


# 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
SCREAMING_SNAKE_CASE       :		List[Any]         =				"\nHuman: <<task>>\n\nAssistant: "


SCREAMING_SNAKE_CASE       :		Optional[Any]         =				"huggingface-tools/default-prompts"
SCREAMING_SNAKE_CASE       :		int         =				{"chat": "chat_prompt_template.txt", "run": "run_prompt_template.txt"}




def     UpperCamelCase    (      _a       ,		_a       ,		_a="run"		)					->							Optional[Any]:
     '''simple docstring'''





     if prompt_or_repo_id is None:
          lowercase_       :int     =       DEFAULT_PROMPTS_REPO

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

     lowercase_       :List[str]     =       cached_file(
         _a       ,		PROMPT_FILES[mode]       ,		repo_type='''dataset'''       ,		user_agent={'''agent''': agent_name}		)
     with open(_a       ,		'''r'''       ,		encoding='''utf-8'''		) as f:
          return f.read()





from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
SCREAMING_SNAKE_CASE       :		List[str]         =				HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE       :		List[Any]         =				parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
SCREAMING_SNAKE_CASE       :		Optional[Any]         =				{
    "vocab_size": len(tokenizer),
    "scale_attn_by_inverse_layer_idx": True,
    "reorder_and_upcast_attn": True,
}

# Load model config (GPT-2 large in this case)
SCREAMING_SNAKE_CASE       :		Any         =				AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
SCREAMING_SNAKE_CASE       :		Dict         =				AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)

