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


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

SCREAMING_SNAKE_CASE__		:					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       snake_case					(      _SCREAMING_SNAKE_CASE						):
		lowercase_			=     "perceiver"




		def __init__(      self     :	List[Any]     , a_     :	Tuple=256     , a_     :	List[str]=1280     , a_     :	List[Any]=768     , a_     :	List[str]=1     , a_     :	str=26     , a_     :	Tuple=8     , a_     :	Any=8     , a_     :	str=None     , a_     :	Dict=None     , a_     :	int="kv"     , a_     :	int=1     , a_     :	Dict=1     , a_     :	str="gelu"     , a_     :	Any=0.1     , a_     :	Any=0.02     , a_     :	Optional[int]=1e-1_2     , a_     :	List[str]=True     , a_     :	Dict=262     , a_     :	Any=2048     , a_     :	int=56     , a_     :	List[str]=[368, 496]     , a_     :	List[Any]=16     , a_     :	int=1920     , a_     :	List[Any]=16     , a_     :	Optional[int]=[1, 16, 224, 224]     , **a_     :	Union[str, Any]     , )->       Any:




							"""simple docstring"""
							super().__init__(**A_				)

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




class       snake_case					(      _SCREAMING_SNAKE_CASE						):




		@property
		def 	__lowercase(      self     :	List[str]				)->       Mapping[str, Mapping[int, str]]:




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




		@property
		def 	__lowercase(      self     :	Dict				)->       float:




							"""simple docstring"""
							return 1e-4




		def 	__lowercase(      self     :	Tuple     , a_     :	List[Any]     , a_     :	List[Any] = -1     , a_     :	Any = -1     , a_     :	Dict = -1     , a_     :	Optional[int] = False     , a_     :	Optional[Any] = None     , a_     :	Tuple = 3     , a_     :	Optional[Any] = 40     , a_     :	Any = 40     , )->       Mapping[str, Any]:




							"""simple docstring"""
							# 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
												SCREAMING_SNAKE_CASE__				:				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
												SCREAMING_SNAKE_CASE__				:				str			=		preprocessor.num_special_tokens_to_add(A_				)
												SCREAMING_SNAKE_CASE__				:				List[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
												SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[' '.join(['a']				) * seq_length] * batch_size
												SCREAMING_SNAKE_CASE__				:				Any			=		dict(preprocessor(A_     , return_tensors=A_				)				)
												SCREAMING_SNAKE_CASE__				:				List[Any]			=		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
												SCREAMING_SNAKE_CASE__				:				List[str]			=		compute_effective_axis_dimension(A_     , fixed_dimension=OnnxConfig.default_fixed_batch				)
												SCREAMING_SNAKE_CASE__				:				Any			=		self._generate_dummy_images(A_     , A_     , A_     , A_				)
												SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		dict(preprocessor(images=A_     , return_tensors=A_				)				)
												SCREAMING_SNAKE_CASE__				:				List[Any]			=		inputs.pop('pixel_values'				)
												return inputs
							else:
												raise ValueError(
												    'Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.'				)




def     _a				(      lowercase__  :			int     ,  lowercase__  :			int ):







     '''simple docstring'''


     return int((input_a, input_a).count(0 ) != 0 )







def     _a				(      ):







     '''simple docstring'''


     assert nand_gate(0     ,  0 ) == 1
     assert nand_gate(0     ,  1 ) == 1
     assert nand_gate(1     ,  0 ) == 1
     assert nand_gate(1     ,  1 ) == 0


if __name__ == "__main__":
      print(nand_gate(0, 0))
      print(nand_gate(0, 1))
      print(nand_gate(1, 0))
      print(nand_gate(1, 1))



