"""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


_a  :  Tuple=				logging.get_logger(__name__)

_a  :  Dict=				{
    "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    (					lowercase		):
				UpperCAmelCase       :   Optional[Any]					=	"""perceiver"""



				def __init__(self       :   Any			,						_A       :   List[str]=2_56			,						_A       :   int=12_80			,						_A       :   Union[str, Any]=7_68			,						_A       :   Any=1			,						_A       :   int=26			,						_A       :   List[Any]=8			,						_A       :   Any=8			,						_A       :   str=None			,						_A       :   Optional[int]=None			,						_A       :   List[str]="kv"			,						_A       :   str=1			,						_A       :   Optional[Any]=1			,						_A       :   List[str]="gelu"			,						_A       :   str=0.1			,						_A       :   Union[str, Any]=0.02			,						_A       :   Tuple=1E-12			,						_A       :   List[str]=True			,						_A       :   int=2_62			,						_A       :   List[Any]=20_48			,						_A       :   str=56			,						_A       :   Optional[int]=[3_68, 4_96]			,						_A       :   Union[str, Any]=16			,						_A       :   Any=19_20			,						_A       :   str=16			,						_A       :   List[str]=[1, 16, 2_24, 2_24]			,						**_A       :   Optional[int]			,						)					->			List[str]:
					super().__init__(**_A)

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







class 	UpperCamelCase    (					lowercase		):



				@property
				def       _lowercase  (self       :   int)					->			Mapping[str, Mapping[int, str]]:
					if self.task == "multiple-choice":
						__snake_case    :							List[Any]   	=				{0: 'batch', 1: 'choice', 2: 'sequence'}
					else:
						__snake_case    :							str   	=				{0: 'batch', 1: 'sequence'}
					return OrderedDict(
					    [
					        ('inputs', dynamic_axis),
					        ('attention_mask', dynamic_axis),
					    ])



				@property
				def       _lowercase  (self       :   Optional[int])					->			float:
					return 1E-4






				def       _lowercase  (self       :   Optional[int]			,						_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
						__snake_case    :							Dict   	=				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
						__snake_case    :							Any   	=				preprocessor.num_special_tokens_to_add(_A)
						__snake_case    :							Dict   	=				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
						__snake_case    :							int   	=				[' '.join(['a']) * seq_length] * batch_size
						__snake_case    :							Any   	=				dict(preprocessor(_A			,						return_tensors=_A))
						__snake_case    :							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
						__snake_case    :							List[Any]   	=				compute_effective_axis_dimension(_A			,						fixed_dimension=OnnxConfig.default_fixed_batch)
						__snake_case    :							Optional[int]   	=				self._generate_dummy_images(_A			,						_A			,						_A			,						_A)
						__snake_case    :							Optional[Any]   	=				dict(preprocessor(images=_A			,						return_tensors=_A))
						__snake_case    :							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"""







import numpy as np
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       np.array						)							->    np.array:

	'''simple docstring'''





	return 1 / (1 + np.exp(-vector						))


if __name__ == "__main__":
	import doctest

	doctest.testmod()





