'''simple docstring'''

from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
 import PIL

if is_torch_available():
 import torch


_A			:      Optional[int]					      =				logging.get_logger(__name__)







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Tuple      	=					["""pixel_values"""]
 def __init__(      self   :	Union[str, Any]      ,       SCREAMING_SNAKE_CASE__   :	bool = True      ,       SCREAMING_SNAKE_CASE__   :	Dict[str, int] = None      ,       SCREAMING_SNAKE_CASE__   :	PILImageResampling = PILImageResampling.BILINEAR      ,       SCREAMING_SNAKE_CASE__   :	bool = True      ,       SCREAMING_SNAKE_CASE__   :	Union[int, float] = 1 / 2_55      ,       SCREAMING_SNAKE_CASE__   :	bool = True      ,       SCREAMING_SNAKE_CASE__   :	Dict[str, int] = None      ,       SCREAMING_SNAKE_CASE__   :	bool = True      ,       **SCREAMING_SNAKE_CASE__   :	Tuple      ,       )  ->    None:
  super().__init__(**SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						size if size is not None else {"""shortest_edge""": 2_24}
  __lowerCAmelCase											=						get_size_dict(SCREAMING_SNAKE_CASE__      ,       default_to_square=SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						crop_size if crop_size is not None else {"""height""": 2_56, """width""": 2_56}
  __lowerCAmelCase											=						get_size_dict(SCREAMING_SNAKE_CASE__      ,       param_name="""crop_size"""							)

  __lowerCAmelCase											=						do_resize
  __lowerCAmelCase											=						size
  __lowerCAmelCase											=						resample
  __lowerCAmelCase											=						do_rescale
  __lowerCAmelCase											=						rescale_factor
  __lowerCAmelCase											=						do_center_crop
  __lowerCAmelCase											=						crop_size
  __lowerCAmelCase											=						do_flip_channel_order
 def a					(      self   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	np.ndarray      ,       SCREAMING_SNAKE_CASE__   :	Dict[str, int]      ,       SCREAMING_SNAKE_CASE__   :	PILImageResampling = PIL.Image.BILINEAR      ,       SCREAMING_SNAKE_CASE__   :	Optional[Union[str, ChannelDimension]] = None      ,       **SCREAMING_SNAKE_CASE__   :	Tuple      ,       )  ->    np.ndarray:
  __lowerCAmelCase											=						get_size_dict(SCREAMING_SNAKE_CASE__      ,       default_to_square=SCREAMING_SNAKE_CASE__							)
  if "shortest_edge" not in size:
   raise ValueError(f"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"""							)
  __lowerCAmelCase											=						get_resize_output_image_size(SCREAMING_SNAKE_CASE__      ,       size=size["""shortest_edge"""]      ,       default_to_square=SCREAMING_SNAKE_CASE__							)
  return resize(SCREAMING_SNAKE_CASE__      ,       size=SCREAMING_SNAKE_CASE__      ,       resample=SCREAMING_SNAKE_CASE__      ,       data_format=SCREAMING_SNAKE_CASE__      ,       **SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	np.ndarray      ,       SCREAMING_SNAKE_CASE__   :	Dict[str, int]      ,       SCREAMING_SNAKE_CASE__   :	Optional[Union[str, ChannelDimension]] = None      ,       **SCREAMING_SNAKE_CASE__   :	Union[str, Any]      ,       )  ->    np.ndarray:
  __lowerCAmelCase											=						get_size_dict(SCREAMING_SNAKE_CASE__							)
  if "height" not in size or "width" not in size:
   raise ValueError(f"""The `size` dictionary must contain the 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 a					(      self   :	Union[str, Any]      ,       SCREAMING_SNAKE_CASE__   :	np.ndarray      ,       SCREAMING_SNAKE_CASE__   :	Union[int, float]      ,       SCREAMING_SNAKE_CASE__   :	Optional[Union[str, ChannelDimension]] = None      ,       **SCREAMING_SNAKE_CASE__   :	List[Any]      ,       )  ->    Optional[Any]:
  return rescale(SCREAMING_SNAKE_CASE__      ,       scale=SCREAMING_SNAKE_CASE__      ,       data_format=SCREAMING_SNAKE_CASE__      ,       **SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	Union[str, Any]      ,       SCREAMING_SNAKE_CASE__   :	np.ndarray      ,       SCREAMING_SNAKE_CASE__   :	Optional[Union[str, ChannelDimension]] = None							)  ->    np.ndarray:
  return flip_channel_order(SCREAMING_SNAKE_CASE__      ,       data_format=SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	ImageInput      ,       SCREAMING_SNAKE_CASE__   :	bool = None      ,       SCREAMING_SNAKE_CASE__   :	Dict[str, int] = None      ,       SCREAMING_SNAKE_CASE__   :	PILImageResampling = None      ,       SCREAMING_SNAKE_CASE__   :	bool = None      ,       SCREAMING_SNAKE_CASE__   :	float = None      ,       SCREAMING_SNAKE_CASE__   :	bool = None      ,       SCREAMING_SNAKE_CASE__   :	Dict[str, int] = None      ,       SCREAMING_SNAKE_CASE__   :	bool = None      ,       SCREAMING_SNAKE_CASE__   :	Optional[Union[str, TensorType]] = None      ,       SCREAMING_SNAKE_CASE__   :	ChannelDimension = ChannelDimension.FIRST      ,       **SCREAMING_SNAKE_CASE__   :	List[str]      ,       )  ->    PIL.Image.Image:
  __lowerCAmelCase											=						do_resize if do_resize is not None else self.do_resize
  __lowerCAmelCase											=						resample if resample is not None else self.resample
  __lowerCAmelCase											=						do_rescale if do_rescale is not None else self.do_rescale
  __lowerCAmelCase											=						rescale_factor if rescale_factor is not None else self.rescale_factor
  __lowerCAmelCase											=						do_center_crop if do_center_crop is not None else self.do_center_crop
  __lowerCAmelCase											=						(
      do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
  )

