




"""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				):







 '''simple docstring'''





 def __init__(       self:	Union[str, Any],   a_:	int,   a_:	Optional[int]=7,   a_:	Any=3,   a_:	Any=18,   a_:	Union[str, Any]=30,   a_:	Union[str, Any]=400,   a_:	Any=True,   a_:	Tuple=None,   a_:	Tuple=True,   ):




   '''simple docstring'''



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


 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   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(  __a ,    unittest.TestCase				):







 '''simple docstring'''





 lowercase__				      =      ImageGPTImageProcessor if is_vision_available() else None


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   _snake_case							:	str			=						ImageGPTImageProcessingTester(self	)


 @property
 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   return self.image_processor_tester.prepare_image_processor_dict()


 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   _snake_case							:	Dict			=						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:	Any	):




   '''simple docstring'''



   _snake_case							:	str			=						self.image_processing_class.from_dict(self.image_processor_dict	)
   self.assertEqual(image_processor.size,   {"""height""": 18, """width""": 18}	)

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


 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   _snake_case							:	Dict			=						self.image_processing_class(**self.image_processor_dict	)
   _snake_case							:	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(a_,   obj[key]	)	)
     else:
       self.assertEqual(obj[key],   a_	)


 def 				UpperCamelCase_     (       self:	Union[str, Any]	):




   '''simple docstring'''



   _snake_case							:	Optional[Any]			=						self.image_processing_class(**self.image_processor_dict	)

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

   _snake_case							:	List[str]			=						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:	Tuple	):




   '''simple docstring'''



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

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

   _snake_case							:	Optional[Any]			=						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:	List[str]	):




   '''simple docstring'''



   pass







def       UpperCAmelCase__ ():
  """simple docstring"""

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

  _snake_case							:	Dict			=						Image.open(dataset[4]["""file"""]					)
  _snake_case							:	Union[str, Any]			=						Image.open(dataset[5]["""file"""]					)

  _snake_case							:	Optional[Any]			=						[imagea, imagea]

  return images






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







 '''simple docstring'''





 @slow
 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   _snake_case							:	str			=						ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""	)

   _snake_case							:	List[str]			=						prepare_images()

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

   self.assertIsInstance(encoding.input_ids,   torch.LongTensor	)
   self.assertEqual(encoding.input_ids.shape,   (1, 1_024)	)

   _snake_case							:	Any			=						[306, 191, 191]
   self.assertEqual(encoding.input_ids[0, :3].tolist(),   a_	)

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

   self.assertIsInstance(encoding.input_ids,   torch.LongTensor	)
   self.assertEqual(encoding.input_ids.shape,   (2, 1_024)	)

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








"""simple docstring"""


import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases


def       UpperCAmelCase__ (snake_case__							:      Optional[Any]	,     snake_case__							:      Dict					):
  """simple docstring"""

  assert isinstance(snake_case__	,     snake_case__					)
  assert dataset.num_rows == 4
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_1", "col_2", "col_3"]
  for feature, expected_dtype in expected_features.items():
    assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize("""keep_in_memory"""	,     [False, True]					)
def       UpperCAmelCase__ (snake_case__							:      str	,     snake_case__							:      Optional[int]	,     snake_case__							:      Dict					):
  """simple docstring"""

  _snake_case							:	str			=						tmp_path / """cache"""
  _snake_case							:	int			=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    _snake_case							:	str			=						ParquetDatasetReader(snake_case__	,     cache_dir=snake_case__	,     keep_in_memory=snake_case__					).read()
  _check_parquet_dataset(snake_case__	,     snake_case__					)


@pytest.mark.parametrize(
    """features"""	,     [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]	,     )
def       UpperCAmelCase__ (snake_case__							:      Dict	,     snake_case__							:      int	,     snake_case__							:      List[Any]					):
  """simple docstring"""

  _snake_case							:	str			=						tmp_path / """cache"""
  _snake_case							:	List[Any]			=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  _snake_case							:	List[Any]			=						features.copy() if features else default_expected_features
  _snake_case							:	List[Any]			=						(
      Features({feature: Value(snake_case__					) for feature, dtype in features.items()}					) if features is not None else None
  )
  _snake_case							:	Optional[Any]			=						ParquetDatasetReader(snake_case__	,     features=snake_case__	,     cache_dir=snake_case__					).read()
  _check_parquet_dataset(snake_case__	,     snake_case__					)


@pytest.mark.parametrize("""split"""	,     [None, NamedSplit("""train"""					), """train""", """test"""]					)
def       UpperCAmelCase__ (snake_case__							:      Optional[int]	,     snake_case__							:      int	,     snake_case__							:      int					):
  """simple docstring"""

  _snake_case							:	List[str]			=						tmp_path / """cache"""
  _snake_case							:	Any			=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  _snake_case							:	int			=						ParquetDatasetReader(snake_case__	,     cache_dir=snake_case__	,     split=snake_case__					).read()
  _check_parquet_dataset(snake_case__	,     snake_case__					)
  assert dataset.split == split if split else "train"


