



import unittest

from transformers import SpeechaTextaConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers.models.speech_to_text_a.modeling_speech_to_text_a import (
          SpeechaTextaDecoder,
          SpeechaTextaForCausalLM,
      )







@require_torch
class       _snake_case				:






   '''simple docstring'''






   def __init__(    self:     Optional[Any]  ,lowerCamelCase_:     Union[str, Any]  ,lowerCamelCase_:     Union[str, Any]=99  ,lowerCamelCase_:     int=13  ,lowerCamelCase_:     List[Any]=16  ,lowerCamelCase_:     int=7  ,lowerCamelCase_:     Optional[Any]=True  ,lowerCamelCase_:     Optional[Any]=True  ,lowerCamelCase_:     Any=True  ,lowerCamelCase_:     str=False  ,lowerCamelCase_:     str=True  ,lowerCamelCase_:     Union[str, Any]=2  ,lowerCamelCase_:     Optional[Any]=32  ,lowerCamelCase_:     Dict=4  ,lowerCamelCase_:     Optional[int]=4  ,lowerCamelCase_:     Optional[int]=30  ,lowerCamelCase_:     Tuple=0  ,lowerCamelCase_:     List[Any]=1  ,lowerCamelCase_:     List[Any]=2  ,lowerCamelCase_:     Optional[int]=None  ,)	->      List[str]:
       UpperCAmelCase_     :       str  		=					parent
       UpperCAmelCase_     :       int  		=					batch_size
       UpperCAmelCase_     :       Any  		=					decoder_seq_length
       # For common tests
       UpperCAmelCase_     :       Tuple  		=					self.decoder_seq_length
       UpperCAmelCase_     :       List[Any]  		=					is_training
       UpperCAmelCase_     :       Any  		=					use_attention_mask
       UpperCAmelCase_     :       List[Any]  		=					use_labels

       UpperCAmelCase_     :       Tuple  		=					vocab_size
       UpperCAmelCase_     :       Dict  		=					d_model
       UpperCAmelCase_     :       List[Any]  		=					d_model
       UpperCAmelCase_     :       Optional[Any]  		=					decoder_layers
       UpperCAmelCase_     :       List[str]  		=					decoder_layers
       UpperCAmelCase_     :       Optional[int]  		=					decoder_ffn_dim
       UpperCAmelCase_     :       Any  		=					decoder_attention_heads
       UpperCAmelCase_     :       Tuple  		=					decoder_attention_heads
       UpperCAmelCase_     :       List[str]  		=					eos_token_id
       UpperCAmelCase_     :       Optional[Any]  		=					bos_token_id
       UpperCAmelCase_     :       Optional[Any]  		=					pad_token_id
       UpperCAmelCase_     :       int  		=					decoder_start_token_id
       UpperCAmelCase_     :       Optional[int]  		=					use_cache
       UpperCAmelCase_     :       List[str]  		=					max_position_embeddings

       UpperCAmelCase_     :       Tuple  		=					None
       UpperCAmelCase_     :       int  		=					decoder_seq_length
       UpperCAmelCase_     :       List[Any]  		=					2
       UpperCAmelCase_     :       str  		=					1


   def       A__    (    self:     Optional[Any]							)	->      Optional[int]:
       UpperCAmelCase_     :       Optional[int]  		=					ids_tensor([self.batch_size, self.decoder_seq_length]  ,self.vocab_size							)

       UpperCAmelCase_     :       List[str]  		=					None
       if self.use_attention_mask:
           UpperCAmelCase_     :       Optional[Any]  		=					ids_tensor([self.batch_size, self.decoder_seq_length]  ,vocab_size=2							)

       UpperCAmelCase_     :       Union[str, Any]  		=					None
       if self.use_labels:
           UpperCAmelCase_     :       List[str]  		=					ids_tensor([self.batch_size, self.decoder_seq_length]  ,self.vocab_size							)

       UpperCAmelCase_     :       Optional[int]  		=					SpeechaTextaConfig(
           vocab_size=self.vocab_size  ,d_model=self.d_model  ,decoder_layers=self.decoder_layers  ,decoder_ffn_dim=self.decoder_ffn_dim  ,decoder_attention_heads=self.decoder_attention_heads  ,eos_token_id=self.eos_token_id  ,bos_token_id=self.bos_token_id  ,use_cache=self.use_cache  ,pad_token_id=self.pad_token_id  ,decoder_start_token_id=self.decoder_start_token_id  ,max_position_embeddings=self.max_position_embeddings  ,)

       return (
           config,
           input_ids,
           attention_mask,
           lm_labels,
       )


   def       A__    (    self:     Optional[Any]  ,lowerCamelCase_:     Dict  ,lowerCamelCase_:     str  ,lowerCamelCase_:     str  ,lowerCamelCase_:     List[Any]  ,)	->      List[str]:
       UpperCAmelCase_     :       Dict  		=					True
       UpperCAmelCase_     :       Dict  		=					SpeechaTextaDecoder(config=lowerCamelCase_							).to(lowerCamelCase_							).eval()
       UpperCAmelCase_     :       Any  		=					input_ids[:2]

       input_ids[input_ids == 0] += 1
       # first forward pass
       UpperCAmelCase_     :       Optional[Any]  		=					model(lowerCamelCase_  ,use_cache=lowerCamelCase_							)
       UpperCAmelCase_     :       List[str]  		=					model(lowerCamelCase_							)
       UpperCAmelCase_     :       int  		=					model(lowerCamelCase_  ,use_cache=lowerCamelCase_							)

       self.parent.assertTrue(len(lowerCamelCase_							) == len(lowerCamelCase_							)							)
       self.parent.assertTrue(len(lowerCamelCase_							) == len(lowerCamelCase_							) + 1							)

