




"""simple docstring"""




import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Union[str, Any]						,  SCREAMING_SNAKE_CASE__     :						Optional[Any]						,  SCREAMING_SNAKE_CASE__     :						Dict    )  ->       List[str]:





     '''simple docstring'''

     _UpperCAmelCase      :     str        =   AlbertConfig.from_json_file(A__    )
     print(f'Building PyTorch model from configuration: {config}'    )
     _UpperCAmelCase      :     int        =   AlbertForPreTraining(A__    )

     # Load weights from tf checkpoint
     load_tf_weights_in_albert(A__						,  A__						,  A__    )

     # Save pytorch-model
     print(f'Save PyTorch model to {pytorch_dump_path}'    )
     torch.save(model.state_dict()						,  A__    )


if __name__ == "__main__":
 _lowerCAmelCase    : List[str]							=							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(
     "--albert_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained ALBERT model. \n"
         "This specifies the model architecture."
     ),
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 _lowerCAmelCase    : Dict							=							parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)





"""simple docstring"""




# Copyright 2023 The HuggingFace Inc. 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 re

from ..utils import cached_file


# docstyle-ignore
_lowerCAmelCase    : Any							=							"\nHuman: <<task>>\n\nAssistant: "


_lowerCAmelCase    : str							=							"huggingface-tools/default-prompts"
_lowerCAmelCase    : Union[str, Any]							=							{"chat": "chat_prompt_template.txt", "run": "run_prompt_template.txt"}

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Union[str, Any]						,  SCREAMING_SNAKE_CASE__     :						Optional[int]						,  SCREAMING_SNAKE_CASE__     :						int="run"    )  ->       int:





     '''simple docstring'''

     if prompt_or_repo_id is None:
          _UpperCAmelCase      :     Optional[int]        =   DEFAULT_PROMPTS_REPO

     # prompt is considered a repo ID when it does not contain any kind of space
     if re.search("\\s"						,  SCREAMING_SNAKE_CASE__    ) is not None:
          return prompt_or_repo_id

     _UpperCAmelCase      :     Dict        =   cached_file(
         SCREAMING_SNAKE_CASE__						,  PROMPT_FILES[mode]						,  repo_type="dataset"						,  user_agent={"agent": agent_name}    )
     with open(SCREAMING_SNAKE_CASE__						,  "r"						,  encoding="utf-8"    ) as f:
          return f.read()
