"""simple docstring"""




import unittest

from transformers import MraConfig, 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, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
			import torch

			from transformers import (
			    MraForMaskedLM,
			    MraForMultipleChoice,
			    MraForQuestionAnswering,
			    MraForSequenceClassification,
			    MraForTokenClassification,
			    MraModel,
			)
			from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST

class       a_      :




					def __init__(	self						:     List[Any]  ,						snake_case__						:     Any  ,						snake_case__						:     Union[str, Any]=2  ,						snake_case__						:     List[str]=8  ,						snake_case__						:     Optional[int]=True  ,						snake_case__						:     Optional[Any]=True  ,						snake_case__						:     Union[str, Any]=True  ,						snake_case__						:     List[str]=True  ,						snake_case__						:     List[Any]=99  ,						snake_case__						:     List[str]=16  ,						snake_case__						:     Dict=5  ,						snake_case__						:     List[Any]=2  ,						snake_case__						:     Optional[Any]=36  ,						snake_case__						:     Any="gelu"  ,						snake_case__						:     Dict=0.0  ,						snake_case__						:     List[str]=0.0  ,						snake_case__						:     str=512  ,						snake_case__						:     Union[str, Any]=16  ,						snake_case__						:     Optional[Any]=2  ,						snake_case__						:     List[str]=0.02  ,						snake_case__						:     Dict=3  ,						snake_case__						:     List[Any]=4  ,						snake_case__						:     Dict=None  ,						):
							lowerCAmelCase__        =   parent
							lowerCAmelCase__        =   batch_size
							lowerCAmelCase__        =   seq_length
							lowerCAmelCase__        =   is_training
							lowerCAmelCase__        =   use_input_mask
							lowerCAmelCase__        =   use_token_type_ids
							lowerCAmelCase__        =   use_labels
							lowerCAmelCase__        =   vocab_size
							lowerCAmelCase__        =   hidden_size
							lowerCAmelCase__        =   num_hidden_layers
							lowerCAmelCase__        =   num_attention_heads
							lowerCAmelCase__        =   intermediate_size
							lowerCAmelCase__        =   hidden_act
							lowerCAmelCase__        =   hidden_dropout_prob
							lowerCAmelCase__        =   attention_probs_dropout_prob
							lowerCAmelCase__        =   max_position_embeddings
							lowerCAmelCase__        =   type_vocab_size
							lowerCAmelCase__        =   type_sequence_label_size
							lowerCAmelCase__        =   initializer_range
							lowerCAmelCase__        =   num_labels
							lowerCAmelCase__        =   num_choices
							lowerCAmelCase__        =   scope




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							lowerCAmelCase__        =   ids_tensor([self.batch_size, self.seq_length]  ,						self.vocab_size     )

							lowerCAmelCase__        =   None
							if self.use_input_mask:
									lowerCAmelCase__        =   random_attention_mask([self.batch_size, self.seq_length]     )

							lowerCAmelCase__        =   None
							if self.use_token_type_ids:
									lowerCAmelCase__        =   ids_tensor([self.batch_size, self.seq_length]  ,						self.type_vocab_size     )

							lowerCAmelCase__        =   None
							lowerCAmelCase__        =   None
							lowerCAmelCase__        =   None
							if self.use_labels:
									lowerCAmelCase__        =   ids_tensor([self.batch_size]  ,						self.type_sequence_label_size     )
									lowerCAmelCase__        =   ids_tensor([self.batch_size, self.seq_length]  ,						self.num_labels     )
									lowerCAmelCase__        =   ids_tensor([self.batch_size]  ,						self.num_choices     )

							lowerCAmelCase__        =   self.get_config()

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							return MraConfig(
							    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  ,						is_decoder=snake_case__  ,						initializer_range=self.initializer_range  ,						)




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   self.get_config()
							lowerCAmelCase__        =   300
							return config




