

'''simple docstring'''







import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest
class 					__a   (		_snake_case  ):
						__SCREAMING_SNAKE_CASE     :					List[str]  =      (DDIMScheduler,)
						__SCREAMING_SNAKE_CASE     :					Any  =      (('eta', 0.0), ('num_inference_steps', 5_0))

						def 	_UpperCAmelCase							(		self	:						Tuple    ,			**lowercase__	:						Optional[int])						->Optional[int]:

							"""simple docstring"""



							_lowercase							=     {
							    """num_train_timesteps""": 10_00,
							    """beta_start""": 0.0001,
							    """beta_end""": 0.02,
							    """beta_schedule""": """linear""",
							    """clip_sample""": True,
							}

							config.update(**lowercase__)
							return config

						def 	_UpperCAmelCase							(		self	:						List[str]    ,			**lowercase__	:						Dict)						->Any:

							"""simple docstring"""



							_lowercase							=     self.scheduler_classes[0]
							_lowercase							=     self.get_scheduler_config(**lowercase__)
							_lowercase							=     scheduler_class(**lowercase__)

							_lowercase		,		_lowercase							=     10, 0.0

							_lowercase							=     self.dummy_model()
							_lowercase							=     self.dummy_sample_deter

							scheduler.set_timesteps(lowercase__)

							for t in scheduler.timesteps:
								_lowercase							=     model(lowercase__    ,			lowercase__)
								_lowercase							=     scheduler.step(lowercase__    ,			lowercase__    ,			lowercase__    ,			lowercase__).prev_sample

							return sample

						def 	_UpperCAmelCase							(		self	:						Dict)						->List[str]:

							"""simple docstring"""



							for timesteps in [1_00, 5_00, 10_00]:
								self.check_over_configs(num_train_timesteps=lowercase__)

						def 	_UpperCAmelCase							(		self	:						Optional[int])						->Tuple:

							"""simple docstring"""



							for steps_offset in [0, 1]:
								self.check_over_configs(steps_offset=lowercase__)

							_lowercase							=     self.scheduler_classes[0]
							_lowercase							=     self.get_scheduler_config(steps_offset=1)
							_lowercase							=     scheduler_class(**lowercase__)
							scheduler.set_timesteps(5)
							assert torch.equal(scheduler.timesteps    ,			torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]))

						def 	_UpperCAmelCase							(		self	:						Optional[Any])						->Optional[int]:

							"""simple docstring"""



							for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]    ,			[0.002, 0.02, 0.2, 2]):
								self.check_over_configs(beta_start=lowercase__    ,			beta_end=lowercase__)

						def 	_UpperCAmelCase							(		self	:						Dict)						->Dict:

							"""simple docstring"""



							for schedule in ["linear", "squaredcos_cap_v2"]:
								self.check_over_configs(beta_schedule=lowercase__)

						def 	_UpperCAmelCase							(		self	:						List[str])						->List[Any]:

							"""simple docstring"""



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

						def 	_UpperCAmelCase							(		self	:						List[str])						->List[str]:

							"""simple docstring"""



							for clip_sample in [True, False]:
								self.check_over_configs(clip_sample=lowercase__)

						def 	_UpperCAmelCase							(		self	:						Any)						->Any:

							"""simple docstring"""



							for timestep_spacing in ["trailing", "leading"]:
								self.check_over_configs(timestep_spacing=lowercase__)

						def 	_UpperCAmelCase							(		self	:						Any)						->Tuple:

							"""simple docstring"""



							for rescale_betas_zero_snr in [True, False]:
								self.check_over_configs(rescale_betas_zero_snr=lowercase__)

						def 	_UpperCAmelCase							(		self	:						List[Any])						->int:

							"""simple docstring"""



							self.check_over_configs(thresholding=lowercase__)
							for threshold in [0.5, 1.0, 2.0]:
								for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(
									    thresholding=lowercase__    ,			prediction_type=lowercase__    ,			sample_max_value=lowercase__    ,			)

						def 	_UpperCAmelCase							(		self	:						Optional[int])						->Any:

							"""simple docstring"""



							for t in [1, 10, 49]:
								self.check_over_forward(time_step=lowercase__)

						def 	_UpperCAmelCase							(		self	:						Optional[int])						->Tuple:

							"""simple docstring"""



							for t, num_inference_steps in zip([1, 10, 50]    ,			[10, 50, 5_00]):
								self.check_over_forward(time_step=lowercase__    ,			num_inference_steps=lowercase__)

						def 	_UpperCAmelCase							(		self	:						str)						->Optional[Any]:

							"""simple docstring"""



							for t, eta in zip([1, 10, 49]    ,			[0.0, 0.5, 1.0]):
								self.check_over_forward(time_step=lowercase__    ,			eta=lowercase__)

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

							"""simple docstring"""



							_lowercase							=     self.scheduler_classes[0]
							_lowercase							=     self.get_scheduler_config()
							_lowercase							=     scheduler_class(**lowercase__)

							assert torch.sum(torch.abs(scheduler._get_variance(0    ,			0) - 0.0)) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_20    ,			4_00) - 0.14771)) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_80    ,			9_60) - 0.32460)) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(0    ,			0) - 0.0)) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_87    ,			4_86) - 0.00979)) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_99    ,			9_98) - 0.02)) < 1e-5

						def 	_UpperCAmelCase							(		self	:						Optional[int])						->Optional[int]:

							"""simple docstring"""



							_lowercase							=     self.full_loop()

							_lowercase							=     torch.sum(torch.abs(lowercase__))
							_lowercase							=     torch.mean(torch.abs(lowercase__))

							assert abs(result_sum.item() - 172.0067) < 1e-2
							assert abs(result_mean.item() - 0.223967) < 1e-3

						def 	_UpperCAmelCase							(		self	:						Optional[Any])						->List[str]:

							"""simple docstring"""



							_lowercase							=     self.full_loop(prediction_type="""v_prediction""")

							_lowercase							=     torch.sum(torch.abs(lowercase__))
							_lowercase							=     torch.mean(torch.abs(lowercase__))

							assert abs(result_sum.item() - 52.5302) < 1e-2
							assert abs(result_mean.item() - 0.0684) < 1e-3

						def 	_UpperCAmelCase							(		self	:						Optional[int])						->Tuple:

							"""simple docstring"""



							_lowercase							=     self.full_loop(set_alpha_to_one=lowercase__    ,			beta_start=0.01)
							_lowercase							=     torch.sum(torch.abs(lowercase__))
							_lowercase							=     torch.mean(torch.abs(lowercase__))

							assert abs(result_sum.item() - 149.8295) < 1e-2
							assert abs(result_mean.item() - 0.1951) < 1e-3


						def 	_UpperCAmelCase							(		self	:						str)						->Any:

							"""simple docstring"""



							_lowercase							=     self.full_loop(set_alpha_to_one=lowercase__    ,			beta_start=0.01)
							_lowercase							=     torch.sum(torch.abs(lowercase__))
							_lowercase							=     torch.mean(torch.abs(lowercase__))

							assert abs(result_sum.item() - 149.0784) < 1e-2
							assert abs(result_mean.item() - 0.1941) < 1e-3







'''simple docstring'''







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		(						):
	_lowercase							=     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"]:
		_lowercase							=     getattr(transformers.models.auto							,       snake_case_							,       snake_case_	)
		if module is None:
			continue
		# all mappings in a single auto modeling file
		_lowercase							=     [
		    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:
			_lowercase							=     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		(						):
	# All model names defined in auto mappings
	_lowercase							=     set(get_all_model_names()	)

	with open("""tests/utils/tiny_model_summary.json"""	) as fp:
		_lowercase							=     json.load(snake_case_	)
	_lowercase							=     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.
	_lowercase							=     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_	):
	_lowercase							=     COMPOSITE_MODELS.values()

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

	_lowercase							=     sorted(_models	)
	# All tiny model names on Hub
	_lowercase							=     {}
	for model in models:
		_lowercase							=     F"""hf-internal-testing/tiny-random-{model}"""
		_lowercase							=     model.split("""-"""	)[0]
		try:
			_lowercase							=     hf_api.repo_info(snake_case_	)
			_lowercase							=     {
			    """tokenizer_classes""": set(),
			    """processor_classes""": set(),
			    """model_classes""": set(),
			    """sha""": repo_info.sha,
			}
		except Exception:
			continue
		try:
			time.sleep(1	)
			_lowercase							=     AutoTokenizer.from_pretrained(snake_case_	)
			content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__	)
		except Exception:
			pass
		try:
			time.sleep(1	)
			_lowercase							=     AutoTokenizer.from_pretrained(snake_case_							,       use_fast=snake_case_	)
			content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__	)
		except Exception:
			pass
		try:
			time.sleep(1	)
			_lowercase							=     AutoImageProcessor.from_pretrained(snake_case_	)
			content["processor_classes"].add(img_p.__class__.__name__	)
		except Exception:
			pass
		try:
			time.sleep(1	)
			_lowercase							=     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	)
			_lowercase							=     getattr(snake_case_							,       snake_case_	)
			_lowercase							=     model_class.from_pretrained(snake_case_	)
			content["model_classes"].add(m.__class__.__name__	)
		except Exception:
			pass
		try:
			time.sleep(1	)
			_lowercase							=     getattr(snake_case_							,       F"""TF{model}"""	)
			_lowercase							=     model_class.from_pretrained(snake_case_	)
			content["model_classes"].add(m.__class__.__name__	)
		except Exception:
			pass

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

		_lowercase							=     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__":
							_lowerCamelCase    =  argparse.ArgumentParser()
							parser.add_argument('--num_workers', default=1, type=int, help='The number of workers to run.')
							_lowerCamelCase    =  parser.parse_args()

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

							_lowerCamelCase    =  'tiny_models'
							_lowerCamelCase    =  True
							_lowerCamelCase    =  None
							_lowerCamelCase    =  get_tiny_model_names_from_repo()
							_lowerCamelCase    =  True
							_lowerCamelCase    =  True
							_lowerCamelCase    =  '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,
							)




