





'''simple docstring'''

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				:      Optional[Any]						      =logging.get_logger(__name__)

__lowerCAmelCase				:      Any						      ={
    "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   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							"""perceiver"""
 def __init__(							self :List[str]		,			lowercase_ :Tuple=2_56		,			lowercase_ :List[Any]=12_80		,			lowercase_ :List[str]=7_68		,			lowercase_ :Union[str, Any]=1		,			lowercase_ :Any=26		,			lowercase_ :Dict=8		,			lowercase_ :Dict=8		,			lowercase_ :str=None		,			lowercase_ :Optional[int]=None		,			lowercase_ :Union[str, Any]="kv"		,			lowercase_ :Tuple=1		,			lowercase_ :Optional[int]=1		,			lowercase_ :Tuple="gelu"		,			lowercase_ :List[str]=0.1		,			lowercase_ :Any=0.0_2		,			lowercase_ :List[str]=1E-12		,			lowercase_ :Optional[int]=True		,			lowercase_ :int=2_62		,			lowercase_ :str=20_48		,			lowercase_ :Tuple=56		,			lowercase_ :Union[str, Any]=[3_68, 4_96]		,			lowercase_ :Dict=16		,			lowercase_ :str=19_20		,			lowercase_ :List[str]=16		,			lowercase_ :List[str]=[1, 16, 2_24, 2_24]		,			**lowercase_ :List[str]		,			)->				str:
     super().__init__(**lowercase_				)

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






class   UpperCAmelCase      (			UpperCamelCase__							):
 @property
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				Mapping[str, Mapping[int, str]]:
     if self.task == "multiple-choice":
         A__							=     {0: "batch", 1: "choice", 2: "sequence"}
     else:
         A__							=     {0: "batch", 1: "sequence"}
     return OrderedDict(
         [
             ("inputs", dynamic_axis),
             ("attention_mask", dynamic_axis),
         ]				)
 @property
 def 	UpperCAmelCase_				(							self :Tuple				)->				float:
     return 1E-4



 def 	UpperCAmelCase_				(							self :Optional[int]		,			lowercase_ :Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]		,			lowercase_ :int = -1		,			lowercase_ :int = -1		,			lowercase_ :int = -1		,			lowercase_ :bool = False		,			lowercase_ :Optional[TensorType] = None		,			lowercase_ :int = 3		,			lowercase_ :int = 40		,			lowercase_ :int = 40		,			)->				Mapping[str, Any]:
     # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

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










'''simple docstring'''

from __future__ import annotations

import math





def 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   int		,			_lowerCamelCase   :   bool		,			_lowerCamelCase   :   list[int]		,			_lowerCamelCase   :   float			):

    if depth < 0:
        raise ValueError("Depth cannot be less than 0"			)

    if len(_lowerCamelCase			) == 0:
        raise ValueError("Scores cannot be empty"			)

    if depth == height:
        return scores[node_index]

    if is_max:
        return max(
            minimax(depth + 1		,			node_index * 2		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)		,			minimax(depth + 1		,			node_index * 2 + 1		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)		,			)

    return min(
        minimax(depth + 1		,			node_index * 2		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)		,			minimax(depth + 1		,			node_index * 2 + 1		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)		,			)





def 		UpperCamelCase							(				):
    A__							=     [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
    A__							=     math.log(len(_lowerCamelCase			)		,			2			)
    print("Optimal value : "		,			end=""			)
    print(minimax(0		,			0		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()