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							(
							    (
							    lowerCAmelCase__
							)   ,					(
							    lowerCAmelCase__
							)   ,					(
							    lowerCAmelCase__
							)   ,					(
							    lowerCAmelCase__
							)   ,					(
							    lowerCAmelCase__
							)   ,					(
							    lowerCAmelCase__
							)   ,					(
							    lowerCAmelCase__
							)   ,					
							)        =   self.prepare_config_and_inputs()

							lowerCAmelCase__        =   True
							lowerCAmelCase__        =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
							lowerCAmelCase__        =   ids_tensor([self.batch_size, self.seq_length]  ,						vocab_size=2     )

							return (
							    config,
							    input_ids,
							    token_type_ids,
							    input_mask,
							    sequence_labels,
							    token_labels,
							    choice_labels,
							    encoder_hidden_states,
							    encoder_attention_mask,
							)




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     List[str]  ,						snake_case__						:     List[Any]  ,						snake_case__						:     List[str]  ,						snake_case__						:     str  ,						snake_case__						:     List[str]  ,						snake_case__						:     Dict  ,						snake_case__						:     Any     ):
							lowerCAmelCase__        =   MraModel(config=snake_case__     )
							model.to(snake_case__     )
							model.eval()
							lowerCAmelCase__        =   model(snake_case__  ,						attention_mask=snake_case__  ,						token_type_ids=snake_case__     )
							lowerCAmelCase__        =   model(snake_case__  ,						token_type_ids=snake_case__     )
							lowerCAmelCase__        =   model(snake_case__     )
							self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.hidden_size)     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     Tuple  ,						snake_case__						:     Optional[Any]  ,						snake_case__						:     Any  ,						snake_case__						:     Tuple  ,						snake_case__						:     Optional[Any]  ,						snake_case__						:     Optional[int]  ,						snake_case__						:     Optional[int]  ,						snake_case__						:     Dict  ,						snake_case__						:     Optional[int]  ,						):
							lowerCAmelCase__        =   True
							lowerCAmelCase__        =   MraModel(snake_case__     )
							model.to(snake_case__     )
							model.eval()
							lowerCAmelCase__        =   model(
							    snake_case__  ,						attention_mask=snake_case__  ,						token_type_ids=snake_case__  ,						encoder_hidden_states=snake_case__  ,						encoder_attention_mask=snake_case__  ,						)
							lowerCAmelCase__        =   model(
							    snake_case__  ,						attention_mask=snake_case__  ,						token_type_ids=snake_case__  ,						encoder_hidden_states=snake_case__  ,						)
							lowerCAmelCase__        =   model(snake_case__  ,						attention_mask=snake_case__  ,						token_type_ids=snake_case__     )
							self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.hidden_size)     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]  ,						snake_case__						:     Any  ,						snake_case__						:     List[Any]  ,						snake_case__						:     List[str]  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     List[Any]  ,						snake_case__						:     List[str]  ,						snake_case__						:     int     ):
							lowerCAmelCase__        =   MraForMaskedLM(config=snake_case__     )
							model.to(snake_case__     )
							model.eval()
							lowerCAmelCase__        =   model(snake_case__  ,						attention_mask=snake_case__  ,						token_type_ids=snake_case__  ,						labels=snake_case__     )
							self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.seq_length, self.vocab_size)     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]  ,						snake_case__						:     Any  ,						snake_case__						:     Any  ,						snake_case__						:     int  ,						snake_case__						:     List[str]  ,						snake_case__						:     Dict  ,						snake_case__						:     int  ,						snake_case__						:     Tuple     ):
