

from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
					import numpy as np
					import tensorflow as tf

					from transformers import (
					    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
					    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
					    TF_MODEL_FOR_MASKED_LM_MAPPING,
					    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
					    TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
					    TF_MODEL_FOR_PRETRAINING_MAPPING,
					    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
					    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
					    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
					    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
					    TFSharedEmbeddings,
					)

					if _tf_gpu_memory_limit is not None:
										lowerCAmelCase_	     =     tf.config.list_physical_devices('''GPU''')
										for gpu in gpus:
															# Restrict TensorFlow to only allocate x GB of memory on the GPUs
															try:
																				tf.config.set_logical_device_configuration(
																				    gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
																				)
																				lowerCAmelCase_	     =     tf.config.list_logical_devices('''GPU''')
																				print('''Logical GPUs''', logical_gpus)
															except RuntimeError as e:
																				# Virtual devices must be set before GPUs have been initialized
																				print(e)





@require_tf
class      snake_case_       :







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       Any						      =    None
			SCREAMING_SNAKE_CASE					:       Union[str, Any]						      =    ()
			SCREAMING_SNAKE_CASE					:       Union[str, Any]						      =    ()
			SCREAMING_SNAKE_CASE					:       List[str]						      =    True
			SCREAMING_SNAKE_CASE					:       Union[str, Any]						      =    True
			SCREAMING_SNAKE_CASE					:       Optional[Any]						      =    True
			SCREAMING_SNAKE_CASE					:       Any						      =    False







			def     snake_case__(					self			:    Union[str, Any]							,       _UpperCamelCase			:    Tuple							,       _UpperCamelCase			:    Tuple							,       _UpperCamelCase			:    str=False							)				->dict:
									snake_case_       =     copy.deepcopy(_UpperCamelCase							)

									if model_class in get_values(_UpperCamelCase							):
															snake_case_       =     {
															    k: tf.tile(tf.expand_dims(_UpperCamelCase							,       1							)							,       (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)							)
															    if isinstance(_UpperCamelCase							,       tf.Tensor							) and v.ndim > 0
															    else v
															    for k, v in inputs_dict.items()
															}

									if return_labels:
															if model_class in get_values(_UpperCamelCase							):
																					snake_case_       =     tf.ones(self.model_tester.batch_size							,       dtype=tf.intaa							)
															elif model_class in get_values(_UpperCamelCase							):
																					snake_case_       =     tf.zeros(self.model_tester.batch_size							,       dtype=tf.intaa							)
																					snake_case_       =     tf.zeros(self.model_tester.batch_size							,       dtype=tf.intaa							)
															elif model_class in [
															    *get_values(_UpperCamelCase							),
															    *get_values(_UpperCamelCase							),
															]:
																					snake_case_       =     tf.zeros(self.model_tester.batch_size							,       dtype=tf.intaa							)
															elif model_class in get_values(_UpperCamelCase							):
																					snake_case_       =     tf.zeros(self.model_tester.batch_size							,       dtype=tf.intaa							)
															elif model_class in [
															    *get_values(_UpperCamelCase							),
															    *get_values(_UpperCamelCase							),
															    *get_values(_UpperCamelCase							),
															    *get_values(_UpperCamelCase							),
															    *get_values(_UpperCamelCase							),
															]:
																					snake_case_       =     tf.zeros(
																					    (self.model_tester.batch_size, self.model_tester.seq_length)							,       dtype=tf.intaa							)
									return inputs_dict







			@slow
			def     snake_case__(					self			:    Optional[Any]							)				->List[Any]:
									snake_case_,   snake_case_       =     self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes[:2]:
															snake_case_       =     self._prepare_for_class(_UpperCamelCase							,       _UpperCamelCase							)
															snake_case_       =     model_class(_UpperCamelCase							)

															@tf.function
															def run_in_graph_mode():
																					return model(_UpperCamelCase							)

															snake_case_       =     run_in_graph_mode()
															self.assertIsNotNone(_UpperCamelCase							)







			@slow
			def     snake_case__(					self			:    Any							)				->Optional[int]:
									snake_case_,   snake_case_       =     self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes[:2]:
															snake_case_       =     self._prepare_for_class(_UpperCamelCase							,       _UpperCamelCase							)
															snake_case_       =     model_class(_UpperCamelCase							)

