

from dataclasses import dataclass, field
from typing import Optional



@dataclass
class 						_A     :





  '''simple docstring'''



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







@dataclass
class 						_A     :





  '''simple docstring'''



  __lowerCamelCase			:      Optional[str]					=   field(
      default='''codeparrot/codeparrot'''						,    metadata={'''help''': '''Model name or path of model to be evaluated.'''}				)
  __lowerCamelCase			:      Optional[str]					=   field(
      default='''codeparrot/codeparrot-clean-valid'''						,    metadata={'''help''': '''Name or path of validation dataset.'''}				)
  __lowerCamelCase			:      Optional[int]					=   field(default=2						,    metadata={'''help''': '''Batch size used for evaluation.'''}				)
  __lowerCamelCase			:      Optional[int]					=   field(
      default=-1						,    metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''}				)
  __lowerCamelCase			:      Optional[int]					=   field(default=1_0_2_4						,    metadata={'''help''': '''Length of sequences to be evaluated.'''}				)
  __lowerCamelCase			:      Optional[int]					=   field(default=1						,    metadata={'''help''': '''Random seed used for evaluation.'''}				)







@dataclass
class 						_A     :





  '''simple docstring'''



  __lowerCamelCase			:      Optional[str]					=   field(
      default='''codeparrot/codeparrot'''						,    metadata={'''help''': '''Model name or path of model to be evaluated.'''}				)
  __lowerCamelCase			:      Optional[int]					=   field(default=snake_case						,    metadata={'''help''': '''Number of workers used for code evaluation.'''}				)
  __lowerCamelCase			:      Optional[int]					=   field(
      default=snake_case						,    metadata={'''help''': '''The number of human-eval tasks to run. If not included all tasks are evaluated.'''}						,    )
  __lowerCamelCase			:      Optional[bool]					=   field(
      default=snake_case						,    metadata={'''help''': '''Sample from the language model\'s output distribution.'''}				)
  __lowerCamelCase			:      Optional[float]					=   field(default=0.2						,    metadata={'''help''': '''Sampling temperature used for generation.'''}				)
  __lowerCamelCase			:      Optional[int]					=   field(default=2_5_6						,    metadata={'''help''': '''Maximum number of newly generated tokens.'''}				)
  __lowerCamelCase			:      Optional[int]					=   field(default=0						,    metadata={'''help''': '''Top-k parameter used for generation.'''}				)
  __lowerCamelCase			:      Optional[float]					=   field(default=0.95						,    metadata={'''help''': '''Top-p parameter used for nucleus sampling.'''}				)
  __lowerCamelCase			:      Optional[int]					=   field(default=1_0						,    metadata={'''help''': '''Number of generations to run in parallel.'''}				)
  __lowerCamelCase			:      Optional[int]					=   field(
      default=2_0_0						,    metadata={'''help''': '''Number of completions to generate for each sample.'''}				)
  __lowerCamelCase			:      Optional[int]					=   field(default=1						,    metadata={'''help''': '''Random seed used for evaluation.'''}				)
  __lowerCamelCase			:      Optional[str]					=   field(
      default='''eval_results.json'''						,    metadata={'''help''': '''Random seed used for evaluation.'''}				)
  __lowerCamelCase			:      Optional[str]					=   field(
      default='''0'''						,    metadata={'''help''': '''Allow `code_eval` to execute Python code on machine'''}				)
  __lowerCamelCase			:      Optional[int]					=   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 						_A     :





  '''simple docstring'''



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







@dataclass
class 						_A     :





  '''simple docstring'''



  __lowerCamelCase			:      Optional[str]					=   field(
      default='''gpt2'''						,    metadata={'''help''': '''Base tokenizer to build new tokenizer from.'''}				)
  __lowerCamelCase			:      Optional[str]					=   field(
      default='''transformersbook/codeparrot-train'''						,    metadata={'''help''': '''Dataset to train tokenizer on.'''}				)
  __lowerCamelCase			:      Optional[str]					=   field(default='''content'''						,    metadata={'''help''': '''Column containing text data to process.'''}				)
  __lowerCamelCase			:      Optional[int]					=   field(default=2_0_0_0_0_0						,    metadata={'''help''': '''Number of examples to train tokenizer on.'''}				)
  __lowerCamelCase			:      Optional[int]					=   field(
      default=3_2_7_6_8						,    metadata={'''help''': '''Number of examples to train the tokenizer on.'''}				)
  __lowerCamelCase			:      Optional[str]					=   field(default='''codeparrot'''						,    metadata={'''help''': '''Name of new tokenizer.'''}				)
  __lowerCamelCase			:      Optional[bool]					=   field(default=snake_case						,    metadata={'''help''': '''Push saved tokenizer to the hub.'''}				)