@pytest.mark.parametrize("""path_type"""	,     [str, list]					)
def       UpperCAmelCase__ (snake_case__							:      Dict	,     snake_case__							:      str	,     snake_case__							:      str					):
  """simple docstring"""

  if issubclass(snake_case__	,     snake_case__					):
    _snake_case							:	Optional[Any]			=						parquet_path
  elif issubclass(snake_case__	,     snake_case__					):
    _snake_case							:	int			=						[parquet_path]
  _snake_case							:	Union[str, Any]			=						tmp_path / """cache"""
  _snake_case							:	Tuple			=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  _snake_case							:	List[str]			=						ParquetDatasetReader(snake_case__	,     cache_dir=snake_case__					).read()
  _check_parquet_dataset(snake_case__	,     snake_case__					)


def       UpperCAmelCase__ (snake_case__							:      str	,     snake_case__							:      Optional[int]	,     snake_case__							:      str=("train",)					):
  """simple docstring"""

  assert isinstance(snake_case__	,     snake_case__					)
  for split in splits:
    _snake_case							:	int			=						dataset_dict[split]
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize("""keep_in_memory"""	,     [False, True]					)
def       UpperCAmelCase__ (snake_case__							:      str	,     snake_case__							:      str	,     snake_case__							:      List[Any]					):
  """simple docstring"""

  _snake_case							:	Tuple			=						tmp_path / """cache"""
  _snake_case							:	Optional[int]			=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    _snake_case							:	Tuple			=						ParquetDatasetReader(
        {"""train""": parquet_path}	,     cache_dir=snake_case__	,     keep_in_memory=snake_case__					).read()
  _check_parquet_datasetdict(snake_case__	,     snake_case__					)


@pytest.mark.parametrize(
    """features"""	,     [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]	,     )
def       UpperCAmelCase__ (snake_case__							:      List[str]	,     snake_case__							:      Tuple	,     snake_case__							:      List[Any]					):
  """simple docstring"""

  _snake_case							:	Optional[int]			=						tmp_path / """cache"""
  _snake_case							:	Dict			=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  _snake_case							:	Optional[Any]			=						features.copy() if features else default_expected_features
  _snake_case							:	Dict			=						(
      Features({feature: Value(snake_case__					) for feature, dtype in features.items()}					) if features is not None else None
  )
  _snake_case							:	Optional[int]			=						ParquetDatasetReader({"""train""": parquet_path}	,     features=snake_case__	,     cache_dir=snake_case__					).read()
  _check_parquet_datasetdict(snake_case__	,     snake_case__					)


@pytest.mark.parametrize("""split"""	,     [None, NamedSplit("""train"""					), """train""", """test"""]					)
def       UpperCAmelCase__ (snake_case__							:      List[str]	,     snake_case__							:      Optional[Any]	,     snake_case__							:      Tuple					):
  """simple docstring"""

  if split:
    _snake_case							:	int			=						{split: parquet_path}
  else:
    _snake_case							:	Optional[Any]			=						"""train"""
    _snake_case							:	int			=						{"""train""": parquet_path, """test""": parquet_path}
  _snake_case							:	Dict			=						tmp_path / """cache"""
  _snake_case							:	Any			=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  _snake_case							:	Union[str, Any]			=						ParquetDatasetReader(snake_case__	,     cache_dir=snake_case__					).read()
  _check_parquet_datasetdict(snake_case__	,     snake_case__	,     splits=list(path.keys()					)					)
  assert all(dataset[split].split == split for split in path.keys()					)


def       UpperCAmelCase__ (snake_case__							:      Tuple	,     snake_case__							:      Tuple					):
  """simple docstring"""

  _snake_case							:	List[Any]			=						ParquetDatasetWriter(snake_case__	,     tmp_path / """foo.parquet"""					)
  assert writer.write() > 0
  _snake_case							:	str			=						pq.ParquetFile(tmp_path / """foo.parquet"""					)
  _snake_case							:	int			=						pf.read()
  assert dataset.data.table == output_table


def       UpperCAmelCase__ (snake_case__							:      int	,     snake_case__							:      int					):
  """simple docstring"""

  _snake_case							:	Optional[Any]			=						str(shared_datadir / """test_image_rgb.jpg"""					)
  _snake_case							:	Tuple			=						{"""image""": [image_path]}
  _snake_case							:	Optional[int]			=						Features({"""image""": Image()}					)
  _snake_case							:	int			=						Dataset.from_dict(snake_case__	,     features=snake_case__					)
  _snake_case							:	Optional[Any]			=						ParquetDatasetWriter(snake_case__	,     tmp_path / """foo.parquet"""					)
  assert writer.write() > 0

  _snake_case							:	List[str]			=						Dataset.from_parquet(str(tmp_path / """foo.parquet"""					)					)
  assert dataset.features == reloaded_dataset.features

  _snake_case							:	Optional[Any]			=						ParquetDatasetReader(str(tmp_path / """foo.parquet"""					)	,     streaming=snake_case__					).read()
  assert dataset.features == reloaded_iterable_dataset.features






@pytest.mark.parametrize(
    """feature, expected"""	,     [
        (Features({"""foo""": Value("""int32"""					)}					), None),
        (Features({"""image""": Image(), """foo""": Value("""int32"""					)}					), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({"""nested""": Sequence(Audio()					)}					), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]	,     )
def       UpperCAmelCase__ (snake_case__							:      List[Any]	,     snake_case__							:      str					):
  """simple docstring"""

  assert get_writer_batch_size(snake_case__					) == expected


