
import argparse
import copy
import json
import multiprocessing
import os
import time

from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
from huggingface_hub import ModelFilter, hf_api

import transformers
from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
from transformers.image_processing_utils import BaseImageProcessor



def        lowerCamelCase_  (   ):
				A_       	=  set()
				# Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
				for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
								A_       	=  getattr(transformers.models.auto ,						__UpperCamelCase ,						__UpperCamelCase     )
								if module is None:
												continue
								# all mappings in a single auto modeling file
								A_       	=  [
								    x
								    for x in dir(__UpperCamelCase     )
								    if x.endswith('''_MAPPING_NAMES'''     )
								    and (x.startswith('''MODEL_'''     ) or x.startswith('''TF_MODEL_'''     ) or x.startswith('''FLAX_MODEL_'''     ))
								]
								for name in mapping_names:
												A_       	=  getattr(__UpperCamelCase ,						__UpperCamelCase     )
												if mapping is not None:
																for v in mapping.values():
																				if isinstance(__UpperCamelCase ,						(list, tuple)     ):
																								model_names.update(__UpperCamelCase     )
																				elif isinstance(__UpperCamelCase ,						__UpperCamelCase     ):
																								model_names.add(__UpperCamelCase     )

				return sorted(__UpperCamelCase     )



def        lowerCamelCase_  (   ):
				# All model names defined in auto mappings
				A_       	=  set(get_all_model_names()     )

				with open('''tests/utils/tiny_model_summary.json'''     ) as fp:
								A_       	=  json.load(__UpperCamelCase     )
				A_       	=  set()
				for model_base_name in tiny_model_info:
								tiny_models_names.update(tiny_model_info[model_base_name]['''model_classes''']     )

				# Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
				A_       	=  model_names.difference(__UpperCamelCase     )
				for model_name in copy.copy(__UpperCamelCase     ):
								if not model_name.startswith('''TF'''     ) and F"TF{model_name}" in not_on_hub:
												tiny_models_names.remove(__UpperCamelCase     )
								elif model_name.startswith('''TF'''     ) and model_name[2:] in not_on_hub:
												tiny_models_names.remove(__UpperCamelCase     )

				return sorted(__UpperCamelCase     )



def        lowerCamelCase_  (   __UpperCamelCase     ):
				A_       	=  COMPOSITE_MODELS.values()

				# All tiny model base names on Hub
				A_       	=  get_all_model_names()
				A_       	=  hf_api.list_models(
				    filter=ModelFilter(
				        author='''hf-internal-testing''' ,						)     )
				A_       	=  set()
				for x in models:
								A_       	=  x.modelId
								A_	,					A_       	=  model.split('''/'''     )
								if not model.startswith('''tiny-random-'''     ):
												continue
								A_       	=  model.replace('''tiny-random-''' ,						''''''     )
								if not model[0].isupper():
												continue
								if model not in model_names and model not in special_models:
												continue
								_models.add(__UpperCamelCase     )

				A_       	=  sorted(_models     )
				# All tiny model names on Hub
				A_       	=  {}
				for model in models:
								A_       	=  F"hf-internal-testing/tiny-random-{model}"
								A_       	=  model.split('''-'''     )[0]
								try:
												A_       	=  hf_api.repo_info(__UpperCamelCase     )
												A_       	=  {
												    '''tokenizer_classes''': set(),
												    '''processor_classes''': set(),
												    '''model_classes''': set(),
												    '''sha''': repo_info.sha,
												}
								except Exception:
												continue
								try:
												time.sleep(1     )
												A_       	=  AutoTokenizer.from_pretrained(__UpperCamelCase     )
												content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__     )
								except Exception:
												pass
								try:
												time.sleep(1     )
												A_       	=  AutoTokenizer.from_pretrained(__UpperCamelCase ,						use_fast=__UpperCamelCase     )
												content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__     )
								except Exception:
												pass
								try:
												time.sleep(1     )
												A_       	=  AutoImageProcessor.from_pretrained(__UpperCamelCase     )
												content["processor_classes"].add(img_p.__class__.__name__     )
								except Exception:
												pass
								try:
												time.sleep(1     )
												A_       	=  AutoFeatureExtractor.from_pretrained(__UpperCamelCase     )
												if not isinstance(__UpperCamelCase ,						__UpperCamelCase     ):
																content["processor_classes"].add(feat_p.__class__.__name__     )
								except Exception:
												pass
								try:
												time.sleep(1     )
												A_       	=  getattr(__UpperCamelCase ,						__UpperCamelCase     )
												A_       	=  model_class.from_pretrained(__UpperCamelCase     )
												content["model_classes"].add(m.__class__.__name__     )
								except Exception:
												pass
								try:
												time.sleep(1     )
												A_       	=  getattr(__UpperCamelCase ,						F"TF{model}"     )
												A_       	=  model_class.from_pretrained(__UpperCamelCase     )
												content["model_classes"].add(m.__class__.__name__     )
								except Exception:
												pass

