





"""simple docstring"""






import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
		import onnxruntime as ort


A 			=  logging.get_logger(__name__)

A 			=  {
    '''tensor(bool)''': np.bool_,
    '''tensor(int8)''': np.inta,
    '''tensor(uint8)''': np.uinta,
    '''tensor(int16)''': np.intaa,
    '''tensor(uint16)''': np.uintaa,
    '''tensor(int32)''': np.intaa,
    '''tensor(uint32)''': np.uintaa,
    '''tensor(int64)''': np.intaa,
    '''tensor(uint64)''': np.uintaa,
    '''tensor(float16)''': np.floataa,
    '''tensor(float)''': np.floataa,
    '''tensor(double)''': np.floataa,
}




class 				__lowercase			:







					'''simple docstring'''




					def __init__(						self	,			_UpperCAmelCase=None	,			**_UpperCAmelCase     ):
									logger.info('''`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'''     )
									__a :						Dict			       =  model
									__a :						int			       =  kwargs.get('''model_save_dir'''	,			_UpperCAmelCase     )
									__a :						Optional[Any]			       =  kwargs.get('''latest_model_name'''	,			_UpperCAmelCase     )







					def __call__(						self	,			**_UpperCAmelCase     ):
									__a :						Dict			       =  {k: np.array(_UpperCAmelCase     ) for k, v in kwargs.items()}
									return self.model.run(_UpperCAmelCase	,			_UpperCAmelCase     )







					@staticmethod
					def  _lowerCamelCase		(						_UpperCAmelCase	,			_UpperCAmelCase=None	,			_UpperCAmelCase=None     ):
									if provider is None:
													logger.info('''No onnxruntime provider specified, using CPUExecutionProvider'''     )
													__a :						List[str]			       =  '''CPUExecutionProvider'''

									return ort.InferenceSession(_UpperCAmelCase	,			providers=[provider]	,			sess_options=_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase = None	,			**_UpperCAmelCase     ):
									__a :						int			       =  file_name if file_name is not None else ONNX_WEIGHTS_NAME

									__a :						List[Any]			       =  self.model_save_dir.joinpath(self.latest_model_name     )
									__a :						int			       =  Path(_UpperCAmelCase     ).joinpath(_UpperCAmelCase     )
									try:
													shutil.copyfile(_UpperCAmelCase	,			_UpperCAmelCase     )
									except shutil.SameFileError:
													pass

									# copy external weights (for models >2GB)
									__a :						Optional[Any]			       =  self.model_save_dir.joinpath(_UpperCAmelCase     )
									if src_path.exists():
													__a :						Any			       =  Path(_UpperCAmelCase     ).joinpath(_UpperCAmelCase     )
													try:
																	shutil.copyfile(_UpperCAmelCase	,			_UpperCAmelCase     )
													except shutil.SameFileError:
																	pass







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			**_UpperCAmelCase	,			):
									if os.path.isfile(_UpperCAmelCase     ):
													logger.error(f"""Provided path ({save_directory}) should be a directory, not a file"""     )
													return

									os.makedirs(_UpperCAmelCase	,			exist_ok=_UpperCAmelCase     )

									# saving model weights/files
									self._save_pretrained(_UpperCAmelCase	,			**_UpperCAmelCase     )







					@classmethod
					def  _lowerCamelCase		(						cls	,			_UpperCAmelCase	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = False	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			**_UpperCAmelCase	,			):
									__a :						Optional[Any]			       =  file_name if file_name is not None else ONNX_WEIGHTS_NAME
									# load model from local directory
									if os.path.isdir(_UpperCAmelCase     ):
													__a :						str			       =  OnnxRuntimeModel.load_model(
													    os.path.join(_UpperCAmelCase	,			_UpperCAmelCase     )	,			provider=_UpperCAmelCase	,			sess_options=_UpperCAmelCase     )
													__a :						List[Any]			       =  Path(_UpperCAmelCase     )
									# load model from hub
									else:
													# download model
													__a :						List[str]			       =  hf_hub_download(
													    repo_id=_UpperCAmelCase	,			filename=_UpperCAmelCase	,			use_auth_token=_UpperCAmelCase	,			revision=_UpperCAmelCase	,			cache_dir=_UpperCAmelCase	,			force_download=_UpperCAmelCase	,			)
													__a :						Optional[Any]			       =  Path(_UpperCAmelCase     ).parent
													__a :						int			       =  Path(_UpperCAmelCase     ).name
													__a :						str			       =  OnnxRuntimeModel.load_model(_UpperCAmelCase	,			provider=_UpperCAmelCase	,			sess_options=_UpperCAmelCase     )
									return cls(model=_UpperCAmelCase	,			**_UpperCAmelCase     )







					@classmethod
					def  _lowerCamelCase		(						cls	,			_UpperCAmelCase	,			_UpperCAmelCase = True	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			**_UpperCAmelCase	,			):
									__a :						Dict			       =  None
									if len(str(_UpperCAmelCase     ).split('''@'''     )     ) == 2:
													__a :						Optional[Any]			       =  model_id.split('''@'''     )

									return cls._from_pretrained(
									    model_id=_UpperCAmelCase	,			revision=_UpperCAmelCase	,			cache_dir=_UpperCAmelCase	,			force_download=_UpperCAmelCase	,			use_auth_token=_UpperCAmelCase	,			**_UpperCAmelCase	,			)






