






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


__a          =			logging.get_logger(__name__)

__a          =			{
    'microsoft/beit-base-patch16-224-pt22k': (
        'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}

class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				'''beit'''





    def __init__(				self       ,_SCREAMING_SNAKE_CASE=8_192       ,_SCREAMING_SNAKE_CASE=768       ,_SCREAMING_SNAKE_CASE=12       ,_SCREAMING_SNAKE_CASE=12       ,_SCREAMING_SNAKE_CASE=3_072       ,_SCREAMING_SNAKE_CASE="gelu"       ,_SCREAMING_SNAKE_CASE=0.0       ,_SCREAMING_SNAKE_CASE=0.0       ,_SCREAMING_SNAKE_CASE=0.02       ,_SCREAMING_SNAKE_CASE=1e-12       ,_SCREAMING_SNAKE_CASE=224       ,_SCREAMING_SNAKE_CASE=16       ,_SCREAMING_SNAKE_CASE=3       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=[3, 5, 7, 11]       ,_SCREAMING_SNAKE_CASE=[1, 2, 3, 6]       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=0.4       ,_SCREAMING_SNAKE_CASE=256       ,_SCREAMING_SNAKE_CASE=1       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=255       ,**_SCREAMING_SNAKE_CASE       ,)			->		Union[str, Any]:
         super().__init__(**_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Optional[int]       =      vocab_size
         UpperCAmelCase_      :							Union[str, Any]       =      hidden_size
         UpperCAmelCase_      :							Any       =      num_hidden_layers
         UpperCAmelCase_      :							Union[str, Any]       =      num_attention_heads
         UpperCAmelCase_      :							Optional[Any]       =      intermediate_size
         UpperCAmelCase_      :							List[str]       =      hidden_act
         UpperCAmelCase_      :							Any       =      hidden_dropout_prob
         UpperCAmelCase_      :							int       =      attention_probs_dropout_prob
         UpperCAmelCase_      :							List[Any]       =      initializer_range
         UpperCAmelCase_      :							Optional[int]       =      layer_norm_eps

         UpperCAmelCase_      :							Dict       =      image_size
         UpperCAmelCase_      :							Optional[Any]       =      patch_size
         UpperCAmelCase_      :							Tuple       =      num_channels
         UpperCAmelCase_      :							Tuple       =      use_mask_token
         UpperCAmelCase_      :							List[Any]       =      use_absolute_position_embeddings
         UpperCAmelCase_      :							List[Any]       =      use_relative_position_bias
         UpperCAmelCase_      :							Tuple       =      use_shared_relative_position_bias
         UpperCAmelCase_      :							Union[str, Any]       =      layer_scale_init_value
         UpperCAmelCase_      :							Optional[int]       =      drop_path_rate
         UpperCAmelCase_      :							List[Any]       =      use_mean_pooling
         # decode head attributes (semantic segmentation)
         UpperCAmelCase_      :							Union[str, Any]       =      out_indices
         UpperCAmelCase_      :							Optional[int]       =      pool_scales
         # auxiliary head attributes (semantic segmentation)
         UpperCAmelCase_      :							Any       =      use_auxiliary_head
         UpperCAmelCase_      :							List[str]       =      auxiliary_loss_weight
         UpperCAmelCase_      :							Dict       =      auxiliary_channels
         UpperCAmelCase_      :							Any       =      auxiliary_num_convs
         UpperCAmelCase_      :							Optional[Any]       =      auxiliary_concat_input
         UpperCAmelCase_      :							Dict       =      semantic_loss_ignore_index


class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				version.parse('''1.11'''       )





    @property
    def 							a__				(				self  )			->		Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ]  )





    @property
    def 							a__				(				self  )			->		float:
         return 1e-4





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=1_0000    ,						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=_a    ,						metadata={'''help''': '''Use gradient checkpointing to reduce memory footprint.'''}       )
    lowerCAmelCase        =				field(default=5_0000    ,						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=_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:






    """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=_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=256    ,						metadata={'''help''': '''Maximum number of newly generated tokens.'''}       )
    lowerCAmelCase        =				field(default=0    ,						metadata={'''help''': '''Top-k parameter used for generation.'''}       )
    lowerCAmelCase        =				field(default=0.95    ,						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=_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=10_0000    ,						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=0.25    ,						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:






    """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=20_0000    ,						metadata={'''help''': '''Number of examples to train tokenizer on.'''}       )
    lowerCAmelCase        =				field(
        default=3_2768    ,						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:






    """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=_a    ,						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=_a    ,						metadata={'''help''': '''Push saved tokenizer to the hub.'''}       )