  __lowerCAmelCase											=						size if size is not None else self.size
  __lowerCAmelCase											=						get_size_dict(SCREAMING_SNAKE_CASE__      ,       default_to_square=SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						crop_size if crop_size is not None else self.crop_size
  __lowerCAmelCase											=						get_size_dict(SCREAMING_SNAKE_CASE__      ,       param_name="""crop_size"""							)

  __lowerCAmelCase											=						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:
   raise ValueError("""Size must be specified if do_resize is True."""							)

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

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

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

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

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

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

  # the pretrained checkpoints assume images are BGR, not RGB
  if do_flip_channel_order:
   __lowerCAmelCase											=						[self.flip_channel_order(image=SCREAMING_SNAKE_CASE__							) for image in images]

  __lowerCAmelCase											=						[to_channel_dimension_format(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							) for image in images]

  __lowerCAmelCase											=						{"""pixel_values""": images}
  return BatchFeature(data=SCREAMING_SNAKE_CASE__      ,       tensor_type=SCREAMING_SNAKE_CASE__							)





 def a					(      self   :	Union[str, Any]      ,       SCREAMING_SNAKE_CASE__   :	Any      ,       SCREAMING_SNAKE_CASE__   :	List[Tuple] = None							)  ->    Optional[Any]:
  __lowerCAmelCase											=						outputs.logits

  # Resize logits and compute semantic segmentation maps
  if target_sizes is not None:
   if len(SCREAMING_SNAKE_CASE__							) != len(SCREAMING_SNAKE_CASE__							):
    raise ValueError(
        """Make sure that you pass in as many target sizes as the batch dimension of the logits"""							)

   if is_torch_tensor(SCREAMING_SNAKE_CASE__							):
    __lowerCAmelCase											=						target_sizes.numpy()

   __lowerCAmelCase											=						[]

   for idx in range(len(SCREAMING_SNAKE_CASE__							)							):
    __lowerCAmelCase											=						torch.nn.functional.interpolate(
        logits[idx].unsqueeze(dim=0							)      ,       size=target_sizes[idx]      ,       mode="""bilinear"""      ,       align_corners=SCREAMING_SNAKE_CASE__							)
    __lowerCAmelCase											=						resized_logits[0].argmax(dim=0							)
    semantic_segmentation.append(SCREAMING_SNAKE_CASE__							)
  else:
   __lowerCAmelCase											=						logits.argmax(dim=1							)
   __lowerCAmelCase											=						[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]							)]

  return semantic_segmentation







'''simple docstring'''

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

from accelerate.utils.dataclasses import DistributedType







class 				_lowercase     :


 '''simple docstring'''






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





 def __getitem__(      self   :	str      ,       SCREAMING_SNAKE_CASE__   :	Dict							)  ->    Optional[int]:
  return {"x": self.x[i], "y": self.y[i]}







class 				_lowercase     (				torch.nn.Module			):


 '''simple docstring'''






 def __init__(      self   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=0      ,       SCREAMING_SNAKE_CASE__   :	Tuple=0      ,       SCREAMING_SNAKE_CASE__   :	Tuple=False							)  ->    Any:
  super().__init__()
  __lowerCAmelCase											=						torch.nn.Parameter(torch.tensor([2, 3]							).float()							)
  __lowerCAmelCase											=						torch.nn.Parameter(torch.tensor([2, 3]							).float()							)
  __lowerCAmelCase											=						True





 def a					(      self   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=None							)  ->    str:
  if self.first_batch:
   print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""							)
   __lowerCAmelCase											=						False
  return x * self.a[0] + self.b[0]







class 				_lowercase     (				torch.nn.Module			):


 '''simple docstring'''






 def __init__(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=0      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=0      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=False							)  ->    Optional[Any]:
  super().__init__()
  __lowerCAmelCase											=						torch.nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE__							).float()							)
  __lowerCAmelCase											=						torch.nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE__							).float()							)
  __lowerCAmelCase											=						True





 def a					(      self   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	Any=None							)  ->    int:
  if self.first_batch:
   print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""							)
   __lowerCAmelCase											=						False
  return x * self.a + self.b







def      UpperCamelCase_						(					snake_case_       :       List[str]						,		snake_case_       :       int = 16      )					->     int:






 '''simple docstring'''

 from datasets import load_dataset
 from transformers import AutoTokenizer

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

 __lowerCAmelCase											=						{v: i for i, v in enumerate(snake_case_      )}

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

 def collate_fn(snake_case_       :       List[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(snake_case_						,		padding="""max_length"""						,		max_length=1_28						,		return_tensors="""pt"""      )
  return tokenizer.pad(snake_case_						,		padding="""longest"""						,		return_tensors="""pt"""      )

 # Instantiate dataloaders.
 __lowerCAmelCase											=						DataLoader(tokenized_datasets["""train"""]						,		shuffle=snake_case_						,		collate_fn=snake_case_						,		batch_size=2      )
 __lowerCAmelCase											=						DataLoader(tokenized_datasets["""validation"""]						,		shuffle=snake_case_						,		collate_fn=snake_case_						,		batch_size=1      )

 return train_dataloader, eval_dataloader






