
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__   (		):







	'''simple docstring'''





	snake_case_		   =					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"]:
		snake_case_		   =					getattr(transformers.models.auto					,     _A					,     _A							)
		if module is None:
			continue
		# all mappings in a single auto modeling file
		snake_case_		   =					[
		    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:
			snake_case_		   =					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 						lowerCamelCase__   (		):







	'''simple docstring'''





	snake_case_		   =					set(get_all_model_names()							)

	with open("tests/utils/tiny_model_summary.json"							) as fp:
		snake_case_		   =					json.load(_A							)
	snake_case_		   =					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.
	snake_case_		   =					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 						lowerCamelCase__   (		_A							):







	'''simple docstring'''





	snake_case_		   =					COMPOSITE_MODELS.values()

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

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

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

		snake_case_		   =					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__":
		lowercase__    :					Optional[Any]					=						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__    :					Tuple					=						"tiny_models"
		lowercase__    :					Any					=						True
		lowercase__    :					Optional[int]					=						None
		lowercase__    :					str					=						get_tiny_model_names_from_repo()
		lowercase__    :					Tuple					=						True
		lowercase__    :					Dict					=						True
		lowercase__    :					List[Any]					=						"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 EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 		UpperCAmelCase       (    UpperCAmelCase__      ):







						'''simple docstring'''





						lowerCAmelCase_							=       (EulerAncestralDiscreteScheduler,)
						lowerCAmelCase_							=       10






						def 	snake_case__						(						self							:					Tuple	,     **__lowercase							:					int						):






							"""simple docstring"""






							snake_case_		   =					{
							    "num_train_timesteps": 11_00,
							    "beta_start": 0.0001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							}

							config.update(**__lowercase						)
							return config






						def 	snake_case__						(						self							:					Union[str, Any]						):






							"""simple docstring"""






							for timesteps in [10, 50, 1_00, 10_00]:
								self.check_over_configs(num_train_timesteps=__lowercase						)






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






							for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	,     [0.0002, 0.002, 0.02]						):
								self.check_over_configs(beta_start=__lowercase	,     beta_end=__lowercase						)






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






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






						def 	snake_case__						(						self							:					Union[str, Any]						):






							"""simple docstring"""






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






						def 	snake_case__						(						self							:					Optional[Any]						):






							"""simple docstring"""






							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config()
							snake_case_		   =					scheduler_class(**__lowercase						)

							scheduler.set_timesteps(self.num_inference_steps						)

							snake_case_		   =					torch.manual_seed(0						)

							snake_case_		   =					self.dummy_model()
							snake_case_		   =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							snake_case_		   =					sample.to(__lowercase						)

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

								snake_case_		   =					model(__lowercase	,     __lowercase						)

								snake_case_		   =					scheduler.step(__lowercase	,     __lowercase	,     __lowercase	,     generator=__lowercase						)
								snake_case_		   =					output.prev_sample

							snake_case_		   =					torch.sum(torch.abs(__lowercase						)						)
							snake_case_		   =					torch.mean(torch.abs(__lowercase						)						)

							assert abs(result_sum.item() - 152.3192						) < 1E-2
							assert abs(result_mean.item() - 0.1983						) < 1E-3






						def 	snake_case__						(						self							:					List[Any]						):






							"""simple docstring"""






							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config(prediction_type="v_prediction"						)
							snake_case_		   =					scheduler_class(**__lowercase						)

							scheduler.set_timesteps(self.num_inference_steps						)

							snake_case_		   =					torch.manual_seed(0						)

							snake_case_		   =					self.dummy_model()
							snake_case_		   =					self.dummy_sample_deter * scheduler.init_noise_sigma
							snake_case_		   =					sample.to(__lowercase						)

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

								snake_case_		   =					model(__lowercase	,     __lowercase						)

								snake_case_		   =					scheduler.step(__lowercase	,     __lowercase	,     __lowercase	,     generator=__lowercase						)
								snake_case_		   =					output.prev_sample

							snake_case_		   =					torch.sum(torch.abs(__lowercase						)						)
							snake_case_		   =					torch.mean(torch.abs(__lowercase						)						)

							assert abs(result_sum.item() - 108.4439						) < 1E-2
							assert abs(result_mean.item() - 0.1412						) < 1E-3






						def 	snake_case__						(						self							:					Optional[int]						):






							"""simple docstring"""






							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config()
							snake_case_		   =					scheduler_class(**__lowercase						)

							scheduler.set_timesteps(self.num_inference_steps	,     device=__lowercase						)
							snake_case_		   =					torch.manual_seed(0						)

							snake_case_		   =					self.dummy_model()
							snake_case_		   =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							snake_case_		   =					sample.to(__lowercase						)

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

								snake_case_		   =					model(__lowercase	,     __lowercase						)

								snake_case_		   =					scheduler.step(__lowercase	,     __lowercase	,     __lowercase	,     generator=__lowercase						)
								snake_case_		   =					output.prev_sample

							snake_case_		   =					torch.sum(torch.abs(__lowercase						)						)
							snake_case_		   =					torch.mean(torch.abs(__lowercase						)						)

							assert abs(result_sum.item() - 152.3192						) < 1E-2
							assert abs(result_mean.item() - 0.1983						) < 1E-3
