


"""simple docstring"""





from dataclasses import dataclass, field
from typing import Optional



@dataclass
class 		snake_case_:
     __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=10_000							,					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=750							,					metadata={'''help''': '''Number of warmup steps in the learning rate schedule.'''}			)
     __UpperCamelCase  =					field(
         default=16							,					metadata={'''help''': '''Number of gradient accumulation steps.'''}			)
     __UpperCamelCase  =					field(
         default=a__							,					metadata={'''help''': '''Use gradient checkpointing to reduce memory footprint.'''}			)
     __UpperCamelCase  =					field(default=50_000							,					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_024							,					metadata={'''help''': '''Sequence lengths used for training.'''}			)
     __UpperCamelCase  =					field(default=1							,					metadata={'''help''': '''Training seed.'''}			)
     __UpperCamelCase  =					field(
         default=1_024							,					metadata={'''help''': '''Interval to save checkpoints. Measured as number of forward passes not training steps.'''}							,					)
     __UpperCamelCase  =					field(
         default=a__							,					metadata={'''help''': '''States path if the training should continue from a checkpoint folder.'''}			)
     __UpperCamelCase  =					field(default=a__							,					metadata={'''help''': '''If True the data is pretokenized.'''}			)



@dataclass
class 		snake_case_:
     __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_024							,					metadata={'''help''': '''Length of sequences to be evaluated.'''}			)
     __UpperCamelCase  =					field(default=1							,					metadata={'''help''': '''Random seed used for evaluation.'''}			)



@dataclass
class 		snake_case_:
     __UpperCamelCase  =					field(
         default='''codeparrot/codeparrot'''							,					metadata={'''help''': '''Model name or path of model to be evaluated.'''}			)
     __UpperCamelCase  =					field(default=a__							,					metadata={'''help''': '''Number of workers used for code evaluation.'''}			)
     __UpperCamelCase  =					field(
         default=a__							,					metadata={'''help''': '''The number of human-eval tasks to run. If not included all tasks are evaluated.'''}							,					)
     __UpperCamelCase  =					field(
         default=a__							,					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=256							,					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=10							,					metadata={'''help''': '''Number of generations to run in parallel.'''}			)
     __UpperCamelCase  =					field(
         default=200							,					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 		snake_case_:
     __UpperCamelCase  =					field(
         default=a__							,					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=100_000							,					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_000							,					metadata={'''help''': '''Maximum line length in file, otherwise file is filtered.'''}			)
     __UpperCamelCase  =					field(
         default=100							,					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=a__							,					metadata={'''help''': '''If True, near-duplicate samples are removed.'''}			)
     __UpperCamelCase  =					field(
         default=0.85							,					metadata={'''help''': '''Jaccard threshold for near-duplicate samples.'''}			)



@dataclass
class 		snake_case_:
     __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=200_000							,					metadata={'''help''': '''Number of examples to train tokenizer on.'''}			)
     __UpperCamelCase  =					field(
         default=32_768							,					metadata={'''help''': '''Number of examples to train the tokenizer on.'''}			)
     __UpperCamelCase  =					field(default='''codeparrot'''							,					metadata={'''help''': '''Name of new tokenizer.'''}			)
     __UpperCamelCase  =					field(default=a__							,					metadata={'''help''': '''Push saved tokenizer to the hub.'''}			)



@dataclass
class 		snake_case_:
     __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=a__							,					metadata={'''help''': '''Number of workers used for code evaluation.'''}			)



@dataclass
class 		snake_case_:
     __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=a__							,					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.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool



class 		snake_case_(       a__			):
     __UpperCamelCase  =					'''philschmid/bart-large-cnn-samsum'''
     __UpperCamelCase  =					(
         '''This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '''
         '''and returns a summary of the text.'''
     )
     __UpperCamelCase  =					'''summarizer'''
     __UpperCamelCase  =					AutoTokenizer
     __UpperCamelCase  =					AutoModelForSeqaSeqLM

     __UpperCamelCase  =					['''text''']
     __UpperCamelCase  =					['''text''']






     def      lowerCamelCase__     (	self  :					Dict					,				UpperCamelCase_  :					int				):
           return self.pre_processor(UpperCamelCase_					,				return_tensors='''pt'''					,				truncation=UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					str				):
           return self.model.generate(**UpperCamelCase_				)[0]






     def      lowerCamelCase__     (	self  :					Any					,				UpperCamelCase_  :					Tuple				):
           return self.pre_processor.decode(UpperCamelCase_					,				skip_special_tokens=UpperCamelCase_					,				clean_up_tokenization_spaces=UpperCamelCase_				)


