
import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
		import torch

		from transformers import (
		    LayoutLMForMaskedLM,
		    LayoutLMForQuestionAnswering,
		    LayoutLMForSequenceClassification,
		    LayoutLMForTokenClassification,
		    LayoutLMModel,
		)


class 						__magic_name__				:





		def __init__(							self      :		List[Any]  ,					lowerCamelCase__      :		int  ,					lowerCamelCase__      :		Dict=1_3  ,					lowerCamelCase__      :		Dict=7  ,					lowerCamelCase__      :		Tuple=True  ,					lowerCamelCase__      :		List[Any]=True  ,					lowerCamelCase__      :		int=True  ,					lowerCamelCase__      :		Dict=True  ,					lowerCamelCase__      :		Union[str, Any]=9_9  ,					lowerCamelCase__      :		Optional[Any]=3_2  ,					lowerCamelCase__      :		List[str]=5  ,					lowerCamelCase__      :		List[Any]=4  ,					lowerCamelCase__      :		str=3_7  ,					lowerCamelCase__      :		Dict="gelu"  ,					lowerCamelCase__      :		int=0.1  ,					lowerCamelCase__      :		Tuple=0.1  ,					lowerCamelCase__      :		Union[str, Any]=5_1_2  ,					lowerCamelCase__      :		Tuple=1_6  ,					lowerCamelCase__      :		Optional[int]=2  ,					lowerCamelCase__      :		Dict=0.0_2  ,					lowerCamelCase__      :		Union[str, Any]=3  ,					lowerCamelCase__      :		List[str]=4  ,					lowerCamelCase__      :		Any=None  ,					lowerCamelCase__      :		Any=1_0_0_0  ,					):
							lowerCAmelCase						:   Any							=       parent
							lowerCAmelCase						:   Optional[Any]							=       batch_size
							lowerCAmelCase						:   Tuple							=       seq_length
							lowerCAmelCase						:   Any							=       is_training
							lowerCAmelCase						:   str							=       use_input_mask
							lowerCAmelCase						:   Tuple							=       use_token_type_ids
							lowerCAmelCase						:   Tuple							=       use_labels
							lowerCAmelCase						:   Dict							=       vocab_size
							lowerCAmelCase						:   str							=       hidden_size
							lowerCAmelCase						:   Union[str, Any]							=       num_hidden_layers
							lowerCAmelCase						:   Optional[Any]							=       num_attention_heads
							lowerCAmelCase						:   Dict							=       intermediate_size
							lowerCAmelCase						:   Tuple							=       hidden_act
							lowerCAmelCase						:   str							=       hidden_dropout_prob
							lowerCAmelCase						:   Union[str, Any]							=       attention_probs_dropout_prob
							lowerCAmelCase						:   List[Any]							=       max_position_embeddings
							lowerCAmelCase						:   Dict							=       type_vocab_size
							lowerCAmelCase						:   Optional[Any]							=       type_sequence_label_size
							lowerCAmelCase						:   int							=       initializer_range
							lowerCAmelCase						:   List[str]							=       num_labels
							lowerCAmelCase						:   Tuple							=       num_choices
							lowerCAmelCase						:   Any							=       scope
							lowerCAmelCase						:   Optional[int]							=       range_bbox





		def  _A							(							self      :		List[str]			):
							lowerCAmelCase						:   Tuple							=       ids_tensor([self.batch_size, self.seq_length]  ,					self.vocab_size			)

							lowerCAmelCase						:   str							=       ids_tensor([self.batch_size, self.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]:
																						lowerCAmelCase						:   Tuple							=       bbox[i, j, 3]
																						lowerCAmelCase						:   Tuple							=       bbox[i, j, 1]
																						lowerCAmelCase						:   Optional[Any]							=       t
																	if bbox[i, j, 2] < bbox[i, j, 0]:
																						lowerCAmelCase						:   List[Any]							=       bbox[i, j, 2]
																						lowerCAmelCase						:   Optional[Any]							=       bbox[i, j, 0]
																						lowerCAmelCase						:   Tuple							=       t

							lowerCAmelCase						:   List[str]							=       None
							if self.use_input_mask:
												lowerCAmelCase						:   List[Any]							=       ids_tensor([self.batch_size, self.seq_length]  ,					vocab_size=2			)

							lowerCAmelCase						:   Union[str, Any]							=       None
							if self.use_token_type_ids:
												lowerCAmelCase						:   Optional[Any]							=       ids_tensor([self.batch_size, self.seq_length]  ,					self.type_vocab_size			)

