

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,
)


snake_case__				= logging.get_logger(__name__)

snake_case__				= 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'),
    ]
)

snake_case__				= _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def       __magic_name__(  __UpperCAmelCase		)						->    Tuple:



      '''simple docstring'''

      for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
            if class_name in extractors:
                  _lowerCamelCase			    =				model_type_to_module_name(__lowercase		)

                  _lowerCamelCase			    =				importlib.import_module(F'.{module_name}' ,						'''transformers.models'''		)
                  try:
                        return getattr(__lowercase ,						__lowercase		)
                  except AttributeError:
                        continue

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

      return None
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase = None ,						__UpperCAmelCase = False ,						__UpperCAmelCase = False ,						__UpperCAmelCase = None ,						__UpperCAmelCase = None ,						__UpperCAmelCase = None ,						__UpperCAmelCase = False ,						**__UpperCAmelCase ,						)						->    Optional[Any]:



      '''simple docstring'''

      _lowerCamelCase			    =				get_file_from_repo(
          __lowercase ,						__lowercase ,						cache_dir=__lowercase ,						force_download=__lowercase ,						resume_download=__lowercase ,						proxies=__lowercase ,						use_auth_token=__lowercase ,						revision=__lowercase ,						local_files_only=__lowercase ,						)
      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(__lowercase ,						encoding='''utf-8'''		) as reader:
            return json.load(__lowercase		)




class  UpperCamelCase				:

     '''simple docstring'''

     def __init__(					self						) ->				List[str]:






           """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(A_						)
     def 		UpperCamelCase_						(					cls				,				A_				,				**A_						) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				kwargs.pop('''config'''				,				A_						)
           _lowerCamelCase			    =				kwargs.pop('''trust_remote_code'''				,				A_						)
           _lowerCamelCase			    =				True

           _lowerCamelCase			    =				ImageProcessingMixin.get_image_processor_dict(A_				,				**A_						)
           _lowerCamelCase			    =				config_dict.get('''image_processor_type'''				,				A_						)
           _lowerCamelCase			    =				None
           if "AutoImageProcessor" in config_dict.get('''auto_map'''				,				{}						):
                 _lowerCamelCase			    =				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:
                 _lowerCamelCase			    =				config_dict.pop('''feature_extractor_type'''				,				A_						)
                 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.'''						)
                       _lowerCamelCase			    =				feature_extractor_class.replace('''FeatureExtractor'''				,				'''ImageProcessor'''						)
                 if "AutoFeatureExtractor" in config_dict.get('''auto_map'''				,				{}						):
                       _lowerCamelCase			    =				config_dict['auto_map']['AutoFeatureExtractor']
                       _lowerCamelCase			    =				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(A_				,				A_						):
                       _lowerCamelCase			    =				AutoConfig.from_pretrained(A_				,				**A_						)
                 # It could be in `config.image_processor_type``
                 _lowerCamelCase			    =				getattr(A_				,				'''image_processor_type'''				,				A_						)
                 if hasattr(A_				,				'''auto_map'''						) and "AutoImageProcessor" in config.auto_map:
                       _lowerCamelCase			    =				config.auto_map['AutoImageProcessor']

           if image_processor_class is not None:
                 _lowerCamelCase			    =				image_processor_class_from_name(A_						)

           _lowerCamelCase			    =				image_processor_auto_map is not None
           _lowerCamelCase			    =				image_processor_class is not None or type(A_						) in IMAGE_PROCESSOR_MAPPING
           _lowerCamelCase			    =				resolve_trust_remote_code(
               A_				,				A_				,				A_				,				A_						)

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

           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_						(					A_				,				A_						) ->				Tuple:






           """simple docstring"""
           IMAGE_PROCESSOR_MAPPING.register(A_				,				A_						)
from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case__				= {
    'configuration_trajectory_transformer': [
        'TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TrajectoryTransformerConfig',
    ],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__				= [
         'TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TrajectoryTransformerModel',
         'TrajectoryTransformerPreTrainedModel',
         'load_tf_weights_in_trajectory_transformer',
     ]


if TYPE_CHECKING:
     from .configuration_trajectory_transformer import (
         TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         TrajectoryTransformerConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_trajectory_transformer import (
              TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TrajectoryTransformerModel,
              TrajectoryTransformerPreTrainedModel,
              load_tf_weights_in_trajectory_transformer,
          )


else:
     import sys

     snake_case__				= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)