
"""simple docstring"""





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:
  lowercase__      =				BlenderbotConfig
  lowercase__      =				{}
  lowercase__      =				'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    ,		)	->	str:






       '''simple docstring'''
       _UpperCamelCase										=  parent
       _UpperCamelCase										=  batch_size
       _UpperCamelCase										=  seq_length
       _UpperCamelCase										=  is_training
       _UpperCamelCase										=  use_labels
       _UpperCamelCase										=  vocab_size
       _UpperCamelCase										=  hidden_size
       _UpperCamelCase										=  num_hidden_layers
       _UpperCamelCase										=  num_attention_heads
       _UpperCamelCase										=  intermediate_size

       _UpperCamelCase										=  hidden_dropout_prob
       _UpperCamelCase										=  attention_probs_dropout_prob
       _UpperCamelCase										=  max_position_embeddings
       _UpperCamelCase										=  eos_token_id
       _UpperCamelCase										=  pad_token_id
       _UpperCamelCase										=  bos_token_id



  def 						UpperCAmelCase							( self)	->	int:






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

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

       _UpperCamelCase										=  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										=  prepare_blenderbot_inputs_dict(__a    ,		__a    ,		__a)
       return config, inputs_dict




  def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	str:






       '''simple docstring'''
       _UpperCamelCase										=  TFBlenderbotModel(config=__a).get_decoder()
       _UpperCamelCase										=  inputs_dict['''input_ids''']

       _UpperCamelCase										=  input_ids[:1, :]
       _UpperCamelCase										=  inputs_dict['''attention_mask'''][:1, :]
       _UpperCamelCase										=  inputs_dict['''head_mask''']
       _UpperCamelCase										=  1

       # first forward pass
       _UpperCamelCase										=  model(__a    ,		attention_mask=__a    ,		head_mask=__a    ,		use_cache=__a)

       _UpperCamelCase			,  _UpperCamelCase										=  outputs.to_tuple()

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

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

       _UpperCamelCase										=  model(__a    ,		attention_mask=__a)[0]
       _UpperCamelCase										=  model(__a    ,		attention_mask=__a    ,		past_key_values=__a)[0]

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

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

       # test that outputs are equal for slice
       tf.debugging.assert_near(__a    ,		__a    ,		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,					)							->		Optional[Any]:


     """simple docstring"""





     if attention_mask is None:
          _UpperCamelCase										=  tf.cast(tf.math.not_equal(__snake_case,					config.pad_token_id     ),					tf.inta     )
     if decoder_attention_mask is None:
          _UpperCamelCase										=  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										=  tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
     if decoder_head_mask is None:
          _UpperCamelCase										=  tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
     if cross_attn_head_mask is None:
          _UpperCamelCase										=  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(   lowerCamelCase				,					lowerCamelCase				,					unittest.TestCase     ):
  lowercase__      =				(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
  lowercase__      =				(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
  lowercase__      =				(
      {
          'conversational': TFBlenderbotForConditionalGeneration,
          'feature-extraction': TFBlenderbotModel,
          'summarization': TFBlenderbotForConditionalGeneration,
          'text2text-generation': TFBlenderbotForConditionalGeneration,
          'translation': TFBlenderbotForConditionalGeneration,
      }
      if is_tf_available()
      else {}
  )
  lowercase__      =				True
  lowercase__      =				False
  lowercase__      =				False



  def 						UpperCAmelCase							( self)	->	Dict:






       '''simple docstring'''
       _UpperCamelCase										=  TFBlenderbotModelTester(self)
       _UpperCamelCase										=  ConfigTester(self    ,		config_class=__a)



  def 						UpperCAmelCase							( self)	->	Any:






       '''simple docstring'''
       self.config_tester.run_common_tests()




  def 						UpperCAmelCase							( self)	->	Dict:






       '''simple docstring'''
       _UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*__a)






@require_tokenizers
@require_tf
class    _UpperCAmelCase(   unittest.TestCase     ):
  lowercase__      =				['My friends are cool but they eat too many carbs.']
  lowercase__      =				'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'''
       _UpperCamelCase										=  TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
       return model




  @slow
  def 						UpperCAmelCase							( self)	->	Any:






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








"""simple docstring"""





from __future__ import annotations



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Optional[int]:


					"""simple docstring"""





					if len(__snake_case     ) <= 1 or n <= 1:
										return

					insert_next(__snake_case,					n - 1     )
					rec_insertion_sort(__snake_case,					n - 1     )



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Dict:


					"""simple docstring"""





					if index >= len(__snake_case     ) or collection[index - 1] <= collection[index]:
										return

					# Swaps adjacent elements since they are not in ascending order
					_UpperCamelCase			,  _UpperCamelCase										=  (
					    collection[index],
					    collection[index - 1],
					)

					insert_next(__snake_case,					index + 1     )


if __name__ == "__main__":
				_a														=		input("""Enter integers separated by spaces: """)
				_a														=		[int(num) for num in numbers.split()]
				rec_insertion_sort(number_list, len(number_list))
				print(number_list)






