
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  ):




 '''simple docstring'''





 def     UpperCamelCase_  (	self :			int			):
     __A             =			"| <pad> <unk> <s> </s> a b c d e f g h i j k".split()
     __A             =			dict(zip(lowerCamelCase_							,range(len(lowerCamelCase_			)			)			)			)

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

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

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

     # load decoder from hub
     __A             =			"hf-internal-testing/ngram-beam-search-decoder"






 def     UpperCamelCase_  (	self :			Optional[Any]							,**A :			str			):
     __A             =			self.add_kwargs_tokens_map.copy()
     kwargs.update(lowerCamelCase_			)
     return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname							,**lowerCamelCase_			)






 def     UpperCamelCase_  (	self :			Optional[int]							,**A :			int			):
     return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname							,**lowerCamelCase_			)






 def     UpperCamelCase_  (	self :			Any							,**A :			Optional[Any]			):
     return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name							,**lowerCamelCase_			)






 def     UpperCamelCase_  (	self :			Dict			):
     shutil.rmtree(self.tmpdirname			)






 def     UpperCamelCase_  (	self :			Any			):
     __A             =			self.get_tokenizer()
     __A             =			self.get_feature_extractor()
     __A             =			self.get_decoder()

     __A             =			WavaVecaProcessorWithLM(tokenizer=lowerCamelCase_							,feature_extractor=lowerCamelCase_							,decoder=lowerCamelCase_			)

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

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

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

     # 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							,lowerCamelCase_			)






 def     UpperCamelCase_  (	self :			List[str]			):
     __A             =			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
     __A             =			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 :			str			):
     __A             =			self.get_tokenizer()
     # add token to trigger raise
     tokenizer.add_tokens(["xx"]			)
     with self.assertRaisesRegex(lowerCamelCase_							,"include"			):
         WavaVecaProcessorWithLM(
             tokenizer=lowerCamelCase_							,feature_extractor=self.get_feature_extractor()							,decoder=self.get_decoder()			)






 def     UpperCamelCase_  (	self :			Union[str, Any]			):
     __A             =			self.get_feature_extractor()
     __A             =			self.get_tokenizer()
     __A             =			self.get_decoder()

     __A             =			WavaVecaProcessorWithLM(tokenizer=lowerCamelCase_							,feature_extractor=lowerCamelCase_							,decoder=lowerCamelCase_			)

     __A             =			floats_list((3, 10_00)			)

     __A             =			feature_extractor(lowerCamelCase_							,return_tensors="np"			)
     __A             =			processor(lowerCamelCase_							,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[int]			):
     __A             =			self.get_feature_extractor()
     __A             =			self.get_tokenizer()
     __A             =			self.get_decoder()

     __A             =			WavaVecaProcessorWithLM(tokenizer=lowerCamelCase_							,feature_extractor=lowerCamelCase_							,decoder=lowerCamelCase_			)

     __A             =			"This is a test string"

     __A             =			processor(text=lowerCamelCase_			)

     __A             =			tokenizer(lowerCamelCase_			)

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






 def     UpperCamelCase_  (	self :			Optional[int]							,A :			str=(2, 10, 16)							,A :			Tuple=77			):
     np.random.seed(lowerCamelCase_			)
     return np.random.rand(*lowerCamelCase_			)






 def     UpperCamelCase_  (	self :			str			):
     __A             =			self.get_feature_extractor()
     __A             =			self.get_tokenizer()
     __A             =			self.get_decoder()

     __A             =			WavaVecaProcessorWithLM(tokenizer=lowerCamelCase_							,feature_extractor=lowerCamelCase_							,decoder=lowerCamelCase_			)

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

     __A             =			processor.decode(lowerCamelCase_			)

     __A             =			decoder.decode_beams(lowerCamelCase_			)[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 :			Any							,A :			Optional[int]			):
     __A             =			self.get_feature_extractor()
     __A             =			self.get_tokenizer()
     __A             =			self.get_decoder()

     __A             =			WavaVecaProcessorWithLM(tokenizer=lowerCamelCase_							,feature_extractor=lowerCamelCase_							,decoder=lowerCamelCase_			)

     __A             =			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:
         __A             =			processor.batch_decode(lowerCamelCase_			)
     else:
         with get_context(lowerCamelCase_			).Pool() as pool:
             __A             =			processor.batch_decode(lowerCamelCase_							,lowerCamelCase_			)

     __A             =			list(lowerCamelCase_			)

     with get_context("fork"			).Pool() as p:
         __A             =			decoder.decode_beams_batch(lowerCamelCase_							,lowerCamelCase_			)

