


'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_tf_available():
					import tensorflow as tf

					from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class 						a__					:





			'''simple docstring'''

			A						:				Any					      =			MBartConfig
			A						:				Dict					      =			{}
			A						:				int					      =			'''gelu'''
			def __init__(  self							:			Any			,    lowerCAmelCase_							:			int			,    lowerCAmelCase_							:			Tuple=13			,    lowerCAmelCase_							:			Any=7			,    lowerCAmelCase_							:			Optional[Any]=True			,    lowerCAmelCase_							:			Any=False			,    lowerCAmelCase_							:			Tuple=99			,    lowerCAmelCase_							:			List[str]=32			,    lowerCAmelCase_							:			Optional[Any]=2			,    lowerCAmelCase_							:			Dict=4			,    lowerCAmelCase_							:			Dict=37			,    lowerCAmelCase_							:			Tuple=0.1			,    lowerCAmelCase_							:			Dict=0.1			,    lowerCAmelCase_							:			List[Any]=20			,    lowerCAmelCase_							:			List[str]=2			,    lowerCAmelCase_							:			List[str]=1			,    lowerCAmelCase_							:			Any=0			,    )      ->		List[str]:
						__A=	parent
						__A=	batch_size
						__A=	seq_length
						__A=	is_training
						__A=	use_labels
						__A=	vocab_size
						__A=	hidden_size
						__A=	num_hidden_layers
						__A=	num_attention_heads
						__A=	intermediate_size
						__A=	hidden_dropout_prob
						__A=	attention_probs_dropout_prob
						__A=	max_position_embeddings
						__A=	eos_token_id
						__A=	pad_token_id
						__A=	bos_token_id
			def 					lowerCAmelCase     (  self							:			Optional[int]	)      ->		Dict:
						__A=	ids_tensor([self.batch_size, self.seq_length - 1]			,    self.vocab_size	)
						__A=	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size	)			,    1	)
						__A=	tf.concat([input_ids, eos_tensor]			,    axis=1	)

						__A=	ids_tensor([self.batch_size, self.seq_length]			,    self.vocab_size	)

						__A=	self.config_cls(
						    vocab_size=self.vocab_size			,    d_model=self.hidden_size			,    encoder_layers=self.num_hidden_layers			,    decoder_layers=self.num_hidden_layers			,    encoder_attention_heads=self.num_attention_heads			,    decoder_attention_heads=self.num_attention_heads			,    encoder_ffn_dim=self.intermediate_size			,    decoder_ffn_dim=self.intermediate_size			,    dropout=self.hidden_dropout_prob			,    attention_dropout=self.attention_probs_dropout_prob			,    max_position_embeddings=self.max_position_embeddings			,    eos_token_ids=[2]			,    bos_token_id=self.bos_token_id			,    pad_token_id=self.pad_token_id			,    decoder_start_token_id=self.pad_token_id			,    **self.config_updates			,    )
						__A=	prepare_mbart_inputs_dict(lowerCAmelCase_			,    lowerCAmelCase_			,    lowerCAmelCase_	)
						return config, inputs_dict

			def 					lowerCAmelCase     (  self							:			Any			,    lowerCAmelCase_							:			Optional[Any]			,    lowerCAmelCase_							:			Union[str, Any]	)      ->		int:
						__A=	TFMBartModel(config=lowerCAmelCase_	).get_decoder()
						__A=	inputs_dict['input_ids']

						__A=	input_ids[:1, :]
						__A=	inputs_dict['attention_mask'][:1, :]
						__A=	inputs_dict['head_mask']
						__A=	1

						# first forward pass
						__A=	model(lowerCAmelCase_			,    attention_mask=lowerCAmelCase_			,    head_mask=lowerCAmelCase_			,    use_cache=lowerCAmelCase_	)

						__A,	__A=	outputs.to_tuple()
						__A=	past_key_values[1]






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: List[Any],_SCREAMING_SNAKE_CASE					: Tuple,_SCREAMING_SNAKE_CASE					: Optional[Any],_SCREAMING_SNAKE_CASE					: Optional[int]=None,_SCREAMING_SNAKE_CASE					: Tuple=None,_SCREAMING_SNAKE_CASE					: Optional[int]=None,_SCREAMING_SNAKE_CASE					: List[str]=None,_SCREAMING_SNAKE_CASE					: Union[str, Any]=None,):






