



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 							lowerCAmelCase__  :
     __A     :    Tuple           =						BlenderbotSmallConfig
     __A     :    int           =						{}
     __A     :    List[str]           =						'gelu'







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

           A__	:							List[Any]						    = hidden_dropout_prob
           A__	:							Tuple						    = attention_probs_dropout_prob
           A__	:							Any						    = max_position_embeddings
           A__	:							str						    = eos_token_id
           A__	:							List[str]						    = pad_token_id
           A__	:							Union[str, Any]						    = bos_token_id







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

           A__	:							Tuple						    = ids_tensor([self.batch_size, self.seq_length]			,		self.vocab_size)

           A__	:							List[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			,		)
           A__	:							Any						    = prepare_blenderbot_small_inputs_dict(_A			,		_A			,		_A)
           return config, inputs_dict







     def  _lowercase			(       self      :     Optional[int]			,		_A      :     Optional[int]			,		_A      :     Optional[int]):
           A__	:							Optional[Any]						    = TFBlenderbotSmallModel(config=_A).get_decoder()
           A__	:							int						    = inputs_dict["input_ids"]

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

           # first forward pass
           A__	:							List[Any]						    = model(_A			,		attention_mask=_A			,		head_mask=_A			,		use_cache=_A)

           A__	:							str						    = outputs.to_tuple()

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

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

           A__	:							Union[str, Any]						    = model(_A			,		attention_mask=_A)[0]
           A__	:							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
           A__	:							Optional[int]						    = int(ids_tensor((1,)			,		output_from_past.shape[-1]))
           A__	:							str						    = 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(_A			,		_A			,		rtol=1e-3)




def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase=None							,     __lowercase=None							,     __lowercase=None							,     __lowercase=None							,     __lowercase=None							,     )		->		Any:







      """simple docstring"""

      if attention_mask is None:
            A__	:							str						    = tf.cast(tf.math.not_equal(__lowercase							,     config.pad_token_id       )							,     tf.inta       )
      if decoder_attention_mask is None:
            A__	:							Optional[int]						    = 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__	:							Tuple						    = tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
      if decoder_head_mask is None:
            A__	:							Tuple						    = tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
      if cross_attn_head_mask is None:
            A__	:							List[str]						    = 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 							lowerCAmelCase__  (	UpperCamelCase					,							UpperCamelCase					,							unittest.TestCase						):
     __A     :    Tuple           =						(
         (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
     )
     __A     :    Dict           =						(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
     __A     :    int           =						(
         {
             'conversational': TFBlenderbotSmallForConditionalGeneration,
             'feature-extraction': TFBlenderbotSmallModel,
             'summarization': TFBlenderbotSmallForConditionalGeneration,
             'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
             'translation': TFBlenderbotSmallForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     __A     :    int           =						True
     __A     :    Optional[int]           =						False
     __A     :    str           =						False







     def  _lowercase			(       self      :     Union[str, Any]):
           A__	:							List[Any]						    = TFBlenderbotSmallModelTester(self)
           A__	:							Any						    = ConfigTester(self			,		config_class=_A)







     def  _lowercase			(       self      :     str):
           self.config_tester.run_common_tests()







     def  _lowercase			(       self      :     Dict):
           A__	:							List[str]						    = self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.check_decoder_model_past_large_inputs(*_A)




@require_tokenizers
@require_tf
class 							lowerCAmelCase__  (	unittest.TestCase						):
     __A     :    str           =						[
         '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?'
     ]
     __A     :    Any           =						'facebook/blenderbot_small-90M'







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







     @cached_property
     def  _lowercase			(       self      :     Optional[int]):
           A__	:							Dict						    = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
           return model







     @slow
     def  _lowercase			(       self      :     Tuple):
           A__	:							str						    = self.tokenizer(self.src_text			,		return_tensors="tf")
           A__	:							Optional[Any]						    = self.model.generate(
               model_inputs.input_ids			,		attention_mask=model_inputs.attention_mask			,		num_beams=2			,		use_cache=_A			,		)
           A__	:							Dict						    = self.tokenizer.batch_decode(generated_ids.numpy()			,		skip_special_tokens=_A)[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.",
           )




snake_case    :						Tuple  =						'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
def 	snake_case__       (					)		->		None:







						"""simple docstring"""

						A__	:							Union[str, Any]						    = input("Enter message: "       )
						A__	:							Tuple						    = input("Enter key [alphanumeric]: "       )
						A__	:							Optional[Any]						    = input("Encrypt/Decrypt [e/d]: "       )

						if mode.lower().startswith("e"       ):
												A__	:							Tuple						    = "encrypt"
												A__	:							str						    = encrypt_message(__lowercase							,     __lowercase       )
						elif mode.lower().startswith("d"       ):
												A__	:							Optional[Any]						    = "decrypt"
												A__	:							Union[str, Any]						    = decrypt_message(__lowercase							,     __lowercase       )

						print(F'\n{mode.title()}ed message:'       )
						print(__lowercase       )
def 	snake_case__       (					__lowercase							,     __lowercase       )		->		str:







						"""simple docstring"""

						return translate_message(__lowercase							,     __lowercase							,     "encrypt"       )
def 	snake_case__       (					__lowercase							,     __lowercase       )		->		str:







						"""simple docstring"""

						return translate_message(__lowercase							,     __lowercase							,     "decrypt"       )
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase       )		->		str:







						"""simple docstring"""

						A__	:							Dict						    = []
						A__	:							Union[str, Any]						    = 0
						A__	:							List[Any]						    = key.upper()

						for symbol in message:
												A__	:							List[str]						    = LETTERS.find(symbol.upper()       )
												if num != -1:
																		if mode == "encrypt":
																								num += LETTERS.find(key[key_index]       )
																		elif mode == "decrypt":
																								num -= LETTERS.find(key[key_index]       )

																		num %= len(__lowercase       )

																		if symbol.isupper():
																								translated.append(LETTERS[num]       )
																		elif symbol.islower():
																								translated.append(LETTERS[num].lower()       )

																		key_index += 1
																		if key_index == len(__lowercase       ):
																								A__	:							Optional[int]						    = 0
												else:
																		translated.append(__lowercase       )
						return "".join(__lowercase       )


if __name__ == "__main__":
		main()