





"""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 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""
				__snake_case   :       Any	 =		MBartConfig
				__snake_case   :       Tuple	 =		{}
				__snake_case   :       List[str]	 =		"""gelu"""




				def __init__(     self  :Optional[Any]						,    __lowercase  :int						,    __lowercase  :Union[str, Any]=13						,    __lowercase  :Union[str, Any]=7						,    __lowercase  :List[Any]=True						,    __lowercase  :str=False						,    __lowercase  :Tuple=99						,    __lowercase  :Any=32						,    __lowercase  :Optional[int]=2						,    __lowercase  :Union[str, Any]=4						,    __lowercase  :Dict=37						,    __lowercase  :int=0.1						,    __lowercase  :Dict=0.1						,    __lowercase  :Dict=20						,    __lowercase  :int=2						,    __lowercase  :Optional[int]=1						,    __lowercase  :List[Any]=0						,    ):
											__lowerCamelCase      :      List[Any]         =parent
											__lowerCamelCase      :      Union[str, Any]         =batch_size
											__lowerCamelCase      :      Dict         =seq_length
											__lowerCamelCase      :      Tuple         =is_training
											__lowerCamelCase      :      List[Any]         =use_labels
											__lowerCamelCase      :      Optional[Any]         =vocab_size
											__lowerCamelCase      :      List[Any]         =hidden_size
											__lowerCamelCase      :      Any         =num_hidden_layers
											__lowerCamelCase      :      List[Any]         =num_attention_heads
											__lowerCamelCase      :      int         =intermediate_size
											__lowerCamelCase      :      Union[str, Any]         =hidden_dropout_prob
											__lowerCamelCase      :      Optional[Any]         =attention_probs_dropout_prob
											__lowerCamelCase      :      Tuple         =max_position_embeddings
											__lowerCamelCase      :      str         =eos_token_id
											__lowerCamelCase      :      Optional[int]         =pad_token_id
											__lowerCamelCase      :      Union[str, Any]         =bos_token_id




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

											__lowerCamelCase      :      List[Any]         =ids_tensor([self.batch_size, self.seq_length]						,    self.vocab_size			)

											__lowerCamelCase      :      Tuple         =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						,    )
											__lowerCamelCase      :      Optional[Any]         =prepare_mbart_inputs_dict(__lowercase						,    __lowercase						,    __lowercase			)
											return config, inputs_dict




				def 					__lowercase  (     self  :List[str]						,    __lowercase  :int						,    __lowercase  :Union[str, Any]			):
											__lowerCamelCase      :      Tuple         =TFMBartModel(config=__lowercase			).get_decoder()
											__lowerCamelCase      :      List[str]         =inputs_dict['''input_ids''']

											__lowerCamelCase      :      Tuple         =input_ids[:1, :]
											__lowerCamelCase      :      Optional[int]         =inputs_dict['''attention_mask'''][:1, :]
											__lowerCamelCase      :      List[str]         =inputs_dict['''head_mask''']
											__lowerCamelCase      :      Optional[int]         =1

											# first forward pass
											__lowerCamelCase      :      Dict         =model(__lowercase						,    attention_mask=__lowercase						,    head_mask=__lowercase						,    use_cache=__lowercase			)

											__lowerCamelCase    ,						__lowerCamelCase      :      int         =outputs.to_tuple()
											__lowerCamelCase      :      Tuple         =past_key_values[1]



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[Any]    ,						SCREAMING_SNAKE_CASE       :							Dict    ,						SCREAMING_SNAKE_CASE       :							Any    ,						SCREAMING_SNAKE_CASE       :							Optional[Any]=None    ,						SCREAMING_SNAKE_CASE       :							Optional[Any]=None    ,						SCREAMING_SNAKE_CASE       :							str=None    ,						SCREAMING_SNAKE_CASE       :							Optional[int]=None    ,						SCREAMING_SNAKE_CASE       :							Optional[int]=None    ,						):


