
import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
			import tensorflow as tf

			from transformers import (
			    TFEfficientFormerForImageClassification,
			    TFEfficientFormerForImageClassificationWithTeacher,
			    TFEfficientFormerModel,
			)
			from transformers.models.efficientformer.modeling_tf_efficientformer import (
			    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
			)


if is_vision_available():
			from PIL import Image

			from transformers import EfficientFormerImageProcessor




class        SCREAMING_SNAKE_CASE__					:





	'''simple docstring'''



	def __init__(  self     :   Dict      ,  lowercase     :   Optional[Any]      ,  lowercase     :   int = 13      ,  lowercase     :   int = 64      ,  lowercase     :   int = 2      ,  lowercase     :   int = 3      ,  lowercase     :   int = 3      ,  lowercase     :   bool = True      ,  lowercase     :   bool = True      ,  lowercase     :   int = 128      ,  lowercase     :   List[Any]=[16, 32, 64, 128]      ,  lowercase     :   int = 7      ,  lowercase     :   int = 4      ,  lowercase     :   int = 37      ,  lowercase     :   str = "gelu"      ,  lowercase     :   float = 0.1      ,  lowercase     :   float = 0.1      ,  lowercase     :   int = 10      ,  lowercase     :   float = 0.02      ,  lowercase     :   int = 2      ,  lowercase     :   int = 1      ,  lowercase     :   int = 128      ,  lowercase     :   List[int] = [2, 2, 2, 2]      ,  lowercase     :   int = 2      ,  lowercase     :   int = 2      ,  ):


				'''simple docstring'''


				_snake_case          =      parent
				_snake_case          =      batch_size
				_snake_case          =      image_size
				_snake_case          =      patch_size
				_snake_case          =      num_channels
				_snake_case          =      is_training
				_snake_case          =      use_labels
				_snake_case          =      hidden_size
				_snake_case          =      num_hidden_layers
				_snake_case          =      num_attention_heads
				_snake_case          =      intermediate_size
				_snake_case          =      hidden_act
				_snake_case          =      hidden_dropout_prob
				_snake_case          =      attention_probs_dropout_prob
				_snake_case          =      type_sequence_label_size
				_snake_case          =      initializer_range
				_snake_case          =      encoder_stride
				_snake_case          =      num_attention_outputs
				_snake_case          =      embed_dim
				_snake_case          =      embed_dim + 1
				_snake_case          =      resolution
				_snake_case          =      depths
				_snake_case          =      hidden_sizes
				_snake_case          =      dim
				_snake_case          =      mlp_expansion_ratio



	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


				_snake_case          =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

				_snake_case          =      None
				if self.use_labels:
							_snake_case          =      ids_tensor([self.batch_size]      ,  self.type_sequence_label_size     )

				_snake_case          =      self.get_config()

				return config, pixel_values, labels



	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				return EfficientFormerConfig(
				    image_size=self.image_size      ,  patch_size=self.patch_size      ,  num_channels=self.num_channels      ,  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      ,  is_decoder=lowercase      ,  initializer_range=self.initializer_range      ,  encoder_stride=self.encoder_stride      ,  resolution=self.resolution      ,  depths=self.depths      ,  hidden_sizes=self.hidden_sizes      ,  dim=self.dim      ,  mlp_expansion_ratio=self.mlp_expansion_ratio      ,  )



	def 			A							(  self     :   List[Any]      ,  lowercase     :   Tuple      ,  lowercase     :   Dict      ,  lowercase     :   int     ):


				'''simple docstring'''


				_snake_case          =      TFEfficientFormerModel(config=lowercase     )
				_snake_case          =      model(lowercase      ,  training=lowercase     )
				self.parent.assertEqual(result.last_hidden_state.shape      ,  (self.batch_size, self.seq_length, self.hidden_size)     )



	def 			A							(  self     :   List[Any]      ,  lowercase     :   Optional[Any]      ,  lowercase     :   Any      ,  lowercase     :   Tuple     ):


				'''simple docstring'''


				_snake_case          =      self.type_sequence_label_size
				_snake_case          =      TFEfficientFormerForImageClassification(lowercase     )
				_snake_case          =      model(lowercase      ,  labels=lowercase      ,  training=lowercase     )
				self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.type_sequence_label_size)     )

				# test greyscale images
				_snake_case          =      1
				_snake_case          =      TFEfficientFormerForImageClassification(lowercase     )

