






'''simple docstring'''

import inspect
import unittest

from transformers import EfficientNetConfig
from transformers.testing_utils import is_pipeline_test, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import EfficientNetForImageClassification, EfficientNetModel
						from transformers.models.efficientnet.modeling_efficientnet import EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor






class 				__magic_name__  :







	"""simple docstring"""



	def __init__(				self	,   _lowercase	,   _lowercase=13	,   _lowercase=32	,   _lowercase=3	,   _lowercase=[3, 3, 5]	,   _lowercase=[32, 16, 24]	,   _lowercase=[16, 24, 40]	,   _lowercase=[1, 1, 2]	,   _lowercase=[1, 1, 2]	,   _lowercase=[1, 6, 6]	,   _lowercase=True	,   _lowercase=True	,   _lowercase=37	,   _lowercase="gelu"	,   _lowercase=10	,   )    ->	Dict:
							lowercase_   : Union[str, Any]   	=      parent
							lowercase_   : Optional[Any]   	=      batch_size
							lowercase_   : List[str]   	=      image_size
							lowercase_   : int   	=      num_channels
							lowercase_   : Union[str, Any]   	=      kernel_sizes
							lowercase_   : Optional[int]   	=      in_channels
							lowercase_   : Optional[int]   	=      out_channels
							lowercase_   : List[str]   	=      strides
							lowercase_   : List[str]   	=      num_block_repeats
							lowercase_   : Union[str, Any]   	=      expand_ratios
							lowercase_   : Optional[Any]   	=      is_training
							lowercase_   : Optional[Any]   	=      hidden_act
							lowercase_   : int   	=      num_labels
							lowercase_   : List[Any]   	=      use_labels
	def 							lowerCamelCase__		(				self							)    ->	int:
							lowercase_   : Union[str, Any]   	=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

							lowercase_   : Optional[int]   	=      None
							if self.use_labels:
													lowercase_   : Dict   	=      ids_tensor([self.batch_size]	,   self.num_labels							)

							lowercase_   : Any   	=      self.get_config()
							return config, pixel_values, labels
	def 							lowerCamelCase__		(				self							)    ->	Tuple:
							return EfficientNetConfig(
							    num_channels=self.num_channels	,   kernel_sizes=self.kernel_sizes	,   in_channels=self.in_channels	,   out_channels=self.out_channels	,   strides=self.strides	,   num_block_repeats=self.num_block_repeats	,   expand_ratios=self.expand_ratios	,   hidden_act=self.hidden_act	,   num_labels=self.num_labels	,   )
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase	,   _lowercase							)    ->	List[Any]:
							lowercase_   : Dict   	=      EfficientNetModel(config=_lowercase							)
							model.to(_lowercase							)
							model.eval()
							lowercase_   : Optional[Any]   	=      model(_lowercase							)
							# expected last hidden states: B, C, H // 4, W // 4
							self.parent.assertEqual(
							    result.last_hidden_state.shape	,   (self.batch_size, config.hidden_dim, self.image_size // 4, self.image_size // 4)	,   )
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase	,   _lowercase							)    ->	Any:
							lowercase_   : Tuple   	=      EfficientNetForImageClassification(_lowercase							)
							model.to(_lowercase							)
							model.eval()
							lowercase_   : int   	=      model(_lowercase	,   labels=_lowercase							)
							self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.num_labels)							)







	def 							lowerCamelCase__		(				self							)    ->	Tuple:
							lowercase_   : int   	=      self.prepare_config_and_inputs()
							lowercase_	,   lowercase_	,   lowercase_   : List[str]   	=      config_and_inputs
							lowercase_   : Any   	=      {'pixel_values': pixel_values}
							return config, inputs_dict






@require_torch
class 				__magic_name__  (						UpperCAmelCase_,    UpperCAmelCase_,    unittest.TestCase					):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_       :   Tuple   =	(EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
	SCREAMING_SNAKE_CASE_       :   Optional[int]   =	(
	    {'feature-extraction': EfficientNetModel, 'image-classification': EfficientNetForImageClassification}
	    if is_torch_available()
	    else {}
	)

