



'''simple docstring'''



import math

import flax.linen as nn
import jax.numpy as jnp



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase = 1  ,   _lowerCAmelCase = 1  ,   _lowerCAmelCase = 1.0e4  ,   _lowerCAmelCase = False  ,   _lowerCAmelCase = 1.0  ,   ):



       """simple docstring"""



       assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
       assert embedding_dim % 2 == 0, f"""Embedding dimension {embedding_dim} should be even"""
       __lowercase       	=float(embedding_dim // 2					)
       __lowercase       	=math.log(max_timescale / min_timescale					) / (num_timescales - freq_shift)
       __lowercase       	=min_timescale * jnp.exp(jnp.arange(_lowerCAmelCase  ,   dtype=jnp.floataa					) * -log_timescale_increment					)
       __lowercase       	=jnp.expand_dims(_lowerCAmelCase  ,   1					) * jnp.expand_dims(_lowerCAmelCase  ,   0					)

       # scale embeddings
       __lowercase       	=scale * emb

       if flip_sin_to_cos:
              __lowercase       	=jnp.concatenate([jnp.cos(_lowerCAmelCase					), jnp.sin(_lowerCAmelCase					)]  ,   axis=1					)
       else:
              __lowercase       	=jnp.concatenate([jnp.sin(_lowerCAmelCase					), jnp.cos(_lowerCAmelCase					)]  ,   axis=1					)
       __lowercase       	=jnp.reshape(_lowerCAmelCase  ,   [jnp.shape(_lowerCAmelCase					)[0], embedding_dim]					)
       return signal





class _UpperCamelCase		(     nn.Module		):





    '''simple docstring'''





    lowerCAmelCase__					    =    32
    lowerCAmelCase__					    =    jnp.floataa

    @nn.compact
    def __call__(							self  :  Any  ,       _lowerCAmelCase  :  List[Any]):







           '''simple docstring'''

           __lowercase       	=nn.Dense(self.time_embed_dim  ,       dtype=self.dtype  ,       name='linear_1')(_lowerCAmelCase)
           __lowercase       	=nn.silu(_lowerCAmelCase)
           __lowercase       	=nn.Dense(self.time_embed_dim  ,       dtype=self.dtype  ,       name='linear_2')(_lowerCAmelCase)
           return temb





class _UpperCamelCase		(     nn.Module		):





    '''simple docstring'''





    lowerCAmelCase__					    =    32
    lowerCAmelCase__					    =    False
    lowerCAmelCase__					    =    1

    @nn.compact
    def __call__(							self  :  Tuple  ,       _lowerCAmelCase  :  str):







           '''simple docstring'''

           return get_sinusoidal_embeddings(
               _lowerCAmelCase  ,       embedding_dim=self.dim  ,       flip_sin_to_cos=self.flip_sin_to_cos  ,       freq_shift=self.freq_shift)








'''simple docstring'''



# 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


lowerCamelCase	 =       """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       (   ):



							"""simple docstring"""



							__lowercase       	=_ask_options(
							    'In which compute environment are you running?'  ,   ['This machine', 'AWS (Amazon SageMaker)']  ,   _convert_compute_environment  ,   )
							if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
														__lowercase       	=get_sagemaker_input()
							else:
														__lowercase       	=get_cluster_input()
							return config



def   _A       (   _lowerCAmelCase=None					):



							"""simple docstring"""



							if subparsers is not None:
														__lowercase       	=subparsers.add_parser('config'  ,   description=_lowerCAmelCase					)
							else:
														__lowercase       	=argparse.ArgumentParser('Accelerate config command'  ,   description=_lowerCAmelCase					)

							parser.add_argument(
							    '--config_file'  ,   default=_lowerCAmelCase  ,   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=_lowerCAmelCase					)
							return parser



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=get_user_input()
							if args.config_file is not None:
														__lowercase       	=args.config_file
							else:
														if not os.path.isdir(_lowerCAmelCase					):
																					os.makedirs(_lowerCAmelCase					)
														__lowercase       	=default_yaml_config_file

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



def   _A       (   ):



							"""simple docstring"""



							__lowercase       	=config_command_parser()
							__lowercase       	=parser.parse_args()
							config_command(_lowerCAmelCase					)


if __name__ == "__main__":
		main()



