

import unittest

import numpy as np

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 MobileViTImageProcessor



class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  def __init__(      self			:   List[Any]	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   Optional[Any]=7	,							lowerCAmelCase			:   List[str]=3	,							lowerCAmelCase			:   int=18	,							lowerCAmelCase			:   int=30	,							lowerCAmelCase			:   Optional[int]=4_00	,							lowerCAmelCase			:   Optional[Any]=True	,							lowerCAmelCase			:   Dict=None	,							lowerCAmelCase			:   List[str]=True	,							lowerCAmelCase			:   Tuple=None	,							lowerCAmelCase			:   Any=True	,							)							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						size if size is not None else {"""shortest_edge""": 20}
     __lowerCAmelCase  :      Any    =						crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
     __lowerCAmelCase  :      str    =						parent
     __lowerCAmelCase  :      List[str]    =						batch_size
     __lowerCAmelCase  :      int    =						num_channels
     __lowerCAmelCase  :      List[str]    =						image_size
     __lowerCAmelCase  :      Optional[int]    =						min_resolution
     __lowerCAmelCase  :      List[str]    =						max_resolution
     __lowerCAmelCase  :      List[Any]    =						do_resize
     __lowerCAmelCase  :      Optional[int]    =						size
     __lowerCAmelCase  :      List[Any]    =						do_center_crop
     __lowerCAmelCase  :      Optional[Any]    =						crop_size
     __lowerCAmelCase  :      int    =						do_flip_channel_order







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






     """simple docstring"""







     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_center_crop": self.do_center_crop,
         "crop_size": self.crop_size,
         "do_flip_channel_order": self.do_flip_channel_order,
     }






@require_torch
@require_vision
class SCREAMING_SNAKE_CASE     (  a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      List[str]										=MobileViTImageProcessor if is_vision_available() else None







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						MobileViTImageProcessingTester(self   )







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






     """simple docstring"""







     return self.image_processor_tester.prepare_image_processor_dict()







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






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						self.image_processing_class(**self.image_processor_dict   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""do_resize"""   )   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""size"""   )   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""do_center_crop"""   )   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""center_crop"""   )   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""do_flip_channel_order"""   )   )







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






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						self.image_processing_class.from_dict(self.image_processor_dict   )
     self.assertEqual(image_processor.size	,							{"""shortest_edge""": 20}   )
     self.assertEqual(image_processor.crop_size	,							{"""height""": 18, """width""": 18}   )

     __lowerCAmelCase  :      Optional[Any]    =						self.image_processing_class.from_dict(self.image_processor_dict	,							size=42	,							crop_size=84   )
     self.assertEqual(image_processor.size	,							{"""shortest_edge""": 42}   )
     self.assertEqual(image_processor.crop_size	,							{"""height""": 84, """width""": 84}   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     int:






     """simple docstring"""







     pass







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






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						self.image_processing_class(**self.image_processor_dict   )
     # create random PIL images
     __lowerCAmelCase  :      Union[str, Any]    =						prepare_image_inputs(self.image_processor_tester	,							equal_resolution=lowerCAmelCase   )
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase	,							Image.Image   )

     # Test not batched input
     __lowerCAmelCase  :      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
     __lowerCAmelCase  :      str    =						image_processing(lowerCAmelCase	,							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			:   int   )							->     Optional[int]:






     """simple docstring"""







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

     # Test not batched input
     __lowerCAmelCase  :      Optional[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
     __lowerCAmelCase  :      Tuple    =						image_processing(lowerCAmelCase	,							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]   )							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						self.image_processing_class(**self.image_processor_dict   )
     # create random PyTorch tensors
     __lowerCAmelCase  :      str    =						prepare_image_inputs(self.image_processor_tester	,							equal_resolution=lowerCAmelCase	,							torchify=lowerCAmelCase   )
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase	,							torch.Tensor   )

     # Test not batched input
     __lowerCAmelCase  :      Optional[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
     __lowerCAmelCase  :      Tuple    =						image_processing(lowerCAmelCase	,							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"""],
         )	,							)





# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

__UpperCAmelCase								=			"""2.13.1"""

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse("""3.7"""):
   raise ImportWarning(
       """To use `datasets`, Python>=3.7 is required, and the current version of Python doesn't match this condition."""
   )

if version.parse(pyarrow.__version__).major < 8:
   raise ImportWarning(
       """To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"""
       """If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."""
   )

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

__UpperCAmelCase								=			concatenate_datasets
__UpperCAmelCase								=			DownloadConfig
__UpperCAmelCase								=			DownloadManager
__UpperCAmelCase								=			DownloadMode
__UpperCAmelCase								=			DownloadConfig
__UpperCAmelCase								=			DownloadMode
__UpperCAmelCase								=			DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager


