
'''simple docstring'''

import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


__UpperCAmelCase												=   1000


class 		_a     :







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE=1.2    ,__SCREAMING_SNAKE_CASE="g"    ,__SCREAMING_SNAKE_CASE=0.5    ,__SCREAMING_SNAKE_CASE="-"    ,__SCREAMING_SNAKE_CASE="test_out.jpg"    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE=False    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE=0.7    ,):
    if isinstance(A_    ,torch.Tensor      ):
     SCREAMING_SNAKE_CASE  :  str								=		img.numpy().astype('np.uint8'      )
    if isinstance(A_    ,A_      ):
     SCREAMING_SNAKE_CASE  :  Optional[Any]								=		img_tensorize(A_      )
    assert isinstance(A_    ,np.ndarray      )

    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  List[Any]								=		img.shape[1], img.shape[0]
    SCREAMING_SNAKE_CASE  :  Tuple								=		mplfigure.Figure(frameon=A_      )
    SCREAMING_SNAKE_CASE  :  Dict								=		fig.get_dpi()
    SCREAMING_SNAKE_CASE  :  str								=		(width * scale + 1e-2) / dpi
    SCREAMING_SNAKE_CASE  :  Tuple								=		(height * scale + 1e-2) / dpi
    fig.set_size_inches(A_    ,A_      )
    SCREAMING_SNAKE_CASE  :  List[Any]								=		fig.add_axes([0.0, 0.0, 1.0, 1.0]      )
    ax.axis('off'      )
    ax.set_xlim(0.0    ,A_      )
    ax.set_ylim(A_      )

    SCREAMING_SNAKE_CASE  :  str								=		saveas
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		rgb
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		pynb
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		img
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		edgecolor
    SCREAMING_SNAKE_CASE  :  List[Any]								=		0.5
    SCREAMING_SNAKE_CASE  :  int								=		linestyle
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		int(np.sqrt(min(A_    ,A_      )      ) * scale // 3      )
    SCREAMING_SNAKE_CASE  :  str								=		width
    SCREAMING_SNAKE_CASE  :  Tuple								=		height
    SCREAMING_SNAKE_CASE  :  Dict								=		scale
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		fig
    SCREAMING_SNAKE_CASE  :  int								=		ax
    SCREAMING_SNAKE_CASE  :  Dict								=		pad
    SCREAMING_SNAKE_CASE  :  Tuple								=		idaobj
    SCREAMING_SNAKE_CASE  :  List[Any]								=		idaattr
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		FigureCanvasAgg(A_      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE=None      ):
    if color is None:
     SCREAMING_SNAKE_CASE  :  Dict								=		self.edgecolor
    ((SCREAMING_SNAKE_CASE)				, (SCREAMING_SNAKE_CASE)				, (SCREAMING_SNAKE_CASE)				, (SCREAMING_SNAKE_CASE))  :  Dict								=		box
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		xa - xa
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		ya - ya
    self.ax.add_patch(
        mpl.patches.Rectangle(
            (xa, ya)    ,A_    ,A_    ,fill=A_    ,edgecolor=A_    ,linewidth=self.font_size // 3    ,alpha=self.alpha    ,linestyle=self.linestyle    ,)      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE=None      ):
    if len(boxes.shape      ) > 2:
     SCREAMING_SNAKE_CASE  :  Tuple								=		boxes[0]
    if len(obj_ids.shape      ) > 1:
     SCREAMING_SNAKE_CASE  :  str								=		obj_ids[0]
    if len(obj_scores.shape      ) > 1:
     SCREAMING_SNAKE_CASE  :  List[Any]								=		obj_scores[0]
    if len(attr_ids.shape      ) > 1:
     SCREAMING_SNAKE_CASE  :  Tuple								=		attr_ids[0]
    if len(attr_scores.shape      ) > 1:
     SCREAMING_SNAKE_CASE  :  int								=		attr_scores[0]
    if isinstance(A_    ,torch.Tensor      ):
     SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		boxes.numpy()
    if isinstance(A_    ,A_      ):
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		np.array(A_      )
    assert isinstance(A_    ,np.ndarray      )
    SCREAMING_SNAKE_CASE  :  List[str]								=		np.prod(boxes[:, 2:] - boxes[:, :2]    ,axis=1      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		np.argsort(-areas      ).tolist()
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		boxes[sorted_idxs] if boxes is not None else None
    SCREAMING_SNAKE_CASE  :  int								=		obj_ids[sorted_idxs] if obj_ids is not None else None
    SCREAMING_SNAKE_CASE  :  Dict								=		obj_scores[sorted_idxs] if obj_scores is not None else None
    SCREAMING_SNAKE_CASE  :  List[Any]								=		attr_ids[sorted_idxs] if attr_ids is not None else None
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		attr_scores[sorted_idxs] if attr_scores is not None else None

    SCREAMING_SNAKE_CASE  :  List[str]								=		[self._random_color(maximum=1      ) for _ in range(len(A_      )      )]
    SCREAMING_SNAKE_CASE  :  List[Any]								=		[assigned_colors[idx] for idx in sorted_idxs]
    if obj_ids is not None:
     SCREAMING_SNAKE_CASE  :  Any								=		self._create_text_labels_attr(A_    ,A_    ,A_    ,A_      )
     for i in range(len(A_      )      ):
      SCREAMING_SNAKE_CASE  :  List[Any]								=		assigned_colors[i]
      self.add_box(boxes[i]    ,A_      )
      self.draw_labels(labels[i]    ,boxes[i]    ,A_      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		box
    SCREAMING_SNAKE_CASE  :  Tuple								=		(xa, ya)
    SCREAMING_SNAKE_CASE  :  Tuple								=		(ya - ya) * (xa - xa)
    SCREAMING_SNAKE_CASE  :  str								=		_SMALL_OBJ * self.scale
    if instance_area < small or ya - ya < 40 * self.scale:
     if ya >= self.height - 5:
      SCREAMING_SNAKE_CASE  :  Any								=		(xa, ya)
     else:
      SCREAMING_SNAKE_CASE  :  Optional[Any]								=		(xa, ya)

    SCREAMING_SNAKE_CASE  :  str								=		(ya - ya) / np.sqrt(self.height * self.width      )
    SCREAMING_SNAKE_CASE  :  Dict								=		self._change_color_brightness(A_    ,brightness_factor=0.7      )
    SCREAMING_SNAKE_CASE  :  Tuple								=		np.clip((height_ratio - 0.02) / 0.08 + 1    ,1.2    ,2      )
    font_size *= 0.75 * self.font_size

    self.draw_text(
        text=A_    ,position=A_    ,color=A_    ,)






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE="g"    ,__SCREAMING_SNAKE_CASE="left"    ,):
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		0
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		self.font_size
    SCREAMING_SNAKE_CASE  :  Dict								=		np.maximum(list(mplc.to_rgb(A_      )      )    ,0.2      )
    SCREAMING_SNAKE_CASE  :  Dict								=		max(0.8    ,np.max(A_      )      )
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		{
        'facecolor': 'black',
        'alpha': self.alpha,
        'pad': self.pad,
        'edgecolor': 'none',
    }
    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  Optional[int]								=		position
    self.ax.text(
        A_    ,A_    ,A_    ,size=font_size * self.scale    ,family='sans-serif'    ,bbox=A_    ,verticalalignment='top'    ,horizontalalignment=A_    ,color=A_    ,zorder=10    ,rotation=A_    ,)






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE=None      ):
    if saveas is None:
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		self.saveas
    if saveas.lower().endswith('.jpg'      ) or saveas.lower().endswith('.png'      ):
     cva.imwrite(
         A_    ,self._get_buffer()[:, :, ::-1]    ,)
    else:
     self.fig.savefig(A_      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		[self.idaobj[i] for i in classes]
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		[self.idaattr[i] for i in attr_classes]
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		[
        f"""{label} {score:.2f} {attr} {attr_score:.2f}"""
        for label, score, attr, attr_score in zip(A_    ,A_    ,A_    ,A_      )
    ]
    return labels






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		[self.idaobj[i] for i in classes]
    if scores is not None:
     if labels is None:
      SCREAMING_SNAKE_CASE  :  Dict								=		['{:.0f}%'.format(s * 100      ) for s in scores]
     else:
      SCREAMING_SNAKE_CASE  :  List[str]								=		['{} {:.0f}%'.format(A_    ,s * 100      ) for li, s in zip(A_    ,A_      )]
    return labels






