






from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block


@dataclass
class  SCREAMING_SNAKE_CASE			(			__snake_case					):
     '''simple docstring'''


     UpperCamelCase_							:		torch.FloatTensor





class  SCREAMING_SNAKE_CASE			(			nn.Module					):
     '''simple docstring'''


     def __init__(				self       :	List[Any]					, UpperCAmelCase_       :	Optional[int]=3					, UpperCAmelCase_       :	Optional[int]=3					, UpperCAmelCase_       :	int=("DownEncoderBlock2D",)					, UpperCAmelCase_       :	str=(64,)					, UpperCAmelCase_       :	Union[str, Any]=2					, UpperCAmelCase_       :	List[Any]=32					, UpperCAmelCase_       :	Tuple="silu"					, UpperCAmelCase_       :	Union[str, Any]=True					, ):
       super().__init__()
       SCREAMING_SNAKE_CASE       :      int							=    layers_per_block

       SCREAMING_SNAKE_CASE       :      List[str]							=    torch.nn.Convad(
           A_					, block_out_channels[0]					, kernel_size=3					, stride=1					, padding=1					, )

       SCREAMING_SNAKE_CASE       :      Dict							=    None
       SCREAMING_SNAKE_CASE       :      List[str]							=    nn.ModuleList([]      )

       # down
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    block_out_channels[0]
       for i, down_block_type in enumerate(A_      ):
         SCREAMING_SNAKE_CASE       :      List[str]							=    output_channel
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    block_out_channels[i]
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    i == len(A_      ) - 1

         SCREAMING_SNAKE_CASE       :      List[str]							=    get_down_block(
             A_					, num_layers=self.layers_per_block					, in_channels=A_					, out_channels=A_					, add_downsample=not is_final_block					, resnet_eps=1E-6					, downsample_padding=0					, resnet_act_fn=A_					, resnet_groups=A_					, attention_head_dim=A_					, temb_channels=A_					, )
         self.down_blocks.append(A_      )

       # mid
       SCREAMING_SNAKE_CASE       :      Dict							=    UNetMidBlockaD(
           in_channels=block_out_channels[-1]					, resnet_eps=1E-6					, resnet_act_fn=A_					, output_scale_factor=1					, resnet_time_scale_shift="default"					, attention_head_dim=block_out_channels[-1]					, resnet_groups=A_					, temb_channels=A_					, )

       # out
       SCREAMING_SNAKE_CASE       :      List[str]							=    nn.GroupNorm(num_channels=block_out_channels[-1]					, num_groups=A_					, eps=1E-6      )
       SCREAMING_SNAKE_CASE       :      Any							=    nn.SiLU()

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    2 * out_channels if double_z else out_channels
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    nn.Convad(block_out_channels[-1]					, A_					, 3					, padding=1      )

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    False






     def 				_A       (				self       :	Dict					, UpperCAmelCase_       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      int							=    x
       SCREAMING_SNAKE_CASE       :      List[Any]							=    self.conv_in(A_      )

       if self.training and self.gradient_checkpointing:

         def create_custom_forward(UpperCAmelCase_       :	str      ):
           def custom_forward(*UpperCAmelCase_       :	Optional[int]      ):
             return module(*A_      )

           return custom_forward

         # down
         if is_torch_version(">="					, "1.11.0"      ):
           for down_block in self.down_blocks:
             SCREAMING_SNAKE_CASE       :      int							=    torch.utils.checkpoint.checkpoint(
                 create_custom_forward(A_      )					, A_					, use_reentrant=A_      )
           # middle
           SCREAMING_SNAKE_CASE       :      str							=    torch.utils.checkpoint.checkpoint(
               create_custom_forward(self.mid_block      )					, A_					, use_reentrant=A_      )
         else:
           for down_block in self.down_blocks:
             SCREAMING_SNAKE_CASE       :      Tuple							=    torch.utils.checkpoint.checkpoint(create_custom_forward(A_      )					, A_      )
           # middle
           SCREAMING_SNAKE_CASE       :      List[str]							=    torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block      )					, A_      )

       else:
         # down
         for down_block in self.down_blocks:
           SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    down_block(A_      )