	SCREAMING_SNAKE_CASE_       :   List[str]   =	False
	SCREAMING_SNAKE_CASE_       :   Union[str, Any]   =	False
	SCREAMING_SNAKE_CASE_       :   str   =	False
	SCREAMING_SNAKE_CASE_       :   str   =	False
	SCREAMING_SNAKE_CASE_       :   List[Any]   =	False
	def 							lowerCamelCase__		(				self							)    ->	Union[str, Any]:
							lowercase_   : Optional[Any]   	=      EfficientNetModelTester(self							)
							lowercase_   : Union[str, Any]   	=      ConfigTester(
							    self	,   config_class=_lowercase	,   has_text_modality=_lowercase	,   hidden_size=37							)
	def 							lowerCamelCase__		(				self							)    ->	Optional[Any]:
							self.create_and_test_config_common_properties()
							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()
	def 							lowerCamelCase__		(				self							)    ->	Tuple:
							return
	@unittest.skip(reason='EfficientNet does not use inputs_embeds'							)
	def 							lowerCamelCase__		(				self							)    ->	List[Any]:
							pass
	@unittest.skip(reason='EfficientNet does not support input and output embeddings'							)
	def 							lowerCamelCase__		(				self							)    ->	str:
							pass
	@unittest.skip(reason='EfficientNet does not use feedforward chunking'							)
	def 							lowerCamelCase__		(				self							)    ->	Any:
							pass
	def 							lowerCamelCase__		(				self							)    ->	Tuple:
							lowercase_	,   lowercase_   : Union[str, Any]   	=      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													lowercase_   : Any   	=      model_class(_lowercase							)
													lowercase_   : str   	=      inspect.signature(model.forward							)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													lowercase_   : List[str]   	=      [*signature.parameters.keys()]

													lowercase_   : Optional[int]   	=      ['pixel_values']
													self.assertListEqual(arg_names[:1]	,   _lowercase							)
	def 							lowerCamelCase__		(				self							)    ->	int:
							lowercase_   : Any   	=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_lowercase							)
	def 							lowerCamelCase__		(				self							)    ->	Dict:
							def check_hidden_states_output(_lowercase	,   _lowercase	,   _lowercase							):
													lowercase_   : Dict   	=      model_class(_lowercase							)
													model.to(_lowercase							)
													model.eval()

													with torch.no_grad():
																			lowercase_   : List[Any]   	=      model(**self._prepare_for_class(_lowercase	,   _lowercase							)							)

													lowercase_   : Dict   	=      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
													lowercase_   : List[Any]   	=      sum(config.num_block_repeats							) * 4
													self.assertEqual(len(_lowercase							)	,   _lowercase							)

													# EfficientNet's feature maps are of shape (batch_size, num_channels, height, width)
													self.assertListEqual(
													    list(hidden_states[0].shape[-2:]							)	,   [self.model_tester.image_size // 2, self.model_tester.image_size // 2]	,   )

							lowercase_	,   lowercase_   : List[Any]   	=      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													lowercase_   : Union[str, Any]   	=      True
													check_hidden_states_output(_lowercase	,   _lowercase	,   _lowercase							)

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													lowercase_   : str   	=      True

													check_hidden_states_output(_lowercase	,   _lowercase	,   _lowercase							)
	def 							lowerCamelCase__		(				self							)    ->	Dict:
							lowercase_   : Dict   	=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_lowercase							)
	@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'							)
	def 							lowerCamelCase__		(				self							)    ->	str:
							pass
	@slow
	def 							lowerCamelCase__		(				self							)    ->	Union[str, Any]:
							for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowercase_   : Tuple   	=      EfficientNetModel.from_pretrained(_lowercase							)
													self.assertIsNotNone(_lowercase							)







	@is_pipeline_test
	@require_vision
	@slow
	def 							lowerCamelCase__		(				self							)    ->	Optional[Any]:
							super().test_pipeline_image_classification()






def 						_UpperCAmelCase     (	)      ->      List[str]:





						"""simple docstring"""
						lowercase_   : str   	=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
						return image






@require_torch
@require_vision
class 				__magic_name__  (						unittest.TestCase					):







	"""simple docstring"""



	@cached_property
	def 							lowerCamelCase__		(				self							)    ->	Optional[Any]:
							return AutoImageProcessor.from_pretrained('google/efficientnet-b7'							) if is_vision_available() else None







	@slow
	def 							lowerCamelCase__		(				self							)    ->	Any:
							lowercase_   : Tuple   	=      EfficientNetForImageClassification.from_pretrained('google/efficientnet-b7'							).to(_lowercase							)

							lowercase_   : Union[str, Any]   	=      self.default_image_processor
							lowercase_   : Any   	=      prepare_img()
							lowercase_   : int   	=      image_processor(images=_lowercase	,   return_tensors='pt'							).to(_lowercase							)

							# forward pass
							with torch.no_grad():
													lowercase_   : Optional[int]   	=      model(**_lowercase							)

							# verify the logits
							lowercase_   : str   	=      torch.Size((1, 1000)							)
							self.assertEqual(outputs.logits.shape	,   _lowercase							)

							lowercase_   : Any   	=      torch.tensor([0.00_01, 0.00_02, 0.00_02]							).to(_lowercase							)
							self.assertTrue(torch.allclose(outputs.logits[0, :3]	,   _lowercase	,   atol=1E-4							)							)








'''simple docstring'''

import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
						from tokenizers import Tokenizer
						from tokenizers.models import WordPiece






class 				__magic_name__  (						unittest.TestCase					):







	"""simple docstring"""



	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	Optional[int]:
							lowercase_   : Optional[int]   	=      list(tokenizer_class.max_model_input_sizes.keys()							)
							for model_name in sa_models[:1]:
													lowercase_   : List[str]   	=      tokenizer_class.from_pretrained(_lowercase							)
													self.assertIsNotNone(_lowercase							)
													self.assertIsInstance(_lowercase	,   _lowercase							)
													self.assertIsInstance(_lowercase	,   _lowercase							)

													for special_tok in tokenizer.all_special_tokens:
																			self.assertIsInstance(_lowercase	,   _lowercase							)
																			lowercase_   : Optional[int]   	=      tokenizer.convert_tokens_to_ids(_lowercase							)
																			self.assertIsInstance(_lowercase	,   _lowercase							)
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase = None							)    ->	str:
							lowercase_   : int   	=      pickle.dumps(_lowercase							)
							self.assertIsNotNone(_lowercase							)

							lowercase_   : Any   	=      pickle.loads(_lowercase							)

							# Ensure is_fast is correctly restored
							self.assertEqual(be_restored.is_fast	,   be_original.is_fast							)

							# Ensure encodings are potentially correctly restored
							if be_original.is_fast:
													self.assertIsNotNone(be_restored.encodings							)
							else:
													self.assertIsNone(be_restored.encodings							)

							# Ensure the keys are the same
							for original_v, restored_v in zip(be_original.values()	,   be_restored.values()							):
													if equal_op:
																			self.assertTrue(equal_op(_lowercase	,   _lowercase							)							)
													else:
																			self.assertEqual(_lowercase	,   _lowercase							)
	@slow
	def 							lowerCamelCase__		(				self							)    ->	List[str]:
							self.check_tokenizer_from_pretrained(_lowercase							)
	def 							lowerCamelCase__		(				self							)    ->	str:
							self.assertEqual(TensorType('tf'							)	,   TensorType.TENSORFLOW							)
							self.assertEqual(TensorType('pt'							)	,   TensorType.PYTORCH							)
							self.assertEqual(TensorType('np'							)	,   TensorType.NUMPY							)
	@require_tokenizers
	def 							lowerCamelCase__		(				self							)    ->	Dict:
							import numpy as np

							lowercase_   : Any   	=      BertTokenizer.from_pretrained('bert-base-cased'							)
							lowercase_   : str   	=      BertTokenizerFast.from_pretrained('bert-base-cased'							)

