






import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
							    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
							    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
							    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
							    LayoutLMvaConfig,
							    LayoutLMvaForQuestionAnswering,
							    LayoutLMvaForSequenceClassification,
							    LayoutLMvaForTokenClassification,
							    LayoutLMvaModel,
							)
							from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
							from PIL import Image

							from transformers import LayoutLMvaImageProcessor

class 				lowerCAmelCase_	:


				def __init__(					self ,						_lowerCAmelCase ,						_lowerCAmelCase=2 ,						_lowerCAmelCase=3 ,						_lowerCAmelCase=4 ,						_lowerCAmelCase=2 ,						_lowerCAmelCase=7 ,						_lowerCAmelCase=True ,						_lowerCAmelCase=True ,						_lowerCAmelCase=True ,						_lowerCAmelCase=True ,						_lowerCAmelCase=9_9 ,						_lowerCAmelCase=3_6 ,						_lowerCAmelCase=3 ,						_lowerCAmelCase=4 ,						_lowerCAmelCase=3_7 ,						_lowerCAmelCase="gelu" ,						_lowerCAmelCase=0.1 ,						_lowerCAmelCase=0.1 ,						_lowerCAmelCase=5_1_2 ,						_lowerCAmelCase=1_6 ,						_lowerCAmelCase=2 ,						_lowerCAmelCase=0.02 ,						_lowerCAmelCase=6 ,						_lowerCAmelCase=6 ,						_lowerCAmelCase=3 ,						_lowerCAmelCase=4 ,						_lowerCAmelCase=None ,						_lowerCAmelCase=1_0_0_0 ,						):
											_lowercase							:   Optional[Any] 							=   parent
											_lowercase							:   Optional[int] 							=   batch_size
											_lowercase							:   List[str] 							=   num_channels
											_lowercase							:   Tuple 							=   image_size
											_lowercase							:   str 							=   patch_size
											_lowercase							:   Union[str, Any] 							=   text_seq_length
											_lowercase							:   List[str] 							=   is_training
											_lowercase							:   Optional[int] 							=   use_input_mask
											_lowercase							:   Dict 							=   use_token_type_ids
											_lowercase							:   Any 							=   use_labels
											_lowercase							:   Optional[int] 							=   vocab_size
											_lowercase							:   int 							=   hidden_size
											_lowercase							:   Dict 							=   num_hidden_layers
											_lowercase							:   Optional[Any] 							=   num_attention_heads
											_lowercase							:   List[Any] 							=   intermediate_size
											_lowercase							:   Optional[int] 							=   hidden_act
											_lowercase							:   Dict 							=   hidden_dropout_prob
											_lowercase							:   List[str] 							=   attention_probs_dropout_prob
											_lowercase							:   Union[str, Any] 							=   max_position_embeddings
											_lowercase							:   Optional[Any] 							=   type_vocab_size
											_lowercase							:   List[str] 							=   type_sequence_label_size
											_lowercase							:   Union[str, Any] 							=   initializer_range
											_lowercase							:   Optional[Any] 							=   coordinate_size
											_lowercase							:   Tuple 							=   shape_size
											_lowercase							:   Any 							=   num_labels
											_lowercase							:   List[str] 							=   num_choices
											_lowercase							:   Optional[Any] 							=   scope
											_lowercase							:   str 							=   range_bbox

											# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
											_lowercase							:   Optional[Any] 							=   text_seq_length
											_lowercase							:   Dict 							=   (image_size // patch_size) ** 2 + 1
											_lowercase							:   Union[str, Any] 							=   self.text_seq_length + self.image_seq_length


				def 						__a					(					self      ):
											_lowercase							:   str 							=   ids_tensor([self.batch_size, self.text_seq_length] ,						self.vocab_size      )

