'''simple docstring'''

import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DetrImageProcessor







class 				_lowercase     (				unittest.TestCase			):


 '''simple docstring'''






 def __init__(      self   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	Dict      ,       SCREAMING_SNAKE_CASE__   :	Dict=7      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=3      ,       SCREAMING_SNAKE_CASE__   :	Dict=30      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=4_00      ,       SCREAMING_SNAKE_CASE__   :	Tuple=True      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=None      ,       SCREAMING_SNAKE_CASE__   :	Tuple=True      ,       SCREAMING_SNAKE_CASE__   :	str=1 / 2_55      ,       SCREAMING_SNAKE_CASE__   :	Dict=True      ,       SCREAMING_SNAKE_CASE__   :	Dict=[0.5, 0.5, 0.5]      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=[0.5, 0.5, 0.5]      ,       SCREAMING_SNAKE_CASE__   :	Dict=True      ,       )  ->    Any:
  # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
  __lowerCAmelCase											=						size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
  __lowerCAmelCase											=						parent
  __lowerCAmelCase											=						batch_size
  __lowerCAmelCase											=						num_channels
  __lowerCAmelCase											=						min_resolution
  __lowerCAmelCase											=						max_resolution
  __lowerCAmelCase											=						do_resize
  __lowerCAmelCase											=						size
  __lowerCAmelCase											=						do_rescale
  __lowerCAmelCase											=						rescale_factor
  __lowerCAmelCase											=						do_normalize
  __lowerCAmelCase											=						image_mean
  __lowerCAmelCase											=						image_std
  __lowerCAmelCase											=						do_pad
 def a					(      self   :	Any							)  ->    Optional[Any]:
  return {
      "do_resize": self.do_resize,
      "size": self.size,
      "do_rescale": self.do_rescale,
      "rescale_factor": self.rescale_factor,
      "do_normalize": self.do_normalize,
      "image_mean": self.image_mean,
      "image_std": self.image_std,
      "do_pad": self.do_pad,
  }





 def a					(      self   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=False							)  ->    Tuple:
  if not batched:
   __lowerCAmelCase											=						image_inputs[0]
   if isinstance(SCREAMING_SNAKE_CASE__      ,       Image.Image							):
    __lowerCAmelCase							,       __lowerCAmelCase											=						image.size
   else:
    __lowerCAmelCase							,       __lowerCAmelCase											=						image.shape[1], image.shape[2]
   if w < h:
    __lowerCAmelCase											=						int(self.size["""shortest_edge"""] * h / w							)
    __lowerCAmelCase											=						self.size["""shortest_edge"""]
   elif w > h:
    __lowerCAmelCase											=						self.size["""shortest_edge"""]
    __lowerCAmelCase											=						int(self.size["""shortest_edge"""] * w / h							)
   else:
    __lowerCAmelCase											=						self.size["""shortest_edge"""]
    __lowerCAmelCase											=						self.size["""shortest_edge"""]

  else:
   __lowerCAmelCase											=						[]
   for image in image_inputs:
    __lowerCAmelCase							,       __lowerCAmelCase											=						self.get_expected_values([image]							)
    expected_values.append((expected_height, expected_width)							)
   __lowerCAmelCase											=						max(SCREAMING_SNAKE_CASE__      ,       key=lambda SCREAMING_SNAKE_CASE__							: item[0]							)[0]
   __lowerCAmelCase											=						max(SCREAMING_SNAKE_CASE__      ,       key=lambda SCREAMING_SNAKE_CASE__							: item[1]							)[1]

