




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  snake_case (							)							->							str:




	'''simple docstring'''


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

	return sorted(A      )






def  snake_case (							)							->							int:




	'''simple docstring'''


	lowerCamelCase_							:		Tuple							       =							set(get_all_model_names()      )

	with open('tests/utils/tiny_model_summary.json'      ) as fp:
		lowerCamelCase_							:		str							       =							json.load(A      )
	lowerCamelCase_							:		Union[str, Any]							       =							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.
	lowerCamelCase_							:		Optional[Any]							       =							model_names.difference(A      )
	for model_name in copy.copy(A      ):
		if not model_name.startswith('TF'      ) and f"TF{model_name}" in not_on_hub:
			tiny_models_names.remove(A      )
		elif model_name.startswith('TF'      ) and model_name[2:] in not_on_hub:
			tiny_models_names.remove(A      )

	return sorted(A      )






def  snake_case (							A      )							->							Dict:




	'''simple docstring'''


	lowerCamelCase_							:		List[str]							       =							COMPOSITE_MODELS.values()

	# All tiny model base names on Hub
	lowerCamelCase_							:		List[Any]							       =							get_all_model_names()
	lowerCamelCase_							:		int							       =							hf_api.list_models(
	    filter=ModelFilter(
	        author='hf-internal-testing'						,       )      )
	lowerCamelCase_							:		int							       =							set()
	for x in models:
		lowerCamelCase_							:		Dict							       =							x.modelId
		lowerCamelCase_       ,      lowerCamelCase_							:		Optional[int]							       =							model.split('/'      )
		if not model.startswith('tiny-random-'      ):
			continue
		lowerCamelCase_							:		Tuple							       =							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(A      )

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

		lowerCamelCase_							:		Optional[Any]							       =							sorted(content['tokenizer_classes']      )
		lowerCamelCase_							:		Union[str, Any]							       =							sorted(content['processor_classes']      )
		lowerCamelCase_							:		Union[str, Any]							       =							sorted(content['model_classes']      )

		lowerCamelCase_							:		List[Any]							       =							content
		with open(os.path.join(A						,       'hub_tiny_model_summary.json'      )						,       'w'      ) as fp:
			json.dump(A						,       A						,       ensure_ascii=A						,       indent=4      )


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

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

							UpperCamelCase__		=						'tiny_models'
							UpperCamelCase__		=						True
							UpperCamelCase__		=						None
							UpperCamelCase__		=						get_tiny_model_names_from_repo()
							UpperCamelCase__		=						True
							UpperCamelCase__		=						True
							UpperCamelCase__		=						'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

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):
					_lowerCamelCase		  =			(HeunDiscreteScheduler,)
					_lowerCamelCase		  =			10

					def      lowercase_							(    self			,	**__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							       =							{
						    'num_train_timesteps': 1_100,
						    'beta_start': 0.00_01,
						    'beta_end': 0.02,
						    'beta_schedule': 'linear',
						}

						config.update(**__lowercase )
						return config

					def      lowercase_							(    self ):







						'''simple docstring'''




						for timesteps in [10, 50, 100, 1_000]:
							self.check_over_configs(num_train_timesteps=__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]			,	[0.00_02, 0.0_02, 0.02] ):
							self.check_over_configs(beta_start=__lowercase			,	beta_end=__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						for schedule in ["linear", "scaled_linear", "exp"]:
							self.check_over_configs(beta_schedule=__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						for clip_sample_range in [1.0, 2.0, 3.0]:
							self.check_over_configs(clip_sample_range=__lowercase			,	clip_sample=__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						for prediction_type in ["epsilon", "v_prediction", "sample"]:
							self.check_over_configs(prediction_type=__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Tuple							       =							self.scheduler_classes[0]
						lowerCamelCase_							:		List[str]							       =							self.get_scheduler_config()
						lowerCamelCase_							:		Union[str, Any]							       =							scheduler_class(**__lowercase )

						scheduler.set_timesteps(self.num_inference_steps )

						lowerCamelCase_							:		Optional[Any]							       =							self.dummy_model()
						lowerCamelCase_							:		Optional[int]							       =							self.dummy_sample_deter * scheduler.init_noise_sigma
						lowerCamelCase_							:		Dict							       =							sample.to(__lowercase )

						for i, t in enumerate(scheduler.timesteps ):
							lowerCamelCase_							:		Any							       =							scheduler.scale_model_input(__lowercase			,	__lowercase )

							lowerCamelCase_							:		int							       =							model(__lowercase			,	__lowercase )

							lowerCamelCase_							:		Optional[int]							       =							scheduler.step(__lowercase			,	__lowercase			,	__lowercase )
							lowerCamelCase_							:		Tuple							       =							output.prev_sample

						lowerCamelCase_							:		str							       =							torch.sum(torch.abs(__lowercase ) )
						lowerCamelCase_							:		Tuple							       =							torch.mean(torch.abs(__lowercase ) )

						if torch_device in ["cpu", "mps"]:
							assert abs(result_sum.item() - 0.12_33 ) < 1E-2
							assert abs(result_mean.item() - 0.00_02 ) < 1E-3
						else:
							# CUDA
							assert abs(result_sum.item() - 0.12_33 ) < 1E-2
							assert abs(result_mean.item() - 0.00_02 ) < 1E-3