											_lowercase							:   Dict 							=   ids_tensor([self.batch_size, self.text_seq_length, 4] ,						self.range_bbox      )
											# Ensure that bbox is legal
											for i in range(bbox.shape[0]      ):
																		for j in range(bbox.shape[1]      ):
																									if bbox[i, j, 3] < bbox[i, j, 1]:
																																_lowercase							:   Optional[int] 							=   bbox[i, j, 3]
																																_lowercase							:   Optional[int] 							=   bbox[i, j, 1]
																																_lowercase							:   str 							=   t
																									if bbox[i, j, 2] < bbox[i, j, 0]:
																																_lowercase							:   Dict 							=   bbox[i, j, 2]
																																_lowercase							:   Any 							=   bbox[i, j, 0]
																																_lowercase							:   int 							=   t

											_lowercase							:   int 							=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

											_lowercase							:   int 							=   None
											if self.use_input_mask:
																		_lowercase							:   Union[str, Any] 							=   random_attention_mask([self.batch_size, self.text_seq_length]      )

											_lowercase							:   int 							=   None
											if self.use_token_type_ids:
																		_lowercase							:   List[str] 							=   ids_tensor([self.batch_size, self.text_seq_length] ,						self.type_vocab_size      )

											_lowercase							:   List[Any] 							=   None
											_lowercase							:   Union[str, Any] 							=   None
											if self.use_labels:
																		_lowercase							:   Dict 							=   ids_tensor([self.batch_size] ,						self.type_sequence_label_size      )
																		_lowercase							:   Dict 							=   ids_tensor([self.batch_size, self.text_seq_length] ,						self.num_labels      )

											_lowercase							:   List[Any] 							=   LayoutLMvaConfig(
											    vocab_size=self.vocab_size ,						hidden_size=self.hidden_size ,						num_hidden_layers=self.num_hidden_layers ,						num_attention_heads=self.num_attention_heads ,						intermediate_size=self.intermediate_size ,						hidden_act=self.hidden_act ,						hidden_dropout_prob=self.hidden_dropout_prob ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob ,						max_position_embeddings=self.max_position_embeddings ,						type_vocab_size=self.type_vocab_size ,						initializer_range=self.initializer_range ,						coordinate_size=self.coordinate_size ,						shape_size=self.shape_size ,						input_size=self.image_size ,						patch_size=self.patch_size ,						)

											return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											_lowercase							:   Optional[int] 							=   LayoutLMvaModel(config=_lowerCAmelCase      )
											model.to(_lowerCAmelCase      )
											model.eval()

											# text + image
											_lowercase							:   str 							=   model(_lowerCAmelCase ,						pixel_values=_lowerCAmelCase      )
											_lowercase							:   Optional[Any] 							=   model(
											    _lowerCAmelCase ,						bbox=_lowerCAmelCase ,						pixel_values=_lowerCAmelCase ,						attention_mask=_lowerCAmelCase ,						token_type_ids=_lowerCAmelCase      )
											_lowercase							:   List[Any] 							=   model(_lowerCAmelCase ,						bbox=_lowerCAmelCase ,						pixel_values=_lowerCAmelCase ,						token_type_ids=_lowerCAmelCase      )
											_lowercase							:   List[str] 							=   model(_lowerCAmelCase ,						bbox=_lowerCAmelCase ,						pixel_values=_lowerCAmelCase      )

											self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, self.seq_length, self.hidden_size)      )

											# text only
											_lowercase							:   List[str] 							=   model(_lowerCAmelCase      )
											self.parent.assertEqual(
											    result.last_hidden_state.shape ,						(self.batch_size, self.text_seq_length, self.hidden_size)      )

											# image only
											_lowercase							:   str 							=   model(pixel_values=_lowerCAmelCase      )
											self.parent.assertEqual(
											    result.last_hidden_state.shape ,						(self.batch_size, self.image_seq_length, self.hidden_size)      )