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE=255      ):
    SCREAMING_SNAKE_CASE  :  int								=		np.random.randint(0    ,len(_COLORS      )      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		_COLORS[idx] * maximum
    if not self.rgb:
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		ret[::-1]
    return ret






   def 					__a			(       self      ):
    if not self.pynb:
     SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  List[Any]								=		self.canvas.print_to_buffer()
     if (width, height) != (self.width, self.height):
      SCREAMING_SNAKE_CASE  :  Any								=		cva.resize(self.img    ,(width, height)      )
     else:
      SCREAMING_SNAKE_CASE  :  List[Any]								=		self.img
    else:
     SCREAMING_SNAKE_CASE  :  int								=		io.BytesIO()  # works for cairo backend
     self.canvas.print_rgba(A_      )
     SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  List[Any]								=		self.width, self.height
     SCREAMING_SNAKE_CASE  :  Tuple								=		buf.getvalue()
     SCREAMING_SNAKE_CASE  :  Optional[Any]								=		self.img

    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		np.frombuffer(A_    ,dtype='uint8'      )
    SCREAMING_SNAKE_CASE  :  Tuple								=		buffer.reshape(A_    ,A_    ,4      )
    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  Dict								=		np.split(A_    ,[3]    ,axis=2      )

    try:
     import numexpr as ne  # fuse them with numexpr

     SCREAMING_SNAKE_CASE  :  List[str]								=		ne.evaluate('img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'      )
    except ImportError:
     SCREAMING_SNAKE_CASE  :  Any								=		alpha.astype('float32'      ) / 255.0
     SCREAMING_SNAKE_CASE  :  Any								=		img * (1 - alpha) + rgb * alpha

    return visualized_image.astype('uint8'      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    assert brightness_factor >= -1.0 and brightness_factor <= 1.0
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		mplc.to_rgb(A_      )
    SCREAMING_SNAKE_CASE  :  List[str]								=		colorsys.rgb_to_hls(*mplc.to_rgb(A_      )      )
    SCREAMING_SNAKE_CASE  :  int								=		polygon_color[1] + (brightness_factor * polygon_color[1])
    SCREAMING_SNAKE_CASE  :  Dict								=		0.0 if modified_lightness < 0.0 else modified_lightness
    SCREAMING_SNAKE_CASE  :  Tuple								=		1.0 if modified_lightness > 1.0 else modified_lightness
    SCREAMING_SNAKE_CASE  :  List[Any]								=		colorsys.hls_to_rgb(polygon_color[0]    ,A_    ,polygon_color[2]      )
    return modified_color



__UpperCAmelCase												=   (
    np.array(
        [
            0.000,
            0.447,
            0.741,
            0.850,
            0.325,
            0.098,
            0.929,
            0.694,
            0.125,
            0.494,
            0.184,
            0.556,
            0.466,
            0.674,
            0.188,
            0.301,
            0.745,
            0.933,
            0.635,
            0.078,
            0.184,
            0.300,
            0.300,
            0.300,
            0.600,
            0.600,
            0.600,
            1.000,
            0.000,
            0.000,
            1.000,
            0.500,
            0.000,
            0.749,
            0.749,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.333,
            0.333,
            0.000,
            0.333,
            0.667,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            0.333,
            0.000,
            0.667,
            0.667,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            1.000,
            0.000,
            0.000,
            0.333,
            0.500,
            0.000,
            0.667,
            0.500,
            0.000,
            1.000,
            0.500,
            0.333,
            0.000,
            0.500,
            0.333,
            0.333,
            0.500,
            0.333,
            0.667,
            0.500,
            0.333,
            1.000,
            0.500,
            0.667,
            0.000,
            0.500,
            0.667,
            0.333,
            0.500,
            0.667,
            0.667,
            0.500,
            0.667,
            1.000,
            0.500,
            1.000,
            0.000,
            0.500,
            1.000,
            0.333,
            0.500,
            1.000,
            0.667,
            0.500,
            1.000,
            1.000,
            0.500,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.333,
            0.333,
            1.000,
            0.333,
            0.667,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.667,
            0.333,
            1.000,
            0.667,
            0.667,
            1.000,
            0.667,
            1.000,
            1.000,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            1.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.143,
            0.143,
            0.143,
            0.857,
            0.857,
            0.857,
            1.000,
            1.000,
            1.000,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)



import importlib
import inspect
import json
import os
from collections import OrderedDict

