


'''simple docstring'''







from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig


A				:					str    						=			logging.get_logger(__name__)

# General docstring
A				:					int    						=			'''RegNetConfig'''

# Base docstring
A				:					List[str]    						=			'''facebook/regnet-y-040'''
A				:					Any    						=			[1, 1088, 7, 7]

# Image classification docstring
A				:					List[str]    						=			'''facebook/regnet-y-040'''
A				:					List[str]    						=			'''tabby, tabby cat'''

A				:					str    						=			[
    '''facebook/regnet-y-040''',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]


class 			__lowerCamelCase			(	nn.Module	):
    """simple docstring"""


    def __init__(  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int = 3				,							SCREAMING_SNAKE_CASE      :							int = 1				,							SCREAMING_SNAKE_CASE      :							int = 1				,							SCREAMING_SNAKE_CASE      :							Optional[str] = "relu"				,							):
           super().__init__()
           _A					:     Any     = nn.Convad(
               SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							kernel_size=SCREAMING_SNAKE_CASE				,							stride=SCREAMING_SNAKE_CASE				,							padding=kernel_size // 2				,							groups=SCREAMING_SNAKE_CASE				,							bias=SCREAMING_SNAKE_CASE				,							)
           _A					:     Any     = nn.BatchNormad(SCREAMING_SNAKE_CASE)
           _A					:     Any     = ACTaFN[activation] if activation is not None else nn.Identity()


    def        A   (  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Dict):
           _A					:     Any     = self.convolution(SCREAMING_SNAKE_CASE)
           _A					:     Dict     = self.normalization(SCREAMING_SNAKE_CASE)
           _A					:     Optional[Any]     = self.activation(SCREAMING_SNAKE_CASE)
           return hidden_state




class 			__lowerCamelCase			(	nn.Module	):
    """simple docstring"""


    def __init__(  self      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							RegNetConfig):
           super().__init__()
           _A					:     int     = RegNetConvLayer(
               config.num_channels				,							config.embedding_size				,							kernel_size=3				,							stride=2				,							activation=config.hidden_act)
           _A					:     str     = config.num_channels


    def        A   (  self      :							int				,							SCREAMING_SNAKE_CASE      :							Any):
           _A					:     List[Any]     = pixel_values.shape[1]
           if num_channels != self.num_channels:
                  raise ValueError(
                      'Make sure that the channel dimension of the pixel values match with the one set in the configuration.')
           _A					:     str     = self.embedder(SCREAMING_SNAKE_CASE)
           return hidden_state




class 			__lowerCamelCase			(	nn.Module	):
    """simple docstring"""


    def __init__(  self      :							int				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int = 2):
           super().__init__()
           _A					:     List[Any]     = nn.Convad(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							kernel_size=1				,							stride=SCREAMING_SNAKE_CASE				,							bias=SCREAMING_SNAKE_CASE)
           _A					:     Any     = nn.BatchNormad(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							int				,							SCREAMING_SNAKE_CASE      :							Tensor):
           _A					:     Optional[int]     = self.convolution(SCREAMING_SNAKE_CASE)
           _A					:     Optional[int]     = self.normalization(SCREAMING_SNAKE_CASE)
           return hidden_state




class 			__lowerCamelCase			(	nn.Module	):
    """simple docstring"""


    def __init__(  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int):
           super().__init__()

           _A					:     str     = nn.AdaptiveAvgPoolad((1, 1))
           _A					:     Any     = nn.Sequential(
               nn.Convad(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							kernel_size=1)				,							nn.ReLU()				,							nn.Convad(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							kernel_size=1)				,							nn.Sigmoid()				,							)


    def        A   (  self      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							str):
           # b c h w -> b c 1 1
           _A					:     Dict     = self.pooler(SCREAMING_SNAKE_CASE)
           _A					:     Optional[Any]     = self.attention(SCREAMING_SNAKE_CASE)
           _A					:     Tuple     = hidden_state * attention
           return hidden_state




class 			__lowerCamelCase			(	nn.Module	):
    """simple docstring"""


    def __init__(  self      :							str				,							SCREAMING_SNAKE_CASE      :							RegNetConfig				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int = 1):
           super().__init__()
           _A					:     Optional[Any]     = in_channels != out_channels or stride != 1
           _A					:     Optional[int]     = max(1				,							out_channels // config.groups_width)
           _A					:     Union[str, Any]     = (
               RegNetShortCut(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							stride=SCREAMING_SNAKE_CASE) if should_apply_shortcut else nn.Identity()
           )
           _A					:     int     = nn.Sequential(
               RegNetConvLayer(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							kernel_size=1				,							activation=config.hidden_act)				,							RegNetConvLayer(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							stride=SCREAMING_SNAKE_CASE				,							groups=SCREAMING_SNAKE_CASE				,							activation=config.hidden_act)				,							RegNetConvLayer(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							kernel_size=1				,							activation=SCREAMING_SNAKE_CASE)				,							)
           _A					:     Dict     = ACTaFN[config.hidden_act]


    def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]):
           _A					:     Any     = hidden_state
           _A					:     Optional[Any]     = self.layer(SCREAMING_SNAKE_CASE)
           _A					:     Dict     = self.shortcut(SCREAMING_SNAKE_CASE)
           hidden_state += residual
           _A					:     Union[str, Any]     = self.activation(SCREAMING_SNAKE_CASE)
           return hidden_state




class 			__lowerCamelCase			(	nn.Module	):
    """simple docstring"""


    def __init__(  self      :							Any				,							SCREAMING_SNAKE_CASE      :							RegNetConfig				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int = 1):
           super().__init__()
           _A					:     str     = in_channels != out_channels or stride != 1
           _A					:     str     = max(1				,							out_channels // config.groups_width)
           _A					:     Dict     = (
               RegNetShortCut(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							stride=SCREAMING_SNAKE_CASE) if should_apply_shortcut else nn.Identity()
           )
           _A					:     Union[str, Any]     = nn.Sequential(
               RegNetConvLayer(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							kernel_size=1				,							activation=config.hidden_act)				,							RegNetConvLayer(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							stride=SCREAMING_SNAKE_CASE				,							groups=SCREAMING_SNAKE_CASE				,							activation=config.hidden_act)				,							RegNetSELayer(SCREAMING_SNAKE_CASE				,							reduced_channels=int(round(in_channels / 4)))				,							RegNetConvLayer(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							kernel_size=1				,							activation=SCREAMING_SNAKE_CASE)				,							)
           _A					:     Any     = ACTaFN[config.hidden_act]


    def        A   (  self      :							int				,							SCREAMING_SNAKE_CASE      :							Optional[Any]):
           _A					:     Dict     = hidden_state
           _A					:     Optional[int]     = self.layer(SCREAMING_SNAKE_CASE)
           _A					:     Optional[Any]     = self.shortcut(SCREAMING_SNAKE_CASE)
           hidden_state += residual
           _A					:     List[str]     = self.activation(SCREAMING_SNAKE_CASE)
           return hidden_state




class 			__lowerCamelCase			(	nn.Module	):
    """simple docstring"""


    def __init__(  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							RegNetConfig				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int = 2				,							SCREAMING_SNAKE_CASE      :							int = 2				,							):
           super().__init__()

           _A					:     List[str]     = RegNetXLayer if config.layer_type == 'x' else RegNetYLayer

           _A					:     Union[str, Any]     = nn.Sequential(
               # downsampling is done in the first layer with stride of 2
               layer(
                   SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							stride=SCREAMING_SNAKE_CASE				,							)				,							*[layer(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE) for _ in range(depth - 1)]				,							)


    def        A   (  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Dict):
           _A					:     int     = self.layers(SCREAMING_SNAKE_CASE)
           return hidden_state




class 			__lowerCamelCase			(	nn.Module	):
    """simple docstring"""


    def __init__(  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							RegNetConfig):
           super().__init__()
           _A					:     Any     = nn.ModuleList([])
           # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
           self.stages.append(
               RegNetStage(
                   SCREAMING_SNAKE_CASE				,							config.embedding_size				,							config.hidden_sizes[0]				,							stride=2 if config.downsample_in_first_stage else 1				,							depth=config.depths[0]				,							))
           _A					:     Union[str, Any]     = zip(config.hidden_sizes				,							config.hidden_sizes[1:])
           for (in_channels, out_channels), depth in zip(SCREAMING_SNAKE_CASE				,							config.depths[1:]):
                  self.stages.append(RegNetStage(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							depth=SCREAMING_SNAKE_CASE))


    def        A   (  self      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Tensor				,							SCREAMING_SNAKE_CASE      :							bool = False				,							SCREAMING_SNAKE_CASE      :							bool = True):
           _A					:     List[Any]     = () if output_hidden_states else None

           for stage_module in self.stages:
                  if output_hidden_states:
                         _A					:     Union[str, Any]     = hidden_states + (hidden_state,)