         # middle
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.mid_block(A_      )

       # post-process
       SCREAMING_SNAKE_CASE       :      Dict							=    self.conv_norm_out(A_      )
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.conv_act(A_      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.conv_out(A_      )

       return sample





class  SCREAMING_SNAKE_CASE			(			nn.Module					):
     '''simple docstring'''


     def __init__(				self       :	List[Any]					, UpperCAmelCase_       :	str=3					, UpperCAmelCase_       :	str=3					, UpperCAmelCase_       :	Tuple=("UpDecoderBlock2D",)					, UpperCAmelCase_       :	Any=(64,)					, UpperCAmelCase_       :	Optional[Any]=2					, UpperCAmelCase_       :	List[str]=32					, UpperCAmelCase_       :	Optional[int]="silu"					, UpperCAmelCase_       :	List[str]="group"					, ):
       super().__init__()
       SCREAMING_SNAKE_CASE       :      List[str]							=    layers_per_block

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    nn.Convad(
           A_					, block_out_channels[-1]					, kernel_size=3					, stride=1					, padding=1					, )

       SCREAMING_SNAKE_CASE       :      Dict							=    None
       SCREAMING_SNAKE_CASE       :      Tuple							=    nn.ModuleList([]      )

       SCREAMING_SNAKE_CASE       :      Dict							=    in_channels if norm_type == "spatial" else None

       # mid
       SCREAMING_SNAKE_CASE       :      Dict							=    UNetMidBlockaD(
           in_channels=block_out_channels[-1]					, resnet_eps=1E-6					, resnet_act_fn=A_					, output_scale_factor=1					, resnet_time_scale_shift="default" if norm_type == "group" else norm_type					, attention_head_dim=block_out_channels[-1]					, resnet_groups=A_					, temb_channels=A_					, )

       # up
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    list(reversed(A_      )      )
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    reversed_block_out_channels[0]
       for i, up_block_type in enumerate(A_      ):
         SCREAMING_SNAKE_CASE       :      Tuple							=    output_channel
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    reversed_block_out_channels[i]

         SCREAMING_SNAKE_CASE       :      str							=    i == len(A_      ) - 1

         SCREAMING_SNAKE_CASE       :      int							=    get_up_block(
             A_					, num_layers=self.layers_per_block + 1					, in_channels=A_					, out_channels=A_					, prev_output_channel=A_					, add_upsample=not is_final_block					, resnet_eps=1E-6					, resnet_act_fn=A_					, resnet_groups=A_					, attention_head_dim=A_					, temb_channels=A_					, resnet_time_scale_shift=A_					, )
         self.up_blocks.append(A_      )
         SCREAMING_SNAKE_CASE       :      Any							=    output_channel

       # out
       if norm_type == "spatial":
         SCREAMING_SNAKE_CASE       :      Tuple							=    SpatialNorm(block_out_channels[0]					, A_      )
       else:
         SCREAMING_SNAKE_CASE       :      str							=    nn.GroupNorm(num_channels=block_out_channels[0]					, num_groups=A_					, eps=1E-6      )
       SCREAMING_SNAKE_CASE       :      str							=    nn.SiLU()
       SCREAMING_SNAKE_CASE       :      str							=    nn.Convad(block_out_channels[0]					, A_					, 3					, padding=1      )

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    False






     def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Optional[Any]=None      ):
       SCREAMING_SNAKE_CASE       :      Tuple							=    z
       SCREAMING_SNAKE_CASE       :      int							=    self.conv_in(A_      )

       SCREAMING_SNAKE_CASE       :      List[str]							=    next(iter(self.up_blocks.parameters()      )      ).dtype
       if self.training and self.gradient_checkpointing:

         def create_custom_forward(UpperCAmelCase_       :	List[Any]      ):
           def custom_forward(*UpperCAmelCase_       :	str      ):
             return module(*A_      )

           return custom_forward

         if is_torch_version(">="					, "1.11.0"      ):
           # middle
           SCREAMING_SNAKE_CASE       :      Any							=    torch.utils.checkpoint.checkpoint(
               create_custom_forward(self.mid_block      )					, A_					, A_					, use_reentrant=A_      )
           SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    sample.to(A_      )

