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     a_			:






 '''simple docstring'''






 UpperCAmelCase_     			= BlenderbotConfig
 UpperCAmelCase_     			= {}
 UpperCAmelCase_     			= 'gelu'




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







        '''simple docstring'''



        lowerCAmelCase__					     = parent
        lowerCAmelCase__					     = batch_size
        lowerCAmelCase__					     = seq_length
        lowerCAmelCase__					     = is_training
        lowerCAmelCase__					     = use_labels
        lowerCAmelCase__					     = vocab_size
        lowerCAmelCase__					     = hidden_size
        lowerCAmelCase__					     = num_hidden_layers
        lowerCAmelCase__					     = num_attention_heads
        lowerCAmelCase__					     = intermediate_size

        lowerCAmelCase__					     = hidden_dropout_prob
        lowerCAmelCase__					     = attention_probs_dropout_prob
        lowerCAmelCase__					     = max_position_embeddings
        lowerCAmelCase__					     = eos_token_id
        lowerCAmelCase__					     = pad_token_id
        lowerCAmelCase__					     = bos_token_id




 def 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



        lowerCAmelCase__					     = ids_tensor([self.batch_size, self.seq_length - 1]      ,   self.vocab_size)
        lowerCAmelCase__					     = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)      ,   1)
        lowerCAmelCase__					     = tf.concat([input_ids, eos_tensor]      ,   axis=1)

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

        lowerCAmelCase__					     = 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__					     = prepare_blenderbot_inputs_dict(lowercase__      ,   lowercase__      ,   lowercase__)
        return config, inputs_dict




 def 		__snake_case						(      self :  Optional[Any]      ,   lowercase__ :  Tuple      ,   lowercase__ :  Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = TFBlenderbotModel(config=lowercase__).get_decoder()
        lowerCAmelCase__					     = inputs_dict['input_ids']

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

        # first forward pass
        lowerCAmelCase__					     = model(lowercase__      ,   attention_mask=lowercase__      ,   head_mask=lowercase__      ,   use_cache=lowercase__)

        lowerCAmelCase__     , lowerCAmelCase__					     = outputs.to_tuple()

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

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

        lowerCAmelCase__					     = model(lowercase__      ,   attention_mask=lowercase__)[0]
        lowerCAmelCase__					     = model(lowercase__      ,   attention_mask=lowercase__      ,   past_key_values=lowercase__)[0]

        self.parent.assertEqual(next_tokens.shape[1]      ,   output_from_past.shape[1])

        # select random slice
        lowerCAmelCase__					     = int(ids_tensor((1,)      ,   output_from_past.shape[-1]))
        lowerCAmelCase__					     = output_from_no_past[:, -3:, random_slice_idx]
        lowerCAmelCase__					     = output_from_past[:, :, random_slice_idx]

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






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__=None     ,  lowerCAmelCase__=None     ,  lowerCAmelCase__=None     ,  lowerCAmelCase__=None     ,  lowerCAmelCase__=None     ,  ):
       if attention_mask is None:
              lowerCAmelCase__					     = tf.cast(tf.math.not_equal(lowerCAmelCase__     ,  config.pad_token_id     )     ,  tf.inta     )
       if decoder_attention_mask is None:
              lowerCAmelCase__					     = 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__					     = tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
       if decoder_head_mask is None:
              lowerCAmelCase__					     = tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
       if cross_attn_head_mask is None:
              lowerCAmelCase__					     = 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_			(						SCREAMING_SNAKE_CASE	,					SCREAMING_SNAKE_CASE	,					unittest.TestCase						):






 '''simple docstring'''






 UpperCAmelCase_     			= (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
 UpperCAmelCase_     			= (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
 UpperCAmelCase_     			= (
     {
         'conversational': TFBlenderbotForConditionalGeneration,
         'feature-extraction': TFBlenderbotModel,
         'summarization': TFBlenderbotForConditionalGeneration,
         'text2text-generation': TFBlenderbotForConditionalGeneration,
         'translation': TFBlenderbotForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 UpperCAmelCase_     			= True
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False




 def 		__snake_case						(      self :  Dict):







        '''simple docstring'''



        lowerCAmelCase__					     = TFBlenderbotModelTester(self)
        lowerCAmelCase__					     = ConfigTester(self      ,   config_class=lowercase__)




 def 		__snake_case						(      self :  Any):







        '''simple docstring'''



        self.config_tester.run_common_tests()




 def 		__snake_case						(      self :  Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*lowercase__)






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






 '''simple docstring'''






 UpperCAmelCase_     			= ['My friends are cool but they eat too many carbs.']
 UpperCAmelCase_     			= 'facebook/blenderbot-400M-distill'




 @cached_property
 def 		__snake_case						(      self :  str):







        '''simple docstring'''



        return BlenderbotTokenizer.from_pretrained(self.model_name)




 @cached_property
 def 		__snake_case						(      self :  int):







        '''simple docstring'''



        lowerCAmelCase__					     = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
        return model




 @slow
 def 		__snake_case						(      self :  List[str]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.tokenizer(self.src_text      ,   return_tensors='tf')
        lowerCAmelCase__					     = self.model.generate(
            model_inputs.input_ids      ,   )
        lowerCAmelCase__					     = self.tokenizer.batch_decode(generated_ids.numpy()      ,   skip_special_tokens=lowercase__)[0]
        assert (
            generated_words
            == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
        )

import numpy as np






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       return 1 / (1 + np.exp(-vector     ))






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       return vector * sigmoid(lowerCAmelCase__     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