                  _A					:     str     = stage_module(SCREAMING_SNAKE_CASE)

           if output_hidden_states:
                  _A					:     Union[str, Any]     = hidden_states + (hidden_state,)

           if not return_dict:
                  return tuple(v for v in [hidden_state, hidden_states] if v is not None)

           return BaseModelOutputWithNoAttention(last_hidden_state=SCREAMING_SNAKE_CASE				,							hidden_states=SCREAMING_SNAKE_CASE)




class 			__lowerCamelCase			(	a_	):
    """simple docstring"""
    a													=   RegNetConfig
    a													=   "regnet"
    a													=   "pixel_values"
    a													=   True


    def        A   (  self      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							str):
           if isinstance(SCREAMING_SNAKE_CASE				,							nn.Convad):
                  nn.init.kaiming_normal_(module.weight				,							mode='fan_out'				,							nonlinearity='relu')
           elif isinstance(SCREAMING_SNAKE_CASE				,							(nn.BatchNormad, nn.GroupNorm)):
                  nn.init.constant_(module.weight				,							1)
                  nn.init.constant_(module.bias				,							0)


    def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							List[str]=False):
           if isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  _A					:     int     = value




A				:					Any    						=			R'''
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''

A				:					Any    						=			R'''
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
'''


@add_start_docstrings(
    "The bare RegNet model outputting raw features without any specific head on top."    , a_    , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 			__lowerCamelCase			(	a_	):
    """simple docstring"""


    def __init__(  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							int):
           super().__init__(SCREAMING_SNAKE_CASE)
           _A					:     str     = config
           _A					:     int     = RegNetEmbeddings(SCREAMING_SNAKE_CASE)
           _A					:     Tuple     = RegNetEncoder(SCREAMING_SNAKE_CASE)
           _A					:     Tuple     = nn.AdaptiveAvgPoolad((1, 1))
           # Initialize weights and apply final processing
           self.post_init()


    @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC				,							output_type=SCREAMING_SNAKE_CASE				,							config_class=_CONFIG_FOR_DOC				,							modality='vision'				,							expected_output=_EXPECTED_OUTPUT_SHAPE				,							)
    def        A   (  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							Tensor				,							SCREAMING_SNAKE_CASE      :							Optional[bool] = None				,							SCREAMING_SNAKE_CASE      :							Optional[bool] = None):
           _A					:     List[str]     = (
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )
           _A					:     List[Any]     = return_dict if return_dict is not None else self.config.use_return_dict

           _A					:     str     = self.embedder(SCREAMING_SNAKE_CASE)

           _A					:     Optional[int]     = self.encoder(
               SCREAMING_SNAKE_CASE				,							output_hidden_states=SCREAMING_SNAKE_CASE				,							return_dict=SCREAMING_SNAKE_CASE)

           _A					:     List[Any]     = encoder_outputs[0]

           _A					:     Tuple     = self.pooler(SCREAMING_SNAKE_CASE)

           if not return_dict:
                  return (last_hidden_state, pooled_output) + encoder_outputs[1:]

           return BaseModelOutputWithPoolingAndNoAttention(
               last_hidden_state=SCREAMING_SNAKE_CASE				,							pooler_output=SCREAMING_SNAKE_CASE				,							hidden_states=encoder_outputs.hidden_states				,							)




@add_start_docstrings(
    "\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "    , a_    , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 			__lowerCamelCase			(	a_	):
    """simple docstring"""


    def __init__(  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Optional[Any]):
           super().__init__(SCREAMING_SNAKE_CASE)
           _A					:     Any     = config.num_labels
           _A					:     Dict     = RegNetModel(SCREAMING_SNAKE_CASE)
           # classification head
           _A					:     Optional[int]     = nn.Sequential(
               nn.Flatten()				,							nn.Linear(config.hidden_sizes[-1]				,							config.num_labels) if config.num_labels > 0 else nn.Identity()				,							)
           # initialize weights and apply final processing
           self.post_init()


