






from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import 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, TFBlenderbotForConditionalGeneration, TFBlenderbotModel



@require_tf
class 				__UpperCAmelCase		:
			__snake_case    :				Optional[Any]									=							BlenderbotConfig
			__snake_case    :				Tuple									=							{}
			__snake_case    :				str									=							"gelu"







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







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  parent
									_SCREAMING_SNAKE_CASE     				=  batch_size
									_SCREAMING_SNAKE_CASE     				=  seq_length
									_SCREAMING_SNAKE_CASE     				=  is_training
									_SCREAMING_SNAKE_CASE     				=  use_labels
									_SCREAMING_SNAKE_CASE     				=  vocab_size
									_SCREAMING_SNAKE_CASE     				=  hidden_size
									_SCREAMING_SNAKE_CASE     				=  num_hidden_layers
									_SCREAMING_SNAKE_CASE     				=  num_attention_heads
									_SCREAMING_SNAKE_CASE     				=  intermediate_size

									_SCREAMING_SNAKE_CASE     				=  hidden_dropout_prob
									_SCREAMING_SNAKE_CASE     				=  attention_probs_dropout_prob
									_SCREAMING_SNAKE_CASE     				=  max_position_embeddings
									_SCREAMING_SNAKE_CASE     				=  eos_token_id
									_SCREAMING_SNAKE_CASE     				=  pad_token_id
									_SCREAMING_SNAKE_CASE     				=  bos_token_id







			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




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

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

									_SCREAMING_SNAKE_CASE     				=  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				,     )
									_SCREAMING_SNAKE_CASE     				=  prepare_blenderbot_inputs_dict(UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_					)
									return config, inputs_dict







			def 							UpperCamelCase				(     self:  Any				,     UpperCAmelCase_:  Dict				,     UpperCAmelCase_:  Dict					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  TFBlenderbotModel(config=UpperCAmelCase_					).get_decoder()
									_SCREAMING_SNAKE_CASE     				=  inputs_dict["""input_ids"""]

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

									# first forward pass
									_SCREAMING_SNAKE_CASE     				=  model(UpperCAmelCase_				,     attention_mask=UpperCAmelCase_				,     head_mask=UpperCAmelCase_				,     use_cache=UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  outputs.to_tuple()

									# create hypothetical next token and extent to next_input_ids
									_SCREAMING_SNAKE_CASE     				=  ids_tensor((self.batch_size, 3)				,     config.vocab_size					)
									_SCREAMING_SNAKE_CASE     				=  tf.cast(ids_tensor((self.batch_size, 3)				,     2					)				,     tf.inta					)

									# append to next input_ids and
									_SCREAMING_SNAKE_CASE     				=  tf.concat([input_ids, next_tokens]				,     axis=-1					)
									_SCREAMING_SNAKE_CASE     				=  tf.concat([attention_mask, next_attn_mask]				,     axis=-1					)

									_SCREAMING_SNAKE_CASE     				=  model(UpperCAmelCase_				,     attention_mask=UpperCAmelCase_					)[0]
									_SCREAMING_SNAKE_CASE     				=  model(UpperCAmelCase_				,     attention_mask=UpperCAmelCase_				,     past_key_values=UpperCAmelCase_					)[0]

									self.parent.assertEqual(next_tokens.shape[1]				,     output_from_past.shape[1]					)

									# select random slice
									_SCREAMING_SNAKE_CASE     				=  int(ids_tensor((1,)				,     output_from_past.shape[-1]					)					)
									_SCREAMING_SNAKE_CASE     				=  output_from_no_past[:, -3:, random_slice_idx]
									_SCREAMING_SNAKE_CASE     				=  output_from_past[:, :, random_slice_idx]

									# test that outputs are equal for slice
									tf.debugging.assert_near(UpperCAmelCase_				,     UpperCAmelCase_				,     rtol=1E-3					)

def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__	,snake_case__=None	,snake_case__=None	,snake_case__=None	,snake_case__=None	,snake_case__=None	,)							->  Dict:






						"""simple docstring"""





						if attention_mask is None:
												_SCREAMING_SNAKE_CASE     				=  tf.cast(tf.math.not_equal(snake_case__	,config.pad_token_id		)	,tf.inta		)
						if decoder_attention_mask is None:
												_SCREAMING_SNAKE_CASE     				=  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:
												_SCREAMING_SNAKE_CASE     				=  tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
						if decoder_head_mask is None:
												_SCREAMING_SNAKE_CASE     				=  tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
						if cross_attn_head_mask is None:
												_SCREAMING_SNAKE_CASE     				=  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		(_UpperCAmelCase	,_UpperCAmelCase	,unittest.TestCase   ):
			__snake_case    :				Union[str, Any]									=							(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
			__snake_case    :				int									=							(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
			__snake_case    :				List[str]									=							(
			    {
			        "conversational": TFBlenderbotForConditionalGeneration,
			        "feature-extraction": TFBlenderbotModel,
			        "summarization": TFBlenderbotForConditionalGeneration,
			        "text2text-generation": TFBlenderbotForConditionalGeneration,
			        "translation": TFBlenderbotForConditionalGeneration,
			    }
			    if is_tf_available()
			    else {}
			)
			__snake_case    :				Dict									=							True
			__snake_case    :				str									=							False
			__snake_case    :				List[Any]									=							False







			def 							UpperCamelCase				(     self:  Tuple					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  TFBlenderbotModelTester(self					)
									_SCREAMING_SNAKE_CASE     				=  ConfigTester(self				,     config_class=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Tuple					):







									'''simple docstring'''




									self.config_tester.run_common_tests()







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase_					)



@require_tokenizers
@require_tf
class 				__UpperCAmelCase		(unittest.TestCase   ):
			__snake_case    :				Optional[int]									=							["My friends are cool but they eat too many carbs."]
			__snake_case    :				int									=							"facebook/blenderbot-400M-distill"







			@cached_property
			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									return BlenderbotTokenizer.from_pretrained(self.model_name					)







			@cached_property
			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




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







			@slow
			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.tokenizer(self.src_text				,     return_tensors="""tf"""					)
									_SCREAMING_SNAKE_CASE     				=  self.model.generate(
									    model_inputs.input_ids				,     )
									_SCREAMING_SNAKE_CASE     				=  self.tokenizer.batch_decode(generated_ids.numpy()				,     skip_special_tokens=UpperCAmelCase_					)[0]
									assert (
									    generated_words
									    == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
									)









def 	__lowerCamelCase      (		snake_case__		)							->  List[Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  len(snake_case__		)
						for i in range(length - 1		):
												_SCREAMING_SNAKE_CASE     				=  i
												for k in range(i + 1	,snake_case__		):
																		if collection[k] < collection[least]:
																								_SCREAMING_SNAKE_CASE     				=  k
												if least != i:
																		_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  (collection[i], collection[least])
						return collection


if __name__ == "__main__":
		UpperCamelCase			    =     input('''Enter numbers separated by a comma:\n''').strip()
		UpperCamelCase			    =     [int(item) for item in user_input.split(''',''')]
		print(selection_sort(unsorted))