			"""simple docstring"""




			if attention_mask is None:
						__A=	tf.cast(tf.math.not_equal(_SCREAMING_SNAKE_CASE,config.pad_token_id ),tf.inta )
			if decoder_attention_mask is None:
						__A=	tf.concat(
						    [
						        tf.ones(decoder_input_ids[:, :1].shape,dtype=tf.inta ),
						        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:],config.pad_token_id ),tf.inta ),
						    ],axis=-1,)
			if head_mask is None:
						__A=	tf.ones((config.encoder_layers, config.encoder_attention_heads) )
			if decoder_head_mask is None:
						__A=	tf.ones((config.decoder_layers, config.decoder_attention_heads) )
			if cross_attn_head_mask is None:
						__A=	tf.ones((config.decoder_layers, config.decoder_attention_heads) )
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": decoder_attention_mask,
			    "head_mask": head_mask,
			    "decoder_head_mask": decoder_head_mask,
			    "cross_attn_head_mask": cross_attn_head_mask,
			}
@require_tf
class 						a__					(					a_      ,  a_      ,  unittest.TestCase   ):





			'''simple docstring'''

			A						:				str					      =			(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
			A						:				str					      =			(TFMBartForConditionalGeneration,) if is_tf_available() else ()
			A						:				Dict					      =			(
			    {
			        '''conversational''': TFMBartForConditionalGeneration,
			        '''feature-extraction''': TFMBartModel,
			        '''summarization''': TFMBartForConditionalGeneration,
			        '''text2text-generation''': TFMBartForConditionalGeneration,
			        '''translation''': TFMBartForConditionalGeneration,
			    }
			    if is_tf_available()
			    else {}
			)
			A						:				List[Any]					      =			True
			A						:				str					      =			False
			A						:				Tuple					      =			False
			def 					lowerCAmelCase     (  self							:			Optional[Any]			,    lowerCAmelCase_							:			Optional[int]			,    lowerCAmelCase_							:			Union[str, Any]			,    lowerCAmelCase_							:			Dict			,    lowerCAmelCase_							:			int			,    lowerCAmelCase_							:			List[Any]	)      ->		Union[str, Any]:
						if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
									# Exception encountered when calling layer '...'
									return True

						return False
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Any:
						__A=	TFMBartModelTester(self	)
						__A=	ConfigTester(self			,    config_class=lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Dict	)      ->		List[Any]:
						self.config_tester.run_common_tests()

			def 					lowerCAmelCase     (  self							:			List[Any]	)      ->		Dict:
						__A=	self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase_	)


@require_sentencepiece
@require_tokenizers
@require_tf
class 						a__					(					unittest.TestCase   ):





			'''simple docstring'''

			A						:				Dict					      =			[
			    ''' UN Chief Says There Is No Military Solution in Syria''',
			]
			A						:				Optional[int]					      =			[
			    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
			]
			A						:				Any					      =			'''facebook/mbart-large-en-ro'''
			@cached_property
			def 					lowerCAmelCase     (  self							:			List[str]	)      ->		Tuple:
						return AutoTokenizer.from_pretrained(self.model_name	)
			@cached_property
			def 					lowerCAmelCase     (  self							:			Optional[int]	)      ->		Any:
						__A=	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name	)
						return model
			def 					lowerCAmelCase     (  self							:			Optional[Any]			,    **lowerCAmelCase_							:			Union[str, Any]	)      ->		Union[str, Any]:
						__A=	self.translate_src_text(**lowerCAmelCase_	)
						self.assertListEqual(self.expected_text			,    lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Tuple			,    **lowerCAmelCase_							:			Dict	)      ->		List[str]:
						__A=	self.tokenizer(self.src_text			,    **lowerCAmelCase_			,    return_tensors='tf'	)
						__A=	self.model.generate(
						    model_inputs.input_ids			,    attention_mask=model_inputs.attention_mask			,    num_beams=2	)
						__A=	self.tokenizer.batch_decode(lowerCAmelCase_			,    skip_special_tokens=lowerCAmelCase_	)
						return generated_words

			@slow
			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		Union[str, Any]:
						self._assert_generated_batch_equal_expected()







