



"""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			:					Any   	=				MBartConfig
		lowerCAmelCase			:					str   	=				{}
		lowerCAmelCase			:					Tuple   	=				"gelu"







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






				"""simple docstring"""



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







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






				"""simple docstring"""



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

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

				lowercase__   :    Optional[int]				=			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__   :    str				=			prepare_mbart_inputs_dict(_A			,_A			,_A						)
				return config, inputs_dict







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






				"""simple docstring"""



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

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

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

				lowercase__		,			lowercase__   :    Any				=			outputs.to_tuple()
				lowercase__   :    str				=			past_key_values[1]





def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase=None						,     __lowerCamelCase=None						,     __lowerCamelCase=None						,     __lowerCamelCase=None						,     __lowerCamelCase=None						,     )							->      List[str]:

		if attention_mask is None:
				lowercase__   :    List[Any]				=			tf.cast(tf.math.not_equal(UpperCamelCase__						,     config.pad_token_id	)						,     tf.inta	)
		if decoder_attention_mask is None:
				lowercase__   :    Optional[int]				=			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__   :    Dict				=			tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
		if decoder_head_mask is None:
				lowercase__   :    Tuple				=			tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
		if cross_attn_head_mask is None:
				lowercase__   :    int				=			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			:					str   	=				(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
		lowerCAmelCase			:					Optional[int]   	=				(TFMBartForConditionalGeneration,) if is_tf_available() else ()
		lowerCAmelCase			:					str   	=				(
		    {
		        "conversational": TFMBartForConditionalGeneration,
		        "feature-extraction": TFMBartModel,
		        "summarization": TFMBartForConditionalGeneration,
		        "text2text-generation": TFMBartForConditionalGeneration,
		        "translation": TFMBartForConditionalGeneration,
		    }
		    if is_tf_available()
		    else {}
		)
		lowerCAmelCase			:					Tuple   	=				True
		lowerCAmelCase			:					List[Any]   	=				False
		lowerCAmelCase			:					List[Any]   	=				False







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






				"""simple docstring"""



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

				return False







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






				"""simple docstring"""



				lowercase__   :    str				=			TFMBartModelTester(self						)
				lowercase__   :    Dict				=			ConfigTester(self			,config_class=_A						)







		def 					UpperCAmelCase    (	self						:  str						)					->		int:






				"""simple docstring"""



				self.config_tester.run_common_tests()







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






				"""simple docstring"""



				lowercase__   :    Optional[int]				=			self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_decoder_model_past_large_inputs(*_A						)





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







		'''simple docstring'''





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







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






				"""simple docstring"""



				return AutoTokenizer.from_pretrained(self.model_name						)







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






				"""simple docstring"""



				lowercase__   :    List[str]				=			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
				return model







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






				"""simple docstring"""



				lowercase__   :    List[Any]				=			self.translate_src_text(**_A						)
				self.assertListEqual(self.expected_text			,_A						)







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






				"""simple docstring"""



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







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






				"""simple docstring"""



				self._assert_generated_batch_equal_expected()







from typing import TYPE_CHECKING

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


__A				:    Dict       =   {
    "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				:    Union[str, Any]       =   ["RoFormerTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    str       =   [
				    "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				:    int       =   [
				    "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				:    Any       =   [
				    "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__)



