
"""simple docstring"""





import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
				from huggingface_hub import snapshot_download
				from pyctcdecode import BeamSearchDecoderCTC

				from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
				from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
				from transformers import WavaVecaForCTC






@require_pyctcdecode
class    _UpperCAmelCase(   unittest.TestCase     ):



		def 						UpperCAmelCase							( self)	->	Optional[int]:






							'''simple docstring'''
							_UpperCamelCase										=  '''| <pad> <unk> <s> </s> a b c d e f g h i j k'''.split()
							_UpperCamelCase										=  dict(zip(UpperCamelCase_    ,		range(len(UpperCamelCase_))))

							_UpperCamelCase										=  {
							    '''unk_token''': '''<unk>''',
							    '''bos_token''': '''<s>''',
							    '''eos_token''': '''</s>''',
							}
							_UpperCamelCase										=  {
							    '''feature_size''': 1,
							    '''padding_value''': 0.0,
							    '''sampling_rate''': 1_60_00,
							    '''return_attention_mask''': False,
							    '''do_normalize''': True,
							}

							_UpperCamelCase										=  tempfile.mkdtemp()
							_UpperCamelCase										=  os.path.join(self.tmpdirname    ,		VOCAB_FILES_NAMES['''vocab_file'''])
							_UpperCamelCase										=  os.path.join(self.tmpdirname    ,		UpperCamelCase_)
							with open(self.vocab_file    ,		'''w'''    ,		encoding='''utf-8''') as fp:
												fp.write(json.dumps(UpperCamelCase_) + '''\n''')

							with open(self.feature_extraction_file    ,		'''w'''    ,		encoding='''utf-8''') as fp:
												fp.write(json.dumps(UpperCamelCase_) + '''\n''')

							# load decoder from hub
							_UpperCamelCase										=  '''hf-internal-testing/ngram-beam-search-decoder'''



		def 						UpperCAmelCase							( self    ,		**__a)	->	List[str]:






							'''simple docstring'''
							_UpperCamelCase										=  self.add_kwargs_tokens_map.copy()
							kwargs.update(UpperCamelCase_)
							return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname    ,		**UpperCamelCase_)



		def 						UpperCAmelCase							( self    ,		**__a)	->	List[Any]:






							'''simple docstring'''
							return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname    ,		**UpperCamelCase_)



		def 						UpperCAmelCase							( self    ,		**__a)	->	Any:






							'''simple docstring'''
							return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name    ,		**UpperCamelCase_)



		def 						UpperCAmelCase							( self)	->	Optional[Any]:






							'''simple docstring'''
							shutil.rmtree(self.tmpdirname)



		def 						UpperCAmelCase							( self)	->	str:






							'''simple docstring'''
							_UpperCamelCase										=  self.get_tokenizer()
							_UpperCamelCase										=  self.get_feature_extractor()
							_UpperCamelCase										=  self.get_decoder()

							_UpperCamelCase										=  WavaVecaProcessorWithLM(tokenizer=UpperCamelCase_    ,		feature_extractor=UpperCamelCase_    ,		decoder=UpperCamelCase_)

							processor.save_pretrained(self.tmpdirname)
							_UpperCamelCase										=  WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname)

							# tokenizer
							self.assertEqual(processor.tokenizer.get_vocab()    ,		tokenizer.get_vocab())
							self.assertIsInstance(processor.tokenizer    ,		UpperCamelCase_)

							# feature extractor
							self.assertEqual(processor.feature_extractor.to_json_string()    ,		feature_extractor.to_json_string())
							self.assertIsInstance(processor.feature_extractor    ,		UpperCamelCase_)

							# decoder
							self.assertEqual(processor.decoder._alphabet.labels    ,		decoder._alphabet.labels)
							self.assertEqual(
							    processor.decoder.model_container[decoder._model_key]._unigram_set    ,		decoder.model_container[decoder._model_key]._unigram_set    ,		)
							self.assertIsInstance(processor.decoder    ,		UpperCamelCase_)



		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  WavaVecaProcessorWithLM(
							    tokenizer=self.get_tokenizer()    ,		feature_extractor=self.get_feature_extractor()    ,		decoder=self.get_decoder())
							processor.save_pretrained(self.tmpdirname)

							# make sure that error is thrown when decoder alphabet doesn't match
							_UpperCamelCase										=  WavaVecaProcessorWithLM.from_pretrained(
							    self.tmpdirname    ,		alpha=5.0    ,		beta=3.0    ,		score_boundary=-7.0    ,		unk_score_offset=3)

							# decoder
							self.assertEqual(processor.language_model.alpha    ,		5.0)
							self.assertEqual(processor.language_model.beta    ,		3.0)
							self.assertEqual(processor.language_model.score_boundary    ,		-7.0)
							self.assertEqual(processor.language_model.unk_score_offset    ,		3)



		def 						UpperCAmelCase							( self)	->	Dict:






