

from collections.abc import Generator
from math import sin
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     if len(_A       ) != 32:
          raise ValueError('''Input must be of length 32'''       )

     SCREAMING_SNAKE_CASE__					=					B''''''
     for i in [3, 2, 1, 0]:
          little_endian += string_aa[8 * i : 8 * i + 8]
     return little_endian
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     if i < 0:
          raise ValueError('''Input must be non-negative'''       )

     SCREAMING_SNAKE_CASE__					=					format(_A   ,	'''08x'''       )[-8:]
     SCREAMING_SNAKE_CASE__					=					B''''''
     for i in [3, 2, 1, 0]:
          little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode('''utf-8'''       )
     return little_endian_hex
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					B''''''
     for char in message:
          bit_string += format(_A   ,	'''08b'''       ).encode('''utf-8'''       )
     SCREAMING_SNAKE_CASE__					=					format(len(_A       )   ,	'''064b'''       ).encode('''utf-8'''       )

     # Pad bit_string to a multiple of 512 chars
     bit_string += b"1"
     while len(_A       ) % 5_12 != 4_48:
          bit_string += b"0"
     bit_string += to_little_endian(start_len[32:]       ) + to_little_endian(start_len[:32]       )

     return bit_string
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     if len(_A       ) % 5_12 != 0:
          raise ValueError('''Input must have length that\'s a multiple of 512'''       )

     for pos in range(0   ,	len(_A       )   ,	5_12       ):
          SCREAMING_SNAKE_CASE__					=					bit_string[pos : pos + 5_12]
          SCREAMING_SNAKE_CASE__					=					[]
          for i in range(0   ,	5_12   ,	32       ):
               block_words.append(int(to_little_endian(block[i : i + 32]       )   ,	2       )       )
          yield block_words
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     if i < 0:
          raise ValueError('''Input must be non-negative'''       )

     SCREAMING_SNAKE_CASE__					=					format(_A   ,	'''032b'''       )
     SCREAMING_SNAKE_CASE__					=					''''''
     for c in i_str:
          new_str += "1" if c == "0" else "0"
     return int(_A   ,	2       )
def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     return (a + b) % 2**32
def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     if i < 0:
          raise ValueError('''Input must be non-negative'''       )
     if shift < 0:
          raise ValueError('''Shift must be non-negative'''       )
     return ((i << shift) ^ (i >> (32 - shift))) % 2**32
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					preprocess(_A       )

     SCREAMING_SNAKE_CASE__					=					[int(2**32 * abs(sin(i + 1       )       )       ) for i in range(64       )]

     # Starting states
     SCREAMING_SNAKE_CASE__					=					0x6_7_4_5_2_3_0_1
     SCREAMING_SNAKE_CASE__					=					0xe_f_c_d_a_b_8_9
     SCREAMING_SNAKE_CASE__					=					0x9_8_b_a_d_c_f_e
     SCREAMING_SNAKE_CASE__					=					0x1_0_3_2_5_4_7_6

     SCREAMING_SNAKE_CASE__					=					[
         7,
         12,
         17,
         22,
         7,
         12,
         17,
         22,
         7,
         12,
         17,
         22,
         7,
         12,
         17,
         22,
         5,
         9,
         14,
         20,
         5,
         9,
         14,
         20,
         5,
         9,
         14,
         20,
         5,
         9,
         14,
         20,
         4,
         11,
         16,
         23,
         4,
         11,
         16,
         23,
         4,
         11,
         16,
         23,
         4,
         11,
         16,
         23,
         6,
         10,
         15,
         21,
         6,
         10,
         15,
         21,
         6,
         10,
         15,
         21,
         6,
         10,
         15,
         21,
     ]

     # Process bit string in chunks, each with 16 32-char words
     for block_words in get_block_words(_A       ):
          SCREAMING_SNAKE_CASE__					=					aa
          SCREAMING_SNAKE_CASE__					=					ba
          SCREAMING_SNAKE_CASE__					=					ca
          SCREAMING_SNAKE_CASE__					=					da

          # Hash current chunk
          for i in range(64       ):
               if i <= 15:
                    # f = (b & c) | (not_32(b) & d)     # Alternate definition for f
                    SCREAMING_SNAKE_CASE__					=					d ^ (b & (c ^ d))
                    SCREAMING_SNAKE_CASE__					=					i
               elif i <= 31:
                    # f = (d & b) | (not_32(d) & c)     # Alternate definition for f
                    SCREAMING_SNAKE_CASE__					=					c ^ (d & (b ^ c))
                    SCREAMING_SNAKE_CASE__					=					(5 * i + 1) % 16
               elif i <= 47:
                    SCREAMING_SNAKE_CASE__					=					b ^ c ^ d
                    SCREAMING_SNAKE_CASE__					=					(3 * i + 5) % 16
               else:
                    SCREAMING_SNAKE_CASE__					=					c ^ (b | not_aa(_A       ))
                    SCREAMING_SNAKE_CASE__					=					(7 * i) % 16
               SCREAMING_SNAKE_CASE__					=					(f + a + added_consts[i] + block_words[g]) % 2**32
               SCREAMING_SNAKE_CASE__					=					d
               SCREAMING_SNAKE_CASE__					=					c
               SCREAMING_SNAKE_CASE__					=					b
               SCREAMING_SNAKE_CASE__					=					sum_aa(_A   ,	left_rotate_aa(_A   ,	shift_amounts[i]       )       )

          # Add hashed chunk to running total
          SCREAMING_SNAKE_CASE__					=					sum_aa(_A   ,	_A       )
          SCREAMING_SNAKE_CASE__					=					sum_aa(_A   ,	_A       )
          SCREAMING_SNAKE_CASE__					=					sum_aa(_A   ,	_A       )
          SCREAMING_SNAKE_CASE__					=					sum_aa(_A   ,	_A       )

     SCREAMING_SNAKE_CASE__					=					reformat_hex(_A       ) + reformat_hex(_A       ) + reformat_hex(_A       ) + reformat_hex(_A       )
     return digest


if __name__ == "__main__":
       import doctest

       doctest.testmod()





from dataclasses import dataclass, field
from typing import Optional






@dataclass
class  UpperCAmelCase__	:






  """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_00_00		,  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_50		,  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_00_00		,  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=10_24		,  metadata={"help": "Sequence lengths used for training."} )
  a     =      field(default=1		,  metadata={"help": "Training seed."} )
  a     =      field(
      default=10_24		,  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  UpperCAmelCase__	:






  """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=10_24		,  metadata={"help": "Length of sequences to be evaluated."} )
  a     =      field(default=1		,  metadata={"help": "Random seed used for evaluation."} )






@dataclass
class  UpperCAmelCase__	:






  """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=2_56		,  metadata={"help": "Maximum number of newly generated tokens."} )
  a     =      field(default=0		,  metadata={"help": "Top-k parameter used for generation."} )
  a     =      field(default=0.9_5		,  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=2_00		,  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  UpperCAmelCase__	:






  """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_00_00		,  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=10_00		,  metadata={"help": "Maximum line length in file, otherwise file is filtered."} )
  a     =      field(
      default=1_00		,  metadata={"help": "Maximum mean line length in file, otherwise file is filtered."} )
  a     =      field(
      default=0.2_5		,  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=0.8_5		,  metadata={"help": "Jaccard threshold for near-duplicate samples."} )






@dataclass
class  UpperCAmelCase__	:






  """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_00_00		,  metadata={"help": "Number of examples to train tokenizer on."} )
  a     =      field(
      default=3_27_68		,  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  UpperCAmelCase__	:






  """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  UpperCAmelCase__	:






  """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."} )