     __A      ,							__A      ,							__A             =			[], [], []
     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(lowerCamelCase_							,decoded_processor.text			)
     self.assertListEqual(["<s> <s> </s>", "<s> <s> <s>"]							,decoded_processor.text			)
     self.assertListEqual(lowerCamelCase_							,decoded_processor.logit_score			)
     self.assertListEqual(lowerCamelCase_							,decoded_processor.lm_score			)






 def     UpperCamelCase_  (	self :			int			):
     __A             =			self.get_feature_extractor()
     __A             =			self.get_tokenizer()
     __A             =			self.get_decoder()

     __A             =			WavaVecaProcessorWithLM(tokenizer=lowerCamelCase_							,feature_extractor=lowerCamelCase_							,decoder=lowerCamelCase_			)

     __A             =			self._get_dummy_logits()

     __A             =			15
     __A             =			-20.0
     __A             =			-4.0

     __A             =			processor.batch_decode(
         lowerCamelCase_							,beam_width=lowerCamelCase_							,beam_prune_logp=lowerCamelCase_							,token_min_logp=lowerCamelCase_							,)
     __A             =			decoded_processor_out.text

     __A             =			list(lowerCamelCase_			)

     with get_context("fork"			).Pool() as pool:
         __A             =			decoder.decode_beams_batch(
             lowerCamelCase_							,lowerCamelCase_							,beam_width=lowerCamelCase_							,beam_prune_logp=lowerCamelCase_							,token_min_logp=lowerCamelCase_							,)

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

     self.assertListEqual(lowerCamelCase_							,lowerCamelCase_			)
     self.assertListEqual(["</s> <s> <s>", "<s> <s> <s>"]							,lowerCamelCase_			)

     self.assertTrue(np.array_equal(lowerCamelCase_							,decoded_processor_out.logit_score			)			)
     self.assertTrue(np.allclose([-20.0_54, -18.4_47]							,lowerCamelCase_							,atol=1E-3			)			)

     self.assertTrue(np.array_equal(lowerCamelCase_							,decoded_processor_out.lm_score			)			)
     self.assertTrue(np.allclose([-15.5_54, -13.94_74]							,lowerCamelCase_							,atol=1E-3			)			)






 def     UpperCamelCase_  (	self :			Any			):
     __A             =			self.get_feature_extractor()
     __A             =			self.get_tokenizer()
     __A             =			self.get_decoder()

     __A             =			WavaVecaProcessorWithLM(tokenizer=lowerCamelCase_							,feature_extractor=lowerCamelCase_							,decoder=lowerCamelCase_			)

     __A             =			self._get_dummy_logits()

     __A             =			2.0
     __A             =			5.0
     __A             =			-20.0
     __A             =			True

     __A             =			processor.batch_decode(
         lowerCamelCase_							,alpha=lowerCamelCase_							,beta=lowerCamelCase_							,unk_score_offset=lowerCamelCase_							,lm_score_boundary=lowerCamelCase_							,)
     __A             =			decoded_processor_out.text

     __A             =			list(lowerCamelCase_			)
     decoder.reset_params(
         alpha=lowerCamelCase_							,beta=lowerCamelCase_							,unk_score_offset=lowerCamelCase_							,lm_score_boundary=lowerCamelCase_							,)

     with get_context("fork"			).Pool() as pool:
         __A             =			decoder.decode_beams_batch(
             lowerCamelCase_							,lowerCamelCase_							,)

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

     self.assertListEqual(lowerCamelCase_							,lowerCamelCase_			)
     self.assertListEqual(["<s> </s> <s> </s> </s>", "</s> </s> <s> </s> </s>"]							,lowerCamelCase_			)
     __A             =			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							,lowerCamelCase_			)






 def     UpperCamelCase_  (	self :			Dict			):
     __A             =			WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"			)

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

     __A             =			os.listdir(lowerCamelCase_			)
     __A             =			["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(lowerCamelCase_							,lowerCamelCase_			)






 def     UpperCamelCase_  (	self :			int			):
     __A             =			snapshot_download("hf-internal-testing/processor_with_lm"			)

     __A             =			WavaVecaProcessorWithLM.from_pretrained(lowerCamelCase_			)

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

     __A             =			os.listdir(lowerCamelCase_			)
     __A             =			os.listdir(lowerCamelCase_			)

     local_decoder_files.sort()
     expected_decoder_files.sort()

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






 def     UpperCamelCase_  (	self :			int			):
     __A             =			WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"			)
     __A             =			AutoProcessor.from_pretrained("hf-internal-testing/processor_with_lm"			)