							'''simple docstring'''
							_UpperCamelCase										=  self.get_tokenizer()
							# add token to trigger raise
							tokenizer.add_tokens(['''xx'''])
							with self.assertRaisesRegex(UpperCamelCase_    ,		'''include'''):
												WavaVecaProcessorWithLM(
												    tokenizer=UpperCamelCase_    ,		feature_extractor=self.get_feature_extractor()    ,		decoder=self.get_decoder())



		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							_UpperCamelCase										=  self.get_feature_extractor()
							_UpperCamelCase										=  self.get_tokenizer()
							_UpperCamelCase										=  self.get_decoder()

							_UpperCamelCase										=  WavaVecaProcessorWithLM(tokenizer=UpperCamelCase_    ,		feature_extractor=UpperCamelCase_    ,		decoder=UpperCamelCase_)

							_UpperCamelCase										=  floats_list((3, 10_00))

							_UpperCamelCase										=  feature_extractor(UpperCamelCase_    ,		return_tensors='''np''')
							_UpperCamelCase										=  processor(UpperCamelCase_    ,		return_tensors='''np''')

							for key in input_feat_extract.keys():
												self.assertAlmostEqual(input_feat_extract[key].sum()    ,		input_processor[key].sum()    ,		delta=1e-2)



		def 						UpperCAmelCase							( self)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  self.get_feature_extractor()
							_UpperCamelCase										=  self.get_tokenizer()
							_UpperCamelCase										=  self.get_decoder()

							_UpperCamelCase										=  WavaVecaProcessorWithLM(tokenizer=UpperCamelCase_    ,		feature_extractor=UpperCamelCase_    ,		decoder=UpperCamelCase_)

							_UpperCamelCase										=  '''This is a test string'''

							_UpperCamelCase										=  processor(text=UpperCamelCase_)

							_UpperCamelCase										=  tokenizer(UpperCamelCase_)

							for key in encoded_tok.keys():
												self.assertListEqual(encoded_tok[key]    ,		encoded_processor[key])



		def 						UpperCAmelCase							( self    ,		__a=(2, 10, 16)    ,		__a=77)	->	Dict:






							'''simple docstring'''
							np.random.seed(UpperCamelCase_)
							return np.random.rand(*UpperCamelCase_)



		def 						UpperCAmelCase							( self)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  self.get_feature_extractor()
							_UpperCamelCase										=  self.get_tokenizer()
							_UpperCamelCase										=  self.get_decoder()

							_UpperCamelCase										=  WavaVecaProcessorWithLM(tokenizer=UpperCamelCase_    ,		feature_extractor=UpperCamelCase_    ,		decoder=UpperCamelCase_)

							_UpperCamelCase										=  self._get_dummy_logits(shape=(10, 16)    ,		seed=13)

							_UpperCamelCase										=  processor.decode(UpperCamelCase_)

							_UpperCamelCase										=  decoder.decode_beams(UpperCamelCase_)[0]

							self.assertEqual(decoded_decoder[0]    ,		decoded_processor.text)
							self.assertEqual('''</s> <s> </s>'''    ,		decoded_processor.text)
							self.assertEqual(decoded_decoder[-2]    ,		decoded_processor.logit_score)
							self.assertEqual(decoded_decoder[-1]    ,		decoded_processor.lm_score)



		@parameterized.expand([[None], ['''fork'''], ['''spawn''']])
		def 						UpperCAmelCase							( self    ,		__a)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  self.get_feature_extractor()
							_UpperCamelCase										=  self.get_tokenizer()
							_UpperCamelCase										=  self.get_decoder()

							_UpperCamelCase										=  WavaVecaProcessorWithLM(tokenizer=UpperCamelCase_    ,		feature_extractor=UpperCamelCase_    ,		decoder=UpperCamelCase_)

							_UpperCamelCase										=  self._get_dummy_logits()

							# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
							#       otherwise, the LM won't be available to the pool's sub-processes.
							# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
							if pool_context is None:
												_UpperCamelCase										=  processor.batch_decode(UpperCamelCase_)
							else:
												with get_context(UpperCamelCase_).Pool() as pool:
																	_UpperCamelCase										=  processor.batch_decode(UpperCamelCase_    ,		UpperCamelCase_)

							_UpperCamelCase										=  list(UpperCamelCase_)

							with get_context('''fork''').Pool() as p:
												_UpperCamelCase										=  decoder.decode_beams_batch(UpperCamelCase_    ,		UpperCamelCase_)

