


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,
)


_A			=						logging.get_logger(__name__)

_A			=						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'),
    ]
)

_A			=						_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : str    ):
     for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
          if class_name in extractors:
               __UpperCamelCase   							=model_type_to_module_name(SCREAMING_SNAKE_CASE__    )

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

     for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
          if getattr(SCREAMING_SNAKE_CASE__      ,					'__name__'      ,					SCREAMING_SNAKE_CASE__    ) == 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(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ):
          return getattr(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )

     return None

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Union[str, os.PathLike]      ,					SCREAMING_SNAKE_CASE__      : Optional[Union[str, os.PathLike]] = None      ,					SCREAMING_SNAKE_CASE__      : bool = False      ,					SCREAMING_SNAKE_CASE__      : bool = False      ,					SCREAMING_SNAKE_CASE__      : Optional[Dict[str, str]] = None      ,					SCREAMING_SNAKE_CASE__      : Optional[Union[bool, str]] = None      ,					SCREAMING_SNAKE_CASE__      : Optional[str] = None      ,					SCREAMING_SNAKE_CASE__      : bool = False      ,					**SCREAMING_SNAKE_CASE__      : Union[str, Any]      ,					):
     __UpperCamelCase   							=get_file_from_repo(
         SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					cache_dir=SCREAMING_SNAKE_CASE__      ,					force_download=SCREAMING_SNAKE_CASE__      ,					resume_download=SCREAMING_SNAKE_CASE__      ,					proxies=SCREAMING_SNAKE_CASE__      ,					use_auth_token=SCREAMING_SNAKE_CASE__      ,					revision=SCREAMING_SNAKE_CASE__      ,					local_files_only=SCREAMING_SNAKE_CASE__      ,					)
     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(SCREAMING_SNAKE_CASE__      ,					encoding='utf-8'    ) as reader:
          return json.load(SCREAMING_SNAKE_CASE__    )







class    UpperCAmelCase__	:



    """simple docstring"""






    def __init__(      self   )  ->	str:
         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(_SCREAMING_SNAKE_CASE   )
    def _a (      cls ,       A_ ,       **A_   )  ->	int:
         __UpperCamelCase   							=kwargs.pop('config' ,       _SCREAMING_SNAKE_CASE   )
         __UpperCamelCase   							=kwargs.pop('trust_remote_code' ,       _SCREAMING_SNAKE_CASE   )
         __UpperCamelCase   							=True

         __UpperCamelCase   							=ImageProcessingMixin.get_image_processor_dict(_SCREAMING_SNAKE_CASE ,       **_SCREAMING_SNAKE_CASE   )
         __UpperCamelCase   							=config_dict.get('image_processor_type' ,       _SCREAMING_SNAKE_CASE   )
         __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' ,       _SCREAMING_SNAKE_CASE   )
              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(_SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE   ):
                   __UpperCamelCase   							=AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE ,       **_SCREAMING_SNAKE_CASE   )
              # It could be in `config.image_processor_type``
              __UpperCamelCase   							=getattr(_SCREAMING_SNAKE_CASE ,       'image_processor_type' ,       _SCREAMING_SNAKE_CASE   )
              if hasattr(_SCREAMING_SNAKE_CASE ,       '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(_SCREAMING_SNAKE_CASE   )

         __UpperCamelCase   							=image_processor_auto_map is not None
         __UpperCamelCase   							=image_processor_class is not None or type(_SCREAMING_SNAKE_CASE   ) in IMAGE_PROCESSOR_MAPPING
         __UpperCamelCase   							=resolve_trust_remote_code(
             _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE   )

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

         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 _a (      A_ ,       A_   )  ->	List[Any]:
         IMAGE_PROCESSOR_MAPPING.register(_SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE   )









import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		torch.FloatTensor
    UpperCAmelCase__						:		Optional[torch.FloatTensor]              =  None

