
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 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	assert isinstance(__SCREAMING_SNAKE_CASE			,							__SCREAMING_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_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   tmp_path / 'cache'
	lowercase					  =   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		lowercase					  =   CsvDatasetReader(__SCREAMING_SNAKE_CASE			,							cache_dir=__SCREAMING_SNAKE_CASE			,							keep_in_memory=__SCREAMING_SNAKE_CASE							).read()
	_check_csv_dataset(__SCREAMING_SNAKE_CASE			,							__SCREAMING_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_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   tmp_path / 'cache'
	# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
	lowercase					  =   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
	lowercase					  =   features.copy() if features else default_expected_features
	lowercase					  =   (
	    Features({feature: Value(__SCREAMING_SNAKE_CASE							) for feature, dtype in features.items()}							) if features is not None else None
	)
	lowercase					  =   CsvDatasetReader(__SCREAMING_SNAKE_CASE			,							features=__SCREAMING_SNAKE_CASE			,							cache_dir=__SCREAMING_SNAKE_CASE							).read()
	_check_csv_dataset(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							)






@pytest.mark.parametrize('split'			,							[None, NamedSplit('train'							), 'train', 'test']							)
def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   tmp_path / 'cache'
	lowercase					  =   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
	lowercase					  =   CsvDatasetReader(__SCREAMING_SNAKE_CASE			,							cache_dir=__SCREAMING_SNAKE_CASE			,							split=__SCREAMING_SNAKE_CASE							).read()
	_check_csv_dataset(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							)
	assert dataset.split == split if split else "train"






@pytest.mark.parametrize('path_type'			,							[str, list]							)
def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	if issubclass(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
		lowercase					  =   csv_path
	elif issubclass(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
		lowercase					  =   [csv_path]
	lowercase					  =   tmp_path / 'cache'
	lowercase					  =   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
	lowercase					  =   CsvDatasetReader(__SCREAMING_SNAKE_CASE			,							cache_dir=__SCREAMING_SNAKE_CASE							).read()
	_check_csv_dataset(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE=("train",)							):
	assert isinstance(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							)
	for split in splits:
		lowercase					  =   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_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   tmp_path / 'cache'
	lowercase					  =   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		lowercase					  =   CsvDatasetReader({'train': csv_path}			,							cache_dir=__SCREAMING_SNAKE_CASE			,							keep_in_memory=__SCREAMING_SNAKE_CASE							).read()
	_check_csv_datasetdict(__SCREAMING_SNAKE_CASE			,							__SCREAMING_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_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   tmp_path / 'cache'
	# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
	lowercase					  =   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
	lowercase					  =   features.copy() if features else default_expected_features
	lowercase					  =   (
	    Features({feature: Value(__SCREAMING_SNAKE_CASE							) for feature, dtype in features.items()}							) if features is not None else None
	)
	lowercase					  =   CsvDatasetReader({'train': csv_path}			,							features=__SCREAMING_SNAKE_CASE			,							cache_dir=__SCREAMING_SNAKE_CASE							).read()
	_check_csv_datasetdict(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							)






@pytest.mark.parametrize('split'			,							[None, NamedSplit('train'							), 'train', 'test']							)
def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	if split:
		lowercase					  =   {split: csv_path}
	else:
		lowercase					  =   {'train': csv_path, 'test': csv_path}
	lowercase					  =   tmp_path / 'cache'
	lowercase					  =   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
	lowercase					  =   CsvDatasetReader(__SCREAMING_SNAKE_CASE			,							cache_dir=__SCREAMING_SNAKE_CASE							).read()
	_check_csv_datasetdict(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							splits=list(path.keys()							)							)
	assert all(dataset[split].split == split for split in path.keys()							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	with open(__SCREAMING_SNAKE_CASE			,							encoding='utf-8'							) as csvfile:
		yield from csv.reader(__SCREAMING_SNAKE_CASE							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   tmp_path / 'cache'
	lowercase					  =   os.path.join(__SCREAMING_SNAKE_CASE			,							'tmp.csv'							)
	lowercase					  =   CsvDatasetReader({'train': csv_path}			,							cache_dir=__SCREAMING_SNAKE_CASE							).read()
	CsvDatasetWriter(dataset['train']			,							__SCREAMING_SNAKE_CASE			,							num_proc=1							).write()

	lowercase					  =   iter_csv_file(__SCREAMING_SNAKE_CASE							)
	lowercase					  =   iter_csv_file(__SCREAMING_SNAKE_CASE							)

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






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   tmp_path / 'cache'
	lowercase					  =   os.path.join(__SCREAMING_SNAKE_CASE			,							'tmp.csv'							)
	lowercase					  =   CsvDatasetReader({'train': csv_path}			,							cache_dir=__SCREAMING_SNAKE_CASE							).read()
	CsvDatasetWriter(dataset['train']			,							__SCREAMING_SNAKE_CASE			,							num_proc=2							).write()

	lowercase					  =   iter_csv_file(__SCREAMING_SNAKE_CASE							)
	lowercase					  =   iter_csv_file(__SCREAMING_SNAKE_CASE							)

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






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   tmp_path / 'cache'
	lowercase					  =   os.path.join(__SCREAMING_SNAKE_CASE			,							'tmp.csv'							)
	lowercase					  =   CsvDatasetReader({'train': csv_path}			,							cache_dir=__SCREAMING_SNAKE_CASE							).read()
	with pytest.raises(__SCREAMING_SNAKE_CASE							):
		CsvDatasetWriter(dataset['train']			,							__SCREAMING_SNAKE_CASE			,							num_proc=0							)



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_video_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import VideoMAEImageProcessor







class 	A_       (      unittest.TestCase       ):




