
'''simple docstring'''





from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


UpperCAmelCase            = logging.get_logger(__name__)  # pylint: disable=invalid-name


UpperCAmelCase            = "\n    Examples:\n        ```py\n        >>> import torch\n\n        >>> from diffusers import ConsistencyModelPipeline\n\n        >>> device = \"cuda\"\n        >>> # Load the cd_imagenet64_l2 checkpoint.\n        >>> model_id_or_path = \"openai/diffusers-cd_imagenet64_l2\"\n        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n        >>> pipe.to(device)\n\n        >>> # Onestep Sampling\n        >>> image = pipe(num_inference_steps=1).images[0]\n        >>> image.save(\"cd_imagenet64_l2_onestep_sample.png\")\n\n        >>> # Onestep sampling, class-conditional image generation\n        >>> # ImageNet-64 class label 145 corresponds to king penguins\n        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]\n        >>> image.save(\"cd_imagenet64_l2_onestep_sample_penguin.png\")\n\n        >>> # Multistep sampling, class-conditional image generation\n        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:\n        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77\n        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]\n        >>> image.save(\"cd_imagenet64_l2_multistep_sample_penguin.png\")\n        ```\n"

class     a (					__magic_name__     ):




				def __init__(				self						:					Dict,     SCREAMING_SNAKE_CASE_						:					UNetaDModel,     SCREAMING_SNAKE_CASE_						:					CMStochasticIterativeScheduler   ):
										super().__init__()

										self.register_modules(
										    unet=SCREAMING_SNAKE_CASE_,     scheduler=SCREAMING_SNAKE_CASE_,     )

										snake_case							:  Optional[int]			=     None




				def 						__snake_case				(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					Optional[int]=0   ):
										if is_accelerate_available() and is_accelerate_version('''>=''',     '''0.14.0'''   ):
																from accelerate import cpu_offload
										else:
																raise ImportError('''`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher'''   )

										snake_case							:  List[str]			=     torch.device(F"""cuda:{gpu_id}"""   )

										if self.device.type != "cpu":
																self.to('''cpu''',     silence_dtype_warnings=SCREAMING_SNAKE_CASE_   )
																torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

										for cpu_offloaded_model in [self.unet]:
																cpu_offload(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )

										if self.safety_checker is not None:
																cpu_offload(self.safety_checker,     execution_device=SCREAMING_SNAKE_CASE_,     offload_buffers=SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					int,     SCREAMING_SNAKE_CASE_						:					int=0   ):
										if is_accelerate_available() and is_accelerate_version('''>=''',     '''0.17.0.dev0'''   ):
																from accelerate import cpu_offload_with_hook
										else:
																raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''   )

										snake_case							:  Union[str, Any]			=     torch.device(F"""cuda:{gpu_id}"""   )

										if self.device.type != "cpu":
																self.to('''cpu''',     silence_dtype_warnings=SCREAMING_SNAKE_CASE_   )
																torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

										snake_case							:  Any			=     None
										for cpu_offloaded_model in [self.unet]:
																snake_case,       snake_case							:  List[str]			=     cpu_offload_with_hook(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     prev_module_hook=SCREAMING_SNAKE_CASE_   )

										if self.safety_checker is not None:
																snake_case,       snake_case							:  Optional[int]			=     cpu_offload_with_hook(self.safety_checker,     SCREAMING_SNAKE_CASE_,     prev_module_hook=SCREAMING_SNAKE_CASE_   )

										# We'll offload the last model manually.
										snake_case							:  str			=     hook




				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def 						__snake_case				(				self						:					List[str]   ):
										if not hasattr(self.unet,     '''_hf_hook'''   ):
																return self.device
										for module in self.unet.modules():
																if (
																    hasattr(SCREAMING_SNAKE_CASE_,     '''_hf_hook'''   )
																    and hasattr(module._hf_hook,     '''execution_device'''   )
																    and module._hf_hook.execution_device is not None
																):
																						return torch.device(module._hf_hook.execution_device   )
										return self.device




				def 						__snake_case				(				self						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					Dict,     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Dict,     SCREAMING_SNAKE_CASE_						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					List[str],     SCREAMING_SNAKE_CASE_						:					List[Any]=None   ):
										snake_case							:  Optional[Any]			=     (batch_size, num_channels, height, width)
										if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ) and len(SCREAMING_SNAKE_CASE_   ) != batch_size:
																raise ValueError(
																    F"""You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE_   )}, but requested an effective batch"""
																    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""   )

										if latents is None:
																snake_case							:  Any			=     randn_tensor(SCREAMING_SNAKE_CASE_,     generator=SCREAMING_SNAKE_CASE_,     device=SCREAMING_SNAKE_CASE_,     dtype=SCREAMING_SNAKE_CASE_   )
										else:
																snake_case							:  Union[str, Any]			=     latents.to(device=SCREAMING_SNAKE_CASE_,     dtype=SCREAMING_SNAKE_CASE_   )

