




'''simple docstring'''


from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

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 TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor

class      A       :







							def __init__(    self			:    int				,     lowerCAmelCase_			:    Dict				,     lowerCAmelCase_			:    List[Any]=13				,     lowerCAmelCase_			:    Any=30				,     lowerCAmelCase_			:    int=2				,     lowerCAmelCase_			:    Union[str, Any]=3				,     lowerCAmelCase_			:    List[Any]=True				,     lowerCAmelCase_			:    Any=True				,     lowerCAmelCase_			:    Dict=32				,     lowerCAmelCase_			:    Optional[Any]=2				,     lowerCAmelCase_			:    Optional[Any]=4				,     lowerCAmelCase_			:    List[Any]=37				,     lowerCAmelCase_			:    str="gelu"				,     lowerCAmelCase_			:    Optional[Any]=0.1				,     lowerCAmelCase_			:    Optional[int]=0.1				,     lowerCAmelCase_			:    Optional[int]=10				,     lowerCAmelCase_			:    List[Any]=0.0_2				,     lowerCAmelCase_			:    List[str]=3				,     lowerCAmelCase_			:    Union[str, Any]=0.6				,     lowerCAmelCase_			:    Union[str, Any]=None				,     )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							parent
														_a      	=							batch_size
														_a      	=							image_size
														_a      	=							patch_size
														_a      	=							num_channels
														_a      	=							is_training
														_a      	=							use_labels
														_a      	=							hidden_size
														_a      	=							num_hidden_layers
														_a      	=							num_attention_heads
														_a      	=							intermediate_size
														_a      	=							hidden_act
														_a      	=							hidden_dropout_prob
														_a      	=							attention_probs_dropout_prob
														_a      	=							type_sequence_label_size
														_a      	=							initializer_range
														_a      	=							mask_ratio
														_a      	=							scope

														# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
														# (we add 1 for the [CLS] token)
														_a      	=							(image_size // patch_size) ** 2
														_a      	=							int(math.ceil((1 - mask_ratio) * (num_patches + 1)   )   )







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				Any:


														"""simple docstring"""


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

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

														_a      	=							self.get_config()

														return config, pixel_values, labels







							def   __lowerCAmelCase     (    self			:    Union[str, Any]   )     ->				Optional[int]:


														"""simple docstring"""


														return ViTMAEConfig(
														    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				,     decoder_hidden_size=self.hidden_size				,     decoder_num_hidden_layers=self.num_hidden_layers				,     decoder_num_attention_heads=self.num_attention_heads				,     decoder_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=lowerCAmelCase_				,     initializer_range=self.initializer_range				,     mask_ratio=self.mask_ratio				,     )







							def   __lowerCAmelCase     (    self			:    List[Any]				,     lowerCAmelCase_			:    Optional[Any]				,     lowerCAmelCase_			:    Optional[int]				,     lowerCAmelCase_			:    str   )     ->				str:


														"""simple docstring"""


														_a      	=							TFViTMAEModel(config=lowerCAmelCase_   )
														_a      	=							model(lowerCAmelCase_				,     training=lowerCAmelCase_   )
														self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size)   )







							def   __lowerCAmelCase     (    self			:    List[str]				,     lowerCAmelCase_			:    List[str]				,     lowerCAmelCase_			:    Any				,     lowerCAmelCase_			:    int   )     ->				Dict:


														"""simple docstring"""


														_a      	=							TFViTMAEForPreTraining(lowerCAmelCase_   )
														_a      	=							model(lowerCAmelCase_				,     training=lowerCAmelCase_   )
														# expected sequence length = num_patches
														_a      	=							(self.image_size // self.patch_size) ** 2
														_a      	=							self.patch_size**2 * self.num_channels
														self.parent.assertEqual(result.logits.shape				,     (self.batch_size, num_patches, expected_num_channels)   )

														# test greyscale images
														_a      	=							1
														_a      	=							TFViTMAEForPreTraining(lowerCAmelCase_   )

														_a      	=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
														_a      	=							model(lowerCAmelCase_				,     training=lowerCAmelCase_   )
														_a      	=							self.patch_size**2
														self.parent.assertEqual(result.logits.shape				,     (self.batch_size, num_patches, expected_num_channels)   )







							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				Any:


														"""simple docstring"""