    @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE)
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT				,							output_type=SCREAMING_SNAKE_CASE				,							config_class=_CONFIG_FOR_DOC				,							expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT				,							)
    def        A   (  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							Optional[torch.FloatTensor] = None				,							SCREAMING_SNAKE_CASE      :							Optional[torch.LongTensor] = None				,							SCREAMING_SNAKE_CASE      :							Optional[bool] = None				,							SCREAMING_SNAKE_CASE      :							Optional[bool] = None				,							):
           _A					:     List[str]     = return_dict if return_dict is not None else self.config.use_return_dict

           _A					:     Optional[int]     = self.regnet(SCREAMING_SNAKE_CASE				,							output_hidden_states=SCREAMING_SNAKE_CASE				,							return_dict=SCREAMING_SNAKE_CASE)

           _A					:     str     = outputs.pooler_output if return_dict else outputs[1]

           _A					:     Union[str, Any]     = self.classifier(SCREAMING_SNAKE_CASE)

           _A					:     Any     = None

           if labels is not None:
                  if self.config.problem_type is None:
                         if self.num_labels == 1:
                                _A					:     List[str]     = 'regression'
                         elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                                _A					:     int     = 'single_label_classification'
                         else:
                                _A					:     Union[str, Any]     = 'multi_label_classification'
                  if self.config.problem_type == "regression":
                         _A					:     Tuple     = MSELoss()
                         if self.num_labels == 1:
                                _A					:     List[Any]     = loss_fct(logits.squeeze()				,							labels.squeeze())
                         else:
                                _A					:     Optional[int]     = loss_fct(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
                  elif self.config.problem_type == "single_label_classification":
                         _A					:     Any     = CrossEntropyLoss()
                         _A					:     Union[str, Any]     = loss_fct(logits.view(-1				,							self.num_labels)				,							labels.view(-1))
                  elif self.config.problem_type == "multi_label_classification":
                         _A					:     Union[str, Any]     = BCEWithLogitsLoss()
                         _A					:     Dict     = loss_fct(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)

           if not return_dict:
                  _A					:     Optional[int]     = (logits,) + outputs[2:]
                  return (loss,) + output if loss is not None else output

           return ImageClassifierOutputWithNoAttention(loss=SCREAMING_SNAKE_CASE				,							logits=SCREAMING_SNAKE_CASE				,							hidden_states=outputs.hidden_states)










'''simple docstring'''







from dataclasses import dataclass, field
from typing import Optional


@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default="codeparrot/codeparrot"    , metadata={"help": "Model name or path of model to be trained."}	)
    a													=   field(
        default="./"    , metadata={"help": "Save dir where model repo is cloned and models updates are saved to."}	)
    a													=   field(
        default="codeparrot/codeparrot-clean-train"    , metadata={"help": "Name or path of training dataset."}	)
    a													=   field(
        default="codeparrot/codeparrot-clean-valid"    , metadata={"help": "Name or path of validation dataset."}	)
    a													=   field(default=2    , metadata={"help": "Batch size for training."}	)
    a													=   field(default=2    , metadata={"help": "Batch size for evaluation."}	)
    a													=   field(default=0.1    , metadata={"help": "Value of weight decay."}	)
    a													=   field(
        default=1_0000    , metadata={"help": "Size of buffer used to shuffle streaming dataset."}	)
    a													=   field(default=2E-4    , metadata={"help": "Learning rate fo training."}	)
    a													=   field(default="cosine"    , metadata={"help": "Learning rate."}	)
    a													=   field(
        default=750    , metadata={"help": "Number of warmup steps in the learning rate schedule."}	)
    a													=   field(
        default=16    , metadata={"help": "Number of gradient accumulation steps."}	)
    a													=   field(
        default=a_    , metadata={"help": "Use gradient checkpointing to reduce memory footprint."}	)
    a													=   field(default=5_0000    , metadata={"help": "Maximum number of training steps."}	)
    a													=   field(
        default=-1    , metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}	)
    a													=   field(default=1024    , metadata={"help": "Sequence lengths used for training."}	)
    a													=   field(default=1    , metadata={"help": "Training seed."}	)
    a													=   field(
        default=1024    , metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."}    , )
    a													=   field(
        default=a_    , metadata={"help": "States path if the training should continue from a checkpoint folder."}	)
    a													=   field(default=a_    , metadata={"help": "If True the data is pretokenized."}	)




