




'''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_       				=      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      (				SCREAMING_SNAKE_CASE__			:      str       ):
 '''simple docstring'''





 for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
  if class_name in processors:
   UpperCAmelCase__										=    model_type_to_module_name(SCREAMING_SNAKE_CASE__       )

   UpperCAmelCase__										=    importlib.import_module(F'''.{module_name}'''       ,						"""transformers.models"""       )
   try:
    return getattr(SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       )
   except AttributeError:
    continue

 for processor in PROCESSOR_MAPPING._extra_content.values():
  if getattr(SCREAMING_SNAKE_CASE__       ,						"""__name__"""       ,						SCREAMING_SNAKE_CASE__       ) == 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__										=    importlib.import_module("""transformers"""       )
 if hasattr(SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       ):
  return getattr(SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       )

 return None







class 						lowerCAmelCase_   :







  '''simple docstring'''







  def __init__(						self    :	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(_UpperCAmelCase )
  def 							SCREAMING_SNAKE_CASE__			(						cls    :	List[str]						,		_UpperCAmelCase    :	Any						,		**_UpperCAmelCase    :	Optional[int] ):






   """simple docstring"""






   UpperCAmelCase__										=    kwargs.pop("""config"""						,		_UpperCAmelCase )
   UpperCAmelCase__										=    kwargs.pop("""trust_remote_code"""						,		_UpperCAmelCase )
   UpperCAmelCase__										=    True

   UpperCAmelCase__										=    None
   UpperCAmelCase__										=    None

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

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

     UpperCAmelCase__										=    config_dict.get("""processor_class"""						,		_UpperCAmelCase )
     if "AutoProcessor" in config_dict.get("""auto_map"""						,		{} ):
      UpperCAmelCase__										=    config_dict["""auto_map"""]["""AutoProcessor"""]

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

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

   if processor_class is not None:
    UpperCAmelCase__										=    processor_class_from_name(_UpperCAmelCase )

   UpperCAmelCase__										=    processor_auto_map is not None
   UpperCAmelCase__										=    processor_class is not None or type(_UpperCAmelCase ) in PROCESSOR_MAPPING
   UpperCAmelCase__										=    resolve_trust_remote_code(
       _UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase )

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

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

    try:
     return AutoFeatureExtractor.from_pretrained(
         _UpperCAmelCase						,		trust_remote_code=_UpperCAmelCase						,		**_UpperCAmelCase )
    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 							SCREAMING_SNAKE_CASE__			(						_UpperCAmelCase    :	Optional[Any]						,		_UpperCAmelCase    :	List[str] ):






   """simple docstring"""






   PROCESSOR_MAPPING.register(_UpperCAmelCase						,		_UpperCAmelCase )






'''simple docstring'''




import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class 						lowerCAmelCase_   ( lowerCamelCase_			,  lowerCamelCase_			,  unittest.TestCase   ):







  '''simple docstring'''







  lowerCAmelCase_				:					Any				     =						IFImgaImgPipeline
  lowerCAmelCase_				:					Dict				     =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
  lowerCAmelCase_				:					Union[str, Any]				     =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  lowerCAmelCase_				:					Dict				     =						PipelineTesterMixin.required_optional_params - {"""latents"""}


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






   """simple docstring"""






   return self._get_dummy_components()


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple						,		_UpperCAmelCase    :	Any						,		_UpperCAmelCase    :	Any=0 ):






   """simple docstring"""






   if str(_UpperCAmelCase ).startswith("""mps""" ):
    UpperCAmelCase__										=    torch.manual_seed(_UpperCAmelCase )
   else:
    UpperCAmelCase__										=    torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )

   UpperCAmelCase__										=    floats_tensor((1, 3, 32, 32)						,		rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )

   UpperCAmelCase__										=    {
       """prompt""": """A painting of a squirrel eating a burger""",
       """image""": image,
       """generator""": generator,
       """num_inference_steps""": 2,
       """output_type""": """numpy""",
   }

   return inputs


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[Any] ):






   """simple docstring"""






   self._test_save_load_optional_components()


  @unittest.skipIf(
      torch_device != """cuda""" or not is_xformers_available()						,		reason="""XFormers attention is only available with CUDA and `xformers` installed"""						,		)
  def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






   """simple docstring"""






   self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )


  @unittest.skipIf(torch_device != """cuda"""						,		reason="""float16 requires CUDA""" )
  def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






   """simple docstring"""






   super().test_save_load_floataa(expected_max_diff=1E-1 )


  @unittest.skipIf(torch_device != """cuda"""						,		reason="""float16 requires CUDA""" )
  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






   """simple docstring"""






   super().test_floataa_inference(expected_max_diff=1E-1 )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str] ):






   """simple docstring"""






   self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






   """simple docstring"""






   self._test_save_load_local()



  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any ):






   """simple docstring"""






   self._test_inference_batch_single_identical(
       expected_max_diff=1E-2						,		)
