

'''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							(			lowercase__      ):
				lowerCamelCase_		       =    'perceiver'







				def __init__(       self     :		List[str]		,					UpperCAmelCase__     :		Optional[int]=256		,					UpperCAmelCase__     :		List[str]=1280		,					UpperCAmelCase__     :		Union[str, Any]=768		,					UpperCAmelCase__     :		Tuple=1		,					UpperCAmelCase__     :		List[Any]=26		,					UpperCAmelCase__     :		Dict=8		,					UpperCAmelCase__     :		str=8		,					UpperCAmelCase__     :		str=None		,					UpperCAmelCase__     :		Any=None		,					UpperCAmelCase__     :		Any="kv"		,					UpperCAmelCase__     :		List[str]=1		,					UpperCAmelCase__     :		List[Any]=1		,					UpperCAmelCase__     :		str="gelu"		,					UpperCAmelCase__     :		Optional[Any]=0.1		,					UpperCAmelCase__     :		Optional[int]=0.02		,					UpperCAmelCase__     :		List[str]=1E-12		,					UpperCAmelCase__     :		int=True		,					UpperCAmelCase__     :		Union[str, Any]=262		,					UpperCAmelCase__     :		Optional[int]=2048		,					UpperCAmelCase__     :		Optional[int]=56		,					UpperCAmelCase__     :		List[str]=[368, 496]		,					UpperCAmelCase__     :		Optional[int]=16		,					UpperCAmelCase__     :		List[str]=1920		,					UpperCAmelCase__     :		Any=16		,					UpperCAmelCase__     :		List[Any]=[1, 16, 224, 224]		,					**UpperCAmelCase__     :		Tuple		,					):
										'''simple docstring'''
										super().__init__(**UpperCAmelCase__   )

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





class 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):







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







				@property
				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										return 1E-4







				def  lowerCamelCase_	(       self     :		Optional[Any]		,					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		,					):
										'''simple docstring'''
										# 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
																lowercase :							str    		=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
																lowercase :							Optional[Any]    		=preprocessor.num_special_tokens_to_add(UpperCAmelCase__   )
																lowercase :							Optional[int]    		=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
																lowercase :							Any    		=[''' '''.join(['''a''']   ) * seq_length] * batch_size
																lowercase :							Optional[Any]    		=dict(preprocessor(UpperCAmelCase__		,					return_tensors=UpperCAmelCase__   )   )
																lowercase :							int    		=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
																lowercase :							Union[str, Any]    		=compute_effective_axis_dimension(UpperCAmelCase__		,					fixed_dimension=OnnxConfig.default_fixed_batch   )
																lowercase :							Dict    		=self._generate_dummy_images(UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__   )
																lowercase :							Union[str, Any]    		=dict(preprocessor(images=UpperCAmelCase__		,					return_tensors=UpperCAmelCase__   )   )
																lowercase :							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.'''   )












import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__           =			logging.get_logger(__name__)

class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   Optional[Any]      =							"""encoder-decoder"""
							snake_case  :   Optional[int]      =							True






							def __init__(    self	,			**__lowerCAmelCase	):
														super().__init__(**__lowerCAmelCase	)
														assert (
														    "encoder" in kwargs and "decoder" in kwargs
														), "Config has to be initialized with encoder and decoder config"
														UpperCamelCase__					  =      kwargs.pop("""encoder"""	)
														UpperCamelCase__					  =      encoder_config.pop("""model_type"""	)
														UpperCamelCase__					  =      kwargs.pop("""decoder"""	)
														UpperCamelCase__					  =      decoder_config.pop("""model_type"""	)

														from ..auto.configuration_auto import AutoConfig

														UpperCamelCase__					  =      AutoConfig.for_model(__lowerCAmelCase	,			**__lowerCAmelCase	)
														UpperCamelCase__					  =      AutoConfig.for_model(__lowerCAmelCase	,			**__lowerCAmelCase	)
														UpperCamelCase__					  =      True






							@classmethod
							def 					_lowerCamelCase		(    cls	,			__lowerCAmelCase	,			__lowerCAmelCase	,			**__lowerCAmelCase	):
														logger.info("""Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"""	)
														UpperCamelCase__					  =      True
														UpperCamelCase__					  =      True

														return cls(encoder=encoder_config.to_dict()	,			decoder=decoder_config.to_dict()	,			**__lowerCAmelCase	)







							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      copy.deepcopy(self.__dict__	)
														UpperCamelCase__					  =      self.encoder.to_dict()
														UpperCamelCase__					  =      self.decoder.to_dict()
														UpperCamelCase__					  =      self.__class__.model_type
														return output