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							self.scheduler_classes[0]
						lowerCamelCase_							:		str							       =							self.get_scheduler_config(prediction_type='v_prediction' )
						lowerCamelCase_							:		Optional[Any]							       =							scheduler_class(**__lowercase )

						scheduler.set_timesteps(self.num_inference_steps )

						lowerCamelCase_							:		Any							       =							self.dummy_model()
						lowerCamelCase_							:		str							       =							self.dummy_sample_deter * scheduler.init_noise_sigma
						lowerCamelCase_							:		List[Any]							       =							sample.to(__lowercase )

						for i, t in enumerate(scheduler.timesteps ):
							lowerCamelCase_							:		int							       =							scheduler.scale_model_input(__lowercase			,	__lowercase )

							lowerCamelCase_							:		Tuple							       =							model(__lowercase			,	__lowercase )

							lowerCamelCase_							:		int							       =							scheduler.step(__lowercase			,	__lowercase			,	__lowercase )
							lowerCamelCase_							:		Dict							       =							output.prev_sample

						lowerCamelCase_							:		Any							       =							torch.sum(torch.abs(__lowercase ) )
						lowerCamelCase_							:		List[Any]							       =							torch.mean(torch.abs(__lowercase ) )

						if torch_device in ["cpu", "mps"]:
							assert abs(result_sum.item() - 4.6934E-07 ) < 1E-2
							assert abs(result_mean.item() - 6.1112E-10 ) < 1E-3
						else:
							# CUDA
							assert abs(result_sum.item() - 4.693428650170972E-07 ) < 1E-2
							assert abs(result_mean.item() - 0.00_02 ) < 1E-3

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							self.scheduler_classes[0]
						lowerCamelCase_							:		int							       =							self.get_scheduler_config()
						lowerCamelCase_							:		Dict							       =							scheduler_class(**__lowercase )

						scheduler.set_timesteps(self.num_inference_steps			,	device=__lowercase )

						lowerCamelCase_							:		str							       =							self.dummy_model()
						lowerCamelCase_							:		Optional[int]							       =							self.dummy_sample_deter.to(__lowercase ) * scheduler.init_noise_sigma

						for t in scheduler.timesteps:
							lowerCamelCase_							:		Tuple							       =							scheduler.scale_model_input(__lowercase			,	__lowercase )

							lowerCamelCase_							:		Tuple							       =							model(__lowercase			,	__lowercase )

							lowerCamelCase_							:		List[Any]							       =							scheduler.step(__lowercase			,	__lowercase			,	__lowercase )
							lowerCamelCase_							:		Union[str, Any]							       =							output.prev_sample

						lowerCamelCase_							:		Dict							       =							torch.sum(torch.abs(__lowercase ) )
						lowerCamelCase_							:		Any							       =							torch.mean(torch.abs(__lowercase ) )

						if str(__lowercase ).startswith('cpu' ):
							# The following sum varies between 148 and 156 on mps. Why?
							assert abs(result_sum.item() - 0.12_33 ) < 1E-2
							assert abs(result_mean.item() - 0.00_02 ) < 1E-3
						elif str(__lowercase ).startswith('mps' ):
							# Larger tolerance on mps
							assert abs(result_mean.item() - 0.00_02 ) < 1E-2
						else:
							# CUDA
							assert abs(result_sum.item() - 0.12_33 ) < 1E-2
							assert abs(result_mean.item() - 0.00_02 ) < 1E-3





					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							self.scheduler_classes[0]
						lowerCamelCase_							:		Optional[Any]							       =							self.get_scheduler_config()
						lowerCamelCase_							:		Dict							       =							scheduler_class(**__lowercase			,	use_karras_sigmas=__lowercase )

						scheduler.set_timesteps(self.num_inference_steps			,	device=__lowercase )

						lowerCamelCase_							:		int							       =							self.dummy_model()
						lowerCamelCase_							:		Optional[Any]							       =							self.dummy_sample_deter.to(__lowercase ) * scheduler.init_noise_sigma
						lowerCamelCase_							:		Tuple							       =							sample.to(__lowercase )

						for t in scheduler.timesteps:
							lowerCamelCase_							:		str							       =							scheduler.scale_model_input(__lowercase			,	__lowercase )

							lowerCamelCase_							:		Tuple							       =							model(__lowercase			,	__lowercase )

							lowerCamelCase_							:		Dict							       =							scheduler.step(__lowercase			,	__lowercase			,	__lowercase )
							lowerCamelCase_							:		Dict							       =							output.prev_sample

						lowerCamelCase_							:		Tuple							       =							torch.sum(torch.abs(__lowercase ) )
						lowerCamelCase_							:		Optional[Any]							       =							torch.mean(torch.abs(__lowercase ) )

						assert abs(result_sum.item() - 0.0_00_15 ) < 1E-2
						assert abs(result_mean.item() - 1.9869554535034695E-07 ) < 1E-2
