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        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''
 lowercase_		=      BlenderbotConfig
 lowercase_		=      {}
 lowercase_		=      '''gelu'''

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



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =parent
   lowerCamelCase__:				str        =batch_size
   lowerCamelCase__:				List[Any]        =seq_length
   lowerCamelCase__:				Dict        =is_training
   lowerCamelCase__:				List[str]        =use_labels
   lowerCamelCase__:				List[Any]        =vocab_size
   lowerCamelCase__:				Tuple        =hidden_size
   lowerCamelCase__:				Dict        =num_hidden_layers
   lowerCamelCase__:				str        =num_attention_heads
   lowerCamelCase__:				int        =intermediate_size

   lowerCamelCase__:				Optional[int]        =hidden_dropout_prob
   lowerCamelCase__:				Tuple        =attention_probs_dropout_prob
   lowerCamelCase__:				Optional[Any]        =max_position_embeddings
   lowerCamelCase__:				Union[str, Any]        =eos_token_id
   lowerCamelCase__:				int        =pad_token_id
   lowerCamelCase__:				Optional[int]        =bos_token_id

 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:				Dict        =ids_tensor([self.batch_size, self.seq_length - 1]	,			self.vocab_size)
   lowerCamelCase__:				Any        =tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)	,			1)
   lowerCamelCase__:				int        =tf.concat([input_ids, eos_tensor]	,			axis=1)

   lowerCamelCase__:				str        =ids_tensor([self.batch_size, self.seq_length]	,			self.vocab_size)

   lowerCamelCase__:				str        =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__:				List[Any]        =prepare_blenderbot_inputs_dict(_a	,			_a	,			_a)
   return config, inputs_dict







 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str]	,			UpperCAmelCase_    :       Optional[Any]	,			UpperCAmelCase_    :       int)	->List[str]:



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        =TFBlenderbotModel(config=_a).get_decoder()
   lowerCamelCase__:				Dict        =inputs_dict['input_ids']

   lowerCamelCase__:				List[str]        =input_ids[:1, :]
   lowerCamelCase__:				Any        =inputs_dict['attention_mask'][:1, :]
   lowerCamelCase__:				Dict        =inputs_dict['head_mask']
   lowerCamelCase__:				Dict        =1

   # first forward pass
   lowerCamelCase__:				Optional[int]        =model(_a	,			attention_mask=_a	,			head_mask=_a	,			use_cache=_a)

   lowerCamelCase__:				str        =outputs.to_tuple()

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

   # append to next input_ids and
   lowerCamelCase__:				Dict        =tf.concat([input_ids, next_tokens]	,			axis=-1)
   lowerCamelCase__:				Union[str, Any]        =tf.concat([attention_mask, next_attn_mask]	,			axis=-1)

   lowerCamelCase__:				int        =model(_a	,			attention_mask=_a)[0]
   lowerCamelCase__:				List[str]        =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
   lowerCamelCase__:				Dict        =int(ids_tensor((1,)	,			output_from_past.shape[-1]))
   lowerCamelCase__:				List[str]        =output_from_no_past[:, -3:, random_slice_idx]
   lowerCamelCase__:				Optional[int]        =output_from_past[:, :, random_slice_idx]

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





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a     ,   __a=None     ,   __a=None     ,   __a=None     ,   __a=None     ,   __a=None     ,   )					->		Any:







  """simple docstring"""





  if attention_mask is None:
    lowerCamelCase__:				Dict        =tf.cast(tf.math.not_equal(__a     ,   config.pad_token_id		)     ,   tf.inta		)
  if decoder_attention_mask is None:
    lowerCamelCase__:				Union[str, 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:
    lowerCamelCase__:				Optional[Any]        =tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
  if decoder_head_mask is None:
    lowerCamelCase__:				Any        =tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
  if cross_attn_head_mask is None:
    lowerCamelCase__:				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        _SCREAMING_SNAKE_CASE						(     lowercase__   ,							lowercase__   ,							unittest.TestCase   ):







 '''simple docstring'''
 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 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int])	->int:



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =TFBlenderbotModelTester(self)
   lowerCamelCase__:				List[str]        =ConfigTester(self	,			config_class=_a)

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any])	->List[Any]:



   '''simple docstring'''



   self.config_tester.run_common_tests()







 def 							SCREAMING_SNAKE_CASE_ (self    :       Any)	->Tuple:



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =self.model_tester.prepare_config_and_inputs_for_common()
   self.model_tester.check_decoder_model_past_large_inputs(*_a)



@require_tokenizers
@require_tf
class        _SCREAMING_SNAKE_CASE						(     unittest.TestCase   ):







 '''simple docstring'''
 lowercase_		=      ['''My friends are cool but they eat too many carbs.''']
 lowercase_		=      '''facebook/blenderbot-400M-distill'''

 @cached_property
 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->int:



   '''simple docstring'''



   return BlenderbotTokenizer.from_pretrained(self.model_name)

 @cached_property
 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
   return model







 @slow
 def 							SCREAMING_SNAKE_CASE_ (self    :       Any)	->str:



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =self.tokenizer(self.src_text	,			return_tensors="tf")
   lowerCamelCase__:				List[Any]        =self.model.generate(
       model_inputs.input_ids	,			)
   lowerCamelCase__:				Dict        =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?"
   )








def 					lowerCAmelCase_			(	__a     ,   __a		)					->		Tuple:







  """simple docstring"""





  assert x is not None
  assert y is not None

  lowerCamelCase__:				Any        =len(__a		)
  lowerCamelCase__:				int        =len(__a		)

  # declaring the array for storing the dp values
  lowerCamelCase__:				List[Any]        =[[0] * (n + 1) for _ in range(m + 1		)]  # noqa: E741

  for i in range(1     ,   m + 1		):
    for j in range(1     ,   n + 1		):
      lowerCamelCase__:				str        =1 if x[i - 1] == y[j - 1] else 0

      lowerCamelCase__:				str        =max(l[i - 1][j]     ,   l[i][j - 1]     ,   l[i - 1][j - 1] + match		)

  lowerCamelCase__:				Any        =""
  lowerCamelCase__  , lowerCamelCase__:				str        =m, n
  while i > 0 and j > 0:
    lowerCamelCase__:				Union[str, Any]        =1 if x[i - 1] == y[j - 1] else 0

    if l[i][j] == l[i - 1][j - 1] + match:
      if match == 1:
        lowerCamelCase__:				Any        =x[i - 1] + seq
      i -= 1
      j -= 1
    elif l[i][j] == l[i - 1][j]:
      i -= 1
    else:
      j -= 1

  return l[m][n], seq


if __name__ == "__main__":
     __A     =    "AGGTAB"
     __A     =    "GXTXAYB"
     __A     =    4
     __A     =    "GTAB"

     __A ,     __A     =    longest_common_subsequence(a, b)
     print("len =", ln, ", sub-sequence =", subseq)
     import doctest

     doctest.testmod()

