



"""simple docstring"""
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import BeitImageProcessor



class       SCREAMING_SNAKE_CASE__  (					unittest.TestCase ):

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=7      ,     _SCREAMING_SNAKE_CASE=3      ,     _SCREAMING_SNAKE_CASE=18      ,     _SCREAMING_SNAKE_CASE=30      ,     _SCREAMING_SNAKE_CASE=400      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=None      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=None      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]      ,     _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]      ,     _SCREAMING_SNAKE_CASE=False      ,     )  ->      List[str]:





    '''simple docstring'''




    UpperCAmelCase	:					Dict	=	size if size is not None else {"""height""": 20, """width""": 20}
    UpperCAmelCase	:					Optional[int]	=	crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
    UpperCAmelCase	:					Optional[int]	=	parent
    UpperCAmelCase	:					Any	=	batch_size
    UpperCAmelCase	:					Dict	=	num_channels
    UpperCAmelCase	:					Union[str, Any]	=	image_size
    UpperCAmelCase	:					int	=	min_resolution
    UpperCAmelCase	:					Any	=	max_resolution
    UpperCAmelCase	:					int	=	do_resize
    UpperCAmelCase	:					Optional[int]	=	size
    UpperCAmelCase	:					Tuple	=	do_center_crop
    UpperCAmelCase	:					List[str]	=	crop_size
    UpperCAmelCase	:					Dict	=	do_normalize
    UpperCAmelCase	:					List[str]	=	image_mean
    UpperCAmelCase	:					Any	=	image_std
    UpperCAmelCase	:					List[Any]	=	do_reduce_labels




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_center_crop": self.do_center_crop,
        "crop_size": self.crop_size,
        "do_normalize": self.do_normalize,
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_reduce_labels": self.do_reduce_labels,
    }

def       _snake_case						(						):
 UpperCAmelCase	:					Tuple	=	load_dataset("""hf-internal-testing/fixtures_ade20k"""			,		split="""test"""						)

 UpperCAmelCase	:					Any	=	Image.open(dataset[0]["""file"""]						)
 UpperCAmelCase	:					Optional[int]	=	Image.open(dataset[1]["""file"""]						)

 return image, map

def       _snake_case						(						):
 UpperCAmelCase	:					Optional[int]	=	load_dataset("""hf-internal-testing/fixtures_ade20k"""			,		split="""test"""						)

 UpperCAmelCase	:					int	=	Image.open(ds[0]["""file"""]						)
 UpperCAmelCase	:					Optional[int]	=	Image.open(ds[1]["""file"""]						)
 UpperCAmelCase	:					Dict	=	Image.open(ds[2]["""file"""]						)
 UpperCAmelCase	:					List[Any]	=	Image.open(ds[3]["""file"""]						)

 return [imagea, imagea], [mapa, mapa]



@require_torch
@require_vision
class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__      ,			unittest.TestCase ):
   __lowerCAmelCase  :   int						=						BeitImageProcessor if is_vision_available() else None

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	BeitImageProcessingTester(self						)

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[str]:





    '''simple docstring'''




    return self.image_processor_tester.prepare_image_processor_dict()

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	:					Union[str, Any]	=	self.image_processing_class(**self.image_processor_dict						)
    self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,     """do_resize"""						)						)
    self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,     """size"""						)						)
    self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,     """do_center_crop"""						)						)
    self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,     """center_crop"""						)						)
    self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,     """do_normalize"""						)						)
    self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,     """image_mean"""						)						)
    self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,     """image_std"""						)						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[int]	=	self.image_processing_class.from_dict(self.image_processor_dict						)
    self.assertEqual(image_processor.size      ,     {"""height""": 20, """width""": 20}						)
    self.assertEqual(image_processor.crop_size      ,     {"""height""": 18, """width""": 18}						)
    self.assertEqual(image_processor.do_reduce_labels      ,     _SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					str	=	self.image_processing_class.from_dict(
        self.image_processor_dict      ,     size=42      ,     crop_size=84      ,     reduce_labels=_SCREAMING_SNAKE_CASE						)
    self.assertEqual(image_processor.size      ,     {"""height""": 42, """width""": 42}						)
    self.assertEqual(image_processor.crop_size      ,     {"""height""": 84, """width""": 84}						)
    self.assertEqual(image_processor.do_reduce_labels      ,     _SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[Any]:





    '''simple docstring'''




    pass

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[str]:





    '''simple docstring'''




    UpperCAmelCase	:					List[Any]	=	self.image_processing_class(**self.image_processor_dict						)
    # create random PIL images
    UpperCAmelCase	:					List[Any]	=	prepare_image_inputs(self.image_processor_tester      ,     equal_resolution=_SCREAMING_SNAKE_CASE						)
    for image in image_inputs:
     self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,     Image.Image						)

    # Test not batched input
    UpperCAmelCase	:					List[Any]	=	image_processing(image_inputs[0]      ,     return_tensors="""pt"""						).pixel_values
    self.assertEqual(
        encoded_images.shape      ,     (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )      ,     )

    # Test batched
    UpperCAmelCase	:					Any	=	image_processing(_SCREAMING_SNAKE_CASE      ,     return_tensors="""pt"""						).pixel_values
    self.assertEqual(
        encoded_images.shape      ,     (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )      ,     )