							lowerCAmelCase__        =   MraForQuestionAnswering(config=snake_case__     )
							model.to(snake_case__     )
							model.eval()
							lowerCAmelCase__        =   model(
							    snake_case__  ,						attention_mask=snake_case__  ,						token_type_ids=snake_case__  ,						start_positions=snake_case__  ,						end_positions=snake_case__  ,						)
							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 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]  ,						snake_case__						:     Any  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     Dict  ,						snake_case__						:     Optional[Any]  ,						snake_case__						:     Tuple  ,						snake_case__						:     Optional[int]  ,						snake_case__						:     List[Any]     ):
							lowerCAmelCase__        =   self.num_labels
							lowerCAmelCase__        =   MraForSequenceClassification(snake_case__     )
							model.to(snake_case__     )
							model.eval()
							lowerCAmelCase__        =   model(snake_case__  ,						attention_mask=snake_case__  ,						token_type_ids=snake_case__  ,						labels=snake_case__     )
							self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_labels)     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						snake_case__						:     Dict  ,						snake_case__						:     List[str]  ,						snake_case__						:     Optional[int]  ,						snake_case__						:     List[Any]  ,						snake_case__						:     int  ,						snake_case__						:     int  ,						snake_case__						:     str     ):
							lowerCAmelCase__        =   self.num_labels
							lowerCAmelCase__        =   MraForTokenClassification(config=snake_case__     )
							model.to(snake_case__     )
							model.eval()
							lowerCAmelCase__        =   model(snake_case__  ,						attention_mask=snake_case__  ,						token_type_ids=snake_case__  ,						labels=snake_case__     )
							self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.seq_length, self.num_labels)     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     int  ,						snake_case__						:     Optional[Any]  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     List[str]  ,						snake_case__						:     str  ,						snake_case__						:     Optional[Any]  ,						snake_case__						:     Optional[int]     ):
							lowerCAmelCase__        =   self.num_choices
							lowerCAmelCase__        =   MraForMultipleChoice(config=snake_case__     )
							model.to(snake_case__     )
							model.eval()
							lowerCAmelCase__        =   input_ids.unsqueeze(1     ).expand(-1  ,						self.num_choices  ,						-1     ).contiguous()
							lowerCAmelCase__        =   token_type_ids.unsqueeze(1     ).expand(-1  ,						self.num_choices  ,						-1     ).contiguous()
							lowerCAmelCase__        =   input_mask.unsqueeze(1     ).expand(-1  ,						self.num_choices  ,						-1     ).contiguous()
							lowerCAmelCase__        =   model(
							    snake_case__  ,						attention_mask=snake_case__  ,						token_type_ids=snake_case__  ,						labels=snake_case__  ,						)
							self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_choices)     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							lowerCAmelCase__        =   self.prepare_config_and_inputs()
							(
							    (
							    lowerCAmelCase__
							)   ,					(
							    lowerCAmelCase__
							)   ,					(
							    lowerCAmelCase__
							)   ,					(
							    lowerCAmelCase__
							)   ,					(
							    lowerCAmelCase__
							)   ,					(
							    lowerCAmelCase__
							)   ,					(
							    lowerCAmelCase__
							)   ,					
							)        =   config_and_inputs
							lowerCAmelCase__        =   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
							return config, inputs_dict

