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


UpperCamelCase__    							= logging.get_logger(__name__)

UpperCamelCase__    							= {
    "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							(    UpperCamelCase_					):
   __UpperCAmelCase			:			Tuple              =      """perceiver"""


   def __init__(self					:						int	,  __UpperCAmelCase					:						Any=2_5_6	,  __UpperCAmelCase					:						Dict=1_2_8_0	,  __UpperCAmelCase					:						List[str]=7_6_8	,  __UpperCAmelCase					:						Tuple=1	,  __UpperCAmelCase					:						Any=2_6	,  __UpperCAmelCase					:						Optional[int]=8	,  __UpperCAmelCase					:						str=8	,  __UpperCAmelCase					:						Optional[Any]=None	,  __UpperCAmelCase					:						Union[str, Any]=None	,  __UpperCAmelCase					:						int="kv"	,  __UpperCAmelCase					:						Any=1	,  __UpperCAmelCase					:						Optional[Any]=1	,  __UpperCAmelCase					:						Any="gelu"	,  __UpperCAmelCase					:						Optional[int]=0.1	,  __UpperCAmelCase					:						Union[str, Any]=0.02	,  __UpperCAmelCase					:						Any=1E-12	,  __UpperCAmelCase					:						List[str]=True	,  __UpperCAmelCase					:						Dict=2_6_2	,  __UpperCAmelCase					:						List[str]=2_0_4_8	,  __UpperCAmelCase					:						int=5_6	,  __UpperCAmelCase					:						Any=[3_6_8, 4_9_6]	,  __UpperCAmelCase					:						Any=1_6	,  __UpperCAmelCase					:						List[str]=1_9_2_0	,  __UpperCAmelCase					:						Optional[Any]=1_6	,  __UpperCAmelCase					:						Any=[1, 1_6, 2_2_4, 2_2_4]	,  **__UpperCAmelCase					:						Dict	,  )						->    Union[str, Any]:
         """simple docstring"""



         super().__init__(**_a		)

         UpperCAmelCase__								=  num_latents
         UpperCAmelCase__								=  d_latents
         UpperCAmelCase__								=  d_model
         UpperCAmelCase__								=  num_blocks
         UpperCAmelCase__								=  num_self_attends_per_block
         UpperCAmelCase__								=  num_self_attention_heads
         UpperCAmelCase__								=  num_cross_attention_heads
         UpperCAmelCase__								=  qk_channels
         UpperCAmelCase__								=  v_channels
         UpperCAmelCase__								=  cross_attention_shape_for_attention
         UpperCAmelCase__								=  self_attention_widening_factor
         UpperCAmelCase__								=  cross_attention_widening_factor
         UpperCAmelCase__								=  hidden_act
         UpperCAmelCase__								=  attention_probs_dropout_prob
         UpperCAmelCase__								=  initializer_range
         UpperCAmelCase__								=  layer_norm_eps
         UpperCAmelCase__								=  use_query_residual
         # masked language modeling attributes
         UpperCAmelCase__								=  vocab_size
         UpperCAmelCase__								=  max_position_embeddings
         # image classification attributes
         UpperCAmelCase__								=  image_size
         # flow attributes
         UpperCAmelCase__								=  train_size
         # multimodal autoencoding attributes
         UpperCAmelCase__								=  num_frames
         UpperCAmelCase__								=  audio_samples_per_frame
         UpperCAmelCase__								=  samples_per_patch
         UpperCAmelCase__								=  output_shape





class 					A							(    UpperCamelCase_					):


   @property
   def       lowercase_				(self					:						Optional[Any]		)						->    Mapping[str, Mapping[int, str]]:
         """simple docstring"""



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


   @property
   def       lowercase_				(self					:						List[str]		)						->    float:
         """simple docstring"""



         return 1E-4





   def       lowercase_				(self					:						Any	,  __UpperCAmelCase					:						List[Any]	,  __UpperCAmelCase					:						Dict = -1	,  __UpperCAmelCase					:						Tuple = -1	,  __UpperCAmelCase					:						Tuple = -1	,  __UpperCAmelCase					:						Any = False	,  __UpperCAmelCase					:						List[Any] = None	,  __UpperCAmelCase					:						List[str] = 3	,  __UpperCAmelCase					:						Optional[int] = 4_0	,  __UpperCAmelCase					:						Union[str, Any] = 4_0	,  )						->    Mapping[str, Any]:
         """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__								=  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__								=  preprocessor.num_special_tokens_to_add(_a		)
               UpperCAmelCase__								=  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__								=  [""" """.join(["a"]		) * seq_length] * batch_size
               UpperCAmelCase__								=  dict(preprocessor(_a	,  return_tensors=_a		)		)
               UpperCAmelCase__								=  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__								=  compute_effective_axis_dimension(_a	,  fixed_dimension=OnnxConfig.default_fixed_batch		)
               UpperCAmelCase__								=  self._generate_dummy_images(_a	,  _a	,  _a	,  _a		)
               UpperCAmelCase__								=  dict(preprocessor(images=_a	,  return_tensors=_a		)		)
               UpperCAmelCase__								=  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        lowerCAmelCase_			(					__A							)					->    list[int]:







      '''simple docstring'''


      if len(__A							) == 0:
            return array

      UpperCAmelCase__		,				UpperCAmelCase__								=  min(__A							), max(__A							)

      # Compute the variables
      UpperCAmelCase__								=  _max - _min + 1
      UpperCAmelCase__		,				UpperCAmelCase__								=  [0] * holes_range, [0] * holes_range

      # Make the sorting.
      for i in array:
            UpperCAmelCase__								=  i - _min
            UpperCAmelCase__								=  i
            holes_repeat[index] += 1

      # Makes the array back by replacing the numbers.
      UpperCAmelCase__								=  0
      for i in range(__A							):
            while holes_repeat[i] > 0:
                  UpperCAmelCase__								=  holes[i]
                  index += 1
                  holes_repeat[i] -= 1

    # Returns the sorted array.
      return array


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      UpperCamelCase__    							= input('Enter numbers separated by comma:\n')
      UpperCamelCase__    							= [int(x) for x in user_input.split(',')]
      print(pigeon_sort(unsorted))



