




from dataclasses import dataclass, field
from typing import Optional







@dataclass
class 		lowercase							:
     _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_0_0_0_0,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=7_5_0,metadata={"help": "Number of warmup steps in the learning rate schedule."}	)
     _a											=		field(
         default=1_6,metadata={"help": "Number of gradient accumulation steps."}	)
     _a											=		field(
         default=UpperCamelCase__,metadata={"help": "Use gradient checkpointing to reduce memory footprint."}	)
     _a											=		field(default=5_0_0_0_0,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=1_0_2_4,metadata={"help": "Sequence lengths used for training."}	)
     _a											=		field(default=1,metadata={"help": "Training seed."}	)
     _a											=		field(
         default=1_0_2_4,metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."},)
     _a											=		field(
         default=UpperCamelCase__,metadata={"help": "States path if the training should continue from a checkpoint folder."}	)
     _a											=		field(default=UpperCamelCase__,metadata={"help": "If True the data is pretokenized."}	)







@dataclass
class 		lowercase							:
     _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=1_0_2_4,metadata={"help": "Length of sequences to be evaluated."}	)
     _a											=		field(default=1,metadata={"help": "Random seed used for evaluation."}	)







@dataclass
class 		lowercase							:
     _a											=		field(
         default="codeparrot/codeparrot",metadata={"help": "Model name or path of model to be evaluated."}	)
     _a											=		field(default=UpperCamelCase__,metadata={"help": "Number of workers used for code evaluation."}	)
     _a											=		field(
         default=UpperCamelCase__,metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."},)
     _a											=		field(
         default=UpperCamelCase__,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=2_5_6,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=1_0,metadata={"help": "Number of generations to run in parallel."}	)
     _a											=		field(
         default=2_0_0,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 		lowercase							:
     _a											=		field(
         default=UpperCamelCase__,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=1_0_0_0_0_0,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=1_0_0_0,metadata={"help": "Maximum line length in file, otherwise file is filtered."}	)
     _a											=		field(
         default=1_0_0,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=UpperCamelCase__,metadata={"help": "If True, near-duplicate samples are removed."}	)
     _a											=		field(
         default=0.85,metadata={"help": "Jaccard threshold for near-duplicate samples."}	)







@dataclass
class 		lowercase							:
     _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=2_0_0_0_0_0,metadata={"help": "Number of examples to train tokenizer on."}	)
     _a											=		field(
         default=3_2_7_6_8,metadata={"help": "Number of examples to train the tokenizer on."}	)
     _a											=		field(default="codeparrot",metadata={"help": "Name of new tokenizer."}	)
     _a											=		field(default=UpperCamelCase__,metadata={"help": "Push saved tokenizer to the hub."}	)







@dataclass
class 		lowercase							:
     _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=UpperCamelCase__,metadata={"help": "Number of workers used for code evaluation."}	)







@dataclass
class 		lowercase							:
     _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=UpperCamelCase__,metadata={"help": "Push saved tokenizer to the hub."}	)







from __future__ import annotations







def        lowerCAmelCase_			(  snake_case_,snake_case_ = None  ):
      _A							:  Tuple    =		word_bank or []
      # create a table
      _A							:  int    =		len(snake_case_  ) + 1

      _A							:  list[list[list[str]]]    =		[]
      for _ in range(snake_case_  ):
            table.append([]  )
      # seed value
      _A							:  Dict    =		[[]]  # because empty string has empty combination

      # iterate through the indices
      for i in range(snake_case_  ):
            # condition
            if table[i] != []:
                  for word in word_bank:
                        # slice condition
                        if target[i : i + len(snake_case_  )] == word:
                              _A							:  list[list[str]]    =		[
                                  [word, *way] for way in table[i]
                              ]
                              # adds the word to every combination the current position holds
                              # now,push that combination to the table[i+len(word)]
                              table[i + len(snake_case_  )] += new_combinations

    # combinations are in reverse order so reverse for better output
      for combination in table[len(snake_case_  )]:
            combination.reverse()

      return table[len(snake_case_  )]


if __name__ == "__main__":
  print(all_construct("jwajalapa", ["jwa", "j", "w", "a", "la", "lapa"]))
  print(all_construct("rajamati", ["s", "raj", "amat", "raja", "ma", "i", "t"]))
  print(
      all_construct(
          "hexagonosaurus",
          ["h", "ex", "hex", "ag", "ago", "ru", "auru", "rus", "go", "no", "o", "s"],
      )
  )

