'''simple docstring'''






from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


SCREAMING_SNAKE_CASE_	   =			logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_	   =			TypeVar('DatasetType', Dataset, IterableDataset)






def  UpperCamelCase__					(					_lowercase    :				List[DatasetType]   ,    _lowercase    :				Optional[List[float]] = None   ,    _lowercase    :				Optional[int] = None   ,    _lowercase    :				Optional[DatasetInfo] = None   ,    _lowercase    :				Optional[NamedSplit] = None   ,    _lowercase    :				Literal["first_exhausted", "all_exhausted"] = "first_exhausted"   ,    )							->	DatasetType:
  from .arrow_dataset import Dataset
  from .iterable_dataset import IterableDataset

  if not datasets:
    raise ValueError("""Unable to interleave an empty list of datasets."""     )
  for i, dataset in enumerate(_lowercase     ):
    if not isinstance(_lowercase   ,    (Dataset, IterableDataset)     ):
      if isinstance(_lowercase   ,    (DatasetDict, IterableDatasetDict)     ):
        if not dataset:
          raise ValueError(
              F'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '''
              """is an empty dataset dictionary."""     )
        raise ValueError(
            F'''Dataset at position {i} has at least one split: {list(_lowercase     )}\n'''
            F'''Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(_lowercase     )     )}\']'''     )
      raise ValueError(
          F'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_lowercase     ).__name__}.'''     )
    if i == 0:
      __UpperCAmelCase, __UpperCAmelCase:					str		= (
          (Dataset, IterableDataset) if isinstance(_lowercase   ,    _lowercase     ) else (IterableDataset, Dataset)
      )
    elif not isinstance(_lowercase   ,    _lowercase     ):
      raise ValueError(
          F'''Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'''     )
  if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
    raise ValueError(F'''{stopping_strategy} is not supported. Please enter a valid stopping_strategy.'''     )
  if dataset_type is Dataset:
    return _interleave_map_style_datasets(
        _lowercase   ,    _lowercase   ,    _lowercase   ,    info=_lowercase   ,    split=_lowercase   ,    stopping_strategy=_lowercase     )
  else:
    return _interleave_iterable_datasets(
        _lowercase   ,    _lowercase   ,    _lowercase   ,    info=_lowercase   ,    split=_lowercase   ,    stopping_strategy=_lowercase     )







def  UpperCamelCase__					(					_lowercase    :				List[DatasetType]   ,    _lowercase    :				Optional[DatasetInfo] = None   ,    _lowercase    :				Optional[NamedSplit] = None   ,    _lowercase    :				int = 0   ,    )							->	DatasetType:

  if not dsets:
    raise ValueError("""Unable to concatenate an empty list of datasets."""     )
  for i, dataset in enumerate(_lowercase     ):
    if not isinstance(_lowercase   ,    (Dataset, IterableDataset)     ):
      if isinstance(_lowercase   ,    (DatasetDict, IterableDatasetDict)     ):
        if not dataset:
          raise ValueError(
              F'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '''
              """is an empty dataset dictionary."""     )
        raise ValueError(
            F'''Dataset at position {i} has at least one split: {list(_lowercase     )}\n'''
            F'''Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(_lowercase     )     )}\']'''     )
      raise ValueError(
          F'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_lowercase     ).__name__}.'''     )
    if i == 0:
      __UpperCAmelCase, __UpperCAmelCase:					Tuple		= (
          (Dataset, IterableDataset) if isinstance(_lowercase   ,    _lowercase     ) else (IterableDataset, Dataset)
      )
    elif not isinstance(_lowercase   ,    _lowercase     ):
      raise ValueError(
          F'''Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'''     )
  if dataset_type is Dataset:
    return _concatenate_map_style_datasets(_lowercase   ,    info=_lowercase   ,    split=_lowercase   ,    axis=_lowercase     )
  else:
    return _concatenate_iterable_datasets(_lowercase   ,    info=_lowercase   ,    split=_lowercase   ,    axis=_lowercase     )
'''simple docstring'''






import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ImageGPTImageProcessor




class    a (       unittest.TestCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_=7   ,    snake_case_=3   ,    snake_case_=18   ,    snake_case_=30   ,    snake_case_=400   ,    snake_case_=True   ,    snake_case_=None   ,    snake_case_=True   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[int]		= size if size is not None else {"""height""": 18, """width""": 18}
    __UpperCAmelCase:					Tuple		= parent
    __UpperCAmelCase:					Any		= batch_size
    __UpperCAmelCase:					str		= num_channels
    __UpperCAmelCase:					Any		= image_size
    __UpperCAmelCase:					Optional[int]		= min_resolution
    __UpperCAmelCase:					Tuple		= max_resolution
    __UpperCAmelCase:					Any		= do_resize
    __UpperCAmelCase:					Any		= size
    __UpperCAmelCase:					Optional[Any]		= do_normalize







  def    lowercase_  (							self					):