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default="codeparrot/codeparrot"    , metadata={"help": "Model name or path of model to be evaluated."}	)
    a													=   field(
        default="codeparrot/codeparrot-clean-valid"    , metadata={"help": "Name or path of validation dataset."}	)
    a													=   field(default=2    , metadata={"help": "Batch size used for evaluation."}	)
    a													=   field(
        default=-1    , metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}	)
    a													=   field(default=1024    , metadata={"help": "Length of sequences to be evaluated."}	)
    a													=   field(default=1    , metadata={"help": "Random seed used for evaluation."}	)




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default="codeparrot/codeparrot"    , metadata={"help": "Model name or path of model to be evaluated."}	)
    a													=   field(default=a_    , metadata={"help": "Number of workers used for code evaluation."}	)
    a													=   field(
        default=a_    , metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}    , )
    a													=   field(
        default=a_    , metadata={"help": "Sample from the language model's output distribution."}	)
    a													=   field(default=0.2    , metadata={"help": "Sampling temperature used for generation."}	)
    a													=   field(default=256    , metadata={"help": "Maximum number of newly generated tokens."}	)
    a													=   field(default=0    , metadata={"help": "Top-k parameter used for generation."}	)
    a													=   field(default=0.95    , metadata={"help": "Top-p parameter used for nucleus sampling."}	)
    a													=   field(default=10    , metadata={"help": "Number of generations to run in parallel."}	)
    a													=   field(
        default=200    , metadata={"help": "Number of completions to generate for each sample."}	)
    a													=   field(default=1    , metadata={"help": "Random seed used for evaluation."}	)
    a													=   field(
        default="eval_results.json"    , metadata={"help": "Random seed used for evaluation."}	)
    a													=   field(
        default="0"    , metadata={"help": "Allow `code_eval` to execute Python code on machine"}	)
    a													=   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 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default=a_    , metadata={
            "help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
        }    , )
    a													=   field(
        default="transformersbook/codeparrot"    , metadata={"help": "Folder or name of dataset to process."}	)
    a													=   field(
        default="codeparrot-clean"    , metadata={"help": "Folder to save processed processed dataset."}	)
    a													=   field(
        default=10_0000    , metadata={"help": "Number of files to save per JSON output file."}	)
    a													=   field(default="content"    , metadata={"help": "Column containing text data to process."}	)
    a													=   field(
        default=1000    , metadata={"help": "Maximum line length in file, otherwise file is filtered."}	)
    a													=   field(
        default=100    , metadata={"help": "Maximum mean line length in file, otherwise file is filtered."}	)
    a													=   field(
        default=0.25    , metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."}	)
    a													=   field(
        default=1.5    , metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."}	)
    a													=   field(
        default=0.7    , metadata={"help": "Probability for filtering config, test and uncommon files."}	)
    a													=   field(
        default="codeparrot/codeparrot"    , metadata={"help": "Name or path to the tokenizer."}    , )
    a													=   field(
        default=a_    , metadata={"help": "If True, near-duplicate samples are removed."}	)
    a													=   field(
        default=0.85    , metadata={"help": "Jaccard threshold for near-duplicate samples."}	)




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default="gpt2"    , metadata={"help": "Base tokenizer to build new tokenizer from."}	)
    a													=   field(
        default="transformersbook/codeparrot-train"    , metadata={"help": "Dataset to train tokenizer on."}	)
    a													=   field(default="content"    , metadata={"help": "Column containing text data to process."}	)
    a													=   field(default=20_0000    , metadata={"help": "Number of examples to train tokenizer on."}	)
    a													=   field(
        default=3_2768    , metadata={"help": "Number of examples to train the tokenizer on."}	)
    a													=   field(default="codeparrot"    , metadata={"help": "Name of new tokenizer."}	)
    a													=   field(default=a_    , metadata={"help": "Push saved tokenizer to the hub."}	)




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




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default="gpt2-large"    , metadata={"help": "Configuration to use for model initialization."}	)
    a													=   field(
        default="codeparrot/codeparrot"    , metadata={"help": "Tokenizer attached to model."}	)
    a													=   field(default="codeparrot"    , metadata={"help": "Name of the created model."}	)
    a													=   field(default=a_    , metadata={"help": "Push saved tokenizer to the hub."}	)