				_snake_case          =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
				_snake_case          =      model(lowercase      ,  labels=lowercase     )
				self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.type_sequence_label_size)     )





	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				_snake_case          =      self.prepare_config_and_inputs()
				_snake_case     , _snake_case     , _snake_case          =      config_and_inputs
				_snake_case          =      {'pixel_values': pixel_values}
				return config, inputs_dict




@require_tf
class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase							,UpperCAmelCase							,unittest.TestCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Optional[int]          = (
	    (
	        TFEfficientFormerModel,
	        TFEfficientFormerForImageClassificationWithTeacher,
	        TFEfficientFormerForImageClassification,
	    )
	    if is_tf_available()
	    else ()
	)
	_UpperCAmelCase	:		Tuple          = (
	    {
	        "feature-extraction": TFEfficientFormerModel,
	        "image-classification": (
	            TFEfficientFormerForImageClassification,
	            TFEfficientFormerForImageClassificationWithTeacher,
	        ),
	    }
	    if is_tf_available()
	    else {}
	)

	_UpperCAmelCase	:		List[Any]          = False

	_UpperCAmelCase	:		Dict          = False
	_UpperCAmelCase	:		List[str]          = False
	_UpperCAmelCase	:		Optional[Any]          = False
	_UpperCAmelCase	:		Optional[int]          = False



	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


				_snake_case          =      TFEfficientFormerModelTester(self     )
				_snake_case          =      ConfigTester(
				    self      ,  config_class=lowercase      ,  has_text_modality=lowercase      ,  hidden_size=37     )



	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				self.config_tester.run_common_tests()



	@unittest.skip(reason='EfficientFormer does not use inputs_embeds'     )
	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				pass



	@unittest.skip(reason='EfficientFormer does not support input and output embeddings'     )
	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				pass



	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case     , _snake_case          =      self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							_snake_case          =      model_class(lowercase     )
							_snake_case          =      inspect.signature(model.call     )
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							_snake_case          =      [*signature.parameters.keys()]

							_snake_case          =      ['pixel_values']
							self.assertListEqual(arg_names[:1]      ,  lowercase     )



	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				def check_hidden_states_output(lowercase     :   Dict      ,  lowercase     :   Dict      ,  lowercase     :   str     ):
							_snake_case          =      model_class(lowercase     )

							_snake_case          =      model(**self._prepare_for_class(lowercase      ,  lowercase     )      ,  training=lowercase     )
							_snake_case          =      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

							_snake_case          =      getattr(
							    self.model_tester      ,  'expected_num_hidden_layers'      ,  self.model_tester.num_hidden_layers + 1     )
							self.assertEqual(len(lowercase     )      ,  lowercase     )

							if hasattr(self.model_tester      ,  'encoder_seq_length'     ):
										_snake_case          =      self.model_tester.encoder_seq_length
										if hasattr(self.model_tester      ,  'chunk_length'     ) and self.model_tester.chunk_length > 1:
													_snake_case          =      seq_length * self.model_tester.chunk_length
							else:
										_snake_case          =      self.model_tester.seq_length

							self.assertListEqual(
							    list(hidden_states[-1].shape[-2:]     )      ,  [seq_length, self.model_tester.hidden_size]      ,  )

							if config.is_encoder_decoder:
										_snake_case          =      outputs.decoder_hidden_states

										self.asseretIsInstance(lowercase      ,  (list, tuple)     )
										self.assertEqual(len(lowercase     )      ,  lowercase     )
										_snake_case          =      getattr(self.model_tester      ,  'seq_length'      ,  lowercase     )
										_snake_case          =      getattr(self.model_tester      ,  'decoder_seq_length'      ,  lowercase     )

										self.assertListEqual(
										    list(hidden_states[-1].shape[-2:]     )      ,  [decoder_seq_length, self.model_tester.hidden_size]      ,  )

				_snake_case     , _snake_case          =      self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							_snake_case          =      True
							check_hidden_states_output(lowercase      ,  lowercase      ,  lowercase     )

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							_snake_case          =      True

							check_hidden_states_output(lowercase      ,  lowercase      ,  lowercase     )



	def 			A							(  self     :   Optional[Any]      ,  lowercase     :   Optional[Any]      ,  lowercase     :   Any      ,  lowercase     :   List[str]=False     ):


				'''simple docstring'''


				_snake_case          =      super()._prepare_for_class(lowercase      ,  lowercase      ,  return_labels=lowercase     )

				if return_labels:
							if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
										del inputs_dict["labels"]

				return inputs_dict



	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowercase     )