															@tf.function(experimental_compile=_UpperCamelCase							)
															def run_in_graph_mode():
																					return model(_UpperCamelCase							)

															snake_case_       =     run_in_graph_mode()
															self.assertIsNotNone(_UpperCamelCase							)







			@slow
			def     snake_case__(					self			:    Any							)				->Optional[Any]:
									# This is a copy of the test_keras_fit method, but we use XLA compilation instead of eager
									snake_case_,   snake_case_       =     self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes[:2]:
															snake_case_       =     model_class(_UpperCamelCase							)
															if getattr(_UpperCamelCase							,       '''hf_compute_loss'''							,       _UpperCamelCase							):
																					# Test that model correctly compute the loss with kwargs
																					snake_case_       =     self._prepare_for_class(inputs_dict.copy()							,       _UpperCamelCase							,       return_labels=_UpperCamelCase							)
																					# Is there a better way to remove these decoder inputs?
																					snake_case_       =     {
																					    key: val
																					    for key, val in prepared_for_class.items()
																					    if key not in ('''head_mask''', '''decoder_head_mask''', '''cross_attn_head_mask''', '''decoder_input_ids''')
																					}

																					snake_case_       =     {
																					    '''labels''',
																					    '''label''',
																					    '''label_ids''',
																					    '''start_positions''',
																					    '''start_position''',
																					    '''end_positions''',
																					    '''end_position''',
																					    '''next_sentence_label''',
																					}
																					snake_case_       =     possible_label_cols.intersection(set(_UpperCamelCase							)							)
																					self.assertGreater(len(_UpperCamelCase							)							,       0							,       msg='''No matching label names found!'''							)
																					snake_case_       =     {key: val for key, val in prepared_for_class.items() if key in label_names}
																					snake_case_       =     {key: val for key, val in prepared_for_class.items() if key not in label_names}
																					self.assertGreater(len(_UpperCamelCase							)							,       0							)

																					# Make sure it works with XLA!
																					model.compile(optimizer=tf.keras.optimizers.SGD(0.0							)							,       jit_compile=_UpperCamelCase							)
																					# Make sure the model fits without crashing regardless of where we pass the labels
																					snake_case_       =     model.fit(
																					    _UpperCamelCase							,       validation_data=_UpperCamelCase							,       steps_per_epoch=1							,       validation_steps=1							,       shuffle=_UpperCamelCase							,       verbose=0							,       )
																					snake_case_       =     history.history['''loss'''][0]
																					self.assertTrue(not isnan(_UpperCamelCase							)							)
																					snake_case_       =     history.history['''val_loss'''][0]
																					self.assertTrue(not isnan(_UpperCamelCase							)							)

																					# Now test it with separate labels, to make sure that path works in XLA too.
																					snake_case_       =     model_class(_UpperCamelCase							)
																					model.compile(optimizer=tf.keras.optimizers.SGD(0.0							)							,       jit_compile=_UpperCamelCase							)
																					snake_case_       =     model.fit(
																					    _UpperCamelCase							,       _UpperCamelCase							,       validation_data=(inputs_minus_labels, labels)							,       steps_per_epoch=1							,       validation_steps=1							,       shuffle=_UpperCamelCase							,       verbose=0							,       )

																					snake_case_       =     history.history['''loss'''][0]
																					self.assertTrue(not isnan(_UpperCamelCase							)							)
																					snake_case_       =     history.history['''val_loss'''][0]
																					self.assertTrue(not isnan(_UpperCamelCase							)							)







			@slow
			def     snake_case__(					self			:    int							)				->str:
									snake_case_,   snake_case_       =     self.model_tester.prepare_config_and_inputs_for_common()
									snake_case_       =     True
									snake_case_       =     True

									if hasattr(_UpperCamelCase							,       '''use_cache'''							):
															snake_case_       =     True

									snake_case_       =     getattr(self.model_tester							,       '''encoder_seq_length'''							,       self.model_tester.seq_length							)
									snake_case_       =     getattr(self.model_tester							,       '''key_length'''							,       _UpperCamelCase							)

									for model_class in self.all_model_classes[:2]:
															snake_case_       =     self._prepare_for_class(_UpperCamelCase							,       _UpperCamelCase							)
															snake_case_       =     model_class(_UpperCamelCase							)
															model.build()
															snake_case_       =     len(model(_UpperCamelCase							)							)

