


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       lowerCamelCase__   :
     SCREAMING_SNAKE_CASE__      			=			BlenderbotConfig
     SCREAMING_SNAKE_CASE__      			=			{}
     SCREAMING_SNAKE_CASE__      			=			'''gelu'''





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase=1_3		,     UpperCAmelCase=7		,     UpperCAmelCase=True		,     UpperCAmelCase=False		,     UpperCAmelCase=9_9		,     UpperCAmelCase=3_2		,     UpperCAmelCase=2		,     UpperCAmelCase=4		,     UpperCAmelCase=3_7		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=2_0		,     UpperCAmelCase=2		,     UpperCAmelCase=1		,     UpperCAmelCase=0		,     )     ->   Dict:
      _lowercase							      =parent
      _lowercase							      =batch_size
      _lowercase							      =seq_length
      _lowercase							      =is_training
      _lowercase							      =use_labels
      _lowercase							      =vocab_size
      _lowercase							      =hidden_size
      _lowercase							      =num_hidden_layers
      _lowercase							      =num_attention_heads
      _lowercase							      =intermediate_size

      _lowercase							      =hidden_dropout_prob
      _lowercase							      =attention_probs_dropout_prob
      _lowercase							      =max_position_embeddings
      _lowercase							      =eos_token_id
      _lowercase							      =pad_token_id
      _lowercase							      =bos_token_id





     def      __A					(self       )     ->   Optional[int]:
      _lowercase							      =ids_tensor([self.batch_size, self.seq_length - 1]		,     self.vocab_size       )
      _lowercase							      =tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size       )		,     1       )
      _lowercase							      =tf.concat([input_ids, eos_tensor]		,     axis=1       )

      _lowercase							      =ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size       )

      _lowercase							      =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		,     )
      _lowercase							      =prepare_blenderbot_inputs_dict(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )
      return config, inputs_dict





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Optional[int]:
      _lowercase							      =TFBlenderbotModel(config=UpperCAmelCase       ).get_decoder()
      _lowercase							      =inputs_dict['''input_ids''']

      _lowercase							      =input_ids[:1, :]
      _lowercase							      =inputs_dict['''attention_mask'''][:1, :]
      _lowercase							      =inputs_dict['''head_mask''']
      _lowercase							      =1

      # first forward pass
      _lowercase							      =model(UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     head_mask=UpperCAmelCase		,     use_cache=UpperCAmelCase       )

      _lowercase      ,    _lowercase							      =outputs.to_tuple()

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

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

      _lowercase							      =model(UpperCAmelCase		,     attention_mask=UpperCAmelCase       )[0]
      _lowercase							      =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
      _lowercase							      =int(ids_tensor((1,)		,     output_from_past.shape[-1]       )       )
      _lowercase							      =output_from_no_past[:, -3:, random_slice_idx]
      _lowercase							      =output_from_past[:, :, random_slice_idx]

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



def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case=None     ,						__snake_case=None     ,						__snake_case=None     ,						__snake_case=None     ,						__snake_case=None     ,						)     ->			Optional[int]:







 """simple docstring"""


 if attention_mask is None:
  _lowercase							      =tf.cast(tf.math.not_equal(__snake_case     ,						config.pad_token_id						)     ,						tf.inta						)
 if decoder_attention_mask is None:
  _lowercase							      =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:
  _lowercase							      =tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
 if decoder_head_mask is None:
  _lowercase							      =tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
 if cross_attn_head_mask is None:
  _lowercase							      =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__   (       lowerCAmelCase   ,							lowerCAmelCase   ,							unittest.TestCase):
     SCREAMING_SNAKE_CASE__      			=			(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
     SCREAMING_SNAKE_CASE__      			=			(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
     SCREAMING_SNAKE_CASE__      			=			(
         {
             '''conversational''': TFBlenderbotForConditionalGeneration,
             '''feature-extraction''': TFBlenderbotModel,
             '''summarization''': TFBlenderbotForConditionalGeneration,
             '''text2text-generation''': TFBlenderbotForConditionalGeneration,
             '''translation''': TFBlenderbotForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     SCREAMING_SNAKE_CASE__      			=			True
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False





     def      __A					(self       )     ->   Optional[Any]:
      _lowercase							      =TFBlenderbotModelTester(self       )
      _lowercase							      =ConfigTester(self		,     config_class=UpperCAmelCase       )





     def      __A					(self       )     ->   Optional[Any]:
      self.config_tester.run_common_tests()





     def      __A					(self       )     ->   Union[str, Any]:
      _lowercase							      =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):
     SCREAMING_SNAKE_CASE__      			=			['''My friends are cool but they eat too many carbs.''']
     SCREAMING_SNAKE_CASE__      			=			'''facebook/blenderbot-400M-distill'''





     @cached_property
     def      __A					(self       )     ->   Union[str, Any]:
      return BlenderbotTokenizer.from_pretrained(self.model_name       )





     @cached_property
     def      __A					(self       )     ->   List[Any]:
      _lowercase							      =TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
      return model





     @slow
     def      __A					(self       )     ->   str:
      _lowercase							      =self.tokenizer(self.src_text		,     return_tensors='''tf'''       )
      _lowercase							      =self.model.generate(
          model_inputs.input_ids		,     )
      _lowercase							      =self.tokenizer.batch_decode(generated_ids.numpy()		,     skip_special_tokens=UpperCAmelCase       )[0]
      assert (
          generated_words
          == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
      )





UpperCAmelCase__   	=							{
    '''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
    '''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''', '''M''': '''--''', '''N''': '''-.''',
    '''O''': '''---''', '''P''': '''.--.''', '''Q''': '''--.-''', '''R''': '''.-.''', '''S''': '''...''', '''T''': '''-''', '''U''': '''..-''',
    '''V''': '''...-''', '''W''': '''.--''', '''X''': '''-..-''', '''Y''': '''-.--''', '''Z''': '''--..''', '''1''': '''.----''',
    '''2''': '''..---''', '''3''': '''...--''', '''4''': '''....-''', '''5''': '''.....''', '''6''': '''-....''', '''7''': '''--...''',
    '''8''': '''---..''', '''9''': '''----.''', '''0''': '''-----''', '''&''': '''.-...''', '''@''': '''.--.-.''',
    ''':''': '''---...''', ''',''': '''--..--''', '''.''': '''.-.-.-''', '''\'''': '''.----.''', '''"''': '''.-..-.''',
    '''?''': '''..--..''', '''/''': '''-..-.''', '''=''': '''-...-''', '''+''': '''.-.-.''', '''-''': '''-....-''',
    '''(''': '''-.--.''', ''')''': '''-.--.-''', '''!''': '''-.-.--''', ''' ''': '''/'''
}  # Exclamation mark is not in ITU-R recommendation
# fmt: on
UpperCAmelCase__   	=							{value: key for key, value in MORSE_CODE_DICT.items()}
def 				UpperCAmelCase_					(							__snake_case						)     ->			str:







 """simple docstring"""


 return " ".join(MORSE_CODE_DICT[char] for char in message.upper()						)
def 				UpperCAmelCase_					(							__snake_case						)     ->			str:







 """simple docstring"""


 return "".join(REVERSE_DICT[char] for char in message.split()						)
def 				UpperCAmelCase_					(							)     ->			None:







 """simple docstring"""


 _lowercase							      ='''Morse code here!'''
 print(__snake_case						)
 _lowercase							      =encrypt(__snake_case						)
 print(__snake_case						)
 _lowercase							      =decrypt(__snake_case						)
 print(__snake_case						)


if __name__ == "__main__":
  main()