								A_       	=  sorted(content['''tokenizer_classes''']     )
								A_       	=  sorted(content['''processor_classes''']     )
								A_       	=  sorted(content['''model_classes''']     )

								A_       	=  content
								with open(os.path.join(__UpperCamelCase ,						'''hub_tiny_model_summary.json'''     ) ,						'''w'''     ) as fp:
												json.dump(__UpperCamelCase ,						__UpperCamelCase ,						ensure_ascii=__UpperCamelCase ,						indent=4     )


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE   :		Union[str, Any]					 =       argparse.ArgumentParser()
						parser.add_argument("--num_workers", default=1, type=int, help="The number of workers to run.")
						SCREAMING_SNAKE_CASE   :		Optional[Any]					 =       parser.parse_args()

						# This has to be `spawn` to avoid hanging forever!
						multiprocessing.set_start_method("spawn")

						SCREAMING_SNAKE_CASE   :		List[str]					 =       "tiny_models"
						SCREAMING_SNAKE_CASE   :		Any					 =       True
						SCREAMING_SNAKE_CASE   :		Tuple					 =       None
						SCREAMING_SNAKE_CASE   :		Tuple					 =       get_tiny_model_names_from_repo()
						SCREAMING_SNAKE_CASE   :		List[str]					 =       True
						SCREAMING_SNAKE_CASE   :		Optional[int]					 =       True
						SCREAMING_SNAKE_CASE   :		Tuple					 =       "hf-internal-testing"

						create_tiny_models(
						    output_path,
						    all,
						    model_types,
						    models_to_skip,
						    no_check,
						    upload,
						    organization,
						    token=os.environ.get("TOKEN", None),
						    num_workers=args.num_workers,
						)

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available






@dataclass
class 					__lowercase		(					A       ):
					__magic_name__						:							int     	=	{
					    '''sld_warmup_steps''': 15,
					    '''sld_guidance_scale''': 20,
					    '''sld_threshold''': 0.0,
					    '''sld_momentum_scale''': 0.0,
					    '''sld_mom_beta''': 0.0,
					}
					__magic_name__						:							Optional[int]     	=	{
					    '''sld_warmup_steps''': 10,
					    '''sld_guidance_scale''': 10_00,
					    '''sld_threshold''': 0.01,
					    '''sld_momentum_scale''': 0.3,
					    '''sld_mom_beta''': 0.4,
					}
					__magic_name__						:							str     	=	{
					    '''sld_warmup_steps''': 7,
					    '''sld_guidance_scale''': 20_00,
					    '''sld_threshold''': 0.0_25,
					    '''sld_momentum_scale''': 0.5,
					    '''sld_mom_beta''': 0.7,
					}
					__magic_name__						:							List[Any]     	=	{
					    '''sld_warmup_steps''': 0,
					    '''sld_guidance_scale''': 50_00,
					    '''sld_threshold''': 1.0,
					    '''sld_momentum_scale''': 0.5,
					    '''sld_mom_beta''': 0.7,
					}






@dataclass
class 					__lowercase		(					A       ):
					__magic_name__						:							Union[List[PIL.Image.Image], np.ndarray]
					__magic_name__						:							Optional[List[bool]]
					__magic_name__						:							Optional[Union[List[PIL.Image.Image], np.ndarray]]
					__magic_name__						:							Optional[str]


if is_transformers_available() and is_torch_available():
						from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
						from .safety_checker import SafeStableDiffusionSafetyChecker