															for key in list(class_inputs_dict.keys()							):
																					# Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
																					if key not in model.input_signature:
																											del class_inputs_dict[key]
																					# Check it's a tensor, in case the inputs dict has some bools in it too
																					elif isinstance(class_inputs_dict[key]							,       tf.Tensor							) and class_inputs_dict[key].dtype.is_integer:
																											snake_case_       =     tf.cast(class_inputs_dict[key]							,       tf.intaa							)

															if set(class_inputs_dict.keys()							) != set(model.input_signature.keys()							):
																					continue  # Some models have inputs that the preparation functions don't create, we skip those

															with tempfile.TemporaryDirectory() as tmpdirname:
																					model.save_pretrained(_UpperCamelCase							,       saved_model=_UpperCamelCase							)
																					snake_case_       =     os.path.join(_UpperCamelCase							,       '''saved_model'''							,       '''1'''							)
																					snake_case_       =     tf.keras.models.load_model(_UpperCamelCase							)
																					snake_case_       =     model(_UpperCamelCase							)

																					if self.is_encoder_decoder:
																											snake_case_       =     outputs['''encoder_hidden_states''']
																											snake_case_       =     outputs['''encoder_attentions''']
																					else:
																											snake_case_       =     outputs['''hidden_states''']
																											snake_case_       =     outputs['''attentions''']

																					self.assertEqual(len(_UpperCamelCase							)							,       _UpperCamelCase							)

																					snake_case_       =     getattr(
																					    self.model_tester							,       '''expected_num_hidden_layers'''							,       self.model_tester.num_hidden_layers + 1							)

																					self.assertEqual(len(_UpperCamelCase							)							,       _UpperCamelCase							)
																					self.assertListEqual(
																					    list(output_hidden_states[0].shape[-2:]							)							,       [self.model_tester.seq_length, self.model_tester.hidden_size]							,       )

																					self.assertEqual(len(_UpperCamelCase							)							,       self.model_tester.num_hidden_layers							)
																					self.assertListEqual(
																					    list(output_attentions[0].shape[-3:]							)							,       [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]							,       )







			@slow
			def     snake_case__(					self			:    str							)				->Dict:
									tf.keras.mixed_precision.set_global_policy('''mixed_float16'''							)

									# try/finally block to ensure subsequent tests run in float32
									try:
															snake_case_,   snake_case_       =     self.model_tester.prepare_config_and_inputs_for_common()
															for model_class in self.all_model_classes[:2]:
																					snake_case_       =     self._prepare_for_class(_UpperCamelCase							,       _UpperCamelCase							)
																					snake_case_       =     model_class(_UpperCamelCase							)
																					snake_case_       =     model(_UpperCamelCase							)

																					self.assertIsNotNone(_UpperCamelCase							)
									finally:
															tf.keras.mixed_precision.set_global_policy('''float32'''							)







			@slow
			def     snake_case__(					self			:    Optional[Any]							)				->Tuple:
									snake_case_,   snake_case_       =     self.model_tester.prepare_config_and_inputs_for_common()
									# head_mask and decoder_head_mask has different shapes than other input args
									if "head_mask" in inputs_dict:
															del inputs_dict["head_mask"]
									if "decoder_head_mask" in inputs_dict:
															del inputs_dict["decoder_head_mask"]
									if "cross_attn_head_mask" in inputs_dict:
															del inputs_dict["cross_attn_head_mask"]
									snake_case_       =     {
									    module_member
									    for model_class in self.all_model_classes
									    for module in (import_module(model_class.__module__							),)
									    for module_member_name in dir(_UpperCamelCase							)
									    if module_member_name.endswith('''MainLayer'''							)
									    for module_member in (getattr(_UpperCamelCase							,       _UpperCamelCase							),)
									    if isinstance(_UpperCamelCase							,       _UpperCamelCase							)
									    and tf.keras.layers.Layer in module_member.__bases__
									    and getattr(_UpperCamelCase							,       '''_keras_serializable'''							,       _UpperCamelCase							)
									}

