




import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


lowercase_	:				int  				= logging.get_logger(__name__)

lowercase_	:				Tuple  				= OrderedDict(
    [
        ('align', 'EfficientNetImageProcessor'),
        ('beit', 'BeitImageProcessor'),
        ('bit', 'BitImageProcessor'),
        ('blip', 'BlipImageProcessor'),
        ('blip-2', 'BlipImageProcessor'),
        ('bridgetower', 'BridgeTowerImageProcessor'),
        ('chinese_clip', 'ChineseCLIPImageProcessor'),
        ('clip', 'CLIPImageProcessor'),
        ('clipseg', 'ViTImageProcessor'),
        ('conditional_detr', 'ConditionalDetrImageProcessor'),
        ('convnext', 'ConvNextImageProcessor'),
        ('convnextv2', 'ConvNextImageProcessor'),
        ('cvt', 'ConvNextImageProcessor'),
        ('data2vec-vision', 'BeitImageProcessor'),
        ('deformable_detr', 'DeformableDetrImageProcessor'),
        ('deit', 'DeiTImageProcessor'),
        ('deta', 'DetaImageProcessor'),
        ('detr', 'DetrImageProcessor'),
        ('dinat', 'ViTImageProcessor'),
        ('donut-swin', 'DonutImageProcessor'),
        ('dpt', 'DPTImageProcessor'),
        ('efficientformer', 'EfficientFormerImageProcessor'),
        ('efficientnet', 'EfficientNetImageProcessor'),
        ('flava', 'FlavaImageProcessor'),
        ('focalnet', 'BitImageProcessor'),
        ('git', 'CLIPImageProcessor'),
        ('glpn', 'GLPNImageProcessor'),
        ('groupvit', 'CLIPImageProcessor'),
        ('imagegpt', 'ImageGPTImageProcessor'),
        ('instructblip', 'BlipImageProcessor'),
        ('layoutlmv2', 'LayoutLMv2ImageProcessor'),
        ('layoutlmv3', 'LayoutLMv3ImageProcessor'),
        ('levit', 'LevitImageProcessor'),
        ('mask2former', 'Mask2FormerImageProcessor'),
        ('maskformer', 'MaskFormerImageProcessor'),
        ('mgp-str', 'ViTImageProcessor'),
        ('mobilenet_v1', 'MobileNetV1ImageProcessor'),
        ('mobilenet_v2', 'MobileNetV2ImageProcessor'),
        ('mobilevit', 'MobileViTImageProcessor'),
        ('mobilevit', 'MobileViTImageProcessor'),
        ('mobilevitv2', 'MobileViTImageProcessor'),
        ('nat', 'ViTImageProcessor'),
        ('oneformer', 'OneFormerImageProcessor'),
        ('owlvit', 'OwlViTImageProcessor'),
        ('perceiver', 'PerceiverImageProcessor'),
        ('pix2struct', 'Pix2StructImageProcessor'),
        ('poolformer', 'PoolFormerImageProcessor'),
        ('regnet', 'ConvNextImageProcessor'),
        ('resnet', 'ConvNextImageProcessor'),
        ('sam', 'SamImageProcessor'),
        ('segformer', 'SegformerImageProcessor'),
        ('swiftformer', 'ViTImageProcessor'),
        ('swin', 'ViTImageProcessor'),
        ('swin2sr', 'Swin2SRImageProcessor'),
        ('swinv2', 'ViTImageProcessor'),
        ('table-transformer', 'DetrImageProcessor'),
        ('timesformer', 'VideoMAEImageProcessor'),
        ('tvlt', 'TvltImageProcessor'),
        ('upernet', 'SegformerImageProcessor'),
        ('van', 'ConvNextImageProcessor'),
        ('videomae', 'VideoMAEImageProcessor'),
        ('vilt', 'ViltImageProcessor'),
        ('vit', 'ViTImageProcessor'),
        ('vit_hybrid', 'ViTHybridImageProcessor'),
        ('vit_mae', 'ViTImageProcessor'),
        ('vit_msn', 'ViTImageProcessor'),
        ('xclip', 'CLIPImageProcessor'),
        ('yolos', 'YolosImageProcessor'),
    ]
)

lowercase_	:				Tuple  				= _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)


def 							__SCREAMING_SNAKE_CASE  (			snake_case_						):

						'''simple docstring'''


						for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
												if class_name in extractors:
																		_UpperCAmelCase													=     model_type_to_module_name(snake_case_						)

																		_UpperCAmelCase													=     importlib.import_module(f""".{module_name}"""	,    "transformers.models"						)
																		try:
																								return getattr(snake_case_	,    snake_case_						)
																		except AttributeError:
																								continue

						for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
												if getattr(snake_case_	,    "__name__"	,    snake_case_						) == class_name:
																		return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
						_UpperCAmelCase													=     importlib.import_module("transformers"						)
						if hasattr(snake_case_	,    snake_case_						):
												return getattr(snake_case_	,    snake_case_						)

						return None


def 							__SCREAMING_SNAKE_CASE  (			snake_case_	,    snake_case_ = None	,    snake_case_ = False	,    snake_case_ = False	,    snake_case_ = None	,    snake_case_ = None	,    snake_case_ = None	,    snake_case_ = False	,    **snake_case_	,    ):

						'''simple docstring'''


						_UpperCAmelCase													=     get_file_from_repo(
						    snake_case_	,    snake_case_	,    cache_dir=snake_case_	,    force_download=snake_case_	,    resume_download=snake_case_	,    proxies=snake_case_	,    use_auth_token=snake_case_	,    revision=snake_case_	,    local_files_only=snake_case_	,    )
						if resolved_config_file is None:
												logger.info(
												    "Could not locate the image processor configuration file, will try to use the model config instead."						)
												return {}

						with open(snake_case_	,    encoding="utf-8"						) as reader:
												return json.load(snake_case_						)






class     __lowerCAmelCase :







			def __init__(       self    :			Tuple      ):




									"""simple docstring"""


									raise EnvironmentError(
									    "AutoImageProcessor is designed to be instantiated "
									    "using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method."      )







			@classmethod
			@replace_list_option_in_docstrings(snake_case__      )
			def UpperCamelCase			(       cls    :			Tuple    ,    snake_case__    :			int    ,    **snake_case__    :			int      ):




									"""simple docstring"""


									_UpperCAmelCase													=     kwargs.pop("config"    ,    snake_case__      )
									_UpperCAmelCase													=     kwargs.pop("trust_remote_code"    ,    snake_case__      )
									_UpperCAmelCase													=     True

									_UpperCAmelCase							, _UpperCAmelCase													=     ImageProcessingMixin.get_image_processor_dict(snake_case__    ,    **snake_case__      )
									_UpperCAmelCase													=     config_dict.get("image_processor_type"    ,    snake_case__      )
									_UpperCAmelCase													=     None
									if "AutoImageProcessor" in config_dict.get("auto_map"    ,    {}      ):
															_UpperCAmelCase													=     config_dict["auto_map"]["AutoImageProcessor"]

									# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
									# and if so, infer the image processor class from there.
									if image_processor_class is None and image_processor_auto_map is None:
															_UpperCAmelCase													=     config_dict.pop("feature_extractor_type"    ,    snake_case__      )
															if feature_extractor_class is not None:
																					logger.warning(
																					    "Could not find image processor class in the image processor config or the model config. Loading"
																					    " based on pattern matching with the model's feature extractor configuration."      )
																					_UpperCAmelCase													=     feature_extractor_class.replace("FeatureExtractor"    ,    "ImageProcessor"      )
															if "AutoFeatureExtractor" in config_dict.get("auto_map"    ,    {}      ):
																					_UpperCAmelCase													=     config_dict["auto_map"]["AutoFeatureExtractor"]
																					_UpperCAmelCase													=     feature_extractor_auto_map.replace("FeatureExtractor"    ,    "ImageProcessor"      )
																					logger.warning(
																					    "Could not find image processor auto map in the image processor config or the model config."
																					    " Loading based on pattern matching with the model's feature extractor configuration."      )

        # If we don't find the image processor class in the image processor config, let's try the model config.
									if image_processor_class is None and image_processor_auto_map is None:
															if not isinstance(snake_case__    ,    snake_case__      ):
																					_UpperCAmelCase													=     AutoConfig.from_pretrained(snake_case__    ,    **snake_case__      )
															# It could be in `config.image_processor_type``
															_UpperCAmelCase													=     getattr(snake_case__    ,    "image_processor_type"    ,    snake_case__      )
															if hasattr(snake_case__    ,    "auto_map"      ) and "AutoImageProcessor" in config.auto_map:
																					_UpperCAmelCase													=     config.auto_map["AutoImageProcessor"]

									if image_processor_class is not None:
															_UpperCAmelCase													=     image_processor_class_from_name(snake_case__      )

