






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'''




      __snake_case       =  BlenderbotSmallConfig
      __snake_case       =  {}
      __snake_case       =  '''gelu'''







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




       """simple docstring"""


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

       a		  =      hidden_dropout_prob
       a		  =      attention_probs_dropout_prob
       a		  =      max_position_embeddings
       a		  =      eos_token_id
       a		  =      pad_token_id
       a		  =      bos_token_id







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




       """simple docstring"""


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

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

       a		  =      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 ,					)
       a		  =      prepare_blenderbot_small_inputs_dict(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase  )
       return config, inputs_dict







      def       __lowerCAmelCase					(      self      :						int ,					__UpperCAmelCase      :						Any ,					__UpperCAmelCase      :						Union[str, Any]  )    ->Dict:




       """simple docstring"""


       a		  =      TFBlenderbotSmallModel(config=__UpperCAmelCase  ).get_decoder()
       a		  =      inputs_dict['''input_ids''']

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

       # first forward pass
       a		  =      model(__UpperCAmelCase ,					attention_mask=__UpperCAmelCase ,					head_mask=__UpperCAmelCase ,					use_cache=__UpperCAmelCase  )

       a  ,     a		  =      outputs.to_tuple()

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

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

       a		  =      model(__UpperCAmelCase ,					attention_mask=__UpperCAmelCase  )[0]
       a		  =      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
       a		  =      int(ids_tensor((1,) ,					output_from_past.shape[-1]  )  )
       a		  =      output_from_no_past[:, -3:, random_slice_idx]
       a		  =      output_from_past[:, :, random_slice_idx]

       # test that outputs are equal for slice
       tf.debugging.assert_near(__UpperCAmelCase ,					__UpperCAmelCase ,					rtol=1e-3  )
def 						_a    (      a :int	,							a :List[str]	,							a :Optional[int]	,							a :str=None	,							a :Optional[Any]=None	,							a :List[str]=None	,							a :Tuple=None	,							a :List[Any]=None	,							)							->			Optional[int]:
 if attention_mask is None:
  a		  =      tf.cast(tf.math.not_equal(a	,							config.pad_token_id )	,							tf.inta )
 if decoder_attention_mask is None:
  a		  =      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:
  a		  =      tf.ones((config.encoder_layers, config.encoder_attention_heads) )
 if decoder_head_mask is None:
  a		  =      tf.ones((config.decoder_layers, config.decoder_attention_heads) )
 if cross_attn_head_mask is None:
  a		  =      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_					(	lowercase ,  lowercase ,  unittest.TestCase   ):






      '''simple docstring'''




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







      def       __lowerCAmelCase					(      self      :						str  )    ->List[Any]:




       """simple docstring"""


       a		  =      TFBlenderbotSmallModelTester(self  )
       a		  =      ConfigTester(self ,					config_class=__UpperCAmelCase  )







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




       """simple docstring"""


       self.config_tester.run_common_tests()







      def       __lowerCAmelCase					(      self      :						int  )    ->Optional[int]:




       """simple docstring"""


       a		  =      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'''




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







      @cached_property
      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->Optional[int]:




       """simple docstring"""


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







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




       """simple docstring"""


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







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




       """simple docstring"""


       a		  =      self.tokenizer(self.src_text ,					return_tensors='''tf'''  )
       a		  =      self.model.generate(
           model_inputs.input_ids ,					attention_mask=model_inputs.attention_mask ,					num_beams=2 ,					use_cache=__UpperCAmelCase ,					)
       a		  =      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.",
       )










import math
def 						_a    (      a :int )							->			list:
 a		  =      [True] * n
 a		  =      False
 a		  =      False
 a		  =      True

 for i in range(3	,							int(n**0.5 + 1 )	,							2 ):
  a		  =      i * 2
  while index < n:
   a		  =      False
   a		  =      index + i

 a		  =      [2]

 for i in range(3	,							a	,							2 ):
  if is_prime[i]:
   primes.append(a )

 return primes
def 						_a    (      a :int = 999_966_663_333 )							->			int:
 a		  =      math.floor(math.sqrt(a ) ) + 100
 a		  =      prime_sieve(a )

 a		  =      0
 a		  =      0
 a		  =      primes[prime_index]

 while (last_prime**2) <= limit:
  a		  =      primes[prime_index + 1]

  a		  =      last_prime**2
  a		  =      next_prime**2

  # Get numbers divisible by lps(current)
  a		  =      lower_bound + last_prime
  while upper_bound > current <= limit:
   matches_sum += current
   current += last_prime

  # Reset the upper_bound
  while (upper_bound - next_prime) > limit:
   upper_bound -= next_prime

  # Add the numbers divisible by ups(current)
  a		  =      upper_bound - next_prime
  while current > lower_bound:
   matches_sum += current
   current -= next_prime

  # Remove the numbers divisible by both ups and lps
  a		  =      0
  while upper_bound > current <= limit:
   if current <= lower_bound:
    # Increment the current number
    current += last_prime * next_prime
    continue

   if current > limit:
    break

   # Remove twice since it was added by both ups and lps
   matches_sum -= current * 2

   # Increment the current number
   current += last_prime * next_prime

  # Setup for next pair
  a		  =      next_prime
  prime_index += 1

 return matches_sum


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