@require_torch
class       a_      (     __UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	Optional[Any]			=    (
					    (
					        MraModel,
					        MraForMaskedLM,
					        MraForMultipleChoice,
					        MraForQuestionAnswering,
					        MraForSequenceClassification,
					        MraForTokenClassification,
					    )
					    if is_torch_available()
					    else ()
					)
					UpperCamelCase_ :	Any			=    False
					UpperCamelCase_ :	Optional[Any]			=    False
					UpperCamelCase_ :	int			=    False
					UpperCamelCase_ :	Optional[int]			=    False

					UpperCamelCase_ :	Optional[Any]			=    ()




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							lowerCAmelCase__        =   MraModelTester(self     )
							lowerCAmelCase__        =   ConfigTester(self  ,						config_class=snake_case__  ,						hidden_size=37     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							self.config_tester.run_common_tests()




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
									lowerCAmelCase__        =   type
									self.model_tester.create_and_check_model(*snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_multiple_choice(*snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*snake_case__     )




					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCAmelCase__        =   MraModel.from_pretrained(snake_case__     )
									self.assertIsNotNone(snake_case__     )




					@unittest.skip(reason="""MRA does not output attentions"""     )
					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							return

@require_torch
class       a_      (     unittest.TestCase			):




					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							lowerCAmelCase__        =   MraModel.from_pretrained("""uw-madison/mra-base-512-4"""     )
							lowerCAmelCase__        =   torch.arange(256     ).unsqueeze(0     )

							with torch.no_grad():
									lowerCAmelCase__        =   model(snake_case__     )[0]

							lowerCAmelCase__        =   torch.Size((1, 256, 768)     )
							self.assertEqual(output.shape  ,						snake_case__     )

							lowerCAmelCase__        =   torch.tensor(
							    [[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]]     )

							self.assertTrue(torch.allclose(output[:, :3, :3]  ,						snake_case__  ,						atol=1E-4     )     )




					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							lowerCAmelCase__        =   MraForMaskedLM.from_pretrained("""uw-madison/mra-base-512-4"""     )
							lowerCAmelCase__        =   torch.arange(256     ).unsqueeze(0     )

							with torch.no_grad():
									lowerCAmelCase__        =   model(snake_case__     )[0]

							lowerCAmelCase__        =   50265

							lowerCAmelCase__        =   torch.Size((1, 256, vocab_size)     )
							self.assertEqual(output.shape  ,						snake_case__     )

							lowerCAmelCase__        =   torch.tensor(
							    [[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]]     )

							self.assertTrue(torch.allclose(output[:, :3, :3]  ,						snake_case__  ,						atol=1E-4     )     )




					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   MraForMaskedLM.from_pretrained("""uw-madison/mra-base-4096-8-d3"""     )
							lowerCAmelCase__        =   torch.arange(4096     ).unsqueeze(0     )

							with torch.no_grad():
									lowerCAmelCase__        =   model(snake_case__     )[0]

							lowerCAmelCase__        =   50265

							lowerCAmelCase__        =   torch.Size((1, 4096, vocab_size)     )
							self.assertEqual(output.shape  ,						snake_case__     )

							lowerCAmelCase__        =   torch.tensor(
							    [[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]]     )

							self.assertTrue(torch.allclose(output[:, :3, :3]  ,						snake_case__  ,						atol=1E-4     )     )





"""simple docstring"""




import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class       a_      (     __UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	str			=    LayoutLMTokenizer
					UpperCamelCase_ :	List[Any]			=    LayoutLMTokenizerFast
					UpperCamelCase_ :	Dict			=    True
					UpperCamelCase_ :	Any			=    True




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							super().setUp()

							lowerCAmelCase__        =   [
							    """[UNK]""",
							    """[CLS]""",
							    """[SEP]""",
							    """want""",
							    """##want""",
							    """##ed""",
							    """wa""",
							    """un""",
							    """runn""",
							    """##ing""",
							    """,""",
							    """low""",
							    """lowest""",
							]
							lowerCAmelCase__        =   os.path.join(self.tmpdirname  ,						VOCAB_FILES_NAMES["""vocab_file"""]     )
							with open(self.vocab_file  ,						"""w"""  ,						encoding="""utf-8"""     ) as vocab_writer:
									vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]     )     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						**snake_case__						:     Union[str, Any]     ):
							return LayoutLMTokenizer.from_pretrained(self.tmpdirname  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						snake_case__						:     Tuple     ):
							lowerCAmelCase__        =   """UNwant\u00E9d,running"""
							lowerCAmelCase__        =   """unwanted, running"""
							return input_text, output_text




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							lowerCAmelCase__        =   self.tokenizer_class(self.vocab_file     )

							lowerCAmelCase__        =   tokenizer.tokenize("""UNwant\u00E9d,running"""     )
							self.assertListEqual(snake_case__  ,						["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]     )
							self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__     )  ,						[7, 4, 5, 10, 8, 9]     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							pass