'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import DistilBertConfig, 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.distilbert.modeling_tf_distilbert import (
					    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TFDistilBertForMaskedLM,
					    TFDistilBertForMultipleChoice,
					    TFDistilBertForQuestionAnswering,
					    TFDistilBertForSequenceClassification,
					    TFDistilBertForTokenClassification,
					    TFDistilBertModel,
					)
class 						a__					:





			'''simple docstring'''

			def __init__(  self							:			str			,    lowerCAmelCase_							:			str			,    )      ->		Optional[int]:
						__A=	parent
						__A=	13
						__A=	7
						__A=	True
						__A=	True
						__A=	False
						__A=	True
						__A=	99
						__A=	32
						__A=	2
						__A=	4
						__A=	37
						__A=	'gelu'
						__A=	0.1
						__A=	0.1
						__A=	512
						__A=	16
						__A=	2
						__A=	0.02
						__A=	3
						__A=	4
						__A=	None
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		str:
						__A=	ids_tensor([self.batch_size, self.seq_length]			,    self.vocab_size	)

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

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

						__A=	DistilBertConfig(
						    vocab_size=self.vocab_size			,    dim=self.hidden_size			,    n_layers=self.num_hidden_layers			,    n_heads=self.num_attention_heads			,    hidden_dim=self.intermediate_size			,    hidden_act=self.hidden_act			,    dropout=self.hidden_dropout_prob			,    attention_dropout=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 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Any			,    lowerCAmelCase_							:			int			,    lowerCAmelCase_							:			int			,    lowerCAmelCase_							:			Dict			,    lowerCAmelCase_							:			Any			,    lowerCAmelCase_							:			Optional[Any]	)      ->		Any:
						__A=	TFDistilBertModel(config=lowerCAmelCase_	)
						__A=	{'input_ids': input_ids, 'attention_mask': input_mask}

						__A=	model(lowerCAmelCase_	)

						__A=	[input_ids, input_mask]

						__A=	model(lowerCAmelCase_	)

						self.parent.assertEqual(result.last_hidden_state.shape			,    (self.batch_size, self.seq_length, self.hidden_size)	)
			def 					lowerCAmelCase     (  self							:			Optional[int]			,    lowerCAmelCase_							:			Optional[Any]			,    lowerCAmelCase_							:			List[Any]			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			Union[str, Any]			,    lowerCAmelCase_							:			Dict			,    lowerCAmelCase_							:			Tuple	)      ->		Optional[int]:
						__A=	TFDistilBertForMaskedLM(config=lowerCAmelCase_	)
						__A=	{'input_ids': input_ids, 'attention_mask': input_mask}
						__A=	model(lowerCAmelCase_	)
						self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.seq_length, self.vocab_size)	)
			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Dict			,    lowerCAmelCase_							:			Optional[Any]			,    lowerCAmelCase_							:			Tuple			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			Dict			,    lowerCAmelCase_							:			List[str]	)      ->		int:
						__A=	TFDistilBertForQuestionAnswering(config=lowerCAmelCase_	)
						__A=	{
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						}
						__A=	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 					lowerCAmelCase     (  self							:			List[str]			,    lowerCAmelCase_							:			Any			,    lowerCAmelCase_							:			Optional[int]			,    lowerCAmelCase_							:			Tuple			,    lowerCAmelCase_							:			int			,    lowerCAmelCase_							:			Dict			,    lowerCAmelCase_							:			List[str]	)      ->		Union[str, Any]:
						__A=	self.num_labels
						__A=	TFDistilBertForSequenceClassification(lowerCAmelCase_	)
						__A=	{'input_ids': input_ids, 'attention_mask': input_mask}
						__A=	model(lowerCAmelCase_	)
						self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.num_labels)	)
			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Any			,    lowerCAmelCase_							:			Union[str, Any]			,    lowerCAmelCase_							:			List[Any]			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			Union[str, Any]			,    lowerCAmelCase_							:			int	)      ->		Optional[Any]:
						__A=	self.num_choices
						__A=	TFDistilBertForMultipleChoice(lowerCAmelCase_	)
						__A=	tf.tile(tf.expand_dims(lowerCAmelCase_			,    1	)			,    (1, self.num_choices, 1)	)
						__A=	tf.tile(tf.expand_dims(lowerCAmelCase_			,    1	)			,    (1, self.num_choices, 1)	)
						__A=	{
						    'input_ids': multiple_choice_inputs_ids,
						    'attention_mask': multiple_choice_input_mask,
						}
						__A=	model(lowerCAmelCase_	)
						self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.num_choices)	)
			def 					lowerCAmelCase     (  self							:			List[Any]			,    lowerCAmelCase_							:			Optional[Any]			,    lowerCAmelCase_							:			Dict			,    lowerCAmelCase_							:			Union[str, Any]			,    lowerCAmelCase_							:			Optional[int]			,    lowerCAmelCase_							:			List[str]			,    lowerCAmelCase_							:			Optional[Any]	)      ->		Dict:
						__A=	self.num_labels
						__A=	TFDistilBertForTokenClassification(lowerCAmelCase_	)
						__A=	{'input_ids': input_ids, 'attention_mask': input_mask}
						__A=	model(lowerCAmelCase_	)
						self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.seq_length, self.num_labels)	)

