



'''simple docstring'''





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=10_000   ,   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=750   ,   metadata={'help': 'Number of warmup steps in the learning rate schedule.'}						)
 __lowerCamelCase    :       Optional[int]          =       field(
     default=16   ,   metadata={'help': 'Number of gradient accumulation steps.'}						)
 __lowerCamelCase    :       Optional[bool]          =       field(
     default=A   ,   metadata={'help': 'Use gradient checkpointing to reduce memory footprint.'}						)
 __lowerCamelCase    :       Optional[int]          =       field(default=50_000   ,   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_024   ,   metadata={'help': 'Sequence lengths used for training.'}						)
 __lowerCamelCase    :       Optional[int]          =       field(default=1   ,   metadata={'help': 'Training seed.'}						)
 __lowerCamelCase    :       Optional[int]          =       field(
     default=1_024   ,   metadata={'help': 'Interval to save checkpoints. Measured as number of forward passes not training steps.'}   ,   )
 __lowerCamelCase    :       Optional[str]          =       field(
     default=A   ,   metadata={'help': 'States path if the training should continue from a checkpoint folder.'}						)
 __lowerCamelCase    :       Optional[bool]          =       field(default=A   ,   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_024   ,   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=A   ,   metadata={'help': 'Number of workers used for code evaluation.'}						)
 __lowerCamelCase    :       Optional[int]          =       field(
     default=A   ,   metadata={'help': 'The number of human-eval tasks to run. If not included all tasks are evaluated.'}   ,   )
 __lowerCamelCase    :       Optional[bool]          =       field(
     default=A   ,   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=256   ,   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=10   ,   metadata={'help': 'Number of generations to run in parallel.'}						)
 __lowerCamelCase    :       Optional[int]          =       field(
     default=200   ,   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=A   ,   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=100_000   ,   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_000   ,   metadata={'help': 'Maximum line length in file, otherwise file is filtered.'}						)
 __lowerCamelCase    :       Optional[float]          =       field(
     default=100   ,   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=A   ,   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=200_000   ,   metadata={'help': 'Number of examples to train tokenizer on.'}						)
 __lowerCamelCase    :       Optional[int]          =       field(
     default=32_768   ,   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=A   ,   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=A   ,   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=A   ,   metadata={'help': 'Push saved tokenizer to the hub.'}						)








'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "microsoft/resnet-50": "https://huggingface.co/microsoft/resnet-50/blob/main/config.json",
}






class __A			(  A   ,   A						):



 '''simple docstring'''







 __lowerCamelCase    :       Tuple          =       'resnet'
 __lowerCamelCase    :       Any          =       ['basic', 'bottleneck']



 def __init__(self ,   A=3 ,   A=64 ,   A=[256, 512, 1_024, 2_048] ,   A=[3, 4, 6, 3] ,   A="bottleneck" ,   A="relu" ,   A=False ,   A=None ,   A=None ,   **A ,   )    ->     Dict:


      """simple docstring"""



      super().__init__(**A       )
      if layer_type not in self.layer_types:
           raise ValueError(f'''layer_type={layer_type} is not one of {','.join(self.layer_types       )}'''       )
      _a      =  num_channels
      _a      =  embedding_size
      _a      =  hidden_sizes
      _a      =  depths
      _a      =  layer_type
      _a      =  hidden_act
      _a      =  downsample_in_first_stage
      _a      =  ['''stem'''] + [f'''stage{idx}''' for idx in range(1 ,   len(A       ) + 1       )]
      _a  ,    _a      =  get_aligned_output_features_output_indices(
          out_features=A ,   out_indices=A ,   stage_names=self.stage_names       )






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Any          =       version.parse('1.11'						)



 @property
 def 					a__  (self       )    ->     Mapping[str, Mapping[int, str]]:


      """simple docstring"""



      return OrderedDict(
          [
              ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
          ]       )



 @property
 def 					a__  (self       )    ->     float:


      """simple docstring"""



      return 1E-3



