
"""simple docstring"""
# Copyright 2022 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

from .config_args import default_config_file, load_config_from_file
from .config_utils import SubcommandHelpFormatter


lowercase		:						List[str] =					'Update an existing config file with the latest defaults while maintaining the old configuration.'


def     lowerCamelCase_    (	lowerCamelCase_ )     ->			Optional[int]:


					'''simple docstring'''
					__a:						Any		    =					args.config_file
					if config_file is None and Path(lowerCamelCase_ ).exists():
										__a:						str		    =					default_config_file
					elif not Path(lowerCamelCase_ ).exists():
										raise ValueError(f"""The passed config file located at {config_file} doesn't exist.""" )
					__a:						Optional[Any]		    =					load_config_from_file(lowerCamelCase_ )

					if config_file.endswith(""".json""" ):
										config.to_json_file(lowerCamelCase_ )
					else:
										config.to_yaml_file(lowerCamelCase_ )
					return config_file


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			Optional[int]:


					'''simple docstring'''
					__a:						List[Any]		    =					parser.add_parser("""update"""	,				parents=lowerCamelCase_	,				help=lowerCamelCase_	,				formatter_class=lowerCamelCase_ )
					parser.add_argument(
					    """--config_file"""	,				default=lowerCamelCase_	,				help=(
					        """The path to the config file to update. 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'."""
					    )	,				)

					parser.set_defaults(func=lowerCamelCase_ )
					return parser




def     lowerCamelCase_    (	lowerCamelCase_ )     ->			List[Any]:


					'''simple docstring'''
					__a:						List[Any]		    =					update_config(lowerCamelCase_ )
					print(f"""Sucessfully updated the configuration file at {config_file}.""" )


"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase		:						List[str] =					{
    'configuration_open_llama': ['OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenLlamaConfig'],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:						Tuple =					['LlamaTokenizer']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:						Dict =					['LlamaTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:						List[Any] =					[
			    'OpenLlamaForCausalLM',
			    'OpenLlamaModel',
			    'OpenLlamaPreTrainedModel',
			    'OpenLlamaForSequenceClassification',
			]


if TYPE_CHECKING:
			from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_open_llama import (
						    OpenLlamaForCausalLM,
						    OpenLlamaForSequenceClassification,
						    OpenLlamaModel,
						    OpenLlamaPreTrainedModel,
						)


else:
			import sys

			lowercase		:						Dict =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
