






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 				UpperCAmelCase_  :
     __lowerCamelCase      				=  BlenderbotConfig
     __lowerCamelCase      				=  {}
     __lowerCamelCase      				=  'gelu'




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

          UpperCAmelCase__      :  Dict				      =			hidden_dropout_prob
          UpperCAmelCase__      :  int				      =			attention_probs_dropout_prob
          UpperCAmelCase__      :  Any				      =			max_position_embeddings
          UpperCAmelCase__      :  Dict				      =			eos_token_id
          UpperCAmelCase__      :  List[Any]				      =			pad_token_id
          UpperCAmelCase__      :  Optional[int]				      =			bos_token_id




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

          UpperCAmelCase__      :  List[Any]				      =			ids_tensor([self.batch_size, self.seq_length]	,						self.vocab_size		)

          UpperCAmelCase__      :  Dict				      =			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__      :  Tuple				      =			prepare_blenderbot_inputs_dict(_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase		)
          return config, inputs_dict




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase		):
          UpperCAmelCase__      :  Union[str, Any]				      =			TFBlenderbotModel(config=_lowerCAmelCase		).get_decoder()
          UpperCAmelCase__      :  Dict				      =			inputs_dict["""input_ids"""]

          UpperCAmelCase__      :  Union[str, Any]				      =			input_ids[:1, :]
          UpperCAmelCase__      :  Dict				      =			inputs_dict["""attention_mask"""][:1, :]
          UpperCAmelCase__      :  List[Any]				      =			inputs_dict["""head_mask"""]
          UpperCAmelCase__      :  Optional[Any]				      =			1

          # first forward pass
          UpperCAmelCase__      :  List[str]				      =			model(_lowerCAmelCase	,						attention_mask=_lowerCAmelCase	,						head_mask=_lowerCAmelCase	,						use_cache=_lowerCAmelCase		)

          UpperCAmelCase__      ,		UpperCAmelCase__      :  List[Any]				      =			outputs.to_tuple()

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

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

          UpperCAmelCase__      :  List[str]				      =			model(_lowerCAmelCase	,						attention_mask=_lowerCAmelCase		)[0]
          UpperCAmelCase__      :  Optional[Any]				      =			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__      :  Any				      =			int(ids_tensor((1,)	,						output_from_past.shape[-1]		)		)
          UpperCAmelCase__      :  Any				      =			output_from_no_past[:, -3:, random_slice_idx]
          UpperCAmelCase__      :  Any				      =			output_from_past[:, :, random_slice_idx]

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





def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase=None ,							__lowerCamelCase=None ,							__lowerCamelCase=None ,							__lowerCamelCase=None ,							__lowerCamelCase=None ,							) ->			List[Any]:

     '''simple docstring'''
     if attention_mask is None:
          UpperCAmelCase__      :  Tuple				      =			tf.cast(tf.math.not_equal(__lowerCamelCase ,							config.pad_token_id       ) ,							tf.inta       )
     if decoder_attention_mask is None:
          UpperCAmelCase__      :  Tuple				      =			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__      :  Any				      =			tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
     if decoder_head_mask is None:
          UpperCAmelCase__      :  int				      =			tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
     if cross_attn_head_mask is None:
          UpperCAmelCase__      :  int				      =			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 				UpperCAmelCase_  (		__lowerCamelCase							,      __lowerCamelCase							,      unittest.TestCase		):
     __lowerCamelCase      				=  (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
     __lowerCamelCase      				=  (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
     __lowerCamelCase      				=  (
         {
             'conversational': TFBlenderbotForConditionalGeneration,
             'feature-extraction': TFBlenderbotModel,
             'summarization': TFBlenderbotForConditionalGeneration,
             'text2text-generation': TFBlenderbotForConditionalGeneration,
             'translation': TFBlenderbotForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     __lowerCamelCase      				=  True
     __lowerCamelCase      				=  False
     __lowerCamelCase      				=  False




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Union[str, Any]				      =			TFBlenderbotModelTester(self		)
          UpperCAmelCase__      :  int				      =			ConfigTester(self	,						config_class=_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self		):
          self.config_tester.run_common_tests()




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Dict				      =			self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_decoder_model_past_large_inputs(*_lowerCAmelCase		)







@require_tokenizers
@require_tf
class 				UpperCAmelCase_  (		unittest.TestCase		):
     __lowerCamelCase      				=  ['My friends are cool but they eat too many carbs.']
     __lowerCamelCase      				=  'facebook/blenderbot-400M-distill'




     @cached_property
     def   __UpperCAmelCase  (				self		):
          return BlenderbotTokenizer.from_pretrained(self.model_name		)




     @cached_property
     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Tuple				      =			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
          return model




     @slow
     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Optional[int]				      =			self.tokenizer(self.src_text	,						return_tensors="""tf"""		)
          UpperCAmelCase__      :  Union[str, Any]				      =			self.model.generate(
              model_inputs.input_ids	,						)
          UpperCAmelCase__      :  List[str]				      =			self.tokenizer.batch_decode(generated_ids.numpy()	,						skip_special_tokens=_lowerCAmelCase		)[0]
          assert (
              generated_words
              == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
          )









def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE = 1000   ):
      '''simple docstring'''







      return sum(2 * a * ((a - 1) // 2) for a in range(3		,				n + 1   )   )


if __name__ == "__main__":
      print(solution())