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



@require_tf
class      A       (						_a	,_a	,unittest.TestCase       ):
							lowercase_							     =							(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
							lowercase_							     =							{'feature-extraction': TFViTMAEModel} if is_tf_available() else {}

							lowercase_							     =							False
							lowercase_							     =							False
							lowercase_							     =							False
							lowercase_							     =							False







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Dict:


														"""simple docstring"""


														_a      	=							TFViTMAEModelTester(self   )
														_a      	=							ConfigTester(self				,     config_class=lowerCAmelCase_				,     has_text_modality=lowerCAmelCase_				,     hidden_size=37   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				Tuple:


														"""simple docstring"""


														self.config_tester.run_common_tests()







							@unittest.skip(reason='''ViTMAE does not use inputs_embeds'''   )
							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				List[Any]:


														"""simple docstring"""


														pass







							def   __lowerCAmelCase     (    self			:    int   )     ->				Dict:


														"""simple docstring"""


														_a	,				_a      	=							self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					_a      	=							model_class(lowerCAmelCase_   )
																					self.assertIsInstance(model.get_input_embeddings()				,     (tf.keras.layers.Layer)   )
																					_a      	=							model.get_output_embeddings()
																					self.assertTrue(x is None or isinstance(lowerCAmelCase_				,     tf.keras.layers.Layer   )   )







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


														"""simple docstring"""


														_a	,				_a      	=							self.model_tester.prepare_config_and_inputs_for_common()

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

																					_a      	=							['''pixel_values''']
																					self.assertListEqual(arg_names[:1]				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				int:


														"""simple docstring"""


														_a      	=							self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_pretraining(*lowerCAmelCase_   )







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


														"""simple docstring"""


														np.random.seed(2   )

														_a	,				_a      	=							self.model_tester.prepare_config_and_inputs_for_common()

														_a      	=							int((config.image_size // config.patch_size) ** 2   )
														_a      	=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )

														for model_class in self.all_model_classes:
																					_a      	=							model_class(lowerCAmelCase_   )
																					_a      	=							self._prepare_for_class(lowerCAmelCase_				,     lowerCAmelCase_   )

																					_a      	=							model(lowerCAmelCase_				,     noise=lowerCAmelCase_   )

																					_a      	=							copy.deepcopy(self._prepare_for_class(lowerCAmelCase_				,     lowerCAmelCase_   )   )
																					_a      	=							model(**lowerCAmelCase_				,     noise=lowerCAmelCase_   )
																					_a      	=							outputs_dict[0].numpy()
																					_a      	=							outputs_keywords[0].numpy()

																					self.assertLess(np.sum(np.abs(output_dict - output_keywords   )   )				,     1e-6   )







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				List[str]:


														"""simple docstring"""


														np.random.seed(2   )

														_a	,				_a      	=							self.model_tester.prepare_config_and_inputs_for_common()

														_a      	=							int((config.image_size // config.patch_size) ** 2   )
														_a      	=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )

														def prepare_numpy_arrays(lowerCAmelCase_			:    List[str]   ):
																					_a      	=							{}
																					for k, v in inputs_dict.items():
																												if tf.is_tensor(lowerCAmelCase_   ):
																																			_a      	=							v.numpy()
																												else:
																																			_a      	=							np.array(lowerCAmelCase_   )

																					return inputs_np_dict

														for model_class in self.all_model_classes:
																					_a      	=							model_class(lowerCAmelCase_   )

																					_a      	=							self._prepare_for_class(lowerCAmelCase_				,     lowerCAmelCase_   )
																					_a      	=							prepare_numpy_arrays(lowerCAmelCase_   )

																					_a      	=							model(lowerCAmelCase_				,     noise=lowerCAmelCase_   )
																					_a      	=							model(**lowerCAmelCase_				,     noise=lowerCAmelCase_   )
																					self.assert_outputs_same(lowerCAmelCase_				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    Union[str, Any]				,     lowerCAmelCase_			:    Optional[int]   )     ->				Any:


														"""simple docstring"""


														np.random.seed(2   )

														_a      	=							int((tf_model.config.image_size // tf_model.config.patch_size) ** 2   )
														_a      	=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )
														_a      	=							tf.constant(lowerCAmelCase_   )

														# Add `noise` argument.
														# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
														_a      	=							tf_noise

														super().check_pt_tf_models(lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Any:


														"""simple docstring"""


														np.random.seed(2   )

														_a	,				_a      	=							self.model_tester.prepare_config_and_inputs_for_common()