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[Any]      ,					SCREAMING_SNAKE_CASE__      : int=0.999      ,					SCREAMING_SNAKE_CASE__      : str="cosine"      ,					):
     if alpha_transform_type == "cosine":

          def alpha_bar_fn(SCREAMING_SNAKE_CASE__      : Any    ):
               return math.cos((t + 0.008) / 1.008 * math.pi / 2    ) ** 2

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(SCREAMING_SNAKE_CASE__      : Optional[Any]    ):
               return math.exp(t * -12.0    )

     else:
          raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'    )

     __UpperCamelCase   							=[]
     for i in range(SCREAMING_SNAKE_CASE__    ):
          __UpperCamelCase   							=i / num_diffusion_timesteps
          __UpperCamelCase   							=(i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE__    ) / alpha_bar_fn(SCREAMING_SNAKE_CASE__    )      ,					SCREAMING_SNAKE_CASE__    )    )
     return torch.tensor(SCREAMING_SNAKE_CASE__      ,					dtype=torch.floataa    )






class    UpperCAmelCase__	( A_       ,  A_		):



    """simple docstring"""






    @register_to_config
    def __init__(      self ,       A_ = 1000 ,       A_ = "fixed_small_log" ,       A_ = True ,       A_ = 1.0 ,       A_ = "epsilon" ,       A_ = "squaredcos_cap_v2" ,       )  ->	Tuple:
         if beta_schedule != "squaredcos_cap_v2":
              raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''   )

         __UpperCamelCase   							=betas_for_alpha_bar(A_   )

         __UpperCamelCase   							=1.0 - self.betas
         __UpperCamelCase   							=torch.cumprod(self.alphas ,       dim=0   )
         __UpperCamelCase   							=torch.tensor(1.0   )

         # standard deviation of the initial noise distribution
         __UpperCamelCase   							=1.0

         # setable values
         __UpperCamelCase   							=None
         __UpperCamelCase   							=torch.from_numpy(np.arange(0 ,       A_   )[::-1].copy()   )

         __UpperCamelCase   							=variance_type






    def _a (      self ,       A_ ,       A_ = None   )  ->	torch.FloatTensor:
         return sample






    def _a (      self ,       A_ ,       A_ = None   )  ->	Tuple:
         __UpperCamelCase   							=num_inference_steps
         __UpperCamelCase   							=(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
         __UpperCamelCase   							=(np.arange(0 ,       A_   ) * step_ratio).round()[::-1].copy().astype(np.intaa   )
         __UpperCamelCase   							=torch.from_numpy(A_   ).to(A_   )






    def _a (      self ,       A_ ,       A_=None ,       A_=None ,       A_=None   )  ->	List[Any]:
         if prev_timestep is None:
              __UpperCamelCase   							=t - 1

         __UpperCamelCase   							=self.alphas_cumprod[t]
         __UpperCamelCase   							=self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
         __UpperCamelCase   							=1 - alpha_prod_t
         __UpperCamelCase   							=1 - alpha_prod_t_prev

         if prev_timestep == t - 1:
              __UpperCamelCase   							=self.betas[t]
         else:
              __UpperCamelCase   							=1 - alpha_prod_t / alpha_prod_t_prev

         # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
         # and sample from it to get previous sample
         # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
         __UpperCamelCase   							=beta_prod_t_prev / beta_prod_t * beta

         if variance_type is None:
              __UpperCamelCase   							=self.config.variance_type

         # hacks - were probably added for training stability
         if variance_type == "fixed_small_log":
              __UpperCamelCase   							=torch.log(torch.clamp(A_ ,       min=1E-20   )   )
              __UpperCamelCase   							=torch.exp(0.5 * variance   )
         elif variance_type == "learned_range":
              # NOTE difference with DDPM scheduler
              __UpperCamelCase   							=variance.log()
              __UpperCamelCase   							=beta.log()

