



"""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,
)


lowercase  							=					logging.get_logger(__name__)

lowercase  							=					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'''),
    ]
)

lowercase  							=					_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)




def 	UpperCAmelCase     (		A	:						str       ):

  '''simple docstring'''


  for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
    if class_name in extractors:
      _UpperCAmelCase							 =		model_type_to_module_name(lowercase_       )

      _UpperCAmelCase							 =		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.
  _UpperCAmelCase							 =		importlib.import_module('transformers'       )
  if hasattr(lowercase_     ,				lowercase_       ):
    return getattr(lowercase_     ,				lowercase_       )

  return None




def 	UpperCAmelCase     (		A	:						int     ,				A	:						Optional[int] = None     ,				A	:						Tuple = False     ,				A	:						Any = False     ,				A	:						List[Any] = None     ,				A	:						Any = None     ,				A	:						Any = None     ,				A	:						Any = False     ,				**A	:						str     ,				):

  '''simple docstring'''


  _UpperCAmelCase							 =		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 			lowercase__			:
       '''simple docstring'''







       def __init__(  self						)			->							List[Any]:
         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        lowerCamelCase_   (  cls	,       snake_case	,       **snake_case						)			->							Optional[Any]:
         _UpperCAmelCase							 =		kwargs.pop('config'	,       __a						)
         _UpperCAmelCase							 =		kwargs.pop('trust_remote_code'	,       __a						)
         _UpperCAmelCase							 =		True

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

         if image_processor_class is not None:
           _UpperCAmelCase							 =		image_processor_class_from_name(__a						)

         _UpperCAmelCase							 =		image_processor_auto_map is not None
         _UpperCAmelCase							 =		image_processor_class is not None or type(__a						) in IMAGE_PROCESSOR_MAPPING
         _UpperCAmelCase							 =		resolve_trust_remote_code(
             __a	,       __a	,       __a	,       __a						)

         if has_remote_code and trust_remote_code:
           _UpperCAmelCase							 =		get_class_from_dynamic_module(
               __a	,       __a	,       **__a						)
           _UpperCAmelCase							 =		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:
           _UpperCAmelCase							 =		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        lowerCamelCase_   (  snake_case	,       snake_case						)			->							str:
         IMAGE_PROCESSOR_MAPPING.register(__a	,       __a						)







"""simple docstring"""





from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class 			lowercase__			(      nn.Module    ):
							'''simple docstring'''







							def __init__(  self	,       snake_case = 16	,       snake_case = 88	,       snake_case = None	,       snake_case = 1	,       snake_case = 0.0	,       snake_case = 32	,       snake_case = None	,       snake_case = False	,       snake_case = None	,       snake_case = None	,       snake_case = "geglu"	,       snake_case = None	,       )			->							str:
									super().__init__()
									_UpperCAmelCase							 =		nn.ModuleList(
									    [
									        TransformeraDModel(
									            num_attention_heads=snake_case	,       attention_head_dim=snake_case	,       in_channels=snake_case	,       num_layers=snake_case	,       dropout=snake_case	,       norm_num_groups=snake_case	,       cross_attention_dim=snake_case	,       attention_bias=snake_case	,       sample_size=snake_case	,       num_vector_embeds=snake_case	,       activation_fn=snake_case	,       num_embeds_ada_norm=snake_case	,       )
									        for _ in range(2						)
									    ]						)

									# Variables that can be set by a pipeline:

									# The ratio of transformer1 to transformer2's output states to be combined during inference
									_UpperCAmelCase							 =		0.5

									# The shape of `encoder_hidden_states` is expected to be
									# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
									_UpperCAmelCase							 =		[77, 257]

									# Which transformer to use to encode which condition.
									# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
									_UpperCAmelCase							 =		[1, 0]






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case=None	,       snake_case=None	,       snake_case=None	,       snake_case = True	,       )			->							Any:
									_UpperCAmelCase							 =		hidden_states

									_UpperCAmelCase							 =		[]
									_UpperCAmelCase							 =		0
									# attention_mask is not used yet
									for i in range(2						):
											# for each of the two transformers, pass the corresponding condition tokens
											_UpperCAmelCase							 =		encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
											_UpperCAmelCase							 =		self.transformer_index_for_condition[i]
											_UpperCAmelCase							 =		self.transformers[transformer_index](
											    snake_case	,       encoder_hidden_states=snake_case	,       timestep=snake_case	,       cross_attention_kwargs=snake_case	,       return_dict=snake_case	,       )[0]
											encoded_states.append(encoded_state - input_states						)
											tokens_start += self.condition_lengths[i]

									_UpperCAmelCase							 =		encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
									_UpperCAmelCase							 =		output_states + input_states

									if not return_dict:
											return (output_states,)

									return TransformeraDModelOutput(sample=snake_case						)