							lowerCAmelCase						:   Optional[int]							=       None
							lowerCAmelCase						:   Optional[int]							=       None
							lowerCAmelCase						:   List[Any]							=       None
							if self.use_labels:
												lowerCAmelCase						:   Union[str, Any]							=       ids_tensor([self.batch_size]  ,					self.type_sequence_label_size			)
												lowerCAmelCase						:   Any							=       ids_tensor([self.batch_size, self.seq_length]  ,					self.num_labels			)
												lowerCAmelCase						:   List[Any]							=       ids_tensor([self.batch_size]  ,					self.num_choices			)

							lowerCAmelCase						:   Tuple							=       self.get_config()

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





		def  _A							(							self      :		List[str]			):
							return LayoutLMConfig(
							    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  ,					)





		def  _A							(							self      :		Union[str, Any]  ,					lowerCamelCase__      :		int  ,					lowerCamelCase__      :		Optional[Any]  ,					lowerCamelCase__      :		int  ,					lowerCamelCase__      :		Optional[int]  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		Optional[Any]  ,					lowerCamelCase__      :		Dict  ,					lowerCamelCase__      :		Any			):
							lowerCAmelCase						:   Any							=       LayoutLMModel(config=lowerCamelCase__			)
							model.to(lowerCamelCase__			)
							model.eval()
							lowerCAmelCase						:   List[Any]							=       model(lowerCamelCase__  ,					lowerCamelCase__  ,					attention_mask=lowerCamelCase__  ,					token_type_ids=lowerCamelCase__			)
							lowerCAmelCase						:   str							=       model(lowerCamelCase__  ,					lowerCamelCase__  ,					token_type_ids=lowerCamelCase__			)
							lowerCAmelCase						:   List[Any]							=       model(lowerCamelCase__  ,					lowerCamelCase__			)
							self.parent.assertEqual(result.last_hidden_state.shape  ,					(self.batch_size, self.seq_length, self.hidden_size)			)
							self.parent.assertEqual(result.pooler_output.shape  ,					(self.batch_size, self.hidden_size)			)





		def  _A							(							self      :		str  ,					lowerCamelCase__      :		int  ,					lowerCamelCase__      :		List[str]  ,					lowerCamelCase__      :		Union[str, Any]  ,					lowerCamelCase__      :		Optional[Any]  ,					lowerCamelCase__      :		Any  ,					lowerCamelCase__      :		Any  ,					lowerCamelCase__      :		Optional[int]  ,					lowerCamelCase__      :		List[Any]			):
							lowerCAmelCase						:   Tuple							=       LayoutLMForMaskedLM(config=lowerCamelCase__			)
							model.to(lowerCamelCase__			)
							model.eval()
							lowerCAmelCase						:   Optional[int]							=       model(lowerCamelCase__  ,					lowerCamelCase__  ,					attention_mask=lowerCamelCase__  ,					token_type_ids=lowerCamelCase__  ,					labels=lowerCamelCase__			)
							self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.seq_length, self.vocab_size)			)





		def  _A							(							self      :		int  ,					lowerCamelCase__      :		Any  ,					lowerCamelCase__      :		Union[str, Any]  ,					lowerCamelCase__      :		Tuple  ,					lowerCamelCase__      :		Tuple  ,					lowerCamelCase__      :		List[str]  ,					lowerCamelCase__      :		Optional[Any]  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		List[str]			):
							lowerCAmelCase						:   int							=       self.num_labels
							lowerCAmelCase						:   Any							=       LayoutLMForSequenceClassification(lowerCamelCase__			)
							model.to(lowerCamelCase__			)
							model.eval()
							lowerCAmelCase						:   Optional[Any]							=       model(
							    lowerCamelCase__  ,					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      :		List[Any]  ,					lowerCamelCase__      :		List[Any]  ,					lowerCamelCase__      :		int  ,					lowerCamelCase__      :		Dict  ,					lowerCamelCase__      :		Union[str, Any]  ,					lowerCamelCase__      :		List[str]  ,					lowerCamelCase__      :		int  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		Optional[int]			):
							lowerCAmelCase						:   Optional[Any]							=       self.num_labels
							lowerCAmelCase						:   Dict							=       LayoutLMForTokenClassification(config=lowerCamelCase__			)
							model.to(lowerCamelCase__			)
							model.eval()
							lowerCAmelCase						:   Any							=       model(lowerCamelCase__  ,					lowerCamelCase__  ,					attention_mask=lowerCamelCase__  ,					token_type_ids=lowerCamelCase__  ,					labels=lowerCamelCase__			)
							self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.seq_length, self.num_labels)			)





