


"""simple docstring"""






from dataclasses import dataclass, field
from typing import Optional




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




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




@dataclass
class 							A_      :
  lowerCAmelCase__      			=	field(
      default='codeparrot/codeparrot'     ,	metadata={'help': 'Model name or path of model to be evaluated.'} )
  lowerCAmelCase__      			=	field(default=_a     ,	metadata={'help': 'Number of workers used for code evaluation.'} )
  lowerCAmelCase__      			=	field(
      default=_a     ,	metadata={'help': 'The number of human-eval tasks to run. If not included all tasks are evaluated.'}     ,	)
  lowerCAmelCase__      			=	field(
      default=_a     ,	metadata={'help': 'Sample from the language model\'s output distribution.'} )
  lowerCAmelCase__      			=	field(default=0.2     ,	metadata={'help': 'Sampling temperature used for generation.'} )
  lowerCAmelCase__      			=	field(default=2_5_6     ,	metadata={'help': 'Maximum number of newly generated tokens.'} )
  lowerCAmelCase__      			=	field(default=0     ,	metadata={'help': 'Top-k parameter used for generation.'} )
  lowerCAmelCase__      			=	field(default=0.95     ,	metadata={'help': 'Top-p parameter used for nucleus sampling.'} )
  lowerCAmelCase__      			=	field(default=1_0     ,	metadata={'help': 'Number of generations to run in parallel.'} )
  lowerCAmelCase__      			=	field(
      default=2_0_0     ,	metadata={'help': 'Number of completions to generate for each sample.'} )
  lowerCAmelCase__      			=	field(default=1     ,	metadata={'help': 'Random seed used for evaluation.'} )
  lowerCAmelCase__      			=	field(
      default='eval_results.json'     ,	metadata={'help': 'Random seed used for evaluation.'} )
  lowerCAmelCase__      			=	field(
      default='0'     ,	metadata={'help': 'Allow `code_eval` to execute Python code on machine'} )
  lowerCAmelCase__      			=	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_      :
  lowerCAmelCase__      			=	field(
      default=_a     ,	metadata={
          'help': 'The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'
      }     ,	)
  lowerCAmelCase__      			=	field(
      default='transformersbook/codeparrot'     ,	metadata={'help': 'Folder or name of dataset to process.'} )
  lowerCAmelCase__      			=	field(
      default='codeparrot-clean'     ,	metadata={'help': 'Folder to save processed processed dataset.'} )
  lowerCAmelCase__      			=	field(
      default=1_0_0_0_0_0     ,	metadata={'help': 'Number of files to save per JSON output file.'} )
  lowerCAmelCase__      			=	field(default='content'     ,	metadata={'help': 'Column containing text data to process.'} )
  lowerCAmelCase__      			=	field(
      default=1_0_0_0     ,	metadata={'help': 'Maximum line length in file, otherwise file is filtered.'} )
  lowerCAmelCase__      			=	field(
      default=1_0_0     ,	metadata={'help': 'Maximum mean line length in file, otherwise file is filtered.'} )
  lowerCAmelCase__      			=	field(
      default=0.25     ,	metadata={'help': 'Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'} )
  lowerCAmelCase__      			=	field(
      default=1.5     ,	metadata={'help': 'Minimum character token ratio for the file, otherwise file is filtered.'} )
  lowerCAmelCase__      			=	field(
      default=0.7     ,	metadata={'help': 'Probability for filtering config, test and uncommon files.'} )
  lowerCAmelCase__      			=	field(
      default='codeparrot/codeparrot'     ,	metadata={'help': 'Name or path to the tokenizer.'}     ,	)
  lowerCAmelCase__      			=	field(
      default=_a     ,	metadata={'help': 'If True, near-duplicate samples are removed.'} )
  lowerCAmelCase__      			=	field(
      default=0.85     ,	metadata={'help': 'Jaccard threshold for near-duplicate samples.'} )




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




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




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





'''simple docstring'''


import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_snake_case : str  				=						os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_snake_case : List[str]  				=						'    \"""\n    Output class for the scheduler\'s step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"""\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n'

class      A       (						unittest.TestCase       ):







							def   __lowerCAmelCase     (    self			:    int   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							tempfile.mkdtemp()
														os.makedirs(os.path.join(self.diffusers_dir				,     '''schedulers/'''   )   )
														_a      	=							self.diffusers_dir
														shutil.copy(
														    os.path.join(lowerCAmelCase_				,     '''src/diffusers/schedulers/scheduling_ddpm.py'''   )				,     os.path.join(self.diffusers_dir				,     '''schedulers/scheduling_ddpm.py'''   )				,     )







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				int:


														"""simple docstring"""


														_a      	=							'''src/diffusers'''
														shutil.rmtree(self.diffusers_dir   )







							def   __lowerCAmelCase     (    self			:    int				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    List[Any]				,     lowerCAmelCase_			:    Union[str, Any]				,     lowerCAmelCase_			:    str=None   )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							comment + F'\nclass {class_name}(nn.Module):\n' + class_code
														if overwrite_result is not None:
																					_a      	=							comment + F'\nclass {class_name}(nn.Module):\n' + overwrite_result
														_a      	=							black.Mode(target_versions={black.TargetVersion.PYaa}				,     line_length=1_19   )
														_a      	=							black.format_str(lowerCAmelCase_				,     mode=lowerCAmelCase_   )
														_a      	=							os.path.join(self.diffusers_dir				,     '''new_code.py'''   )
														with open(lowerCAmelCase_				,     '''w'''				,     newline='''\n'''   ) as f:
																					f.write(lowerCAmelCase_   )
														if overwrite_result is None:
																					self.assertTrue(len(check_copies.is_copy_consistent(lowerCAmelCase_   )   ) == 0   )
														else:
																					check_copies.is_copy_consistent(f.name				,     overwrite=lowerCAmelCase_   )
																					with open(lowerCAmelCase_				,     '''r'''   ) as f:
																												self.assertTrue(f.read()				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput'''   )
														self.assertEqual(lowerCAmelCase_				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Union[str, Any]:


														"""simple docstring"""


														self.check_copy_consistency(
														    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'''				,     '''DDPMSchedulerOutput'''				,     REFERENCE_CODE + '''\n'''				,     )

														# With no empty line at the end
														self.check_copy_consistency(
														    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'''				,     '''DDPMSchedulerOutput'''				,     lowerCAmelCase_				,     )

														# Copy consistency with rename
														self.check_copy_consistency(
														    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'''				,     '''TestSchedulerOutput'''				,     re.sub('''DDPM'''				,     '''Test'''				,     lowerCAmelCase_   )				,     )

														# Copy consistency with a really long name
														_a      	=							'''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
														self.check_copy_consistency(
														    F'# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}'				,     F'{long_class_name}SchedulerOutput'				,     re.sub('''Bert'''				,     lowerCAmelCase_				,     lowerCAmelCase_   )				,     )

														# Copy consistency with overwrite
														self.check_copy_consistency(
														    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'''				,     '''TestSchedulerOutput'''				,     lowerCAmelCase_				,     overwrite_result=re.sub('''DDPM'''				,     '''Test'''				,     lowerCAmelCase_   )				,     )