							_UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase										=  [], [], []
							for beams in decoded_beams:
												texts_decoder.append(beams[0][0])
												logit_scores_decoder.append(beams[0][-2])
												lm_scores_decoder.append(beams[0][-1])

							self.assertListEqual(UpperCamelCase_    ,		decoded_processor.text)
							self.assertListEqual(['''<s> <s> </s>''', '''<s> <s> <s>''']    ,		decoded_processor.text)
							self.assertListEqual(UpperCamelCase_    ,		decoded_processor.logit_score)
							self.assertListEqual(UpperCamelCase_    ,		decoded_processor.lm_score)



		def 						UpperCAmelCase							( self)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  self.get_feature_extractor()
							_UpperCamelCase										=  self.get_tokenizer()
							_UpperCamelCase										=  self.get_decoder()

							_UpperCamelCase										=  WavaVecaProcessorWithLM(tokenizer=UpperCamelCase_    ,		feature_extractor=UpperCamelCase_    ,		decoder=UpperCamelCase_)

							_UpperCamelCase										=  self._get_dummy_logits()

							_UpperCamelCase										=  15
							_UpperCamelCase										=  -20.0
							_UpperCamelCase										=  -4.0

							_UpperCamelCase										=  processor.batch_decode(
							    UpperCamelCase_    ,		beam_width=UpperCamelCase_    ,		beam_prune_logp=UpperCamelCase_    ,		token_min_logp=UpperCamelCase_    ,		)
							_UpperCamelCase										=  decoded_processor_out.text

							_UpperCamelCase										=  list(UpperCamelCase_)

							with get_context('''fork''').Pool() as pool:
												_UpperCamelCase										=  decoder.decode_beams_batch(
												    UpperCamelCase_    ,		UpperCamelCase_    ,		beam_width=UpperCamelCase_    ,		beam_prune_logp=UpperCamelCase_    ,		token_min_logp=UpperCamelCase_    ,		)

							_UpperCamelCase										=  [d[0][0] for d in decoded_decoder_out]
							_UpperCamelCase										=  [d[0][2] for d in decoded_decoder_out]
							_UpperCamelCase										=  [d[0][3] for d in decoded_decoder_out]

							self.assertListEqual(UpperCamelCase_    ,		UpperCamelCase_)
							self.assertListEqual(['''</s> <s> <s>''', '''<s> <s> <s>''']    ,		UpperCamelCase_)

							self.assertTrue(np.array_equal(UpperCamelCase_    ,		decoded_processor_out.logit_score))
							self.assertTrue(np.allclose([-20.054, -18.447]    ,		UpperCamelCase_    ,		atol=1e-3))

							self.assertTrue(np.array_equal(UpperCamelCase_    ,		decoded_processor_out.lm_score))
							self.assertTrue(np.allclose([-15.554, -13.9474]    ,		UpperCamelCase_    ,		atol=1e-3))



		def 						UpperCAmelCase							( self)	->	Dict:






							'''simple docstring'''
							_UpperCamelCase										=  self.get_feature_extractor()
							_UpperCamelCase										=  self.get_tokenizer()
							_UpperCamelCase										=  self.get_decoder()

							_UpperCamelCase										=  WavaVecaProcessorWithLM(tokenizer=UpperCamelCase_    ,		feature_extractor=UpperCamelCase_    ,		decoder=UpperCamelCase_)

							_UpperCamelCase										=  self._get_dummy_logits()

							_UpperCamelCase										=  2.0
							_UpperCamelCase										=  5.0
							_UpperCamelCase										=  -20.0
							_UpperCamelCase										=  True

							_UpperCamelCase										=  processor.batch_decode(
							    UpperCamelCase_    ,		alpha=UpperCamelCase_    ,		beta=UpperCamelCase_    ,		unk_score_offset=UpperCamelCase_    ,		lm_score_boundary=UpperCamelCase_    ,		)
							_UpperCamelCase										=  decoded_processor_out.text

							_UpperCamelCase										=  list(UpperCamelCase_)
							decoder.reset_params(
							    alpha=UpperCamelCase_    ,		beta=UpperCamelCase_    ,		unk_score_offset=UpperCamelCase_    ,		lm_score_boundary=UpperCamelCase_    ,		)

							with get_context('''fork''').Pool() as pool:
												_UpperCamelCase										=  decoder.decode_beams_batch(
												    UpperCamelCase_    ,		UpperCamelCase_    ,		)