  return expected_height, expected_width







@require_torch
@require_vision
class 				_lowercase     (				UpperCAmelCase__  ,							unittest.TestCase			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Any      	=					DetrImageProcessor if is_vision_available() else None
 def a					(      self   :	List[Any]							)  ->    List[Any]:
  __lowerCAmelCase											=						DetrImageProcessingTester(self							)
 @property
 def a					(      self   :	List[Any]							)  ->    Union[str, Any]:
  return self.image_processor_tester.prepare_image_processor_dict()
 def a					(      self   :	Dict							)  ->    Union[str, Any]:
  __lowerCAmelCase											=						self.image_processing_class(**self.image_processor_dict							)
  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,       """image_mean"""							)							)
  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,       """image_std"""							)							)
  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,       """do_normalize"""							)							)
  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,       """do_rescale"""							)							)
  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,       """rescale_factor"""							)							)
  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,       """do_resize"""							)							)
  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,       """size"""							)							)
  self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__      ,       """do_pad"""							)							)
 def a					(      self   :	str							)  ->    Optional[int]:
  __lowerCAmelCase											=						self.image_processing_class.from_dict(self.image_processor_dict							)
  self.assertEqual(image_processor.size      ,       {"""shortest_edge""": 18, """longest_edge""": 13_33}							)
  self.assertEqual(image_processor.do_pad      ,       SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						self.image_processing_class.from_dict(
      self.image_processor_dict      ,       size=42      ,       max_size=84      ,       pad_and_return_pixel_mask=SCREAMING_SNAKE_CASE__							)
  self.assertEqual(image_processor.size      ,       {"""shortest_edge""": 42, """longest_edge""": 84}							)
  self.assertEqual(image_processor.do_pad      ,       SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	Optional[int]							)  ->    Dict:
  pass
 def a					(      self   :	int							)  ->    str:
  # Initialize image_processing
  __lowerCAmelCase											=						self.image_processing_class(**self.image_processor_dict							)
  # create random PIL images
  __lowerCAmelCase											=						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
  __lowerCAmelCase											=						image_processing(image_inputs[0]      ,       return_tensors="""pt"""							).pixel_values

  __lowerCAmelCase							,       __lowerCAmelCase											=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__							)

  self.assertEqual(
      encoded_images.shape      ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,       )

  # Test batched
  __lowerCAmelCase							,       __lowerCAmelCase											=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__      ,       batched=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						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,
          expected_height,
          expected_width,
      )      ,       )
 def a					(      self   :	Union[str, Any]							)  ->    List[Any]:
  # Initialize image_processing
  __lowerCAmelCase											=						self.image_processing_class(**self.image_processor_dict							)
  # create random numpy tensors
  __lowerCAmelCase											=						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
  __lowerCAmelCase											=						image_processing(image_inputs[0]      ,       return_tensors="""pt"""							).pixel_values

  __lowerCAmelCase							,       __lowerCAmelCase											=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__							)

  self.assertEqual(
      encoded_images.shape      ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,       )

  # Test batched
  __lowerCAmelCase											=						image_processing(SCREAMING_SNAKE_CASE__      ,       return_tensors="""pt"""							).pixel_values

  __lowerCAmelCase							,       __lowerCAmelCase											=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__      ,       batched=SCREAMING_SNAKE_CASE__							)

  self.assertEqual(
      encoded_images.shape      ,       (
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_channels,
          expected_height,
          expected_width,
      )      ,       )
 def a					(      self   :	int							)  ->    int:
  # Initialize image_processing
  __lowerCAmelCase											=						self.image_processing_class(**self.image_processor_dict							)
  # create random PyTorch tensors
  __lowerCAmelCase											=						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
  __lowerCAmelCase											=						image_processing(image_inputs[0]      ,       return_tensors="""pt"""							).pixel_values

  __lowerCAmelCase							,       __lowerCAmelCase											=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__							)

  self.assertEqual(
      encoded_images.shape      ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,       )

  # Test batched
  __lowerCAmelCase											=						image_processing(SCREAMING_SNAKE_CASE__      ,       return_tensors="""pt"""							).pixel_values

  __lowerCAmelCase							,       __lowerCAmelCase											=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__      ,       batched=SCREAMING_SNAKE_CASE__							)

  self.assertEqual(
      encoded_images.shape      ,       (
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_channels,
          expected_height,
          expected_width,
      )      ,       )
 @slow
 def a					(      self   :	Any							)  ->    int:
  # prepare image and target
  __lowerCAmelCase											=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
  with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""      ,       """r"""							) as f:
   __lowerCAmelCase											=						json.loads(f.read()							)

  __lowerCAmelCase											=						{"""image_id""": 3_97_69, """annotations""": target}

  # encode them
  __lowerCAmelCase											=						DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50"""							)
  __lowerCAmelCase											=						image_processing(images=SCREAMING_SNAKE_CASE__      ,       annotations=SCREAMING_SNAKE_CASE__      ,       return_tensors="""pt"""							)

