



"""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,
)


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 			snake_case		(		lowerCAmelCase_	)						->				str:
  for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
    if class_name in extractors:
      _snake_case   	=      model_type_to_module_name(lowerCAmelCase_	)

      _snake_case   	=      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   	=      importlib.import_module('''transformers'''	)
  if hasattr(lowerCAmelCase_  ,						lowerCAmelCase_	):
    return getattr(lowerCAmelCase_  ,						lowerCAmelCase_	)

  return None




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = False  ,						lowerCAmelCase_ = False  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = False  ,						**lowerCAmelCase_  ,						)						->				int:
  _snake_case   	=      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     UpperCAmelCase    :



   def __init__(       self    :	Any				):



     """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(__lowerCamelCase				)
   def    __UpperCAmelCase    (       cls    :	List[str]							,       __lowerCamelCase    :	List[str]							,       **__lowerCamelCase    :	Dict				):



     """simple docstring"""
     _snake_case   	=      kwargs.pop('''config'''							,       __lowerCamelCase				)
     _snake_case   	=      kwargs.pop('''trust_remote_code'''							,       __lowerCamelCase				)
     _snake_case   	=      True

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

     if image_processor_class is not None:
       _snake_case   	=      image_processor_class_from_name(__lowerCamelCase				)

     _snake_case   	=      image_processor_auto_map is not None
     _snake_case   	=      image_processor_class is not None or type(__lowerCamelCase				) in IMAGE_PROCESSOR_MAPPING
     _snake_case   	=      resolve_trust_remote_code(
         __lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase				)

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

     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    (       __lowerCamelCase    :	int							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     IMAGE_PROCESSOR_MAPPING.register(__lowerCamelCase							,       __lowerCamelCase				)










"""simple docstring"""






import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import MobileNetVaForImageClassification, MobileNetVaModel
     from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import MobileNetVaImageProcessor



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     _snake_case   	=      self.config_class(**self.inputs_dict				)
     self.parent.assertTrue(hasattr(__lowerCamelCase							,       '''tf_padding'''				)				)
     self.parent.assertTrue(hasattr(__lowerCamelCase							,       '''depth_multiplier'''				)				)



class     UpperCAmelCase    :



   def __init__(       self    :	List[str]							,       __lowerCamelCase    :	List[str]							,       __lowerCamelCase    :	Tuple=1_3							,       __lowerCamelCase    :	List[Any]=3							,       __lowerCamelCase    :	List[str]=3_2							,       __lowerCamelCase    :	Dict=0.2_5							,       __lowerCamelCase    :	Optional[Any]=8							,       __lowerCamelCase    :	Dict=True							,       __lowerCamelCase    :	List[Any]=1_0_2_4							,       __lowerCamelCase    :	Union[str, Any]=3_2							,       __lowerCamelCase    :	int="relu6"							,       __lowerCamelCase    :	Optional[int]=0.1							,       __lowerCamelCase    :	Tuple=0.0_2							,       __lowerCamelCase    :	Tuple=True							,       __lowerCamelCase    :	Dict=True							,       __lowerCamelCase    :	Union[str, Any]=1_0							,       __lowerCamelCase    :	Optional[Any]=None							,       ):



     """simple docstring"""
     _snake_case   	=      parent
     _snake_case   	=      batch_size
     _snake_case   	=      num_channels
     _snake_case   	=      image_size
     _snake_case   	=      depth_multiplier
     _snake_case   	=      min_depth
     _snake_case   	=      tf_padding
     _snake_case   	=      int(last_hidden_size * depth_multiplier				)
     _snake_case   	=      output_stride
     _snake_case   	=      hidden_act
     _snake_case   	=      classifier_dropout_prob
     _snake_case   	=      use_labels
     _snake_case   	=      is_training
     _snake_case   	=      num_labels
     _snake_case   	=      initializer_range
     _snake_case   	=      scope



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     _snake_case   	=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

     _snake_case   	=      None
     _snake_case   	=      None
     if self.use_labels:
       _snake_case   	=      ids_tensor([self.batch_size]							,       self.num_labels				)
       _snake_case   	=      ids_tensor([self.batch_size, self.image_size, self.image_size]							,       self.num_labels				)

