


import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


A			:						Union[str, Any]        =  logging.get_logger(__name__)

A			:						str        =  {
    'facebook/detr-resnet-50': 'https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json',
    # See all DETR models at https://huggingface.co/models?filter=detr
}



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					'''detr'''
					A__     =					['''past_key_values''']
					A__     =					{
					    '''hidden_size''': '''d_model''',
					    '''num_attention_heads''': '''encoder_attention_heads''',
					}

					def __init__(self       :		Any ,   _UpperCAmelCase       :		Tuple=True ,   _UpperCAmelCase       :		Optional[int]=None ,   _UpperCAmelCase       :		str=3 ,   _UpperCAmelCase       :		str=100 ,   _UpperCAmelCase       :		Tuple=6 ,   _UpperCAmelCase       :		Optional[Any]=2048 ,   _UpperCAmelCase       :		Optional[int]=8 ,   _UpperCAmelCase       :		int=6 ,   _UpperCAmelCase       :		Optional[int]=2048 ,   _UpperCAmelCase       :		str=8 ,   _UpperCAmelCase       :		Tuple=0.0 ,   _UpperCAmelCase       :		List[str]=0.0 ,   _UpperCAmelCase       :		Optional[Any]=True ,   _UpperCAmelCase       :		str="relu" ,   _UpperCAmelCase       :		int=256 ,   _UpperCAmelCase       :		Union[str, Any]=0.1 ,   _UpperCAmelCase       :		List[Any]=0.0 ,   _UpperCAmelCase       :		List[Any]=0.0 ,   _UpperCAmelCase       :		List[str]=0.02 ,   _UpperCAmelCase       :		Union[str, Any]=1.0 ,   _UpperCAmelCase       :		Optional[Any]=False ,   _UpperCAmelCase       :		Optional[Any]="sine" ,   _UpperCAmelCase       :		Union[str, Any]="resnet50" ,   _UpperCAmelCase       :		str=True ,   _UpperCAmelCase       :		Optional[int]=False ,   _UpperCAmelCase       :		int=1 ,   _UpperCAmelCase       :		Dict=5 ,   _UpperCAmelCase       :		Optional[int]=2 ,   _UpperCAmelCase       :		Tuple=1 ,   _UpperCAmelCase       :		Optional[Any]=1 ,   _UpperCAmelCase       :		int=5 ,   _UpperCAmelCase       :		Dict=2 ,   _UpperCAmelCase       :		List[str]=0.1 ,   **_UpperCAmelCase       :		str ,   )		->  str:




							"""simple docstring"""


							if backbone_config is not None and use_timm_backbone:
									raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""						)

							if not use_timm_backbone:
									if backbone_config is None:
											logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""						)
											lowercase__	     =						CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]						)
									elif isinstance(_UpperCAmelCase ,   _UpperCAmelCase						):
											lowercase__	     =						backbone_config.get("""model_type"""						)
											lowercase__	     =						CONFIG_MAPPING[backbone_model_type]
											lowercase__	     =						config_class.from_dict(_UpperCAmelCase						)
									# set timm attributes to None
									lowercase__     ,      lowercase__     ,      lowercase__	     =						None, None, None

							lowercase__	     =						use_timm_backbone
							lowercase__	     =						backbone_config
							lowercase__	     =						num_channels
							lowercase__	     =						num_queries
							lowercase__	     =						d_model
							lowercase__	     =						encoder_ffn_dim
							lowercase__	     =						encoder_layers
							lowercase__	     =						encoder_attention_heads
							lowercase__	     =						decoder_ffn_dim
							lowercase__	     =						decoder_layers
							lowercase__	     =						decoder_attention_heads
							lowercase__	     =						dropout
							lowercase__	     =						attention_dropout
							lowercase__	     =						activation_dropout
							lowercase__	     =						activation_function
							lowercase__	     =						init_std
							lowercase__	     =						init_xavier_std
							lowercase__	     =						encoder_layerdrop
							lowercase__	     =						decoder_layerdrop
							lowercase__	     =						encoder_layers
							lowercase__	     =						auxiliary_loss
							lowercase__	     =						position_embedding_type
							lowercase__	     =						backbone
							lowercase__	     =						use_pretrained_backbone
							lowercase__	     =						dilation
							# Hungarian matcher
							lowercase__	     =						class_cost
							lowercase__	     =						bbox_cost
							lowercase__	     =						giou_cost
							# Loss coefficients
							lowercase__	     =						mask_loss_coefficient
							lowercase__	     =						dice_loss_coefficient
							lowercase__	     =						bbox_loss_coefficient
							lowercase__	     =						giou_loss_coefficient
							lowercase__	     =						eos_coefficient
							super().__init__(is_encoder_decoder=_UpperCAmelCase ,   **_UpperCAmelCase						)

					@property
					def     lowerCamelCase__	(self       :		List[Any]						)		->  int:




							"""simple docstring"""


							return self.encoder_attention_heads

					@property
					def     lowerCamelCase__	(self       :		int						)		->  int:




							"""simple docstring"""


							return self.d_model

					@classmethod
					def     lowerCamelCase__	(cls       :		Tuple ,   _UpperCAmelCase       :		PretrainedConfig ,   **_UpperCAmelCase       :		List[str]						)		->  Optional[Any]:




							"""simple docstring"""


							return cls(backbone_config=_UpperCAmelCase ,   **_UpperCAmelCase						)


					def     lowerCamelCase__	(self       :		Any						)		->  Dict[str, any]:




							"""simple docstring"""


							lowercase__	     =						copy.deepcopy(self.__dict__						)
							if output["backbone_config"] is not None:
									lowercase__	     =						self.backbone_config.to_dict()
							lowercase__	     =						self.__class__.model_type
							return output






class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					version.parse('''1.11'''				)

					@property
					def     lowerCamelCase__	(self       :		Tuple						)		->  Mapping[str, Mapping[int, str]]:




							"""simple docstring"""


							return OrderedDict(
							    [
							        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
							        ("""pixel_mask""", {0: """batch"""}),
							    ]						)

					@property
					def     lowerCamelCase__	(self       :		Optional[int]						)		->  float:




							"""simple docstring"""


							return 1E-5


					@property
					def     lowerCamelCase__	(self       :		Any						)		->  int:




							"""simple docstring"""


							return 12



'''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


lowerCAmelCase					:    Any           = logging.get_logger(__name__)

lowerCAmelCase					:    Optional[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 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							"""perceiver"""







				def __init__(       self						,				A_=256						,				A_=1280						,				A_=768						,				A_=1						,				A_=26						,				A_=8						,				A_=8						,				A_=None						,				A_=None						,				A_="kv"						,				A_=1						,				A_=1						,				A_="gelu"						,				A_=0.1						,				A_=0.02						,				A_=1e-12						,				A_=True						,				A_=262						,				A_=2048						,				A_=56						,				A_=[368, 496]						,				A_=16						,				A_=1920						,				A_=16						,				A_=[1, 16, 224, 224]						,				**A_						,				)->		str:







											'''simple docstring'''






											super().__init__(**A_     )

											UpperCamelCase   			=     num_latents
											UpperCamelCase   			=     d_latents
											UpperCamelCase   			=     d_model
											UpperCamelCase   			=     num_blocks
											UpperCamelCase   			=     num_self_attends_per_block
											UpperCamelCase   			=     num_self_attention_heads
											UpperCamelCase   			=     num_cross_attention_heads
											UpperCamelCase   			=     qk_channels
											UpperCamelCase   			=     v_channels
											UpperCamelCase   			=     cross_attention_shape_for_attention
											UpperCamelCase   			=     self_attention_widening_factor
											UpperCamelCase   			=     cross_attention_widening_factor
											UpperCamelCase   			=     hidden_act
											UpperCamelCase   			=     attention_probs_dropout_prob
											UpperCamelCase   			=     initializer_range
											UpperCamelCase   			=     layer_norm_eps
											UpperCamelCase   			=     use_query_residual
											# masked language modeling attributes
											UpperCamelCase   			=     vocab_size
											UpperCamelCase   			=     max_position_embeddings
											# image classification attributes
											UpperCamelCase   			=     image_size
											# flow attributes
											UpperCamelCase   			=     train_size
											# multimodal autoencoding attributes
											UpperCamelCase   			=     num_frames
											UpperCamelCase   			=     audio_samples_per_frame
											UpperCamelCase   			=     samples_per_patch
											UpperCamelCase   			=     output_shape


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







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







											'''simple docstring'''






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







				@property
				def 						UpperCAmelCase_    (       self     )->		float:







											'''simple docstring'''






											return 1e-4







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = -1						,				A_ = -1						,				A_ = -1						,				A_ = False						,				A_ = None						,				A_ = 3						,				A_ = 40						,				A_ = 40						,				)->		Mapping[str, Any]:







											'''simple docstring'''






											if isinstance(A_						,				A_     ):
																		# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
																		UpperCamelCase   			=     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
																		UpperCamelCase   			=     preprocessor.num_special_tokens_to_add(A_     )
																		UpperCamelCase   			=     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
																		UpperCamelCase   			=     [' '.join(['a']     ) * seq_length] * batch_size
																		UpperCamelCase   			=     dict(preprocessor(A_						,				return_tensors=A_     )     )
																		UpperCamelCase   			=     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
																		UpperCamelCase   			=     compute_effective_axis_dimension(A_						,				fixed_dimension=OnnxConfig.default_fixed_batch     )
																		UpperCamelCase   			=     self._generate_dummy_images(A_						,				A_						,				A_						,				A_     )
																		UpperCamelCase   			=     dict(preprocessor(images=A_						,				return_tensors=A_     )     )
																		UpperCamelCase   			=     inputs.pop('pixel_values'     )
																		return inputs
											else:
																		raise ValueError(
																		    'Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.'     )




