


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

__lowerCamelCase      :Optional[int]	    =		{
    '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__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					List[str]													='''perceiver'''

     def __init__(	self:						Dict     ,		__a:						Any=256     ,		__a:						int=1_280     ,		__a:						int=768     ,		__a:						Any=1     ,		__a:						Optional[Any]=26     ,		__a:						List[Any]=8     ,		__a:						List[str]=8     ,		__a:						Union[str, Any]=None     ,		__a:						str=None     ,		__a:						str="kv"     ,		__a:						List[str]=1     ,		__a:						List[Any]=1     ,		__a:						List[str]="gelu"     ,		__a:						Optional[int]=0.1     ,		__a:						Optional[int]=0.02     ,		__a:						int=1e-1_2     ,		__a:						Any=True     ,		__a:						Any=262     ,		__a:						Any=2_048     ,		__a:						List[str]=56     ,		__a:						Any=[368, 496]     ,		__a:						List[Any]=16     ,		__a:						Any=1_920     ,		__a:						int=16     ,		__a:						List[Any]=[1, 16, 224, 224]     ,		**__a:						Union[str, Any]     ,		)->    List[str]:
           super().__init__(**__a	)

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


class 			A__	(						__lowercase):


     """simple docstring"""

     @property
     def 		a__						(	self:						int	)->    Mapping[str, Mapping[int, str]]:
           if self.task == "multiple-choice":
                 lowerCamelCase					:					Dict        =	{0: """batch""", 1: """choice""", 2: """sequence"""}
           else:
                 lowerCamelCase					:					Tuple        =	{0: """batch""", 1: """sequence"""}
           return OrderedDict(
               [
                   ("""inputs""", dynamic_axis),
                   ("""attention_mask""", dynamic_axis),
               ]	)

     @property
     def 		a__						(	self:						List[str]	)->    float:
           return 1e-4







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

           if isinstance(__a     ,		__a	):
                 # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
                 lowerCamelCase					:					int        =	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
                 lowerCamelCase					:					List[str]        =	preprocessor.num_special_tokens_to_add(__a	)
                 lowerCamelCase					:					Optional[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
                 lowerCamelCase					:					Optional[Any]        =	[""" """.join(["""a"""]	) * seq_length] * batch_size
                 lowerCamelCase					:					str        =	dict(preprocessor(__a     ,		return_tensors=__a	)	)
                 lowerCamelCase					:					List[str]        =	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
                 lowerCamelCase					:					Optional[Any]        =	compute_effective_axis_dimension(__a     ,		fixed_dimension=OnnxConfig.default_fixed_batch	)
                 lowerCamelCase					:					Dict        =	self._generate_dummy_images(__a     ,		__a     ,		__a     ,		__a	)
                 lowerCamelCase					:					str        =	dict(preprocessor(images=__a     ,		return_tensors=__a	)	)
                 lowerCamelCase					:					str        =	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

__lowerCamelCase      :int	    =		10




def   snake_case		(     UpperCamelCase__       :						list[int]    )   ->      list[int]:
      lowerCamelCase					:					int        =	1
      lowerCamelCase					:					Union[str, Any]        =	max(UpperCamelCase__    )
      while placement <= max_digit:
            # declare and initialize empty buckets
            lowerCamelCase					:					list[list]        =	[[] for _ in range(UpperCamelCase__    )]
            # split list_of_ints between the buckets
            for i in list_of_ints:
                  lowerCamelCase					:					Any        =	int((i / placement) % RADIX    )
                  buckets[tmp].append(UpperCamelCase__    )
            # put each buckets' contents into list_of_ints
            lowerCamelCase					:					Dict        =	0
            for b in range(UpperCamelCase__    ):
                  for i in buckets[b]:
                        lowerCamelCase					:					List[str]        =	i
                        a += 1
        # move to next
            placement *= RADIX
      return list_of_ints


if __name__ == "__main__":
    import doctest

    doctest.testmod()




