

"""simple docstring"""





from dataclasses import dataclass, field
from typing import Optional







@dataclass
class 							SCREAMING_SNAKE_CASE   :






					"""simple docstring"""



					lowercase__												=			field(
					    default="codeparrot/codeparrot"       ,				metadata={"help": "Model name or path of model to be trained."}			)
					lowercase__												=			field(
					    default="./"       ,				metadata={"help": "Save dir where model repo is cloned and models updates are saved to."}			)
					lowercase__												=			field(
					    default="codeparrot/codeparrot-clean-train"       ,				metadata={"help": "Name or path of training dataset."}			)
					lowercase__												=			field(
					    default="codeparrot/codeparrot-clean-valid"       ,				metadata={"help": "Name or path of validation dataset."}			)
					lowercase__												=			field(default=2       ,				metadata={"help": "Batch size for training."}			)
					lowercase__												=			field(default=2       ,				metadata={"help": "Batch size for evaluation."}			)
					lowercase__												=			field(default=0.1       ,				metadata={"help": "Value of weight decay."}			)
					lowercase__												=			field(
					    default=10000       ,				metadata={"help": "Size of buffer used to shuffle streaming dataset."}			)
					lowercase__												=			field(default=2e-4       ,				metadata={"help": "Learning rate fo training."}			)
					lowercase__												=			field(default="cosine"       ,				metadata={"help": "Learning rate."}			)
					lowercase__												=			field(
					    default=750       ,				metadata={"help": "Number of warmup steps in the learning rate schedule."}			)
					lowercase__												=			field(
					    default=16       ,				metadata={"help": "Number of gradient accumulation steps."}			)
					lowercase__												=			field(
					    default=a_       ,				metadata={"help": "Use gradient checkpointing to reduce memory footprint."}			)
					lowercase__												=			field(default=50000       ,				metadata={"help": "Maximum number of training steps."}			)
					lowercase__												=			field(
					    default=-1       ,				metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}			)
					lowercase__												=			field(default=1024       ,				metadata={"help": "Sequence lengths used for training."}			)
					lowercase__												=			field(default=1       ,				metadata={"help": "Training seed."}			)
					lowercase__												=			field(
					    default=1024       ,				metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."}       ,				)
					lowercase__												=			field(
					    default=a_       ,				metadata={"help": "States path if the training should continue from a checkpoint folder."}			)
					lowercase__												=			field(default=a_       ,				metadata={"help": "If True the data is pretokenized."}			)







@dataclass
class 							SCREAMING_SNAKE_CASE   :






					"""simple docstring"""



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







@dataclass
class 							SCREAMING_SNAKE_CASE   :






					"""simple docstring"""



					lowercase__												=			field(
					    default="codeparrot/codeparrot"       ,				metadata={"help": "Model name or path of model to be evaluated."}			)
					lowercase__												=			field(default=a_       ,				metadata={"help": "Number of workers used for code evaluation."}			)
					lowercase__												=			field(
					    default=a_       ,				metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}       ,				)
					lowercase__												=			field(
					    default=a_       ,				metadata={"help": "Sample from the language model's output distribution."}			)
					lowercase__												=			field(default=0.2       ,				metadata={"help": "Sampling temperature used for generation."}			)
					lowercase__												=			field(default=256       ,				metadata={"help": "Maximum number of newly generated tokens."}			)
					lowercase__												=			field(default=0       ,				metadata={"help": "Top-k parameter used for generation."}			)
					lowercase__												=			field(default=0.95       ,				metadata={"help": "Top-p parameter used for nucleus sampling."}			)
					lowercase__												=			field(default=10       ,				metadata={"help": "Number of generations to run in parallel."}			)
					lowercase__												=			field(
					    default=200       ,				metadata={"help": "Number of completions to generate for each sample."}			)
					lowercase__												=			field(default=1       ,				metadata={"help": "Random seed used for evaluation."}			)
					lowercase__												=			field(
					    default="eval_results.json"       ,				metadata={"help": "Random seed used for evaluation."}			)
					lowercase__												=			field(
					    default="0"       ,				metadata={"help": "Allow `code_eval` to execute Python code on machine"}			)
					lowercase__												=			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 							SCREAMING_SNAKE_CASE   :






					"""simple docstring"""