           # up
           for up_block in self.up_blocks:
             SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    torch.utils.checkpoint.checkpoint(
                 create_custom_forward(A_      )					, A_					, A_					, use_reentrant=A_      )
         else:
           # middle
           SCREAMING_SNAKE_CASE       :      List[Any]							=    torch.utils.checkpoint.checkpoint(
               create_custom_forward(self.mid_block      )					, A_					, A_      )
           SCREAMING_SNAKE_CASE       :      List[Any]							=    sample.to(A_      )

           # up
           for up_block in self.up_blocks:
             SCREAMING_SNAKE_CASE       :      int							=    torch.utils.checkpoint.checkpoint(create_custom_forward(A_      )					, A_					, A_      )
       else:
         # middle
         SCREAMING_SNAKE_CASE       :      str							=    self.mid_block(A_					, A_      )
         SCREAMING_SNAKE_CASE       :      List[str]							=    sample.to(A_      )

         # up
         for up_block in self.up_blocks:
           SCREAMING_SNAKE_CASE       :      Optional[int]							=    up_block(A_					, A_      )

         # post-process
       if latent_embeds is None:
         SCREAMING_SNAKE_CASE       :      int							=    self.conv_norm_out(A_      )
       else:
         SCREAMING_SNAKE_CASE       :      int							=    self.conv_norm_out(A_					, A_      )
       SCREAMING_SNAKE_CASE       :      int							=    self.conv_act(A_      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.conv_out(A_      )

       return sample





class  SCREAMING_SNAKE_CASE			(			nn.Module					):
     '''simple docstring'''


     def __init__(				self       :	int					, UpperCAmelCase_       :	str					, UpperCAmelCase_       :	Optional[Any]					, UpperCAmelCase_       :	List[str]					, UpperCAmelCase_       :	str=None					, UpperCAmelCase_       :	Any="random"					, UpperCAmelCase_       :	Tuple=False					, UpperCAmelCase_       :	List[str]=True      ):
       super().__init__()
       SCREAMING_SNAKE_CASE       :      List[Any]							=    n_e
       SCREAMING_SNAKE_CASE       :      Dict							=    vq_embed_dim
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    beta
       SCREAMING_SNAKE_CASE       :      int							=    legacy

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    nn.Embedding(self.n_e					, self.vq_embed_dim      )
       self.embedding.weight.data.uniform_(-1.0 / self.n_e					, 1.0 / self.n_e      )

       SCREAMING_SNAKE_CASE       :      str							=    remap
       if self.remap is not None:
         self.register_buffer("used"					, torch.tensor(np.load(self.remap      )      )      )
         SCREAMING_SNAKE_CASE       :      str							=    self.used.shape[0]
         SCREAMING_SNAKE_CASE       :      List[Any]							=    unknown_index  # "random" or "extra" or integer
         if self.unknown_index == "extra":
           SCREAMING_SNAKE_CASE       :      Tuple							=    self.re_embed
           SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.re_embed + 1
         print(
             f'''Remapping {self.n_e} indices to {self.re_embed} indices. '''
             f'''Using {self.unknown_index} for unknown indices.'''      )
       else:
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    n_e

       SCREAMING_SNAKE_CASE       :      Tuple							=    sane_index_shape


     def 				_A       (				self       :	Optional[Any]					, UpperCAmelCase_       :	List[str]      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    inds.shape
       assert len(A_      ) > 1
       SCREAMING_SNAKE_CASE       :      Dict							=    inds.reshape(ishape[0]					, -1      )
       SCREAMING_SNAKE_CASE       :      Any							=    self.used.to(A_      )
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    (inds[:, :, None] == used[None, None, ...]).long()
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    match.argmax(-1      )
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    match.sum(2      ) < 1
       if self.unknown_index == "random":
         SCREAMING_SNAKE_CASE       :      Dict							=    torch.randint(0					, self.re_embed					, size=new[unknown].shape      ).to(device=new.device      )
       else:
         SCREAMING_SNAKE_CASE       :      List[Any]							=    self.unknown_index
       return new.reshape(A_      )


