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 					_lowercase    (     unittest.TestCase			):






    '''simple docstring'''




    def __init__(    self       ,						lowerCamelCase__       ,						lowerCamelCase__=7       ,						lowerCamelCase__=3       ,						lowerCamelCase__=18       ,						lowerCamelCase__=30       ,						lowerCamelCase__=400       ,						lowerCamelCase__=True       ,						lowerCamelCase__=None       ,						lowerCamelCase__=True       ,						lowerCamelCase__=None       ,						lowerCamelCase__=True       ,						):
       lowerCAmelCase_:							Dict       =	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_:							Optional[Any]       =	parent
       lowerCAmelCase_:							int       =	batch_size
       lowerCAmelCase_:							Dict       =	num_channels
       lowerCAmelCase_:							str       =	image_size
       lowerCAmelCase_:							Any       =	min_resolution
       lowerCAmelCase_:							List[Any]       =	max_resolution
       lowerCAmelCase_:							Dict       =	do_resize
       lowerCAmelCase_:							Any       =	size
       lowerCAmelCase_:							int       =	do_center_crop
       lowerCAmelCase_:							Union[str, Any]       =	crop_size
       lowerCAmelCase_:							List[Any]       =	do_flip_channel_order




    def      _a    (    self ):
       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 					_lowercase    (     UpperCAmelCase__ ,  unittest.TestCase			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Optional[int]          =    MobileViTImageProcessor if is_vision_available() else None




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[Any]       =	MobileViTImageProcessingTester(self )




    @property
    def      _a    (    self ):
       return self.image_processor_tester.prepare_image_processor_dict()




    def      _a    (    self ):
       lowerCAmelCase_:							Union[str, 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      _a    (    self ):
       lowerCAmelCase_:							List[Any]       =	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_:							List[str]       =	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      _a    (    self ):
       pass




    def      _a    (    self ):
       # Initialize image_processing
       lowerCAmelCase_:							List[Any]       =	self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       lowerCAmelCase_:							Tuple       =	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_:							Union[str, 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_:							Optional[Any]       =	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      _a    (    self ):
       # Initialize image_processing
       lowerCAmelCase_:							List[str]       =	self.image_processing_class(**self.image_processor_dict )
       # create random numpy tensors
       lowerCAmelCase_:							int       =	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_:							Dict       =	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      _a    (    self ):
       # Initialize image_processing
       lowerCAmelCase_:							Optional[int]       =	self.image_processing_class(**self.image_processor_dict )
       # create random PyTorch tensors
       lowerCAmelCase_:							Optional[int]       =	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_:							Dict       =	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"],
           )       ,						)
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 		snake_case__	(		lowercase							,				lowercase					):
   assert isinstance(lowercase							,				lowercase					)
   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 		snake_case__	(		lowercase							,				lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							Any       =	tmp_path / "cache"
   lowerCAmelCase_:							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():
      lowerCAmelCase_:							Optional[int]       =	SqlDatasetReader(
          "dataset"							,				"sqlite:///" + sqlite_path							,				cache_dir=lowercase							,				keep_in_memory=lowercase					).read()
   _check_sql_dataset(lowercase							,				lowercase					)
@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 		snake_case__	(		lowercase							,				lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							List[str]       =	tmp_path / "cache"
   lowerCAmelCase_:							int       =	{"col_1": "string", "col_2": "int64", "col_3": "float64"}
   lowerCAmelCase_:							List[str]       =	features.copy() if features else default_expected_features
   lowerCAmelCase_:							Any       =	(
       Features({feature: Value(lowercase					) for feature, dtype in features.items()}					) if features is not None else None
   )
   lowerCAmelCase_:							int       =	SqlDatasetReader("dataset"							,				"sqlite:///" + sqlite_path							,				features=lowercase							,				cache_dir=lowercase					).read()
   _check_sql_dataset(lowercase							,				lowercase					)
def 		snake_case__	(		lowercase					):
   with contextlib.closing(sqlitea.connect(lowercase					)					) as con:
      lowerCAmelCase_:							Any       =	con.cursor()
      cur.execute("SELECT * FROM dataset"					)
      for row in cur:
         yield row
@require_sqlalchemy
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							Optional[int]       =	tmp_path / "cache"
   lowerCAmelCase_:							Optional[Any]       =	os.path.join(lowercase							,				"tmp.sql"					)
   lowerCAmelCase_:							str       =	SqlDatasetReader("dataset"							,				"sqlite:///" + sqlite_path							,				cache_dir=lowercase					).read()
   SqlDatasetWriter(lowercase							,				"dataset"							,				"sqlite:///" + output_sqlite_path							,				num_proc=1					).write()

   lowerCAmelCase_:							Union[str, Any]       =	iter_sql_file(lowercase					)
   lowerCAmelCase_:							str       =	iter_sql_file(lowercase					)

   for rowa, rowa in zip(lowercase							,				lowercase					):
      assert rowa == rowa
@require_sqlalchemy
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							str       =	tmp_path / "cache"
   lowerCAmelCase_:							Optional[int]       =	os.path.join(lowercase							,				"tmp.sql"					)
   lowerCAmelCase_:							Optional[Any]       =	SqlDatasetReader("dataset"							,				"sqlite:///" + sqlite_path							,				cache_dir=lowercase					).read()
   SqlDatasetWriter(lowercase							,				"dataset"							,				"sqlite:///" + output_sqlite_path							,				num_proc=2					).write()

   lowerCAmelCase_:							Optional[Any]       =	iter_sql_file(lowercase					)
   lowerCAmelCase_:							Optional[int]       =	iter_sql_file(lowercase					)

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







@require_sqlalchemy
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							Union[str, Any]       =	tmp_path / "cache"
   lowerCAmelCase_:							int       =	os.path.join(lowercase							,				"tmp.sql"					)
   lowerCAmelCase_:							Any       =	SqlDatasetReader("dataset"							,				"sqlite:///" + sqlite_path							,				cache_dir=lowercase					).read()
   with pytest.raises(lowercase					):
      SqlDatasetWriter(lowercase							,				"dataset"							,				"sqlite:///" + output_sqlite_path							,				num_proc=0					).write()