    '''simple docstring'''







    return {
        # here we create 2 clusters for the sake of simplicity
        "clusters": np.asarray(
            [
                [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
                [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
            ]					),
        "do_resize": self.do_resize,
        "size": self.size,
        "do_normalize": self.do_normalize,
    }




@require_torch
@require_vision
class    a (       __lowerCAmelCase						,  unittest.TestCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					ImageGPTImageProcessor if is_vision_available() else None







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					str		= ImageGPTImageProcessingTester(self					)







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    return self.image_processor_tester.prepare_image_processor_dict()







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Tuple		= self.image_processing_class(**self.image_processor_dict					)
    self.assertTrue(hasattr(snake_case_   ,    """clusters"""					)					)
    self.assertTrue(hasattr(snake_case_   ,    """do_resize"""					)					)
    self.assertTrue(hasattr(snake_case_   ,    """size"""					)					)
    self.assertTrue(hasattr(snake_case_   ,    """do_normalize"""					)					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= self.image_processing_class.from_dict(self.image_processor_dict					)
    self.assertEqual(image_processor.size   ,    {"""height""": 18, """width""": 18}					)

    __UpperCAmelCase:					int		= self.image_processing_class.from_dict(self.image_processor_dict   ,    size=42					)
    self.assertEqual(image_processor.size   ,    {"""height""": 42, """width""": 42}					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					int		= self.image_processing_class(**self.image_processor_dict					)
    __UpperCAmelCase:					str		= json.loads(image_processor.to_json_string()					)
    for key, value in self.image_processor_dict.items():
      if key == "clusters":
        self.assertTrue(np.array_equal(snake_case_   ,    obj[key]					)					)
      else:
        self.assertEqual(obj[key]   ,    snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= self.image_processing_class(**self.image_processor_dict					)

    with tempfile.TemporaryDirectory() as tmpdirname:
      __UpperCAmelCase:					List[Any]		= os.path.join(snake_case_   ,    """image_processor.json"""					)
      image_processor_first.to_json_file(snake_case_					)
      __UpperCAmelCase:					List[str]		= self.image_processing_class.from_json_file(snake_case_					).to_dict()

    __UpperCAmelCase:					str		= image_processor_first.to_dict()
    for key, value in image_processor_first.items():
      if key == "clusters":
        self.assertTrue(np.array_equal(snake_case_   ,    image_processor_second[key]					)					)
      else:
        self.assertEqual(image_processor_first[key]   ,    snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= self.image_processing_class(**self.image_processor_dict					)

    with tempfile.TemporaryDirectory() as tmpdirname:
      image_processor_first.save_pretrained(snake_case_					)
      __UpperCAmelCase:					Any		= self.image_processing_class.from_pretrained(snake_case_					).to_dict()

    __UpperCAmelCase:					str		= image_processor_first.to_dict()
    for key, value in image_processor_first.items():
      if key == "clusters":
        self.assertTrue(np.array_equal(snake_case_   ,    image_processor_second[key]					)					)
      else:
        self.assertEqual(image_processor_first[key]   ,    snake_case_					)







  @unittest.skip("""ImageGPT requires clusters at initialization"""					)
  def    lowercase_  (							self					):

    '''simple docstring'''







    pass






def  UpperCamelCase__					(					)							->	List[str]:
  __UpperCAmelCase:					Any		= load_dataset("""hf-internal-testing/fixtures_image_utils"""   ,    split="""test"""     )

  __UpperCAmelCase:					List[Any]		= Image.open(dataset[4]["""file"""]     )
  __UpperCAmelCase:					Dict		= Image.open(dataset[5]["""file"""]     )

  __UpperCAmelCase:					Dict		= [imagea, imagea]

  return images







@require_vision
@require_torch
class    a (       unittest.TestCase				):



  """simple docstring"""







  @slow
  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""					)

    __UpperCAmelCase:					Dict		= prepare_images()

    # test non-batched
    __UpperCAmelCase:					Tuple		= image_processing(images[0]   ,    return_tensors="""pt"""					)

    self.assertIsInstance(encoding.input_ids   ,    torch.LongTensor					)
    self.assertEqual(encoding.input_ids.shape   ,    (1, 1024)					)

    __UpperCAmelCase:					int		= [306, 191, 191]
    self.assertEqual(encoding.input_ids[0, :3].tolist()   ,    snake_case_					)

    # test batched
    __UpperCAmelCase:					Union[str, Any]		= image_processing(snake_case_   ,    return_tensors="""pt"""					)

    self.assertIsInstance(encoding.input_ids   ,    torch.LongTensor					)
    self.assertEqual(encoding.input_ids.shape   ,    (2, 1024)					)

    __UpperCAmelCase:					Tuple		= [303, 13, 13]
    self.assertEqual(encoding.input_ids[1, -3:].tolist()   ,    snake_case_					)