






'''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


_UpperCamelCase		:		Optional[int]								=			logging.get_logger(__name__)

_UpperCamelCase		:		Union[str, 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'),
    ]
)

_UpperCamelCase		:		str								=			_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)
def __UpperCAmelCase (			A			:	str						)  -> List[str]:

			for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
						if class_name in processors:
									UpperCAmelCase_     :   Optional[int]       =   model_type_to_module_name(A						)

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

			return None







class 				snake_case__     :




					def __init__(       self       :						str     )  ->    Optional[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(SCREAMING_SNAKE_CASE_     )
					def 						A     (       cls       :						List[str]							,				_A       :						int							,				**_A       :						Tuple     )  ->    Optional[Any]:
								UpperCAmelCase_     :   Optional[int]       =   kwargs.pop('''config'''							,				SCREAMING_SNAKE_CASE_     )
								UpperCAmelCase_     :   int       =   kwargs.pop('''trust_remote_code'''							,				SCREAMING_SNAKE_CASE_     )
								UpperCAmelCase_     :   Optional[int]       =   True

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

								# First, let's see if we have a preprocessor config.
								# Filter the kwargs for `get_file_from_repo`.
								UpperCAmelCase_     :   str       =   {
								    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
								UpperCAmelCase_     :   str       =   get_file_from_repo(
								    SCREAMING_SNAKE_CASE_							,				SCREAMING_SNAKE_CASE_							,				**SCREAMING_SNAKE_CASE_     )
								if preprocessor_config_file is not None:
											UpperCAmelCase_  ,     UpperCAmelCase_     :   Dict       =   ImageProcessingMixin.get_image_processor_dict(SCREAMING_SNAKE_CASE_							,				**SCREAMING_SNAKE_CASE_     )
											UpperCAmelCase_     :   List[Any]       =   config_dict.get('''processor_class'''							,				SCREAMING_SNAKE_CASE_     )
											if "AutoProcessor" in config_dict.get('''auto_map'''							,				{}     ):
														UpperCAmelCase_     :   Tuple       =   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:
											UpperCAmelCase_  ,     UpperCAmelCase_     :   Optional[int]       =   FeatureExtractionMixin.get_feature_extractor_dict(SCREAMING_SNAKE_CASE_							,				**SCREAMING_SNAKE_CASE_     )
											UpperCAmelCase_     :   Union[str, Any]       =   config_dict.get('''processor_class'''							,				SCREAMING_SNAKE_CASE_     )
											if "AutoProcessor" in config_dict.get('''auto_map'''							,				{}     ):
														UpperCAmelCase_     :   List[Any]       =   config_dict['''auto_map''']['''AutoProcessor''']

								if processor_class is None:
											# Next, let's check whether the processor class is saved in a tokenizer
											UpperCAmelCase_     :   str       =   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:
																	UpperCAmelCase_     :   int       =   json.load(SCREAMING_SNAKE_CASE_     )

														UpperCAmelCase_     :   Optional[Any]       =   config_dict.get('''processor_class'''							,				SCREAMING_SNAKE_CASE_     )
														if "AutoProcessor" in config_dict.get('''auto_map'''							,				{}     ):
																	UpperCAmelCase_     :   List[str]       =   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_     ):
														UpperCAmelCase_     :   Tuple       =   AutoConfig.from_pretrained(
														    SCREAMING_SNAKE_CASE_							,				trust_remote_code=SCREAMING_SNAKE_CASE_							,				**SCREAMING_SNAKE_CASE_     )

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

								if processor_class is not None:
											UpperCAmelCase_     :   Tuple       =   processor_class_from_name(SCREAMING_SNAKE_CASE_     )

								UpperCAmelCase_     :   str       =   processor_auto_map is not None
								UpperCAmelCase_     :   Dict       =   processor_class is not None or type(SCREAMING_SNAKE_CASE_     ) in PROCESSOR_MAPPING
								UpperCAmelCase_     :   Dict       =   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_     :   List[Any]       =   get_class_from_dynamic_module(
											    SCREAMING_SNAKE_CASE_							,				SCREAMING_SNAKE_CASE_							,				**SCREAMING_SNAKE_CASE_     )
											UpperCAmelCase_     :   Any       =   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 						A     (       _A       :						str							,				_A       :						Optional[int]     )  ->    int:
								PROCESSOR_MAPPING.register(SCREAMING_SNAKE_CASE_							,				SCREAMING_SNAKE_CASE_     )














'''simple docstring'''





import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference


@slow
@require_torch_gpu
class 				snake_case__     (			unittest.TestCase):




					def 						A     (       self       :						Optional[Any]     )  ->    List[str]:
								UpperCAmelCase_     :   Optional[Any]       =   '''runwayml/stable-diffusion-v1-5'''
								UpperCAmelCase_     :   Optional[int]       =   TextToVideoZeroPipeline.from_pretrained(_A							,				torch_dtype=torch.floataa     ).to('''cuda'''     )
								UpperCAmelCase_     :   Tuple       =   DDIMScheduler.from_config(pipe.scheduler.config     )
								UpperCAmelCase_     :   Tuple       =   torch.Generator(device='''cuda'''     ).manual_seed(0     )

								UpperCAmelCase_     :   List[Any]       =   '''A bear is playing a guitar on Times Square'''
								UpperCAmelCase_     :   List[Any]       =   pipe(prompt=_A							,				generator=_A     ).images

								UpperCAmelCase_     :   Any       =   load_pt(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt'''     )

								assert_mean_pixel_difference(_A							,				_A     )