							'''simple docstring'''







							if attention_mask is None:
														__lowerCamelCase      :      Tuple         =tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE    ,						config.pad_token_id   )    ,						tf.inta   )
							if decoder_attention_mask is None:
														__lowerCamelCase      :      Any         =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:
														__lowerCamelCase      :      Tuple         =tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
							if decoder_head_mask is None:
														__lowerCamelCase      :      Any         =tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
							if cross_attn_head_mask is None:
														__lowerCamelCase      :      Union[str, 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 		SCREAMING_SNAKE_CASE_							(    snake_case__       ,  snake_case__       ,  unittest.TestCase   ):
				"""simple docstring"""
				__snake_case   :       List[str]	 =		(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
				__snake_case   :       str	 =		(TFMBartForConditionalGeneration,) if is_tf_available() else ()
				__snake_case   :       List[Any]	 =		(
				    {
				        """conversational""": TFMBartForConditionalGeneration,
				        """feature-extraction""": TFMBartModel,
				        """summarization""": TFMBartForConditionalGeneration,
				        """text2text-generation""": TFMBartForConditionalGeneration,
				        """translation""": TFMBartForConditionalGeneration,
				    }
				    if is_tf_available()
				    else {}
				)
				__snake_case   :       Any	 =		True
				__snake_case   :       List[str]	 =		False
				__snake_case   :       str	 =		False




				def 					__lowercase  (     self  :Any						,    __lowercase  :Tuple						,    __lowercase  :Any						,    __lowercase  :Any						,    __lowercase  :List[str]						,    __lowercase  :Tuple			):
											if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
																		# Exception encountered when calling layer '...'
																		return True

											return False




				def 					__lowercase  (     self  :Any			):
											__lowerCamelCase      :      List[str]         =TFMBartModelTester(self			)
											__lowerCamelCase      :      int         =ConfigTester(self						,    config_class=__lowercase			)




				def 					__lowercase  (     self  :List[str]			):
											self.config_tester.run_common_tests()




				def 					__lowercase  (     self  :Dict			):
											__lowerCamelCase      :      int         =self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.check_decoder_model_past_large_inputs(*__lowercase			)

@require_sentencepiece
@require_tokenizers
@require_tf
class 		SCREAMING_SNAKE_CASE_							(    unittest.TestCase   ):
				"""simple docstring"""
				__snake_case   :       List[Any]	 =		[
				    """ UN Chief Says There Is No Military Solution in Syria""",
				]
				__snake_case   :       Any	 =		[
				    """Şeful ONU declară că nu există o soluţie militară în Siria""",
				]
				__snake_case   :       Tuple	 =		"""facebook/mbart-large-en-ro"""




				@cached_property
				def 					__lowercase  (     self  :str			):
											return AutoTokenizer.from_pretrained(self.model_name			)




				@cached_property
				def 					__lowercase  (     self  :Dict			):
											__lowerCamelCase      :      List[str]         =TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name			)
											return model




				def 					__lowercase  (     self  :Optional[Any]						,    **__lowercase  :List[Any]			):
											__lowerCamelCase      :      List[Any]         =self.translate_src_text(**__lowercase			)
											self.assertListEqual(self.expected_text						,    __lowercase			)




				def 					__lowercase  (     self  :List[Any]						,    **__lowercase  :Dict			):
											__lowerCamelCase      :      Optional[Any]         =self.tokenizer(self.src_text						,    **__lowercase						,    return_tensors='''tf'''			)
											__lowerCamelCase      :      int         =self.model.generate(
											    model_inputs.input_ids						,    attention_mask=model_inputs.attention_mask						,    num_beams=2			)
											__lowerCamelCase      :      Optional[int]         =self.tokenizer.batch_decode(__lowercase						,    skip_special_tokens=__lowercase			)
											return generated_words




				@slow
				def 					__lowercase  (     self  :Union[str, Any]			):
											self._assert_generated_batch_equal_expected()











"""simple docstring"""





import warnings

from ...utils import logging
from .image_processing_flava import FlavaImageProcessor


_UpperCamelCase							=      logging.get_logger(__name__)
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""




				def __init__(     self  :List[str]						,    *__lowercase  :int						,    **__lowercase  :Any			):
											warnings.warn(
											    '''The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
											    ''' use FlavaImageProcessor instead.'''						,    __lowercase						,    )
											super().__init__(*__lowercase						,    **__lowercase			)




