


'''simple docstring'''

from math import ceil

def _SCREAMING_SNAKE_CASE (A = 1_001			)			->			int:






       """simple docstring"""







       lowercase__      =					1

       for i in range(1    ,					int(ceil(n / 2.0			)			)			):
              lowercase__      =					2 * i + 1
              lowercase__      =					2 * i
              lowercase__      =					total + 4 * odd**2 - 6 * even

       return total


if __name__ == "__main__":
 import sys

 if len(sys.argv) == 1:
  print(solution())
 else:
  try:
   lowerCamelCase				:  int    		=      int(sys.argv[1])
   print(solution(n))
  except ValueError:
   print('Invalid entry - please enter a number')







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   UpperCamelCase			:
    __UpperCamelCase    =BlenderbotSmallConfig
    __UpperCamelCase    ={}
    __UpperCamelCase    ="gelu"

    def __init__(      self			:   Optional[int]				,     snake_case__			:   Dict				,     snake_case__			:   Optional[int]=1_3				,     snake_case__			:   Optional[int]=7				,     snake_case__			:   Optional[Any]=True				,     snake_case__			:   List[str]=False				,     snake_case__			:   int=9_9				,     snake_case__			:   Any=3_2				,     snake_case__			:   Tuple=2				,     snake_case__			:   Optional[int]=4				,     snake_case__			:   int=3_7				,     snake_case__			:   Optional[Any]=0.1				,     snake_case__			:   int=0.1				,     snake_case__			:   Any=2_0				,     snake_case__			:   List[Any]=2				,     snake_case__			:   int=1				,     snake_case__			:   int=0				,     ):

     """simple docstring"""






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

     SCREAMING_SNAKE_CASE		    =      hidden_dropout_prob
     SCREAMING_SNAKE_CASE		    =      attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE		    =      max_position_embeddings
     SCREAMING_SNAKE_CASE		    =      eos_token_id
     SCREAMING_SNAKE_CASE		    =      pad_token_id
     SCREAMING_SNAKE_CASE		    =      bos_token_id

    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






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

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

     SCREAMING_SNAKE_CASE		    =      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				,     )
     SCREAMING_SNAKE_CASE		    =      prepare_blenderbot_small_inputs_dict(snake_case__				,     snake_case__				,     snake_case__     )
     return config, inputs_dict




    def UpperCamelCase      (      self			:   Dict				,     snake_case__			:   Tuple				,     snake_case__			:   Any     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      TFBlenderbotSmallModel(config=snake_case__     ).get_decoder()
     SCREAMING_SNAKE_CASE		    =      inputs_dict['input_ids']

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

     # first forward pass
     SCREAMING_SNAKE_CASE		    =      model(snake_case__				,     attention_mask=snake_case__				,     head_mask=snake_case__				,     use_cache=snake_case__     )

     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      outputs.to_tuple()

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

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

     SCREAMING_SNAKE_CASE		    =      model(snake_case__				,     attention_mask=snake_case__     )[0]
     SCREAMING_SNAKE_CASE		    =      model(snake_case__				,     attention_mask=snake_case__				,     past_key_values=snake_case__     )[0]

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

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

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


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Dict		,				_UpperCamelCase		:	Optional[int]		,				_UpperCamelCase		:	str		,				_UpperCamelCase		:	Dict=None		,				_UpperCamelCase		:	Optional[int]=None		,				_UpperCamelCase		:	Any=None		,				_UpperCamelCase		:	List[str]=None		,				_UpperCamelCase		:	Optional[Any]=None		,				)		->				str:
 '''simple docstring'''


 if attention_mask is None:
  SCREAMING_SNAKE_CASE		    =      tf.cast(tf.math.not_equal(_UpperCamelCase		,				config.pad_token_id						)		,				tf.inta						)
 if decoder_attention_mask is None:
  SCREAMING_SNAKE_CASE		    =      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:
  SCREAMING_SNAKE_CASE		    =      tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
 if decoder_head_mask is None:
  SCREAMING_SNAKE_CASE		    =      tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
 if cross_attn_head_mask is None:
  SCREAMING_SNAKE_CASE		    =      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			(  SCREAMING_SNAKE_CASE						,      SCREAMING_SNAKE_CASE						,      unittest.TestCase ):
    __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 UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      TFBlenderbotSmallModelTester(self     )
     SCREAMING_SNAKE_CASE		    =      ConfigTester(self				,     config_class=snake_case__     )

    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     self.config_tester.run_common_tests()




    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_decoder_model_past_large_inputs(*snake_case__     )




@require_tokenizers
@require_tf
class   UpperCamelCase			(  unittest.TestCase ):
    __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 UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






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

    @cached_property
    def UpperCamelCase      (      self			:   Optional[int]     ):

     """simple docstring"""






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




    @slow
    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






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