"""simple docstring"""






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 			=  logging.get_logger(__name__)

A 			=  {
    '''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 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




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







					def __init__(						self	,			_UpperCAmelCase=True	,			_UpperCAmelCase=None	,			_UpperCAmelCase=3	,			_UpperCAmelCase=100	,			_UpperCAmelCase=6	,			_UpperCAmelCase=2048	,			_UpperCAmelCase=8	,			_UpperCAmelCase=6	,			_UpperCAmelCase=2048	,			_UpperCAmelCase=8	,			_UpperCAmelCase=0.0	,			_UpperCAmelCase=0.0	,			_UpperCAmelCase=True	,			_UpperCAmelCase="relu"	,			_UpperCAmelCase=256	,			_UpperCAmelCase=0.1	,			_UpperCAmelCase=0.0	,			_UpperCAmelCase=0.0	,			_UpperCAmelCase=0.0_2	,			_UpperCAmelCase=1.0	,			_UpperCAmelCase=False	,			_UpperCAmelCase="sine"	,			_UpperCAmelCase="resnet50"	,			_UpperCAmelCase=True	,			_UpperCAmelCase=False	,			_UpperCAmelCase=1	,			_UpperCAmelCase=5	,			_UpperCAmelCase=2	,			_UpperCAmelCase=1	,			_UpperCAmelCase=1	,			_UpperCAmelCase=5	,			_UpperCAmelCase=2	,			_UpperCAmelCase=0.1	,			**_UpperCAmelCase	,			):
									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.'''     )
																	__a :						Any			       =  CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']     )
													elif isinstance(_UpperCAmelCase	,			_UpperCAmelCase     ):
																	__a :						Optional[int]			       =  backbone_config.get('''model_type'''     )
																	__a :						Tuple			       =  CONFIG_MAPPING[backbone_model_type]
																	__a :						Union[str, Any]			       =  config_class.from_dict(_UpperCAmelCase     )
													# set timm attributes to None
													__a       ,						__a       ,						__a :						Union[str, Any]			       =  None, None, None

									__a :						Union[str, Any]			       =  use_timm_backbone
									__a :						Any			       =  backbone_config
									__a :						Tuple			       =  num_channels
									__a :						int			       =  num_queries
									__a :						str			       =  d_model
									__a :						Any			       =  encoder_ffn_dim
									__a :						int			       =  encoder_layers
									__a :						Optional[int]			       =  encoder_attention_heads
									__a :						Any			       =  decoder_ffn_dim
									__a :						str			       =  decoder_layers
									__a :						Union[str, Any]			       =  decoder_attention_heads
									__a :						List[Any]			       =  dropout
									__a :						Union[str, Any]			       =  attention_dropout
									__a :						int			       =  activation_dropout
									__a :						Dict			       =  activation_function
									__a :						str			       =  init_std
									__a :						int			       =  init_xavier_std
									__a :						Optional[Any]			       =  encoder_layerdrop
									__a :						Optional[int]			       =  decoder_layerdrop
									__a :						str			       =  encoder_layers
									__a :						List[str]			       =  auxiliary_loss
									__a :						Optional[Any]			       =  position_embedding_type
									__a :						Any			       =  backbone
									__a :						Tuple			       =  use_pretrained_backbone
									__a :						int			       =  dilation
									# Hungarian matcher
									__a :						str			       =  class_cost
									__a :						Optional[Any]			       =  bbox_cost
									__a :						Any			       =  giou_cost
									# Loss coefficients
									__a :						List[str]			       =  mask_loss_coefficient
									__a :						Dict			       =  dice_loss_coefficient
									__a :						str			       =  bbox_loss_coefficient
									__a :						str			       =  giou_loss_coefficient
									__a :						Any			       =  eos_coefficient
									super().__init__(is_encoder_decoder=_UpperCAmelCase	,			**_UpperCAmelCase     )







					@property
					def  _lowerCamelCase		(						self     ):
									return self.encoder_attention_heads







					@property
					def  _lowerCamelCase		(						self     ):
									return self.d_model







					@classmethod
					def  _lowerCamelCase		(						cls	,			_UpperCAmelCase	,			**_UpperCAmelCase     ):
									return cls(backbone_config=_UpperCAmelCase	,			**_UpperCAmelCase     )







					def  _lowerCamelCase		(						self     ):
									__a :						Dict			       =  copy.deepcopy(self.__dict__     )
									if output["backbone_config"] is not None:
													__a :						Tuple			       =  self.backbone_config.to_dict()
									__a :						Dict			       =  self.__class__.model_type
									return output






class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




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







					@property
					def  _lowerCamelCase		(						self     ):
									return OrderedDict(
									    [
									        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
									        ('''pixel_mask''', {0: '''batch'''}),
									    ]     )







					@property
					def  _lowerCamelCase		(						self     ):
									return 1e-5







					@property
					def  _lowerCamelCase		(						self     ):
									return 12