
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,
)


__lowerCAmelCase					:			Any			      =		logging.get_logger(__name__)

__lowerCAmelCase					:			Dict			      =		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"),
    ]
)

__lowerCAmelCase					:			List[Any]			      =		_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						Dict:
    for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
        if class_name in extractors:
            __lowercase		:			str	    =       model_type_to_module_name(UpperCAmelCase__	)

            __lowercase		:			Optional[Any]	    =       importlib.import_module(F'.{module_name}'	,					'''transformers.models'''	)
            try:
                return getattr(UpperCAmelCase__	,					UpperCAmelCase__	)
            except AttributeError:
                continue

    for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
        if getattr(UpperCAmelCase__	,					'''__name__'''	,					UpperCAmelCase__	) == 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.
    __lowercase		:			int	    =       importlib.import_module('''transformers'''	)
    if hasattr(UpperCAmelCase__	,					UpperCAmelCase__	):
        return getattr(UpperCAmelCase__	,					UpperCAmelCase__	)

    return None


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase = None	,					__lowerCAmelCase = False	,					__lowerCAmelCase = False	,					__lowerCAmelCase = None	,					__lowerCAmelCase = None	,					__lowerCAmelCase = None	,					__lowerCAmelCase = False	,					**__lowerCAmelCase	,					) ->						Optional[Any]:
    __lowercase		:			List[Any]	    =       get_file_from_repo(
        UpperCAmelCase__	,					UpperCAmelCase__	,					cache_dir=UpperCAmelCase__	,					force_download=UpperCAmelCase__	,					resume_download=UpperCAmelCase__	,					proxies=UpperCAmelCase__	,					use_auth_token=UpperCAmelCase__	,					revision=UpperCAmelCase__	,					local_files_only=UpperCAmelCase__	,					)
    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(UpperCAmelCase__	,					encoding='''utf-8'''	) as reader:
        return json.load(UpperCAmelCase__	)





class 						__lowerCAmelCase	:



      """simple docstring"""







      def __init__(      self		:       List[str]				):
          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(__UpperCamelCase				)
      def 		snake_case_			(      cls		:       Tuple				,       _snake_case		:       Union[str, Any]				,       **_snake_case		:       Optional[Any]				):
          __lowercase		:			Union[str, Any]	    =       kwargs.pop('''config'''				,       __UpperCamelCase				)
          __lowercase		:			List[Any]	    =       kwargs.pop('''trust_remote_code'''				,       __UpperCamelCase				)
          __lowercase		:			Optional[Any]	    =       True

          __lowercase    ,      __lowercase		:			str	    =       ImageProcessingMixin.get_image_processor_dict(__UpperCamelCase				,       **__UpperCamelCase				)
          __lowercase		:			Tuple	    =       config_dict.get('''image_processor_type'''				,       __UpperCamelCase				)
          __lowercase		:			str	    =       None
          if "AutoImageProcessor" in config_dict.get('''auto_map'''				,       {}				):
              __lowercase		:			Dict	    =       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:
              __lowercase		:			List[str]	    =       config_dict.pop('''feature_extractor_type'''				,       __UpperCamelCase				)
              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.'''				)
                  __lowercase		:			str	    =       feature_extractor_class.replace('''FeatureExtractor'''				,       '''ImageProcessor'''				)
              if "AutoFeatureExtractor" in config_dict.get('''auto_map'''				,       {}				):
                  __lowercase		:			Tuple	    =       config_dict['''auto_map''']['''AutoFeatureExtractor''']
                  __lowercase		:			Any	    =       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(__UpperCamelCase				,       __UpperCamelCase				):
                  __lowercase		:			Union[str, Any]	    =       AutoConfig.from_pretrained(__UpperCamelCase				,       **__UpperCamelCase				)
              # It could be in `config.image_processor_type``
              __lowercase		:			int	    =       getattr(__UpperCamelCase				,       '''image_processor_type'''				,       __UpperCamelCase				)
              if hasattr(__UpperCamelCase				,       '''auto_map'''				) and "AutoImageProcessor" in config.auto_map:
                  __lowercase		:			Optional[Any]	    =       config.auto_map['''AutoImageProcessor''']

          if image_processor_class is not None:
              __lowercase		:			List[str]	    =       image_processor_class_from_name(__UpperCamelCase				)

