
import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader







class 	A_       (      __lowerCamelCase       ):




	'''simple docstring'''


	def __init__(	self		,   snake_case		,   snake_case = None		,   snake_case = None		,   snake_case = None		,   snake_case = False		,   snake_case = False		,   snake_case = None		,   **snake_case		,   ):
		super().__init__(
		    __A		,   split=__A		,   features=__A		,   cache_dir=__A		,   keep_in_memory=__A		,   streaming=__A		,   num_proc=__A		,   **__A		,   )
		lowercase					  =   path_or_paths if isinstance(__A		,   __A	) else {self.split: path_or_paths}
		lowercase					  =   Csv(
		    cache_dir=__A		,   data_files=__A		,   features=__A		,   **__A		,   )





	def      SCREAMING_SNAKE_CASE__						(	self	):





		if self.streaming:
			lowercase					  =   self.builder.as_streaming_dataset(split=self.split	)
		# Build regular (map-style) dataset
		else:
			lowercase					  =   None
			lowercase					  =   None
			lowercase					  =   None
			lowercase					  =   None

			self.builder.download_and_prepare(
			    download_config=__A		,   download_mode=__A		,   verification_mode=__A		,   base_path=__A		,   num_proc=self.num_proc		,   )
			lowercase					  =   self.builder.as_dataset(
			    split=self.split		,   verification_mode=__A		,   in_memory=self.keep_in_memory	)
		return dataset







class 	A_       :




	'''simple docstring'''


	def __init__(	self		,   snake_case		,   snake_case		,   snake_case = None		,   snake_case = None		,   **snake_case		,   ):





		if num_proc is not None and num_proc <= 0:
			raise ValueError(F'''num_proc {num_proc} must be an integer > 0.'''	)

		lowercase					  =   dataset
		lowercase					  =   path_or_buf
		lowercase					  =   batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
		lowercase					  =   num_proc
		lowercase					  =   "utf-8"
		lowercase					  =   to_csv_kwargs





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   self.to_csv_kwargs.pop('path_or_buf'		,   __A	)
		lowercase					  =   self.to_csv_kwargs.pop('header'		,   __A	)
		lowercase					  =   self.to_csv_kwargs.pop('index'		,   __A	)

		if isinstance(self.path_or_buf		,   (str, bytes, os.PathLike)	):
			with open(self.path_or_buf		,   'wb+'	) as buffer:
				lowercase					  =   self._write(file_obj=__A		,   header=__A		,   index=__A		,   **self.to_csv_kwargs	)
		else:
			lowercase					  =   self._write(file_obj=self.path_or_buf		,   header=__A		,   index=__A		,   **self.to_csv_kwargs	)
		return written





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		lowercase					  =   args

		lowercase					  =   query_table(
		    table=self.dataset.data		,   key=slice(__A		,   offset + self.batch_size	)		,   indices=self.dataset._indices		,   )
		lowercase					  =   batch.to_pandas().to_csv(
		    path_or_buf=__A		,   header=header if (offset == 0) else False		,   index=__A		,   **__A	)
		return csv_str.encode(self.encoding	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case		,   **snake_case	):
		lowercase					  =   0

		if self.num_proc is None or self.num_proc == 1:
			for offset in logging.tqdm(
			    range(0		,   len(self.dataset	)		,   self.batch_size	)		,   unit='ba'		,   disable=not logging.is_progress_bar_enabled()		,   desc='Creating CSV from Arrow format'		,   ):
				lowercase					  =   self._batch_csv((offset, header, index, to_csv_kwargs)	)
				written += file_obj.write(__A	)

		else:
			lowercase					  =   len(self.dataset	), self.batch_size
			with multiprocessing.Pool(self.num_proc	) as pool:
				for csv_str in logging.tqdm(
				    pool.imap(
				        self._batch_csv		,   [(offset, header, index, to_csv_kwargs) for offset in range(0		,   __A		,   __A	)]		,   )		,   total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size		,   unit='ba'		,   disable=not logging.is_progress_bar_enabled()		,   desc='Creating CSV from Arrow format'		,   ):
					written += file_obj.write(__A	)

		return written



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'],
		    )		,   )