		def  _A							(							self      :		List[str]  ,					lowerCamelCase__      :		Tuple  ,					lowerCamelCase__      :		Union[str, Any]  ,					lowerCamelCase__      :		Dict  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		Optional[Any]  ,					lowerCamelCase__      :		Optional[Any]  ,					lowerCamelCase__      :		List[str]  ,					lowerCamelCase__      :		List[Any]			):
							lowerCAmelCase						:   Any							=       LayoutLMForQuestionAnswering(config=lowerCamelCase__			)
							model.to(lowerCamelCase__			)
							model.eval()
							lowerCAmelCase						:   Optional[int]							=       model(
							    lowerCamelCase__  ,					bbox=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      :		int			):
							lowerCAmelCase						:   Optional[Any]							=       self.prepare_config_and_inputs()
							(
							    (
							    lowerCAmelCase
							)		,							(
							    lowerCAmelCase
							)		,							(
							    lowerCAmelCase
							)		,							(
							    lowerCAmelCase
							)		,							(
							    lowerCAmelCase
							)		,							(
							    lowerCAmelCase
							)		,							(
							    lowerCAmelCase
							)		,							(
							    lowerCAmelCase
							)		,							
							)						:   Dict							=       config_and_inputs
							lowerCAmelCase						:   Union[str, Any]							=       {
							    '''input_ids''': input_ids,
							    '''bbox''': bbox,
							    '''token_type_ids''': token_type_ids,
							    '''attention_mask''': input_mask,
							}
							return config, inputs_dict



@require_torch
class 						__magic_name__				(  snake_case,			snake_case,			unittest.TestCase      ):
		_lowerCAmelCase						=		(
		    (
		        LayoutLMModel,
		        LayoutLMForMaskedLM,
		        LayoutLMForSequenceClassification,
		        LayoutLMForTokenClassification,
		        LayoutLMForQuestionAnswering,
		    )
		    if is_torch_available()
		    else None
		)
		_lowerCAmelCase						=		(
		    {
		        "document-question-answering": LayoutLMForQuestionAnswering,
		        "feature-extraction": LayoutLMModel,
		        "fill-mask": LayoutLMForMaskedLM,
		        "text-classification": LayoutLMForSequenceClassification,
		        "token-classification": LayoutLMForTokenClassification,
		        "zero-shot": LayoutLMForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		_lowerCAmelCase						=		True





		def  _A							(							self      :		Any			):
							lowerCAmelCase						:   List[Any]							=       LayoutLMModelTester(self			)
							lowerCAmelCase						:   Optional[Any]							=       ConfigTester(self  ,					config_class=lowerCamelCase__  ,					hidden_size=3_7			)





		def  _A							(							self      :		List[Any]			):
							self.config_tester.run_common_tests()





		def  _A							(							self      :		List[Any]			):
							lowerCAmelCase						:   Optional[int]							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCamelCase__			)





		def  _A							(							self      :		List[str]			):
							lowerCAmelCase						:   Dict							=       self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
												lowerCAmelCase						:   Dict							=       type
												self.model_tester.create_and_check_model(*lowerCamelCase__			)





		def  _A							(							self      :		str			):
							lowerCAmelCase						:   Optional[int]							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase__			)





		def  _A							(							self      :		Dict			):
							lowerCAmelCase						:   Optional[Any]							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__			)





		def  _A							(							self      :		List[Any]			):
							lowerCAmelCase						:   int							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__			)





		def  _A							(							self      :		List[str]			):
							lowerCAmelCase						:   str							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__			)





		@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''			)
		def  _A							(							self      :		List[str]			):
							pass



def       UpperCAmelCase__							(    ):


