
"""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.

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


_A						=			'Create a default config file for Accelerate with only a few flags set.'
def 		UpperCAmelCase (		a_="no",						a_ = default_json_config_file,						a_ = False      ):





		'''simple docstring'''


		lowerCamelCase					:      Dict          =   Path(a_      )
		path.parent.mkdir(parents=a_,						exist_ok=a_      )
		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
		lowerCamelCase					:      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}"""      )
		lowerCamelCase					:      Any          =   {
		    'compute_environment': 'LOCAL_MACHINE',
		    'mixed_precision': mixed_precision,
		}
		if torch.cuda.is_available():
				lowerCamelCase					:      List[str]          =   torch.cuda.device_count()
				lowerCamelCase					:      str          =   num_gpus
				lowerCamelCase					:      int          =   False
				if num_gpus > 1:
						lowerCamelCase					:      int          =   'MULTI_GPU'
				else:
						lowerCamelCase					:      Optional[Any]          =   'NO'
		elif is_xpu_available() and use_xpu:
				lowerCamelCase					:      Optional[int]          =   torch.xpu.device_count()
				lowerCamelCase					:      Dict          =   num_xpus
				lowerCamelCase					:      Optional[int]          =   False
				if num_xpus > 1:
						lowerCamelCase					:      Any          =   'MULTI_XPU'
				else:
						lowerCamelCase					:      Any          =   'NO'
		elif is_npu_available():
				lowerCamelCase					:      Optional[Any]          =   torch.npu.device_count()
				lowerCamelCase					:      List[Any]          =   num_npus
				lowerCamelCase					:      Any          =   False
				if num_npus > 1:
						lowerCamelCase					:      Tuple          =   'MULTI_NPU'
				else:
						lowerCamelCase					:      List[Any]          =   'NO'
		else:
				lowerCamelCase					:      Tuple          =   0
				lowerCamelCase					:      Union[str, Any]          =   True
				lowerCamelCase					:      Optional[int]          =   1
				lowerCamelCase					:      int          =   'NO'
		lowerCamelCase					:      Dict          =   ClusterConfig(**a_      )
		config.to_json_file(a_      )
		return path
def 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


		lowerCamelCase					:      str          =   parser.add_parser('default',						parents=a_,						help=a_,						formatter_class=a_      )
		parser.add_argument(
		    '--config_file',						default=a_,						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=a_,						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=a_      )
		return parser





def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


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








"""simple docstring"""

from ....configuration_utils import PretrainedConfig
from ....utils import logging


_A						=			logging.get_logger(__name__)

_A						=			{
    'CarlCochet/trajectory-transformer-halfcheetah-medium-v2': (
        'https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json'
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     'trajectory_transformer'
	lowercase_				=     ['past_key_values']
	lowercase_				=     {
	    'hidden_size': 'n_embd',
	    'num_attention_heads': 'n_head',
	    'num_hidden_layers': 'n_layer',
	}




	def __init__(			self		,			UpperCAmelCase_=100		,			UpperCAmelCase_=5		,			UpperCAmelCase_=1		,			UpperCAmelCase_=1		,			UpperCAmelCase_=249		,			UpperCAmelCase_=6		,			UpperCAmelCase_=17		,			UpperCAmelCase_=25		,			UpperCAmelCase_=4		,			UpperCAmelCase_=4		,			UpperCAmelCase_=128		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.0006		,			UpperCAmelCase_=512		,			UpperCAmelCase_=0.02		,			UpperCAmelCase_=1E-1_2		,			UpperCAmelCase_=1		,			UpperCAmelCase_=True		,			UpperCAmelCase_=1		,			UpperCAmelCase_=50256		,			UpperCAmelCase_=50256		,			**UpperCAmelCase_		,			)		->							List[Any]:
			lowerCamelCase					:      int          =   vocab_size
			lowerCamelCase					:      List[str]          =   action_weight
			lowerCamelCase					:      List[Any]          =   reward_weight
			lowerCamelCase					:      List[str]          =   value_weight
			lowerCamelCase					:      Tuple          =   max_position_embeddings
			lowerCamelCase					:      List[str]          =   block_size
			lowerCamelCase					:      Any          =   action_dim
			lowerCamelCase					:      List[Any]          =   observation_dim
			lowerCamelCase					:      Any          =   transition_dim
			lowerCamelCase					:      int          =   learning_rate
			lowerCamelCase					:      Union[str, Any]          =   n_layer
			lowerCamelCase					:      Tuple          =   n_head
			lowerCamelCase					:      Any          =   n_embd
			lowerCamelCase					:      Union[str, Any]          =   embd_pdrop
			lowerCamelCase					:      Optional[int]          =   attn_pdrop
			lowerCamelCase					:      int          =   resid_pdrop
			lowerCamelCase					:      Optional[int]          =   initializer_range
			lowerCamelCase					:      List[str]          =   layer_norm_eps
			lowerCamelCase					:      Any          =   kaiming_initializer_range
			lowerCamelCase					:      str          =   use_cache
			super().__init__(pad_token_id=UpperCAmelCase_		,			bos_token_id=UpperCAmelCase_		,			eos_token_id=UpperCAmelCase_		,			**UpperCAmelCase_ )