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import FEATURE_EXTRACTOR_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,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


snake_case__						      =			logging.get_logger(__name__)

snake_case__						      =			OrderedDict(
    [
        ('align', 'AlignProcessor'),
        ('altclip', 'AltCLIPProcessor'),
        ('bark', 'BarkProcessor'),
        ('blip', 'BlipProcessor'),
        ('blip-2', 'Blip2Processor'),
        ('bridgetower', 'BridgeTowerProcessor'),
        ('chinese_clip', 'ChineseCLIPProcessor'),
        ('clap', 'ClapProcessor'),
        ('clip', 'CLIPProcessor'),
        ('clipseg', 'CLIPSegProcessor'),
        ('flava', 'FlavaProcessor'),
        ('git', 'GitProcessor'),
        ('groupvit', 'CLIPProcessor'),
        ('hubert', 'Wav2Vec2Processor'),
        ('instructblip', 'InstructBlipProcessor'),
        ('layoutlmv2', 'LayoutLMv2Processor'),
        ('layoutlmv3', 'LayoutLMv3Processor'),
        ('markuplm', 'MarkupLMProcessor'),
        ('mctct', 'MCTCTProcessor'),
        ('mgp-str', 'MgpstrProcessor'),
        ('oneformer', 'OneFormerProcessor'),
        ('owlvit', 'OwlViTProcessor'),
        ('pix2struct', 'Pix2StructProcessor'),
        ('sam', 'SamProcessor'),
        ('sew', 'Wav2Vec2Processor'),
        ('sew-d', 'Wav2Vec2Processor'),
        ('speech_to_text', 'Speech2TextProcessor'),
        ('speech_to_text_2', 'Speech2Text2Processor'),
        ('speecht5', 'SpeechT5Processor'),
        ('trocr', 'TrOCRProcessor'),
        ('tvlt', 'TvltProcessor'),
        ('unispeech', 'Wav2Vec2Processor'),
        ('unispeech-sat', 'Wav2Vec2Processor'),
        ('vilt', 'ViltProcessor'),
        ('vision-text-dual-encoder', 'VisionTextDualEncoderProcessor'),
        ('wav2vec2', 'Wav2Vec2Processor'),
        ('wav2vec2-conformer', 'Wav2Vec2Processor'),
        ('wavlm', 'Wav2Vec2Processor'),
        ('whisper', 'WhisperProcessor'),
        ('xclip', 'XCLIPProcessor'),
    ]
)

snake_case__						      =			_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)
def    __magic_name__(	__UpperCAmelCase		)    ->     List[str]:



      '''simple docstring'''

      for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
            if class_name in processors:
                  _lowerCamelCase           =					model_type_to_module_name(__UpperCAmelCase		)

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

      for processor in PROCESSOR_MAPPING._extra_content.values():
            if getattr(__UpperCAmelCase			,    '''__name__'''			,    __UpperCAmelCase		) == class_name:
                  return processor

    # 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(__UpperCAmelCase			,    __UpperCAmelCase		):
            return getattr(__UpperCAmelCase			,    __UpperCAmelCase		)

      return None




class 			UpperCamelCase					:

     '''simple docstring'''

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






           """simple docstring"""
           raise EnvironmentError(
               '''AutoProcessor is designed to be instantiated '''
               '''using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method.'''    )



     @classmethod
     @replace_list_option_in_docstrings(A_    )
     def 		UpperCamelCase_    (       cls					,					A_					,					**A_    )			->					List[Any]:






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

           _lowerCamelCase           =					None
           _lowerCamelCase           =					None