	@unittest.skip(reason='EfficientFormer does not implement masked image modeling yet'     )
	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				_snake_case          =      self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_image_modeling(*lowercase     )



	def 			A							(  self     :   str     ):


				'''simple docstring'''


				_snake_case          =      self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*lowercase     )



	@slow
	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							_snake_case          =      TFEfficientFormerModel.from_pretrained(lowercase     )
							self.assertIsNotNone(lowercase     )



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				_snake_case     , _snake_case          =      self.model_tester.prepare_config_and_inputs_for_common()
				_snake_case          =      True

				_snake_case          =      getattr(self.model_tester      ,  'seq_length'      ,  lowercase     )
				_snake_case          =      getattr(self.model_tester      ,  'encoder_seq_length'      ,  lowercase     )
				_snake_case          =      getattr(self.model_tester      ,  'key_length'      ,  lowercase     )
				_snake_case          =      getattr(self.model_tester      ,  'chunk_length'      ,  lowercase     )

				if chunk_length is not None and hasattr(self.model_tester      ,  'num_hashes'     ):
							_snake_case          =      encoder_seq_length * self.model_tester.num_hashes

				for model_class in self.all_model_classes:
							_snake_case          =      True
							_snake_case          =      False
							_snake_case          =      True
							_snake_case          =      model_class(lowercase     )

							_snake_case          =      model(**self._prepare_for_class(lowercase      ,  lowercase     )      ,  training=lowercase     )
							_snake_case          =      outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
							self.assertEqual(len(lowercase     )      ,  self.model_tester.num_attention_outputs     )

							# check that output_attentions also work using config
							del inputs_dict["output_attentions"]
							_snake_case          =      True
							_snake_case          =      model_class(lowercase     )
							_snake_case          =      model(**self._prepare_for_class(lowercase      ,  lowercase     )      ,  training=lowercase     )

							_snake_case          =      outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
							self.assertEqual(len(lowercase     )      ,  self.model_tester.num_attention_outputs     )

							if chunk_length is not None:
										self.assertListEqual(
										    list(attentions[0].shape[-4:]     )      ,  [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]      ,  )
							else:
										self.assertListEqual(
										    list(attentions[0].shape[-3:]     )      ,  [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]      ,  )





	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				_snake_case     , _snake_case          =      self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							# Prepare our model
							_snake_case          =      model_class(lowercase     )
							# These are maximally general inputs for the model, with multiple None dimensions
							# Hopefully this will catch any conditionals that fail for flexible shapes
							_snake_case          =      {
							    key: tf.keras.Input(shape=val.shape[1:]      ,  dtype=val.dtype      ,  name=lowercase     )
							    for key, val in model.input_signature.items()
							    if key in model.dummy_inputs
							}
							_snake_case          =      model(lowercase     )
							self.assertTrue(outputs_dict is not None     )






def 	a_		(	)			->  Any:
			_snake_case          =      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
			return image




@require_tf
@require_vision
class        SCREAMING_SNAKE_CASE__					(     unittest.TestCase ):





	'''simple docstring'''



	@cached_property
	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				return (
				    EfficientFormerImageProcessor.from_pretrained('snap-research/efficientformer-l1-300'     )
				    if is_vision_available()
				    else None
				)



	@slow
	def 			A							(  self     :   int     ):


				'''simple docstring'''


				_snake_case          =      TFEfficientFormerForImageClassification.from_pretrained('snap-research/efficientformer-l1-300'     )
				_snake_case          =      self.default_image_processor
				_snake_case          =      prepare_img()
				_snake_case          =      image_processor(images=lowercase      ,  return_tensors='tf'     )
				# forward pass
				_snake_case          =      model(**lowercase      ,  training=lowercase     )
				# verify the logits
				_snake_case          =      tf.TensorShape((1, 1_000)     )
				self.assertEqual(outputs.logits.shape      ,  lowercase     )
				_snake_case          =      tf.constant([-0.0555, 0.4825, -0.0852]     )
				self.assertTrue(np.allclose(outputs.logits[0, :3]      ,  lowercase      ,  atol=1E-4     )     )