														_a      	=							{
														    module_member
														    for model_class in self.all_model_classes
														    for module in (import_module(model_class.__module__   ),)
														    for module_member_name in dir(lowerCAmelCase_   )
														    if module_member_name.endswith('''MainLayer'''   )
														    # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
														    and module_member_name[: -len('''MainLayer'''   )] == model_class.__name__[: -len('''Model'''   )]
														    for module_member in (getattr(lowerCAmelCase_				,     lowerCAmelCase_   ),)
														    if isinstance(lowerCAmelCase_				,     lowerCAmelCase_   )
														    and tf.keras.layers.Layer in module_member.__bases__
														    and getattr(lowerCAmelCase_				,     '''_keras_serializable'''				,     lowerCAmelCase_   )
														}

														_a      	=							int((config.image_size // config.patch_size) ** 2   )
														_a      	=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )
														_a      	=							tf.convert_to_tensor(lowerCAmelCase_   )
														inputs_dict.update({'''noise''': noise}   )

														for main_layer_class in tf_main_layer_classes:
																					_a      	=							main_layer_class(lowerCAmelCase_   )

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

																					_a      	=							tf.keras.Model(lowerCAmelCase_				,     outputs=main_layer(lowerCAmelCase_   )   )
																					_a      	=							model(lowerCAmelCase_   )

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												_a      	=							os.path.join(lowerCAmelCase_				,     '''keras_model.h5'''   )
																												model.save(lowerCAmelCase_   )
																												_a      	=							tf.keras.models.load_model(
																												    lowerCAmelCase_				,     custom_objects={main_layer_class.__name__: main_layer_class}   )
																												assert isinstance(lowerCAmelCase_				,     tf.keras.Model   )
																												_a      	=							model(lowerCAmelCase_   )
																												self.assert_outputs_same(lowerCAmelCase_				,     lowerCAmelCase_   )







							@slow
							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				str:


														"""simple docstring"""


														np.random.seed(2   )

														_a	,				_a      	=							self.model_tester.prepare_config_and_inputs_for_common()

														_a      	=							int((config.image_size // config.patch_size) ** 2   )
														_a      	=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )

														for model_class in self.all_model_classes:
																					_a      	=							model_class(lowerCAmelCase_   )
																					_a      	=							self._prepare_for_class(lowerCAmelCase_				,     lowerCAmelCase_   )
																					_a      	=							model(lowerCAmelCase_				,     noise=lowerCAmelCase_   )

																					if model_class.__name__ == "TFViTMAEModel":
																												_a      	=							outputs.last_hidden_state.numpy()
																												_a      	=							0
																					else:
																												_a      	=							outputs.logits.numpy()
																												_a      	=							0

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												model.save_pretrained(lowerCAmelCase_				,     saved_model=lowerCAmelCase_   )
																												_a      	=							model_class.from_pretrained(lowerCAmelCase_   )
																												_a      	=							model(lowerCAmelCase_				,     noise=lowerCAmelCase_   )

																												if model_class.__name__ == "TFViTMAEModel":
																																			_a      	=							after_outputs['''last_hidden_state'''].numpy()
																																			_a      	=							0
																												else:
																																			_a      	=							after_outputs['''logits'''].numpy()
																																			_a      	=							0

																												_a      	=							np.amax(np.abs(out_a - out_a   )   )
																												self.assertLessEqual(lowerCAmelCase_				,     1e-5   )







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				Tuple:


														"""simple docstring"""


														np.random.seed(2   )

														_a	,				_a      	=							self.model_tester.prepare_config_and_inputs_for_common()

														_a      	=							int((config.image_size // config.patch_size) ** 2   )
														_a      	=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )

														for model_class in self.all_model_classes:
																					_a      	=							model_class(lowerCAmelCase_   )
																					_a      	=							self._prepare_for_class(lowerCAmelCase_				,     lowerCAmelCase_   )

																					_a      	=							model(lowerCAmelCase_				,     noise=lowerCAmelCase_   )
																					_a      	=							model.get_config()
																					# make sure that returned config is jsonifiable, which is required by keras
																					json.dumps(lowerCAmelCase_   )
																					_a      	=							model_class.from_config(model.get_config()   )
																					# make sure it also accepts a normal config
																					_a      	=							model_class.from_config(model.config   )
																					_a      	=							new_model(lowerCAmelCase_   )  # Build model
																					new_model.set_weights(model.get_weights()   )
																					_a      	=							new_model(lowerCAmelCase_				,     noise=lowerCAmelCase_   )

																					self.assert_outputs_same(lowerCAmelCase_				,     lowerCAmelCase_   )







