# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

from .config_args import default_config_file, load_config_from_file
from .config_utils import SubcommandHelpFormatter


__snake_case         =     '''Update an existing config file with the latest defaults while maintaining the old configuration.'''





def 	lowerCAmelCase_			(					__lowerCAmelCase    )->	List[str]:






      '''simple docstring'''





      UpperCAmelCase     :			str     	=args.config_file
      if config_file is None and Path(__lowerCAmelCase    ).exists():
            UpperCAmelCase     :			int     	=default_config_file
      elif not Path(__lowerCAmelCase    ).exists():
            raise ValueError(f'''The passed config file located at {config_file} doesn\'t exist.'''    )
      UpperCAmelCase     :			Tuple     	=load_config_from_file(__lowerCAmelCase    )

      if config_file.endswith('''.json'''    ):
            config.to_json_file(__lowerCAmelCase    )
      else:
            config.to_yaml_file(__lowerCAmelCase    )
      return config_file





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase    )->	str:






      '''simple docstring'''





      UpperCAmelCase     :			str     	=parser.add_parser('''update'''		,       parents=__lowerCAmelCase		,       help=__lowerCAmelCase		,       formatter_class=__lowerCAmelCase    )
      parser.add_argument(
          '''--config_file'''		,       default=__lowerCAmelCase		,       help=(
              '''The path to the config file to update. Will default to a file named default_config.yaml in the cache '''
              '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
              '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
              '''with \'huggingface\'.'''
          )		,       )

      parser.set_defaults(func=__lowerCAmelCase    )
      return parser





def 	lowerCAmelCase_			(					__lowerCAmelCase    )->	int:






      '''simple docstring'''





      UpperCAmelCase     :			str     	=update_config(__lowerCAmelCase    )
      print(f'''Sucessfully updated the configuration file at {config_file}.'''    )

import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest







class       __snake_case (  lowerCamelCase__ ):
 __lowerCamelCase     : Dict 	=   (VQDiffusionScheduler,)
 def 						UpperCAmelCase__							(	self		, **snake_case__    )						->				Dict:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	={
           '''num_vec_classes''': 4097,
           '''num_train_timesteps''': 100,
       }

       config.update(**snake_case__    )
       return config
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				List[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			List[str]     	=4
       UpperCAmelCase     :			Optional[int]     	=8
       UpperCAmelCase     :			Optional[Any]     	=8

       UpperCAmelCase     :			str     	=torch.randint(0		, snake_case__		, (batch_size, height * width)    )

       return sample
 @property
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       assert False
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				Tuple:
       '''simple docstring'''





       def model(snake_case__		, snake_case__		, *snake_case__    ):
             UpperCAmelCase	,    UpperCAmelCase     :			Union[str, Any]     	=sample.shape
             UpperCAmelCase     :			Tuple     	=torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)    )
             UpperCAmelCase     :			List[str]     	=F.log_softmax(logits.double()		, dim=1    ).float()
             return return_value

       return model
 def 						UpperCAmelCase__							(	self    )						->				Dict:
       '''simple docstring'''





       for timesteps in [2, 5, 100, 1000]:
             self.check_over_configs(num_train_timesteps=snake_case__    )
 def 						UpperCAmelCase__							(	self    )						->				Optional[Any]:
       '''simple docstring'''





       for num_vec_classes in [5, 100, 1000, 4000]:
             self.check_over_configs(num_vec_classes=snake_case__    )
 def 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





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






 def 						UpperCAmelCase__							(	self    )						->				int:
       '''simple docstring'''





       pass
