






"""simple docstring"""


from manim import *


class 					snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     __A									= Rectangle(height=0.5,       width=0.5  )
     __A									= Rectangle(height=0.46,       width=0.46  ).set_stroke(width=0  )

     __A									= [mem.copy() for i in range(6  )]
     __A									= [mem.copy() for i in range(6  )]
     __A									= VGroup(*_lowerCamelCase  ).arrange(_lowerCamelCase,       buff=0  )
     __A									= VGroup(*_lowerCamelCase  ).arrange(_lowerCamelCase,       buff=0  )
     __A									= VGroup(_lowerCamelCase,       _lowerCamelCase  ).arrange(_lowerCamelCase,       buff=0  )
     __A									= Text('''CPU''',       font_size=24  )
     __A									= Group(_lowerCamelCase,       _lowerCamelCase  ).arrange(_lowerCamelCase,       buff=0.5,       aligned_edge=_lowerCamelCase  )
     cpu.move_to([-2.5, -0.5, 0]  )
     self.add(_lowerCamelCase  )

     __A									= [mem.copy() for i in range(1  )]
     __A									= VGroup(*_lowerCamelCase  ).arrange(_lowerCamelCase,       buff=0  )
     __A									= Text('''GPU''',       font_size=24  )
     __A									= Group(_lowerCamelCase,       _lowerCamelCase  ).arrange(_lowerCamelCase,       buff=0.5,       aligned_edge=_lowerCamelCase  )
     gpu.align_to(_lowerCamelCase,       _lowerCamelCase  )
     gpu.set_x(gpu.get_x() - 1  )

     self.add(_lowerCamelCase  )

     __A									= [mem.copy() for i in range(6  )]
     __A									= VGroup(*_lowerCamelCase  ).arrange(_lowerCamelCase,       buff=0  )

     __A									= Text('''Model''',       font_size=24  )
     __A									= Group(_lowerCamelCase,       _lowerCamelCase  ).arrange(_lowerCamelCase,       buff=0.5,       aligned_edge=_lowerCamelCase  )
     model.move_to([3, -1.0, 0]  )

     self.play(
         Create(_lowerCamelCase,       run_time=1  ),       Create(_lowerCamelCase,       run_time=1  ),       Create(_lowerCamelCase,       run_time=1  ),       )

     __A									= MarkupText(
         f'First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.',       font_size=24,       )

     __A									= Square(side_length=2.2  )
     key.move_to([-5, 2, 0]  )

     __A									= MarkupText(
         f'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model',       font_size=18,       )

     key_text.move_to([-5, 2.4, 0]  )

     step_a.move_to([2, 2, 0]  )
     self.play(Write(_lowerCamelCase,       run_time=2.5  ),       Write(_lowerCamelCase  ),       Write(_lowerCamelCase  )  )

     self.add(_lowerCamelCase  )

     __A									= []
     __A									= []
     __A									= []
     for i, rect in enumerate(_lowerCamelCase  ):
       __A									= Rectangle(height=0.46,       width=0.46  ).set_stroke(width=0.0  ).set_fill(_lowerCamelCase,       opacity=0.7  )
       cpu_target.move_to(_lowerCamelCase  )
       cpu_target.generate_target()
       __A									= 0.46 / 4
       __A									= 0.46 / 3

       if i == 0:
         cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT  ),       buff=0.02,       direction=_lowerCamelCase  )
         cpu_target.target.set_x(cpu_target.target.get_x() + 0.1  )
       elif i == 3:
         cpu_target.target.next_to(cpu_targs[0].target,       direction=_lowerCamelCase,       buff=0.0  )
       else:
         cpu_target.target.next_to(cpu_targs[i - 1].target,       direction=_lowerCamelCase,       buff=0.0  )
       cpu_targs.append(_lowerCamelCase  )

       first_animations.append(rect.animate(run_time=0.5  ).set_stroke(_lowerCamelCase  )  )
       second_animations.append(MoveToTarget(_lowerCamelCase,       run_time=1.5  )  )

     self.play(*_lowerCamelCase  )
     self.play(*_lowerCamelCase  )

     self.wait()












"""simple docstring"""


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

from accelerate.utils.dataclasses import DistributedType


class 					snake_case		:


   '''simple docstring'''

   def __init__(	self :     Optional[int],       _lowerCamelCase :     Optional[int]=2,       _lowerCamelCase :     Optional[int]=3,       _lowerCamelCase :     int=64,       _lowerCamelCase :     List[str]=None  ):

     '''simple docstring'''
     __A									= np.random.default_rng(_lowerCamelCase  )
     __A									= length
     __A									= rng.normal(size=(length,)  ).astype(np.floataa  )
     __A									= a * self.x + b + rng.normal(scale=0.1,       size=(length,)  ).astype(np.floataa  )


   def __len__(	self :     str  ):

     '''simple docstring'''
     return self.length






   def __getitem__(	self :     Dict,       _lowerCamelCase :     Optional[int]  ):

     '''simple docstring'''
     return {"x": self.x[i], "y": self.y[i]}





class 					snake_case		(							torch.nn.Module						):


   '''simple docstring'''

   def __init__(	self :     Optional[Any],       _lowerCamelCase :     Tuple=0,       _lowerCamelCase :     Any=0,       _lowerCamelCase :     Optional[Any]=False  ):

     '''simple docstring'''
     super().__init__()
     __A									= torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
     __A									= torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
     __A									= True






   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str],       _lowerCamelCase :     Optional[Any]=None  ):

     '''simple docstring'''
     if self.first_batch:
       print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'  )
       __A									= False
     return x * self.a[0] + self.b[0]





class 					snake_case		(							torch.nn.Module						):


   '''simple docstring'''

   def __init__(	self :     str,       _lowerCamelCase :     Optional[Any]=0,       _lowerCamelCase :     Any=0,       _lowerCamelCase :     List[Any]=False  ):

     '''simple docstring'''
     super().__init__()
     __A									= torch.nn.Parameter(torch.tensor(_lowerCamelCase  ).float()  )
     __A									= torch.nn.Parameter(torch.tensor(_lowerCamelCase  ).float()  )
     __A									= True






   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any],       _lowerCamelCase :     List[str]=None  ):

     '''simple docstring'''
     if self.first_batch:
       print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'  )
       __A									= False
     return x * self.a + self.b





def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase = 1_6 ):




  """simple docstring"""







  from datasets import load_dataset
  from transformers import AutoTokenizer

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

  __A									= {v: i for i, v in enumerate(__UpperCamelCase )}

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

  def collate_fn(__UpperCamelCase ):
    # 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(__UpperCamelCase		,       padding='''max_length'''		,       max_length=1_2_8		,       return_tensors='''pt''' )
    return tokenizer.pad(__UpperCamelCase		,       padding='''longest'''		,       return_tensors='''pt''' )

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

  return train_dataloader, eval_dataloader