							_UpperCamelCase										=  [d[0][0] for d in decoded_decoder_out]

							self.assertListEqual(UpperCamelCase_    ,		UpperCamelCase_)
							self.assertListEqual(['''<s> </s> <s> </s> </s>''', '''</s> </s> <s> </s> </s>''']    ,		UpperCamelCase_)
							_UpperCamelCase										=  processor.decoder.model_container[processor.decoder._model_key]
							self.assertEqual(lm_model.alpha    ,		2.0)
							self.assertEqual(lm_model.beta    ,		5.0)
							self.assertEqual(lm_model.unk_score_offset    ,		-20.0)
							self.assertEqual(lm_model.score_boundary    ,		UpperCamelCase_)



		def 						UpperCAmelCase							( self)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''')

							_UpperCamelCase										=  processor.decoder.model_container[processor.decoder._model_key]
							_UpperCamelCase										=  Path(language_model._kenlm_model.path.decode('''utf-8''')).parent.parent.absolute()

							_UpperCamelCase										=  os.listdir(UpperCamelCase_)
							_UpperCamelCase										=  ['''alphabet.json''', '''language_model''']

							downloaded_decoder_files.sort()
							expected_decoder_files.sort()

							# test that only decoder relevant files from
							# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
							# are downloaded and none of the rest (e.g. README.md, ...)
							self.assertListEqual(UpperCamelCase_    ,		UpperCamelCase_)



		def 						UpperCAmelCase							( self)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  snapshot_download('''hf-internal-testing/processor_with_lm''')

							_UpperCamelCase										=  WavaVecaProcessorWithLM.from_pretrained(UpperCamelCase_)

							_UpperCamelCase										=  processor.decoder.model_container[processor.decoder._model_key]
							_UpperCamelCase										=  Path(language_model._kenlm_model.path.decode('''utf-8''')).parent.parent.absolute()

							_UpperCamelCase										=  os.listdir(UpperCamelCase_)
							_UpperCamelCase										=  os.listdir(UpperCamelCase_)

							local_decoder_files.sort()
							expected_decoder_files.sort()

							# test that both decoder form hub and local files in cache are the same
							self.assertListEqual(UpperCamelCase_    ,		UpperCamelCase_)



		def 						UpperCAmelCase							( self)	->	Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase										=  WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''')
							_UpperCamelCase										=  AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm''')

							_UpperCamelCase										=  floats_list((3, 10_00))

							_UpperCamelCase										=  processor_wavaveca(UpperCamelCase_    ,		return_tensors='''np''')
							_UpperCamelCase										=  processor_auto(UpperCamelCase_    ,		return_tensors='''np''')

							for key in input_wavaveca.keys():
												self.assertAlmostEqual(input_wavaveca[key].sum()    ,		input_auto[key].sum()    ,		delta=1e-2)

							_UpperCamelCase										=  self._get_dummy_logits()

							_UpperCamelCase										=  processor_wavaveca.batch_decode(UpperCamelCase_)
							_UpperCamelCase										=  processor_auto.batch_decode(UpperCamelCase_)

							self.assertListEqual(decoded_wavaveca.text    ,		decoded_auto.text)



		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							_UpperCamelCase										=  self.get_feature_extractor()
							_UpperCamelCase										=  self.get_tokenizer()
							_UpperCamelCase										=  self.get_decoder()

							_UpperCamelCase										=  WavaVecaProcessorWithLM(tokenizer=UpperCamelCase_    ,		feature_extractor=UpperCamelCase_    ,		decoder=UpperCamelCase_)

							self.assertListEqual(
							    processor.model_input_names    ,		feature_extractor.model_input_names    ,		msg='''`processor` and `feature_extractor` model input names do not match'''    ,		)



		@staticmethod
		def 						UpperCAmelCase							( __a    ,		__a)	->	Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase										=  [d[key] for d in offsets]
							return retrieved_list



		def 						UpperCAmelCase							( self)	->	Optional[int]:






							'''simple docstring'''
							_UpperCamelCase										=  WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''')
							_UpperCamelCase										=  self._get_dummy_logits()[0]

							_UpperCamelCase										=  processor.decode(UpperCamelCase_    ,		output_word_offsets=UpperCamelCase_)
							# check Wav2Vec2CTCTokenizerOutput keys for word
							self.assertEqual(len(outputs.keys())    ,		4)
							self.assertTrue('''text''' in outputs)
							self.assertTrue('''word_offsets''' in outputs)
							self.assertTrue(isinstance(UpperCamelCase_    ,		UpperCamelCase_))

							self.assertEqual(''' '''.join(self.get_from_offsets(outputs['''word_offsets''']    ,		'''word'''))    ,		outputs.text)
							self.assertListEqual(self.get_from_offsets(outputs['''word_offsets''']    ,		'''word''')    ,		['''<s>''', '''<s>''', '''</s>'''])
							self.assertListEqual(self.get_from_offsets(outputs['''word_offsets''']    ,		'''start_offset''')    ,		[0, 2, 4])
							self.assertListEqual(self.get_from_offsets(outputs['''word_offsets''']    ,		'''end_offset''')    ,		[1, 3, 5])



		def 						UpperCAmelCase							( self)	->	Optional[int]:






							'''simple docstring'''
							_UpperCamelCase										=  WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''')
							_UpperCamelCase										=  self._get_dummy_logits()

							_UpperCamelCase										=  processor.batch_decode(UpperCamelCase_    ,		output_word_offsets=UpperCamelCase_)

							# check Wav2Vec2CTCTokenizerOutput keys for word
							self.assertEqual(len(outputs.keys())    ,		4)
							self.assertTrue('''text''' in outputs)
							self.assertTrue('''word_offsets''' in outputs)
							self.assertTrue(isinstance(UpperCamelCase_    ,		UpperCamelCase_))

							self.assertListEqual(
							    [''' '''.join(self.get_from_offsets(UpperCamelCase_    ,		'''word''')) for o in outputs['''word_offsets''']]    ,		outputs.text)
							self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0]    ,		'''word''')    ,		['''<s>''', '''<s>''', '''</s>'''])
							self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0]    ,		'''start_offset''')    ,		[0, 2, 4])
							self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0]    ,		'''end_offset''')    ,		[1, 3, 5])




		@slow
		@require_torch
		@require_torchaudio
		def 						UpperCAmelCase							( self)	->	str:






							'''simple docstring'''
							import torch

							_UpperCamelCase										=  load_dataset('''common_voice'''    ,		'''en'''    ,		split='''train'''    ,		streaming=UpperCamelCase_)
							_UpperCamelCase										=  ds.cast_column('''audio'''    ,		datasets.Audio(sampling_rate=1_60_00))
							_UpperCamelCase										=  iter(UpperCamelCase_)
							_UpperCamelCase										=  next(UpperCamelCase_)

							_UpperCamelCase										=  AutoProcessor.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''')
							_UpperCamelCase										=  WavaVecaForCTC.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''')

							# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
							_UpperCamelCase										=  processor(sample['''audio''']['''array''']    ,		return_tensors='''pt''').input_values

							with torch.no_grad():
												_UpperCamelCase										=  model(UpperCamelCase_).logits.cpu().numpy()

							_UpperCamelCase										=  processor.decode(logits[0]    ,		output_word_offsets=UpperCamelCase_)

							_UpperCamelCase										=  model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
							_UpperCamelCase										=  [
							    {
							        '''start_time''': d['''start_offset'''] * time_offset,
							        '''end_time''': d['''end_offset'''] * time_offset,
							        '''word''': d['''word'''],
							    }
							    for d in output['''word_offsets''']
							]

							_UpperCamelCase										=  '''WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'''

							# output words
							self.assertEqual(''' '''.join(self.get_from_offsets(UpperCamelCase_    ,		'''word'''))    ,		UpperCamelCase_)
							self.assertEqual(''' '''.join(self.get_from_offsets(UpperCamelCase_    ,		'''word'''))    ,		output.text)

							# output times
							_UpperCamelCase										=  torch.tensor(self.get_from_offsets(UpperCamelCase_    ,		'''start_time'''))
							_UpperCamelCase										=  torch.tensor(self.get_from_offsets(UpperCamelCase_    ,		'''end_time'''))

							# fmt: off
							_UpperCamelCase										=  torch.tensor([1.4199, 1.6599, 2.2599, 3.0, 3.24, 3.5999, 3.7999, 4.0999, 4.26, 4.94, 5.28, 5.6599, 5.78, 5.94, 6.32, 6.5399, 6.6599])
							_UpperCamelCase										=  torch.tensor([1.5399, 1.8999, 2.9, 3.16, 3.5399, 3.72, 4.0199, 4.1799, 4.76, 5.1599, 5.5599, 5.6999, 5.86, 6.1999, 6.38, 6.6199, 6.94])
							# fmt: on

							self.assertTrue(torch.allclose(UpperCamelCase_    ,		UpperCamelCase_    ,		atol=0.01))
							self.assertTrue(torch.allclose(UpperCamelCase_    ,		UpperCamelCase_    ,		atol=0.01))








"""simple docstring"""





from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers.models.layoutlm.modeling_tf_layoutlm import (
				    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFLayoutLMForMaskedLM,
				    TFLayoutLMForQuestionAnswering,
				    TFLayoutLMForSequenceClassification,
				    TFLayoutLMForTokenClassification,
				    TFLayoutLMModel,
				)






class    _UpperCAmelCase:



		def __init__( self    ,		__a    ,		__a=13    ,		__a=7    ,		__a=True    ,		__a=True    ,		__a=True    ,		__a=True    ,		__a=99    ,		__a=32    ,		__a=2    ,		__a=4    ,		__a=37    ,		__a="gelu"    ,		__a=0.1    ,		__a=0.1    ,		__a=5_12    ,		__a=16    ,		__a=2    ,		__a=0.02    ,		__a=3    ,		__a=4    ,		__a=None    ,		__a=10_00    ,		)	->	str:






							'''simple docstring'''
							_UpperCamelCase										=  parent
							_UpperCamelCase										=  batch_size
							_UpperCamelCase										=  seq_length
							_UpperCamelCase										=  is_training
							_UpperCamelCase										=  use_input_mask
							_UpperCamelCase										=  use_token_type_ids
							_UpperCamelCase										=  use_labels
							_UpperCamelCase										=  vocab_size
							_UpperCamelCase										=  hidden_size
							_UpperCamelCase										=  num_hidden_layers
							_UpperCamelCase										=  num_attention_heads
							_UpperCamelCase										=  intermediate_size
							_UpperCamelCase										=  hidden_act
							_UpperCamelCase										=  hidden_dropout_prob
							_UpperCamelCase										=  attention_probs_dropout_prob
							_UpperCamelCase										=  max_position_embeddings
							_UpperCamelCase										=  type_vocab_size
							_UpperCamelCase										=  type_sequence_label_size
							_UpperCamelCase										=  initializer_range
							_UpperCamelCase										=  num_labels
							_UpperCamelCase										=  num_choices
							_UpperCamelCase										=  scope
							_UpperCamelCase										=  range_bbox



		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							_UpperCamelCase										=  ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size)

							# convert bbox to numpy since TF does not support item assignment
							_UpperCamelCase										=  ids_tensor([self.batch_size, self.seq_length, 4]    ,		self.range_bbox).numpy()
							# Ensure that bbox is legal
							for i in range(bbox.shape[0]):
												for j in range(bbox.shape[1]):
																	if bbox[i, j, 3] < bbox[i, j, 1]:
																						_UpperCamelCase										=  bbox[i, j, 3]
																						_UpperCamelCase										=  bbox[i, j, 1]
																						_UpperCamelCase										=  t
																	if bbox[i, j, 2] < bbox[i, j, 0]:
																						_UpperCamelCase										=  bbox[i, j, 2]
																						_UpperCamelCase										=  bbox[i, j, 0]
																						_UpperCamelCase										=  t
							_UpperCamelCase										=  tf.convert_to_tensor(__a)

							_UpperCamelCase										=  None
							if self.use_input_mask:
												_UpperCamelCase										=  random_attention_mask([self.batch_size, self.seq_length])

							_UpperCamelCase										=  None
							if self.use_token_type_ids:
												_UpperCamelCase										=  ids_tensor([self.batch_size, self.seq_length]    ,		self.type_vocab_size)

							_UpperCamelCase										=  None
							_UpperCamelCase										=  None
							_UpperCamelCase										=  None
							if self.use_labels:
												_UpperCamelCase										=  ids_tensor([self.batch_size]    ,		self.type_sequence_label_size)
												_UpperCamelCase										=  ids_tensor([self.batch_size, self.seq_length]    ,		self.num_labels)
												_UpperCamelCase										=  ids_tensor([self.batch_size]    ,		self.num_choices)

							_UpperCamelCase										=  LayoutLMConfig(
							    vocab_size=self.vocab_size    ,		hidden_size=self.hidden_size    ,		num_hidden_layers=self.num_hidden_layers    ,		num_attention_heads=self.num_attention_heads    ,		intermediate_size=self.intermediate_size    ,		hidden_act=self.hidden_act    ,		hidden_dropout_prob=self.hidden_dropout_prob    ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,		max_position_embeddings=self.max_position_embeddings    ,		type_vocab_size=self.type_vocab_size    ,		initializer_range=self.initializer_range    ,		)

							return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a)	->	List[str]:






							'''simple docstring'''
							_UpperCamelCase										=  TFLayoutLMModel(config=__a)

							_UpperCamelCase										=  model(__a    ,		__a    ,		attention_mask=__a    ,		token_type_ids=__a)
							_UpperCamelCase										=  model(__a    ,		__a    ,		token_type_ids=__a)
							_UpperCamelCase										=  model(__a    ,		__a)
							self.parent.assertEqual(result.last_hidden_state.shape    ,		(self.batch_size, self.seq_length, self.hidden_size))
							self.parent.assertEqual(result.pooler_output.shape    ,		(self.batch_size, self.hidden_size))



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a)	->	Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase										=  TFLayoutLMForMaskedLM(config=__a)

							_UpperCamelCase										=  model(__a    ,		__a    ,		attention_mask=__a    ,		token_type_ids=__a    ,		labels=__a)
							self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.seq_length, self.vocab_size))