     _snake_case   	=      self.get_config()

     return config, pixel_values, labels, pixel_labels



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     return MobileNetVaConfig(
         num_channels=self.num_channels							,       image_size=self.image_size							,       depth_multiplier=self.depth_multiplier							,       min_depth=self.min_depth							,       tf_padding=self.tf_padding							,       hidden_act=self.hidden_act							,       classifier_dropout_prob=self.classifier_dropout_prob							,       initializer_range=self.initializer_range							,       )



   def    __UpperCAmelCase    (       self    :	Dict							,       __lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	List[str]							,       __lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      MobileNetVaModel(config=__lowerCamelCase				)
     model.to(__lowerCamelCase				)
     model.eval()
     _snake_case   	=      model(__lowerCamelCase				)
     self.parent.assertEqual(
         result.last_hidden_state.shape							,       (
             self.batch_size,
             self.last_hidden_size,
             self.image_size // self.output_stride,
             self.image_size // self.output_stride,
         )							,       )



   def    __UpperCAmelCase    (       self    :	str							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Union[str, Any]							,       __lowerCamelCase    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case   	=      self.num_labels
     _snake_case   	=      MobileNetVaForImageClassification(__lowerCamelCase				)
     model.to(__lowerCamelCase				)
     model.eval()
     _snake_case   	=      model(__lowerCamelCase							,       labels=__lowerCamelCase				)
     self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.num_labels)				)





   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     _snake_case   	=      self.prepare_config_and_inputs()
     _snake_case	,     _snake_case	,     _snake_case	,     _snake_case   	=      config_and_inputs
     _snake_case   	=      {'''pixel_values''': pixel_values}
     return config, inputs_dict



@require_torch
class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,unittest.TestCase  ):
   A__		:	List[Any]           =  (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
   A__		:	Optional[int]           =  (
       {'''feature-extraction''': MobileNetVaModel, '''image-classification''': MobileNetVaForImageClassification}
       if is_torch_available()
       else {}
   )

   A__		:	List[str]           =  False
   A__		:	Tuple           =  False
   A__		:	List[Any]           =  False
   A__		:	List[str]           =  False



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     _snake_case   	=      MobileNetVaModelTester(self				)
     _snake_case   	=      MobileNetVaConfigTester(self							,       config_class=__lowerCamelCase							,       has_text_modality=__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     self.config_tester.run_common_tests()



   @unittest.skip(reason='''MobileNetV1 does not use inputs_embeds'''				)
   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     pass



   @unittest.skip(reason='''MobileNetV1 does not support input and output embeddings'''				)
   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     pass



   @unittest.skip(reason='''MobileNetV1 does not output attentions'''				)
   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     pass



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       _snake_case   	=      model_class(__lowerCamelCase				)
       _snake_case   	=      inspect.signature(model.forward				)
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       _snake_case   	=      [*signature.parameters.keys()]

       _snake_case   	=      ['''pixel_values''']
       self.assertListEqual(arg_names[:1]							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case   	=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     def check_hidden_states_output(__lowerCamelCase    :	Any							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	Union[str, Any]				):
       _snake_case   	=      model_class(__lowerCamelCase				)
       model.to(__lowerCamelCase				)
       model.eval()

       with torch.no_grad():
         _snake_case   	=      model(**self._prepare_for_class(__lowerCamelCase							,       __lowerCamelCase				)				)

       _snake_case   	=      outputs.hidden_states

       _snake_case   	=      2_6
       self.assertEqual(len(__lowerCamelCase				)							,       __lowerCamelCase				)

     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       _snake_case   	=      True
       check_hidden_states_output(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase				)

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       _snake_case   	=      True

       check_hidden_states_output(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase				)





   @slow
   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       _snake_case   	=      MobileNetVaModel.from_pretrained(__lowerCamelCase				)
       self.assertIsNotNone(__lowerCamelCase				)




def 			snake_case		(		)						->				int:
  _snake_case   	=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
  return image







@require_torch
@require_vision
class     UpperCAmelCase    (	unittest.TestCase  ):



   @cached_property
   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     return (
         MobileNetVaImageProcessor.from_pretrained('''google/mobilenet_v1_1.0_224'''				) if is_vision_available() else None
     )





   @slow
   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v1_1.0_224'''				).to(__lowerCamelCase				)

     _snake_case   	=      self.default_image_processor
     _snake_case   	=      prepare_img()
     _snake_case   	=      image_processor(images=__lowerCamelCase							,       return_tensors='''pt'''				).to(__lowerCamelCase				)

     # forward pass
     with torch.no_grad():
       _snake_case   	=      model(**__lowerCamelCase				)

     # verify the logits
     _snake_case   	=      torch.Size((1, 1_0_0_1)				)
     self.assertEqual(outputs.logits.shape							,       __lowerCamelCase				)

     _snake_case   	=      torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]				).to(__lowerCamelCase				)

     self.assertTrue(torch.allclose(outputs.logits[0, :3]							,       __lowerCamelCase							,       atol=1E-4				)				)