							# Python no tensor
							with self.subTest('BatchEncoding (Python, return_tensors=None)'							):
													self.assert_dump_and_restore(tokenizer_p('Small example to encode'							)							)

							with self.subTest('BatchEncoding (Python, return_tensors=NUMPY)'							):
													self.assert_dump_and_restore(
													    tokenizer_p('Small example to encode'	,   return_tensors=TensorType.NUMPY							)	,   np.array_equal							)

							with self.subTest('BatchEncoding (Rust, return_tensors=None)'							):
													self.assert_dump_and_restore(tokenizer_r('Small example to encode'							)							)

							with self.subTest('BatchEncoding (Rust, return_tensors=NUMPY)'							):
													self.assert_dump_and_restore(
													    tokenizer_r('Small example to encode'	,   return_tensors=TensorType.NUMPY							)	,   np.array_equal							)
	@require_tf
	@require_tokenizers
	def 							lowerCamelCase__		(				self							)    ->	Optional[int]:
							import tensorflow as tf

							def tf_array_equals(_lowercase	,   _lowercase							):
													return tf.reduce_all(tf.equal(_lowercase	,   _lowercase							)							)

							lowercase_   : Optional[Any]   	=      BertTokenizer.from_pretrained('bert-base-cased'							)
							lowercase_   : str   	=      BertTokenizerFast.from_pretrained('bert-base-cased'							)

							with self.subTest('BatchEncoding (Python, return_tensors=TENSORFLOW)'							):
													self.assert_dump_and_restore(
													    tokenizer_p('Small example to encode'	,   return_tensors=TensorType.TENSORFLOW							)	,   _lowercase							)

							with self.subTest('BatchEncoding (Rust, return_tensors=TENSORFLOW)'							):
													self.assert_dump_and_restore(
													    tokenizer_r('Small example to encode'	,   return_tensors=TensorType.TENSORFLOW							)	,   _lowercase							)
	@require_torch
	@require_tokenizers
	def 							lowerCamelCase__		(				self							)    ->	List[str]:
							import torch

							lowercase_   : Dict   	=      BertTokenizer.from_pretrained('bert-base-cased'							)
							lowercase_   : Optional[int]   	=      BertTokenizerFast.from_pretrained('bert-base-cased'							)

							with self.subTest('BatchEncoding (Python, return_tensors=PYTORCH)'							):
													self.assert_dump_and_restore(
													    tokenizer_p('Small example to encode'	,   return_tensors=TensorType.PYTORCH							)	,   torch.equal							)

							with self.subTest('BatchEncoding (Rust, return_tensors=PYTORCH)'							):
													self.assert_dump_and_restore(
													    tokenizer_r('Small example to encode'	,   return_tensors=TensorType.PYTORCH							)	,   torch.equal							)
	@require_tokenizers
	def 							lowerCamelCase__		(				self							)    ->	Tuple:
							lowercase_   : Dict   	=      BertTokenizer.from_pretrained('bert-base-cased'							)
							lowercase_   : Tuple   	=      BertTokenizerFast.from_pretrained('bert-base-cased'							)

							with self.subTest('Python Tokenizer'							):
													self.assertFalse(tokenizer_p('Small example to_encode'							).is_fast							)

							with self.subTest('Rust Tokenizer'							):
													self.assertTrue(tokenizer_r('Small example to_encode'							).is_fast							)
	@require_tokenizers
	def 							lowerCamelCase__		(				self							)    ->	List[Any]:
							lowercase_   : Optional[Any]   	=      BertTokenizerFast.from_pretrained('bert-base-cased'							)
							lowercase_   : Dict   	=      tokenizer_r(['Test', '\xad', 'test']	,   is_split_into_words=_lowercase							)

