


"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import MPNetConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers.models.mpnet.modeling_tf_mpnet import (
					    TFMPNetForMaskedLM,
					    TFMPNetForMultipleChoice,
					    TFMPNetForQuestionAnswering,
					    TFMPNetForSequenceClassification,
					    TFMPNetForTokenClassification,
					    TFMPNetModel,
					)


class 			SCREAMING_SNAKE_CASE_  :




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__=13	,					lowerCamelCase__=7	,					lowerCamelCase__=True	,					lowerCamelCase__=True	,					lowerCamelCase__=False	,					lowerCamelCase__=True	,					lowerCamelCase__=99	,					lowerCamelCase__=64	,					lowerCamelCase__=2	,					lowerCamelCase__=4	,					lowerCamelCase__=64	,					lowerCamelCase__="gelu"	,					lowerCamelCase__=0.1	,					lowerCamelCase__=0.1	,					lowerCamelCase__=512	,					lowerCamelCase__=16	,					lowerCamelCase__=2	,					lowerCamelCase__=0.02	,					lowerCamelCase__=3	,					lowerCamelCase__=4	,					lowerCamelCase__=None	,					)   ->	str:







								'''simple docstring'''




								snake_case__  :						Dict		=      parent
								snake_case__  :						Dict		=      batch_size
								snake_case__  :						Optional[int]		=      seq_length
								snake_case__  :						Union[str, Any]		=      is_training
								snake_case__  :						Optional[Any]		=      use_input_mask
								snake_case__  :						Union[str, Any]		=      use_token_type_ids
								snake_case__  :						List[str]		=      use_labels
								snake_case__  :						Optional[Any]		=      vocab_size
								snake_case__  :						List[Any]		=      hidden_size
								snake_case__  :						Optional[Any]		=      num_hidden_layers
								snake_case__  :						int		=      num_attention_heads
								snake_case__  :						List[str]		=      intermediate_size
								snake_case__  :						Optional[int]		=      hidden_act
								snake_case__  :						str		=      hidden_dropout_prob
								snake_case__  :						str		=      attention_probs_dropout_prob
								snake_case__  :						List[Any]		=      max_position_embeddings
								snake_case__  :						str		=      type_vocab_size
								snake_case__  :						Any		=      type_sequence_label_size
								snake_case__  :						int		=      initializer_range
								snake_case__  :						int		=      num_labels
								snake_case__  :						List[Any]		=      num_choices
								snake_case__  :						Any		=      scope




							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								snake_case__  :						Union[str, Any]		=      ids_tensor([self.batch_size, self.seq_length]	,					self.vocab_size)

								snake_case__  :						List[str]		=      None
								if self.use_input_mask:
									snake_case__  :						int		=      random_attention_mask([self.batch_size, self.seq_length])

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

								snake_case__  :						Any		=      MPNetConfig(
								    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	,					initializer_range=self.initializer_range	,					)
								return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__)   ->	Union[str, Any]:







								'''simple docstring'''




								snake_case__  :						Optional[Any]		=      TFMPNetModel(config=__lowerCamelCase)
								snake_case__  :						Optional[Any]		=      {"input_ids": input_ids, "attention_mask": input_mask}
								snake_case__  :						List[str]		=      model(__lowerCamelCase)
								snake_case__  :						List[Any]		=      [input_ids, input_mask]
								snake_case__  :						int		=      model(__lowerCamelCase)
								self.parent.assertEqual(result.last_hidden_state.shape	,					(self.batch_size, self.seq_length, self.hidden_size))




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__)   ->	List[Any]:







								'''simple docstring'''




								snake_case__  :						int		=      TFMPNetForMaskedLM(config=__lowerCamelCase)
								snake_case__  :						Tuple		=      {"input_ids": input_ids, "attention_mask": input_mask}
								snake_case__  :						Dict		=      model(__lowerCamelCase)
								self.parent.assertEqual(result.logits.shape	,					(self.batch_size, self.seq_length, self.vocab_size))




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__)   ->	int:







								'''simple docstring'''




								snake_case__  :						Tuple		=      TFMPNetForQuestionAnswering(config=__lowerCamelCase)
								snake_case__  :						List[Any]		=      {
								    "input_ids": input_ids,
								    "attention_mask": input_mask,
								}
								snake_case__  :						int		=      model(__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        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__)   ->	str:







								'''simple docstring'''




								snake_case__  :						Dict		=      self.num_labels
								snake_case__  :						Dict		=      TFMPNetForSequenceClassification(__lowerCamelCase)
								snake_case__  :						List[str]		=      {"input_ids": input_ids, "attention_mask": input_mask}
								snake_case__  :						int		=      model(__lowerCamelCase)
								self.parent.assertEqual(result.logits.shape	,					(self.batch_size, self.num_labels))




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__)   ->	Union[str, Any]:







								'''simple docstring'''




								snake_case__  :						Optional[int]		=      self.num_choices
								snake_case__  :						Union[str, Any]		=      TFMPNetForMultipleChoice(__lowerCamelCase)
								snake_case__  :						str		=      tf.tile(tf.expand_dims(__lowerCamelCase	,					1)	,					(1, self.num_choices, 1))
								snake_case__  :						Dict		=      tf.tile(tf.expand_dims(__lowerCamelCase	,					1)	,					(1, self.num_choices, 1))
								snake_case__  :						List[Any]		=      {
								    "input_ids": multiple_choice_inputs_ids,
								    "attention_mask": multiple_choice_input_mask,
								}
								snake_case__  :						Optional[int]		=      model(__lowerCamelCase)
								self.parent.assertEqual(result.logits.shape	,					(self.batch_size, self.num_choices))




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__)   ->	List[str]:







