





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

from accelerate.utils.dataclasses import DistributedType





class      lowercase__    :







  def __init__(	self    ,						_lowercase=2    ,						_lowercase=3    ,						_lowercase=64    ,						_lowercase=None						):
       lowerCAmelCase_					:       List[Any]    =		np.random.default_rng(_lowercase						)
       lowerCAmelCase_					:       List[Any]    =		length
       lowerCAmelCase_					:       Tuple    =		rng.normal(size=(length,)						).astype(np.floataa						)
       lowerCAmelCase_					:       str    =		a * self.x + b + rng.normal(scale=0.1    ,						size=(length,)						).astype(np.floataa						)







  def __len__(	self						):
       return self.length







  def __getitem__(	self    ,						_lowercase						):
       return {"x": self.x[i], "y": self.y[i]}





class      lowercase__    (							torch.nn.Module  ):







  def __init__(	self    ,						_lowercase=0    ,						_lowercase=0    ,						_lowercase=False						):
       super().__init__()
       lowerCAmelCase_					:       Dict    =		torch.nn.Parameter(torch.tensor([2, 3]						).float()						)
       lowerCAmelCase_					:       Union[str, Any]    =		torch.nn.Parameter(torch.tensor([2, 3]						).float()						)
       lowerCAmelCase_					:       List[str]    =		True







  def 	UpperCAmelCase__		(	self    ,						_lowercase=None						):
       if self.first_batch:
            print(F'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'						)
            lowerCAmelCase_					:       Dict    =		False
       return x * self.a[0] + self.b[0]





class      lowercase__    (							torch.nn.Module  ):







  def __init__(	self    ,						_lowercase=0    ,						_lowercase=0    ,						_lowercase=False						):
       super().__init__()
       lowerCAmelCase_					:       Dict    =		torch.nn.Parameter(torch.tensor(_lowercase						).float()						)
       lowerCAmelCase_					:       str    =		torch.nn.Parameter(torch.tensor(_lowercase						).float()						)
       lowerCAmelCase_					:       Any    =		True







  def 	UpperCAmelCase__		(	self    ,						_lowercase=None						):
       if self.first_batch:
            print(F'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'						)
            lowerCAmelCase_					:       List[Any]    =		False
       return x * self.a + self.b






def 							_lowerCAmelCase (  _a   :					Dict					,   _a   :					int = 16			)	->			int:
     from datasets import load_dataset
     from transformers import AutoTokenizer

     lowerCAmelCase_					:       int    =		AutoTokenizer.from_pretrained("""bert-base-cased"""			)
     lowerCAmelCase_					:       str    =		{"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
     lowerCAmelCase_					:       int    =		load_dataset("""csv"""					,   data_files=_a			)
     lowerCAmelCase_					:       List[str]    =		datasets["""train"""].unique("""label"""			)

     lowerCAmelCase_					:       List[str]    =		{v: i for i, v in enumerate(_a			)}

     def tokenize_function(_a   :					int			):
          # max_length=None => use the model max length (it's actually the default)
          lowerCAmelCase_					:       str    =		tokenizer(
              examples["""sentence1"""]					,   examples["""sentence2"""]					,   truncation=_a					,   max_length=_a					,   padding="""max_length"""			)
          if "label" in examples:
               lowerCAmelCase_					:       List[str]    =		[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
     lowerCAmelCase_					:       str    =		datasets.map(
         _a					,   batched=_a					,   remove_columns=["""sentence1""", """sentence2""", """label"""]					,   )

     def collate_fn(_a   :					str			):
          # 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(_a					,   padding="""max_length"""					,   max_length=1_28					,   return_tensors="""pt"""			)
          return tokenizer.pad(_a					,   padding="""longest"""					,   return_tensors="""pt"""			)