			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		int:
						__A=	self.prepare_config_and_inputs()
						((__A),	(__A),	(__A),	(__A),	(__A),	(__A))=	config_and_inputs
						__A=	{'input_ids': input_ids, 'attention_mask': input_mask}
						return config, inputs_dict


@require_tf
class 						a__					(					a_      ,  a_      ,  unittest.TestCase   ):





			'''simple docstring'''

			A						:				Optional[Any]					      =			(
			    (
			        TFDistilBertModel,
			        TFDistilBertForMaskedLM,
			        TFDistilBertForQuestionAnswering,
			        TFDistilBertForSequenceClassification,
			        TFDistilBertForTokenClassification,
			        TFDistilBertForMultipleChoice,
			    )
			    if is_tf_available()
			    else None
			)
			A						:				Optional[int]					      =			(
			    {
			        '''feature-extraction''': TFDistilBertModel,
			        '''fill-mask''': TFDistilBertForMaskedLM,
			        '''question-answering''': TFDistilBertForQuestionAnswering,
			        '''text-classification''': TFDistilBertForSequenceClassification,
			        '''token-classification''': TFDistilBertForTokenClassification,
			        '''zero-shot''': TFDistilBertForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			A						:				str					      =			False
			A						:				List[Any]					      =			False
			def 					lowerCAmelCase     (  self							:			List[str]	)      ->		Optional[Any]:
						__A=	TFDistilBertModelTester(self	)
						__A=	ConfigTester(self			,    config_class=lowerCAmelCase_			,    dim=37	)
			def 					lowerCAmelCase     (  self							:			Dict	)      ->		Tuple:
						self.config_tester.run_common_tests()
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Optional[int]:
						__A=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_model(*lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Optional[int]	)      ->		Optional[Any]:
						__A=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_for_masked_lm(*lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			str	)      ->		Any:
						__A=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_for_question_answering(*lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		Optional[Any]:
						__A=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_for_sequence_classification(*lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		Optional[Any]:
						__A=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_for_multiple_choice(*lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Any	)      ->		Optional[int]:
						__A=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_distilbert_for_token_classification(*lowerCAmelCase_	)

			@slow
			def 					lowerCAmelCase     (  self							:			int	)      ->		Tuple:
						for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]	):
									__A=	TFDistilBertModel.from_pretrained(lowerCAmelCase_	)
									self.assertIsNotNone(lowerCAmelCase_	)


@require_tf
class 						a__					(					unittest.TestCase   ):





			'''simple docstring'''

			@slow
			def 					lowerCAmelCase     (  self							:			Any	)      ->		List[Any]:
						__A=	TFDistilBertModel.from_pretrained('distilbert-base-uncased'	)
						__A=	tf.constant([[0, 1, 2, 3, 4, 5]]	)
						__A=	model(lowerCAmelCase_	)[0]

						__A=	[1, 6, 768]
						self.assertEqual(output.shape			,    lowerCAmelCase_	)

						__A=	tf.constant(
						    [
						        [
						            [0.19_26_18_85, -0.13_73_29_55, 0.4_11_97_99],
						            [0.22_15_01_56, -0.07_42_26_61, 0.39_03_72_04],
						            [0.22_75_60_18, -0.0_89_64_14, 0.3_70_14_67],
						        ]
						    ]	)
						tf.debugging.assert_near(output[:, :3, :3]			,    lowerCAmelCase_			,    atol=1E-4	)