							@unittest.skip(
							    reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''   )
							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				Optional[int]:


														"""simple docstring"""


														pass







							@unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'''   )
							def   __lowerCAmelCase     (    self			:    Union[str, Any]   )     ->				List[Any]:


														"""simple docstring"""


														pass







							@slow
							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				str:


														"""simple docstring"""


														_a      	=							TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224'''   )
														self.assertIsNotNone(lowerCAmelCase_   )



def 						snake_case_       ():





							'''simple docstring'''


							_a      	=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
							return image

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







							@cached_property
							def   __lowerCAmelCase     (    self			:    int   )     ->				Optional[Any]:


														"""simple docstring"""


														return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base'''   ) if is_vision_available() else None







							@slow
							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Any:


														"""simple docstring"""


														np.random.seed(2   )

														_a      	=							TFViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''   )

														_a      	=							self.default_image_processor
														_a      	=							prepare_img()
														_a      	=							image_processor(images=lowerCAmelCase_				,     return_tensors='''tf'''   )

														# prepare a noise vector that will be also used for testing the TF model
														# (this way we can ensure that the PT and TF models operate on the same inputs)
														_a      	=							ViTMAEConfig()
														_a      	=							int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2   )
														_a      	=							np.random.uniform(size=(1, num_patches)   )

														# forward pass
														_a      	=							model(**lowerCAmelCase_				,     noise=lowerCAmelCase_   )

														# verify the logits
														_a      	=							tf.convert_to_tensor([1, 1_96, 7_68]   )
														self.assertEqual(outputs.logits.shape				,     lowerCAmelCase_   )

														_a      	=							tf.convert_to_tensor(
														    [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]]   )

														tf.debugging.assert_near(outputs.logits[0, :3, :3]				,     lowerCAmelCase_				,     atol=1e-4   )







'''simple docstring'''


import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class      A       (						_a	,unittest.TestCase       ):
							lowercase_							     =							LEDTokenizer
							lowercase_							     =							LEDTokenizerFast
							lowercase_							     =							True







							def   __lowerCAmelCase     (    self			:    int   )     ->				List[Any]:


														"""simple docstring"""


														super().setUp()
														_a      	=							[
														    '''l''',
														    '''o''',
														    '''w''',
														    '''e''',
														    '''r''',
														    '''s''',
														    '''t''',
														    '''i''',
														    '''d''',
														    '''n''',
														    '''\u0120''',
														    '''\u0120l''',
														    '''\u0120n''',
														    '''\u0120lo''',
														    '''\u0120low''',
														    '''er''',
														    '''\u0120lowest''',
														    '''\u0120newer''',
														    '''\u0120wider''',
														    '''<unk>''',
														]
														_a      	=							dict(zip(lowerCAmelCase_				,     range(len(lowerCAmelCase_   )   )   )   )
														_a      	=							['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
														_a      	=							{'''unk_token''': '''<unk>'''}

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







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     **lowerCAmelCase_			:    int   )     ->				Optional[int]:


														"""simple docstring"""


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







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     **lowerCAmelCase_			:    Any   )     ->				int:


														"""simple docstring"""


														kwargs.update(self.special_tokens_map   )
														return self.rust_tokenizer_class.from_pretrained(self.tmpdirname				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     lowerCAmelCase_			:    Dict   )     ->				List[str]:


														"""simple docstring"""


														return "lower newer", "lower newer"







							@cached_property
							def   __lowerCAmelCase     (    self			:    Dict   )     ->				int:


														"""simple docstring"""


														return LEDTokenizer.from_pretrained('''allenai/led-base-16384'''   )







							@cached_property
							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				Union[str, Any]:


														"""simple docstring"""


														return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384'''   )







							@require_torch
							def   __lowerCAmelCase     (    self			:    int   )     ->				Tuple:


														"""simple docstring"""


														_a      	=							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
														_a      	=							[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					_a      	=							tokenizer(lowerCAmelCase_				,     max_length=len(lowerCAmelCase_   )				,     padding=lowerCAmelCase_				,     return_tensors='''pt'''   )
																					self.assertIsInstance(lowerCAmelCase_				,     lowerCAmelCase_   )

																					self.assertEqual((2, 9)				,     batch.input_ids.shape   )
																					self.assertEqual((2, 9)				,     batch.attention_mask.shape   )
																					_a      	=							batch.input_ids.tolist()[0]
																					self.assertListEqual(lowerCAmelCase_				,     lowerCAmelCase_   )







