


"""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


UpperCamelCase    :					Dict							   =   logging.get_logger(__name__)

UpperCamelCase    :					List[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       lowerCamelCase__    ( a_      ):
						lowerCAmelCase  	=  """detr"""
						lowerCAmelCase  	=  ["""past_key_values"""]
						lowerCAmelCase  	=  {
						    """hidden_size""": """d_model""",
						    """num_attention_heads""": """encoder_attention_heads""",
						}







						def __init__(				self :     str      ,      _lowercase :     str=True      ,      _lowercase :     List[Any]=None      ,      _lowercase :     Dict=3      ,      _lowercase :     int=100      ,      _lowercase :     List[str]=6      ,      _lowercase :     Optional[Any]=2_048      ,      _lowercase :     str=8      ,      _lowercase :     Optional[int]=6      ,      _lowercase :     Any=2_048      ,      _lowercase :     Optional[int]=8      ,      _lowercase :     Optional[int]=0.0      ,      _lowercase :     List[str]=0.0      ,      _lowercase :     Tuple=True      ,      _lowercase :     List[Any]="relu"      ,      _lowercase :     List[Any]=256      ,      _lowercase :     Dict=0.1      ,      _lowercase :     Optional[int]=0.0      ,      _lowercase :     Tuple=0.0      ,      _lowercase :     Optional[Any]=0.0_2      ,      _lowercase :     Optional[Any]=1.0      ,      _lowercase :     Tuple=False      ,      _lowercase :     Optional[int]="sine"      ,      _lowercase :     int="resnet50"      ,      _lowercase :     Union[str, Any]=True      ,      _lowercase :     Optional[Any]=False      ,      _lowercase :     List[str]=1      ,      _lowercase :     Dict=5      ,      _lowercase :     Union[str, Any]=2      ,      _lowercase :     List[str]=1      ,      _lowercase :     List[Any]=1      ,      _lowercase :     Optional[Any]=5      ,      _lowercase :     Any=2      ,      _lowercase :     Any=0.1      ,      **_lowercase :     List[str]      ,      ):
								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       							=			CONFIG_MAPPING["resnet"](out_features=['stage4']		)
										elif isinstance(_a      ,      _a		):
												A       							=			backbone_config.get('model_type'		)
												A       							=			CONFIG_MAPPING[backbone_model_type]
												A       							=			config_class.from_dict(_a		)
										# set timm attributes to None
										A       							=			None, None, None

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







						@property
						def __a    (				self :     Tuple		):
								return self.encoder_attention_heads







						@property
						def __a    (				self :     Dict		):
								return self.d_model







						@classmethod
						def __a    (				cls :     Any      ,      _lowercase :     Tuple      ,      **_lowercase :     List[str]		):
								return cls(backbone_config=_a      ,      **_a		)







						def __a    (				self :     Union[str, Any]		):
								A       							=			copy.deepcopy(self.__dict__		)
								if output["backbone_config"] is not None:
										A       							=			self.backbone_config.to_dict()
								A       							=			self.__class__.model_type
								return output



class       lowerCamelCase__    ( a_      ):
						lowerCAmelCase  	=  version.parse("""1.11"""      )







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







						@property
						def __a    (				self :     Union[str, Any]		):
								return 1e-5







						@property
						def __a    (				self :     str		):
								return 12







"""simple docstring"""




from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


_A     :			Union[str, Any]  						=			logging.get_logger(__name__)  # pylint: disable=invalid-name


def     __magic_name__      ( __snake_case							:			str    )					->							List[str]:
       if not path:
              return "pipe"

       for ext in PipelineDataFormat.SUPPORTED_FORMATS:
              if path.endswith(__snake_case    ):
                     return ext

       raise Exception(
           f"""Unable to determine file format from file extension {path}. """
           f"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"""    )


def     __magic_name__      ( __snake_case							:			str    )					->							Optional[int]:
       lowercase       :							int									=				pipeline(
           task=args.task	,					model=args.model if args.model else None	,					config=args.config	,					tokenizer=args.tokenizer	,					device=args.device	,					)
       lowercase       :							Optional[Any]									=				try_infer_format_from_ext(args.input    ) if args.format == "infer" else args.format
       lowercase       :							Optional[int]									=				PipelineDataFormat.from_str(
           format=__snake_case	,					output_path=args.output	,					input_path=args.input	,					column=args.column if args.column else nlp.default_input_names	,					overwrite=args.overwrite	,					)
       return RunCommand(__snake_case	,					__snake_case    )

class 						a__		( a_		):
   def __init__(     self      ,       _a      ,       _a							):
          lowercase       :							List[str]									=				nlp
          lowercase       :							Optional[Any]									=				reader
   @staticmethod
   def      __magic_name__     (     _a							):
          lowercase       :							Any									=				parser.add_parser("run"      ,       help="Run a pipeline through the CLI"							)
          run_parser.add_argument("--task"      ,       choices=get_supported_tasks()      ,       help="Task to run"							)
          run_parser.add_argument("--input"      ,       type=_a      ,       help="Path to the file to use for inference"							)
          run_parser.add_argument("--output"      ,       type=_a      ,       help="Path to the file that will be used post to write results."							)
          run_parser.add_argument("--model"      ,       type=_a      ,       help="Name or path to the model to instantiate."							)
          run_parser.add_argument("--config"      ,       type=_a      ,       help="Name or path to the model's config to instantiate."							)
          run_parser.add_argument(
              "--tokenizer"      ,       type=_a      ,       help="Name of the tokenizer to use. (default: same as the model name)"							)
          run_parser.add_argument(
              "--column"      ,       type=_a      ,       help="Name of the column to use as input. (For multi columns input as QA use column1,columns2)"      ,       )
          run_parser.add_argument(
              "--format"      ,       type=_a      ,       default="infer"      ,       choices=PipelineDataFormat.SUPPORTED_FORMATS      ,       help="Input format to read from"      ,       )
          run_parser.add_argument(
              "--device"      ,       type=_a      ,       default=-1      ,       help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"      ,       )
          run_parser.add_argument("--overwrite"      ,       action="store_true"      ,       help="Allow overwriting the output file."							)
          run_parser.set_defaults(func=_a							)

   def      __magic_name__     (     self							):
          lowercase						,       lowercase       :							int									=				self._nlp, []

          for entry in self._reader:
                 lowercase       :							str									=				nlp(**_a							) if self._reader.is_multi_columns else nlp(_a							)
                 if isinstance(_a      ,       _a							):
                        outputs.append(_a							)
                 else:
                        outputs += output

        # Saving data
          if self._nlp.binary_output:
                 lowercase       :							Dict									=				self._reader.save_binary(_a							)
                 logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}"""							)
          else:
                 self._reader.save(_a							)