										# scale the initial noise by the standard deviation required by the scheduler
										snake_case							:  Optional[Any]			=     latents * self.scheduler.init_noise_sigma
										return latents




				def 						__snake_case				(				self						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					torch.FloatTensor,     SCREAMING_SNAKE_CASE_						:					str = "pil"   ):
										if output_type not in ["pt", "np", "pil"]:
																raise ValueError(
																    F"""output_type={output_type} is not supported. Make sure to choose one of ['pt', 'np', or 'pil']"""   )

										# Equivalent to diffusers.VaeImageProcessor.denormalize
										snake_case							:  List[Any]			=     (sample / 2 + 0.5).clamp(0,     1   )
										if output_type == "pt":
																return sample

										# Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
										snake_case							:  Optional[int]			=     sample.cpu().permute(0,     2,     3,     1   ).numpy()
										if output_type == "np":
																return sample

										# Output_type must be 'pil'
										snake_case							:  Any			=     self.numpy_to_pil(SCREAMING_SNAKE_CASE_   )
										return sample




				def 						__snake_case				(				self						:					int,     SCREAMING_SNAKE_CASE_						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					List[str]=None   ):
										if self.unet.config.num_class_embeds is not None:
																if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ):
																						snake_case							:  str			=     torch.tensor(SCREAMING_SNAKE_CASE_,     dtype=torch.int   )
																elif isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ):
																						assert batch_size == 1, "Batch size must be 1 if classes is an int"
																						snake_case							:  Any			=     torch.tensor([class_labels],     dtype=torch.int   )
																elif class_labels is None:
																						# Randomly generate batch_size class labels
																						# TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
																						snake_case							:  str			=     torch.randint(0,     self.unet.config.num_class_embeds,     size=(batch_size,)   )
																snake_case							:  str			=     class_labels.to(SCREAMING_SNAKE_CASE_   )
										else:
																snake_case							:  List[Any]			=     None
										return class_labels




				def 						__snake_case				(				self						:					List[str],     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					str,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					List[Any],     SCREAMING_SNAKE_CASE_						:					Dict,     SCREAMING_SNAKE_CASE_						:					Tuple   ):
										if num_inference_steps is None and timesteps is None:
																raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''   )

										if num_inference_steps is not None and timesteps is not None:
																logger.warning(
																    F"""Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;"""
																    ''' `timesteps` will be used over `num_inference_steps`.'''   )

										if latents is not None:
																snake_case							:  Dict			=     (batch_size, 3, img_size, img_size)
																if latents.shape != expected_shape:
																						raise ValueError(F"""The shape of latents is {latents.shape} but is expected to be {expected_shape}."""   )

										if (callback_steps is None) or (
										    callback_steps is not None and (not isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ) or callback_steps <= 0)
										):
																raise ValueError(
																    F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
																    F""" {type(SCREAMING_SNAKE_CASE_   )}."""   )




				@torch.no_grad()
				@replace_example_docstring(SCREAMING_SNAKE_CASE_   )
				def __call__(				self						:					str,     SCREAMING_SNAKE_CASE_						:					int = 1,     SCREAMING_SNAKE_CASE_						:					Optional[Union[torch.Tensor, List[int], int]] = None,     SCREAMING_SNAKE_CASE_						:					int = 1,     SCREAMING_SNAKE_CASE_						:					List[int] = None,     SCREAMING_SNAKE_CASE_						:					Optional[Union[torch.Generator, List[torch.Generator]]] = None,     SCREAMING_SNAKE_CASE_						:					Optional[torch.FloatTensor] = None,     SCREAMING_SNAKE_CASE_						:					Optional[str] = "pil",     SCREAMING_SNAKE_CASE_						:					bool = True,     SCREAMING_SNAKE_CASE_						:					Optional[Callable[[int, int, torch.FloatTensor], None]] = None,     SCREAMING_SNAKE_CASE_						:					int = 1,     ):
										snake_case							:  List[str]			=     self.unet.config.sample_size
										snake_case							:  Optional[Any]			=     self._execution_device

