




'''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    __lowercase						(  unittest.TestCase ):







   def __init__(	self      ,							UpperCamelCase      ,							UpperCamelCase=7      ,							UpperCamelCase=3      ,							UpperCamelCase=18      ,							UpperCamelCase=30      ,							UpperCamelCase=400      ,							UpperCamelCase=True      ,							UpperCamelCase=None      ,							UpperCamelCase=True      ,							)				->    Any:
     __a			=					size if size is not None else {'height': 18, 'width': 18}
     __a			=					parent
     __a			=					batch_size
     __a			=					num_channels
     __a			=					image_size
     __a			=					min_resolution
     __a			=					max_resolution
     __a			=					do_resize
     __a			=					size
     __a			=					do_normalize







   def 		UpperCamelCase__			(	self      )				->    int:
     return {
         # here we create 2 clusters for the sake of simplicity
         "clusters": np.asarray(
             [
                 [0.8_866_443_634_033_203, 0.6_618_829_369_544_983, 0.3_891_746_401_786_804],
                 [-0.6_042_559_146_881_104, -0.02_295_008_860_528_469, 0.5_423_797_369_003_296],
             ]      ),
         "do_resize": self.do_resize,
         "size": self.size,
         "do_normalize": self.do_normalize,
     }





@require_torch
@require_vision
class    __lowercase						(  __lowercase       ,							unittest.TestCase ):
   _a							=      ImageGPTImageProcessor if is_vision_available() else None







   def 		UpperCamelCase__			(	self      )				->    int:
     __a			=					ImageGPTImageProcessingTester(self      )







   @property
   def 		UpperCamelCase__			(	self      )				->    Any:
     return self.image_processor_tester.prepare_image_processor_dict()







   def 		UpperCamelCase__			(	self      )				->    str:
     __a			=					self.image_processing_class(**self.image_processor_dict      )
     self.assertTrue(hasattr(_A      ,							'clusters'      )      )
     self.assertTrue(hasattr(_A      ,							'do_resize'      )      )
     self.assertTrue(hasattr(_A      ,							'size'      )      )
     self.assertTrue(hasattr(_A      ,							'do_normalize'      )      )







   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     __a			=					self.image_processing_class.from_dict(self.image_processor_dict      )
     self.assertEqual(image_processor.size      ,							{'height': 18, 'width': 18}      )

     __a			=					self.image_processing_class.from_dict(self.image_processor_dict      ,							size=42      )
     self.assertEqual(image_processor.size      ,							{'height': 42, 'width': 42}      )







   def 		UpperCamelCase__			(	self      )				->    Dict:
     __a			=					self.image_processing_class(**self.image_processor_dict      )
     __a			=					json.loads(image_processor.to_json_string()      )
     for key, value in self.image_processor_dict.items():
       if key == "clusters":
         self.assertTrue(np.array_equal(_A      ,							obj[key]      )      )
       else:
         self.assertEqual(obj[key]      ,							_A      )







   def 		UpperCamelCase__			(	self      )				->    List[str]:
     __a			=					self.image_processing_class(**self.image_processor_dict      )

     with tempfile.TemporaryDirectory() as tmpdirname:
       __a			=					os.path.join(_A      ,							'image_processor.json'      )
       image_processor_first.to_json_file(_A      )
       __a			=					self.image_processing_class.from_json_file(_A      ).to_dict()

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







   def 		UpperCamelCase__			(	self      )				->    Any:
     __a			=					self.image_processing_class(**self.image_processor_dict      )

     with tempfile.TemporaryDirectory() as tmpdirname:
       image_processor_first.save_pretrained(_A      )
       __a			=					self.image_processing_class.from_pretrained(_A      ).to_dict()

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







   @unittest.skip('ImageGPT requires clusters at initialization'      )
   def 		UpperCamelCase__			(	self      )				->    Any:
     pass


def 						SCREAMING_SNAKE_CASE       (      ):
  __a			=					load_dataset('hf-internal-testing/fixtures_image_utils'				,					split='test'			)

  __a			=					Image.open(dataset[4]['file']			)
  __a			=					Image.open(dataset[5]['file']			)

  __a			=					[imagea, imagea]

  return images





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







   @slow
   def 		UpperCamelCase__			(	self      )				->    List[Any]:
     __a			=					ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'      )

     __a			=					prepare_images()

     # test non-batched
     __a			=					image_processing(images[0]      ,							return_tensors='pt'      )

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

     __a			=					[306, 191, 191]
     self.assertEqual(encoding.input_ids[0, :3].tolist()      ,							_A      )

     # test batched
     __a			=					image_processing(_A      ,							return_tensors='pt'      )

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

     __a			=					[303, 13, 13]
     self.assertEqual(encoding.input_ids[1, -3:].tolist()      ,							_A      )








'''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


UpperCAmelCase_ 		=      logging.get_logger(__name__)


UpperCAmelCase_ 		=      TypeVar("DatasetType", Dataset, IterableDataset)


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[DatasetType]				,					a_	:							Optional[List[float]] = None				,					a_	:							Optional[int] = None				,					a_	:							Optional[DatasetInfo] = None				,					a_	:							Optional[NamedSplit] = None				,					a_	:							Literal["first_exhausted", "all_exhausted"] = "first_exhausted"				,					):
  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(a_			):
    if not isinstance(a_				,					(Dataset, IterableDataset)			):
      if isinstance(a_				,					(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(a_			)}\n"
            f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(a_			)			)}']"			)
      raise ValueError(
          f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(a_			).__name__}."			)
    if i == 0:
      __a	,       __a			=					(
          (Dataset, IterableDataset) if isinstance(a_				,					a_			) else (IterableDataset, Dataset)
      )
    elif not isinstance(a_				,					a_			):
      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(
        a_				,					a_				,					a_				,					info=a_				,					split=a_				,					stopping_strategy=a_			)
  else:
    return _interleave_iterable_datasets(
        a_				,					a_				,					a_				,					info=a_				,					split=a_				,					stopping_strategy=a_			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[DatasetType]				,					a_	:							Optional[DatasetInfo] = None				,					a_	:							Optional[NamedSplit] = None				,					a_	:							int = 0				,					):

  if not dsets:
    raise ValueError('Unable to concatenate an empty list of datasets.'			)
  for i, dataset in enumerate(a_			):
    if not isinstance(a_				,					(Dataset, IterableDataset)			):
      if isinstance(a_				,					(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(a_			)}\n"
            f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(a_			)			)}']"			)
      raise ValueError(
          f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(a_			).__name__}."			)
    if i == 0:
      __a	,       __a			=					(
          (Dataset, IterableDataset) if isinstance(a_				,					a_			) else (IterableDataset, Dataset)
      )
    elif not isinstance(a_				,					a_			):
      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(a_				,					info=a_				,					split=a_				,					axis=a_			)
  else:
    return _concatenate_iterable_datasets(a_				,					info=a_				,					split=a_				,					axis=a_			)



