



"""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 				lowercase__       :







       '''simple docstring'''


       UpperCamelCase						=					BlenderbotSmallConfig
       UpperCamelCase						=					{}
       UpperCamelCase						=					'''gelu'''



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


             '''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 					lowercase__    (							self      :  Tuple   )			->						Tuple:


             '''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(_UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase   )
             return config, inputs_dict







       def 					lowercase__    (							self      :  int		,    _UpperCAmelCase      :  str		,    _UpperCAmelCase      :  Tuple   )			->						List[Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							TFBlenderbotSmallModel(config=_UpperCAmelCase   ).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(_UpperCAmelCase		,    attention_mask=_UpperCAmelCase		,    head_mask=_UpperCAmelCase		,    use_cache=_UpperCAmelCase   )

             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(_UpperCAmelCase		,    attention_mask=_UpperCAmelCase   )[0]
             UpperCAmelCase_      			=							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
             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(_UpperCAmelCase		,    _UpperCAmelCase		,    rtol=1e-3   )



def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__=None     ,      lowerCAmelCase__=None     ,      lowerCAmelCase__=None     ,      lowerCAmelCase__=None     ,      lowerCAmelCase__=None     ,      ):
      if attention_mask is None:
            UpperCAmelCase_      			=							tf.cast(tf.math.not_equal(lowerCAmelCase__     ,      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 				lowercase__       (   SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , unittest.TestCase				):







       '''simple docstring'''


       UpperCamelCase						=					(
           (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
       )
       UpperCamelCase						=					(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
       UpperCamelCase						=					(
           {
               '''conversational''': TFBlenderbotSmallForConditionalGeneration,
               '''feature-extraction''': TFBlenderbotSmallModel,
               '''summarization''': TFBlenderbotSmallForConditionalGeneration,
               '''text2text-generation''': TFBlenderbotSmallForConditionalGeneration,
               '''translation''': TFBlenderbotSmallForConditionalGeneration,
           }
           if is_tf_available()
           else {}
       )
       UpperCamelCase						=					True
       UpperCamelCase						=					False
       UpperCamelCase						=					False



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


             '''simple docstring'''




             UpperCAmelCase_      			=							TFBlenderbotSmallModelTester(self   )
             UpperCAmelCase_      			=							ConfigTester(self		,    config_class=_UpperCAmelCase   )



       def 					lowercase__    (							self      :  str   )			->						Optional[int]:


             '''simple docstring'''




             self.config_tester.run_common_tests()







       def 					lowercase__    (							self      :  Tuple   )			->						Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.check_decoder_model_past_large_inputs(*_UpperCAmelCase   )



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







       '''simple docstring'''


       UpperCamelCase						=					[
           '''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						=					'''facebook/blenderbot_small-90M'''



       @cached_property
       def 					lowercase__    (							self      :  int   )			->						Any:


             '''simple docstring'''




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



       @cached_property
       def 					lowercase__    (							self      :  Union[str, Any]   )			->						int:


             '''simple docstring'''




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







       @slow
       def 					lowercase__    (							self      :  Optional[int]   )			->						int:


             '''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=_UpperCAmelCase		,    )
             UpperCAmelCase_      			=							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"""





from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass
@dataclass
class 				lowercase__       :







       '''simple docstring'''


       UpperCamelCase						=					42
       UpperCamelCase						=					None
       UpperCamelCase						=					None



lowerCamelCase       	=						namedtuple("""CoinsDistribResult""", """moves excess""")
def        a__     (				lowerCAmelCase__				):

      if root is None:
            return 0

      # Validation
      def count_nodes(lowerCAmelCase__				) -> int:
            if node is None:
                  return 0

            return count_nodes(node.left				) + count_nodes(node.right				) + 1

      def count_coins(lowerCAmelCase__				) -> int:
            if node is None:
                  return 0

            return count_coins(node.left				) + count_coins(node.right				) + node.data

      if count_nodes(lowerCAmelCase__				) != count_coins(lowerCAmelCase__				):
            raise ValueError("The nodes number should be same as the number of coins"				)

      # Main calculation
      def get_distrib(lowerCAmelCase__				) -> CoinsDistribResult:

            if node is None:
                  return CoinsDistribResult(0     ,      1				)

            UpperCAmelCase_			,   UpperCAmelCase_      			=							get_distrib(node.left				)
            UpperCAmelCase_			,   UpperCAmelCase_      			=							get_distrib(node.right				)

            UpperCAmelCase_      			=							1 - left_distrib_excess
            UpperCAmelCase_      			=							1 - right_distrib_excess

            UpperCAmelCase_      			=							(
                left_distrib_moves
                + right_distrib_moves
                + abs(lowerCAmelCase__				)
                + abs(lowerCAmelCase__				)
            )
            UpperCAmelCase_      			=							node.data - coins_to_left - coins_to_right

            return CoinsDistribResult(lowerCAmelCase__     ,      lowerCAmelCase__				)

      return get_distrib(lowerCAmelCase__				)[0]


if __name__ == "__main__":
  import doctest

  doctest.testmod()




