
'''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__						:				Optional[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__						:				str           =    _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)





def     snake_case							(						UpperCAmelCase		)->     Union[str, Any]:







      """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[int]     )			->     Union[str, Any]:







       '''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					:List[Any]		,			_A					:List[str]		,			**_A					:Optional[int]     )			->     str:







       '''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					:List[str]		,			_A					:Tuple     )			->     List[str]:







       '''simple docstring'''



       PROCESSOR_MAPPING.register(_A		,			_A     )








'''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 	UpperCamelCase__					(      unittest.TestCase):






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







       '''simple docstring'''



       __A    		=     'runwayml/stable-diffusion-v1-5'
       __A    		=     TextToVideoZeroPipeline.from_pretrained(_A		,			torch_dtype=torch.floataa     ).to('cuda'     )
       __A    		=     DDIMScheduler.from_config(pipe.scheduler.config     )
       __A    		=     torch.Generator(device='cuda'     ).manual_seed(0     )

       __A    		=     'A bear is playing a guitar on Times Square'
       __A    		=     pipe(prompt=_A		,			generator=_A     ).images

       __A    		=     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     )