	'''simple docstring'''


	def __init__(	self		,   snake_case		,   snake_case=7		,   snake_case=3		,   snake_case=10		,   snake_case=18		,   snake_case=30		,   snake_case=400		,   snake_case=True		,   snake_case=None		,   snake_case=True		,   snake_case=[0.5, 0.5, 0.5]		,   snake_case=[0.5, 0.5, 0.5]		,   snake_case=None		,   ):
		lowercase					  =   size if size is not None else {'shortest_edge': 18}
		lowercase					  =   crop_size if crop_size is not None else {'height': 18, 'width': 18}

		lowercase					  =   parent
		lowercase					  =   batch_size
		lowercase					  =   num_channels
		lowercase					  =   num_frames
		lowercase					  =   image_size
		lowercase					  =   min_resolution
		lowercase					  =   max_resolution
		lowercase					  =   do_resize
		lowercase					  =   size
		lowercase					  =   do_normalize
		lowercase					  =   image_mean
		lowercase					  =   image_std
		lowercase					  =   crop_size





	def      SCREAMING_SNAKE_CASE__						(	self	):
		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_size": self.crop_size,
		}







@require_torch
@require_vision
class 	A_       (      __lowerCamelCase				,     unittest.TestCase       ):




	'''simple docstring'''


	_UpperCamelCase					:					List[str]										=	VideoMAEImageProcessor if is_vision_available() else None





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   VideoMAEImageProcessingTester(self	)





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





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   self.image_processing_class(**self.image_processor_dict	)
		self.assertTrue(hasattr(snake_case		,   'image_mean'	)	)
		self.assertTrue(hasattr(snake_case		,   'image_std'	)	)
		self.assertTrue(hasattr(snake_case		,   'do_normalize'	)	)
		self.assertTrue(hasattr(snake_case		,   'do_resize'	)	)
		self.assertTrue(hasattr(snake_case		,   'do_center_crop'	)	)
		self.assertTrue(hasattr(snake_case		,   'size'	)	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   self.image_processing_class.from_dict(self.image_processor_dict	)
		self.assertEqual(image_processor.size		,   {'shortest_edge': 18}	)
		self.assertEqual(image_processor.crop_size		,   {'height': 18, 'width': 18}	)

		lowercase					  =   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      SCREAMING_SNAKE_CASE__						(	self	):
		pass





	def      SCREAMING_SNAKE_CASE__						(	self	):
		# Initialize image_processing
		lowercase					  =   self.image_processing_class(**self.image_processor_dict	)
		# create random PIL videos
		lowercase					  =   prepare_video_inputs(self.image_processor_tester		,   equal_resolution=snake_case	)
		for video in video_inputs:
			self.assertIsInstance(snake_case		,   snake_case	)
			self.assertIsInstance(video[0]		,   Image.Image	)

		# Test not batched input
		lowercase					  =   image_processing(video_inputs[0]		,   return_tensors='pt'	).pixel_values
		self.assertEqual(
		    encoded_videos.shape		,   (
		        1,
		        self.image_processor_tester.num_frames,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size['height'],
		        self.image_processor_tester.crop_size['width'],
		    )		,   )

		# Test batched
		lowercase					  =   image_processing(snake_case		,   return_tensors='pt'	).pixel_values
		self.assertEqual(
		    encoded_videos.shape		,   (
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_frames,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size['height'],
		        self.image_processor_tester.crop_size['width'],
		    )		,   )





	def      SCREAMING_SNAKE_CASE__						(	self	):
		# Initialize image_processing
		lowercase					  =   self.image_processing_class(**self.image_processor_dict	)
		# create random numpy tensors
		lowercase					  =   prepare_video_inputs(self.image_processor_tester		,   equal_resolution=snake_case		,   numpify=snake_case	)
		for video in video_inputs:
			self.assertIsInstance(snake_case		,   snake_case	)
			self.assertIsInstance(video[0]		,   np.ndarray	)

		# Test not batched input
		lowercase					  =   image_processing(video_inputs[0]		,   return_tensors='pt'	).pixel_values
		self.assertEqual(
		    encoded_videos.shape		,   (
		        1,
		        self.image_processor_tester.num_frames,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size['height'],
		        self.image_processor_tester.crop_size['width'],
		    )		,   )

		# Test batched
		lowercase					  =   image_processing(snake_case		,   return_tensors='pt'	).pixel_values
		self.assertEqual(
		    encoded_videos.shape		,   (
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_frames,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size['height'],
		        self.image_processor_tester.crop_size['width'],
		    )		,   )





	def      SCREAMING_SNAKE_CASE__						(	self	):
		# Initialize image_processing
		lowercase					  =   self.image_processing_class(**self.image_processor_dict	)
		# create random PyTorch tensors
		lowercase					  =   prepare_video_inputs(self.image_processor_tester		,   equal_resolution=snake_case		,   torchify=snake_case	)
		for video in video_inputs:
			self.assertIsInstance(snake_case		,   snake_case	)
			self.assertIsInstance(video[0]		,   torch.Tensor	)

		# Test not batched input
		lowercase					  =   image_processing(video_inputs[0]		,   return_tensors='pt'	).pixel_values
		self.assertEqual(
		    encoded_videos.shape		,   (
		        1,
		        self.image_processor_tester.num_frames,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size['height'],
		        self.image_processor_tester.crop_size['width'],
		    )		,   )

		# Test batched
		lowercase					  =   image_processing(snake_case		,   return_tensors='pt'	).pixel_values
		self.assertEqual(
		    encoded_videos.shape		,   (
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_frames,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size['height'],
		        self.image_processor_tester.crop_size['width'],
		    )		,   )