							self.assertEqual(encoded.word_to_tokens(0							)	,   TokenSpan(start=1	,   end=2							)							)
							self.assertEqual(encoded.word_to_tokens(1							)	,   _lowercase							)
							self.assertEqual(encoded.word_to_tokens(2							)	,   TokenSpan(start=2	,   end=3							)							)
	def 							lowerCamelCase__		(				self							)    ->	str:
							lowercase_   : Dict   	=      BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}							)
							lowercase_   : Any   	=      batch.convert_to_tensors(tensor_type='np'							)
							self.assertEqual(tensor_batch['inputs'].shape	,   (2, 3)							)
							self.assertEqual(tensor_batch['labels'].shape	,   (2,)							)
							# test converting the converted
							with CaptureStderr() as cs:
													lowercase_   : Dict   	=      batch.convert_to_tensors(tensor_type='np'							)
							self.assertFalse(len(cs.err							)	,   msg=f"should have no warning, but got {cs.err}"							)

							lowercase_   : Union[str, Any]   	=      BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}							)
							lowercase_   : Optional[int]   	=      batch.convert_to_tensors(tensor_type='np'	,   prepend_batch_axis=_lowercase							)
							self.assertEqual(tensor_batch['inputs'].shape	,   (1, 3)							)
							self.assertEqual(tensor_batch['labels'].shape	,   (1,)							)
	@require_torch
	def 							lowerCamelCase__		(				self							)    ->	Union[str, Any]:
							lowercase_   : Dict   	=      BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}							)
							lowercase_   : Optional[Any]   	=      batch.convert_to_tensors(tensor_type='pt'							)
							self.assertEqual(tensor_batch['inputs'].shape	,   (2, 3)							)
							self.assertEqual(tensor_batch['labels'].shape	,   (2,)							)
							# test converting the converted
							with CaptureStderr() as cs:
													lowercase_   : Optional[Any]   	=      batch.convert_to_tensors(tensor_type='pt'							)
							self.assertFalse(len(cs.err							)	,   msg=f"should have no warning, but got {cs.err}"							)

							lowercase_   : int   	=      BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}							)
							lowercase_   : Union[str, Any]   	=      batch.convert_to_tensors(tensor_type='pt'	,   prepend_batch_axis=_lowercase							)
							self.assertEqual(tensor_batch['inputs'].shape	,   (1, 3)							)
							self.assertEqual(tensor_batch['labels'].shape	,   (1,)							)
	@require_tf
	def 							lowerCamelCase__		(				self							)    ->	Tuple:
							lowercase_   : Dict   	=      BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}							)
							lowercase_   : List[Any]   	=      batch.convert_to_tensors(tensor_type='tf'							)
							self.assertEqual(tensor_batch['inputs'].shape	,   (2, 3)							)
							self.assertEqual(tensor_batch['labels'].shape	,   (2,)							)
							# test converting the converted
							with CaptureStderr() as cs:
													lowercase_   : Tuple   	=      batch.convert_to_tensors(tensor_type='tf'							)
							self.assertFalse(len(cs.err							)	,   msg=f"should have no warning, but got {cs.err}"							)

							lowercase_   : int   	=      BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}							)
							lowercase_   : Optional[Any]   	=      batch.convert_to_tensors(tensor_type='tf'	,   prepend_batch_axis=_lowercase							)
							self.assertEqual(tensor_batch['inputs'].shape	,   (1, 3)							)
							self.assertEqual(tensor_batch['labels'].shape	,   (1,)							)
	@require_flax
	def 							lowerCamelCase__		(				self							)    ->	Union[str, Any]:
							lowercase_   : int   	=      BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}							)
							lowercase_   : Optional[Any]   	=      batch.convert_to_tensors(tensor_type='jax'							)
							self.assertEqual(tensor_batch['inputs'].shape	,   (2, 3)							)
							self.assertEqual(tensor_batch['labels'].shape	,   (2,)							)
							# test converting the converted
							with CaptureStderr() as cs:
													lowercase_   : Dict   	=      batch.convert_to_tensors(tensor_type='jax'							)
							self.assertFalse(len(cs.err							)	,   msg=f"should have no warning, but got {cs.err}"							)

