'''simple docstring'''

from dataclasses import dataclass, field
from typing import Optional



@dataclass
class 			UpperCAmelCase  :





       '''simple docstring'''



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



@dataclass
class 			UpperCAmelCase  :





       '''simple docstring'''



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



@dataclass
class 			UpperCAmelCase  :





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    field(
           default="codeparrot/codeparrot"    ,						metadata={"help": "Model name or path of model to be evaluated."}					)
       SCREAMING_SNAKE_CASE							=    field(default=a__    ,						metadata={"help": "Number of workers used for code evaluation."}					)
       SCREAMING_SNAKE_CASE							=    field(
           default=a__    ,						metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}    ,						)
       SCREAMING_SNAKE_CASE							=    field(
           default=a__    ,						metadata={"help": "Sample from the language model's output distribution."}					)
       SCREAMING_SNAKE_CASE							=    field(default=0.2    ,						metadata={"help": "Sampling temperature used for generation."}					)
       SCREAMING_SNAKE_CASE							=    field(default=2_5_6    ,						metadata={"help": "Maximum number of newly generated tokens."}					)
       SCREAMING_SNAKE_CASE							=    field(default=0    ,						metadata={"help": "Top-k parameter used for generation."}					)
       SCREAMING_SNAKE_CASE							=    field(default=0.95    ,						metadata={"help": "Top-p parameter used for nucleus sampling."}					)
       SCREAMING_SNAKE_CASE							=    field(default=1_0    ,						metadata={"help": "Number of generations to run in parallel."}					)
       SCREAMING_SNAKE_CASE							=    field(
           default=2_0_0    ,						metadata={"help": "Number of completions to generate for each sample."}					)
       SCREAMING_SNAKE_CASE							=    field(default=1    ,						metadata={"help": "Random seed used for evaluation."}					)
       SCREAMING_SNAKE_CASE							=    field(
           default="eval_results.json"    ,						metadata={"help": "Random seed used for evaluation."}					)
       SCREAMING_SNAKE_CASE							=    field(
           default="0"    ,						metadata={"help": "Allow `code_eval` to execute Python code on machine"}					)
       SCREAMING_SNAKE_CASE							=    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 			UpperCAmelCase  :





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    field(
           default=a__    ,						metadata={
               "help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
           }    ,						)
       SCREAMING_SNAKE_CASE							=    field(
           default="transformersbook/codeparrot"    ,						metadata={"help": "Folder or name of dataset to process."}					)
       SCREAMING_SNAKE_CASE							=    field(
           default="codeparrot-clean"    ,						metadata={"help": "Folder to save processed processed dataset."}					)
       SCREAMING_SNAKE_CASE							=    field(
           default=1_0_0_0_0_0    ,						metadata={"help": "Number of files to save per JSON output file."}					)
       SCREAMING_SNAKE_CASE							=    field(default="content"    ,						metadata={"help": "Column containing text data to process."}					)
       SCREAMING_SNAKE_CASE							=    field(
           default=1_0_0_0    ,						metadata={"help": "Maximum line length in file, otherwise file is filtered."}					)
       SCREAMING_SNAKE_CASE							=    field(
           default=1_0_0    ,						metadata={"help": "Maximum mean line length in file, otherwise file is filtered."}					)
       SCREAMING_SNAKE_CASE							=    field(
           default=0.25    ,						metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."}					)
       SCREAMING_SNAKE_CASE							=    field(
           default=1.5    ,						metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."}					)
       SCREAMING_SNAKE_CASE							=    field(
           default=0.7    ,						metadata={"help": "Probability for filtering config, test and uncommon files."}					)
       SCREAMING_SNAKE_CASE							=    field(
           default="codeparrot/codeparrot"    ,						metadata={"help": "Name or path to the tokenizer."}    ,						)
       SCREAMING_SNAKE_CASE							=    field(
           default=a__    ,						metadata={"help": "If True, near-duplicate samples are removed."}					)
       SCREAMING_SNAKE_CASE							=    field(
           default=0.85    ,						metadata={"help": "Jaccard threshold for near-duplicate samples."}					)



@dataclass
class 			UpperCAmelCase  :





       '''simple docstring'''



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



@dataclass
class 			UpperCAmelCase  :





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    field(
           default="codeparrot/codeparrot"    ,						metadata={"help": "Name or path to the tokenizer."}					)
       SCREAMING_SNAKE_CASE							=    field(
           default="codeparrot/codeparrot-clean-train"    ,						metadata={"help": "Name or path to the dataset to pretokenize."}					)
       SCREAMING_SNAKE_CASE							=    field(
           default="tokenized-codeparrot-train"    ,						metadata={"help": "Repo name of the pretokenized data."}					)
       SCREAMING_SNAKE_CASE							=    field(default=a__    ,						metadata={"help": "Number of workers used for code evaluation."}					)



@dataclass
class 			UpperCAmelCase  :





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    field(
           default="gpt2-large"    ,						metadata={"help": "Configuration to use for model initialization."}					)
       SCREAMING_SNAKE_CASE							=    field(
           default="codeparrot/codeparrot"    ,						metadata={"help": "Tokenizer attached to model."}					)
       SCREAMING_SNAKE_CASE							=    field(default="codeparrot"    ,						metadata={"help": "Name of the created model."}					)
       SCREAMING_SNAKE_CASE							=    field(default=a__    ,						metadata={"help": "Push saved tokenizer to the hub."}					)






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase          =       logging.get_logger(__name__)

__UpperCAmelCase          =       {
    '''facebook/s2t-wav2vec2-large-en-de''': (
        '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}



class 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					Optional[Any]        =  "speech_to_text_2"
       UpperCAmelCase__				:					List[Any]        =  ["past_key_values"]
       UpperCAmelCase__				:					Any        =  {"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}
       def __init__(			self,			SCREAMING_SNAKE_CASE_=1_0000,			SCREAMING_SNAKE_CASE_=6,			SCREAMING_SNAKE_CASE_=2048,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_="relu",			SCREAMING_SNAKE_CASE_=256,			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=0.02,			SCREAMING_SNAKE_CASE_=2,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=1,			SCREAMING_SNAKE_CASE_=0,			SCREAMING_SNAKE_CASE_=2,			SCREAMING_SNAKE_CASE_=1024,			**SCREAMING_SNAKE_CASE_,			)     ->		int:
          UpperCamelCase    :  Optional[int]							=	vocab_size
          UpperCamelCase    :  List[str]							=	d_model
          UpperCamelCase    :  List[str]							=	decoder_ffn_dim
          UpperCamelCase    :  Optional[Any]							=	decoder_layers
          UpperCamelCase    :  Any							=	decoder_attention_heads
          UpperCamelCase    :  Tuple							=	dropout
          UpperCamelCase    :  str							=	attention_dropout
          UpperCamelCase    :  str							=	activation_dropout
          UpperCamelCase    :  Union[str, Any]							=	activation_function
          UpperCamelCase    :  Optional[int]							=	init_std
          UpperCamelCase    :  Tuple							=	decoder_layerdrop
          UpperCamelCase    :  Dict							=	use_cache
          UpperCamelCase    :  Any							=	decoder_layers
          UpperCamelCase    :  Union[str, Any]							=	scale_embedding  # scale factor will be sqrt(d_model) if True
          UpperCamelCase    :  Union[str, Any]							=	max_target_positions

          super().__init__(
              pad_token_id=SCREAMING_SNAKE_CASE_,			bos_token_id=SCREAMING_SNAKE_CASE_,			eos_token_id=SCREAMING_SNAKE_CASE_,			decoder_start_token_id=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_,			)



