




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        a__       :







  """simple docstring"""




  UpperCAmelCase__      : Union[str, Any]			       =BlenderbotConfig
  UpperCAmelCase__      : List[str]			       ={}
  UpperCAmelCase__      : str			       ="""gelu"""


  def __init__(			self    : int				,				UpperCAmelCase__    : List[Any]				,				UpperCAmelCase__    : str=1_3				,				UpperCAmelCase__    : Any=7				,				UpperCAmelCase__    : Union[str, Any]=True				,				UpperCAmelCase__    : List[Any]=False				,				UpperCAmelCase__    : Dict=9_9				,				UpperCAmelCase__    : Optional[Any]=3_2				,				UpperCAmelCase__    : Tuple=2				,				UpperCAmelCase__    : List[str]=4				,				UpperCAmelCase__    : Union[str, Any]=3_7				,				UpperCAmelCase__    : str=0.1				,				UpperCAmelCase__    : List[Any]=0.1				,				UpperCAmelCase__    : int=2_0				,				UpperCAmelCase__    : int=2				,				UpperCAmelCase__    : Optional[Any]=1				,				UpperCAmelCase__    : Union[str, Any]=0				,				)					->List[str]:
        """simple docstring"""







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

        SCREAMING_SNAKE_CASE :      Optional[int]       =   hidden_dropout_prob
        SCREAMING_SNAKE_CASE :      int       =   attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE :      Any       =   max_position_embeddings
        SCREAMING_SNAKE_CASE :      Any       =   eos_token_id
        SCREAMING_SNAKE_CASE :      Dict       =   pad_token_id
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   bos_token_id


  def  _lowercase      (			self    : str		)					->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Tuple       =   ids_tensor([self.batch_size, self.seq_length - 1]				,				self.vocab_size		)
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)				,				1		)
        SCREAMING_SNAKE_CASE :      List[Any]       =   tf.concat([input_ids, eos_tensor]				,				axis=1		)

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

        SCREAMING_SNAKE_CASE :      List[Any]       =   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 :      List[Any]       =   prepare_blenderbot_inputs_dict(_A				,				_A				,				_A		)
        return config, inputs_dict




  def  _lowercase      (			self    : Tuple				,				UpperCAmelCase__    : Any				,				UpperCAmelCase__    : Optional[int]		)					->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      str       =   TFBlenderbotModel(config=_A		).get_decoder()
        SCREAMING_SNAKE_CASE :      Optional[int]       =   inputs_dict["""input_ids"""]

        SCREAMING_SNAKE_CASE :      Dict       =   input_ids[:1, :]
        SCREAMING_SNAKE_CASE :      List[str]       =   inputs_dict["""attention_mask"""][:1, :]
        SCREAMING_SNAKE_CASE :      Dict       =   inputs_dict["""head_mask"""]
        SCREAMING_SNAKE_CASE :      str       =   1

        # first forward pass
        SCREAMING_SNAKE_CASE :      Tuple       =   model(_A				,				attention_mask=_A				,				head_mask=_A				,				use_cache=_A		)

        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      int       =   outputs.to_tuple()

        # create hypothetical next token and extent to next_input_ids
        SCREAMING_SNAKE_CASE :      List[Any]       =   ids_tensor((self.batch_size, 3)				,				config.vocab_size		)
        SCREAMING_SNAKE_CASE :      Dict       =   tf.cast(ids_tensor((self.batch_size, 3)				,				2		)				,				tf.inta		)

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

        SCREAMING_SNAKE_CASE :      Optional[int]       =   model(_A				,				attention_mask=_A		)[0]
        SCREAMING_SNAKE_CASE :      List[Any]       =   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
        SCREAMING_SNAKE_CASE :      int       =   int(ids_tensor((1,)				,				output_from_past.shape[-1]		)		)
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   output_from_no_past[:, -3:, random_slice_idx]
        SCREAMING_SNAKE_CASE :      List[str]       =   output_from_past[:, :, random_slice_idx]

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






def 					__lowercase      (     _A						,    _A						,    _A						,    _A=None						,    _A=None						,    _A=None						,    _A=None						,    _A=None						,    )     ->						str:
      if attention_mask is None:
            SCREAMING_SNAKE_CASE :      int       =   tf.cast(tf.math.not_equal(__snake_case						,    config.pad_token_id			)						,    tf.inta			)
      if decoder_attention_mask is None:
            SCREAMING_SNAKE_CASE :      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:
            SCREAMING_SNAKE_CASE :      Dict       =   tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
      if decoder_head_mask is None:
            SCREAMING_SNAKE_CASE :      Tuple       =   tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
      if cross_attn_head_mask is None:
            SCREAMING_SNAKE_CASE :      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        a__       (   __lowercase	,			__lowercase	,			unittest.TestCase       ):







  """simple docstring"""




  UpperCAmelCase__      : Any			       =(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
  UpperCAmelCase__      : Union[str, Any]			       =(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
  UpperCAmelCase__      : Any			       =(
      {
          """conversational""": TFBlenderbotForConditionalGeneration,
          """feature-extraction""": TFBlenderbotModel,
          """summarization""": TFBlenderbotForConditionalGeneration,
          """text2text-generation""": TFBlenderbotForConditionalGeneration,
          """translation""": TFBlenderbotForConditionalGeneration,
      }
      if is_tf_available()
      else {}
  )
  UpperCAmelCase__      : Tuple			       =True
  UpperCAmelCase__      : str			       =False
  UpperCAmelCase__      : int			       =False


  def  _lowercase      (			self    : Any		)					->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[str]       =   TFBlenderbotModelTester(self		)
        SCREAMING_SNAKE_CASE :      Optional[int]       =   ConfigTester(self				,				config_class=_A		)


  def  _lowercase      (			self    : List[Any]		)					->Optional[int]:
        """simple docstring"""







        self.config_tester.run_common_tests()




  def  _lowercase      (			self    : Tuple		)					->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Any       =   self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*_A		)







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







  """simple docstring"""




  UpperCAmelCase__      : Optional[Any]			       =["""My friends are cool but they eat too many carbs."""]
  UpperCAmelCase__      : List[str]			       ="""facebook/blenderbot-400M-distill"""


  @cached_property
  def  _lowercase      (			self    : Any		)					->str:
        """simple docstring"""







        return BlenderbotTokenizer.from_pretrained(self.model_name		)


  @cached_property
  def  _lowercase      (			self    : Tuple		)					->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      str       =   TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
        return model




  @slow
  def  _lowercase      (			self    : Tuple		)					->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      str       =   self.tokenizer(self.src_text				,				return_tensors="""tf"""		)
        SCREAMING_SNAKE_CASE :      List[Any]       =   self.model.generate(
            model_inputs.input_ids				,				)
        SCREAMING_SNAKE_CASE :      Tuple       =   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 					__lowercase      (     _A = 50			)     ->						int:
      SCREAMING_SNAKE_CASE :      Tuple       =   [1] * (length + 1)

      for row_length in range(3						,    length + 1			):
            for block_length in range(3						,    row_length + 1			):
                  for block_start in range(row_length - block_length			):
                        ways_number[row_length] += ways_number[
                            row_length - block_start - block_length - 1
                        ]

                  ways_number[row_length] += 1

      return ways_number[length]


if __name__ == "__main__":
  print(F"""{solution() = }""")




