



import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def 		UpperCamelCase	(   snake_case__       :    List[str]       ,     snake_case__       :    List[Any]	)	->     List[str]:
   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
@require_sqlalchemy
@pytest.mark.parametrize('keep_in_memory'       ,     [False, True]	)
def 		UpperCamelCase	(   snake_case__       :    int       ,     snake_case__       :    str       ,     snake_case__       :    Tuple       ,     snake_case__       :    Optional[Any]	)	->     Dict:
   UpperCamelCase    :  Optional[int]							=	tmp_path / 'cache'
   UpperCamelCase    :  Any							=	{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      UpperCamelCase    :  Tuple							=	SqlDatasetReader(
          'dataset'       ,     'sqlite:///' + sqlite_path       ,     cache_dir=snake_case__       ,     keep_in_memory=snake_case__	).read()
   _check_sql_dataset(snake_case__       ,     snake_case__	)
@require_sqlalchemy
@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__       :    str       ,     snake_case__       :    int       ,     snake_case__       :    List[str]       ,     snake_case__       :    int	)	->     List[str]:
   UpperCamelCase    :  str							=	tmp_path / 'cache'
   UpperCamelCase    :  Dict							=	{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
   UpperCamelCase    :  Optional[int]							=	features.copy() if features else default_expected_features
   UpperCamelCase    :  Union[str, Any]							=	(
       Features({feature: Value(snake_case__	) for feature, dtype in features.items()}	) if features is not None else None
   )
   UpperCamelCase    :  int							=	SqlDatasetReader('dataset'       ,     'sqlite:///' + sqlite_path       ,     features=snake_case__       ,     cache_dir=snake_case__	).read()
   _check_sql_dataset(snake_case__       ,     snake_case__	)
def 		UpperCamelCase	(   snake_case__       :    Dict	)	->     Tuple:
   with contextlib.closing(sqlitea.connect(snake_case__	)	) as con:
      UpperCamelCase    :  List[str]							=	con.cursor()
      cur.execute('SELECT * FROM dataset'	)
      for row in cur:
         yield row
@require_sqlalchemy
def 		UpperCamelCase	(   snake_case__       :    Optional[Any]       ,     snake_case__       :    Optional[int]       ,     snake_case__       :    Any	)	->     Optional[Any]:
   UpperCamelCase    :  Any							=	tmp_path / 'cache'
   UpperCamelCase    :  Any							=	os.path.join(snake_case__       ,     'tmp.sql'	)
   UpperCamelCase    :  Tuple							=	SqlDatasetReader('dataset'       ,     'sqlite:///' + sqlite_path       ,     cache_dir=snake_case__	).read()
   SqlDatasetWriter(snake_case__       ,     'dataset'       ,     'sqlite:///' + output_sqlite_path       ,     num_proc=1	).write()

   UpperCamelCase    :  Dict							=	iter_sql_file(snake_case__	)
   UpperCamelCase    :  Optional[int]							=	iter_sql_file(snake_case__	)

   for rowa, rowa in zip(snake_case__       ,     snake_case__	):
      assert rowa == rowa
@require_sqlalchemy
def 		UpperCamelCase	(   snake_case__       :    List[Any]       ,     snake_case__       :    Union[str, Any]       ,     snake_case__       :    Dict	)	->     Union[str, Any]:
   UpperCamelCase    :  List[str]							=	tmp_path / 'cache'
   UpperCamelCase    :  List[Any]							=	os.path.join(snake_case__       ,     'tmp.sql'	)
   UpperCamelCase    :  Union[str, Any]							=	SqlDatasetReader('dataset'       ,     'sqlite:///' + sqlite_path       ,     cache_dir=snake_case__	).read()
   SqlDatasetWriter(snake_case__       ,     'dataset'       ,     'sqlite:///' + output_sqlite_path       ,     num_proc=2	).write()

   UpperCamelCase    :  Tuple							=	iter_sql_file(snake_case__	)
   UpperCamelCase    :  List[str]							=	iter_sql_file(snake_case__	)

   for rowa, rowa in zip(snake_case__       ,     snake_case__	):
      assert rowa == rowa


@require_sqlalchemy
def 		UpperCamelCase	(   snake_case__       :    List[Any]       ,     snake_case__       :    str       ,     snake_case__       :    List[str]	)	->     List[Any]:
   UpperCamelCase    :  List[Any]							=	tmp_path / 'cache'
   UpperCamelCase    :  Tuple							=	os.path.join(snake_case__       ,     'tmp.sql'	)
   UpperCamelCase    :  int							=	SqlDatasetReader('dataset'       ,     'sqlite:///' + sqlite_path       ,     cache_dir=snake_case__	).read()
   with pytest.raises(snake_case__	):
      SqlDatasetWriter(snake_case__       ,     'dataset'       ,     'sqlite:///' + output_sqlite_path       ,     num_proc=0	).write()








