


"""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 _lowerCAmelCase    :
   __UpperCAmelCase			:	Any							  =  BlenderbotSmallConfig
   __UpperCAmelCase			:	List[str]							  =  {}
   __UpperCAmelCase			:	str							  =  '''gelu'''







   def __init__(     self						,     UpperCamelCase__						,     UpperCamelCase__=13						,     UpperCamelCase__=7						,     UpperCamelCase__=True						,     UpperCamelCase__=False						,     UpperCamelCase__=99						,     UpperCamelCase__=32						,     UpperCamelCase__=2						,     UpperCamelCase__=4						,     UpperCamelCase__=37						,     UpperCamelCase__=0.1						,     UpperCamelCase__=0.1						,     UpperCamelCase__=20						,     UpperCamelCase__=2						,     UpperCamelCase__=1						,     UpperCamelCase__=0						,     )     ->		Dict:

       '''simple docstring'''




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

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







   def 			lowerCamelCase		(     self						)     ->		Dict:

       '''simple docstring'''




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

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

       snake_case   :				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						,     )
       snake_case   :				str				 =   prepare_blenderbot_small_inputs_dict(UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)
       return config, inputs_dict







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		str:

       '''simple docstring'''




       snake_case   :				Any				 =   TFBlenderbotSmallModel(config=UpperCamelCase__						).get_decoder()
       snake_case   :				Tuple				 =   inputs_dict["input_ids"]

       snake_case   :				Optional[Any]				 =   input_ids[:1, :]
       snake_case   :				Tuple				 =   inputs_dict["attention_mask"][:1, :]
       snake_case   :				List[str]				 =   inputs_dict["head_mask"]
       snake_case   :				Tuple				 =   1

       # first forward pass
       snake_case   :				str				 =   model(UpperCamelCase__						,     attention_mask=UpperCamelCase__						,     head_mask=UpperCamelCase__						,     use_cache=UpperCamelCase__						)

       snake_case   :				Dict				 =   outputs.to_tuple()

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

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

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

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







def   __lowerCAmelCase						(						lowercase  :  str						,							lowercase  :  Union[str, Any]						,							lowercase  :  int						,							lowercase  :  int=None						,							lowercase  :  Any=None						,							lowercase  :  Any=None						,							lowercase  :  Optional[Any]=None						,							lowercase  :  Optional[Any]=None						,							)     ->						str:


    """simple docstring"""





    if attention_mask is None:
        snake_case   :				Optional[Any]				 =   tf.cast(tf.math.not_equal(lowercase						,							config.pad_token_id			)						,							tf.inta			)
    if decoder_attention_mask is None:
        snake_case   :				Dict				 =   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:
        snake_case   :				Optional[Any]				 =   tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
    if decoder_head_mask is None:
        snake_case   :				int				 =   tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
    if cross_attn_head_mask is None:
        snake_case   :				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    (				snake_case_   ,							snake_case_   ,							unittest.TestCase     ):
   __UpperCAmelCase			:	Optional[Any]							  =  (
       (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
   )
   __UpperCAmelCase			:	Optional[Any]							  =  (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
   __UpperCAmelCase			:	Optional[int]							  =  (
       {
           '''conversational''': TFBlenderbotSmallForConditionalGeneration,
           '''feature-extraction''': TFBlenderbotSmallModel,
           '''summarization''': TFBlenderbotSmallForConditionalGeneration,
           '''text2text-generation''': TFBlenderbotSmallForConditionalGeneration,
           '''translation''': TFBlenderbotSmallForConditionalGeneration,
       }
       if is_tf_available()
       else {}
   )
   __UpperCAmelCase			:	Optional[int]							  =  True
   __UpperCAmelCase			:	Any							  =  False
   __UpperCAmelCase			:	Tuple							  =  False







   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       snake_case   :				str				 =   TFBlenderbotSmallModelTester(self						)
       snake_case   :				Optional[int]				 =   ConfigTester(self						,     config_class=UpperCamelCase__						)







   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       self.config_tester.run_common_tests()







   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       snake_case   :				Optional[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 _lowerCAmelCase    (				unittest.TestCase     ):
   __UpperCAmelCase			:	List[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?'''
   ]
   __UpperCAmelCase			:	Tuple							  =  '''facebook/blenderbot_small-90M'''







   @cached_property
   def 			lowerCamelCase		(     self						)     ->		Tuple:

       '''simple docstring'''




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







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

       '''simple docstring'''




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







   @slow
   def 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   self.tokenizer(self.src_text						,     return_tensors="tf"						)
       snake_case   :				List[Any]				 =   self.model.generate(
           model_inputs.input_ids						,     attention_mask=model_inputs.attention_mask						,     num_beams=2						,     use_cache=UpperCamelCase__						,     )
       snake_case   :				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"""



import string







def   __lowerCAmelCase						(						lowercase  :  str			)     ->						str:


    """simple docstring"""





    snake_case   :				List[str]				 =   ""
    for i in sequence:
        snake_case   :				Optional[Any]				 =   ord(lowercase			)
        if 65 <= extract <= 90:
            output += chr(155 - extract			)
        elif 97 <= extract <= 122:
            output += chr(219 - extract			)
        else:
            output += i
    return output







def   __lowerCAmelCase						(						lowercase  :  str			)     ->						str:


    """simple docstring"""





    snake_case   :				Dict				 =   string.ascii_letters
    snake_case   :				List[Any]				 =   string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
    return "".join(
        letters_reversed[letters.index(lowercase			)] if c in letters else c for c in sequence			)







def   __lowerCAmelCase						(						)     ->						None:


    """simple docstring"""





    from timeit import timeit

    print("Running performance benchmarks..."			)
    snake_case   :				Optional[int]				 =   "from string import printable ; from __main__ import atbash, atbash_slow"
    print(F'> atbash_slow(): {timeit("atbash_slow(printable)"						,							setup=lowercase			)} seconds'			)
    print(F'>      atbash(): {timeit("atbash(printable)"						,							setup=lowercase			)} seconds'			)


if __name__ == "__main__":
  for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
    print(F'''{example} encrypted in atbash: {atbash(example)}''')
  benchmark()





