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 __snake_case    :
			lowerCAmelCase_														=							BlenderbotConfig
			lowerCAmelCase_														=							{}
			lowerCAmelCase_														=							"gelu"




			def __init__(   self      :    Tuple	,     _lowercase      :    Any	,     _lowercase      :    Optional[int]=13	,     _lowercase      :    Tuple=7	,     _lowercase      :    Dict=True	,     _lowercase      :    int=False	,     _lowercase      :    Tuple=99	,     _lowercase      :    str=32	,     _lowercase      :    Optional[int]=2	,     _lowercase      :    Dict=4	,     _lowercase      :    Any=37	,     _lowercase      :    Any=0.1	,     _lowercase      :    Union[str, Any]=0.1	,     _lowercase      :    Any=20	,     _lowercase      :    str=2	,     _lowercase      :    Optional[Any]=1	,     _lowercase      :    Dict=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      __a						(   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(_lowercase	,     _lowercase	,     _lowercase				)
					return config, inputs_dict




			def      __a						(   self      :    Any	,     _lowercase      :    List[Any]	,     _lowercase      :    int				):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__      							=				TFBlenderbotModel(config=_lowercase				).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(_lowercase	,     attention_mask=_lowercase	,     head_mask=_lowercase	,     use_cache=_lowercase				)

					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(_lowercase	,     attention_mask=_lowercase				)[0]
					SCREAMING_SNAKE_CASE__      							=				model(_lowercase	,     attention_mask=_lowercase	,     past_key_values=_lowercase				)[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(_lowercase	,     _lowercase	,     rtol=1E-3				)





def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Any			,					__UpperCamelCase   :	str			,					__UpperCamelCase   :	Any			,					__UpperCamelCase   :	List[Any]=None			,					__UpperCamelCase   :	Any=None			,					__UpperCamelCase   :	Tuple=None			,					__UpperCamelCase   :	List[Any]=None			,					__UpperCamelCase   :	List[str]=None			,					)							-> List[Any]:







		"""simple docstring"""





		if attention_mask is None:
				SCREAMING_SNAKE_CASE__      							=				tf.cast(tf.math.not_equal(__UpperCamelCase			,					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 __snake_case    (						lowerCamelCase_       ,		lowerCamelCase_       ,		unittest.TestCase		):
			lowerCAmelCase_														=							(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
			lowerCAmelCase_														=							(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
			lowerCAmelCase_														=							(
			    {
			        "conversational": TFBlenderbotForConditionalGeneration,
			        "feature-extraction": TFBlenderbotModel,
			        "summarization": TFBlenderbotForConditionalGeneration,
			        "text2text-generation": TFBlenderbotForConditionalGeneration,
			        "translation": TFBlenderbotForConditionalGeneration,
			    }
			    if is_tf_available()
			    else {}
			)
			lowerCAmelCase_														=							True
			lowerCAmelCase_														=							False
			lowerCAmelCase_														=							False




			def      __a						(   self      :    Optional[int]				):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__      							=				TFBlenderbotModelTester(self				)
					SCREAMING_SNAKE_CASE__      							=				ConfigTester(self	,     config_class=_lowercase				)




			def      __a						(   self      :    Optional[Any]				):



					"""simple docstring"""






					self.config_tester.run_common_tests()




			def      __a						(   self      :    Optional[Any]				):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__      							=				self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.check_decoder_model_past_large_inputs(*_lowercase				)







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




			@cached_property
			def      __a						(   self      :    Optional[Any]				):



					"""simple docstring"""






					return BlenderbotTokenizer.from_pretrained(self.model_name				)




			@cached_property
			def      __a						(   self      :    Optional[int]				):



					"""simple docstring"""






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




			@slow
			def      __a						(   self      :    Any				):



					"""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=_lowercase				)[0]
					assert (
					    generated_words
					    == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
					)

def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Union[str, Any]     )							-> int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				len(__UpperCamelCase     )
  for i in range(length - 1     ):
    SCREAMING_SNAKE_CASE__      							=				i
    for k in range(i + 1			,					__UpperCamelCase     ):
      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__":
   __lowerCamelCase      :			Any							       =		input('''Enter numbers separated by a comma:\n''').strip()
   __lowerCamelCase      :			Dict							       =		[int(item) for item in user_input.split(''',''')]
   print(selection_sort(unsorted))