       UpperCAmelCase_     :       Dict  		=					outputs["""past_key_values"""]

       # create hypothetical next token and extent to next_input_ids
       UpperCAmelCase_     :       Dict  		=					ids_tensor((2, 1)  ,config.vocab_size - 1							) + 1

       # append to next input_ids and
       UpperCAmelCase_     :       int  		=					torch.cat([input_ids, next_tokens]  ,dim=-1							)

       print(lowerCamelCase_							)

       UpperCAmelCase_     :       Dict  		=					model(lowerCamelCase_							)["""last_hidden_state"""]
       UpperCAmelCase_     :       int  		=					model(lowerCamelCase_  ,past_key_values=lowerCamelCase_							)["""last_hidden_state"""]

       # select random slice
       UpperCAmelCase_     :       List[str]  		=					ids_tensor((1,)  ,output_from_past.shape[-1]							).item()
       UpperCAmelCase_     :       Optional[Any]  		=					output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
       UpperCAmelCase_     :       Any  		=					output_from_past[:, 0, random_slice_idx].detach()

       # test that outputs are equal for slice
       assert torch.allclose(lowerCamelCase_  ,lowerCamelCase_  ,atol=1e-3							)






   def       A__    (    self:     Optional[Any]							)	->      List[str]:
       UpperCAmelCase_     :       Dict  		=					self.prepare_config_and_inputs()
       (
           (
           UpperCAmelCase_
       )		, (
           UpperCAmelCase_
       )		, (
           UpperCAmelCase_
       )		, (
           UpperCAmelCase_
       )		, 
       )     :       Dict  		=					config_and_inputs

       UpperCAmelCase_     :       Any  		=					{
           """input_ids""": input_ids,
           """attention_mask""": attention_mask,
       }
       return config, inputs_dict







@require_torch
class       _snake_case				(     __snake_case ,							__snake_case ,							__snake_case ,							unittest.TestCase ):






   '''simple docstring'''






   A__      : Optional[Any]       =						(SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
   A__      : List[Any]       =						(SpeechaTextaForCausalLM,) if is_torch_available() else ()
   A__      : Any       =						{"text-generation": SpeechaTextaForCausalLM} if is_torch_available() else {}
   A__      : Optional[int]       =						True
   A__      : Union[str, Any]       =						False


   def       A__    (    self:     Tuple  ,)	->      Any:
       UpperCAmelCase_     :       Any  		=					SpeechaTextaStandaloneDecoderModelTester(self  ,is_training=lowerCamelCase_							)
       UpperCAmelCase_     :       List[Any]  		=					ConfigTester(self  ,config_class=lowerCamelCase_							)


   def       A__    (    self:     Dict							)	->      int:
       pass


   def       A__    (    self:     str							)	->      int:
       pass


   def       A__    (    self:     Union[str, Any]							)	->      Optional[int]:
       pass


   def       A__    (    self:     List[str]							)	->      int:
       self.config_tester.run_common_tests()


   def       A__    (    self:     int							)	->      Dict:
       UpperCAmelCase_     :       List[str]  		=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_decoder_model_past(*lowerCamelCase_							)






   def       A__    (    self:     Optional[int]							)	->      Any:
       return


"""simple docstring"""






import math
def    UpperCamelCase						(					UpperCAmelCase     )						->bool:

   """simple docstring"""



   if 1 < number < 4:
      # 2 and 3 are primes
      return True
   elif number < 2 or number % 2 == 0 or number % 3 == 0:
      # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
      return False

   # All primes number are in format of 6k +/- 1
   for i in range(5 ,   int(math.sqrt(UpperCAmelCase     ) + 1     ) ,   6     ):
      if number % i == 0 or number % (i + 2) == 0:
         return False
   return True
def    UpperCamelCase						(					UpperCAmelCase = 600_851_475_143     )						->int:

   """simple docstring"""



   try:
      a_						   =     int(UpperCAmelCase     )
   except (TypeError, ValueError):
      raise TypeError("Parameter n must be int or castable to int."     )
   if n <= 0:
      raise ValueError("Parameter n must be greater than or equal to one."     )
   a_						   =     0
   if is_prime(UpperCAmelCase     ):
      return n
   while n % 2 == 0:
      n //= 2
   if is_prime(UpperCAmelCase     ):
      return n
   for i in range(3 ,   int(math.sqrt(UpperCAmelCase     )     ) + 1 ,   2     ):
      if n % i == 0:
         if is_prime(n // i     ):
            a_						   =     n // i
            break
         elif is_prime(UpperCAmelCase     ):
            a_						   =     i
   return max_number


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