





from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


__lowerCamelCase :  List[str]										=  logging.get_logger(__name__)

__lowerCamelCase :  Tuple										=  {
    """deepmind/language-perceiver""": """https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json""",
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:int							=     'perceiver'





      def __init__(				self ,							A_=256 ,							A_=1280 ,							A_=768 ,							A_=1 ,							A_=26 ,							A_=8 ,							A_=8 ,							A_=None ,							A_=None ,							A_="kv" ,							A_=1 ,							A_=1 ,							A_="gelu" ,							A_=0.1 ,							A_=0.02 ,							A_=1e-12 ,							A_=True ,							A_=262 ,							A_=2048 ,							A_=56 ,							A_=[368, 496] ,							A_=16 ,							A_=1920 ,							A_=16 ,							A_=[1, 16, 224, 224] ,							**A_ ,							):
       '''simple docstring'''




       super().__init__(**A_     )

       UpperCamelCase			:      List[Any] 			=     num_latents
       UpperCamelCase			:      int 			=     d_latents
       UpperCamelCase			:      List[Any] 			=     d_model
       UpperCamelCase			:      Union[str, Any] 			=     num_blocks
       UpperCamelCase			:      List[Any] 			=     num_self_attends_per_block
       UpperCamelCase			:      List[str] 			=     num_self_attention_heads
       UpperCamelCase			:      str 			=     num_cross_attention_heads
       UpperCamelCase			:      Optional[Any] 			=     qk_channels
       UpperCamelCase			:      List[Any] 			=     v_channels
       UpperCamelCase			:      Tuple 			=     cross_attention_shape_for_attention
       UpperCamelCase			:      Optional[int] 			=     self_attention_widening_factor
       UpperCamelCase			:      List[str] 			=     cross_attention_widening_factor
       UpperCamelCase			:      List[str] 			=     hidden_act
       UpperCamelCase			:      List[str] 			=     attention_probs_dropout_prob
       UpperCamelCase			:      Optional[int] 			=     initializer_range
       UpperCamelCase			:      Any 			=     layer_norm_eps
       UpperCamelCase			:      Optional[Any] 			=     use_query_residual
       # masked language modeling attributes
       UpperCamelCase			:      Optional[int] 			=     vocab_size
       UpperCamelCase			:      Optional[int] 			=     max_position_embeddings
       # image classification attributes
       UpperCamelCase			:      Dict 			=     image_size
       # flow attributes
       UpperCamelCase			:      str 			=     train_size
       # multimodal autoencoding attributes
       UpperCamelCase			:      Optional[int] 			=     num_frames
       UpperCamelCase			:      List[Any] 			=     audio_samples_per_frame
       UpperCamelCase			:      int 			=     samples_per_patch
       UpperCamelCase			:      str 			=     output_shape



class       A__     ( __snake_case    ):





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       if self.task == "multiple-choice":
        UpperCamelCase			:      Any 			=     {0: "batch", 1: "choice", 2: "sequence"}
       else:
        UpperCamelCase			:      Any 			=     {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("inputs", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]     )





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return 1e-4





      def  __UpperCamelCase(				self ,							A_ ,							A_ = -1 ,							A_ = -1 ,							A_ = -1 ,							A_ = False ,							A_ = None ,							A_ = 3 ,							A_ = 40 ,							A_ = 40 ,							):
       '''simple docstring'''




       if isinstance(A_ ,							A_     ):
        # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
        UpperCamelCase			:      Union[str, Any] 			=     compute_effective_axis_dimension(
            A_ ,							fixed_dimension=OnnxConfig.default_fixed_batch ,							num_token_to_add=0     )
        # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
        UpperCamelCase			:      Optional[int] 			=     preprocessor.num_special_tokens_to_add(A_     )
        UpperCamelCase			:      List[Any] 			=     compute_effective_axis_dimension(
            A_ ,							fixed_dimension=OnnxConfig.default_fixed_sequence ,							num_token_to_add=A_     )
        # Generate dummy inputs according to compute batch and sequence
        UpperCamelCase			:      str 			=     [" ".join(["a"]     ) * seq_length] * batch_size
        UpperCamelCase			:      Tuple 			=     dict(preprocessor(A_ ,							return_tensors=A_     )     )
        UpperCamelCase			:      int 			=     inputs.pop("input_ids"     )
        return inputs
       elif isinstance(A_ ,							A_     ) and preprocessor.model_input_names[0] == "pixel_values":
        # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
        UpperCamelCase			:      List[str] 			=     compute_effective_axis_dimension(A_ ,							fixed_dimension=OnnxConfig.default_fixed_batch     )
        UpperCamelCase			:      List[Any] 			=     self._generate_dummy_images(A_ ,							A_ ,							A_ ,							A_     )
        UpperCamelCase			:      Tuple 			=     dict(preprocessor(images=A_ ,							return_tensors=A_     )     )
        UpperCamelCase			:      Any 			=     inputs.pop("pixel_values"     )
        return inputs
       else:
        raise ValueError(
            "Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."     )







from __future__ import annotations



def 	A_       (  _lowerCAmelCase  )		->				list[int]:
 UpperCamelCase			:      Optional[Any] 			=     [True] * limit
 UpperCamelCase			:      Optional[Any] 			=     False
 UpperCamelCase			:      List[str] 			=     False
 UpperCamelCase			:      Tuple 			=     True

 for i in range(3							,     int(limit**0.5 + 1  )							,     2  ):
  UpperCamelCase			:      Optional[Any] 			=     i * 2
  while index < limit:
   UpperCamelCase			:      int 			=     False
   UpperCamelCase			:      Optional[int] 			=     index + i

 UpperCamelCase			:      Any 			=     [2]

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

 return primes



def 	A_       (  _lowerCAmelCase = 100_0000  )		->				int:
 UpperCamelCase			:      Union[str, Any] 			=     prime_sieve(_lowerCAmelCase  )
 UpperCamelCase			:      List[str] 			=     0
 UpperCamelCase			:      Union[str, Any] 			=     0

 for i in range(len(_lowerCAmelCase  )  ):
  for j in range(i + length							,     len(_lowerCAmelCase  )  ):
   UpperCamelCase			:      Dict 			=     sum(primes[i:j]  )
   if sol >= ceiling:
    break

   if sol in primes:
    UpperCamelCase			:      int 			=     j - i
    UpperCamelCase			:      Dict 			=     sol

 return largest


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