"""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    lowercase_   (   _lowerCamelCase:	Optional[int]="no"						,   _lowerCamelCase:	str = default_json_config_file						,   _lowerCamelCase:	bool = False      ) ->      List[Any]:



							'''simple docstring'''

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


def    lowercase_   (   _lowerCamelCase:	Optional[int]						,   _lowerCamelCase:	Union[str, Any]      ) ->      List[str]:



							'''simple docstring'''

							__lowerCamelCase					:		Union[str, Any]		   =     parser.add_parser("default"						,   parents=_lowerCamelCase						,   help=_lowerCamelCase						,   formatter_class=_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'."
							    )						,   dest="save_location"						,   )

							parser.add_argument(
							    "--mixed_precision"						,   choices=["no", "fp16", "bf16"]						,   type=_lowerCamelCase						,   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=_lowerCamelCase      )
							return parser







def    lowercase_   (   _lowerCamelCase:	List[str]      ) ->      Optional[int]:



							'''simple docstring'''

							__lowerCamelCase					:		str		   =     write_basic_config(args.mixed_precision						,   args.save_location      )
							if config_file:
														print(F"""accelerate configuration saved at {config_file}"""      )
"""simple docstring"""





import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def    lowercase_   (   _lowerCamelCase:	Dict						,   _lowerCamelCase:	str						,   _lowerCamelCase:	Dict      ) ->      List[str]:



							'''simple docstring'''

							__lowerCamelCase					:		List[Any]		   =     TaConfig.from_json_file(_lowerCamelCase      )
							print(F"""Building PyTorch model from configuration: {config}"""      )
							__lowerCamelCase					:		Optional[int]		   =     TaForConditionalGeneration(_lowerCamelCase      )

							# Load weights from tf checkpoint
							load_tf_weights_in_ta(_lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase      )

							# Save pytorch-model
							print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
							model.save_pretrained(_lowerCamelCase      )


if __name__ == "__main__":
				__A  =				argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
				)
				parser.add_argument(
				    '''--config_file''',
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
				    ),
				)
				parser.add_argument(
				    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
				)
				__A  =				parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)