					'''simple docstring'''

					lowerCAmelCase						:   int							=       torch.tensor([[1_01,10_19,10_14,10_16,10_37,1_28_49,47_47,10_04,1_42_46,22_78,54_39,45_24,50_02,29_30,21_93,29_30,43_41,32_08,10_05,10_55,21_71,28_48,1_13_00,35_31,1_02],[1_01,40_70,40_34,70_20,10_24,30_58,10_15,10_13,28_61,10_13,60_70,1_92_74,27_72,62_05,2_78_14,1_61_47,1_61_47,43_43,20_47,1_02_83,1_09_69,1_43_89,10_12,23_38,1_02]]							,					device=__magic_name__					)  # noqa: E231
					lowerCAmelCase						:   Optional[int]							=       torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]							,					device=__magic_name__					)  # noqa: E231
					lowerCAmelCase						:   Optional[Any]							=       torch.tensor([[[0,0,0,0],[4_23,2_37,4_40,2_51],[4_27,2_72,4_41,2_87],[4_19,1_15,4_37,1_29],[9_61,8_85,9_92,9_12],[2_56,38,3_30,58],[2_56,38,3_30,58],[3_36,42,3_53,57],[3_60,39,4_01,56],[3_60,39,4_01,56],[4_11,39,4_71,59],[4_79,41,5_28,59],[5_33,39,6_30,60],[67,1_13,1_34,1_31],[1_41,1_15,2_09,1_32],[68,1_49,1_33,1_66],[1_41,1_49,1_87,1_64],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[2_95,1_48,3_49,1_65],[4_41,1_49,4_92,1_66],[4_97,1_49,5_46,1_64],[64,2_01,1_25,2_18],[10_00,10_00,10_00,10_00]],[[0,0,0,0],[6_62,1_50,7_54,1_66],[6_65,1_99,7_42,2_11],[5_19,2_13,5_54,2_28],[5_19,2_13,5_54,2_28],[1_34,4_33,1_87,4_54],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[3_14,4_69,3_76,4_82],[5_04,6_84,5_82,7_06],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[6_10,7_49,6_52,7_65],[1_30,6_59,1_68,6_72],[1_76,6_57,2_37,6_72],[2_38,6_57,3_12,6_72],[4_43,6_53,6_28,6_72],[4_43,6_53,6_28,6_72],[7_16,3_01,8_25,3_17],[10_00,10_00,10_00,10_00]]]							,					device=__magic_name__					)  # noqa: E231
					lowerCAmelCase						:   Union[str, Any]							=       torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]							,					device=__magic_name__					)  # noqa: E231
					# these are sequence labels (i.e. at the token level)
					lowerCAmelCase						:   int							=       torch.tensor([[-1_00,10,10,10,9,1,-1_00,7,7,-1_00,7,7,4,2,5,2,8,8,-1_00,-1_00,5,0,3,2,-1_00],[-1_00,12,12,12,-1_00,12,10,-1_00,-1_00,-1_00,-1_00,10,12,9,-1_00,-1_00,-1_00,10,10,10,9,12,-1_00,10,-1_00]]							,					device=__magic_name__					)  # noqa: E231
					# fmt: on

					return input_ids, attention_mask, bbox, token_type_ids, labels


@require_torch
class 						__magic_name__				(  unittest.TestCase      ):





		@slow
		def  _A							(							self      :		str			):
							lowerCAmelCase						:   str							=       LayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased'''			).to(lowerCamelCase__			)

							lowerCAmelCase		,							lowerCAmelCase		,							lowerCAmelCase		,							lowerCAmelCase		,							lowerCAmelCase						:   Union[str, Any]							=       prepare_layoutlm_batch_inputs()

							# forward pass
							lowerCAmelCase						:   int							=       model(input_ids=lowerCamelCase__  ,					bbox=lowerCamelCase__  ,					attention_mask=lowerCamelCase__  ,					token_type_ids=lowerCamelCase__			)

							# test the sequence output on [0, :3, :3]
							lowerCAmelCase						:   Optional[Any]							=       torch.tensor(
							    [[0.1_7_8_5, -0.1_9_4_7, -0.0_4_2_5], [-0.3_2_5_4, -0.2_8_0_7, 0.2_5_5_3], [-0.5_3_9_1, -0.3_3_2_2, 0.3_3_6_4]]  ,					device=lowerCamelCase__  ,					)

							self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]  ,					lowerCamelCase__  ,					atol=1E-3			)			)

							# test the pooled output on [1, :3]
							lowerCAmelCase						:   Any							=       torch.tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2]  ,					device=lowerCamelCase__			)

							self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]  ,					lowerCamelCase__  ,					atol=1E-3			)			)





		@slow
		def  _A							(							self      :		Optional[Any]			):
							# initialize model with randomly initialized sequence classification head
							lowerCAmelCase						:   Union[str, Any]							=       LayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''  ,					num_labels=2			).to(
							    lowerCamelCase__			)

							lowerCAmelCase		,							lowerCAmelCase		,							lowerCAmelCase		,							lowerCAmelCase		,							lowerCAmelCase						:   Any							=       prepare_layoutlm_batch_inputs()

