




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 				__SCREAMING_SNAKE_CASE    (     ):

						'''simple docstring'''


						_UpperCAmelCase						=	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"]:
												_UpperCAmelCase						=	getattr(transformers.models.auto      ,       snake_case_      ,       snake_case_		)
												if module is None:
																		continue
												# all mappings in a single auto modeling file
												_UpperCAmelCase						=	[
												    x
												    for x in dir(snake_case_		)
												    if x.endswith("_MAPPING_NAMES"		)
												    and (x.startswith("MODEL_"		) or x.startswith("TF_MODEL_"		) or x.startswith("FLAX_MODEL_"		))
												]
												for name in mapping_names:
																		_UpperCAmelCase						=	getattr(snake_case_      ,       snake_case_		)
																		if mapping is not None:
																								for v in mapping.values():
																														if isinstance(snake_case_      ,       (list, tuple)		):
																																				model_names.update(snake_case_		)
																														elif isinstance(snake_case_      ,       snake_case_		):
																																				model_names.add(snake_case_		)

						return sorted(snake_case_		)


def 				__SCREAMING_SNAKE_CASE    (     ):

						'''simple docstring'''


						_UpperCAmelCase						=	set(get_all_model_names()		)

						with open("tests/utils/tiny_model_summary.json"		) as fp:
												_UpperCAmelCase						=	json.load(snake_case_		)
						_UpperCAmelCase						=	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.
						_UpperCAmelCase						=	model_names.difference(snake_case_		)
						for model_name in copy.copy(snake_case_		):
												if not model_name.startswith("TF"		) and f"""TF{model_name}""" in not_on_hub:
																		tiny_models_names.remove(snake_case_		)
												elif model_name.startswith("TF"		) and model_name[2:] in not_on_hub:
																		tiny_models_names.remove(snake_case_		)

						return sorted(snake_case_		)


def 				__SCREAMING_SNAKE_CASE    (     snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase						=	COMPOSITE_MODELS.values()

						# All tiny model base names on Hub
						_UpperCAmelCase						=	get_all_model_names()
						_UpperCAmelCase						=	hf_api.list_models(
						    filter=ModelFilter(
						        author="hf-internal-testing"      ,       )		)
						_UpperCAmelCase						=	set()
						for x in models:
												_UpperCAmelCase						=	x.modelId
												_UpperCAmelCase				,					_UpperCAmelCase						=	model.split("/"		)
												if not model.startswith("tiny-random-"		):
																		continue
												_UpperCAmelCase						=	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(snake_case_		)

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

												_UpperCAmelCase						=	sorted(content["tokenizer_classes"]		)
												_UpperCAmelCase						=	sorted(content["processor_classes"]		)
												_UpperCAmelCase						=	sorted(content["model_classes"]		)

												_UpperCAmelCase						=	content
												with open(os.path.join(snake_case_      ,       "hub_tiny_model_summary.json"		)      ,       "w"		) as fp:
																		json.dump(snake_case_      ,       snake_case_      ,       ensure_ascii=snake_case_      ,       indent=4		)


if __name__ == "__main__":
				lowercase_      :							int    							=					argparse.ArgumentParser()
				parser.add_argument('--num_workers', default=1, type=int, help='The number of workers to run.')
				lowercase_      :							int    							=					parser.parse_args()

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

				lowercase_      :							Optional[Any]    							=					'tiny_models'
				lowercase_      :							Dict    							=					True
				lowercase_      :							Tuple    							=					None
				lowercase_      :							Union[str, Any]    							=					get_tiny_model_names_from_repo()
				lowercase_      :							Optional[int]    							=					True
				lowercase_      :							Optional[int]    							=					True
				lowercase_      :							Dict    							=					'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,
				)










import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest






class        __lowerCAmelCase					(			UpperCAmelCase__					):
			snake_case_ :					List[str]						=	(VQDiffusionScheduler,)







			def UpperCamelCase     (			self       :     Dict       ,       **snake_case__       :     int ):




									"""simple docstring"""


									_UpperCAmelCase						=	{
									    "num_vec_classes": 4_097,
									    "num_train_timesteps": 100,
									}

									config.update(**snake_case__ )
									return config







			def UpperCamelCase     (			self       :     Optional[int]       ,       snake_case__       :     Any ):




									"""simple docstring"""


									_UpperCAmelCase						=	4
									_UpperCAmelCase						=	8
									_UpperCAmelCase						=	8

									_UpperCAmelCase						=	torch.randint(0       ,       snake_case__       ,       (batch_size, height * width) )

									return sample







			@property
			def UpperCamelCase     (			self       :     str ):




									"""simple docstring"""


									assert False







			def UpperCamelCase     (			self       :     List[str]       ,       snake_case__       :     Optional[Any] ):




									"""simple docstring"""


									def model(snake_case__       :     Dict       ,       snake_case__       :     Tuple       ,       *snake_case__       :     str ):
															_UpperCAmelCase				,					_UpperCAmelCase						=	sample.shape
															_UpperCAmelCase						=	torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels) )
															_UpperCAmelCase						=	F.log_softmax(logits.double()       ,       dim=1 ).float()
															return return_value

									return model







			def UpperCamelCase     (			self       :     Optional[int] ):




									"""simple docstring"""


									for timesteps in [2, 5, 100, 1_000]:
															self.check_over_configs(num_train_timesteps=snake_case__ )







			def UpperCamelCase     (			self       :     Optional[int] ):




									"""simple docstring"""


									for num_vec_classes in [5, 100, 1_000, 4_000]:
															self.check_over_configs(num_vec_classes=snake_case__ )







			def UpperCamelCase     (			self       :     Optional[Any] ):




									"""simple docstring"""


									for t in [0, 50, 99]:
															self.check_over_forward(time_step=snake_case__ )







			def UpperCamelCase     (			self       :     Tuple ):




									"""simple docstring"""


									pass