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											_lowercase							:   Any 							=   self.num_labels
											_lowercase							:   str 							=   LayoutLMvaForSequenceClassification(_lowerCAmelCase      )
											model.to(_lowerCAmelCase      )
											model.eval()
											_lowercase							:   Any 							=   model(
											    _lowerCAmelCase ,						bbox=_lowerCAmelCase ,						pixel_values=_lowerCAmelCase ,						attention_mask=_lowerCAmelCase ,						token_type_ids=_lowerCAmelCase ,						labels=_lowerCAmelCase ,						)
											self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_labels)      )


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											_lowercase							:   List[Any] 							=   self.num_labels
											_lowercase							:   Optional[Any] 							=   LayoutLMvaForTokenClassification(config=_lowerCAmelCase      )
											model.to(_lowerCAmelCase      )
											model.eval()
											_lowercase							:   Tuple 							=   model(
											    _lowerCAmelCase ,						bbox=_lowerCAmelCase ,						pixel_values=_lowerCAmelCase ,						attention_mask=_lowerCAmelCase ,						token_type_ids=_lowerCAmelCase ,						labels=_lowerCAmelCase ,						)
											self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.text_seq_length, self.num_labels)      )


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											_lowercase							:   str 							=   LayoutLMvaForQuestionAnswering(config=_lowerCAmelCase      )
											model.to(_lowerCAmelCase      )
											model.eval()
											_lowercase							:   Union[str, Any] 							=   model(
											    _lowerCAmelCase ,						bbox=_lowerCAmelCase ,						pixel_values=_lowerCAmelCase ,						attention_mask=_lowerCAmelCase ,						token_type_ids=_lowerCAmelCase ,						start_positions=_lowerCAmelCase ,						end_positions=_lowerCAmelCase ,						)
											self.parent.assertEqual(result.start_logits.shape ,						(self.batch_size, self.seq_length)      )
											self.parent.assertEqual(result.end_logits.shape ,						(self.batch_size, self.seq_length)      )


				def 						__a					(					self      ):
											_lowercase							:   List[Any] 							=   self.prepare_config_and_inputs()
											(
											    (
											    _lowercase
											)							,							(
											    _lowercase
											)							,							(
											    _lowercase
											)							,							(
											    _lowercase
											)							,							(
											    _lowercase
											)							,							(
											    _lowercase
											)							,							(
											    _lowercase
											)							,							(
											    _lowercase
											)							,							
											)							:   Union[str, Any] 							=   config_and_inputs
											_lowercase							:   Dict 							=   {
											    'input_ids': input_ids,
											    'bbox': bbox,
											    'pixel_values': pixel_values,
											    'token_type_ids': token_type_ids,
											    'attention_mask': input_mask,
											}
											return config, inputs_dict




@require_torch
class 				lowerCAmelCase_	(		__snake_case   ,		__snake_case   ,		unittest.TestCase			):
				_UpperCamelCase					:       List[Any]							       = False
				_UpperCamelCase					:       str							       = False
				_UpperCamelCase					:       Any							       = False

				_UpperCamelCase					:       Dict							       = (
				    (
				        LayoutLMvaModel,
				        LayoutLMvaForSequenceClassification,
				        LayoutLMvaForTokenClassification,
				        LayoutLMvaForQuestionAnswering,
				    )
				    if is_torch_available()
				    else ()
				)
				_UpperCamelCase					:       Any							       = (
				    {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
				    if is_torch_available()
				    else {}
				)


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											# `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
											# embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
											# the sequence dimension of the text embedding only.
											# (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
											return True


				def 						__a					(					self      ):
											_lowercase							:   List[Any] 							=   LayoutLMvaModelTester(self      )
											_lowercase							:   Optional[int] 							=   ConfigTester(self ,						config_class=_lowerCAmelCase ,						hidden_size=3_7      )