@dataclass
class 						_A     :





  '''simple docstring'''



  __lowerCamelCase			:      Optional[str]					=   field(
      default='''codeparrot/codeparrot'''						,    metadata={'''help''': '''Name or path to the tokenizer.'''}				)
  __lowerCamelCase			:      Optional[str]					=   field(
      default='''codeparrot/codeparrot-clean-train'''						,    metadata={'''help''': '''Name or path to the dataset to pretokenize.'''}				)
  __lowerCamelCase			:      Optional[str]					=   field(
      default='''tokenized-codeparrot-train'''						,    metadata={'''help''': '''Repo name of the pretokenized data.'''}				)
  __lowerCamelCase			:      Optional[int]					=   field(default=snake_case						,    metadata={'''help''': '''Number of workers used for code evaluation.'''}				)







@dataclass
class 						_A     :





  '''simple docstring'''



  __lowerCamelCase			:      Optional[str]					=   field(
      default='''gpt2-large'''						,    metadata={'''help''': '''Configuration to use for model initialization.'''}				)
  __lowerCamelCase			:      Optional[str]					=   field(
      default='''codeparrot/codeparrot'''						,    metadata={'''help''': '''Tokenizer attached to model.'''}				)
  __lowerCamelCase			:      Optional[str]					=   field(default='''codeparrot'''						,    metadata={'''help''': '''Name of the created model.'''}				)
  __lowerCamelCase			:      Optional[bool]					=   field(default=snake_case						,    metadata={'''help''': '''Push saved tokenizer to the hub.'''}				)










import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY




@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class        SCREAMING_SNAKE_CASE__					(		unittest.TestCase ):
 __SCREAMING_SNAKE_CASE							=      MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase					):
        A__		   =		hf_hub_download(
            repo_id='''nateraw/video-demo''',filename='''archery.mp4''',repo_type='''dataset'''					)
        A__		   =		VideoClassificationPipeline(model=__lowerCamelCase,image_processor=__lowerCamelCase,top_k=2					)
        A__		   =		[
            example_video_filepath,
            '''https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4''',
        ]
        return video_classifier, examples



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase					):
        for example in examples:
               A__		   =		video_classifier(__lowerCamelCase					)

               self.assertEqual(
                   __lowerCamelCase,[
                       {'''score''': ANY(__lowerCamelCase					), '''label''': ANY(__lowerCamelCase					)},
                       {'''score''': ANY(__lowerCamelCase					), '''label''': ANY(__lowerCamelCase					)},
                   ],)



 @require_torch
 def 							UpperCamelCase     (				self					):
        A__		   =		'''hf-internal-testing/tiny-random-VideoMAEForVideoClassification'''
        A__		   =		VideoMAEFeatureExtractor(
            size={'''shortest_edge''': 10},crop_size={'''height''': 10, '''width''': 10}					)
        A__		   =		pipeline(
            '''video-classification''',model=__lowerCamelCase,feature_extractor=__lowerCamelCase,frame_sampling_rate=4					)

        A__		   =		hf_hub_download(repo_id='''nateraw/video-demo''',filename='''archery.mp4''',repo_type='''dataset'''					)
        A__		   =		video_classifier(__lowerCamelCase,top_k=2					)
        self.assertEqual(
            nested_simplify(__lowerCamelCase,decimals=4					),[{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],)

        A__		   =		video_classifier(
            [
                video_file_path,
                video_file_path,
            ],top_k=2,)
        self.assertEqual(
            nested_simplify(__lowerCamelCase,decimals=4					),[
                [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
                [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
            ],)




 @require_tf
 def 							UpperCamelCase     (				self					):
        pass





