


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

from accelerate.utils.dataclasses import DistributedType

class 		__a				:
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE=2      ,   _SCREAMING_SNAKE_CASE=3      ,   _SCREAMING_SNAKE_CASE=64      ,   _SCREAMING_SNAKE_CASE=None						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      np.random.default_rng(_SCREAMING_SNAKE_CASE						)
									_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						)			->	List[Any]:




									"""simple docstring"""





									return self.length






		def __getitem__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[Any]:




									"""simple docstring"""





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



class 		__a				(    torch.nn.Module      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE=0      ,   _SCREAMING_SNAKE_CASE=0      ,   _SCREAMING_SNAKE_CASE=False						)			->	str:




									"""simple docstring"""





									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      ,   _SCREAMING_SNAKE_CASE=None						)			->	int:




									"""simple docstring"""





									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 		__a				(    torch.nn.Module      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE=0      ,   _SCREAMING_SNAKE_CASE=0      ,   _SCREAMING_SNAKE_CASE=False						)			->	Any:




									"""simple docstring"""





									super().__init__()
									_UpperCAmelCase					=      torch.nn.Parameter(torch.tensor(_SCREAMING_SNAKE_CASE						).float()						)
									_UpperCAmelCase					=      torch.nn.Parameter(torch.tensor(_SCREAMING_SNAKE_CASE						).float()						)
									_UpperCAmelCase					=      True






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE=None						)			->	List[str]:




									"""simple docstring"""





									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 	lowerCAmelCase__			(      a__:		int      ,  a__:		int = 1_6	)  ->		List[Any]:


							'''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=a__	)
							_UpperCAmelCase					=      datasets['train'].unique('label'	)

							_UpperCAmelCase					=      {v: i for i, v in enumerate(a__	)}

							def tokenize_function(a__:		str	):
														# max_length=None => use the model max length (it's actually the default)
														_UpperCAmelCase					=      tokenizer(
														    examples['sentence1']      ,  examples['sentence2']      ,  truncation=a__      ,  max_length=a__      ,  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(
							    a__      ,  batched=a__      ,  remove_columns=['sentence1', 'sentence2', 'label']      ,  )

							def collate_fn(a__:		Union[str, 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(a__      ,  padding='max_length'      ,  max_length=1_2_8      ,  return_tensors='pt'	)
														return tokenizer.pad(a__      ,  padding='longest'      ,  return_tensors='pt'	)

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

							return train_dataloader, eval_dataloader




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


lowerCAmelCase__  :List[str]             =							logging.get_logger(__name__)

class 		__a				(    UpperCAmelCase      ):
		_a     :  Optional[int]           =     ['pixel_values']
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = PIL.Image.BICUBIC      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = 1 / 255      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	None:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      size if size is not None else {'height': 256, 'width': 256}
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      crop_size if crop_size is not None else {'height': 224, 'width': 224}
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE      ,   param_name='crop_size'						)

									_UpperCAmelCase					=      do_resize
									_UpperCAmelCase					=      size
									_UpperCAmelCase					=      resample
									_UpperCAmelCase					=      do_center_crop
									_UpperCAmelCase					=      crop_size
									_UpperCAmelCase					=      do_rescale
									_UpperCAmelCase					=      rescale_factor
									_UpperCAmelCase					=      do_normalize
									_UpperCAmelCase					=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									_UpperCAmelCase					=      image_std if image_std is not None else IMAGENET_STANDARD_STD
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = PIL.Image.BICUBIC      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	np.ndarray:




									"""simple docstring"""





									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE						)
									if "height" not in size or "width" not in size:
																raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
									return resize(
									    _SCREAMING_SNAKE_CASE      ,   size=(size['height'], size['width'])      ,   resample=_SCREAMING_SNAKE_CASE      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	np.ndarray:




									"""simple docstring"""





									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE						)
									if "height" not in size or "width" not in size:
																raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
									return center_crop(_SCREAMING_SNAKE_CASE      ,   size=(size['height'], size['width'])      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	str:




									"""simple docstring"""





									return rescale(_SCREAMING_SNAKE_CASE      ,   scale=_SCREAMING_SNAKE_CASE      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	np.ndarray:




									"""simple docstring"""





									return normalize(_SCREAMING_SNAKE_CASE      ,   mean=_SCREAMING_SNAKE_CASE      ,   std=_SCREAMING_SNAKE_CASE      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = ChannelDimension.FIRST      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	PIL.Image.Image:




									"""simple docstring"""





									_UpperCAmelCase					=      do_resize if do_resize is not None else self.do_resize
									_UpperCAmelCase					=      resample if resample is not None else self.resample
									_UpperCAmelCase					=      do_center_crop if do_center_crop is not None else self.do_center_crop
									_UpperCAmelCase					=      do_rescale if do_rescale is not None else self.do_rescale
									_UpperCAmelCase					=      rescale_factor if rescale_factor is not None else self.rescale_factor
									_UpperCAmelCase					=      do_normalize if do_normalize is not None else self.do_normalize
									_UpperCAmelCase					=      image_mean if image_mean is not None else self.image_mean
									_UpperCAmelCase					=      image_std if image_std is not None else self.image_std

									_UpperCAmelCase					=      size if size is not None else self.size
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      crop_size if crop_size is not None else self.crop_size
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE      ,   param_name='crop_size'						)

									_UpperCAmelCase					=      make_list_of_images(_SCREAMING_SNAKE_CASE						)

									if not valid_images(_SCREAMING_SNAKE_CASE						):
																raise ValueError(
																    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																    'torch.Tensor, tf.Tensor or jax.ndarray.'						)

									if do_resize and size is None or resample is None:
																raise ValueError('Size and resample must be specified if do_resize is True.'						)

									if do_center_crop and crop_size is None:
																raise ValueError('Crop size must be specified if do_center_crop is True.'						)

									if do_rescale and rescale_factor is None:
																raise ValueError('Rescale factor must be specified if do_rescale is True.'						)

									if do_normalize and (image_mean is None or image_std is None):
																raise ValueError('Image mean and std must be specified if do_normalize is True.'						)

									# All transformations expect numpy arrays.
									_UpperCAmelCase					=      [to_numpy_array(_SCREAMING_SNAKE_CASE						) for image in images]

									if do_resize:
																_UpperCAmelCase					=      [self.resize(image=_SCREAMING_SNAKE_CASE      ,   size=_SCREAMING_SNAKE_CASE      ,   resample=_SCREAMING_SNAKE_CASE						) for image in images]

									if do_center_crop:
																_UpperCAmelCase					=      [self.center_crop(image=_SCREAMING_SNAKE_CASE      ,   size=_SCREAMING_SNAKE_CASE						) for image in images]

									if do_rescale:
																_UpperCAmelCase					=      [self.rescale(image=_SCREAMING_SNAKE_CASE      ,   scale=_SCREAMING_SNAKE_CASE						) for image in images]

									if do_normalize:
																_UpperCAmelCase					=      [self.normalize(image=_SCREAMING_SNAKE_CASE      ,   mean=_SCREAMING_SNAKE_CASE      ,   std=_SCREAMING_SNAKE_CASE						) for image in images]

									_UpperCAmelCase					=      [to_channel_dimension_format(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						) for image in images]

									_UpperCAmelCase					=      {'pixel_values': images}
									return BatchFeature(data=_SCREAMING_SNAKE_CASE      ,   tensor_type=_SCREAMING_SNAKE_CASE						)