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase=False      ):
											_lowercase							:   Optional[int] 							=   copy.deepcopy(_lowerCAmelCase      )
											if model_class in get_values(_lowerCAmelCase      ):
																		_lowercase							:   Tuple 							=   {
																		    k: v.unsqueeze(1      ).expand(-1 ,						self.model_tester.num_choices ,						-1      ).contiguous()
																		    if isinstance(_lowerCAmelCase ,						torch.Tensor      ) and v.ndim > 1
																		    else v
																		    for k, v in inputs_dict.items()
																		}
											if return_labels:
																		if model_class in get_values(_lowerCAmelCase      ):
																									_lowercase							:   Tuple 							=   torch.ones(self.model_tester.batch_size ,						dtype=torch.long ,						device=_lowerCAmelCase      )
																		elif model_class in get_values(_lowerCAmelCase      ):
																									_lowercase							:   str 							=   torch.zeros(
																									    self.model_tester.batch_size ,						dtype=torch.long ,						device=_lowerCAmelCase      )
																									_lowercase							:   Any 							=   torch.zeros(
																									    self.model_tester.batch_size ,						dtype=torch.long ,						device=_lowerCAmelCase      )
																		elif model_class in [
																		    *get_values(_lowerCAmelCase      ),
																		]:
																									_lowercase							:   Any 							=   torch.zeros(
																									    self.model_tester.batch_size ,						dtype=torch.long ,						device=_lowerCAmelCase      )
																		elif model_class in [
																		    *get_values(_lowerCAmelCase      ),
																		]:
																									_lowercase							:   List[Any] 							=   torch.zeros(
																									    (self.model_tester.batch_size, self.model_tester.text_seq_length) ,						dtype=torch.long ,						device=_lowerCAmelCase ,						)

											return inputs_dict


				def 						__a					(					self      ):
											self.config_tester.run_common_tests()


				def 						__a					(					self      ):
											_lowercase							:   Dict 							=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_lowerCAmelCase      )


				def 						__a					(					self      ):
											_lowercase							:   Tuple 							=   self.model_tester.prepare_config_and_inputs()
											for type in ["absolute", "relative_key", "relative_key_query"]:
																		_lowercase							:   Union[str, Any] 							=   type
																		self.model_tester.create_and_check_model(*_lowerCAmelCase      )


				def 						__a					(					self      ):
											_lowercase							:   int 							=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*_lowerCAmelCase      )


				def 						__a					(					self      ):
											_lowercase							:   List[Any] 							=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*_lowerCAmelCase      )


				def 						__a					(					self      ):
											_lowercase							:   List[str] 							=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*_lowerCAmelCase      )


				@slow
				def 						__a					(					self      ):
											for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		_lowercase							:   Tuple 							=   LayoutLMvaModel.from_pretrained(_lowerCAmelCase      )
																		self.assertIsNotNone(_lowerCAmelCase      )






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






@require_torch
class 				lowerCAmelCase_	(		unittest.TestCase			):


				@cached_property
				def 						__a					(					self      ):
											return LayoutLMvaImageProcessor(apply_ocr=_lowerCAmelCase      ) if is_vision_available() else None


				@slow
				def 						__a					(					self      ):
											_lowercase							:   Tuple 							=   LayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'      ).to(_lowerCAmelCase      )

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

											_lowercase							:   Tuple 							=   torch.tensor([[1, 2]]      )
											_lowercase							:   Any 							=   torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]      ).unsqueeze(0      )

											# forward pass
											_lowercase							:   int 							=   model(
											    input_ids=input_ids.to(_lowerCAmelCase      ) ,						bbox=bbox.to(_lowerCAmelCase      ) ,						pixel_values=pixel_values.to(_lowerCAmelCase      ) ,						)

											# verify the logits
											_lowercase							:   Union[str, Any] 							=   torch.Size((1, 1_9_9, 7_6_8)      )
											self.assertEqual(outputs.last_hidden_state.shape ,						_lowerCAmelCase      )

											_lowercase							:   int 							=   torch.tensor(
											    [[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]]      ).to(_lowerCAmelCase      )

											self.assertTrue(torch.allclose(outputs.last_hidden_state[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>''']   )

