





"""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    __lowerCamelCase     (							)		->							List[str]:






       """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,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)
              if module is None:
                     continue
              # all mappings in a single auto modeling file
              _UpperCAmelCase			     =  [
                  x
                  for x in dir(SCREAMING_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(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)
                     if mapping is not None:
                            for v in mapping.values():
                                   if isinstance(SCREAMING_SNAKE_CASE,(list, tuple)				):
                                          model_names.update(SCREAMING_SNAKE_CASE				)
                                   elif isinstance(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				):
                                          model_names.add(SCREAMING_SNAKE_CASE				)

       return sorted(SCREAMING_SNAKE_CASE				)

def    __lowerCamelCase     (							)		->							Tuple:






       """simple docstring"""




       _UpperCAmelCase			     =  set(get_all_model_names()				)

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

       return sorted(SCREAMING_SNAKE_CASE				)

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							str:






       """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(SCREAMING_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(SCREAMING_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(SCREAMING_SNAKE_CASE				)
                     content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__				)
              except Exception:
                     pass
              try:
                     time.sleep(1				)
                     _UpperCAmelCase			     =  AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE,use_fast=SCREAMING_SNAKE_CASE				)
                     content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__				)
              except Exception:
                     pass
              try:
                     time.sleep(1				)
                     _UpperCAmelCase			     =  AutoImageProcessor.from_pretrained(SCREAMING_SNAKE_CASE				)
                     content["processor_classes"].add(img_p.__class__.__name__				)
              except Exception:
                     pass
              try:
                     time.sleep(1				)
                     _UpperCAmelCase			     =  AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE				)
                     if not isinstance(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				):
                            content["processor_classes"].add(feat_p.__class__.__name__				)
              except Exception:
                     pass
              try:
                     time.sleep(1				)
                     _UpperCAmelCase			     =  getattr(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)
                     _UpperCAmelCase			     =  model_class.from_pretrained(SCREAMING_SNAKE_CASE				)
                     content["model_classes"].add(m.__class__.__name__				)
              except Exception:
                     pass
              try:
                     time.sleep(1				)
                     _UpperCAmelCase			     =  getattr(SCREAMING_SNAKE_CASE,F"""TF{model}"""				)
                     _UpperCAmelCase			     =  model_class.from_pretrained(SCREAMING_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(SCREAMING_SNAKE_CASE,'hub_tiny_model_summary.json'				),'w'				) as fp:
                     json.dump(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,ensure_ascii=SCREAMING_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,
    )













"""simple docstring"""






import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 						lowerCAmelCase       (   snake_case				):
       lowerCAmelCase__    			=	(LMSDiscreteScheduler,)
       lowerCAmelCase__    			=	10






       def 				__A			( self     ,	**a__  ):
              _UpperCAmelCase			     =  {
                  'num_train_timesteps': 11_00,
                  'beta_start': 0.0_001,
                  'beta_end': 0.02,
                  'beta_schedule': 'linear',
              }

              config.update(**a__  )
              return config






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






       def 				__A			( self  ):
              for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]     ,	[0.0_002, 0.002, 0.02]  ):
                     self.check_over_configs(beta_start=a__     ,	beta_end=a__  )






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






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






       def 				__A			( self  ):
              for t in [0, 5_00, 8_00]:
                     self.check_over_forward(time_step=a__  )






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config()
              _UpperCAmelCase			     =  scheduler_class(**a__  )

              scheduler.set_timesteps(self.num_inference_steps  )

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter * scheduler.init_noise_sigma

              for i, t in enumerate(scheduler.timesteps  ):
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 1_006.388  ) < 1E-2
              assert abs(result_mean.item() - 1.31  ) < 1E-3






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config(prediction_type='v_prediction'  )
              _UpperCAmelCase			     =  scheduler_class(**a__  )

              scheduler.set_timesteps(self.num_inference_steps  )

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter * scheduler.init_noise_sigma

              for i, t in enumerate(scheduler.timesteps  ):
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 0.0_017  ) < 1E-2
              assert abs(result_mean.item() - 2.2676E-06  ) < 1E-3






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config()
              _UpperCAmelCase			     =  scheduler_class(**a__  )

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

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
              _UpperCAmelCase			     =  sample.to(a__  )

              for i, t in enumerate(scheduler.timesteps  ):
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 1_006.388  ) < 1E-2
              assert abs(result_mean.item() - 1.31  ) < 1E-3






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config()
              _UpperCAmelCase			     =  scheduler_class(**a__     ,	use_karras_sigmas=a__  )

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

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter.to(a__  ) * scheduler.init_noise_sigma
              _UpperCAmelCase			     =  sample.to(a__  )

              for t in scheduler.timesteps:
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 3_812.9_927  ) < 2E-2
              assert abs(result_mean.item() - 4.9_648  ) < 1E-3