							@require_torch
							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					_a      	=							tokenizer(lowerCAmelCase_				,     padding=lowerCAmelCase_				,     return_tensors='''pt'''   )
																					self.assertIn('''input_ids'''				,     lowerCAmelCase_   )
																					self.assertIn('''attention_mask'''				,     lowerCAmelCase_   )
																					self.assertNotIn('''labels'''				,     lowerCAmelCase_   )
																					self.assertNotIn('''decoder_attention_mask'''				,     lowerCAmelCase_   )







							@require_torch
							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				str:


														"""simple docstring"""


														_a      	=							[
														    '''Summary of the text.''',
														    '''Another summary.''',
														]
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					_a      	=							tokenizer(text_target=lowerCAmelCase_				,     max_length=32				,     padding='''max_length'''				,     return_tensors='''pt'''   )
																					self.assertEqual(32				,     targets['''input_ids'''].shape[1]   )







							@require_torch
							def   __lowerCAmelCase     (    self			:    Any   )     ->				Union[str, Any]:


														"""simple docstring"""


														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					_a      	=							tokenizer(
																					    ['''I am a small frog''' * 10_24, '''I am a small frog''']				,     padding=lowerCAmelCase_				,     truncation=lowerCAmelCase_				,     return_tensors='''pt'''   )
																					self.assertIsInstance(lowerCAmelCase_				,     lowerCAmelCase_   )
																					self.assertEqual(batch.input_ids.shape				,     (2, 51_22)   )







							@require_torch
							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							['''A long paragraph for summarization.''']
														_a      	=							[
														    '''Summary of the text.''',
														]
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					_a      	=							tokenizer(lowerCAmelCase_				,     return_tensors='''pt'''   )
																					_a      	=							tokenizer(text_target=lowerCAmelCase_				,     return_tensors='''pt'''   )
																					_a      	=							inputs['''input_ids''']
																					_a      	=							targets['''input_ids''']
																					self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()   )
																					self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()   )
																					self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()   )
																					self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()   )







							@require_torch
							def   __lowerCAmelCase     (    self			:    Any   )     ->				Union[str, Any]:


														"""simple docstring"""


														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					_a      	=							['''Summary of the text.''', '''Another summary.''']
																					_a      	=							[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

																					_a      	=							tokenizer(lowerCAmelCase_				,     padding=lowerCAmelCase_   )
																					_a      	=							[[0] * len(lowerCAmelCase_   ) for x in encoded_output['''input_ids''']]
																					_a      	=							tokenizer.pad(lowerCAmelCase_   )
																					self.assertSequenceEqual(outputs['''global_attention_mask''']				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Any   )     ->				Dict:


														"""simple docstring"""


														pass







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


														"""simple docstring"""


														for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																					with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'   ):
																												_a      	=							self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_				,     **lowerCAmelCase_   )
																												_a      	=							self.tokenizer_class.from_pretrained(lowerCAmelCase_				,     **lowerCAmelCase_   )
																												_a      	=							'''A, <mask> AllenNLP sentence.'''
																												_a      	=							tokenizer_r.encode_plus(lowerCAmelCase_				,     add_special_tokens=lowerCAmelCase_				,     return_token_type_ids=lowerCAmelCase_   )
																												_a      	=							tokenizer_p.encode_plus(lowerCAmelCase_				,     add_special_tokens=lowerCAmelCase_				,     return_token_type_ids=lowerCAmelCase_   )
																												self.assertEqual(sum(tokens_r['''token_type_ids''']   )				,     sum(tokens_p['''token_type_ids''']   )   )
																												self.assertEqual(
																												    sum(tokens_r['''attention_mask''']   ) / len(tokens_r['''attention_mask''']   )				,     sum(tokens_p['''attention_mask''']   ) / len(tokens_p['''attention_mask''']   )				,     )

																												_a      	=							tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']   )
																												_a      	=							tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']   )
																												self.assertSequenceEqual(tokens_p['''input_ids''']				,     [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]   )
																												self.assertSequenceEqual(tokens_r['''input_ids''']				,     [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]   )

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

