
'''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     UpperCAmelCase_	:






						'''simple docstring'''






						_lowercase						: List[str]										=  MBartConfig
						_lowercase						: Any										=  {}
						_lowercase						: Union[str, Any]										=  '''gelu'''




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

												"""simple docstring"""



												_lowerCAmelCase    		=					parent
												_lowerCAmelCase    		=					batch_size
												_lowerCAmelCase    		=					seq_length
												_lowerCAmelCase    		=					is_training
												_lowerCAmelCase    		=					use_labels
												_lowerCAmelCase    		=					vocab_size
												_lowerCAmelCase    		=					hidden_size
												_lowerCAmelCase    		=					num_hidden_layers
												_lowerCAmelCase    		=					num_attention_heads
												_lowerCAmelCase    		=					intermediate_size
												_lowerCAmelCase    		=					hidden_dropout_prob
												_lowerCAmelCase    		=					attention_probs_dropout_prob
												_lowerCAmelCase    		=					max_position_embeddings
												_lowerCAmelCase    		=					eos_token_id
												_lowerCAmelCase    		=					pad_token_id
												_lowerCAmelCase    		=					bos_token_id




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					ids_tensor([self.batch_size, self.seq_length - 1]       ,  self.vocab_size )
												_lowerCAmelCase    		=					tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size )       ,  1 )
												_lowerCAmelCase    		=					tf.concat([input_ids, eos_tensor]       ,  axis=1 )

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

												_lowerCAmelCase    		=					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    		=					prepare_mbart_inputs_dict(_lowercase       ,  _lowercase       ,  _lowercase )
												return config, inputs_dict




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					TFMBartModel(config=_lowercase ).get_decoder()
												_lowerCAmelCase    		=					inputs_dict["""input_ids"""]

												_lowerCAmelCase    		=					input_ids[:1, :]
												_lowerCAmelCase    		=					inputs_dict["""attention_mask"""][:1, :]
												_lowerCAmelCase    		=					inputs_dict["""head_mask"""]
												_lowerCAmelCase    		=					1

												# first forward pass
												_lowerCAmelCase    		=					model(_lowercase       ,  attention_mask=_lowercase       ,  head_mask=_lowercase       ,  use_cache=_lowercase )

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






def A (__lowerCamelCase :List[str]					,      __lowerCamelCase :Union[str, Any]					,      __lowerCamelCase :Union[str, Any]					,      __lowerCamelCase :Union[str, Any]=None					,      __lowerCamelCase :Optional[int]=None					,      __lowerCamelCase :Optional[int]=None					,      __lowerCamelCase :List[Any]=None					,      __lowerCamelCase :Optional[Any]=None					,      ):
						if attention_mask is None:
												_lowerCAmelCase    		=					tf.cast(tf.math.not_equal(__lowerCamelCase					,      config.pad_token_id						)					,      tf.inta						)
						if decoder_attention_mask is None:
												_lowerCAmelCase    		=					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    		=					tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
						if decoder_head_mask is None:
												_lowerCAmelCase    		=					tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
						if cross_attn_head_mask is None:
												_lowerCAmelCase    		=					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     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE			,				unittest.TestCase					):






						'''simple docstring'''






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




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase ):

												"""simple docstring"""



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

												return False




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					TFMBartModelTester(self )
												_lowerCAmelCase    		=					ConfigTester(self       ,  config_class=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												self.config_tester.run_common_tests()




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					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     UpperCAmelCase_	(						unittest.TestCase					):






						'''simple docstring'''






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




						@cached_property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return AutoTokenizer.from_pretrained(self.model_name )




						@cached_property
						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
												return model




						def    _lowercase      (   self       ,  **_lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.translate_src_text(**_lowercase )
												self.assertListEqual(self.expected_text       ,  _lowercase )




						def    _lowercase      (   self       ,  **_lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.tokenizer(self.src_text       ,  **_lowercase       ,  return_tensors="""tf""" )
												_lowerCAmelCase    		=					self.model.generate(
												    model_inputs.input_ids       ,  attention_mask=model_inputs.attention_mask       ,  num_beams=2 )
												_lowerCAmelCase    		=					self.tokenizer.batch_decode(_lowercase       ,  skip_special_tokens=_lowercase )
												return generated_words




						@slow
						def    _lowercase      (   self ):

												"""simple docstring"""



												self._assert_generated_batch_equal_expected()


'''simple docstring'''



from itertools import product


def A (__lowerCamelCase :int					,      __lowerCamelCase :int						):
						_lowerCAmelCase    		=					sides_number
						_lowerCAmelCase    		=					max_face_number * dice_number
						_lowerCAmelCase    		=					[0] * (max_total + 1)

						_lowerCAmelCase    		=					1
						_lowerCAmelCase    		=					range(__lowerCamelCase					,      max_face_number + 1						)
						for dice_numbers in product(__lowerCamelCase					,      repeat=__lowerCamelCase						):
												_lowerCAmelCase    		=					sum(__lowerCamelCase						)
												totals_frequencies[total] += 1

						return totals_frequencies


def A ():
						_lowerCAmelCase    		=					total_frequency_distribution(
						    sides_number=4					,      dice_number=9						)
						_lowerCAmelCase    		=					total_frequency_distribution(
						    sides_number=6					,      dice_number=6						)

						_lowerCAmelCase    		=					0
						_lowerCAmelCase    		=					9
						_lowerCAmelCase    		=					4 * 9
						_lowerCAmelCase    		=					6
						for peter_total in range(__lowerCamelCase					,      max_peter_total + 1						):
												peter_wins_count += peter_totals_frequencies[peter_total] * sum(
												    colin_totals_frequencies[min_colin_total:peter_total]						)

						_lowerCAmelCase    		=					(4**9) * (6**6)
						_lowerCAmelCase    		=					peter_wins_count / total_games_number

						_lowerCAmelCase    		=					round(__lowerCamelCase					,      ndigits=7						)

						return rounded_peter_win_probability


if __name__ == "__main__":
						print(F"""{solution() = }""")