              __UpperCamelCase   							=(predicted_variance + 1) / 2
              __UpperCamelCase   							=frac * max_log + (1 - frac) * min_log

         return variance






    def _a (      self ,       A_ ,       A_ ,       A_ ,       A_ = None ,       A_=None ,       A_ = True ,       )  ->	Union[UnCLIPSchedulerOutput, Tuple]:
         __UpperCamelCase   							=timestep

         if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
              __UpperCamelCase							,				__UpperCamelCase   							=torch.split(A_ ,       sample.shape[1] ,       dim=1   )
         else:
              __UpperCamelCase   							=None

         # 1. compute alphas, betas
         if prev_timestep is None:
              __UpperCamelCase   							=t - 1

         __UpperCamelCase   							=self.alphas_cumprod[t]
         __UpperCamelCase   							=self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
         __UpperCamelCase   							=1 - alpha_prod_t
         __UpperCamelCase   							=1 - alpha_prod_t_prev

         if prev_timestep == t - 1:
              __UpperCamelCase   							=self.betas[t]
              __UpperCamelCase   							=self.alphas[t]
         else:
              __UpperCamelCase   							=1 - alpha_prod_t / alpha_prod_t_prev
              __UpperCamelCase   							=1 - beta

         # 2. compute predicted original sample from predicted noise also called
         # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
         if self.config.prediction_type == "epsilon":
              __UpperCamelCase   							=(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         elif self.config.prediction_type == "sample":
              __UpperCamelCase   							=model_output
         else:
              raise ValueError(
                  f'prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'
                  ' for the UnCLIPScheduler.'   )

         # 3. Clip "predicted x_0"
         if self.config.clip_sample:
              __UpperCamelCase   							=torch.clamp(
                  A_ ,       -self.config.clip_sample_range ,       self.config.clip_sample_range   )

         # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
         # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
         __UpperCamelCase   							=(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
         __UpperCamelCase   							=alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

         # 5. Compute predicted previous sample µ_t
         # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
         __UpperCamelCase   							=pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

         # 6. Add noise
         __UpperCamelCase   							=0
         if t > 0:
              __UpperCamelCase   							=randn_tensor(
                  model_output.shape ,       dtype=model_output.dtype ,       generator=A_ ,       device=model_output.device   )

              __UpperCamelCase   							=self._get_variance(
                  A_ ,       predicted_variance=A_ ,       prev_timestep=A_ ,       )

              if self.variance_type == "fixed_small_log":
                   __UpperCamelCase   							=variance
              elif self.variance_type == "learned_range":
                   __UpperCamelCase   							=(0.5 * variance).exp()
              else:
                   raise ValueError(
                       f'variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'
                       ' for the UnCLIPScheduler.'   )

              __UpperCamelCase   							=variance * variance_noise

         __UpperCamelCase   							=pred_prev_sample + variance

         if not return_dict:
              return (pred_prev_sample,)

         return UnCLIPSchedulerOutput(prev_sample=A_ ,       pred_original_sample=A_   )






    def _a (      self ,       A_ ,       A_ ,       A_ ,       )  ->	torch.FloatTensor:
         # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
         __UpperCamelCase   							=self.alphas_cumprod.to(device=original_samples.device ,       dtype=original_samples.dtype   )
         __UpperCamelCase   							=timesteps.to(original_samples.device   )

         __UpperCamelCase   							=alphas_cumprod[timesteps] ** 0.5
         __UpperCamelCase   							=sqrt_alpha_prod.flatten()
         while len(sqrt_alpha_prod.shape   ) < len(original_samples.shape   ):
              __UpperCamelCase   							=sqrt_alpha_prod.unsqueeze(-1   )

         __UpperCamelCase   							=(1 - alphas_cumprod[timesteps]) ** 0.5
         __UpperCamelCase   							=sqrt_one_minus_alpha_prod.flatten()
         while len(sqrt_one_minus_alpha_prod.shape   ) < len(original_samples.shape   ):
              __UpperCamelCase   							=sqrt_one_minus_alpha_prod.unsqueeze(-1   )

         __UpperCamelCase   							=sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
         return noisy_samples





