
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 ConvNextImageProcessor







class      lowerCamelCase		(  unittest.TestCase  ):





     def __init__(self      :    Optional[Any]				,    _A      :    Optional[int]				,    _A      :    Any=7				,    _A      :    Union[str, Any]=3				,    _A      :    List[str]=1_8				,    _A      :    Optional[int]=3_0				,    _A      :    int=4_0_0				,    _A      :    int=True				,    _A      :    List[Any]=None				,    _A      :    Tuple=0.8_75				,    _A      :    str=True				,    _A      :    Optional[Any]=[0.5, 0.5, 0.5]				,    _A      :    List[Any]=[0.5, 0.5, 0.5]				,    )						->						List[Any]:
         snake_case      =					size if size is not None else {"shortest_edge": 2_0}
         snake_case      =					parent
         snake_case      =					batch_size
         snake_case      =					num_channels
         snake_case      =					image_size
         snake_case      =					min_resolution
         snake_case      =					max_resolution
         snake_case      =					do_resize
         snake_case      =					size
         snake_case      =					crop_pct
         snake_case      =					do_normalize
         snake_case      =					image_mean
         snake_case      =					image_std





     def 		UpperCAmelCase(self      :    str   )						->						List[str]:
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "crop_pct": self.crop_pct,
         }







@require_torch
@require_vision
class      lowerCamelCase		(  A_							, unittest.TestCase  ):
     UpperCAmelCase__				:							str           =		ConvNextImageProcessor if is_vision_available() else None





     def 		UpperCAmelCase(self      :    Optional[int]   )						->						Union[str, Any]:
         snake_case      =					ConvNextImageProcessingTester(self   )





     @property
     def 		UpperCAmelCase(self      :    str   )						->						Optional[Any]:
         return self.image_processor_tester.prepare_image_processor_dict()





     def 		UpperCAmelCase(self      :    str   )						->						List[Any]:
         snake_case      =					self.image_processing_class(**self.image_processor_dict   )
         self.assertTrue(hasattr(UpperCAmelCase_				,    "do_resize"   )   )
         self.assertTrue(hasattr(UpperCAmelCase_				,    "size"   )   )
         self.assertTrue(hasattr(UpperCAmelCase_				,    "crop_pct"   )   )
         self.assertTrue(hasattr(UpperCAmelCase_				,    "do_normalize"   )   )
         self.assertTrue(hasattr(UpperCAmelCase_				,    "image_mean"   )   )
         self.assertTrue(hasattr(UpperCAmelCase_				,    "image_std"   )   )





     def 		UpperCAmelCase(self      :    Union[str, Any]   )						->						Optional[Any]:
         snake_case      =					self.image_processing_class.from_dict(self.image_processor_dict   )
         self.assertEqual(image_processor.size				,    {"shortest_edge": 2_0}   )

         snake_case      =					self.image_processing_class.from_dict(self.image_processor_dict				,    size=4_2   )
         self.assertEqual(image_processor.size				,    {"shortest_edge": 4_2}   )





     def 		UpperCAmelCase(self      :    List[str]   )						->						List[str]:
         pass





     def 		UpperCAmelCase(self      :    Optional[Any]   )						->						Optional[int]:
         # Initialize image_processing
         snake_case      =					self.image_processing_class(**self.image_processor_dict   )
         # create random PIL images
         snake_case      =					prepare_image_inputs(self.image_processor_tester				,    equal_resolution=UpperCAmelCase_   )
         for image in image_inputs:
             self.assertIsInstance(UpperCAmelCase_				,    Image.Image   )

         # Test not batched input
         snake_case      =					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.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             )				,    )

         # Test batched
         snake_case      =					image_processing(UpperCAmelCase_				,    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.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             )				,    )





     def 		UpperCAmelCase(self      :    Optional[Any]   )						->						str:
         # Initialize image_processing
         snake_case      =					self.image_processing_class(**self.image_processor_dict   )
         # create random numpy tensors
         snake_case      =					prepare_image_inputs(self.image_processor_tester				,    equal_resolution=UpperCAmelCase_				,    numpify=UpperCAmelCase_   )
         for image in image_inputs:
             self.assertIsInstance(UpperCAmelCase_				,    np.ndarray   )

         # Test not batched input
         snake_case      =					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.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             )				,    )

         # Test batched
         snake_case      =					image_processing(UpperCAmelCase_				,    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.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             )				,    )





     def 		UpperCAmelCase(self      :    Tuple   )						->						Optional[Any]:
         # Initialize image_processing
         snake_case      =					self.image_processing_class(**self.image_processor_dict   )
         # create random PyTorch tensors
         snake_case      =					prepare_image_inputs(self.image_processor_tester				,    equal_resolution=UpperCAmelCase_				,    torchify=UpperCAmelCase_   )
         for image in image_inputs:
             self.assertIsInstance(UpperCAmelCase_				,    torch.Tensor   )

         # Test not batched input
         snake_case      =					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.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             )				,    )