	@slow
	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				_snake_case          =      TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
				    'snap-research/efficientformer-l1-300'     )
				_snake_case          =      self.default_image_processor
				_snake_case          =      prepare_img()
				_snake_case          =      image_processor(images=lowercase      ,  return_tensors='tf'     )
				# forward pass
				_snake_case          =      model(**lowercase      ,  training=lowercase     )
				# verify the logits
				_snake_case          =      tf.TensorShape((1, 1_000)     )
				self.assertEqual(outputs.logits.shape      ,  lowercase     )
				_snake_case          =      tf.constant([-0.1312, 0.4353, -1.0499]     )
				self.assertTrue(np.allclose(outputs.logits[0, :3]      ,  lowercase      ,  atol=1E-4     )     )

import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase							,unittest.TestCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Tuple          = RobertaTokenizer
	_UpperCAmelCase	:		Dict          = RobertaTokenizerFast
	_UpperCAmelCase	:		List[Any]          = True
	_UpperCAmelCase	:		Any          = {"cls_token": "<s>"}



	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				super().setUp()

				# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
				_snake_case          =      [
				    'l',
				    'o',
				    'w',
				    'e',
				    'r',
				    's',
				    't',
				    'i',
				    'd',
				    'n',
				    '\u0120',
				    '\u0120l',
				    '\u0120n',
				    '\u0120lo',
				    '\u0120low',
				    'er',
				    '\u0120lowest',
				    '\u0120newer',
				    '\u0120wider',
				    '<unk>',
				]
				_snake_case          =      dict(zip(lowercase      ,  range(len(lowercase     )     )     )     )
				_snake_case          =      ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
				_snake_case          =      {'unk_token': '<unk>'}

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



	def 			A							(  self     :   List[str]      ,  **lowercase     :   List[str]     ):


				'''simple docstring'''


				kwargs.update(self.special_tokens_map     )
				return self.tokenizer_class.from_pretrained(self.tmpdirname      ,  **lowercase     )



	def 			A							(  self     :   List[str]      ,  **lowercase     :   int     ):


				'''simple docstring'''


				kwargs.update(self.special_tokens_map     )
				return RobertaTokenizerFast.from_pretrained(self.tmpdirname      ,  **lowercase     )



	def 			A							(  self     :   Optional[Any]      ,  lowercase     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      'lower newer'
				_snake_case          =      'lower newer'
				return input_text, output_text



	def 			A							(  self     :   str     ):


				'''simple docstring'''


				_snake_case          =      self.tokenizer_class(self.vocab_file      ,  self.merges_file      ,  **self.special_tokens_map     )
				_snake_case          =      'lower newer'
				_snake_case          =      ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
				_snake_case          =      tokenizer.tokenize(lowercase     )  # , add_prefix_space=True)
				self.assertListEqual(lowercase      ,  lowercase     )

				_snake_case          =      tokens + [tokenizer.unk_token]
				_snake_case          =      [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
				self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase     )      ,  lowercase     )



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      self.get_tokenizer()

				self.assertListEqual(tokenizer.encode('Hello world!'      ,  add_special_tokens=lowercase     )      ,  [0, 31_414, 232, 328, 2]     )
				self.assertListEqual(
				    tokenizer.encode('Hello world! cécé herlolip 418'      ,  add_special_tokens=lowercase     )      ,  [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]      ,  )



	@slow
	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


				_snake_case          =      self.tokenizer_class.from_pretrained('roberta-base'     )

				_snake_case          =      tokenizer.encode('sequence builders'      ,  add_special_tokens=lowercase     )
				_snake_case          =      tokenizer.encode('multi-sequence build'      ,  add_special_tokens=lowercase     )

				_snake_case          =      tokenizer.encode(
				    'sequence builders'      ,  add_special_tokens=lowercase      ,  add_prefix_space=lowercase     )
				_snake_case          =      tokenizer.encode(
				    'sequence builders'      ,  'multi-sequence build'      ,  add_special_tokens=lowercase      ,  add_prefix_space=lowercase     )

				_snake_case          =      tokenizer.build_inputs_with_special_tokens(lowercase     )
				_snake_case          =      tokenizer.build_inputs_with_special_tokens(lowercase      ,  lowercase     )

				assert encoded_sentence == encoded_text_from_decode
				assert encoded_pair == encoded_pair_from_decode



	def 			A							(  self     :   int     ):


				'''simple docstring'''


				_snake_case          =      self.get_tokenizer()

				_snake_case          =      'Encode this sequence.'
				_snake_case          =      tokenizer.byte_encoder[' '.encode('utf-8'     )[0]]