										# 1. Check inputs
										self.check_inputs(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )

										# 2. Prepare image latents
										# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
										snake_case							:  int			=     self.prepare_latents(
										    batch_size=SCREAMING_SNAKE_CASE_,     num_channels=self.unet.config.in_channels,     height=SCREAMING_SNAKE_CASE_,     width=SCREAMING_SNAKE_CASE_,     dtype=self.unet.dtype,     device=SCREAMING_SNAKE_CASE_,     generator=SCREAMING_SNAKE_CASE_,     latents=SCREAMING_SNAKE_CASE_,     )

										# 3. Handle class_labels for class-conditional models
										snake_case							:  Any			=     self.prepare_class_labels(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     class_labels=SCREAMING_SNAKE_CASE_   )

										# 4. Prepare timesteps
										if timesteps is not None:
																self.scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_,     device=SCREAMING_SNAKE_CASE_   )
																snake_case							:  Union[str, Any]			=     self.scheduler.timesteps
																snake_case							:  List[Any]			=     len(SCREAMING_SNAKE_CASE_   )
										else:
																self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_   )
																snake_case							:  List[Any]			=     self.scheduler.timesteps

										# 5. Denoising loop
										# Multistep sampling: implements Algorithm 1 in the paper
										with self.progress_bar(total=SCREAMING_SNAKE_CASE_   ) as progress_bar:
																for i, t in enumerate(SCREAMING_SNAKE_CASE_   ):
																						snake_case							:  Union[str, Any]			=     self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )
																						snake_case							:  List[str]			=     self.unet(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     class_labels=SCREAMING_SNAKE_CASE_,     return_dict=SCREAMING_SNAKE_CASE_   )[0]

																						snake_case							:  List[Any]			=     self.scheduler.step(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     generator=SCREAMING_SNAKE_CASE_   )[0]

																						# call the callback, if provided
																						progress_bar.update()
																						if callback is not None and i % callback_steps == 0:
																												callback(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )

        # 6. Post-process image sample
										snake_case							:  List[Any]			=     self.postprocess_image(SCREAMING_SNAKE_CASE_,     output_type=SCREAMING_SNAKE_CASE_   )

										# Offload last model to CPU
										if hasattr(self,     '''final_offload_hook'''   ) and self.final_offload_hook is not None:
																self.final_offload_hook.offload()

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_   )






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


UpperCAmelCase_			      =  logging.get_logger(__name__)

UpperCAmelCase_			      =  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'''),
    ]
)

UpperCAmelCase_			      =  _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)
def   UpperCAmelCase							(		A__     )							->					Dict:
	for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
		if class_name in processors:
			_snake_case					:							str					     =   model_type_to_module_name(A__     )

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

	for processor in PROCESSOR_MAPPING._extra_content.values():
		if getattr(A__				,  """__name__"""				,  A__     ) == 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.
	_snake_case					:							Optional[int]					     =   importlib.import_module("""transformers"""     )
	if hasattr(A__				,  A__     ):
		return getattr(A__				,  A__     )

	return None





class 		__SCREAMING_SNAKE_CASE      :






				"""simple docstring"""

				def __init__(				self      ):






					"""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(SCREAMING_SNAKE_CASE__      )
				def 		__lowerCamelCase(				cls						,	SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							List[str]					     =   kwargs.pop("""config"""						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Tuple					     =   kwargs.pop("""trust_remote_code"""						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Optional[int]					     =   True

					_snake_case					:							Optional[int]					     =   None
					_snake_case					:							List[Any]					     =   None