									for main_layer_class in tf_main_layer_classes:
															# T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
															if "T5" in main_layer_class.__name__:
																					# Take the same values than in TFT5ModelTester for this shared layer
																					snake_case_       =     TFSharedEmbeddings(self.model_tester.vocab_size							,       self.model_tester.hidden_size							,       name='''shared'''							)
																					snake_case_       =     False
																					snake_case_       =     main_layer_class(_UpperCamelCase							,       embed_tokens=_UpperCamelCase							)
															else:
																					snake_case_       =     main_layer_class(_UpperCamelCase							)

															snake_case_       =     {
															    name: tf.keras.Input(tensor.shape[1:]							,       dtype=tensor.dtype							) for name, tensor in inputs_dict.items()
															}

															if hasattr(self.model_tester							,       '''num_labels'''							):
																					snake_case_       =     self.model_tester.num_labels
															else:
																					snake_case_       =     2

															snake_case_       =     tf.data.Dataset.from_tensor_slices(
															    (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)							))							).batch(1							)

															snake_case_       =     main_layer(_UpperCamelCase							)[0]
															snake_case_       =     tf.keras.layers.Dense(_UpperCamelCase							,       activation='''softmax'''							,       name='''outputs'''							)(_UpperCamelCase							)
															snake_case_       =     tf.keras.models.Model(inputs=_UpperCamelCase							,       outputs=[outputs]							)

															model.compile(loss='''binary_crossentropy'''							,       optimizer='''adam'''							,       metrics=['''binary_accuracy''']							)
															model.fit(_UpperCamelCase							,       epochs=1							)

															with tempfile.TemporaryDirectory() as tmpdirname:
																					snake_case_       =     os.path.join(_UpperCamelCase							,       '''keras_model.h5'''							)
																					model.save(_UpperCamelCase							)
																					if "T5" in main_layer_class.__name__:
																											snake_case_       =     tf.keras.models.load_model(
																											    _UpperCamelCase							,       custom_objects={
																											        main_layer_class.__name__: main_layer_class,
																											        '''TFSharedEmbeddings''': TFSharedEmbeddings,
																											    }							,       )
																					else:
																											snake_case_       =     tf.keras.models.load_model(
																											    _UpperCamelCase							,       custom_objects={main_layer_class.__name__: main_layer_class}							)
																					assert isinstance(_UpperCamelCase							,       tf.keras.Model							)
																					model(_UpperCamelCase							)







			@slow
			def     snake_case__(					self			:    List[str]							)				->List[str]:
									snake_case_,   snake_case_       =     self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes[:2]:
															snake_case_       =     model_class(_UpperCamelCase							)

															snake_case_       =     copy.deepcopy(_UpperCamelCase							)

															if not self.is_encoder_decoder:
																					snake_case_       =     inputs['''input_ids''']
																					del inputs["input_ids"]
															else:
																					snake_case_       =     inputs['''input_ids''']
																					snake_case_       =     inputs.get('''decoder_input_ids'''							,       _UpperCamelCase							)
																					del inputs["input_ids"]
																					inputs.pop('''decoder_input_ids'''							,       _UpperCamelCase							)

															if not self.is_encoder_decoder:
																					snake_case_       =     model.get_input_embeddings()(_UpperCamelCase							)
															else:
																					snake_case_       =     model.get_input_embeddings()(_UpperCamelCase							)
																					snake_case_       =     model.get_input_embeddings()(_UpperCamelCase							)

															snake_case_       =     self._prepare_for_class(_UpperCamelCase							,       _UpperCamelCase							)

															@tf.function
															def run_in_graph_mode():
																					return model(_UpperCamelCase							)

															snake_case_       =     run_in_graph_mode()
															self.assertIsNotNone(_UpperCamelCase							)







			def     snake_case__(					self			:    Union[str, Any]							,       _UpperCamelCase			:    int							,       _UpperCamelCase			:    Tuple							)				->Any:
									# special tokens cannot be bad tokens
									snake_case_       =     []
									if model.config.bos_token_id is not None:
															special_tokens.append(model.config.bos_token_id							)
									if model.config.pad_token_id is not None:
															special_tokens.append(model.config.pad_token_id							)
									if model.config.eos_token_id is not None:
															special_tokens.append(model.config.eos_token_id							)