  # verify pixel values
  __lowerCAmelCase											=						torch.Size([1, 3, 8_00, 10_66]							)
  self.assertEqual(encoding["""pixel_values"""].shape      ,       SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]							)
  self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]      ,       SCREAMING_SNAKE_CASE__      ,       atol=1e-4							)							)

  # verify area
  __lowerCAmelCase											=						torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]							)
  self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]      ,       SCREAMING_SNAKE_CASE__							)							)
  # verify boxes
  __lowerCAmelCase											=						torch.Size([6, 4]							)
  self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,       SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]							)
  self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]      ,       SCREAMING_SNAKE_CASE__      ,       atol=1e-3							)							)
  # verify image_id
  __lowerCAmelCase											=						torch.tensor([3_97_69]							)
  self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,       SCREAMING_SNAKE_CASE__							)							)
  # verify is_crowd
  __lowerCAmelCase											=						torch.tensor([0, 0, 0, 0, 0, 0]							)
  self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,       SCREAMING_SNAKE_CASE__							)							)
  # verify class_labels
  __lowerCAmelCase											=						torch.tensor([75, 75, 63, 65, 17, 17]							)
  self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,       SCREAMING_SNAKE_CASE__							)							)
  # verify orig_size
  __lowerCAmelCase											=						torch.tensor([4_80, 6_40]							)
  self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,       SCREAMING_SNAKE_CASE__							)							)
  # verify size
  __lowerCAmelCase											=						torch.tensor([8_00, 10_66]							)
  self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,       SCREAMING_SNAKE_CASE__							)							)





 @slow
 def a					(      self   :	int							)  ->    str:
  # prepare image, target and masks_path
  __lowerCAmelCase											=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
  with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""      ,       """r"""							) as f:
   __lowerCAmelCase											=						json.loads(f.read()							)

  __lowerCAmelCase											=						{"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}

  __lowerCAmelCase											=						pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""							)

  # encode them
  __lowerCAmelCase											=						DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50-panoptic"""							)
  __lowerCAmelCase											=						image_processing(images=SCREAMING_SNAKE_CASE__      ,       annotations=SCREAMING_SNAKE_CASE__      ,       masks_path=SCREAMING_SNAKE_CASE__      ,       return_tensors="""pt"""							)

  # verify pixel values
  __lowerCAmelCase											=						torch.Size([1, 3, 8_00, 10_66]							)
  self.assertEqual(encoding["""pixel_values"""].shape      ,       SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]							)
  self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]      ,       SCREAMING_SNAKE_CASE__      ,       atol=1e-4							)							)

  # verify area
  __lowerCAmelCase											=						torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]							)
  self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]      ,       SCREAMING_SNAKE_CASE__							)							)
  # verify boxes
  __lowerCAmelCase											=						torch.Size([6, 4]							)
  self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,       SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]							)
  self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]      ,       SCREAMING_SNAKE_CASE__      ,       atol=1e-3							)							)
  # verify image_id
  __lowerCAmelCase											=						torch.tensor([3_97_69]							)
  self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,       SCREAMING_SNAKE_CASE__							)							)
  # verify is_crowd
  __lowerCAmelCase											=						torch.tensor([0, 0, 0, 0, 0, 0]							)
  self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,       SCREAMING_SNAKE_CASE__							)							)
  # verify class_labels
  __lowerCAmelCase											=						torch.tensor([17, 17, 63, 75, 75, 93]							)
  self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,       SCREAMING_SNAKE_CASE__							)							)
  # verify masks
  __lowerCAmelCase											=						82_28_73
  self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()      ,       SCREAMING_SNAKE_CASE__							)
  # verify orig_size
  __lowerCAmelCase											=						torch.tensor([4_80, 6_40]							)
  self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,       SCREAMING_SNAKE_CASE__							)							)
  # verify size
  __lowerCAmelCase											=						torch.tensor([8_00, 10_66]							)
  self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,       SCREAMING_SNAKE_CASE__							)							)







'''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






