


"""simple docstring"""







from dataclasses import dataclass, field
from typing import Optional






@dataclass
class       __A				:






 """simple docstring"""




 __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=10000,    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=750,    metadata={"help": "Number of warmup steps in the learning rate schedule."}			)
 __lowerCAmelCase 					=  field(
     default=16,    metadata={"help": "Number of gradient accumulation steps."}			)
 __lowerCAmelCase 					=  field(
     default=_SCREAMING_SNAKE_CASE,    metadata={"help": "Use gradient checkpointing to reduce memory footprint."}			)
 __lowerCAmelCase 					=  field(default=50000,    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=1024,    metadata={"help": "Sequence lengths used for training."}			)
 __lowerCAmelCase 					=  field(default=1,    metadata={"help": "Training seed."}			)
 __lowerCAmelCase 					=  field(
     default=1024,    metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."},    )
 __lowerCAmelCase 					=  field(
     default=_SCREAMING_SNAKE_CASE,    metadata={"help": "States path if the training should continue from a checkpoint folder."}			)
 __lowerCAmelCase 					=  field(default=_SCREAMING_SNAKE_CASE,    metadata={"help": "If True the data is pretokenized."}			)






@dataclass
class       __A				:






 """simple docstring"""




 __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=1024,    metadata={"help": "Length of sequences to be evaluated."}			)
 __lowerCAmelCase 					=  field(default=1,    metadata={"help": "Random seed used for evaluation."}			)






@dataclass
class       __A				:






 """simple docstring"""




 __lowerCAmelCase 					=  field(
     default="codeparrot/codeparrot",    metadata={"help": "Model name or path of model to be evaluated."}			)
 __lowerCAmelCase 					=  field(default=_SCREAMING_SNAKE_CASE,    metadata={"help": "Number of workers used for code evaluation."}			)
 __lowerCAmelCase 					=  field(
     default=_SCREAMING_SNAKE_CASE,    metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."},    )
 __lowerCAmelCase 					=  field(
     default=_SCREAMING_SNAKE_CASE,    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=256,    metadata={"help": "Maximum number of newly generated tokens."}			)
 __lowerCAmelCase 					=  field(default=0,    metadata={"help": "Top-k parameter used for generation."}			)
 __lowerCAmelCase 					=  field(default=0.9_5,    metadata={"help": "Top-p parameter used for nucleus sampling."}			)
 __lowerCAmelCase 					=  field(default=10,    metadata={"help": "Number of generations to run in parallel."}			)
 __lowerCAmelCase 					=  field(
     default=200,    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				:






 """simple docstring"""




 __lowerCAmelCase 					=  field(
     default=_SCREAMING_SNAKE_CASE,    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=100000,    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=1000,    metadata={"help": "Maximum line length in file, otherwise file is filtered."}			)
 __lowerCAmelCase 					=  field(
     default=100,    metadata={"help": "Maximum mean line length in file, otherwise file is filtered."}			)
 __lowerCAmelCase 					=  field(
     default=0.2_5,    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=_SCREAMING_SNAKE_CASE,    metadata={"help": "If True, near-duplicate samples are removed."}			)
 __lowerCAmelCase 					=  field(
     default=0.8_5,    metadata={"help": "Jaccard threshold for near-duplicate samples."}			)






@dataclass
class       __A				:






 """simple docstring"""




 __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=200000,    metadata={"help": "Number of examples to train tokenizer on."}			)
 __lowerCAmelCase 					=  field(
     default=32768,    metadata={"help": "Number of examples to train the tokenizer on."}			)
 __lowerCAmelCase 					=  field(default="codeparrot",    metadata={"help": "Name of new tokenizer."}			)
 __lowerCAmelCase 					=  field(default=_SCREAMING_SNAKE_CASE,    metadata={"help": "Push saved tokenizer to the hub."}			)






@dataclass
class       __A				:






 """simple docstring"""




 __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=_SCREAMING_SNAKE_CASE,    metadata={"help": "Number of workers used for code evaluation."}			)






@dataclass
class       __A				:






 """simple docstring"""




 __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=_SCREAMING_SNAKE_CASE,    metadata={"help": "Push saved tokenizer to the hub."}			)
"""simple docstring"""


from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase__     						=  logging.get_logger(__name__)

UpperCAmelCase__     						=  {
    """google/efficientnet-b7""": """https://huggingface.co/google/efficientnet-b7/resolve/main/config.json""",
}


class 				a (						lowerCAmelCase_      ):
    _snake_case   :     Any   							=							'efficientnet'


    def __init__(    self		:     Any      , __lowerCAmelCase		:     int = 3      , __lowerCAmelCase		:     int = 600      , __lowerCAmelCase		:     float = 2.0      , __lowerCAmelCase		:     float = 3.1      , __lowerCAmelCase		:     int = 8      , __lowerCAmelCase		:     List[int] = [3, 3, 5, 3, 5, 5, 3]      , __lowerCAmelCase		:     List[int] = [32, 16, 24, 40, 80, 112, 192]      , __lowerCAmelCase		:     List[int] = [16, 24, 40, 80, 112, 192, 320]      , __lowerCAmelCase		:     List[int] = []      , __lowerCAmelCase		:     List[int] = [1, 2, 2, 2, 1, 2, 1]      , __lowerCAmelCase		:     List[int] = [1, 2, 2, 3, 3, 4, 1]      , __lowerCAmelCase		:     List[int] = [1, 6, 6, 6, 6, 6, 6]      , __lowerCAmelCase		:     float = 0.25      , __lowerCAmelCase		:     str = "swish"      , __lowerCAmelCase		:     int = 2560      , __lowerCAmelCase		:     str = "mean"      , __lowerCAmelCase		:     float = 0.02      , __lowerCAmelCase		:     float = 0.001      , __lowerCAmelCase		:     float = 0.99      , __lowerCAmelCase		:     float = 0.5      , __lowerCAmelCase		:     float = 0.2      , **__lowerCAmelCase		:     List[Any]      , ):
      super().__init__(**__lowerCAmelCase    )

      _UpperCAmelCase         =					num_channels
      _UpperCAmelCase         =					image_size
      _UpperCAmelCase         =					width_coefficient
      _UpperCAmelCase         =					depth_coefficient
      _UpperCAmelCase         =					depth_divisor
      _UpperCAmelCase         =					kernel_sizes
      _UpperCAmelCase         =					in_channels
      _UpperCAmelCase         =					out_channels
      _UpperCAmelCase         =					depthwise_padding
      _UpperCAmelCase         =					strides
      _UpperCAmelCase         =					num_block_repeats
      _UpperCAmelCase         =					expand_ratios
      _UpperCAmelCase         =					squeeze_expansion_ratio
      _UpperCAmelCase         =					hidden_act
      _UpperCAmelCase         =					hidden_dim
      _UpperCAmelCase         =					pooling_type
      _UpperCAmelCase         =					initializer_range
      _UpperCAmelCase         =					batch_norm_eps
      _UpperCAmelCase         =					batch_norm_momentum
      _UpperCAmelCase         =					dropout_rate
      _UpperCAmelCase         =					drop_connect_rate
      _UpperCAmelCase         =					sum(__lowerCAmelCase    ) * 4



class 				a (						lowerCAmelCase_      ):
    _snake_case   :     Dict   							=							version.parse('1.11'      )


    @property
    def    lowerCAmelCase_     (    self		:     Any    ):
      return OrderedDict(
          [
              ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
          ]    )


    @property
    def    lowerCAmelCase_     (    self		:     int    ):
      return 1e-5
