



"""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'''





		lowerCAmelCase			:					List[Any]   	=				MBartConfig
		lowerCAmelCase			:					int   	=				{}
		lowerCAmelCase			:					Optional[int]   	=				"gelu"







		def __init__(	self						:  str			,_snake_case						:  Any			,_snake_case						:  Optional[int]=13			,_snake_case						:  str=7			,_snake_case						:  Any=True			,_snake_case						:  Union[str, Any]=False			,_snake_case						:  List[str]=99			,_snake_case						:  Any=32			,_snake_case						:  Optional[int]=2			,_snake_case						:  Dict=4			,_snake_case						:  str=37			,_snake_case						:  Optional[int]=0.1			,_snake_case						:  Dict=0.1			,_snake_case						:  List[str]=20			,_snake_case						:  Optional[Any]=2			,_snake_case						:  Optional[Any]=1			,_snake_case						:  Optional[int]=0			,)					->		Optional[int]:






				"""simple docstring"""



				lowercase__   :    List[str]				=			parent
				lowercase__   :    Optional[Any]				=			batch_size
				lowercase__   :    Union[str, Any]				=			seq_length
				lowercase__   :    Tuple				=			is_training
				lowercase__   :    Optional[Any]				=			use_labels
				lowercase__   :    Any				=			vocab_size
				lowercase__   :    Tuple				=			hidden_size
				lowercase__   :    Dict				=			num_hidden_layers
				lowercase__   :    Optional[Any]				=			num_attention_heads
				lowercase__   :    Any				=			intermediate_size
				lowercase__   :    Any				=			hidden_dropout_prob
				lowercase__   :    Optional[int]				=			attention_probs_dropout_prob
				lowercase__   :    str				=			max_position_embeddings
				lowercase__   :    Union[str, Any]				=			eos_token_id
				lowercase__   :    List[Any]				=			pad_token_id
				lowercase__   :    Tuple				=			bos_token_id







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






				"""simple docstring"""



				lowercase__   :    Optional[Any]				=			ids_tensor([self.batch_size, self.seq_length - 1]			,self.vocab_size						)
				lowercase__   :    List[str]				=			tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)			,1						)
				lowercase__   :    Optional[int]				=			tf.concat([input_ids, eos_tensor]			,axis=1						)

				lowercase__   :    str				=			ids_tensor([self.batch_size, self.seq_length]			,self.vocab_size						)

				lowercase__   :    Union[str, Any]				=			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			,)
				lowercase__   :    Tuple				=			prepare_mbart_inputs_dict(_snake_case			,_snake_case			,_snake_case						)
				return config, inputs_dict







		def 					UpperCAmelCase    (	self						:  int			,_snake_case						:  Dict			,_snake_case						:  Optional[Any]						)					->		str:






				"""simple docstring"""



				lowercase__   :    Union[str, Any]				=			TFMBartModel(config=_snake_case						).get_decoder()
				lowercase__   :    List[str]				=			inputs_dict['''input_ids''']

				lowercase__   :    Dict				=			input_ids[:1, :]
				lowercase__   :    Any				=			inputs_dict['''attention_mask'''][:1, :]
				lowercase__   :    List[Any]				=			inputs_dict['''head_mask''']
				lowercase__   :    List[Any]				=			1

				# first forward pass
				lowercase__   :    Any				=			model(_snake_case			,attention_mask=_snake_case			,head_mask=_snake_case			,use_cache=_snake_case						)

				lowercase__		,			lowercase__   :    int				=			outputs.to_tuple()
				lowercase__   :    Dict				=			past_key_values[1]





def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase=None						,     __lowerCamelCase=None						,     __lowerCamelCase=None						,     __lowerCamelCase=None						,     __lowerCamelCase=None						,     )							->      int:
		if attention_mask is None:
				lowercase__   :    int				=			tf.cast(tf.math.not_equal(__lowerCamelCase						,     config.pad_token_id	)						,     tf.inta	)
		if decoder_attention_mask is None:
				lowercase__   :    str				=			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:
				lowercase__   :    Optional[int]				=			tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
		if decoder_head_mask is None:
				lowercase__   :    List[Any]				=			tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
		if cross_attn_head_mask is None:
				lowercase__   :    Any				=			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'''





		lowerCAmelCase			:					Optional[Any]   	=				(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
		lowerCAmelCase			:					Any   	=				(TFMBartForConditionalGeneration,) if is_tf_available() else ()
		lowerCAmelCase			:					Optional[Any]   	=				(
		    {
		        "conversational": TFMBartForConditionalGeneration,
		        "feature-extraction": TFMBartModel,
		        "summarization": TFMBartForConditionalGeneration,
		        "text2text-generation": TFMBartForConditionalGeneration,
		        "translation": TFMBartForConditionalGeneration,
		    }
		    if is_tf_available()
		    else {}
		)
		lowerCAmelCase			:					Optional[int]   	=				True
		lowerCAmelCase			:					Optional[Any]   	=				False
		lowerCAmelCase			:					str   	=				False







		def 					UpperCAmelCase    (	self						:  Optional[Any]			,_snake_case						:  List[Any]			,_snake_case						:  Dict			,_snake_case						:  int			,_snake_case						:  List[Any]			,_snake_case						:  List[Any]						)					->		List[str]:






				"""simple docstring"""



				if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
						# Exception encountered when calling layer '...'
						return True

				return False







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






				"""simple docstring"""



				lowercase__   :    Dict				=			TFMBartModelTester(self						)
				lowercase__   :    Optional[Any]				=			ConfigTester(self			,config_class=_snake_case						)







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






				"""simple docstring"""



				self.config_tester.run_common_tests()







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






				"""simple docstring"""



				lowercase__   :    List[Any]				=			self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_decoder_model_past_large_inputs(*_snake_case						)





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







		'''simple docstring'''





		lowerCAmelCase			:					List[Any]   	=				[
		    " UN Chief Says There Is No Military Solution in Syria",
		]
		lowerCAmelCase			:					Dict   	=				[
		    "Şeful ONU declară că nu există o soluţie militară în Siria",
		]
		lowerCAmelCase			:					str   	=				"facebook/mbart-large-en-ro"







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






				"""simple docstring"""



				return AutoTokenizer.from_pretrained(self.model_name						)







		@cached_property
		def 					UpperCAmelCase    (	self						:  List[str]						)					->		Optional[int]:






				"""simple docstring"""



				lowercase__   :    Dict				=			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
				return model







		def 					UpperCAmelCase    (	self						:  List[Any]			,**_snake_case						:  str						)					->		Optional[int]:






				"""simple docstring"""



				lowercase__   :    Dict				=			self.translate_src_text(**_snake_case						)
				self.assertListEqual(self.expected_text			,_snake_case						)







		def 					UpperCAmelCase    (	self						:  Any			,**_snake_case						:  List[str]						)					->		Optional[Any]:






				"""simple docstring"""



				lowercase__   :    str				=			self.tokenizer(self.src_text			,**_snake_case			,return_tensors='''tf'''						)
				lowercase__   :    Dict				=			self.model.generate(
				    model_inputs.input_ids			,attention_mask=model_inputs.attention_mask			,num_beams=2						)
				lowercase__   :    List[str]				=			self.tokenizer.batch_decode(_snake_case			,skip_special_tokens=_snake_case						)
				return generated_words







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






				"""simple docstring"""



				self._assert_generated_batch_equal_expected()




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a__				:							str						=     {
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							int						=     ['''RoFormerTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							int						=     [
		    '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''RoFormerForCausalLM''',
		    '''RoFormerForMaskedLM''',
		    '''RoFormerForMultipleChoice''',
		    '''RoFormerForQuestionAnswering''',
		    '''RoFormerForSequenceClassification''',
		    '''RoFormerForTokenClassification''',
		    '''RoFormerLayer''',
		    '''RoFormerModel''',
		    '''RoFormerPreTrainedModel''',
		    '''load_tf_weights_in_roformer''',
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							List[Any]						=     [
		    '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFRoFormerForCausalLM''',
		    '''TFRoFormerForMaskedLM''',
		    '''TFRoFormerForMultipleChoice''',
		    '''TFRoFormerForQuestionAnswering''',
		    '''TFRoFormerForSequenceClassification''',
		    '''TFRoFormerForTokenClassification''',
		    '''TFRoFormerLayer''',
		    '''TFRoFormerModel''',
		    '''TFRoFormerPreTrainedModel''',
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							Tuple						=     [
		    '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''FlaxRoFormerForMaskedLM''',
		    '''FlaxRoFormerForMultipleChoice''',
		    '''FlaxRoFormerForQuestionAnswering''',
		    '''FlaxRoFormerForSequenceClassification''',
		    '''FlaxRoFormerForTokenClassification''',
		    '''FlaxRoFormerModel''',
		    '''FlaxRoFormerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
		from .tokenization_roformer import RoFormerTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_roformer_fast import RoFormerTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roformer import (
				    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoFormerForCausalLM,
				    RoFormerForMaskedLM,
				    RoFormerForMultipleChoice,
				    RoFormerForQuestionAnswering,
				    RoFormerForSequenceClassification,
				    RoFormerForTokenClassification,
				    RoFormerLayer,
				    RoFormerModel,
				    RoFormerPreTrainedModel,
				    load_tf_weights_in_roformer,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_roformer import (
				    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFRoFormerForCausalLM,
				    TFRoFormerForMaskedLM,
				    TFRoFormerForMultipleChoice,
				    TFRoFormerForQuestionAnswering,
				    TFRoFormerForSequenceClassification,
				    TFRoFormerForTokenClassification,
				    TFRoFormerLayer,
				    TFRoFormerModel,
				    TFRoFormerPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_roformer import (
				    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    FlaxRoFormerForMaskedLM,
				    FlaxRoFormerForMultipleChoice,
				    FlaxRoFormerForQuestionAnswering,
				    FlaxRoFormerForSequenceClassification,
				    FlaxRoFormerForTokenClassification,
				    FlaxRoFormerModel,
				    FlaxRoFormerPreTrainedModel,
				)


else:
		import sys

		a__				:							int						=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