          __lowercase		:			Optional[Any]	    =       image_processor_auto_map is not None
          __lowercase		:			Tuple	    =       image_processor_class is not None or type(__UpperCamelCase				) in IMAGE_PROCESSOR_MAPPING
          __lowercase		:			Tuple	    =       resolve_trust_remote_code(
              __UpperCamelCase				,       __UpperCamelCase				,       __UpperCamelCase				,       __UpperCamelCase				)

          if has_remote_code and trust_remote_code:
              __lowercase		:			Optional[int]	    =       get_class_from_dynamic_module(
                  __UpperCamelCase				,       __UpperCamelCase				,       **__UpperCamelCase				)
              __lowercase		:			Optional[Any]	    =       kwargs.pop('''code_revision'''				,       __UpperCamelCase				)
              if os.path.isdir(__UpperCamelCase				):
                  image_processor_class.register_for_auto_class()
              return image_processor_class.from_dict(__UpperCamelCase				,       **__UpperCamelCase				)
          elif image_processor_class is not None:
              return image_processor_class.from_dict(__UpperCamelCase				,       **__UpperCamelCase				)
          # Last try: we use the IMAGE_PROCESSOR_MAPPING.
          elif type(__UpperCamelCase				) in IMAGE_PROCESSOR_MAPPING:
              __lowercase		:			Tuple	    =       IMAGE_PROCESSOR_MAPPING[type(__UpperCamelCase				)]
              return image_processor_class.from_dict(__UpperCamelCase				,       **__UpperCamelCase				)

          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 		snake_case_			(      _snake_case		:       Optional[int]				,       _snake_case		:       Tuple				):
          IMAGE_PROCESSOR_MAPPING.register(__UpperCamelCase				,       __UpperCamelCase				)


import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast


@dataclass
class 						__lowerCAmelCase	(	datasets.BuilderConfig      ):



      """simple docstring"""







      A__	:			Optional[datasets.Features]        =    None






class 						__lowerCAmelCase	(	datasets.ArrowBasedBuilder      ):



      """simple docstring"""







      A__	:			List[str]        =    PandasConfig




      def 		snake_case_			(      self		:       int				):
          return datasets.DatasetInfo(features=self.config.features				)




      def 		snake_case_			(      self		:       int				,       _snake_case		:       Optional[int]				):
          if not self.config.data_files:
              raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}'				)
          __lowercase		:			Any	    =       dl_manager.download_and_extract(self.config.data_files				)
          if isinstance(_snake_case				,       (str, list, tuple)				):
              __lowercase		:			str	    =       data_files
              if isinstance(_snake_case				,       _snake_case				):
                  __lowercase		:			Any	    =       [files]
              # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
              __lowercase		:			Optional[int]	    =       [dl_manager.iter_files(_snake_case				) for file in files]
              return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,       gen_kwargs={'''files''': files}				)]
          __lowercase		:			str	    =       []
          for split_name, files in data_files.items():
              if isinstance(_snake_case				,       _snake_case				):
                  __lowercase		:			str	    =       [files]
              # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
              __lowercase		:			int	    =       [dl_manager.iter_files(_snake_case				) for file in files]
              splits.append(datasets.SplitGenerator(name=_snake_case				,       gen_kwargs={'''files''': files}				)				)
          return splits




      def 		snake_case_			(      self		:       Optional[int]				,       _snake_case		:       pa.Table				):
          if self.config.features is not None:
              # more expensive cast to support nested features with keys in a different order
              # allows str <-> int/float or str to Audio for example
              __lowercase		:			List[str]	    =       table_cast(_snake_case				,       self.config.features.arrow_schema				)
          return pa_table




      def 		snake_case_			(      self		:       int				,       _snake_case		:       Union[str, Any]				):
          for i, file in enumerate(itertools.chain.from_iterable(_snake_case				)				):
              with open(_snake_case				,       '''rb'''				) as f:
                  __lowercase		:			Dict	    =       pa.Table.from_pandas(pd.read_pickle(_snake_case				)				)
                  yield i, self._cast_table(_snake_case				)


