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

_a  :  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 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   Tuple					=	"""perceiver"""



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

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







class 	UpperCamelCase    (					lowercase		):



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



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






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





