
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


snake_case__  							=							logging.get_logger(__name__)

snake_case__  							=							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"""),
    ]
)

snake_case__  							=							_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)

def        lowerCamelCase_      (			UpperCAmelCase_							:				str       ):
						for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
												if class_name in processors:
																		lowercase       :    List[Any]											=  model_type_to_module_name(UpperCAmelCase_       )

																		lowercase       :    Optional[int]											=  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.
						lowercase       :    List[str]											=  importlib.import_module('''transformers'''       )
						if hasattr(UpperCAmelCase_							,			UpperCAmelCase_       ):
												return getattr(UpperCAmelCase_							,			UpperCAmelCase_       )

						return None







class 							UpperCAmelCase	:

					def __init__(			self      :					Union[str, Any]					):
											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(lowerCAmelCase					)
					def        _lowerCAmelCase	(			cls      :					Tuple ,      lowerCAmelCase      :					int ,      **lowerCAmelCase      :					int					):
											lowercase       :    str											=  kwargs.pop('''config''' ,      lowerCAmelCase					)
											lowercase       :    Optional[Any]											=  kwargs.pop('''trust_remote_code''' ,      lowerCAmelCase					)
											lowercase       :    List[str]											=  True

											lowercase       :    List[str]											=  None
											lowercase       :    Dict											=  None

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

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

																							lowercase       :    List[Any]											=  config_dict.get('''processor_class''' ,      lowerCAmelCase					)
																							if "AutoProcessor" in config_dict.get('''auto_map''' ,      {}					):
																													lowercase       :    Any											=  config_dict['''auto_map''']['''AutoProcessor''']

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

																	# And check if the config contains the processor class.
																	lowercase       :    Tuple											=  getattr(lowerCAmelCase ,      '''processor_class''' ,      lowerCAmelCase					)
																	if hasattr(lowerCAmelCase ,      '''auto_map'''					) and "AutoProcessor" in config.auto_map:
																							lowercase       :    List[str]											=  config.auto_map['''AutoProcessor''']

											if processor_class is not None:
																	lowercase       :    List[str]											=  processor_class_from_name(lowerCAmelCase					)

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

											if has_remote_code and trust_remote_code:
																	lowercase       :    List[Any]											=  get_class_from_dynamic_module(
																	    lowerCAmelCase ,      lowerCAmelCase ,      **lowerCAmelCase					)
																	lowercase       :    Union[str, Any]											=  kwargs.pop('''code_revision''' ,      lowerCAmelCase					)
																	if os.path.isdir(lowerCAmelCase					):
																							processor_class.register_for_auto_class()
																	return processor_class.from_pretrained(
																	    lowerCAmelCase ,      trust_remote_code=lowerCAmelCase ,      **lowerCAmelCase					)
											elif processor_class is not None:
																	return processor_class.from_pretrained(
																	    lowerCAmelCase ,      trust_remote_code=lowerCAmelCase ,      **lowerCAmelCase					)
											# Last try: we use the PROCESSOR_MAPPING.
											elif type(lowerCAmelCase					) in PROCESSOR_MAPPING:
																	return PROCESSOR_MAPPING[type(lowerCAmelCase					)].from_pretrained(lowerCAmelCase ,      **lowerCAmelCase					)

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

																	try:
																							return AutoFeatureExtractor.from_pretrained(
																							    lowerCAmelCase ,      trust_remote_code=lowerCAmelCase ,      **lowerCAmelCase					)
																	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	(			lowerCAmelCase      :					str ,      lowerCAmelCase      :					Optional[Any]					):
											PROCESSOR_MAPPING.register(lowerCAmelCase ,      lowerCAmelCase					)




from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 							UpperCAmelCase	(   __lowerCamelCase					):
					a__: UNetaDModel
					a__: PNDMScheduler

					def __init__(			self      :					List[str] ,      lowerCAmelCase      :					UNetaDModel ,      lowerCAmelCase      :					PNDMScheduler					):
											super().__init__()

											lowercase       :    int											=  PNDMScheduler.from_config(scheduler.config					)

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




					@torch.no_grad()
					def __call__(			self      :					List[str] ,      lowerCAmelCase      :					int = 1 ,      lowerCAmelCase      :					int = 50 ,      lowerCAmelCase      :					Optional[Union[torch.Generator, List[torch.Generator]]] = None ,      lowerCAmelCase      :					Optional[str] = "pil" ,      lowerCAmelCase      :					bool = True ,      **lowerCAmelCase      :					int ,      ):
											lowercase       :    Tuple											=  randn_tensor(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,      generator=lowerCAmelCase ,      device=self.device ,      )

											self.scheduler.set_timesteps(lowerCAmelCase					)
											for t in self.progress_bar(self.scheduler.timesteps					):
																	lowercase       :    Dict											=  self.unet(lowerCAmelCase ,      lowerCAmelCase					).sample

																	lowercase       :    Tuple											=  self.scheduler.step(lowerCAmelCase ,      lowerCAmelCase ,      lowerCAmelCase					).prev_sample

											lowercase       :    Optional[Any]											=  (image / 2 + 0.5).clamp(0 ,      1					)
											lowercase       :    Optional[int]											=  image.cpu().permute(0 ,      2 ,      3 ,      1					).numpy()
											if output_type == "pil":
																	lowercase       :    Union[str, Any]											=  self.numpy_to_pil(lowerCAmelCase					)

											if not return_dict:
																	return (image,)

											return ImagePipelineOutput(images=lowerCAmelCase					)