									# create random bad tokens that are not special tokens
									snake_case_       =     []
									while len(_UpperCamelCase							) < num_bad_tokens:
															snake_case_       =     tf.squeeze(ids_tensor((1, 1)							,       self.model_tester.vocab_size							)							,       0							).numpy()[0]
															if token not in special_tokens:
																					bad_tokens.append(_UpperCamelCase							)
									return bad_tokens







			def     snake_case__(					self			:    List[str]							,       _UpperCamelCase			:    int							)				->List[Any]:
									for token_id in output_ids[0].numpy().tolist():
															self.assertGreaterEqual(_UpperCamelCase							,       0							)
															self.assertLess(_UpperCamelCase							,       self.model_tester.vocab_size							)







			def     snake_case__(					self			:    Dict							,       _UpperCamelCase			:    str							,       _UpperCamelCase			:    Dict							)				->str:
									# for all bad word tokens
									for bad_word_ids in bad_words_ids:
															# for all slices in batch
															for generated_ids_slice in generated_ids:
																					# for all word idx
																					for i in range(len(_UpperCamelCase							)							,       len(_UpperCamelCase							)							):
																											# if tokens match
																											if generated_ids_slice[i - len(_UpperCamelCase							) : i] == bad_word_ids:
																																	return True
									return False


import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class      snake_case_       (		__A     ,	unittest.TestCase ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       str						      =    PreTrainedTokenizerFast
			SCREAMING_SNAKE_CASE					:       List[str]						      =    False
			SCREAMING_SNAKE_CASE					:       Tuple						      =    True
			SCREAMING_SNAKE_CASE					:       str						      =    "tokenizer_file"







			def     snake_case__(					self			:    Dict							)				->int:
									snake_case_       =     False  # because we don't have pretrained_vocab_files_map
									super().setUp()
									snake_case_       =     True

									snake_case_       =     ['''robot-test/dummy-tokenizer-fast''', '''robot-test/dummy-tokenizer-wordlevel''']
									snake_case_       =     '''SaulLu/dummy-tokenizer-bytelevel-bpe'''

									# Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
									snake_case_       =     [(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

									snake_case_       =     PreTrainedTokenizerFast.from_pretrained(model_paths[0]							)
									tokenizer.save_pretrained(self.tmpdirname							)







			def     snake_case__(					self			:    Union[str, Any]							)				->Dict:
									# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
									# model
									pass







			def     snake_case__(					self			:    str							)				->Any:
									# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
									# model
									pass







			def     snake_case__(					self			:    List[Any]							)				->str:
									# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
									# model
									pass







			def     snake_case__(					self			:    str							)				->List[Any]:
									# PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
									pass







			def     snake_case__(					self			:    Any							)				->Union[str, Any]:
									snake_case_       =     self.tmpdirname
									# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
																					try:
																											snake_case_       =     tempfile.mkdtemp()
																											snake_case_       =     self.rust_tokenizer_class.from_pretrained(_UpperCamelCase							,       **_UpperCamelCase							)

																											tokenizer.save_pretrained(self.tmpdirname							)
																											super().test_training_new_tokenizer()
																					finally:
																											# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
																											# is restored
																											shutil.rmtree(self.tmpdirname							)
																											snake_case_       =     tmpdirname_orig







			def     snake_case__(					self			:    List[str]							)				->List[Any]:
									snake_case_       =     self.tmpdirname
									# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
																					try:
																											snake_case_       =     tempfile.mkdtemp()
																											snake_case_       =     self.rust_tokenizer_class.from_pretrained(_UpperCamelCase							,       **_UpperCamelCase							)

																											tokenizer.save_pretrained(self.tmpdirname							)
																											super().test_training_new_tokenizer_with_special_tokens_change()
																					finally:
																											# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
																											# is restored
																											shutil.rmtree(self.tmpdirname							)
																											snake_case_       =     tmpdirname_orig







			def     snake_case__(					self			:    Optional[int]							)				->Optional[Any]:
									snake_case_       =     self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name							)

									snake_case_       =     ('''a''' for _ in range(1_0_0_0							))
									snake_case_       =     tokenizer.train_new_from_iterator(text_iterator=_UpperCamelCase							,       length=1_0_0_0							,       vocab_size=5_0							)

									snake_case_       =     new_tokenizer.encode('''a🤗'''							)
									self.assertEqual(_UpperCamelCase							,       [6_4, 1_7_2, 2_5_3, 9_7, 2_4_5]							)






@require_tokenizers
class      snake_case_       (		unittest.TestCase ):