					# First, let's see if we have a preprocessor config.
					# Filter the kwargs for `get_file_from_repo`.
					_snake_case					:							Tuple					     =   {
					    key: kwargs[key] for key in inspect.signature(SCREAMING_SNAKE_CASE__      ).parameters.keys() if key in kwargs
					}
					# Let's start by checking whether the processor class is saved in an image processor
					_snake_case					:							List[str]					     =   get_file_from_repo(
					    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__      )
					if preprocessor_config_file is not None:
						_snake_case     , _snake_case					:							Dict					     =   ImageProcessingMixin.get_image_processor_dict(SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__      )
						_snake_case					:							str					     =   config_dict.get("""processor_class"""						,	SCREAMING_SNAKE_CASE__      )
						if "AutoProcessor" in config_dict.get("""auto_map"""						,	{}      ):
							_snake_case					:							Any					     =   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:
						_snake_case     , _snake_case					:							List[Any]					     =   FeatureExtractionMixin.get_feature_extractor_dict(SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__      )
						_snake_case					:							List[Any]					     =   config_dict.get("""processor_class"""						,	SCREAMING_SNAKE_CASE__      )
						if "AutoProcessor" in config_dict.get("""auto_map"""						,	{}      ):
							_snake_case					:							List[str]					     =   config_dict["""auto_map"""]["""AutoProcessor"""]

					if processor_class is None:
						# Next, let's check whether the processor class is saved in a tokenizer
						_snake_case					:							Optional[int]					     =   get_file_from_repo(
						    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__      )
						if tokenizer_config_file is not None:
							with open(SCREAMING_SNAKE_CASE__						,	encoding="""utf-8"""      ) as reader:
								_snake_case					:							Optional[Any]					     =   json.load(SCREAMING_SNAKE_CASE__      )

							_snake_case					:							Optional[int]					     =   config_dict.get("""processor_class"""						,	SCREAMING_SNAKE_CASE__      )
							if "AutoProcessor" in config_dict.get("""auto_map"""						,	{}      ):
								_snake_case					:							Tuple					     =   config_dict["""auto_map"""]["""AutoProcessor"""]

					if processor_class is None:
						# Otherwise, load config, if it can be loaded.
						if not isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):
							_snake_case					:							List[Any]					     =   AutoConfig.from_pretrained(
							    SCREAMING_SNAKE_CASE__						,	trust_remote_code=SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__      )

						# And check if the config contains the processor class.
						_snake_case					:							Optional[Any]					     =   getattr(SCREAMING_SNAKE_CASE__						,	"""processor_class"""						,	SCREAMING_SNAKE_CASE__      )
						if hasattr(SCREAMING_SNAKE_CASE__						,	"""auto_map"""      ) and "AutoProcessor" in config.auto_map:
							_snake_case					:							List[str]					     =   config.auto_map["""AutoProcessor"""]

					if processor_class is not None:
						_snake_case					:							Any					     =   processor_class_from_name(SCREAMING_SNAKE_CASE__      )

					_snake_case					:							Optional[Any]					     =   processor_auto_map is not None
					_snake_case					:							Optional[int]					     =   processor_class is not None or type(SCREAMING_SNAKE_CASE__      ) in PROCESSOR_MAPPING
					_snake_case					:							List[Any]					     =   resolve_trust_remote_code(
					    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )

					if has_remote_code and trust_remote_code:
						_snake_case					:							int					     =   get_class_from_dynamic_module(
						    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__      )
						_snake_case					:							Optional[int]					     =   kwargs.pop("""code_revision"""						,	SCREAMING_SNAKE_CASE__      )
						if os.path.isdir(SCREAMING_SNAKE_CASE__      ):
							processor_class.register_for_auto_class()
						return processor_class.from_pretrained(
						    SCREAMING_SNAKE_CASE__						,	trust_remote_code=SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__      )
					elif processor_class is not None:
						return processor_class.from_pretrained(
						    SCREAMING_SNAKE_CASE__						,	trust_remote_code=SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__      )
					# Last try: we use the PROCESSOR_MAPPING.
					elif type(SCREAMING_SNAKE_CASE__      ) in PROCESSOR_MAPPING:
						return PROCESSOR_MAPPING[type(SCREAMING_SNAKE_CASE__      )].from_pretrained(SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__      )

					# 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(
						    SCREAMING_SNAKE_CASE__						,	trust_remote_code=SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__      )
					except Exception:
						try:
							return AutoImageProcessor.from_pretrained(
							    SCREAMING_SNAKE_CASE__						,	trust_remote_code=SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__      )
						except Exception:
							pass

						try:
							return AutoFeatureExtractor.from_pretrained(
							    SCREAMING_SNAKE_CASE__						,	trust_remote_code=SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__      )
						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 		__lowerCamelCase(				SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					PROCESSOR_MAPPING.register(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )





