





'''simple docstring'''






import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType







class 		UpperCAmelCase   :


  """simple docstring"""







  def __init__(				self    :					Optional[Any]							,	UpperCamelCase__    :					int=2							,	UpperCamelCase__    :					Optional[Any]=3							,	UpperCamelCase__    :					Any=64							,	UpperCamelCase__    :					Optional[int]=None  )     ->						Any:
    _UpperCamelCase       			=np.random.default_rng(UpperCamelCase__  )
    _UpperCamelCase       			=length
    _UpperCamelCase       			=rng.normal(size=(length,)  ).astype(np.floataa  )
    _UpperCamelCase       			=a * self.x + b + rng.normal(scale=0.1							,	size=(length,)  ).astype(np.floataa  )


  def __len__(				self    :					Optional[int]  )     ->						List[Any]:
    return self.length




  def __getitem__(				self    :					Optional[int]							,	UpperCamelCase__    :					Tuple  )     ->						Optional[Any]:
    return {"x": self.x[i], "y": self.y[i]}







class 		UpperCAmelCase   (				torch.nn.Module):


  """simple docstring"""







  def __init__(				self    :					Optional[int]							,	UpperCamelCase__    :					Optional[int]=0							,	UpperCamelCase__    :					List[str]=0							,	UpperCamelCase__    :					str=False  )     ->						List[str]:
    super().__init__()
    _UpperCamelCase       			=torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
    _UpperCamelCase       			=torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
    _UpperCamelCase       			=True




  def   UpperCamelCase__      (				self    :					List[str]							,	UpperCamelCase__    :					Union[str, Any]=None  )     ->						Any:
    if self.first_batch:
      print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''  )
      _UpperCamelCase       			=False
    return x * self.a[0] + self.b[0]







class 		UpperCAmelCase   (				torch.nn.Module):


  """simple docstring"""







  def __init__(				self    :					Optional[Any]							,	UpperCamelCase__    :					List[str]=0							,	UpperCamelCase__    :					Tuple=0							,	UpperCamelCase__    :					Union[str, Any]=False  )     ->						Optional[int]:
    super().__init__()
    _UpperCamelCase       			=torch.nn.Parameter(torch.tensor(UpperCamelCase__  ).float()  )
    _UpperCamelCase       			=torch.nn.Parameter(torch.tensor(UpperCamelCase__  ).float()  )
    _UpperCamelCase       			=True




  def   UpperCamelCase__      (				self    :					Tuple							,	UpperCamelCase__    :					Dict=None  )     ->						Any:
    if self.first_batch:
      print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''  )
      _UpperCamelCase       			=False
    return x * self.a + self.b




def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE = 16   ):


  """simple docstring"""

  from datasets import load_dataset
  from transformers import AutoTokenizer

  _UpperCamelCase       			=AutoTokenizer.from_pretrained('''bert-base-cased'''   )
  _UpperCamelCase       			={'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
  _UpperCamelCase       			=load_dataset('''csv'''  ,      data_files=__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=datasets['''train'''].unique('''label'''   )

  _UpperCamelCase       			={v: i for i, v in enumerate(__SCREAMING_SNAKE_CASE   )}

  def tokenize_function(__SCREAMING_SNAKE_CASE   ):
    # max_length=None => use the model max length (it's actually the default)
    _UpperCamelCase       			=tokenizer(
        examples['''sentence1''']  ,      examples['''sentence2''']  ,      truncation=__SCREAMING_SNAKE_CASE  ,      max_length=__SCREAMING_SNAKE_CASE  ,      padding='''max_length'''   )
    if "label" in examples:
      _UpperCamelCase       			=[label_to_id[l] for l in examples['''label''']]
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  _UpperCamelCase       			=datasets.map(
      __SCREAMING_SNAKE_CASE  ,      batched=__SCREAMING_SNAKE_CASE  ,      remove_columns=['''sentence1''', '''sentence2''', '''label''']  ,      )

  def collate_fn(__SCREAMING_SNAKE_CASE   ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    if accelerator.distributed_type == DistributedType.TPU:
      return tokenizer.pad(__SCREAMING_SNAKE_CASE  ,      padding='''max_length'''  ,      max_length=128  ,      return_tensors='''pt'''   )
    return tokenizer.pad(__SCREAMING_SNAKE_CASE  ,      padding='''longest'''  ,      return_tensors='''pt'''   )

  # Instantiate dataloaders.
  _UpperCamelCase       			=DataLoader(tokenized_datasets['''train''']  ,      shuffle=__SCREAMING_SNAKE_CASE  ,      collate_fn=__SCREAMING_SNAKE_CASE  ,      batch_size=2   )
  _UpperCamelCase       			=DataLoader(tokenized_datasets['''validation''']  ,      shuffle=__SCREAMING_SNAKE_CASE  ,      collate_fn=__SCREAMING_SNAKE_CASE  ,      batch_size=1   )

  return train_dataloader, eval_dataloader













'''simple docstring'''






from collections.abc import Generator
from math import sin


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

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

  _UpperCamelCase       			=b''''''
  for i in [3, 2, 1, 0]:
    little_endian += string_aa[8 * i : 8 * i + 8]
  return little_endian


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

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

  _UpperCamelCase       			=format(__SCREAMING_SNAKE_CASE  ,      '''08x'''   )[-8:]
  _UpperCamelCase       			=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 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

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

  # Pad bit_string to a multiple of 512 chars
  bit_string += b"1"
  while len(__SCREAMING_SNAKE_CASE   ) % 512 != 448:
    bit_string += b"0"
  bit_string += to_little_endian(start_len[32:]   ) + to_little_endian(start_len[:32]   )

  return bit_string


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

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

  for pos in range(0  ,      len(__SCREAMING_SNAKE_CASE   )  ,      512   ):
    _UpperCamelCase       			=bit_string[pos : pos + 512]
    _UpperCamelCase       			=[]
    for i in range(0  ,      512  ,      32   ):
      block_words.append(int(to_little_endian(block[i : i + 32]   )  ,      2   )   )
    yield block_words


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

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

  _UpperCamelCase       			=format(__SCREAMING_SNAKE_CASE  ,      '''032b'''   )
  _UpperCamelCase       			=''''''
  for c in i_str:
    new_str += "1" if c == "0" else "0"
  return int(__SCREAMING_SNAKE_CASE  ,      2   )


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  return (a + b) % 2**32


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """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 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=preprocess(__SCREAMING_SNAKE_CASE   )

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

  # Starting states
  _UpperCamelCase       			=0X6745_2301
  _UpperCamelCase       			=0Xefcd_ab89
  _UpperCamelCase       			=0X98ba_dcfe
  _UpperCamelCase       			=0X1032_5476

  _UpperCamelCase       			=[
      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(__SCREAMING_SNAKE_CASE   ):
    _UpperCamelCase       			=aa
    _UpperCamelCase       			=ba
    _UpperCamelCase       			=ca
    _UpperCamelCase       			=da

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

    # Add hashed chunk to running total
    _UpperCamelCase       			=sum_aa(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
    _UpperCamelCase       			=sum_aa(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
    _UpperCamelCase       			=sum_aa(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
    _UpperCamelCase       			=sum_aa(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )

  _UpperCamelCase       			=reformat_hex(__SCREAMING_SNAKE_CASE   ) + reformat_hex(__SCREAMING_SNAKE_CASE   ) + reformat_hex(__SCREAMING_SNAKE_CASE   ) + reformat_hex(__SCREAMING_SNAKE_CASE   )
  return digest


if __name__ == "__main__":
      import doctest

      doctest.testmod()