     def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	str      ):
       SCREAMING_SNAKE_CASE       :      Tuple							=    inds.shape
       assert len(A_      ) > 1
       SCREAMING_SNAKE_CASE       :      int							=    inds.reshape(ishape[0]					, -1      )
       SCREAMING_SNAKE_CASE       :      Dict							=    self.used.to(A_      )
       if self.re_embed > self.used.shape[0]:  # extra token
         SCREAMING_SNAKE_CASE       :      List[Any]							=    0  # simply set to zero
       SCREAMING_SNAKE_CASE       :      str							=    torch.gather(used[None, :][inds.shape[0] * [0], :]					, 1					, A_      )
       return back.reshape(A_      )


     def 				_A       (				self       :	str					, UpperCAmelCase_       :	Optional[Any]      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    z.permute(0					, 2					, 3					, 1      ).contiguous()
       SCREAMING_SNAKE_CASE       :      Any							=    z.view(-1					, self.vq_embed_dim      )

       # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    torch.argmin(torch.cdist(A_					, self.embedding.weight      )					, dim=1      )

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.embedding(A_      ).view(z.shape      )
       SCREAMING_SNAKE_CASE       :      Dict							=    None
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    None

       # compute loss for embedding
       if not self.legacy:
         SCREAMING_SNAKE_CASE       :      List[Any]							=    self.beta * torch.mean((z_q.detach() - z) ** 2      ) + torch.mean((z_q - z.detach()) ** 2      )
       else:
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    torch.mean((z_q.detach() - z) ** 2      ) + self.beta * torch.mean((z_q - z.detach()) ** 2      )

       # preserve gradients
       SCREAMING_SNAKE_CASE       :      Dict							=    z + (z_q - z).detach()

       # reshape back to match original input shape
       SCREAMING_SNAKE_CASE       :      Any							=    z_q.permute(0					, 3					, 1					, 2      ).contiguous()

       if self.remap is not None:
         SCREAMING_SNAKE_CASE       :      List[Any]							=    min_encoding_indices.reshape(z.shape[0]					, -1      )  # add batch axis
         SCREAMING_SNAKE_CASE       :      Dict							=    self.remap_to_used(A_      )
         SCREAMING_SNAKE_CASE       :      int							=    min_encoding_indices.reshape(-1					, 1      )  # flatten

       if self.sane_index_shape:
         SCREAMING_SNAKE_CASE       :      Tuple							=    min_encoding_indices.reshape(z_q.shape[0]					, z_q.shape[2]					, z_q.shape[3]      )

       return z_q, loss, (perplexity, min_encodings, min_encoding_indices)






     def 				_A       (				self       :	Any					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	Optional[Any]      ):




       if self.remap is not None:
         SCREAMING_SNAKE_CASE       :      Dict							=    indices.reshape(shape[0]					, -1      )  # add batch axis
         SCREAMING_SNAKE_CASE       :      List[str]							=    self.unmap_to_all(A_      )
         SCREAMING_SNAKE_CASE       :      List[str]							=    indices.reshape(-1      )  # flatten again

       # get quantized latent vectors
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.embedding(A_      )

       if shape is not None:
         SCREAMING_SNAKE_CASE       :      int							=    z_q.view(A_      )
         # reshape back to match original input shape
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    z_q.permute(0					, 3					, 1					, 2      ).contiguous()

       return z_q





class  SCREAMING_SNAKE_CASE			(			__snake_case					):
     '''simple docstring'''


     def __init__(				self       :	List[str]					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	List[Any]=False      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    parameters
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    torch.chunk(A_					, 2					, dim=1      )
       SCREAMING_SNAKE_CASE       :      List[Any]							=    torch.clamp(self.logvar					, -30.0					, 20.0      )
       SCREAMING_SNAKE_CASE       :      Dict							=    deterministic
       SCREAMING_SNAKE_CASE       :      str							=    torch.exp(0.5 * self.logvar      )
       SCREAMING_SNAKE_CASE       :      Any							=    torch.exp(self.logvar      )
       if self.deterministic:
         SCREAMING_SNAKE_CASE       :      List[Any]							=    torch.zeros_like(
             self.mean					, device=self.parameters.device					, dtype=self.parameters.dtype      )


     def 				_A       (				self       :	List[str]					, UpperCAmelCase_       :	List[str] = None      ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    randn_tensor(
           self.mean.shape					, generator=A_					, device=self.parameters.device					, dtype=self.parameters.dtype      )
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.mean + self.std * sample
       return x