         # Test batched
         snake_case      =					image_processing(UpperCAmelCase_				,    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.size["shortest_edge"],
                 self.image_processor_tester.size["shortest_edge"],
             )				,    )









import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases


def      lowerCamelCase__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  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


@pytest.mark.parametrize("keep_in_memory"  ,   [False, True] )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Dict		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			CsvDatasetReader(lowercase  ,   cache_dir=lowercase  ,   keep_in_memory=lowercase ).read()
  _check_csv_dataset(lowercase  ,   lowercase )


@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      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Any		  =			tmp_path / "cache"
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE				:					Any		  =			features.copy() if features else default_expected_features
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			(
      Features({feature: Value(lowercase ) for feature, dtype in features.items()} ) if features is not None else None
  )
  SCREAMING_SNAKE_CASE				:					Tuple		  =			CsvDatasetReader(lowercase  ,   features=lowercase  ,   cache_dir=lowercase ).read()
  _check_csv_dataset(lowercase  ,   lowercase )


@pytest.mark.parametrize("split"  ,   [None, NamedSplit("train" ), "train", "test"] )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Dict		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Dict		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			CsvDatasetReader(lowercase  ,   cache_dir=lowercase  ,   split=lowercase ).read()
  _check_csv_dataset(lowercase  ,   lowercase )
  assert dataset.split == split if split else "train"


@pytest.mark.parametrize("path_type"  ,   [str, list] )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  if issubclass(lowercase  ,   lowercase ):
    SCREAMING_SNAKE_CASE				:					Tuple		  =			csv_path
  elif issubclass(lowercase  ,   lowercase ):
    SCREAMING_SNAKE_CASE				:					int		  =			[csv_path]
  SCREAMING_SNAKE_CASE				:					Any		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE				:					Tuple		  =			CsvDatasetReader(lowercase  ,   cache_dir=lowercase ).read()
  _check_csv_dataset(lowercase  ,   lowercase )


def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase=("train",) ):

  """simple docstring"""






  assert isinstance(lowercase  ,   lowercase )
  for split in splits:
    SCREAMING_SNAKE_CASE				:					str		  =			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      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			CsvDatasetReader({"train": csv_path}  ,   cache_dir=lowercase  ,   keep_in_memory=lowercase ).read()
  _check_csv_datasetdict(lowercase  ,   lowercase )


@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      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					int		  =			tmp_path / "cache"
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  SCREAMING_SNAKE_CASE				:					Any		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			features.copy() if features else default_expected_features
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			(
      Features({feature: Value(lowercase ) for feature, dtype in features.items()} ) if features is not None else None
  )
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			CsvDatasetReader({"train": csv_path}  ,   features=lowercase  ,   cache_dir=lowercase ).read()
  _check_csv_datasetdict(lowercase  ,   lowercase )


@pytest.mark.parametrize("split"  ,   [None, NamedSplit("train" ), "train", "test"] )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  if split:
    SCREAMING_SNAKE_CASE				:					Dict		  =			{split: csv_path}
  else:
    SCREAMING_SNAKE_CASE				:					str		  =			{"train": csv_path, "test": csv_path}
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Dict		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE				:					Tuple		  =			CsvDatasetReader(lowercase  ,   cache_dir=lowercase ).read()
  _check_csv_datasetdict(lowercase  ,   lowercase  ,   splits=list(path.keys() ) )
  assert all(dataset[split].split == split for split in path.keys() )


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  with open(lowercase  ,   encoding="utf-8" ) as csvfile:
    yield from csv.reader(lowercase )


def      lowerCamelCase__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					List[Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			os.path.join(lowercase  ,   "tmp.csv" )
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			CsvDatasetReader({"train": csv_path}  ,   cache_dir=lowercase ).read()
  CsvDatasetWriter(dataset["train"]  ,   lowercase  ,   num_proc=1 ).write()

  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			iter_csv_file(lowercase )
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			iter_csv_file(lowercase )

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


def      lowerCamelCase__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			os.path.join(lowercase  ,   "tmp.csv" )
  SCREAMING_SNAKE_CASE				:					List[str]		  =			CsvDatasetReader({"train": csv_path}  ,   cache_dir=lowercase ).read()
  CsvDatasetWriter(dataset["train"]  ,   lowercase  ,   num_proc=2 ).write()

  SCREAMING_SNAKE_CASE				:					Dict		  =			iter_csv_file(lowercase )
  SCREAMING_SNAKE_CASE				:					Tuple		  =			iter_csv_file(lowercase )

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


def      lowerCamelCase__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			os.path.join(lowercase  ,   "tmp.csv" )
  SCREAMING_SNAKE_CASE				:					List[str]		  =			CsvDatasetReader({"train": csv_path}  ,   cache_dir=lowercase ).read()
  with pytest.raises(lowercase ):
    CsvDatasetWriter(dataset["train"]  ,   lowercase  ,   num_proc=0 )