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  self.num_labels
							_UpperCamelCase										=  TFLayoutLMForSequenceClassification(config=__a)

							_UpperCamelCase										=  model(__a    ,		__a    ,		attention_mask=__a    ,		token_type_ids=__a)
							self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_labels))



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a)	->	Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase										=  self.num_labels
							_UpperCamelCase										=  TFLayoutLMForTokenClassification(config=__a)

							_UpperCamelCase										=  model(__a    ,		__a    ,		attention_mask=__a    ,		token_type_ids=__a    ,		labels=__a)
							self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.seq_length, self.num_labels))



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a)	->	Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase										=  TFLayoutLMForQuestionAnswering(config=__a)

							_UpperCamelCase										=  model(__a    ,		__a    ,		attention_mask=__a    ,		token_type_ids=__a)
							self.parent.assertEqual(result.start_logits.shape    ,		(self.batch_size, self.seq_length))
							self.parent.assertEqual(result.end_logits.shape    ,		(self.batch_size, self.seq_length))




		def 						UpperCAmelCase							( self)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  self.prepare_config_and_inputs()
							(
							    (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  
							)										=  config_and_inputs
							_UpperCamelCase										=  {
							    '''input_ids''': input_ids,
							    '''bbox''': bbox,
							    '''token_type_ids''': token_type_ids,
							    '''attention_mask''': input_mask,
							}
							return config, inputs_dict






@require_tf
class    _UpperCAmelCase(   lowerCamelCase				,					lowerCamelCase				,					unittest.TestCase     ):
		lowercase__      =				(
		    (
		        TFLayoutLMModel,
		        TFLayoutLMForMaskedLM,
		        TFLayoutLMForTokenClassification,
		        TFLayoutLMForSequenceClassification,
		        TFLayoutLMForQuestionAnswering,
		    )
		    if is_tf_available()
		    else ()
		)
		lowercase__      =				(
		    {
		        'feature-extraction': TFLayoutLMModel,
		        'fill-mask': TFLayoutLMForMaskedLM,
		        'text-classification': TFLayoutLMForSequenceClassification,
		        'token-classification': TFLayoutLMForTokenClassification,
		        'zero-shot': TFLayoutLMForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)
		lowercase__      =				False
		lowercase__      =				True
		lowercase__      =				10



		def 						UpperCAmelCase							( self)	->	Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase										=  TFLayoutLMModelTester(self)
							_UpperCamelCase										=  ConfigTester(self    ,		config_class=__a    ,		hidden_size=37)



		def 						UpperCAmelCase							( self)	->	str:






							'''simple docstring'''
							self.config_tester.run_common_tests()



		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__a)



		def 						UpperCAmelCase							( self)	->	Dict:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*__a)



		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*__a)



		def 						UpperCAmelCase							( self)	->	Optional[int]:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*__a)



		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*__a)



		@slow
		def 						UpperCAmelCase							( self)	->	str:






							'''simple docstring'''
							for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_UpperCamelCase										=  TFLayoutLMModel.from_pretrained(__a)
												self.assertIsNotNone(__a)




		@unittest.skip('''Onnx compliancy broke with TF 2.10''')
		def 						UpperCAmelCase							( self)	->	List[str]:






							'''simple docstring'''
							pass





def 				lowerCamelCase__		(      )							->		Union[str, Any]:


					"""simple docstring"""





