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_    :		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__							(       __snake_case		):
	A__    :       Dict        =      'perceiver'



	def __init__(						self				,						UpperCAmelCase=2_5_6				,						UpperCAmelCase=1_2_8_0				,						UpperCAmelCase=7_6_8				,						UpperCAmelCase=1				,						UpperCAmelCase=2_6				,						UpperCAmelCase=8				,						UpperCAmelCase=8				,						UpperCAmelCase=None				,						UpperCAmelCase=None				,						UpperCAmelCase="kv"				,						UpperCAmelCase=1				,						UpperCAmelCase=1				,						UpperCAmelCase="gelu"				,						UpperCAmelCase=0.1				,						UpperCAmelCase=0.02				,						UpperCAmelCase=1e-12				,						UpperCAmelCase=True				,						UpperCAmelCase=2_6_2				,						UpperCAmelCase=2_0_4_8				,						UpperCAmelCase=5_6				,						UpperCAmelCase=[3_6_8, 4_9_6]				,						UpperCAmelCase=1_6				,						UpperCAmelCase=1_9_2_0				,						UpperCAmelCase=1_6				,						UpperCAmelCase=[1, 1_6, 2_2_4, 2_2_4]				,						**UpperCAmelCase				,						)			->   Optional[int]:
						super().__init__(**UpperCAmelCase   )

						__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 						a__							(       __snake_case		):



	@property
	def __SCREAMING_SNAKE_CASE							(						self   )			->   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 __SCREAMING_SNAKE_CASE							(						self   )			->   float:
						return 1e-4







	def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = -1				,						UpperCAmelCase = -1				,						UpperCAmelCase = -1				,						UpperCAmelCase = False				,						UpperCAmelCase = None				,						UpperCAmelCase = 3				,						UpperCAmelCase = 4_0				,						UpperCAmelCase = 4_0				,						)			->   Mapping[str, Any]:
						# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

						if isinstance(UpperCAmelCase				,						UpperCAmelCase   ):
											# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
											__a		 =       compute_effective_axis_dimension(
											    UpperCAmelCase				,						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(UpperCAmelCase   )
											__a		 =       compute_effective_axis_dimension(
											    UpperCAmelCase				,						fixed_dimension=OnnxConfig.default_fixed_sequence				,						num_token_to_add=UpperCAmelCase   )
											# Generate dummy inputs according to compute batch and sequence
											__a		 =       [' '.join(['a']   ) * seq_length] * batch_size
											__a		 =       dict(preprocessor(UpperCAmelCase				,						return_tensors=UpperCAmelCase   )   )
											__a		 =       inputs.pop('input_ids'   )
											return inputs
						elif isinstance(UpperCAmelCase				,						UpperCAmelCase   ) 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(UpperCAmelCase				,						fixed_dimension=OnnxConfig.default_fixed_batch   )
											__a		 =       self._generate_dummy_images(UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )
											__a		 =       dict(preprocessor(images=UpperCAmelCase				,						return_tensors=UpperCAmelCase   )   )
											__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.'   )





def 							lowerCAmelCase(     __lowerCamelCase						):
					if not all(char in '01' for char in bin_string						):
										raise ValueError('Non-binary value was passed to the function'						)
					if not bin_string:
										raise ValueError('Empty string was passed to the function'						)
					__a		 =       ''
					while len(__lowerCamelCase						) % 3 != 0:
										__a		 =       '0' + bin_string
					__a		 =       [
					    bin_string[index : index + 3]
					    for index in range(len(__lowerCamelCase						)						)
					    if index % 3 == 0
					]
					for bin_group in bin_string_in_3_list:
										__a		 =       0
										for index, val in enumerate(__lowerCamelCase						):
															oct_val += int(2 ** (2 - index) * int(__lowerCamelCase						)						)
										oct_string += str(__lowerCamelCase						)
					return oct_string


if __name__ == "__main__":
	from doctest import testmod

	testmod()