				# Testing encoder arguments
				_snake_case          =      tokenizer.encode(lowercase      ,  add_special_tokens=lowercase      ,  add_prefix_space=lowercase     )
				_snake_case          =      tokenizer.convert_ids_to_tokens(encoded[0]     )[0]
				self.assertNotEqual(lowercase      ,  lowercase     )

				_snake_case          =      tokenizer.encode(lowercase      ,  add_special_tokens=lowercase      ,  add_prefix_space=lowercase     )
				_snake_case          =      tokenizer.convert_ids_to_tokens(encoded[0]     )[0]
				self.assertEqual(lowercase      ,  lowercase     )

				tokenizer.add_special_tokens({'bos_token': '<s>'}     )
				_snake_case          =      tokenizer.encode(lowercase      ,  add_special_tokens=lowercase     )
				_snake_case          =      tokenizer.convert_ids_to_tokens(encoded[1]     )[0]
				self.assertNotEqual(lowercase      ,  lowercase     )

				# Testing spaces after special tokens
				_snake_case          =      '<mask>'
				tokenizer.add_special_tokens(
				    {'mask_token': AddedToken(lowercase      ,  lstrip=lowercase      ,  rstrip=lowercase     )}     )  # mask token has a left space
				_snake_case          =      tokenizer.convert_tokens_to_ids(lowercase     )

				_snake_case          =      'Encode <mask> sequence'
				_snake_case          =      'Encode <mask>sequence'

				_snake_case          =      tokenizer.encode(lowercase     )
				_snake_case          =      encoded.index(lowercase     )
				_snake_case          =      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]     )[0]
				self.assertEqual(lowercase      ,  lowercase     )

				_snake_case          =      tokenizer.encode(lowercase     )
				_snake_case          =      encoded.index(lowercase     )
				_snake_case          =      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]     )[0]
				self.assertNotEqual(lowercase      ,  lowercase     )



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				pass



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
										_snake_case          =      self.rust_tokenizer_class.from_pretrained(lowercase      ,  **lowercase     )
										_snake_case          =      self.tokenizer_class.from_pretrained(lowercase      ,  **lowercase     )
										_snake_case          =      'A, <mask> AllenNLP sentence.'
										_snake_case          =      tokenizer_r.encode_plus(lowercase      ,  add_special_tokens=lowercase      ,  return_token_type_ids=lowercase     )
										_snake_case          =      tokenizer_p.encode_plus(lowercase      ,  add_special_tokens=lowercase      ,  return_token_type_ids=lowercase     )

										# token_type_ids should put 0 everywhere
										self.assertEqual(sum(tokens_r['token_type_ids']     )      ,  sum(tokens_p['token_type_ids']     )     )

										# attention_mask should put 1 everywhere, so sum over length should be 1
										self.assertEqual(
										    sum(tokens_r['attention_mask']     ) / len(tokens_r['attention_mask']     )      ,  sum(tokens_p['attention_mask']     ) / len(tokens_p['attention_mask']     )      ,  )

										_snake_case          =      tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']     )
										_snake_case          =      tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']     )

										# Rust correctly handles the space before the mask while python doesnt
										self.assertSequenceEqual(tokens_p['input_ids']      ,  [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]     )
										self.assertSequenceEqual(tokens_r['input_ids']      ,  [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]     )

										self.assertSequenceEqual(
										    lowercase      ,  ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']     )
										self.assertSequenceEqual(
										    lowercase      ,  ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']     )



	def 			A							(  self     :   str     ):


				'''simple docstring'''


				for trim_offsets, add_prefix_space in itertools.product([True, False]      ,  repeat=2     ):
							_snake_case          =      self.rust_tokenizer_class.from_pretrained(
							    self.tmpdirname      ,  use_fast=lowercase      ,  add_prefix_space=lowercase      ,  trim_offsets=lowercase     )

							_snake_case          =      json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()     )
							_snake_case          =      json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()     )

							self.assertEqual(pre_tokenizer_state['add_prefix_space']      ,  lowercase     )

							self.assertEqual(post_processor_state['add_prefix_space']      ,  lowercase     )
							self.assertEqual(post_processor_state['trim_offsets']      ,  lowercase     )





	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
										_snake_case          =      'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
										_snake_case          =      f'''{text_of_1_token} {text_of_1_token}'''