									_UpperCAmelCase													=     image_processor_auto_map is not None
									_UpperCAmelCase													=     image_processor_class is not None or type(snake_case__      ) in IMAGE_PROCESSOR_MAPPING
									_UpperCAmelCase													=     resolve_trust_remote_code(
									    snake_case__    ,    snake_case__    ,    snake_case__    ,    snake_case__      )

									if has_remote_code and trust_remote_code:
															_UpperCAmelCase													=     get_class_from_dynamic_module(
															    snake_case__    ,    snake_case__    ,    **snake_case__      )
															_UpperCAmelCase													=     kwargs.pop("code_revision"    ,    snake_case__      )
															if os.path.isdir(snake_case__      ):
																					image_processor_class.register_for_auto_class()
															return image_processor_class.from_dict(snake_case__    ,    **snake_case__      )
									elif image_processor_class is not None:
															return image_processor_class.from_dict(snake_case__    ,    **snake_case__      )
									# Last try: we use the IMAGE_PROCESSOR_MAPPING.
									elif type(snake_case__      ) in IMAGE_PROCESSOR_MAPPING:
															_UpperCAmelCase													=     IMAGE_PROCESSOR_MAPPING[type(snake_case__      )]
															return image_processor_class.from_dict(snake_case__    ,    **snake_case__      )

									raise ValueError(
									    F"""Unrecognized image processor in {pretrained_model_name_or_path}. Should have a """
									    F"""`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following """
									    F"""`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()      )}"""      )







			@staticmethod
			def UpperCamelCase			(       snake_case__    :			Tuple    ,    snake_case__    :			Optional[int]      ):




									"""simple docstring"""


									IMAGE_PROCESSOR_MAPPING.register(snake_case__    ,    snake_case__      )











"""simple docstring"""





from argparse import ArgumentParser

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


SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)  # pylint: disable=invalid-name


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str			):







      '''simple docstring'''




      if not path:
            return "pipe"

      for ext in PipelineDataFormat.SUPPORTED_FORMATS:
            if path.endswith(SCREAMING_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 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Optional[Any]			):







      '''simple docstring'''




      lowerCAmelCase  						=       pipeline(
          task=args.task							,						model=args.model if args.model else None							,						config=args.config							,						tokenizer=args.tokenizer							,						device=args.device							,						)
      lowerCAmelCase  						=       try_infer_format_from_ext(args.input			) if args.format == """infer""" else args.format
      lowerCAmelCase  						=       PipelineDataFormat.from_str(
          format=SCREAMING_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(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)






class 		lowercase					(					_UpperCAmelCase					):





      def __init__(       self      ,						lowercase      ,						lowercase )		->     Union[str, Any]:
            lowerCAmelCase  						=       nlp
            lowerCAmelCase  						=       reader





      @staticmethod
      def     _snake_case			(       lowercase )		->     Optional[int]:
            lowerCAmelCase  						=       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=lowercase      ,						help="""Path to the file to use for inference""" )
            run_parser.add_argument("""--output"""      ,						type=lowercase      ,						help="""Path to the file that will be used post to write results.""" )
            run_parser.add_argument("""--model"""      ,						type=lowercase      ,						help="""Name or path to the model to instantiate.""" )
            run_parser.add_argument("""--config"""      ,						type=lowercase      ,						help="""Name or path to the model's config to instantiate.""" )
            run_parser.add_argument(
                """--tokenizer"""      ,						type=lowercase      ,						help="""Name of the tokenizer to use. (default: same as the model name)""" )
            run_parser.add_argument(
                """--column"""      ,						type=lowercase      ,						help="""Name of the column to use as input. (For multi columns input as QA use column1,columns2)"""      ,						)
            run_parser.add_argument(
                """--format"""      ,						type=lowercase      ,						default="""infer"""      ,						choices=PipelineDataFormat.SUPPORTED_FORMATS      ,						help="""Input format to read from"""      ,						)
            run_parser.add_argument(
                """--device"""      ,						type=lowercase      ,						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=lowercase )





      def     _snake_case			(       self )		->     int:
            lowerCAmelCase						,		lowerCAmelCase  						=       self._nlp, []

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

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






