



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


_UpperCAmelCase     =			logging.get_logger(__name__)
class 					a	(  UpperCAmelCase__       ):
  UpperCamelCase	:				Tuple     						=					['pixel_values']



  def __init__(						self :					Any       ,		lowerCAmelCase :					bool = True       ,		lowerCAmelCase :					Dict[str, int] = None       ,		lowerCAmelCase :					PILImageResampling = PILImageResampling.BILINEAR       ,		lowerCAmelCase :					bool = True       ,		lowerCAmelCase :					Union[int, float] = 1 / 255       ,		lowerCAmelCase :					bool = True       ,		lowerCAmelCase :					Dict[str, int] = None       ,		lowerCAmelCase :					bool = True       ,		**lowerCAmelCase :					str       ,		)     ->					None:


     '''simple docstring'''




     super().__init__(**lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[str]     			=size if size is not None else {"""shortest_edge""": 224}
     SCREAMING_SNAKE_CASE_: List[str]     			=get_size_dict(lowerCAmelCase       ,		default_to_square=lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: str     			=crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
     SCREAMING_SNAKE_CASE_: Any     			=get_size_dict(lowerCAmelCase       ,		param_name="""crop_size"""		)

     SCREAMING_SNAKE_CASE_: Any     			=do_resize
     SCREAMING_SNAKE_CASE_: List[Any]     			=size
     SCREAMING_SNAKE_CASE_: str     			=resample
     SCREAMING_SNAKE_CASE_: Tuple     			=do_rescale
     SCREAMING_SNAKE_CASE_: Tuple     			=rescale_factor
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=do_center_crop
     SCREAMING_SNAKE_CASE_: Any     			=crop_size
     SCREAMING_SNAKE_CASE_: List[Any]     			=do_flip_channel_order



  def 	lowerCamelCase__       (						self :					int       ,		lowerCAmelCase :					np.ndarray       ,		lowerCAmelCase :					Dict[str, int]       ,		lowerCAmelCase :					PILImageResampling = PIL.Image.BILINEAR       ,		lowerCAmelCase :					Optional[Union[str, ChannelDimension]] = None       ,		**lowerCAmelCase :					str       ,		)     ->					np.ndarray:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[Any]     			=get_size_dict(lowerCAmelCase       ,		default_to_square=lowerCAmelCase		)
     if "shortest_edge" not in size:
        raise ValueError(f'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''		)
     SCREAMING_SNAKE_CASE_: Any     			=get_resize_output_image_size(lowerCAmelCase       ,		size=size["""shortest_edge"""]       ,		default_to_square=lowerCAmelCase		)
     return resize(lowerCAmelCase       ,		size=lowerCAmelCase       ,		resample=lowerCAmelCase       ,		data_format=lowerCAmelCase       ,		**lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					List[Any]       ,		lowerCAmelCase :					np.ndarray       ,		lowerCAmelCase :					Dict[str, int]       ,		lowerCAmelCase :					Optional[Union[str, ChannelDimension]] = None       ,		**lowerCAmelCase :					Any       ,		)     ->					np.ndarray:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=get_size_dict(lowerCAmelCase		)
     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(lowerCAmelCase       ,		size=(size["""height"""], size["""width"""])       ,		data_format=lowerCAmelCase       ,		**lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					int       ,		lowerCAmelCase :					np.ndarray       ,		lowerCAmelCase :					Union[int, float]       ,		lowerCAmelCase :					Optional[Union[str, ChannelDimension]] = None       ,		**lowerCAmelCase :					Union[str, Any]       ,		)     ->					Optional[Any]:


     '''simple docstring'''




     return rescale(lowerCAmelCase       ,		scale=lowerCAmelCase       ,		data_format=lowerCAmelCase       ,		**lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					str       ,		lowerCAmelCase :					np.ndarray       ,		lowerCAmelCase :					Optional[Union[str, ChannelDimension]] = None		)     ->					np.ndarray:


     '''simple docstring'''




     return flip_channel_order(lowerCAmelCase       ,		data_format=lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					List[str]       ,		lowerCAmelCase :					ImageInput       ,		lowerCAmelCase :					bool = None       ,		lowerCAmelCase :					Dict[str, int] = None       ,		lowerCAmelCase :					PILImageResampling = None       ,		lowerCAmelCase :					bool = None       ,		lowerCAmelCase :					float = None       ,		lowerCAmelCase :					bool = None       ,		lowerCAmelCase :					Dict[str, int] = None       ,		lowerCAmelCase :					bool = None       ,		lowerCAmelCase :					Optional[Union[str, TensorType]] = None       ,		lowerCAmelCase :					ChannelDimension = ChannelDimension.FIRST       ,		**lowerCAmelCase :					Dict       ,		)     ->					PIL.Image.Image:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[Any]     			=do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE_: List[str]     			=resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE_: str     			=do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE_: Tuple     			=do_center_crop if do_center_crop is not None else self.do_center_crop
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=(
         do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
     )

     SCREAMING_SNAKE_CASE_: List[str]     			=size if size is not None else self.size
     SCREAMING_SNAKE_CASE_: Dict     			=get_size_dict(lowerCAmelCase       ,		default_to_square=lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Dict     			=crop_size if crop_size is not None else self.crop_size
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=get_size_dict(lowerCAmelCase       ,		param_name="""crop_size"""		)

