from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer



@dataclass
class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     torch.FloatTensor







class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_   ,						lowerCAmelCase_			):
  '''simple docstring'''


  @register_to_config
  def __init__(				self    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					int = 3      ,	SCREAMING_SNAKE_CASE__    :					int = 3      ,	SCREAMING_SNAKE_CASE__    :					Tuple[str] = ("DownEncoderBlock2D",)      ,	SCREAMING_SNAKE_CASE__    :					Tuple[str] = ("UpDecoderBlock2D",)      ,	SCREAMING_SNAKE_CASE__    :					Tuple[int] = (64,)      ,	SCREAMING_SNAKE_CASE__    :					int = 1      ,	SCREAMING_SNAKE_CASE__    :					str = "silu"      ,	SCREAMING_SNAKE_CASE__    :					int = 3      ,	SCREAMING_SNAKE_CASE__    :					int = 32      ,	SCREAMING_SNAKE_CASE__    :					int = 2_56      ,	SCREAMING_SNAKE_CASE__    :					int = 32      ,	SCREAMING_SNAKE_CASE__    :					Optional[int] = None      ,	SCREAMING_SNAKE_CASE__    :					float = 0.1_8_2_1_5      ,	SCREAMING_SNAKE_CASE__    :					str = "group"      ,	)    ->				Tuple:
         super().__init__()

         # pass init params to Encoder
         A						:     Any         =Encoder(
             in_channels=SCREAMING_SNAKE_CASE__      ,	out_channels=SCREAMING_SNAKE_CASE__      ,	down_block_types=SCREAMING_SNAKE_CASE__      ,	block_out_channels=SCREAMING_SNAKE_CASE__      ,	layers_per_block=SCREAMING_SNAKE_CASE__      ,	act_fn=SCREAMING_SNAKE_CASE__      ,	norm_num_groups=SCREAMING_SNAKE_CASE__      ,	double_z=SCREAMING_SNAKE_CASE__      ,	)

         A						:     str         =vq_embed_dim if vq_embed_dim is not None else latent_channels

         A						:     List[str]         =nn.Convad(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	1     )
         A						:     Tuple         =VectorQuantizer(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	beta=0.2_5      ,	remap=SCREAMING_SNAKE_CASE__      ,	sane_index_shape=SCREAMING_SNAKE_CASE__     )
         A						:     Tuple         =nn.Convad(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	1     )

         # pass init params to Decoder
         A						:     Dict         =Decoder(
             in_channels=SCREAMING_SNAKE_CASE__      ,	out_channels=SCREAMING_SNAKE_CASE__      ,	up_block_types=SCREAMING_SNAKE_CASE__      ,	block_out_channels=SCREAMING_SNAKE_CASE__      ,	layers_per_block=SCREAMING_SNAKE_CASE__      ,	act_fn=SCREAMING_SNAKE_CASE__      ,	norm_num_groups=SCREAMING_SNAKE_CASE__      ,	norm_type=SCREAMING_SNAKE_CASE__      ,	)







  @apply_forward_hook
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					torch.FloatTensor      ,	SCREAMING_SNAKE_CASE__    :					bool = True     )    ->				VQEncoderOutput:
         A						:     List[Any]         =self.encoder(SCREAMING_SNAKE_CASE__     )
         A						:     Union[str, Any]         =self.quant_conv(SCREAMING_SNAKE_CASE__     )

         if not return_dict:
                return (h,)

         return VQEncoderOutput(latents=SCREAMING_SNAKE_CASE__     )







  @apply_forward_hook
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					torch.FloatTensor      ,	SCREAMING_SNAKE_CASE__    :					bool = False      ,	SCREAMING_SNAKE_CASE__    :					bool = True     )    ->				Union[DecoderOutput, torch.FloatTensor]:
         # also go through quantization layer
         if not force_not_quantize:
                A  ,  A  ,  A						:     Dict         =self.quantize(SCREAMING_SNAKE_CASE__     )
         else:
                A						:     Any         =h
         A						:     List[str]         =self.post_quant_conv(SCREAMING_SNAKE_CASE__     )
         A						:     Any         =self.decoder(SCREAMING_SNAKE_CASE__      ,	quant if self.config.norm_type == 'spatial' else None     )

         if not return_dict:
                return (dec,)

         return DecoderOutput(sample=SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					torch.FloatTensor      ,	SCREAMING_SNAKE_CASE__    :					bool = True     )    ->				Union[DecoderOutput, torch.FloatTensor]:
         A						:     Optional[Any]         =sample
         A						:     List[Any]         =self.encode(SCREAMING_SNAKE_CASE__     ).latents
         A						:     str         =self.decode(SCREAMING_SNAKE_CASE__     ).sample

         if not return_dict:
                return (dec,)

         return DecoderOutput(sample=SCREAMING_SNAKE_CASE__     )







# Copyright 2021 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.

import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


_lowercase							:      List[str]      							='''Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'''



def A__  (			)				->		List[Any]:
       A						:     Any         =_ask_options(
           'In which compute environment are you running?',		['This machine', 'AWS (Amazon SageMaker)'],		_convert_compute_environment,		)
       if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
              A						:     Tuple         =get_sagemaker_input()
       else:
              A						:     str         =get_cluster_input()
       return config



def A__  (			lowercase:     int=None					)				->		str:
       if subparsers is not None:
              A						:     List[str]         =subparsers.add_parser('config',		description=lowercase					)
       else:
              A						:     Union[str, Any]         =argparse.ArgumentParser('Accelerate config command',		description=lowercase					)

       parser.add_argument(
           '--config_file',		default=lowercase,		help=(
               'The path to use to store the config file. 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\'.'
           ),		)

       if subparsers is not None:
              parser.set_defaults(func=lowercase					)
       return parser



def A__  (			lowercase:     Tuple					)				->		List[Any]:
       A						:     Union[str, Any]         =get_user_input()
       if args.config_file is not None:
              A						:     Optional[Any]         =args.config_file
       else:
              if not os.path.isdir(lowercase					):
                     os.makedirs(lowercase					)
              A						:     Union[str, Any]         =default_yaml_config_file

       if config_file.endswith('.json'					):
              config.to_json_file(lowercase					)
       else:
              config.to_yaml_file(lowercase					)
       print(F'accelerate configuration saved at {config_file}'					)



def A__  (			)				->		Optional[int]:
       A						:     Any         =config_command_parser()
       A						:     int         =parser.parse_args()
       config_command(lowercase					)


if __name__ == "__main__":
    main()