			'''simple docstring'''

			def     snake_case__(					self			:    Optional[int]							)				->int:
									snake_case_       =     AutoTokenizer.from_pretrained('''bert-base-cased'''							)
									snake_case_       =     json.loads(tokenizer._tokenizer.to_str()							)
									snake_case_       =     len(_UpperCamelCase							)

									with tempfile.TemporaryDirectory() as tmp_dir:
															# Hack to save this in the tokenizer_config.json
															snake_case_       =     ['''tokenizer.4.0.0.json''']
															tokenizer.save_pretrained(_UpperCamelCase							)
															json.dump(_UpperCamelCase							,       open(os.path.join(_UpperCamelCase							,       '''tokenizer.4.0.0.json'''							)							,       '''w'''							)							)

															# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
															snake_case_       =     AutoTokenizer.from_pretrained(_UpperCamelCase							)
															self.assertEqual(len(_UpperCamelCase							)							,       len(_UpperCamelCase							) + 1							)
															snake_case_       =     json.loads(new_tokenizer._tokenizer.to_str()							)
															self.assertIn('''huggingface'''							,       json_tokenizer['''model''']['''vocab''']							)

															# Will need to be adjusted if we reach v42 and this test is still here.
															# Should pick the old tokenizer file as the version of Transformers is < 4.0.0
															shutil.move(os.path.join(_UpperCamelCase							,       '''tokenizer.4.0.0.json'''							)							,       os.path.join(_UpperCamelCase							,       '''tokenizer.42.0.0.json'''							)							)
															snake_case_       =     ['''tokenizer.42.0.0.json''']
															tokenizer.save_pretrained(_UpperCamelCase							)
															snake_case_       =     AutoTokenizer.from_pretrained(_UpperCamelCase							)
															self.assertEqual(len(_UpperCamelCase							)							,       len(_UpperCamelCase							)							)
															snake_case_       =     json.loads(new_tokenizer._tokenizer.to_str()							)
															self.assertNotIn('''huggingface'''							,       json_tokenizer['''model''']['''vocab''']							)







			def     snake_case__(					self			:    List[str]							)				->Optional[Any]:
									# This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower.
									snake_case_       =     '''hf-internal-testing/test-two-tokenizers'''

									# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
									snake_case_       =     AutoTokenizer.from_pretrained(_UpperCamelCase							)
									self.assertEqual(len(_UpperCamelCase							)							,       2_8_9_9_7							)
									snake_case_       =     json.loads(tokenizer._tokenizer.to_str()							)
									self.assertIn('''huggingface'''							,       json_tokenizer['''model''']['''vocab''']							)

									# Testing an older version by monkey-patching the version in the module it's used.
									import transformers as old_transformers

									snake_case_       =     '''3.0.0'''
									snake_case_       =     old_transformers.models.auto.AutoTokenizer.from_pretrained(_UpperCamelCase							)
									self.assertEqual(len(_UpperCamelCase							)							,       2_8_9_9_6							)
									snake_case_       =     json.loads(old_tokenizer._tokenizer.to_str()							)
									self.assertNotIn('''huggingface'''							,       json_tokenizer['''model''']['''vocab''']							)






@require_tokenizers
class      snake_case_       (		unittest.TestCase ):







			'''simple docstring'''

			def     snake_case__(					self			:    str							)				->Optional[int]:
									# See https://github.com/huggingface/transformers/pull/12550
									# and https://github.com/huggingface/tokenizers/issues/537
									snake_case_       =     PreTrainedTokenizerFast.from_pretrained('''robot-test/dummy-tokenizer-wordlevel'''							)
									snake_case_       =     '''The Matrix is a 1999 science fiction action film.'''

									with concurrent.futures.ThreadPoolExecutor() as executor:
															snake_case_       =     [executor.submit(self.fetch							,       _UpperCamelCase							,       _UpperCamelCase							) for i in range(1_0							)]
															snake_case_       =     [future.result() for future in futures]
															self.assertEqual(_UpperCamelCase							,       [[1, 1_0, 0, 8, 0, 1_8, 0, 0, 0, 2] for i in range(1_0							)]							)







			def     snake_case__(					self			:    Optional[int]							,       _UpperCamelCase			:    Dict							,       _UpperCamelCase			:    str							)				->Tuple:
									return tokenizer.encode(_UpperCamelCase							,       truncation='''longest_first'''							,       padding='''longest'''							)