							# forward pass
							lowerCAmelCase						:   Optional[Any]							=       model(
							    input_ids=lowerCamelCase__  ,					bbox=lowerCamelCase__  ,					attention_mask=lowerCamelCase__  ,					token_type_ids=lowerCamelCase__  ,					labels=torch.tensor([1, 1]  ,					device=lowerCamelCase__			)  ,					)

							# test whether we get a loss as a scalar
							lowerCAmelCase						:   Any							=       outputs.loss
							lowerCAmelCase						:   int							=       torch.Size([]			)
							self.assertEqual(loss.shape  ,					lowerCamelCase__			)

							# test the shape of the logits
							lowerCAmelCase						:   Union[str, Any]							=       outputs.logits
							lowerCAmelCase						:   Dict							=       torch.Size((2, 2)			)
							self.assertEqual(logits.shape  ,					lowerCamelCase__			)





		@slow
		def  _A							(							self      :		Tuple			):
							# initialize model with randomly initialized token classification head
							lowerCAmelCase						:   Tuple							=       LayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''  ,					num_labels=1_3			).to(
							    lowerCamelCase__			)

							lowerCAmelCase		,							lowerCAmelCase		,							lowerCAmelCase		,							lowerCAmelCase		,							lowerCAmelCase						:   List[Any]							=       prepare_layoutlm_batch_inputs()

							# forward pass
							lowerCAmelCase						:   str							=       model(
							    input_ids=lowerCamelCase__  ,					bbox=lowerCamelCase__  ,					attention_mask=lowerCamelCase__  ,					token_type_ids=lowerCamelCase__  ,					labels=lowerCamelCase__			)

							# test the loss calculation to be around 2.65
							# expected_loss = torch.tensor(2.65, device=torch_device)

							# The loss is currently somewhat random and can vary between 0.1-0.3 atol.
							# self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

							# test the shape of the logits
							lowerCAmelCase						:   Any							=       outputs.logits
							lowerCAmelCase						:   Tuple							=       torch.Size((2, 2_5, 1_3)			)
							self.assertEqual(logits.shape  ,					lowerCamelCase__			)





		@slow
		def  _A							(							self      :		str			):
							# initialize model with randomly initialized token classification head
							lowerCAmelCase						:   str							=       LayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased'''			).to(lowerCamelCase__			)

							lowerCAmelCase		,							lowerCAmelCase		,							lowerCAmelCase		,							lowerCAmelCase		,							lowerCAmelCase						:   Optional[Any]							=       prepare_layoutlm_batch_inputs()

							# forward pass
							lowerCAmelCase						:   Dict							=       model(input_ids=lowerCamelCase__  ,					bbox=lowerCamelCase__  ,					attention_mask=lowerCamelCase__  ,					token_type_ids=lowerCamelCase__			)

							# test the shape of the logits
							lowerCAmelCase						:   Optional[Any]							=       torch.Size((2, 2_5)			)
							self.assertEqual(outputs.start_logits.shape  ,					lowerCamelCase__			)
							self.assertEqual(outputs.end_logits.shape  ,					lowerCamelCase__			)


from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpta.tokenization_gpta import bytes_to_unicode
def       UpperCAmelCase__							(    __magic_name__  :       str=10					):


					'''simple docstring'''

					for _ in tqdm(range(0							,					args.n_examples							,					__magic_name__					)					):
										yield [next(__magic_name__					)[args.text_column] for _ in range(__magic_name__					)]


# Configuration
__SCREAMING_SNAKE_CASE     :				Dict             =       HfArgumentParser(TokenizerTrainingArguments)
__SCREAMING_SNAKE_CASE     :				str             =       parser.parse_args()

# Base tokenizer
__SCREAMING_SNAKE_CASE     :				Tuple             =       AutoTokenizer.from_pretrained(args.base_tokenizer)
__SCREAMING_SNAKE_CASE     :				List[str]             =       list(bytes_to_unicode().values())

# Load dataset
__SCREAMING_SNAKE_CASE     :				Union[str, Any]             =       load_dataset(args.dataset_name, split='train', streaming=True)
__SCREAMING_SNAKE_CASE     :				Any             =       iter(dataset)


# Training and saving
__SCREAMING_SNAKE_CASE     :				Optional[int]             =       tokenizer.train_new_from_iterator(
    batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
)
new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)