					lowercase__												=			field(
					    default=a_       ,				metadata={
					        "help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
					    }       ,				)
					lowercase__												=			field(
					    default="transformersbook/codeparrot"       ,				metadata={"help": "Folder or name of dataset to process."}			)
					lowercase__												=			field(
					    default="codeparrot-clean"       ,				metadata={"help": "Folder to save processed processed dataset."}			)
					lowercase__												=			field(
					    default=100000       ,				metadata={"help": "Number of files to save per JSON output file."}			)
					lowercase__												=			field(default="content"       ,				metadata={"help": "Column containing text data to process."}			)
					lowercase__												=			field(
					    default=1000       ,				metadata={"help": "Maximum line length in file, otherwise file is filtered."}			)
					lowercase__												=			field(
					    default=100       ,				metadata={"help": "Maximum mean line length in file, otherwise file is filtered."}			)
					lowercase__												=			field(
					    default=0.25       ,				metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."}			)
					lowercase__												=			field(
					    default=1.5       ,				metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."}			)
					lowercase__												=			field(
					    default=0.7       ,				metadata={"help": "Probability for filtering config, test and uncommon files."}			)
					lowercase__												=			field(
					    default="codeparrot/codeparrot"       ,				metadata={"help": "Name or path to the tokenizer."}       ,				)
					lowercase__												=			field(
					    default=a_       ,				metadata={"help": "If True, near-duplicate samples are removed."}			)
					lowercase__												=			field(
					    default=0.85       ,				metadata={"help": "Jaccard threshold for near-duplicate samples."}			)







@dataclass
class 							SCREAMING_SNAKE_CASE   :






					"""simple docstring"""



					lowercase__												=			field(
					    default="gpt2"       ,				metadata={"help": "Base tokenizer to build new tokenizer from."}			)
					lowercase__												=			field(
					    default="transformersbook/codeparrot-train"       ,				metadata={"help": "Dataset to train tokenizer on."}			)
					lowercase__												=			field(default="content"       ,				metadata={"help": "Column containing text data to process."}			)
					lowercase__												=			field(default=200000       ,				metadata={"help": "Number of examples to train tokenizer on."}			)
					lowercase__												=			field(
					    default=32768       ,				metadata={"help": "Number of examples to train the tokenizer on."}			)
					lowercase__												=			field(default="codeparrot"       ,				metadata={"help": "Name of new tokenizer."}			)
					lowercase__												=			field(default=a_       ,				metadata={"help": "Push saved tokenizer to the hub."}			)







@dataclass
class 							SCREAMING_SNAKE_CASE   :






					"""simple docstring"""



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







@dataclass
class 							SCREAMING_SNAKE_CASE   :






					"""simple docstring"""



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







"""simple docstring"""





from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase

def     __SCREAMING_SNAKE_CASE    ( A_					):
	lowerCAmelCase__							,lowerCAmelCase__      :		Dict    							=      analyze_text(A_					)
	lowerCAmelCase__      :		Any    							=      list(''' ''' + ascii_lowercase					)
	# what is our total sum of probabilities.
	lowerCAmelCase__      :		Optional[Any]    							=      sum(single_char_strings.values()					)

	# one length string
	lowerCAmelCase__      :		int    							=      0
	# for each alpha we go in our dict and if it is in it we calculate entropy
	for ch in my_alphas:
		if ch in single_char_strings:
			lowerCAmelCase__      :		Union[str, Any]    							=      single_char_strings[ch]
			lowerCAmelCase__      :		Dict    							=      my_str / all_sum
			my_fir_sum += prob * math.loga(A_					)  # entropy formula.

    # print entropy
	print(f'{round(-1 * my_fir_sum					):.1f}'					)

	# two len string
	lowerCAmelCase__      :		List[Any]    							=      sum(two_char_strings.values()					)
	lowerCAmelCase__      :		Optional[int]    							=      0
	# for each alpha (two in size) calculate entropy.
	for cha in my_alphas:
		for cha in my_alphas:
			lowerCAmelCase__      :		int    							=      cha + cha
			if sequence in two_char_strings:
				lowerCAmelCase__      :		Optional[Any]    							=      two_char_strings[sequence]
				lowerCAmelCase__      :		int    							=      int(A_					) / all_sum
				my_sec_sum += prob * math.loga(A_					)

    # print second entropy
	print(f'{round(-1 * my_sec_sum					):.1f}'					)

	# print the difference between them
	print(f'{round((-1 * my_sec_sum) - (-1 * my_fir_sum)					):.1f}'					)

def     __SCREAMING_SNAKE_CASE    ( A_					):
	lowerCAmelCase__      :		List[str]    							=      Counter()  # type: ignore
	lowerCAmelCase__      :		Optional[int]    							=      Counter()  # type: ignore
	single_char_strings[text[-1]] += 1

	# first case when we have space at start.
	two_char_strings[" " + text[0]] += 1
	for i in range(0		,	len(A_					) - 1					):
		single_char_strings[text[i]] += 1
		two_char_strings[text[i : i + 2]] += 1
	return single_char_strings, two_char_strings

def     __SCREAMING_SNAKE_CASE    ( ):
	import doctest

	doctest.testmod()
	# text = (
	#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
	#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
	#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
	#     "beloved evident through no service elderly is. Blind there if every no so "
	#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
	#     "of message cottage windows do besides against uncivil.  Delightful "
	#     "unreserved impossible few estimating men favourable see entreaties. She "
	#     "propriety immediate was improving. He or entrance humoured likewise "
	#     "moderate. Much nor game son say feel. Fat make met can must form into "
	#     "gate. Me we offending prevailed discovery. "
	# )

	# calculate_prob(text)


if __name__ == "__main__":
							main()




