





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__       :
						_UpperCAmelCase     :List[Any]						   =       MBartConfig
						_UpperCAmelCase     :Dict						   =       {}
						_UpperCAmelCase     :Dict						   =       'gelu'





						def __init__(			self	,							A_	,							A_=13	,							A_=7	,							A_=True	,							A_=False	,							A_=99	,							A_=32	,							A_=2	,							A_=4	,							A_=37	,							A_=0.1	,							A_=0.1	,							A_=20	,							A_=2	,							A_=1	,							A_=0	,							):
							'''simple docstring'''




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





						def 	__UpperCamelCase(			self       ):
							'''simple docstring'''




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

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

							UpperCamelCase     : List[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	,							)
							UpperCamelCase     : Optional[int]	     =       prepare_mbart_inputs_dict(A_	,							A_	,							A_       )
							return config, inputs_dict





						def 	__UpperCamelCase(			self	,							A_	,							A_       ):
							'''simple docstring'''




							UpperCamelCase     : Tuple	     =       TFMBartModel(config=A_       ).get_decoder()
							UpperCamelCase     : Any	     =       inputs_dict["input_ids"]

							UpperCamelCase     : Union[str, Any]	     =       input_ids[:1, :]
							UpperCamelCase     : Dict	     =       inputs_dict["attention_mask"][:1, :]
							UpperCamelCase     : Optional[int]	     =       inputs_dict["head_mask"]
							UpperCamelCase     : Tuple	     =       1

							# first forward pass
							UpperCamelCase     : Union[str, Any]	     =       model(A_	,							attention_mask=A_	,							head_mask=A_	,							use_cache=A_       )

							UpperCamelCase     : Optional[int]	     =       outputs.to_tuple()
							UpperCamelCase     : str	     =       past_key_values[1]



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase=None							,       _lowerCAmelCase=None							,       _lowerCAmelCase=None							,       _lowerCAmelCase=None							,       _lowerCAmelCase=None							,       )						->			Union[str, Any]:
	if attention_mask is None:
		UpperCamelCase     : Optional[Any]	     =       tf.cast(tf.math.not_equal(_lowerCAmelCase							,       config.pad_token_id					)							,       tf.inta					)
	if decoder_attention_mask is None:
		UpperCamelCase     : Union[str, 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:
		UpperCamelCase     : Optional[Any]	     =       tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
	if decoder_head_mask is None:
		UpperCamelCase     : Optional[Any]	     =       tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
	if cross_attn_head_mask is None:
		UpperCamelCase     : List[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__       (	__snake_case	,       __snake_case	,       unittest.TestCase      ):
						_UpperCAmelCase     :Optional[int]						   =       (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
						_UpperCAmelCase     :List[Any]						   =       (TFMBartForConditionalGeneration,) if is_tf_available() else ()
						_UpperCAmelCase     :List[str]						   =       (
						    {
						        'conversational': TFMBartForConditionalGeneration,
						        'feature-extraction': TFMBartModel,
						        'summarization': TFMBartForConditionalGeneration,
						        'text2text-generation': TFMBartForConditionalGeneration,
						        'translation': TFMBartForConditionalGeneration,
						    }
						    if is_tf_available()
						    else {}
						)
						_UpperCAmelCase     :str						   =       True
						_UpperCAmelCase     :Optional[Any]						   =       False
						_UpperCAmelCase     :List[str]						   =       False





						def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_	,							A_	,							A_       ):
							'''simple docstring'''




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

							return False





						def 	__UpperCamelCase(			self       ):
							'''simple docstring'''




							UpperCamelCase     : Any	     =       TFMBartModelTester(self       )
							UpperCamelCase     : Tuple	     =       ConfigTester(self	,							config_class=A_       )





						def 	__UpperCamelCase(			self       ):
							'''simple docstring'''




							self.config_tester.run_common_tests()





						def 	__UpperCamelCase(			self       ):
							'''simple docstring'''




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





						@cached_property
						def 	__UpperCamelCase(			self       ):
							'''simple docstring'''




							return AutoTokenizer.from_pretrained(self.model_name       )





						@cached_property
						def 	__UpperCamelCase(			self       ):
							'''simple docstring'''




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





						def 	__UpperCamelCase(			self	,							**A_       ):
							'''simple docstring'''




							UpperCamelCase     : Optional[Any]	     =       self.translate_src_text(**A_       )
							self.assertListEqual(self.expected_text	,							A_       )





						def 	__UpperCamelCase(			self	,							**A_       ):
							'''simple docstring'''




							UpperCamelCase     : str	     =       self.tokenizer(self.src_text	,							**A_	,							return_tensors="tf"       )
							UpperCamelCase     : List[Any]	     =       self.model.generate(
							    model_inputs.input_ids	,							attention_mask=model_inputs.attention_mask	,							num_beams=2       )
							UpperCamelCase     : int	     =       self.tokenizer.batch_decode(A_	,							skip_special_tokens=A_       )
							return generated_words





						@slow
						def 	__UpperCamelCase(			self       ):
							'''simple docstring'''




							self._assert_generated_batch_equal_expected()







from __future__ import annotations



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			list[str]:
 if partitions <= 0:
  raise ValueError("partitions must be a positive number!"					)
 if partitions > number_of_bytes:
  raise ValueError("partitions can not > number_of_bytes!"					)
 UpperCamelCase     : str	     =       number_of_bytes // partitions
 UpperCamelCase     : List[Any]	     =       []
 for i in range(_lowerCAmelCase					):
  UpperCamelCase     : Optional[Any]	     =       i * bytes_per_partition + 1
  UpperCamelCase     : Any	     =       (
      number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
  )
  allocation_list.append(F"""{start_bytes}-{end_bytes}"""					)
 return allocation_list


if __name__ == "__main__":
    import doctest

    doctest.testmod()
