





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      _SCREAMING_SNAKE_CASE		:







 '''simple docstring'''
 lowercase_	=    BlenderbotSmallConfig
 lowercase_	=    {}
 lowercase_	=    "gelu"

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



   '''simple docstring'''



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

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

 def 							SCREAMING_SNAKE_CASE_      (self       :     Tuple) ->int:



   '''simple docstring'''



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

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

   lowerCamelCase__:   Union[str, 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 ,				)
   lowerCamelCase__:   Dict    						=prepare_blenderbot_small_inputs_dict(UpperCAmelCase_ ,				UpperCAmelCase_ ,				UpperCAmelCase_)
   return config, inputs_dict







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



   '''simple docstring'''



   lowerCamelCase__:   List[str]    						=TFBlenderbotSmallModel(config=UpperCAmelCase_).get_decoder()
   lowerCamelCase__:   Tuple    						=inputs_dict["input_ids"]

   lowerCamelCase__:   Union[str, Any]    						=input_ids[:1, :]
   lowerCamelCase__:   List[str]    						=inputs_dict["attention_mask"][:1, :]
   lowerCamelCase__:   Optional[int]    						=inputs_dict["head_mask"]
   lowerCamelCase__:   Optional[int]    						=1

   # first forward pass
   lowerCamelCase__:   str    						=model(UpperCAmelCase_ ,				attention_mask=UpperCAmelCase_ ,				head_mask=UpperCAmelCase_ ,				use_cache=UpperCAmelCase_)

   lowerCamelCase__:   List[str]    						=outputs.to_tuple()

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

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

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

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





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







  """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__:   List[str]    						=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__:   List[str]    						=tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
  if decoder_head_mask is None:
    lowerCamelCase__:   List[Any]    						=tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
  if cross_attn_head_mask is None:
    lowerCamelCase__:   Union[str, 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		(			__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							unittest.TestCase					):







 '''simple docstring'''
 lowercase_	=    (
     (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
 )
 lowercase_	=    (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
 lowercase_	=    (
     {
         "conversational": TFBlenderbotSmallForConditionalGeneration,
         "feature-extraction": TFBlenderbotSmallModel,
         "summarization": TFBlenderbotSmallForConditionalGeneration,
         "text2text-generation": TFBlenderbotSmallForConditionalGeneration,
         "translation": TFBlenderbotSmallForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 lowercase_	=    True
 lowercase_	=    False
 lowercase_	=    False

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



   '''simple docstring'''



   lowerCamelCase__:   Any    						=TFBlenderbotSmallModelTester(self)
   lowerCamelCase__:   int    						=ConfigTester(self ,				config_class=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_      (self       :     str) ->Any:



   '''simple docstring'''



   self.config_tester.run_common_tests()







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



   '''simple docstring'''



   lowerCamelCase__:   int    						=self.model_tester.prepare_config_and_inputs_for_common()
   self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase_)



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







 '''simple docstring'''
 lowercase_	=    [
     "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?"
 ]
 lowercase_	=    "facebook/blenderbot_small-90M"

 @cached_property
 def 							SCREAMING_SNAKE_CASE_      (self       :     Dict) ->Optional[int]:



   '''simple docstring'''



   return BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M")

 @cached_property
 def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[int]) ->Any:



   '''simple docstring'''



   lowerCamelCase__:   Any    						=TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
   return model







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



   '''simple docstring'''



   lowerCamelCase__:   Tuple    						=self.tokenizer(self.src_text ,				return_tensors="tf")
   lowerCamelCase__:   List[str]    						=self.model.generate(
       model_inputs.input_ids ,				attention_mask=model_inputs.attention_mask ,				num_beams=2 ,				use_cache=UpperCAmelCase_ ,				)
   lowerCamelCase__:   Optional[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.",
   )












def   lowerCAmelCase_				( __a	)  ->	str:







  """simple docstring"""





  lowerCamelCase__:   List[str]    						=""
  for ch in key:
    if ch == " " or ch not in key_no_dups and ch.isalpha():
      key_no_dups += ch
  return key_no_dups





def   lowerCAmelCase_				( __a	)  ->	dict[str, str]:







  """simple docstring"""





  lowerCamelCase__:   Dict    						=[chr(i + 65	) for i in range(26	)]
  # Remove duplicate characters from key
  lowerCamelCase__:   Union[str, Any]    						=remove_duplicates(key.upper()	)
  lowerCamelCase__:   List[Any]    						=len(__a	)
  # First fill cipher with key characters
  lowerCamelCase__:   Optional[Any]    						={alphabet[i]: char for i, char in enumerate(__a	)}
  # Then map remaining characters in alphabet to
  # the alphabet from the beginning
  for i in range(len(__a	)			,					26	):
    lowerCamelCase__:   Optional[int]    						=alphabet[i - offset]
    # Ensure we are not mapping letters to letters previously mapped
    while char in key:
      offset -= 1
      lowerCamelCase__:   List[Any]    						=alphabet[i - offset]
    lowerCamelCase__:   str    						=char
  return cipher_alphabet





def   lowerCAmelCase_				( __a			,					__a	)  ->	str:







  """simple docstring"""





  return "".join(cipher_map.get(__a			,					__a	) for ch in message.upper()	)





def   lowerCAmelCase_				( __a			,					__a	)  ->	str:







  """simple docstring"""





  lowerCamelCase__:   List[Any]    						={v: k for k, v in cipher_map.items()}
  return "".join(rev_cipher_map.get(__a			,					__a	) for ch in message.upper()	)





def   lowerCAmelCase_				( )  ->	None:







  """simple docstring"""





  lowerCamelCase__:   Optional[Any]    						=input("Enter message to encode or decode: "	).strip()
  lowerCamelCase__:   Dict    						=input("Enter keyword: "	).strip()
  lowerCamelCase__:   Any    						=input("Encipher or decipher? E/D:"	).strip()[0].lower()
  try:
    lowerCamelCase__:   Union[str, Any]    						={"e": encipher, "d": decipher}[option]
  except KeyError:
    raise KeyError("invalid input option"	)
  lowerCamelCase__:   List[Any]    						=create_cipher_map(__a	)
  print(func(__a			,					__a	)	)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()