								'''simple docstring'''




								snake_case__  :						Union[str, Any]		=      self.num_labels
								snake_case__  :						Tuple		=      TFMPNetForTokenClassification(__lowerCamelCase)
								snake_case__  :						Tuple		=      {"input_ids": input_ids, "attention_mask": input_mask}
								snake_case__  :						Optional[Any]		=      model(__lowerCamelCase)
								self.parent.assertEqual(result.logits.shape	,					(self.batch_size, self.seq_length, self.num_labels))







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







								'''simple docstring'''




								snake_case__  :						Union[str, Any]		=      self.prepare_config_and_inputs()
								((snake_case__),					(snake_case__),					(snake_case__),					(snake_case__),					(snake_case__),					(snake_case__))  :						int		=      config_and_inputs
								snake_case__  :						Union[str, Any]		=      {"input_ids": input_ids, "attention_mask": input_mask}
								return config, inputs_dict


@require_tf
class 			SCREAMING_SNAKE_CASE_  (   snake_case_	,      snake_case_	,      unittest.TestCase):




							'''simple docstring'''





							__magic_name__					:     Optional[Any]				  =  (
							    (
							        TFMPNetForMaskedLM,
							        TFMPNetForMultipleChoice,
							        TFMPNetForQuestionAnswering,
							        TFMPNetForSequenceClassification,
							        TFMPNetForTokenClassification,
							        TFMPNetModel,
							    )
							    if is_tf_available()
							    else ()
							)
							__magic_name__					:     Dict				  =  (
							    {
							        '''feature-extraction''': TFMPNetModel,
							        '''fill-mask''': TFMPNetForMaskedLM,
							        '''question-answering''': TFMPNetForQuestionAnswering,
							        '''text-classification''': TFMPNetForSequenceClassification,
							        '''token-classification''': TFMPNetForTokenClassification,
							        '''zero-shot''': TFMPNetForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							__magic_name__					:     int				  =  False
							__magic_name__					:     Union[str, Any]				  =  False




							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								snake_case__  :						Optional[Any]		=      TFMPNetModelTester(self)
								snake_case__  :						Optional[Any]		=      ConfigTester(self	,					config_class=__lowerCamelCase	,					hidden_size=37)




							def        UpperCAmelCase (						self)   ->	Optional[int]:







								'''simple docstring'''




								self.config_tester.run_common_tests()




							def        UpperCAmelCase (						self)   ->	List[str]:







								'''simple docstring'''




								snake_case__  :						Optional[Any]		=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mpnet_model(*__lowerCamelCase)




							def        UpperCAmelCase (						self)   ->	Dict:







								'''simple docstring'''




								snake_case__  :						Union[str, Any]		=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mpnet_for_masked_lm(*__lowerCamelCase)




							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								snake_case__  :						str		=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mpnet_for_question_answering(*__lowerCamelCase)




							def        UpperCAmelCase (						self)   ->	List[Any]:







								'''simple docstring'''




								snake_case__  :						Any		=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mpnet_for_sequence_classification(*__lowerCamelCase)




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







								'''simple docstring'''




								snake_case__  :						Tuple		=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mpnet_for_multiple_choice(*__lowerCamelCase)




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







								'''simple docstring'''




								snake_case__  :						Any		=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mpnet_for_token_classification(*__lowerCamelCase)







							@slow
							def        UpperCAmelCase (						self)   ->	Union[str, Any]:







								'''simple docstring'''




								for model_name in ["microsoft/mpnet-base"]:
									snake_case__  :						List[str]		=      TFMPNetModel.from_pretrained(__lowerCamelCase)
									self.assertIsNotNone(__lowerCamelCase)


@require_tf
class 			SCREAMING_SNAKE_CASE_  (   unittest.TestCase):




							'''simple docstring'''





							@slow
							def        UpperCAmelCase (						self)   ->	Union[str, Any]:







								'''simple docstring'''




								snake_case__  :						Tuple		=      TFMPNetModel.from_pretrained("microsoft/mpnet-base")
								snake_case__  :						int		=      tf.constant([[0, 1, 2, 3, 4, 5]])
								snake_case__  :						List[Any]		=      model(__lowerCamelCase)[0]

								snake_case__  :						Union[str, Any]		=      [1, 6, 768]
								self.assertEqual(output.shape	,					__lowerCamelCase)

								snake_case__  :						Dict		=      tf.constant(
								    [
								        [
								            [-0.1_06_71_72, 0.08_21_64_73, 0.0_02_45_43],
								            [-0.03_46_58_79, 0.8_35_41_18, -0.03_25_22_88],
								            [-0.06_56_94_76, -0.12_42_41_11, -0.0_49_44_36],
								        ]
								    ])
								tf.debugging.assert_near(output[:, :3, :3]	,					__lowerCamelCase	,					atol=1E-4)













"""simple docstring"""

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
			import torch

			from transformers import CamembertModel

@require_torch
@require_sentencepiece
@require_tokenizers
class     A_				(   unittest.TestCase	):







				@slow
				def      _snake_case       (				self				:    Tuple						)					->       Dict:
										__magic_name__      =  CamembertModel.from_pretrained("camembert-base"						)
										model.to(__lowerCamelCase						)

										__magic_name__      =  torch.tensor(
										    [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]    ,     device=__lowerCamelCase    ,     dtype=torch.long    ,     )  # J'aime le camembert !
										with torch.no_grad():
																__magic_name__      =  model(__lowerCamelCase						)["last_hidden_state"]
										__magic_name__      =  torch.Size((1, 1_0, 7_6_8)						)
										self.assertEqual(output.shape    ,     __lowerCamelCase						)
										# compare the actual values for a slice.
										__magic_name__      =  torch.tensor(
										    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]    ,     device=__lowerCamelCase    ,     dtype=torch.float    ,     )
										# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
										# camembert.eval()
										# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

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