           # First, let's see if we have a preprocessor config.
           # Filter the kwargs for `get_file_from_repo`.
           _lowerCamelCase           =					{
               key: kwargs[key] for key in inspect.signature(A_    ).parameters.keys() if key in kwargs
           }
           # Let's start by checking whether the processor class is saved in an image processor
           _lowerCamelCase           =					get_file_from_repo(
               A_					,					A_					,					**A_    )
           if preprocessor_config_file is not None:
                 _lowerCamelCase     ,       _lowerCamelCase           =					ImageProcessingMixin.get_image_processor_dict(A_					,					**A_    )
                 _lowerCamelCase           =					config_dict.get('''processor_class'''					,					A_    )
                 if "AutoProcessor" in config_dict.get('''auto_map'''					,					{}    ):
                       _lowerCamelCase           =					config_dict['''auto_map''']['''AutoProcessor''']

        # If not found, let's check whether the processor class is saved in a feature extractor config
           if preprocessor_config_file is not None and processor_class is None:
                 _lowerCamelCase     ,       _lowerCamelCase           =					FeatureExtractionMixin.get_feature_extractor_dict(A_					,					**A_    )
                 _lowerCamelCase           =					config_dict.get('''processor_class'''					,					A_    )
                 if "AutoProcessor" in config_dict.get('''auto_map'''					,					{}    ):
                       _lowerCamelCase           =					config_dict['''auto_map''']['''AutoProcessor''']

           if processor_class is None:
                 # Next, let's check whether the processor class is saved in a tokenizer
                 _lowerCamelCase           =					get_file_from_repo(
                     A_					,					A_					,					**A_    )
                 if tokenizer_config_file is not None:
                       with open(A_					,					encoding='''utf-8'''    ) as reader:
                             _lowerCamelCase           =					json.load(A_    )

                       _lowerCamelCase           =					config_dict.get('''processor_class'''					,					A_    )
                       if "AutoProcessor" in config_dict.get('''auto_map'''					,					{}    ):
                             _lowerCamelCase           =					config_dict['''auto_map''']['''AutoProcessor''']

           if processor_class is None:
                 # Otherwise, load config, if it can be loaded.
                 if not isinstance(A_					,					A_    ):
                       _lowerCamelCase           =					AutoConfig.from_pretrained(
                           A_					,					trust_remote_code=A_					,					**A_    )

                 # And check if the config contains the processor class.
                 _lowerCamelCase           =					getattr(A_					,					'''processor_class'''					,					A_    )
                 if hasattr(A_					,					'''auto_map'''    ) and "AutoProcessor" in config.auto_map:
                       _lowerCamelCase           =					config.auto_map['''AutoProcessor''']

           if processor_class is not None:
                 _lowerCamelCase           =					processor_class_from_name(A_    )

           _lowerCamelCase           =					processor_auto_map is not None
           _lowerCamelCase           =					processor_class is not None or type(A_    ) in 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_    ):
                       processor_class.register_for_auto_class()
                 return processor_class.from_pretrained(
                     A_					,					trust_remote_code=A_					,					**A_    )
           elif processor_class is not None:
                 return processor_class.from_pretrained(
                     A_					,					trust_remote_code=A_					,					**A_    )
           # Last try: we use the PROCESSOR_MAPPING.
           elif type(A_    ) in PROCESSOR_MAPPING:
                 return PROCESSOR_MAPPING[type(A_    )].from_pretrained(A_					,					**A_    )

           # At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
           # tokenizer.
           try:
                 return AutoTokenizer.from_pretrained(
                     A_					,					trust_remote_code=A_					,					**A_    )
           except Exception:
                 try:
                       return AutoImageProcessor.from_pretrained(
                           A_					,					trust_remote_code=A_					,					**A_    )
                 except Exception:
                       pass

                 try:
                       return AutoFeatureExtractor.from_pretrained(
                           A_					,					trust_remote_code=A_					,					**A_    )
                 except Exception:
                       pass

           raise ValueError(
               F'Unrecognized processing class in {pretrained_model_name_or_path}. Can\'t instantiate a processor, a '
               '''tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains'''
               '''the files of at least one of those processing classes.'''    )





     @staticmethod
     def 		UpperCamelCase_    (       A_					,					A_    )			->					List[Any]:






           """simple docstring"""
           PROCESSOR_MAPPING.register(A_					,					A_    )