     SCREAMING_SNAKE_CASE_: Tuple     			=make_list_of_images(lowerCAmelCase		)

     if not valid_images(lowerCAmelCase		):
        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.
     SCREAMING_SNAKE_CASE_: Dict     			=[to_numpy_array(lowerCAmelCase		) for image in images]

     if do_resize:
        SCREAMING_SNAKE_CASE_: Any     			=[self.resize(image=lowerCAmelCase       ,		size=lowerCAmelCase       ,		resample=lowerCAmelCase		) for image in images]

     if do_center_crop:
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=[self.center_crop(image=lowerCAmelCase       ,		size=lowerCAmelCase		) for image in images]

     if do_rescale:
        SCREAMING_SNAKE_CASE_: str     			=[self.rescale(image=lowerCAmelCase       ,		scale=lowerCAmelCase		) for image in images]

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

     SCREAMING_SNAKE_CASE_: int     			=[to_channel_dimension_format(lowerCAmelCase       ,		lowerCAmelCase		) for image in images]

     SCREAMING_SNAKE_CASE_: Optional[Any]     			={"""pixel_values""": images}
     return BatchFeature(data=lowerCAmelCase       ,		tensor_type=lowerCAmelCase		)





  def 	lowerCamelCase__       (						self :					int       ,		lowerCAmelCase :					Optional[Any]       ,		lowerCAmelCase :					List[Tuple] = None		)     ->					List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Dict     			=outputs.logits

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

        if is_torch_tensor(lowerCAmelCase		):
           SCREAMING_SNAKE_CASE_: List[str]     			=target_sizes.numpy()

        SCREAMING_SNAKE_CASE_: int     			=[]

        for idx in range(len(lowerCAmelCase		)		):
           SCREAMING_SNAKE_CASE_: Optional[int]     			=torch.nn.functional.interpolate(
               logits[idx].unsqueeze(dim=0		)       ,		size=target_sizes[idx]       ,		mode="""bilinear"""       ,		align_corners=lowerCAmelCase		)
           SCREAMING_SNAKE_CASE_: str     			=resized_logits[0].argmax(dim=0		)
           semantic_segmentation.append(lowerCAmelCase		)
     else:
        SCREAMING_SNAKE_CASE_: Any     			=logits.argmax(dim=1		)
        SCREAMING_SNAKE_CASE_: Optional[int]     			=[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 					a	:



  def __init__(						self :					Union[str, Any]       ,		lowerCAmelCase :					List[str]=2       ,		lowerCAmelCase :					int=3       ,		lowerCAmelCase :					Optional[Any]=64       ,		lowerCAmelCase :					Union[str, Any]=None		)     ->					Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=np.random.default_rng(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=length
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=rng.normal(size=(length,)		).astype(np.floataa		)
     SCREAMING_SNAKE_CASE_: Tuple     			=a * self.x + b + rng.normal(scale=0.1       ,		size=(length,)		).astype(np.floataa		)



  def __len__(						self :					List[Any]		)     ->					str:


     '''simple docstring'''




     return self.length





  def __getitem__(						self :					Union[str, Any]       ,		lowerCAmelCase :					Any		)     ->					List[str]:


     '''simple docstring'''




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







class 					a	(  torch.nn.Module       ):



  def __init__(						self :					Optional[int]       ,		lowerCAmelCase :					str=0       ,		lowerCAmelCase :					List[Any]=0       ,		lowerCAmelCase :					Optional[int]=False		)     ->					Tuple:


     '''simple docstring'''




     super().__init__()
     SCREAMING_SNAKE_CASE_: int     			=torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
     SCREAMING_SNAKE_CASE_: int     			=torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
     SCREAMING_SNAKE_CASE_: Dict     			=True





  def 	lowerCamelCase__       (						self :					str       ,		lowerCAmelCase :					Tuple=None		)     ->					int:


     '''simple docstring'''




     if self.first_batch:
        print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''		)
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=False
     return x * self.a[0] + self.b[0]







class 					a	(  torch.nn.Module       ):



  def __init__(						self :					Union[str, Any]       ,		lowerCAmelCase :					Any=0       ,		lowerCAmelCase :					List[Any]=0       ,		lowerCAmelCase :					List[Any]=False		)     ->					str:


     '''simple docstring'''




     super().__init__()
     SCREAMING_SNAKE_CASE_: List[str]     			=torch.nn.Parameter(torch.tensor(lowerCAmelCase		).float()		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=torch.nn.Parameter(torch.tensor(lowerCAmelCase		).float()		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=True





  def 	lowerCamelCase__       (						self :					List[str]       ,		lowerCAmelCase :					int=None		)     ->					Any:


     '''simple docstring'''




     if self.first_batch:
        print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''		)
        SCREAMING_SNAKE_CASE_: Optional[int]     			=False
     return x * self.a + self.b







def      __magic_name__				(							lowercase							,			lowercase = 16 ):
   from datasets import load_dataset
   from transformers import AutoTokenizer

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

   SCREAMING_SNAKE_CASE_: List[Any]     			={v: i for i, v in enumerate(lowercase )}

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

   def collate_fn(lowercase ):
      # 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(lowercase							,			padding="""max_length"""							,			max_length=128							,			return_tensors="""pt""" )
      return tokenizer.pad(lowercase							,			padding="""longest"""							,			return_tensors="""pt""" )

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

   return train_dataloader, eval_dataloader



