





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

from accelerate.utils.dataclasses import DistributedType



class 		UpperCamelCase__  :
   def __init__(self			: str ,    snake_case_			: Dict=2 ,    snake_case_			: Optional[Any]=3 ,    snake_case_			: Optional[int]=6_4 ,    snake_case_			: Optional[int]=None       ):
    __a			:  List[Any]		    =  np.random.default_rng(A__       )
    __a			:  Optional[int]		    =  length
    __a			:  Tuple		    =  rng.normal(size=(length,)       ).astype(np.floataa       )
    __a			:  int		    =  a * self.x + b + rng.normal(scale=0.1 ,    size=(length,)       ).astype(np.floataa       )
   def __len__(self			: Optional[Any]       ):
    return self.length







   def __getitem__(self			: int ,    snake_case_			: List[Any]       ):
    return {"x": self.x[i], "y": self.y[i]}





class 		UpperCamelCase__  (	torch.nn.Module   ):
   def __init__(self			: Any ,    snake_case_			: Optional[int]=0 ,    snake_case_			: Any=0 ,    snake_case_			: int=False       ):
    super().__init__()
    __a			:  int		    =  torch.nn.Parameter(torch.tensor([2, 3]       ).float()       )
    __a			:  Any		    =  torch.nn.Parameter(torch.tensor([2, 3]       ).float()       )
    __a			:  Tuple		    =  True







   def 				lowerCAmelCase      (self			: Union[str, Any] ,    snake_case_			: str=None       ):
    if self.first_batch:
     print(f"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"       )
     __a			:  Optional[int]		    =  False
    return x * self.a[0] + self.b[0]





class 		UpperCamelCase__  (	torch.nn.Module   ):
   def __init__(self			: Union[str, Any] ,    snake_case_			: Tuple=0 ,    snake_case_			: Any=0 ,    snake_case_			: Union[str, Any]=False       ):
    super().__init__()
    __a			:  Dict		    =  torch.nn.Parameter(torch.tensor(A__       ).float()       )
    __a			:  int		    =  torch.nn.Parameter(torch.tensor(A__       ).float()       )
    __a			:  List[str]		    =  True







   def 				lowerCAmelCase      (self			: Dict ,    snake_case_			: Union[str, Any]=None       ):
    if self.first_batch:
     print(f"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"       )
     __a			:  Tuple		    =  False
    return x * self.a + self.b





def        __UpperCamelCase					(    lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    Optional[Any] = 1_6  ):
 from datasets import load_dataset
 from transformers import AutoTokenizer

 __a			:  Union[str, Any]		    =  AutoTokenizer.from_pretrained('''bert-base-cased'''  )
 __a			:  List[Any]		    =  {'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
 __a			:  int		    =  load_dataset('''csv'''  ,       data_files=lowerCAmelCase__  )
 __a			:  Dict		    =  datasets['''train'''].unique('''label'''  )

 __a			:  Dict		    =  {v: i for i, v in enumerate(lowerCAmelCase__  )}

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

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

 # Instantiate dataloaders.
 __a			:  Union[str, Any]		    =  DataLoader(tokenized_datasets['''train''']  ,       shuffle=lowerCAmelCase__  ,       collate_fn=lowerCAmelCase__  ,       batch_size=2  )
 __a			:  int		    =  DataLoader(tokenized_datasets['''validation''']  ,       shuffle=lowerCAmelCase__  ,       collate_fn=lowerCAmelCase__  ,       batch_size=1  )

 return train_dataloader, eval_dataloader


import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowercase__          =50000
lowercase__          =5000

lowercase__       ,      lowercase__          =os.path.split(__file__)
lowercase__          =os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))




@get_duration
def        __UpperCamelCase					(    lowerCAmelCase__							:    datasets.Dataset  ,       lowerCAmelCase__							:    List[str]  ):
 for i in range(lowerCAmelCase__  ):
  __a			:  str		    =  dataset[i]




@get_duration
def        __UpperCamelCase					(    lowerCAmelCase__							:    datasets.Dataset  ,       lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    Tuple  ):
 for i in range(0  ,       len(lowerCAmelCase__  )  ,       lowerCAmelCase__  ):
  __a			:  Optional[int]		    =  dataset[i : i + batch_size]




@get_duration
def        __UpperCamelCase					(    lowerCAmelCase__							:    datasets.Dataset  ,       lowerCAmelCase__							:    Union[str, Any]  ,       lowerCAmelCase__							:    str  ):
 with dataset.formatted_as(type=lowerCAmelCase__  ):
  for i in range(lowerCAmelCase__  ):
   __a			:  Dict		    =  dataset[i]




@get_duration
def        __UpperCamelCase					(    lowerCAmelCase__							:    datasets.Dataset  ,       lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    int  ):
 with dataset.formatted_as(type=lowerCAmelCase__  ):
  for i in range(0  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ):
   __a			:  int		    =  dataset[i : i + batch_size]




def        __UpperCamelCase					(    ):
 __a			:  Any		    =  {'''num examples''': SPEED_TEST_N_EXAMPLES}
 __a			:  List[Any]		    =  [
     (read, {'''length''': SMALL_TEST}),
     (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
     (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_0}),
     (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_0_0}),
     (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_0_0_0}),
     (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
     (read_formatted, {'''type''': '''pandas''', '''length''': SMALL_TEST}),
     (read_formatted, {'''type''': '''torch''', '''length''': SMALL_TEST}),
     (read_formatted, {'''type''': '''tensorflow''', '''length''': SMALL_TEST}),
     (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1_0}),
     (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1_0_0_0}),
 ]

 __a			:  Union[str, Any]		    =  [
     (read, {'''length''': SMALL_TEST}),
     (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
     (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_0}),
     (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_0_0}),
     (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_0_0_0}),
     (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
     (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1_0}),
     (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1_0_0_0}),
 ]
 with tempfile.TemporaryDirectory() as tmp_dir:
  print('''generating dataset'''  )
  __a			:  Optional[Any]		    =  datasets.Features(
      {'''list''': datasets.Sequence(datasets.Value('''float32'''  )  ), '''numbers''': datasets.Value('''float32'''  )}  )
  __a			:  Optional[int]		    =  generate_example_dataset(
      os.path.join(lowerCAmelCase__  ,       '''dataset.arrow'''  )  ,       lowerCAmelCase__  ,       num_examples=lowerCAmelCase__  ,       seq_shapes={'''list''': (1_0_0,)}  ,       )
  print('''first set of iterations'''  )
  for func, kwargs in functions:
   print(func.__name__  ,       str(lowerCAmelCase__  )  )
   __a			:  str		    =  func(lowerCAmelCase__  ,       **lowerCAmelCase__  )

  print('''shuffling dataset'''  )
  __a			:  int		    =  dataset.shuffle()
  print('''Second set of iterations (after shuffling'''  )
  for func, kwargs in functions_shuffled:
   print('''shuffled '''  ,       func.__name__  ,       str(lowerCAmelCase__  )  )
   __a			:  List[Any]		    =  func(
       lowerCAmelCase__  ,       **lowerCAmelCase__  )

 with open(lowerCAmelCase__  ,       '''wb'''  ) as f:
  f.write(json.dumps(lowerCAmelCase__  ).encode('''utf-8'''  )  )


if __name__ == "__main__":  # useful to run the profiler
      benchmark_iterating()
