


'''simple docstring'''






from dataclasses import dataclass, field
from typing import Optional






@dataclass
class  __magic_name__							:
       UpperCAmelCase										=field(
           default="codeparrot/codeparrot" ,metadata={"help": "Model name or path of model to be trained."}    )
       UpperCAmelCase										=field(
           default="./" ,metadata={"help": "Save dir where model repo is cloned and models updates are saved to."}    )
       UpperCAmelCase										=field(
           default="codeparrot/codeparrot-clean-train" ,metadata={"help": "Name or path of training dataset."}    )
       UpperCAmelCase										=field(
           default="codeparrot/codeparrot-clean-valid" ,metadata={"help": "Name or path of validation dataset."}    )
       UpperCAmelCase										=field(default=2 ,metadata={"help": "Batch size for training."}    )
       UpperCAmelCase										=field(default=2 ,metadata={"help": "Batch size for evaluation."}    )
       UpperCAmelCase										=field(default=0.1 ,metadata={"help": "Value of weight decay."}    )
       UpperCAmelCase										=field(
           default=1_0_0_0_0 ,metadata={"help": "Size of buffer used to shuffle streaming dataset."}    )
       UpperCAmelCase										=field(default=2e-4 ,metadata={"help": "Learning rate fo training."}    )
       UpperCAmelCase										=field(default="cosine" ,metadata={"help": "Learning rate."}    )
       UpperCAmelCase										=field(
           default=7_5_0 ,metadata={"help": "Number of warmup steps in the learning rate schedule."}    )
       UpperCAmelCase										=field(
           default=1_6 ,metadata={"help": "Number of gradient accumulation steps."}    )
       UpperCAmelCase										=field(
           default=__UpperCamelCase ,metadata={"help": "Use gradient checkpointing to reduce memory footprint."}    )
       UpperCAmelCase										=field(default=5_0_0_0_0 ,metadata={"help": "Maximum number of training steps."}    )
       UpperCAmelCase										=field(
           default=-1 ,metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}    )
       UpperCAmelCase										=field(default=1_0_2_4 ,metadata={"help": "Sequence lengths used for training."}    )
       UpperCAmelCase										=field(default=1 ,metadata={"help": "Training seed."}    )
       UpperCAmelCase										=field(
           default=1_0_2_4 ,metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."} ,)
       UpperCAmelCase										=field(
           default=__UpperCamelCase ,metadata={"help": "States path if the training should continue from a checkpoint folder."}    )
       UpperCAmelCase										=field(default=__UpperCamelCase ,metadata={"help": "If True the data is pretokenized."}    )






@dataclass
class  __magic_name__							:
       UpperCAmelCase										=field(
           default="codeparrot/codeparrot" ,metadata={"help": "Model name or path of model to be evaluated."}    )
       UpperCAmelCase										=field(
           default="codeparrot/codeparrot-clean-valid" ,metadata={"help": "Name or path of validation dataset."}    )
       UpperCAmelCase										=field(default=2 ,metadata={"help": "Batch size used for evaluation."}    )
       UpperCAmelCase										=field(
           default=-1 ,metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}    )
       UpperCAmelCase										=field(default=1_0_2_4 ,metadata={"help": "Length of sequences to be evaluated."}    )
       UpperCAmelCase										=field(default=1 ,metadata={"help": "Random seed used for evaluation."}    )






@dataclass
class  __magic_name__							:
       UpperCAmelCase										=field(
           default="codeparrot/codeparrot" ,metadata={"help": "Model name or path of model to be evaluated."}    )
       UpperCAmelCase										=field(default=__UpperCamelCase ,metadata={"help": "Number of workers used for code evaluation."}    )
       UpperCAmelCase										=field(
           default=__UpperCamelCase ,metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."} ,)
       UpperCAmelCase										=field(
           default=__UpperCamelCase ,metadata={"help": "Sample from the language model's output distribution."}    )
       UpperCAmelCase										=field(default=0.2 ,metadata={"help": "Sampling temperature used for generation."}    )
       UpperCAmelCase										=field(default=2_5_6 ,metadata={"help": "Maximum number of newly generated tokens."}    )
       UpperCAmelCase										=field(default=0 ,metadata={"help": "Top-k parameter used for generation."}    )
       UpperCAmelCase										=field(default=0.95 ,metadata={"help": "Top-p parameter used for nucleus sampling."}    )
       UpperCAmelCase										=field(default=1_0 ,metadata={"help": "Number of generations to run in parallel."}    )
       UpperCAmelCase										=field(
           default=2_0_0 ,metadata={"help": "Number of completions to generate for each sample."}    )
       UpperCAmelCase										=field(default=1 ,metadata={"help": "Random seed used for evaluation."}    )
       UpperCAmelCase										=field(
           default="eval_results.json" ,metadata={"help": "Random seed used for evaluation."}    )
       UpperCAmelCase										=field(
           default="0" ,metadata={"help": "Allow `code_eval` to execute Python code on machine"}    )
       UpperCAmelCase										=field(
           default=-1 ,metadata={
               "help": (
                   "Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive"
                   " number corresponds to which GPU device id to run on."
               )
           } ,)