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase	:					List[str]	=	self.image_processing_class(**self.image_processor_dict						)
    # create random numpy tensors
    UpperCAmelCase	:					List[Any]	=	prepare_image_inputs(self.image_processor_tester      ,     equal_resolution=_SCREAMING_SNAKE_CASE      ,     numpify=_SCREAMING_SNAKE_CASE						)
    for image in image_inputs:
     self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,     np.ndarray						)

    # Test not batched input
    UpperCAmelCase	:					Any	=	image_processing(image_inputs[0]      ,     return_tensors="""pt"""						).pixel_values
    self.assertEqual(
        encoded_images.shape      ,     (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )      ,     )

    # Test batched
    UpperCAmelCase	:					str	=	image_processing(_SCREAMING_SNAKE_CASE      ,     return_tensors="""pt"""						).pixel_values
    self.assertEqual(
        encoded_images.shape      ,     (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )      ,     )

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	self.image_processing_class(**self.image_processor_dict						)
    # create random PyTorch tensors
    UpperCAmelCase	:					Optional[Any]	=	prepare_image_inputs(self.image_processor_tester      ,     equal_resolution=_SCREAMING_SNAKE_CASE      ,     torchify=_SCREAMING_SNAKE_CASE						)
    for image in image_inputs:
     self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,     torch.Tensor						)

    # Test not batched input
    UpperCAmelCase	:					int	=	image_processing(image_inputs[0]      ,     return_tensors="""pt"""						).pixel_values
    self.assertEqual(
        encoded_images.shape      ,     (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )      ,     )

    # Test batched
    UpperCAmelCase	:					Optional[Any]	=	image_processing(_SCREAMING_SNAKE_CASE      ,     return_tensors="""pt"""						).pixel_values
    self.assertEqual(
        encoded_images.shape      ,     (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )      ,     )

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[int]:





    '''simple docstring'''




    UpperCAmelCase	:					Any	=	self.image_processing_class(**self.image_processor_dict						)
    # create random PyTorch tensors
    UpperCAmelCase	:					Any	=	prepare_image_inputs(self.image_processor_tester      ,     equal_resolution=_SCREAMING_SNAKE_CASE      ,     torchify=_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Tuple	=	[]
    for image in image_inputs:
     self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,     torch.Tensor						)
     maps.append(torch.zeros(image.shape[-2:]						).long()						)

    # Test not batched input
    UpperCAmelCase	:					Optional[int]	=	image_processing(image_inputs[0]      ,     maps[0]      ,     return_tensors="""pt"""						)
    self.assertEqual(
        encoding["""pixel_values"""].shape      ,     (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )      ,     )
    self.assertEqual(
        encoding["""labels"""].shape      ,     (
            1,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )      ,     )
    self.assertEqual(encoding["""labels"""].dtype      ,     torch.long						)
    self.assertTrue(encoding["""labels"""].min().item() >= 0						)
    self.assertTrue(encoding["""labels"""].max().item() <= 255						)

    # Test batched
    UpperCAmelCase	:					Tuple	=	image_processing(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     return_tensors="""pt"""						)
    self.assertEqual(
        encoding["""pixel_values"""].shape      ,     (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )      ,     )
    self.assertEqual(
        encoding["""labels"""].shape      ,     (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )      ,     )
    self.assertEqual(encoding["""labels"""].dtype      ,     torch.long						)
    self.assertTrue(encoding["""labels"""].min().item() >= 0						)
    self.assertTrue(encoding["""labels"""].max().item() <= 255						)

    # Test not batched input (PIL images)
    UpperCAmelCase	,		UpperCAmelCase	:					Any	=	prepare_semantic_single_inputs()

    UpperCAmelCase	:					int	=	image_processing(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     return_tensors="""pt"""						)
    self.assertEqual(
        encoding["""pixel_values"""].shape      ,     (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )      ,     )
    self.assertEqual(
        encoding["""labels"""].shape      ,     (
            1,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )      ,     )
    self.assertEqual(encoding["""labels"""].dtype      ,     torch.long						)
    self.assertTrue(encoding["""labels"""].min().item() >= 0						)
    self.assertTrue(encoding["""labels"""].max().item() <= 255						)

    # Test batched input (PIL images)
    UpperCAmelCase	,		UpperCAmelCase	:					List[str]	=	prepare_semantic_batch_inputs()

    UpperCAmelCase	:					Union[str, Any]	=	image_processing(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     return_tensors="""pt"""						)
    self.assertEqual(
        encoding["""pixel_values"""].shape      ,     (
            2,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )      ,     )
    self.assertEqual(
        encoding["""labels"""].shape      ,     (
            2,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )      ,     )
    self.assertEqual(encoding["""labels"""].dtype      ,     torch.long						)
    self.assertTrue(encoding["""labels"""].min().item() >= 0						)
    self.assertTrue(encoding["""labels"""].max().item() <= 255						)




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[int]:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	self.image_processing_class(**self.image_processor_dict						)

    # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
    UpperCAmelCase	,		UpperCAmelCase	:					Dict	=	prepare_semantic_single_inputs()
    UpperCAmelCase	:					Union[str, Any]	=	image_processing(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     return_tensors="""pt"""						)
    self.assertTrue(encoding["""labels"""].min().item() >= 0						)
    self.assertTrue(encoding["""labels"""].max().item() <= 150						)

    UpperCAmelCase	:					List[Any]	=	True
    UpperCAmelCase	:					Union[str, Any]	=	image_processing(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     return_tensors="""pt"""						)
    self.assertTrue(encoding["""labels"""].min().item() >= 0						)
    self.assertTrue(encoding["""labels"""].max().item() <= 255						)








"""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




