'''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


UpperCamelCase_      	=	logging.get_logger(__name__)

UpperCamelCase_      	=	{
    '''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 _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):
						A_			:							Optional[Any]     		= 'perceiver'




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

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




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):




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




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




						def      __lowerCamelCase       (    self :       Dict							,							UpperCamelCase_ :       Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]							,							UpperCamelCase_ :       int = -1							,							UpperCamelCase_ :       int = -1							,							UpperCamelCase_ :       int = -1							,							UpperCamelCase_ :       bool = False							,							UpperCamelCase_ :       Optional[TensorType] = None							,							UpperCamelCase_ :       int = 3							,							UpperCamelCase_ :       int = 40							,							UpperCamelCase_ :       int = 40							,							)     ->				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
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = 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
																				SCREAMING_SNAKE_CASE__     :List[str]						    = preprocessor.num_special_tokens_to_add(UpperCamelCase_      )
																				SCREAMING_SNAKE_CASE__     :Tuple						    = 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
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = [' '.join(['a']      ) * seq_length] * batch_size
																				SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = dict(preprocessor(UpperCamelCase_							,							return_tensors=UpperCamelCase_      )      )
																				SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 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
																				SCREAMING_SNAKE_CASE__     :str						    = compute_effective_axis_dimension(UpperCamelCase_							,							fixed_dimension=OnnxConfig.default_fixed_batch      )
																				SCREAMING_SNAKE_CASE__     :List[str]						    = self._generate_dummy_images(UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_      )
																				SCREAMING_SNAKE_CASE__     :str						    = dict(preprocessor(images=UpperCamelCase_							,							return_tensors=UpperCamelCase_      )      )
																				SCREAMING_SNAKE_CASE__     :Tuple						    = 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'''







def    lowerCamelCase       (			UpperCAmelCase__				:  int		)			->	int:


							'''simple docstring'''






							assert (
							    isinstance(UpperCAmelCase__				,						UpperCAmelCase__		) and number_of_steps > 0
							), F'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
							if number_of_steps == 1:
														return 1
							SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Dict						    = 1, 1
							for _ in range(number_of_steps - 1		):
														SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = current + previous, current
							return current


if __name__ == "__main__":
		import doctest

		doctest.testmod()






