





'''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					:






       """simple docstring"""
       _snake_case     :					int						    =   BlenderbotSmallConfig
       _snake_case     :					Optional[int]						    =   {}
       _snake_case     :					Dict						    =   'gelu'





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

            '''simple docstring'''




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

            _UpperCamelCase       =	hidden_dropout_prob
            _UpperCamelCase       =	attention_probs_dropout_prob
            _UpperCamelCase       =	max_position_embeddings
            _UpperCamelCase       =	eos_token_id
            _UpperCamelCase       =	pad_token_id
            _UpperCamelCase       =	bos_token_id





       def snake_case__						(							self       :		List[str]					)   ->					Union[str, Any]:

            '''simple docstring'''




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

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

            _UpperCamelCase       =	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	,			)
            _UpperCamelCase       =	prepare_blenderbot_small_inputs_dict(lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__					)
            return config, inputs_dict







       def snake_case__						(							self       :		int	,			lowerCAmelCase__       :		List[str]	,			lowerCAmelCase__       :		Tuple					)   ->					Tuple:

            '''simple docstring'''




            _UpperCamelCase       =	TFBlenderbotSmallModel(config=lowerCAmelCase__					).get_decoder()
            _UpperCamelCase       =	inputs_dict['''input_ids''']

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

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

            _UpperCamelCase   ,    _UpperCamelCase       =	outputs.to_tuple()

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

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

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

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

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

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







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





     """simple docstring"""

     if attention_mask is None:
          _UpperCamelCase       =	tf.cast(tf.math.not_equal(lowercase, config.pad_token_id				), tf.inta				)
     if decoder_attention_mask is None:
          _UpperCamelCase       =	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:
          _UpperCamelCase       =	tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
     if decoder_head_mask is None:
          _UpperCamelCase       =	tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
     if cross_attn_head_mask is None:
          _UpperCamelCase       =	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					(   __magic_name__						,						__magic_name__						,						unittest.TestCase			):






       """simple docstring"""
       _snake_case     :					Optional[Any]						    =   (
           (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
       )
       _snake_case     :					Dict						    =   (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
       _snake_case     :					Optional[int]						    =   (
           {
               'conversational': TFBlenderbotSmallForConditionalGeneration,
               'feature-extraction': TFBlenderbotSmallModel,
               'summarization': TFBlenderbotSmallForConditionalGeneration,
               'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
               'translation': TFBlenderbotSmallForConditionalGeneration,
           }
           if is_tf_available()
           else {}
       )
       _snake_case     :					List[Any]						    =   True
       _snake_case     :					Tuple						    =   False
       _snake_case     :					Any						    =   False





       def snake_case__						(							self       :		Union[str, Any]					)   ->					Any:

            '''simple docstring'''




            _UpperCamelCase       =	TFBlenderbotSmallModelTester(self					)
            _UpperCamelCase       =	ConfigTester(self	,			config_class=lowerCAmelCase__					)





       def snake_case__						(							self       :		Dict					)   ->					Optional[int]:

            '''simple docstring'''




            self.config_tester.run_common_tests()







       def snake_case__						(							self       :		Optional[int]					)   ->					List[Any]:

            '''simple docstring'''




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

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






       """simple docstring"""
       _snake_case     :					Any						    =   [
           '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?'
       ]
       _snake_case     :					List[str]						    =   'facebook/blenderbot_small-90M'





       @cached_property
       def snake_case__						(							self       :		int					)   ->					List[str]:

            '''simple docstring'''




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





       @cached_property
       def snake_case__						(							self       :		Dict					)   ->					Any:

            '''simple docstring'''




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







       @slow
       def snake_case__						(							self       :		List[Any]					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	self.tokenizer(self.src_text	,			return_tensors='''tf'''					)
            _UpperCamelCase       =	self.model.generate(
                model_inputs.input_ids	,			attention_mask=model_inputs.attention_mask	,			num_beams=2	,			use_cache=lowerCAmelCase__	,			)
            _UpperCamelCase       =	self.tokenizer.batch_decode(generated_ids.numpy()	,			skip_special_tokens=lowerCAmelCase__					)[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'''

def  a__    (    lowercase :					int				)		->		int:





     """simple docstring"""

     if not isinstance(lowercase, lowercase				):
          raise TypeError('''Input value must be an \'int\' type'''				)

     _UpperCamelCase       =	0
     while number:
          position += 1
          number >>= 1

     return position


if __name__ == "__main__":
   import doctest

   doctest.testmod()