@dataclass
class  __magic_name__							:
       UpperCAmelCase										=field(
           default=__UpperCamelCase ,metadata={
               "help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
           } ,)
       UpperCAmelCase										=field(
           default="transformersbook/codeparrot" ,metadata={"help": "Folder or name of dataset to process."}    )
       UpperCAmelCase										=field(
           default="codeparrot-clean" ,metadata={"help": "Folder to save processed processed dataset."}    )
       UpperCAmelCase										=field(
           default=1_0_0_0_0_0 ,metadata={"help": "Number of files to save per JSON output file."}    )
       UpperCAmelCase										=field(default="content" ,metadata={"help": "Column containing text data to process."}    )
       UpperCAmelCase										=field(
           default=1_0_0_0 ,metadata={"help": "Maximum line length in file, otherwise file is filtered."}    )
       UpperCAmelCase										=field(
           default=1_0_0 ,metadata={"help": "Maximum mean line length in file, otherwise file is filtered."}    )
       UpperCAmelCase										=field(
           default=0.25 ,metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."}    )
       UpperCAmelCase										=field(
           default=1.5 ,metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."}    )
       UpperCAmelCase										=field(
           default=0.7 ,metadata={"help": "Probability for filtering config, test and uncommon files."}    )
       UpperCAmelCase										=field(
           default="codeparrot/codeparrot" ,metadata={"help": "Name or path to the tokenizer."} ,)
       UpperCAmelCase										=field(
           default=__UpperCamelCase ,metadata={"help": "If True, near-duplicate samples are removed."}    )
       UpperCAmelCase										=field(
           default=0.85 ,metadata={"help": "Jaccard threshold for near-duplicate samples."}    )






@dataclass
class  __magic_name__							:
       UpperCAmelCase										=field(
           default="gpt2" ,metadata={"help": "Base tokenizer to build new tokenizer from."}    )
       UpperCAmelCase										=field(
           default="transformersbook/codeparrot-train" ,metadata={"help": "Dataset to train tokenizer on."}    )
       UpperCAmelCase										=field(default="content" ,metadata={"help": "Column containing text data to process."}    )
       UpperCAmelCase										=field(default=2_0_0_0_0_0 ,metadata={"help": "Number of examples to train tokenizer on."}    )
       UpperCAmelCase										=field(
           default=3_2_7_6_8 ,metadata={"help": "Number of examples to train the tokenizer on."}    )
       UpperCAmelCase										=field(default="codeparrot" ,metadata={"help": "Name of new tokenizer."}    )
       UpperCAmelCase										=field(default=__UpperCamelCase ,metadata={"help": "Push saved tokenizer to the hub."}    )






@dataclass
class  __magic_name__							:
       UpperCAmelCase										=field(
           default="codeparrot/codeparrot" ,metadata={"help": "Name or path to the tokenizer."}    )
       UpperCAmelCase										=field(
           default="codeparrot/codeparrot-clean-train" ,metadata={"help": "Name or path to the dataset to pretokenize."}    )
       UpperCAmelCase										=field(
           default="tokenized-codeparrot-train" ,metadata={"help": "Repo name of the pretokenized data."}    )
       UpperCAmelCase										=field(default=__UpperCamelCase ,metadata={"help": "Number of workers used for code evaluation."}    )






@dataclass
class  __magic_name__							:
       UpperCAmelCase										=field(
           default="gpt2-large" ,metadata={"help": "Configuration to use for model initialization."}    )
       UpperCAmelCase										=field(
           default="codeparrot/codeparrot" ,metadata={"help": "Tokenizer attached to model."}    )
       UpperCAmelCase										=field(default="codeparrot" ,metadata={"help": "Name of the created model."}    )
       UpperCAmelCase										=field(default=__UpperCamelCase ,metadata={"help": "Push saved tokenizer to the hub."}    )






'''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
lowercase							='\nHuman: <<task>>\n\nAssistant: '


lowercase							='huggingface-tools/default-prompts'
lowercase							={'chat': 'chat_prompt_template.txt', 'run': 'run_prompt_template.txt'}





def lowerCamelCase__      (						__lowerCamelCase	:							Union[str, Any]	,   __lowerCamelCase	:							Union[str, Any]	,   __lowerCamelCase	:							int="run"							):







   '''simple docstring'''




   if prompt_or_repo_id is None:
      _UpperCAmelCase       :       List[str]							   =DEFAULT_PROMPTS_REPO

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

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

