
'''simple docstring'''





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


a__		: List[Any]  							=							logging.get_logger(__name__)

a__		: Optional[Any]  							=							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"),
    ]
)

a__		: Tuple  							=							_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)





def 							snake_case				(		UpperCAmelCase						)->      Dict:







      """simple docstring"""
      for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
            if class_name in processors:
                  __A													=      model_type_to_module_name(UpperCAmelCase						)

                  __A													=      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.
      __A													=      importlib.import_module('transformers'						)
      if hasattr(UpperCAmelCase   ,	UpperCAmelCase						):
            return getattr(UpperCAmelCase   ,	UpperCAmelCase						)

      return None


class 			UpperCamelCase__	:






 def __init__(      self	:Optional[Any]      )					->      Tuple:







       '''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 						lowercase_    (      cls	:Union[str, Any]						,					_A	:Optional[int]						,					**_A	:int      )					->      Dict:







       '''simple docstring'''



       __A													=      kwargs.pop('config'						,					_A      )
       __A													=      kwargs.pop('trust_remote_code'						,					_A      )
       __A													=      True

       __A													=      None
       __A													=      None

       # First, let's see if we have a preprocessor config.
       # Filter the kwargs for `get_file_from_repo`.
       __A													=      {
           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
       __A													=      get_file_from_repo(
           _A						,					_A						,					**_A      )
       if preprocessor_config_file is not None:
             __A						,   __A													=      ImageProcessingMixin.get_image_processor_dict(_A						,					**_A      )
             __A													=      config_dict.get('processor_class'						,					_A      )
             if "AutoProcessor" in config_dict.get('auto_map'						,					{}      ):
                   __A													=      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:
             __A						,   __A													=      FeatureExtractionMixin.get_feature_extractor_dict(_A						,					**_A      )
             __A													=      config_dict.get('processor_class'						,					_A      )
             if "AutoProcessor" in config_dict.get('auto_map'						,					{}      ):
                   __A													=      config_dict['auto_map']['AutoProcessor']

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

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

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

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

       if processor_class is not None:
             __A													=      processor_class_from_name(_A      )

       __A													=      processor_auto_map is not None
       __A													=      processor_class is not None or type(_A      ) in PROCESSOR_MAPPING
       __A													=      resolve_trust_remote_code(
           _A						,					_A						,					_A						,					_A      )

       if has_remote_code and trust_remote_code:
             __A													=      get_class_from_dynamic_module(
                 _A						,					_A						,					**_A      )
             __A													=      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 						lowercase_    (      _A	:Optional[int]						,					_A	:int      )					->      List[str]:







       '''simple docstring'''



       PROCESSOR_MAPPING.register(_A						,					_A      )








'''simple docstring'''





import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE   ,  unittest.TestCase):
 UpperCAmelCase__     :     Union[str, Any]    					=       RePaintPipeline
 UpperCAmelCase__     :     Any    					=       IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
 UpperCAmelCase__     :     Optional[int]    					=       PipelineTesterMixin.required_optional_params - {
     'latents',
     'num_images_per_prompt',
     'callback',
     'callback_steps',
 }
 UpperCAmelCase__     :     Optional[int]    					=       IMAGE_INPAINTING_BATCH_PARAMS
 UpperCAmelCase__     :     Any    					=       False






 def 						lowercase_    (      self	:Any      )					->      int:







       '''simple docstring'''



       torch.manual_seed(0      )
       torch.manual_seed(0      )
       __A													=      UNetaDModel(
           block_out_channels=(32, 64)						,					layers_per_block=2						,					sample_size=32						,					in_channels=3						,					out_channels=3						,					down_block_types=('DownBlock2D', 'AttnDownBlock2D')						,					up_block_types=('AttnUpBlock2D', 'UpBlock2D')						,					)
       __A													=      RePaintScheduler()
       __A													=      {'unet': unet, 'scheduler': scheduler}
       return components






 def 						lowercase_    (      self	:Any						,					_A	:List[Any]						,					_A	:Tuple=0      )					->      List[str]:







       '''simple docstring'''



       if str(_A      ).startswith('mps'      ):
             __A													=      torch.manual_seed(_A      )
       else:
             __A													=      torch.Generator(device=_A      ).manual_seed(_A      )
       __A													=      np.random.RandomState(_A      ).standard_normal((1, 3, 32, 32)      )
       __A													=      torch.from_numpy(_A      ).to(device=_A						,					dtype=torch.floataa      )
       __A													=      (image > 0).to(device=_A						,					dtype=torch.floataa      )
       __A													=      {
           'image': image,
           'mask_image': mask,
           'generator': generator,
           'num_inference_steps': 5,
           'eta': 0.0,
           'jump_length': 2,
           'jump_n_sample': 2,
           'output_type': 'numpy',
       }
       return inputs






 def 						lowercase_    (      self	:Union[str, Any]      )					->      Optional[int]:







       '''simple docstring'''



       __A													=      'cpu'  # ensure determinism for the device-dependent torch.Generator
       __A													=      self.get_dummy_components()
       __A													=      RePaintPipeline(**_A      )
       __A													=      sd_pipe.to(_A      )
       sd_pipe.set_progress_bar_config(disable=_A      )

       __A													=      self.get_dummy_inputs(_A      )
       __A													=      sd_pipe(**_A      ).images
       __A													=      image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       __A													=      np.array([1.0_000, 0.5_426, 0.5_497, 0.2_200, 1.0_000, 1.0_000, 0.5_623, 1.0_000, 0.6_274]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3






 @skip_mps
 def 						lowercase_    (      self	:Dict      )					->      Optional[int]:







       '''simple docstring'''



       return super().test_save_load_local()






 @unittest.skip('non-deterministic pipeline'      )
 def 						lowercase_    (      self	:Union[str, Any]      )					->      int:







       '''simple docstring'''



       return super().test_inference_batch_single_identical()






 @skip_mps
 def 						lowercase_    (      self	:Dict      )					->      Union[str, Any]:







       '''simple docstring'''



       return super().test_dict_tuple_outputs_equivalent()






 @skip_mps
 def 						lowercase_    (      self	:List[str]      )					->      int:







       '''simple docstring'''



       return super().test_save_load_optional_components()






 @skip_mps
 def 						lowercase_    (      self	:Optional[int]      )					->      Optional[Any]:







       '''simple docstring'''



       return super().test_attention_slicing_forward_pass()


@nightly
@require_torch_gpu
class 			UpperCamelCase__	(     unittest.TestCase):






 def 						lowercase_    (      self	:Optional[int]      )					->      int:







       '''simple docstring'''



       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






 def 						lowercase_    (      self	:Dict      )					->      Optional[Any]:







       '''simple docstring'''



       __A													=      load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
           'repaint/celeba_hq_256.png'      )
       __A													=      load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'      )
       __A													=      load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
           'repaint/celeba_hq_256_result.npy'      )

       __A													=      'google/ddpm-ema-celebahq-256'
       __A													=      UNetaDModel.from_pretrained(_A      )
       __A													=      RePaintScheduler.from_pretrained(_A      )

       __A													=      RePaintPipeline(unet=_A						,					scheduler=_A      ).to(_A      )
       repaint.set_progress_bar_config(disable=_A      )
       repaint.enable_attention_slicing()

       __A													=      torch.manual_seed(0      )
       __A													=      repaint(
           _A						,					_A						,					num_inference_steps=250						,					eta=0.0						,					jump_length=10						,					jump_n_sample=10						,					generator=_A						,					output_type='np'						,					)
       __A													=      output.images[0]

       assert image.shape == (256, 256, 3)
       assert np.abs(expected_image - image      ).mean() < 1E-2






