
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[Any]    =			logging.get_logger(__name__)

_UpperCAmelCase   :					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"),
    ]
)

_UpperCAmelCase   :					List[str]    =			_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)


def  A			(   lowercase					)  ->    int:
		'''simple docstring'''

		for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
				if class_name in processors:
						UpperCamelCase            =	model_type_to_module_name(lowercase					)

						UpperCamelCase            =	importlib.import_module(f'''.{module_name}'''       ,			'transformers.models'					)
						try:
								return getattr(lowercase       ,			lowercase					)
						except AttributeError:
								continue

		for processor in PROCESSOR_MAPPING._extra_content.values():
				if getattr(lowercase       ,			'__name__'       ,			lowercase					) == 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(lowercase       ,			lowercase					):
				return getattr(lowercase       ,			lowercase					)

		return None


class 							lowercase   :



		def __init__(					self			)    ->   Optional[int]:
				"""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 __UpperCamelCase					(					cls							, A_							, **A_			)    ->   Dict:
				"""simple docstring"""
				UpperCamelCase            =	kwargs.pop('config'							, SCREAMING_SNAKE_CASE_			)
				UpperCamelCase            =	kwargs.pop('trust_remote_code'							, SCREAMING_SNAKE_CASE_			)
				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(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            =	get_file_from_repo(
				    SCREAMING_SNAKE_CASE_							, SCREAMING_SNAKE_CASE_							, **SCREAMING_SNAKE_CASE_			)
				if preprocessor_config_file is not None:
						UpperCamelCase      ,    UpperCamelCase            =	ImageProcessingMixin.get_image_processor_dict(SCREAMING_SNAKE_CASE_							, **SCREAMING_SNAKE_CASE_			)
						UpperCamelCase            =	config_dict.get('processor_class'							, SCREAMING_SNAKE_CASE_			)
						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(SCREAMING_SNAKE_CASE_							, **SCREAMING_SNAKE_CASE_			)
						UpperCamelCase            =	config_dict.get('processor_class'							, SCREAMING_SNAKE_CASE_			)
						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(
						    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            =	json.load(SCREAMING_SNAKE_CASE_			)

								UpperCamelCase            =	config_dict.get('processor_class'							, SCREAMING_SNAKE_CASE_			)
								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(SCREAMING_SNAKE_CASE_							, SCREAMING_SNAKE_CASE_			):
								UpperCamelCase            =	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            =	getattr(SCREAMING_SNAKE_CASE_							, 'processor_class'							, SCREAMING_SNAKE_CASE_			)
						if hasattr(SCREAMING_SNAKE_CASE_							, 'auto_map'			) and "AutoProcessor" in config.auto_map:
								UpperCamelCase            =	config.auto_map['AutoProcessor']

				if processor_class is not None:
						UpperCamelCase            =	processor_class_from_name(SCREAMING_SNAKE_CASE_			)

				UpperCamelCase            =	processor_auto_map is not None
				UpperCamelCase            =	processor_class is not None or type(SCREAMING_SNAKE_CASE_			) in PROCESSOR_MAPPING
				UpperCamelCase            =	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            =	get_class_from_dynamic_module(
						    SCREAMING_SNAKE_CASE_							, SCREAMING_SNAKE_CASE_							, **SCREAMING_SNAKE_CASE_			)
						UpperCamelCase            =	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 __UpperCamelCase					(					A_							, A_			)    ->   str:
				"""simple docstring"""
				PROCESSOR_MAPPING.register(SCREAMING_SNAKE_CASE_							, SCREAMING_SNAKE_CASE_			)


import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowercase__				:				List[str]						=      False






@nightly
@require_torch_gpu
class 							SCREAMING_SNAKE_CASE__     (				unittest.TestCase     ):

							"""simple docstring"""






							def      A__		(	self      )-> List[Any]:





													'''simple docstring'''






													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()



							def      A__		(	self      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''      )
													# remove text_unet
													pipe.remove_unused_weights()
													pipe.to(SCREAMING_SNAKE_CASE_      )
													pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''      )

													__UpperCamelCase  	=      torch.manual_seed(0      )
													__UpperCamelCase  	=      pipe(
													    prompt='''first prompt'''  ,						image=SCREAMING_SNAKE_CASE_  ,						text_to_image_strength=0.7_5  ,						generator=SCREAMING_SNAKE_CASE_  ,						guidance_scale=7.5  ,						num_inference_steps=2  ,						output_type='''numpy'''  ,						).images

													with tempfile.TemporaryDirectory() as tmpdirname:
																			pipe.save_pretrained(SCREAMING_SNAKE_CASE_      )
																			__UpperCamelCase  	=      VersatileDiffusionDualGuidedPipeline.from_pretrained(SCREAMING_SNAKE_CASE_      )

													pipe.to(SCREAMING_SNAKE_CASE_      )
													pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      generator.manual_seed(0      )
													__UpperCamelCase  	=      pipe(
													    prompt='''first prompt'''  ,						image=SCREAMING_SNAKE_CASE_  ,						text_to_image_strength=0.7_5  ,						generator=SCREAMING_SNAKE_CASE_  ,						guidance_scale=7.5  ,						num_inference_steps=2  ,						output_type='''numpy'''  ,						).images

													assert np.abs(image - new_image      ).sum() < 1E-5, "Models don't have the same forward pass"



							def      A__		(	self      )-> int:





													'''simple docstring'''






													__UpperCamelCase  	=      VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''      )
													pipe.remove_unused_weights()
													pipe.to(SCREAMING_SNAKE_CASE_      )
													pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      '''cyberpunk 2077'''
													__UpperCamelCase  	=      load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''      )
													__UpperCamelCase  	=      torch.manual_seed(0      )
													__UpperCamelCase  	=      pipe(
													    prompt=SCREAMING_SNAKE_CASE_  ,						image=SCREAMING_SNAKE_CASE_  ,						text_to_image_strength=0.7_5  ,						generator=SCREAMING_SNAKE_CASE_  ,						guidance_scale=7.5  ,						num_inference_steps=50  ,						output_type='''numpy'''  ,						).images

													__UpperCamelCase  	=      image[0, 253:256, 253:256, -1]

													assert image.shape == (1, 512, 512, 3)
													__UpperCamelCase  	=      np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]      )

													assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2