     def 				_A       (				self       :	Tuple					, UpperCAmelCase_       :	Tuple=None      ):




       if self.deterministic:
         return torch.Tensor([0.0]      )
       else:
         if other is None:
           return 0.5 * torch.sum(torch.pow(self.mean					, 2      ) + self.var - 1.0 - self.logvar					, dim=[1, 2, 3]      )
         else:
           return 0.5 * torch.sum(
               torch.pow(self.mean - other.mean					, 2      ) / other.var
               + self.var / other.var
               - 1.0
               - self.logvar
               + other.logvar					, dim=[1, 2, 3]					, )


     def 				_A       (				self       :	List[Any]					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	List[Any]=[1, 2, 3]      ):




       if self.deterministic:
         return torch.Tensor([0.0]      )
       SCREAMING_SNAKE_CASE       :      str							=    np.log(2.0 * np.pi      )
       return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean					, 2      ) / self.var					, dim=A_      )






     def 				_A       (				self       :	Any      ):
       return self.mean














# 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.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


snake_case    				=		"""Create a default config file for Accelerate with only a few flags set."""


def   lowerCamelCase__			(     lowercase="no"						,     lowercase = default_json_config_file						,     lowercase = False				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Tuple							=    Path(lowercase				)
  path.parent.mkdir(parents=lowercase						,     exist_ok=lowercase				)
  if path.exists():
    print(
        F'''Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'''				)
    return False
  SCREAMING_SNAKE_CASE       :      int							=    mixed_precision.lower()
  if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
    raise ValueError(
        F'''`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'''				)
  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    {
      "compute_environment": "LOCAL_MACHINE",
      "mixed_precision": mixed_precision,
  }
  if torch.cuda.is_available():
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    torch.cuda.device_count()
    SCREAMING_SNAKE_CASE       :      int							=    num_gpus
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    False
    if num_gpus > 1:
      SCREAMING_SNAKE_CASE       :      Tuple							=    "MULTI_GPU"
    else:
      SCREAMING_SNAKE_CASE       :      Optional[Any]							=    "NO"
  elif is_xpu_available() and use_xpu:
    SCREAMING_SNAKE_CASE       :      List[str]							=    torch.xpu.device_count()
    SCREAMING_SNAKE_CASE       :      str							=    num_xpus
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    False
    if num_xpus > 1:
      SCREAMING_SNAKE_CASE       :      Any							=    "MULTI_XPU"
    else:
      SCREAMING_SNAKE_CASE       :      str							=    "NO"
  elif is_npu_available():
    SCREAMING_SNAKE_CASE       :      List[Any]							=    torch.npu.device_count()
    SCREAMING_SNAKE_CASE       :      Optional[Any]							=    num_npus
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    False
    if num_npus > 1:
      SCREAMING_SNAKE_CASE       :      str							=    "MULTI_NPU"
    else:
      SCREAMING_SNAKE_CASE       :      int							=    "NO"
  else:
    SCREAMING_SNAKE_CASE       :      Dict							=    0
    SCREAMING_SNAKE_CASE       :      List[Any]							=    True
    SCREAMING_SNAKE_CASE       :      Dict							=    1
    SCREAMING_SNAKE_CASE       :      int							=    "NO"
  SCREAMING_SNAKE_CASE       :      Dict							=    ClusterConfig(**lowercase				)
  config.to_json_file(lowercase				)
  return path


def   lowerCamelCase__			(     lowercase						,     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Dict							=    parser.add_parser("default"						,     parents=lowercase						,     help=lowercase						,     formatter_class=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'."
      )						,     dest="save_location"						,     )

  parser.add_argument(
      "--mixed_precision"						,     choices=["no", "fp16", "bf16"]						,     type=lowercase						,     help="Whether or not to use mixed precision training. "
      "Choose between FP16 and BF16 (bfloat16) training. "
      "BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later."						,     default="no"						,     )
  parser.set_defaults(func=lowercase				)
  return parser


def   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Optional[int]							=    write_basic_config(args.mixed_precision						,     args.save_location				)
  if config_file:
    print(F'''accelerate configuration saved at {config_file}'''				)