					_UpperCamelCase										=  tf.convert_to_tensor([[1_01,10_19,10_14,10_16,10_37,1_28_49,47_47,10_04,1_42_46,22_78,54_39,45_24,50_02,29_30,21_93,29_30,43_41,32_08,10_05,10_55,21_71,28_48,1_13_00,35_31,1_02],[1_01,40_70,40_34,70_20,10_24,30_58,10_15,10_13,28_61,10_13,60_70,1_92_74,27_72,62_05,2_78_14,1_61_47,1_61_47,43_43,20_47,1_02_83,1_09_69,1_43_89,10_12,23_38,1_02]]     )  # noqa: E231
					_UpperCamelCase										=  tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]     )  # noqa: E231
					_UpperCamelCase										=  tf.convert_to_tensor([[[0,0,0,0],[4_23,2_37,4_40,2_51],[4_27,2_72,4_41,2_87],[4_19,1_15,4_37,1_29],[9_61,8_85,9_92,9_12],[2_56,38,3_30,58],[2_56,38,3_30,58],[3_36,42,3_53,57],[3_60,39,4_01,56],[3_60,39,4_01,56],[4_11,39,4_71,59],[4_79,41,5_28,59],[5_33,39,6_30,60],[67,1_13,1_34,1_31],[1_41,1_15,2_09,1_32],[68,1_49,1_33,1_66],[1_41,1_49,1_87,1_64],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[2_95,1_48,3_49,1_65],[4_41,1_49,4_92,1_66],[4_97,1_49,5_46,1_64],[64,2_01,1_25,2_18],[10_00,10_00,10_00,10_00]],[[0,0,0,0],[6_62,1_50,7_54,1_66],[6_65,1_99,7_42,2_11],[5_19,2_13,5_54,2_28],[5_19,2_13,5_54,2_28],[1_34,4_33,1_87,4_54],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[3_14,4_69,3_76,4_82],[5_04,6_84,5_82,7_06],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[6_10,7_49,6_52,7_65],[1_30,6_59,1_68,6_72],[1_76,6_57,2_37,6_72],[2_38,6_57,3_12,6_72],[4_43,6_53,6_28,6_72],[4_43,6_53,6_28,6_72],[7_16,3_01,8_25,3_17],[10_00,10_00,10_00,10_00]]]     )  # noqa: E231
					_UpperCamelCase										=  tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]     )  # noqa: E231
					# these are sequence labels (i.e. at the token level)
					_UpperCamelCase										=  tf.convert_to_tensor([[-1_00,10,10,10,9,1,-1_00,7,7,-1_00,7,7,4,2,5,2,8,8,-1_00,-1_00,5,0,3,2,-1_00],[-1_00,12,12,12,-1_00,12,10,-1_00,-1_00,-1_00,-1_00,10,12,9,-1_00,-1_00,-1_00,10,10,10,9,12,-1_00,10,-1_00]]     )  # noqa: E231
					# fmt: on

					return input_ids, attention_mask, bbox, token_type_ids, labels






@require_tf
class    _UpperCAmelCase(   unittest.TestCase     ):



		@slow
		def 						UpperCAmelCase							( self)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  TFLayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased''')

							_UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase										=  prepare_layoutlm_batch_inputs()

							# forward pass
							_UpperCamelCase										=  model(input_ids=__a    ,		bbox=__a    ,		attention_mask=__a    ,		token_type_ids=__a)

							# test the sequence output on [0, :3, :3]
							_UpperCamelCase										=  tf.convert_to_tensor(
							    [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]]    ,		)

							self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]    ,		__a    ,		atol=1e-3))

							# test the pooled output on [1, :3]
							_UpperCamelCase										=  tf.convert_to_tensor([-0.6580, -0.0214, 0.8552])

							self.assertTrue(np.allclose(outputs.pooler_output[1, :3]    ,		__a    ,		atol=1e-3))



		@slow
		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							# initialize model with randomly initialized sequence classification head
							_UpperCamelCase										=  TFLayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''    ,		num_labels=2)

							_UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase										=  prepare_layoutlm_batch_inputs()

							# forward pass
							_UpperCamelCase										=  model(
							    input_ids=__a    ,		bbox=__a    ,		attention_mask=__a    ,		token_type_ids=__a    ,		labels=tf.convert_to_tensor([1, 1])    ,		)

							# test whether we get a loss as a scalar
							_UpperCamelCase										=  outputs.loss
							_UpperCamelCase										=  (2,)
							self.assertEqual(loss.shape    ,		__a)

							# test the shape of the logits
							_UpperCamelCase										=  outputs.logits
							_UpperCamelCase										=  (2, 2)
							self.assertEqual(logits.shape    ,		__a)



		@slow
		def 						UpperCAmelCase							( self)	->	List[str]:






							'''simple docstring'''
							# initialize model with randomly initialized token classification head
							_UpperCamelCase										=  TFLayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''    ,		num_labels=13)

							_UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase										=  prepare_layoutlm_batch_inputs()

							# forward pass
							_UpperCamelCase										=  model(
							    input_ids=__a    ,		bbox=__a    ,		attention_mask=__a    ,		token_type_ids=__a    ,		labels=__a)

							# test the shape of the logits
							_UpperCamelCase										=  outputs.logits
							_UpperCamelCase										=  tf.convert_to_tensor((2, 25, 13))
							self.assertEqual(logits.shape    ,		__a)




		@slow
		def 						UpperCAmelCase							( self)	->	Dict:






							'''simple docstring'''
							# initialize model with randomly initialized token classification head
							_UpperCamelCase										=  TFLayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased''')

							_UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase										=  prepare_layoutlm_batch_inputs()

							# forward pass
							_UpperCamelCase										=  model(input_ids=__a    ,		bbox=__a    ,		attention_mask=__a    ,		token_type_ids=__a)

							# test the shape of the logits
							_UpperCamelCase										=  tf.convert_to_tensor((2, 25))
							self.assertEqual(outputs.start_logits.shape    ,		__a)
							self.assertEqual(outputs.end_logits.shape    ,		__a)






