'''simple docstring'''





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 :
    a__			:		Optional[int]			  =    BlenderbotConfig
    a__			:		List[str]			  =    {}
    a__			:		Optional[Any]			  =    """gelu"""



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






    def    _lowercase		(self       :       Optional[int]  ,     __a       :       str  ,     __a       :       Tuple					):
         UpperCAmelCase_			       =  TFBlenderbotModel(config=__a					).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(__a  ,     attention_mask=__a  ,     head_mask=__a  ,     use_cache=__a					)

         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(__a  ,     attention_mask=__a					)[0]
         UpperCAmelCase_			       =  model(__a  ,     attention_mask=__a  ,     past_key_values=__a					)[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(__a  ,     __a  ,     rtol=1E-3					)





def       lowerCAmelCase_							(   snake_case_			:					List[Any]   ,       snake_case_			:					str   ,       snake_case_			:					Any   ,       snake_case_			:					Tuple=None   ,       snake_case_			:					Tuple=None   ,       snake_case_			:					Union[str, Any]=None   ,       snake_case_			:					Optional[Any]=None   ,       snake_case_			:					Any=None   ,       )       -> List[Any]:


     '''simple docstring'''




     if attention_mask is None:
          UpperCAmelCase_			       =  tf.cast(tf.math.not_equal(snake_case_   ,       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 			__A (					UpperCamelCase__						,      UpperCamelCase__						,      unittest.TestCase      ):
    a__			:		List[Any]			  =    (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
    a__			:		int			  =    (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
    a__			:		Any			  =    (
        {
            """conversational""": TFBlenderbotForConditionalGeneration,
            """feature-extraction""": TFBlenderbotModel,
            """summarization""": TFBlenderbotForConditionalGeneration,
            """text2text-generation""": TFBlenderbotForConditionalGeneration,
            """translation""": TFBlenderbotForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    a__			:		Dict			  =    True
    a__			:		Union[str, Any]			  =    False
    a__			:		Union[str, Any]			  =    False



    def    _lowercase		(self       :       Optional[Any]					):
         UpperCAmelCase_			       =  TFBlenderbotModelTester(self					)
         UpperCAmelCase_			       =  ConfigTester(self  ,     config_class=__a					)



    def    _lowercase		(self       :       Any					):
         self.config_tester.run_common_tests()






    def    _lowercase		(self       :       Dict					):
         UpperCAmelCase_			       =  self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_decoder_model_past_large_inputs(*__a					)




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



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



    @cached_property
    def    _lowercase		(self       :       Union[str, Any]					):
         UpperCAmelCase_			       =  TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name					)
         return model






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









from __future__ import annotations

from math import ceil, floor, sqrt





def 		a__      (		_UpperCamelCase      :			int = 2_00_00_00				):
   __lowerCamelCase    				=     [0]
   __lowerCamelCase    				=     42

   for idx in range(1			,ceil(sqrt(target * 2				) * 1.1				)				):
      triangle_numbers.append(triangle_numbers[-1] + idx				)

   # we want this to be as close as possible to target
   __lowerCamelCase    				=     0
   # the area corresponding to the grid that gives the product closest to target
   __lowerCamelCase    				=     0
   # an estimate of b, using the quadratic formula
   __lowerCamelCase    				=     42
   # the largest integer less than b_estimate
   __lowerCamelCase    				=     42
   # the largest integer less than b_estimate
   __lowerCamelCase    				=     42
   # the triangle number corresponding to b_floor
   __lowerCamelCase    				=     42
   # the triangle number corresponding to b_ceil
   __lowerCamelCase    				=     42

   for idx_a, triangle_a in enumerate(triangle_numbers[1:]			,1				):
      __lowerCamelCase    				=     (-1 + sqrt(1 + 8 * target / triangle_a				)) / 2
      __lowerCamelCase    				=     floor(_UpperCamelCase				)
      __lowerCamelCase    				=     ceil(_UpperCamelCase				)
      __lowerCamelCase    				=     triangle_numbers[b_floor]
      __lowerCamelCase    				=     triangle_numbers[b_ceil]

      if abs(target - triangle_b_first_guess * triangle_a				) < abs(
          target - best_product				):
         __lowerCamelCase    				=     triangle_b_first_guess * triangle_a
         __lowerCamelCase    				=     idx_a * b_floor

      if abs(target - triangle_b_second_guess * triangle_a				) < abs(
          target - best_product				):
         __lowerCamelCase    				=     triangle_b_second_guess * triangle_a
         __lowerCamelCase    				=     idx_a * b_ceil

   return area


if __name__ == "__main__":
  print(f"{solution() = }")