							lowercase_   : List[Any]   	=      BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}							)
							lowercase_   : Dict   	=      batch.convert_to_tensors(tensor_type='jax'	,   prepend_batch_axis=_lowercase							)
							self.assertEqual(tensor_batch['inputs'].shape	,   (1, 3)							)
							self.assertEqual(tensor_batch['labels'].shape	,   (1,)							)
	def 							lowerCamelCase__		(				self							)    ->	List[str]:
							lowercase_   : Any   	=      [{'input_ids': np.array([0, 1, 2]							)}, {'input_ids': np.array([0, 1, 2, 3]							)}]
							lowercase_   : str   	=      BertTokenizer.from_pretrained('bert-base-cased'							)

							lowercase_   : Any   	=      tokenizer.pad(_lowercase	,   padding=_lowercase							)
							self.assertTrue(isinstance(batch['input_ids']	,   np.ndarray							)							)
							self.assertEqual(batch['input_ids'].tolist()	,   [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]							)
							lowercase_   : List[str]   	=      tokenizer.pad(_lowercase	,   padding=_lowercase	,   return_tensors='np'							)
							self.assertTrue(isinstance(batch['input_ids']	,   np.ndarray							)							)
							self.assertEqual(batch['input_ids'].tolist()	,   [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]							)
	@require_torch
	def 							lowerCamelCase__		(				self							)    ->	List[Any]:
							import torch

							lowercase_   : Union[str, Any]   	=      [{'input_ids': torch.tensor([0, 1, 2]							)}, {'input_ids': torch.tensor([0, 1, 2, 3]							)}]
							lowercase_   : Optional[int]   	=      BertTokenizer.from_pretrained('bert-base-cased'							)

							lowercase_   : str   	=      tokenizer.pad(_lowercase	,   padding=_lowercase							)
							self.assertTrue(isinstance(batch['input_ids']	,   torch.Tensor							)							)
							self.assertEqual(batch['input_ids'].tolist()	,   [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]							)
							lowercase_   : Tuple   	=      tokenizer.pad(_lowercase	,   padding=_lowercase	,   return_tensors='pt'							)
							self.assertTrue(isinstance(batch['input_ids']	,   torch.Tensor							)							)
							self.assertEqual(batch['input_ids'].tolist()	,   [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]							)
	@require_tf
	def 							lowerCamelCase__		(				self							)    ->	Any:
							import tensorflow as tf

							lowercase_   : Any   	=      [{'input_ids': tf.constant([0, 1, 2]							)}, {'input_ids': tf.constant([0, 1, 2, 3]							)}]
							lowercase_   : Dict   	=      BertTokenizer.from_pretrained('bert-base-cased'							)

							lowercase_   : Any   	=      tokenizer.pad(_lowercase	,   padding=_lowercase							)
							self.assertTrue(isinstance(batch['input_ids']	,   tf.Tensor							)							)
							self.assertEqual(batch['input_ids'].numpy().tolist()	,   [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]							)
							lowercase_   : Dict   	=      tokenizer.pad(_lowercase	,   padding=_lowercase	,   return_tensors='tf'							)
							self.assertTrue(isinstance(batch['input_ids']	,   tf.Tensor							)							)
							self.assertEqual(batch['input_ids'].numpy().tolist()	,   [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]							)
	@require_tokenizers
	def 							lowerCamelCase__		(				self							)    ->	str:
							lowercase_   : Optional[int]   	=      Tokenizer(WordPiece(unk_token='[UNK]'							)							)
							PreTrainedTokenizerFast(tokenizer_object=_lowercase							)







	@require_tokenizers
	def 							lowerCamelCase__		(				self							)    ->	int:
							lowercase_   : Union[str, Any]   	=      Tokenizer(WordPiece(unk_token='[UNK]'							)							)
							with tempfile.TemporaryDirectory() as tmpdirname:
													bert_tokenizer.save(os.path.join(_lowercase	,   'tokenizer.json'							)							)
													PreTrainedTokenizerFast(tokenizer_file=os.path.join(_lowercase	,   'tokenizer.json'							)							)
