






'''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 							__magic_name__     :







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      List[Any]        =  MBartConfig
	SCREAMING_SNAKE_CASE_					:      List[Any]        =  {}
	SCREAMING_SNAKE_CASE_					:      str        =  """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  ,      )						->  Optional[Any]:
							lowercase_      :   Union[str, Any]        =			parent
							lowercase_      :   List[str]        =			batch_size
							lowercase_      :   Union[str, Any]        =			seq_length
							lowercase_      :   Optional[Any]        =			is_training
							lowercase_      :   int        =			use_labels
							lowercase_      :   Dict        =			vocab_size
							lowercase_      :   Optional[Any]        =			hidden_size
							lowercase_      :   Optional[int]        =			num_hidden_layers
							lowercase_      :   Optional[Any]        =			num_attention_heads
							lowercase_      :   List[str]        =			intermediate_size
							lowercase_      :   int        =			hidden_dropout_prob
							lowercase_      :   int        =			attention_probs_dropout_prob
							lowercase_      :   Union[str, Any]        =			max_position_embeddings
							lowercase_      :   Tuple        =			eos_token_id
							lowercase_      :   int        =			pad_token_id
							lowercase_      :   List[str]        =			bos_token_id
	def        lowerCamelCase__		(							self       )						->  Union[str, Any]:
							lowercase_      :   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_      :   Dict        =			tf.concat([input_ids, eos_tensor]  ,      axis=1       )

							lowercase_      :   Optional[Any]        =			ids_tensor([self.batch_size, self.seq_length]  ,      self.vocab_size       )

							lowercase_      :   Dict        =			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_      :   int        =			prepare_mbart_inputs_dict(UpperCAmelCase_  ,      UpperCAmelCase_  ,      UpperCAmelCase_       )
							return config, inputs_dict







	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase       )						->  Any:
							lowercase_      :   Any        =			TFMBartModel(config=UpperCAmelCase_       ).get_decoder()
							lowercase_      :   Optional[int]        =			inputs_dict['input_ids']

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

							# first forward pass
							lowercase_      :   Dict        =			model(UpperCAmelCase_  ,      attention_mask=UpperCAmelCase_  ,      head_mask=UpperCAmelCase_  ,      use_cache=UpperCAmelCase_       )

							lowercase_  ,      lowercase_      :   Dict        =			outputs.to_tuple()
							lowercase_      :   List[str]        =			past_key_values[1]






def 	_UpperCAmelCase    (  a		:	Tuple						,			a		:	Dict						,			a		:	Optional[Any]						,			a		:	Optional[int]=None						,			a		:	Any=None						,			a		:	List[str]=None						,			a		:	int=None						,			a		:	Tuple=None						,			)			->			List[str]:





						"""simple docstring"""
						if attention_mask is None:
												lowercase_      :   Optional[Any]        =			tf.cast(tf.math.not_equal(_snake_case						,			config.pad_token_id							)						,			tf.inta							)
						if decoder_attention_mask is None:
												lowercase_      :   List[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:
												lowercase_      :   Any        =			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_      :   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 							__magic_name__     (	__UpperCAmelCase,						__UpperCAmelCase,						unittest.TestCase				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      Tuple        =  (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
	SCREAMING_SNAKE_CASE_					:      Dict        =  (TFMBartForConditionalGeneration,) if is_tf_available() else ()
	SCREAMING_SNAKE_CASE_					:      List[Any]        =  (
	    {
	        """conversational""": TFMBartForConditionalGeneration,
	        """feature-extraction""": TFMBartModel,
	        """summarization""": TFMBartForConditionalGeneration,
	        """text2text-generation""": TFMBartForConditionalGeneration,
	        """translation""": TFMBartForConditionalGeneration,
	    }
	    if is_tf_available()
	    else {}
	)
	SCREAMING_SNAKE_CASE_					:      Optional[Any]        =  True
	SCREAMING_SNAKE_CASE_					:      int        =  False
	SCREAMING_SNAKE_CASE_					:      Optional[Any]        =  False
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase       )						->  Any:
							if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
													# Exception encountered when calling layer '...'
													return True

							return False
	def        lowerCamelCase__		(							self       )						->  int:
							lowercase_      :   Dict        =			TFMBartModelTester(self       )
							lowercase_      :   List[str]        =			ConfigTester(self  ,      config_class=UpperCAmelCase_       )
	def        lowerCamelCase__		(							self       )						->  Union[str, Any]:
							self.config_tester.run_common_tests()







	def        lowerCamelCase__		(							self       )						->  Union[str, Any]:
							lowercase_      :   List[str]        =			self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase_       )






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







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      Tuple        =  [
	    """ UN Chief Says There Is No Military Solution in Syria""",
	]
	SCREAMING_SNAKE_CASE_					:      Any        =  [
	    """Şeful ONU declară că nu există o soluţie militară în Siria""",
	]
	SCREAMING_SNAKE_CASE_					:      Optional[Any]        =  """facebook/mbart-large-en-ro"""
	@cached_property
	def        lowerCamelCase__		(							self       )						->  Optional[int]:
							return AutoTokenizer.from_pretrained(self.model_name       )
	@cached_property
	def        lowerCamelCase__		(							self       )						->  str:
							lowercase_      :   str        =			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
							return model
	def        lowerCamelCase__		(							self  ,      **_lowercase       )						->  Optional[Any]:
							lowercase_      :   Union[str, Any]        =			self.translate_src_text(**UpperCAmelCase_       )
							self.assertListEqual(self.expected_text  ,      UpperCAmelCase_       )
	def        lowerCamelCase__		(							self  ,      **_lowercase       )						->  List[Any]:
							lowercase_      :   Tuple        =			self.tokenizer(self.src_text  ,      **UpperCAmelCase_  ,      return_tensors='tf'       )
							lowercase_      :   Union[str, Any]        =			self.model.generate(
							    model_inputs.input_ids  ,      attention_mask=model_inputs.attention_mask  ,      num_beams=2       )
							lowercase_      :   Dict        =			self.tokenizer.batch_decode(UpperCAmelCase_  ,      skip_special_tokens=UpperCAmelCase_       )
							return generated_words







	@slow
	def        lowerCamelCase__		(							self       )						->  List[Any]:
							self._assert_generated_batch_equal_expected()








'''simple docstring'''

def 	_UpperCAmelCase    (  a		:	list							)			->			list:





						"""simple docstring"""
						for i in range(len(a							) - 1						,			0						,			-1							):
												lowercase_      :   Any        =			False

												for j in range(a						,			0						,			-1							):
																		if unsorted[j] < unsorted[j - 1]:
																								lowercase_  ,      lowercase_      :   Any        =			unsorted[j - 1], unsorted[j]
																								lowercase_      :   int        =			True

												for j in range(a							):
																		if unsorted[j] > unsorted[j + 1]:
																								lowercase_  ,      lowercase_      :   Union[str, Any]        =			unsorted[j + 1], unsorted[j]
																								lowercase_      :   Optional[Any]        =			True

												if not swapped:
																		break
						return unsorted


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						A:      Union[str, Any]  				=				input("Enter numbers separated by a comma:\n").strip()
						A:      Tuple  				=				[int(item) for item in user_input.split(",")]
						print(f"""{cocktail_shaker_sort(unsorted) = }""")