										_snake_case          =      self.rust_tokenizer_class.from_pretrained(
										    lowercase      ,  use_fast=lowercase      ,  add_prefix_space=lowercase      ,  trim_offsets=lowercase     )
										_snake_case          =      tokenizer_r(lowercase      ,  return_offsets_mapping=lowercase      ,  add_special_tokens=lowercase     )
										self.assertEqual(encoding.offset_mapping[0]      ,  (0, len(lowercase     ))     )
										self.assertEqual(
										    encoding.offset_mapping[1]      ,  (len(lowercase     ) + 1, len(lowercase     ) + 1 + len(lowercase     ))      ,  )

										_snake_case          =      self.rust_tokenizer_class.from_pretrained(
										    lowercase      ,  use_fast=lowercase      ,  add_prefix_space=lowercase      ,  trim_offsets=lowercase     )
										_snake_case          =      tokenizer_r(lowercase      ,  return_offsets_mapping=lowercase      ,  add_special_tokens=lowercase     )
										self.assertEqual(encoding.offset_mapping[0]      ,  (0, len(lowercase     ))     )
										self.assertEqual(
										    encoding.offset_mapping[1]      ,  (len(lowercase     ) + 1, len(lowercase     ) + 1 + len(lowercase     ))      ,  )

										_snake_case          =      self.rust_tokenizer_class.from_pretrained(
										    lowercase      ,  use_fast=lowercase      ,  add_prefix_space=lowercase      ,  trim_offsets=lowercase     )
										_snake_case          =      tokenizer_r(lowercase      ,  return_offsets_mapping=lowercase      ,  add_special_tokens=lowercase     )
										self.assertEqual(encoding.offset_mapping[0]      ,  (0, len(lowercase     ))     )
										self.assertEqual(
										    encoding.offset_mapping[1]      ,  (len(lowercase     ), len(lowercase     ) + 1 + len(lowercase     ))      ,  )

										_snake_case          =      self.rust_tokenizer_class.from_pretrained(
										    lowercase      ,  use_fast=lowercase      ,  add_prefix_space=lowercase      ,  trim_offsets=lowercase     )
										_snake_case          =      tokenizer_r(lowercase      ,  return_offsets_mapping=lowercase      ,  add_special_tokens=lowercase     )
										self.assertEqual(encoding.offset_mapping[0]      ,  (0, len(lowercase     ))     )
										self.assertEqual(
										    encoding.offset_mapping[1]      ,  (len(lowercase     ), len(lowercase     ) + 1 + len(lowercase     ))      ,  )

										_snake_case          =      f''' {text}'''

										# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
										#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
										# )
										# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
										# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
										# self.assertEqual(
										#     encoding.offset_mapping[1],
										#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
										# )

										_snake_case          =      self.rust_tokenizer_class.from_pretrained(
										    lowercase      ,  use_fast=lowercase      ,  add_prefix_space=lowercase      ,  trim_offsets=lowercase     )
										_snake_case          =      tokenizer_r(lowercase      ,  return_offsets_mapping=lowercase      ,  add_special_tokens=lowercase     )
										self.assertEqual(encoding.offset_mapping[0]      ,  (1, 1 + len(lowercase     ))     )
										self.assertEqual(
										    encoding.offset_mapping[1]      ,  (1 + len(lowercase     ) + 1, 1 + len(lowercase     ) + 1 + len(lowercase     ))      ,  )

										_snake_case          =      self.rust_tokenizer_class.from_pretrained(
										    lowercase      ,  use_fast=lowercase      ,  add_prefix_space=lowercase      ,  trim_offsets=lowercase     )
										_snake_case          =      tokenizer_r(lowercase      ,  return_offsets_mapping=lowercase      ,  add_special_tokens=lowercase     )
										self.assertEqual(encoding.offset_mapping[0]      ,  (0, 1 + len(lowercase     ))     )
										self.assertEqual(
										    encoding.offset_mapping[1]      ,  (1 + len(lowercase     ), 1 + len(lowercase     ) + 1 + len(lowercase     ))      ,  )

										_snake_case          =      self.rust_tokenizer_class.from_pretrained(
										    lowercase      ,  use_fast=lowercase      ,  add_prefix_space=lowercase      ,  trim_offsets=lowercase     )
										_snake_case          =      tokenizer_r(lowercase      ,  return_offsets_mapping=lowercase      ,  add_special_tokens=lowercase     )
										self.assertEqual(encoding.offset_mapping[0]      ,  (0, 1 + len(lowercase     ))     )
										self.assertEqual(
										    encoding.offset_mapping[1]      ,  (1 + len(lowercase     ), 1 + len(lowercase     ) + 1 + len(lowercase     ))      ,  )