     __A             =			floats_list((3, 10_00)			)

     __A             =			processor_wavaveca(lowerCamelCase_							,return_tensors="np"			)
     __A             =			processor_auto(lowerCamelCase_							,return_tensors="np"			)

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

     __A             =			self._get_dummy_logits()

     __A             =			processor_wavaveca.batch_decode(lowerCamelCase_			)
     __A             =			processor_auto.batch_decode(lowerCamelCase_			)

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






 def     UpperCamelCase_  (	self :			str			):
     __A             =			self.get_feature_extractor()
     __A             =			self.get_tokenizer()
     __A             =			self.get_decoder()

     __A             =			WavaVecaProcessorWithLM(tokenizer=lowerCamelCase_							,feature_extractor=lowerCamelCase_							,decoder=lowerCamelCase_			)

     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 :			List[str]							,A :			Tuple			):
     __A             =			[d[key] for d in offsets]
     return retrieved_list






 def     UpperCamelCase_  (	self :			List[str]			):
     __A             =			WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"			)
     __A             =			self._get_dummy_logits()[0]

     __A             =			processor.decode(lowerCamelCase_							,output_word_offsets=lowerCamelCase_			)
     # 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(lowerCamelCase_							,lowerCamelCase_			)			)

     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 :			int			):
     __A             =			WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"			)
     __A             =			self._get_dummy_logits()

     __A             =			processor.batch_decode(lowerCamelCase_							,output_word_offsets=lowerCamelCase_			)

     # 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(lowerCamelCase_							,lowerCamelCase_			)			)

     self.assertListEqual(
         [" ".join(self.get_from_offsets(lowerCamelCase_							,"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 :			Tuple			):
     import torch

     __A             =			load_dataset("common_voice"							,"en"							,split="train"							,streaming=lowerCamelCase_			)
     __A             =			ds.cast_column("audio"							,datasets.Audio(sampling_rate=1_60_00			)			)
     __A             =			iter(lowerCamelCase_			)
     __A             =			next(lowerCamelCase_			)

     __A             =			AutoProcessor.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm"			)
     __A             =			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
     __A             =			processor(sample["audio"]["array"]							,return_tensors="pt"			).input_values

     with torch.no_grad():
         __A             =			model(lowerCamelCase_			).logits.cpu().numpy()

     __A             =			processor.decode(logits[0]							,output_word_offsets=lowerCamelCase_			)

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

     __A             =			"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(lowerCamelCase_							,"word"			)			)							,lowerCamelCase_			)
     self.assertEqual(" ".join(self.get_from_offsets(lowerCamelCase_							,"word"			)			)							,output.text			)

     # output times
     __A             =			torch.tensor(self.get_from_offsets(lowerCamelCase_							,"start_time"			)			)
     __A             =			torch.tensor(self.get_from_offsets(lowerCamelCase_							,"end_time"			)			)

     # fmt: off
     __A             =			torch.tensor([1.41_99, 1.65_99, 2.25_99, 3.0, 3.24, 3.59_99, 3.79_99, 4.09_99, 4.26, 4.94, 5.28, 5.65_99, 5.78, 5.94, 6.32, 6.53_99, 6.65_99]			)
     __A             =			torch.tensor([1.53_99, 1.89_99, 2.9, 3.16, 3.53_99, 3.72, 4.01_99, 4.17_99, 4.76, 5.15_99, 5.55_99, 5.69_99, 5.86, 6.19_99, 6.38, 6.61_99, 6.94]			)
     # fmt: on

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


import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Value
from .base import TaskTemplate





@dataclass(frozen=__SCREAMING_SNAKE_CASE  )
class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					field(default="text-classification"      ,      metadata={"include_in_asdict_even_if_is_default": True}  )
	snake_case_    =					Features({"text": Value("string"  )}  )
	snake_case_    =					Features({"labels": ClassLabel}  )
	snake_case_    =					"text"
	snake_case_    =					"labels"






	def     UpperCamelCase_  (	self :			Optional[Any]							,A :			Dict			):
					if self.label_column not in features:
									raise ValueError(f'''Column {self.label_column} is not present in features.'''			)
					if not isinstance(features[self.label_column]							,A			):
									raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''			)
					__A             =			copy.deepcopy(self			)
					__A             =			self.label_schema.copy()
					__A             =			features[self.label_column]
					__A             =			label_schema
					return task_template







	@property
	def     UpperCamelCase_  (	self :			Dict			):
					return {
					    self.text_column: "text",
					    self.label_column: "labels",
					}
