


"""simple docstring"""







from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, 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, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel






@require_tf
class 						_A  :







    """simple docstring"""




    UpperCAmelCase	:							Union[str, Any]     =	BlenderbotSmallConfig
    UpperCAmelCase	:							Optional[Any]     =	{}
    UpperCAmelCase	:							Optional[int]     =	"""gelu"""






    def __init__( self    :   Optional[int]    ,			__UpperCAmelCase    :   Dict    ,			__UpperCAmelCase    :   str=13    ,			__UpperCAmelCase    :   Any=7    ,			__UpperCAmelCase    :   Any=True    ,			__UpperCAmelCase    :   Tuple=False    ,			__UpperCAmelCase    :   Tuple=99    ,			__UpperCAmelCase    :   Tuple=32    ,			__UpperCAmelCase    :   int=2    ,			__UpperCAmelCase    :   List[str]=4    ,			__UpperCAmelCase    :   str=37    ,			__UpperCAmelCase    :   str=0.1    ,			__UpperCAmelCase    :   str=0.1    ,			__UpperCAmelCase    :   Dict=20    ,			__UpperCAmelCase    :   Dict=2    ,			__UpperCAmelCase    :   List[Any]=1    ,			__UpperCAmelCase    :   str=0    ,			):
      a						:		Optional[int]											=      parent
      a						:		List[str]											=      batch_size
      a						:		int											=      seq_length
      a						:		Union[str, Any]											=      is_training
      a						:		Tuple											=      use_labels
      a						:		int											=      vocab_size
      a						:		Optional[int]											=      hidden_size
      a						:		Tuple											=      num_hidden_layers
      a						:		Union[str, Any]											=      num_attention_heads
      a						:		Optional[int]											=      intermediate_size

      a						:		Optional[int]											=      hidden_dropout_prob
      a						:		Dict											=      attention_probs_dropout_prob
      a						:		List[str]											=      max_position_embeddings
      a						:		Union[str, Any]											=      eos_token_id
      a						:		str											=      pad_token_id
      a						:		str											=      bos_token_id






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

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

      a						:		Optional[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    ,			)
      a						:		Optional[Any]											=      prepare_blenderbot_small_inputs_dict(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)
      return config, inputs_dict






    def      __snake_case							( self    :   Dict    ,			__UpperCAmelCase    :   List[str]    ,			__UpperCAmelCase    :   Tuple):
      a						:		List[Any]											=      TFBlenderbotSmallModel(config=__UpperCAmelCase).get_decoder()
      a						:		Any											=      inputs_dict["input_ids"]

      a						:		List[Any]											=      input_ids[:1, :]
      a						:		Optional[int]											=      inputs_dict["attention_mask"][:1, :]
      a						:		Tuple											=      inputs_dict["head_mask"]
      a						:		Optional[int]											=      1

      # first forward pass
      a						:		Any											=      model(__UpperCAmelCase    ,			attention_mask=__UpperCAmelCase    ,			head_mask=__UpperCAmelCase    ,			use_cache=__UpperCAmelCase)

      a						:		List[Any]											=      outputs.to_tuple()

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

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

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

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






def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_=None  ,       A_=None  ,       A_=None  ,       A_=None  ,       A_=None  ,       )->				int:
  '''simple docstring'''







  if attention_mask is None:
    a						:		Optional[Any]											=      tf.cast(tf.math.not_equal(A_  ,       config.pad_token_id	)  ,       tf.inta	)
  if decoder_attention_mask is None:
    a						:		Optional[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:
    a						:		Optional[int]											=      tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
  if decoder_head_mask is None:
    a						:		Optional[int]											=      tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
  if cross_attn_head_mask is None:
    a						:		Dict											=      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  (  _a   ,_a   ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase	:							int     =	(
        (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
    )
    UpperCAmelCase	:							Any     =	(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
    UpperCAmelCase	:							List[str]     =	(
        {
            """conversational""": TFBlenderbotSmallForConditionalGeneration,
            """feature-extraction""": TFBlenderbotSmallModel,
            """summarization""": TFBlenderbotSmallForConditionalGeneration,
            """text2text-generation""": TFBlenderbotSmallForConditionalGeneration,
            """translation""": TFBlenderbotSmallForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    UpperCAmelCase	:							List[Any]     =	True
    UpperCAmelCase	:							List[Any]     =	False
    UpperCAmelCase	:							Optional[int]     =	False






    def      __snake_case							( self    :   int):
      a						:		Union[str, Any]											=      TFBlenderbotSmallModelTester(self)
      a						:		Optional[int]											=      ConfigTester(self    ,			config_class=__UpperCAmelCase)






    def      __snake_case							( self    :   Tuple):
      self.config_tester.run_common_tests()






    def      __snake_case							( self    :   Tuple):
      a						:		Dict											=      self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*__UpperCAmelCase)






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







    """simple docstring"""




    UpperCAmelCase	:							int     =	[
        """Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like """
        """  i'm going to throw up.\nand why is that?"""
    ]
    UpperCAmelCase	:							Union[str, Any]     =	"""facebook/blenderbot_small-90M"""






    @cached_property
    def      __snake_case							( self    :   str):
      # use "old" tokenizer here because of bug when downloading new tokenizer
      return BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M")






    @cached_property
    def      __snake_case							( self    :   Union[str, Any]):
      a						:		Dict											=      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
      return model






    @slow
    def      __snake_case							( self    :   Dict):
      a						:		Tuple											=      self.tokenizer(self.src_text    ,			return_tensors="tf")
      a						:		Dict											=      self.model.generate(
          model_inputs.input_ids    ,			attention_mask=model_inputs.attention_mask    ,			num_beams=2    ,			use_cache=__UpperCAmelCase    ,			)
      a						:		List[Any]											=      self.tokenizer.batch_decode(generated_ids.numpy()    ,			skip_special_tokens=__UpperCAmelCase)[0]
      assert generated_words in (
          "i don't know. i just feel like i'm going to throw up. it's not fun.",
          "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
          "i'm not sure. i just feel like i've been in a bad situation.",
      )










"""simple docstring"""







from __future__ import annotations






class 						_A  :







    """simple docstring"""




    def __init__( self    :   Tuple    ,			__UpperCAmelCase    :   List[Any]=None):
      a						:		int											=      data
      a						:		Dict											=      None






    def __repr__( self    :   Dict):
      a						:		List[Any]											=      []
      a						:		str											=      self
      while temp:
        string_rep.append(f'''{temp.data}''')
        a						:		Tuple											=      temp.next
      return "->".join(__UpperCAmelCase)






def 					lowercase      (    A_	)->				Any:
  '''simple docstring'''







  if not elements_list:
    raise Exception("The Elements List is empty"	)

  a						:		Any											=      Node(elements_list[0]	)
  for i in range(1  ,       len(A_	)	):
    a						:		int											=      Node(elements_list[i]	)
    a						:		Optional[Any]											=      current.next
  return head


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if head_node is not None and isinstance(A_  ,       A_	):
    print_reverse(head_node.next	)
    print(head_node.data	)


def 					lowercase      (    )->				List[Any]:
  '''simple docstring'''







  from doctest import testmod

  testmod()

  a						:		Union[str, Any]											=      make_linked_list([14, 52, 14, 12, 43]	)
  print("Linked List:"	)
  print(A_	)
  print("Elements in Reverse:"	)
  print_reverse(A_	)


if __name__ == "__main__":
 main()