     # Instantiate dataloaders.
     lowerCAmelCase_					:       Optional[int]    =		DataLoader(tokenized_datasets["""train"""]					,   shuffle=_a					,   collate_fn=_a					,   batch_size=2			)
     lowerCAmelCase_					:       Optional[int]    =		DataLoader(tokenized_datasets["""validation"""]					,   shuffle=_a					,   collate_fn=_a					,   batch_size=1			)

     return train_dataloader, eval_dataloader







from collections.abc import Generator
from math import sin






def 							_lowerCAmelCase (  _a   :					bytes			)	->			bytes:
     if len(_a			) != 32:
          raise ValueError("""Input must be of length 32"""			)

     lowerCAmelCase_					:       Any    =		B""""""
     for i in [3, 2, 1, 0]:
          little_endian += string_aa[8 * i : 8 * i + 8]
     return little_endian






def 							_lowerCAmelCase (  _a   :					int			)	->			bytes:
     if i < 0:
          raise ValueError("""Input must be non-negative"""			)

     lowerCAmelCase_					:       Tuple    =		format(_a					,   """08x"""			)[-8:]
     lowerCAmelCase_					:       Any    =		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 							_lowerCAmelCase (  _a   :					bytes			)	->			bytes:
     lowerCAmelCase_					:       Tuple    =		B""""""
     for char in message:
          bit_string += format(_a					,   """08b"""			).encode("""utf-8"""			)
     lowerCAmelCase_					:       Dict    =		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 							_lowerCAmelCase (  _a   :					bytes			)	->			Generator[list[int], None, None]:
     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			):
          lowerCAmelCase_					:       int    =		bit_string[pos : pos + 5_12]
          lowerCAmelCase_					:       Any    =		[]
          for i in range(0					,   5_12					,   32			):
               block_words.append(int(to_little_endian(block[i : i + 32]			)					,   2			)			)
          yield block_words






def 							_lowerCAmelCase (  _a   :					int			)	->			int:
     if i < 0:
          raise ValueError("""Input must be non-negative"""			)

     lowerCAmelCase_					:       List[str]    =		format(_a					,   """032b"""			)
     lowerCAmelCase_					:       Optional[int]    =		""""""
     for c in i_str:
          new_str += "1" if c == "0" else "0"
     return int(_a					,   2			)






def 							_lowerCAmelCase (  _a   :					int					,   _a   :					int			)	->			int:
     return (a + b) % 2**32






def 							_lowerCAmelCase (  _a   :					int					,   _a   :					int			)	->			int:
     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 							_lowerCAmelCase (  _a   :					bytes			)	->			bytes:
     lowerCAmelCase_					:       Union[str, Any]    =		preprocess(_a			)

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

     # Starting states
     lowerCAmelCase_					:       Tuple    =		0X67452301
     lowerCAmelCase_					:       Optional[int]    =		0XEFCDAB89
     lowerCAmelCase_					:       Tuple    =		0X98BADCFE
     lowerCAmelCase_					:       Tuple    =		0X10325476

     lowerCAmelCase_					:       Dict    =		[
         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			):
          lowerCAmelCase_					:       int    =		aa
          lowerCAmelCase_					:       Any    =		ba
          lowerCAmelCase_					:       List[str]    =		ca
          lowerCAmelCase_					:       Optional[Any]    =		da

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

          # Add hashed chunk to running total
          lowerCAmelCase_					:       Optional[int]    =		sum_aa(_a					,   _a			)
          lowerCAmelCase_					:       Optional[int]    =		sum_aa(_a					,   _a			)
          lowerCAmelCase_					:       Dict    =		sum_aa(_a					,   _a			)
          lowerCAmelCase_					:       Tuple    =		sum_aa(_a					,   _a			)

     lowerCAmelCase_					:       int    =		reformat_hex(_a			) + reformat_hex(_a			) + reformat_hex(_a			) + reformat_hex(_a			)
     return digest


if __name__ == "__main__":
      import doctest

      doctest.testmod()
