





'''simple docstring'''

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,
)


UpperCAmelCase					:		str		=							logging.get_logger(__name__)

UpperCAmelCase					:		Optional[Any]		=							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'),
    ]
)

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




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
            if class_name in extractors:
                  _snake_case		:	Any					     =     model_type_to_module_name(lowerCAmelCase_	)

                  _snake_case		:	Tuple					     =     importlib.import_module(f'''.{module_name}'''			,      '''transformers.models'''	)
                  try:
                        return getattr(lowerCAmelCase_			,      lowerCAmelCase_	)
                  except AttributeError:
                        continue

      for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
            if getattr(lowerCAmelCase_			,      '''__name__'''			,      lowerCAmelCase_	) == 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.
      _snake_case		:	List[str]					     =     importlib.import_module('''transformers'''	)
      if hasattr(lowerCAmelCase_			,      lowerCAmelCase_	):
            return getattr(lowerCAmelCase_			,      lowerCAmelCase_	)

      return None




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_ = None			,      lowerCAmelCase_ = False			,      lowerCAmelCase_ = False			,      lowerCAmelCase_ = None			,      lowerCAmelCase_ = None			,      lowerCAmelCase_ = None			,      lowerCAmelCase_ = False			,      **lowerCAmelCase_			,      ):





      """simple docstring"""



      _snake_case		:	Optional[Any]					     =     get_file_from_repo(
          lowerCAmelCase_			,      lowerCAmelCase_			,      cache_dir=lowerCAmelCase_			,      force_download=lowerCAmelCase_			,      resume_download=lowerCAmelCase_			,      proxies=lowerCAmelCase_			,      use_auth_token=lowerCAmelCase_			,      revision=lowerCAmelCase_			,      local_files_only=lowerCAmelCase_			,      )
      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(lowerCAmelCase_			,      encoding='''utf-8'''	) as reader:
            return json.load(lowerCAmelCase_	)





class lowerCamelCase						:







  def __init__(		self		)		->						Dict:






        """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(lowercase__		)
  def 				UpperCAmelCase_				(		cls     ,     lowercase__     ,     **lowercase__		)		->						List[Any]:






        """simple docstring"""





        _snake_case		:	str					     =     kwargs.pop('''config'''     ,     lowercase__		)
        _snake_case		:	int					     =     kwargs.pop('''trust_remote_code'''     ,     lowercase__		)
        _snake_case		:	List[str]					     =     True

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

        if image_processor_class is not None:
              _snake_case		:	Optional[Any]					     =     image_processor_class_from_name(lowercase__		)

        _snake_case		:	Optional[Any]					     =     image_processor_auto_map is not None
        _snake_case		:	Dict					     =     image_processor_class is not None or type(lowercase__		) in IMAGE_PROCESSOR_MAPPING
        _snake_case		:	Dict					     =     resolve_trust_remote_code(
            lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__		)

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

        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_				(		lowercase__     ,     lowercase__		)		->						Optional[int]:






        """simple docstring"""





        IMAGE_PROCESSOR_MAPPING.register(lowercase__     ,     lowercase__		)









'''simple docstring'''

import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
   import optax
   from flax.training.common_utils import onehot

   from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
   from transformers.models.ta.modeling_flax_ta import shift_tokens_right





@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class lowerCamelCase						(unittest.TestCase					):







  @slow
  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        _snake_case		:	Tuple					     =     FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''		)
        _snake_case		:	Any					     =     AutoTokenizer.from_pretrained('''google/mt5-small'''		)

        _snake_case		:	List[str]					     =     tokenizer('''Hello there'''     ,     return_tensors='''np'''		).input_ids
        _snake_case		:	Dict					     =     tokenizer('''Hi I am'''     ,     return_tensors='''np'''		).input_ids

        _snake_case		:	Any					     =     shift_tokens_right(lowercase__     ,     model.config.pad_token_id     ,     model.config.decoder_start_token_id		)

        _snake_case		:	Any					     =     model(lowercase__     ,     decoder_input_ids=lowercase__		).logits
        _snake_case		:	Tuple					     =     optax.softmax_cross_entropy(lowercase__     ,     onehot(lowercase__     ,     logits.shape[-1]		)		).mean()

        _snake_case		:	Tuple					     =     -(labels.shape[-1] * loss.item())

        _snake_case		:	Union[str, Any]					     =     -84.9_127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1E-4		)