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 				lowerCAmelCase_		(  unittest.TestCase							):
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=7,			SCREAMING_SNAKE_CASE_=3,			SCREAMING_SNAKE_CASE_=18,			SCREAMING_SNAKE_CASE_=30,			SCREAMING_SNAKE_CASE_=400,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=True,			)     ->		Union[str, Any]:
          UpperCamelCase    :  str							=	size if size is not None else {'height': 18, 'width': 18}
          UpperCamelCase    :  int							=	parent
          UpperCamelCase    :  List[Any]							=	batch_size
          UpperCamelCase    :  int							=	num_channels
          UpperCamelCase    :  Any							=	image_size
          UpperCamelCase    :  Optional[int]							=	min_resolution
          UpperCamelCase    :  Optional[Any]							=	max_resolution
          UpperCamelCase    :  Union[str, Any]							=	do_resize
          UpperCamelCase    :  List[Any]							=	size
          UpperCamelCase    :  int							=	do_normalize






       def 					snake_case_       (			self       )     ->		Tuple:
          return {
              # here we create 2 clusters for the sake of simplicity
              "clusters": np.asarray(
                  [
                      [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
                      [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
                  ]       ),
              "do_resize": self.do_resize,
              "size": self.size,
              "do_normalize": self.do_normalize,
          }






@require_torch
@require_vision
class 				lowerCAmelCase_		(  a__       ,    unittest.TestCase							):
       UpperCAmelCase__				:					Any        =  ImageGPTImageProcessor if is_vision_available() else None
       def 					snake_case_       (			self       )     ->		int:
          UpperCamelCase    :  str							=	ImageGPTImageProcessingTester(self       )
       @property
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          return self.image_processor_tester.prepare_image_processor_dict()
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  int							=	self.image_processing_class(**self.image_processor_dict       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'clusters'       )       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'do_resize'       )       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'size'       )       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'do_normalize'       )       )
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  Tuple							=	self.image_processing_class.from_dict(self.image_processor_dict       )
          self.assertEqual(image_processor.size,			{'height': 18, 'width': 18}       )

          UpperCamelCase    :  Dict							=	self.image_processing_class.from_dict(self.image_processor_dict,			size=42       )
          self.assertEqual(image_processor.size,			{'height': 42, 'width': 42}       )
       def 					snake_case_       (			self       )     ->		List[str]:
          UpperCamelCase    :  str							=	self.image_processing_class(**self.image_processor_dict       )
          UpperCamelCase    :  int							=	json.loads(image_processor.to_json_string()       )
          for key, value in self.image_processor_dict.items():
             if key == "clusters":
                self.assertTrue(np.array_equal(SCREAMING_SNAKE_CASE_,			obj[key]       )       )
             else:
                self.assertEqual(obj[key],			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Dict:
          UpperCamelCase    :  Tuple							=	self.image_processing_class(**self.image_processor_dict       )

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

          UpperCamelCase    :  List[Any]							=	image_processor_first.to_dict()
          for key, value in image_processor_first.items():
             if key == "clusters":
                self.assertTrue(np.array_equal(SCREAMING_SNAKE_CASE_,			image_processor_second[key]       )       )
             else:
                self.assertEqual(image_processor_first[key],			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Optional[int]:
          UpperCamelCase    :  Optional[Any]							=	self.image_processing_class(**self.image_processor_dict       )

          with tempfile.TemporaryDirectory() as tmpdirname:
             image_processor_first.save_pretrained(SCREAMING_SNAKE_CASE_       )
             UpperCamelCase    :  str							=	self.image_processing_class.from_pretrained(SCREAMING_SNAKE_CASE_       ).to_dict()

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






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






def 		UpperCamelCase	(   )	->     int:
   UpperCamelCase    :  Optional[int]							=	load_dataset('hf-internal-testing/fixtures_image_utils'       ,     split='test'	)

   UpperCamelCase    :  int							=	Image.open(dataset[4]['file']	)
   UpperCamelCase    :  Optional[Any]							=	Image.open(dataset[5]['file']	)

   UpperCamelCase    :  str							=	[imagea, imagea]

   return images



@require_vision
@require_torch
class 				lowerCAmelCase_		(  unittest.TestCase							):
       @slow
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  List[str]							=	ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'       )

          UpperCamelCase    :  List[str]							=	prepare_images()

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

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

          UpperCamelCase    :  Union[str, Any]							=	[306, 191, 191]
          self.assertEqual(encoding.input_ids[0, :3].tolist(),			SCREAMING_SNAKE_CASE_       )

          # test batched
          UpperCamelCase    :  Tuple							=	image_processing(SCREAMING_SNAKE_CASE_,			return_tensors='pt'       )

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

          UpperCamelCase    :  Optional[Any]							=	[303, 13, 13]
          self.assertEqual(encoding.input_ids[1, -3:].tolist(),			SCREAMING_SNAKE_CASE_       )



