




'''simple docstring'''

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


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import ChineseCLIPImageProcessor






class 		a							(  unittest.TestCase	):


				def __init__(						self       :   Dict			,							lowercase_       :   List[Any]			,							lowercase_       :   List[str]=7			,							lowercase_       :   Tuple=3			,							lowercase_       :   Tuple=18			,							lowercase_       :   int=30			,							lowercase_       :   List[Any]=400			,							lowercase_       :   int=True			,							lowercase_       :   List[Any]=None			,							lowercase_       :   Optional[Any]=True			,							lowercase_       :   List[str]=None			,							lowercase_       :   Optional[Any]=True			,							lowercase_       :   int=[0.4814_5466, 0.457_8275, 0.4082_1073]			,							lowercase_       :   Any=[0.2686_2954, 0.2613_0258, 0.2757_7711]			,							lowercase_       :   Dict=True			,							):
									snake_case_		     =							size if size is not None else {'''height''': 224, '''width''': 224}
									snake_case_		     =							crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
									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_		     =							do_center_crop
									snake_case_		     =							crop_size
									snake_case_		     =							do_normalize
									snake_case_		     =							image_mean
									snake_case_		     =							image_std
									snake_case_		     =							do_convert_rgb


				def     A_					(						self       :   int   ):
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_center_crop": self.do_center_crop,
									    "crop_size": self.crop_size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_convert_rgb": self.do_convert_rgb,
									}


				def     A_					(						self       :   List[Any]			,							lowercase_       :   Any=False			,							lowercase_       :   List[Any]=False			,							lowercase_       :   List[Any]=False   ):
									assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

									if equal_resolution:
														snake_case_		     =							[]
														for i in range(self.batch_size   ):
																			image_inputs.append(
																			    np.random.randint(
																			        255			,							size=(self.num_channels, self.max_resolution, self.max_resolution)			,							dtype=np.uinta   )   )
									else:
														snake_case_		     =							[]
														for i in range(self.batch_size   ):
																			snake_case_     ,snake_case_		     =							np.random.choice(np.arange(self.min_resolution			,							self.max_resolution   )			,							2   )
																			image_inputs.append(np.random.randint(255			,							size=(self.num_channels, width, height)			,							dtype=np.uinta   )   )

									if not numpify and not torchify:
														# PIL expects the channel dimension as last dimension
														snake_case_		     =							[Image.fromarray(np.moveaxis(lowercase_			,							0			,							-1   )   ) for x in image_inputs]

									if torchify:
														snake_case_		     =							[torch.from_numpy(lowercase_   ) for x in image_inputs]

									return image_inputs







@require_torch
@require_vision
class 		a							(  _lowerCamelCase  ,	unittest.TestCase	):
				snake_case_           =						ChineseCLIPImageProcessor if is_vision_available() else None


				def     A_					(						self       :   Dict   ):
									snake_case_		     =							ChineseCLIPImageProcessingTester(self			,							do_center_crop=lowercase_   )


				@property
				def     A_					(						self       :   str   ):
									return self.image_processor_tester.prepare_image_processor_dict()


				def     A_					(						self       :   Union[str, Any]   ):
									snake_case_		     =							self.image_processing_class(**self.image_processor_dict   )
									self.assertTrue(hasattr(lowercase_			,							'''do_resize'''   )   )
									self.assertTrue(hasattr(lowercase_			,							'''size'''   )   )
									self.assertTrue(hasattr(lowercase_			,							'''do_center_crop'''   )   )
									self.assertTrue(hasattr(lowercase_			,							'''center_crop'''   )   )
									self.assertTrue(hasattr(lowercase_			,							'''do_normalize'''   )   )
									self.assertTrue(hasattr(lowercase_			,							'''image_mean'''   )   )
									self.assertTrue(hasattr(lowercase_			,							'''image_std'''   )   )
									self.assertTrue(hasattr(lowercase_			,							'''do_convert_rgb'''   )   )


				def     A_					(						self       :   Optional[Any]   ):
									snake_case_		     =							self.image_processing_class.from_dict(self.image_processor_dict   )
									self.assertEqual(image_processor.size			,							{'''height''': 224, '''width''': 224}   )
									self.assertEqual(image_processor.crop_size			,							{'''height''': 18, '''width''': 18}   )

									snake_case_		     =							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       :   Optional[int]   ):
									pass


				def     A_					(						self       :   List[str]   ):
									# Initialize image_processing
									snake_case_		     =							self.image_processing_class(**self.image_processor_dict   )
									# create random PIL images
									snake_case_		     =							self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_   )
									for image in image_inputs:
														self.assertIsInstance(lowercase_			,							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.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )			,							)

									# Test batched
									snake_case_		     =							image_processing(lowercase_			,							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       :   Dict   ):
									# Initialize image_processing
									snake_case_		     =							self.image_processing_class(**self.image_processor_dict   )
									# create random numpy tensors
									snake_case_		     =							self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_			,							numpify=lowercase_   )
									for image in image_inputs:
														self.assertIsInstance(lowercase_			,							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.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )			,							)

									# Test batched
									snake_case_		     =							image_processing(lowercase_			,							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       :   int   ):
									# Initialize image_processing
									snake_case_		     =							self.image_processing_class(**self.image_processor_dict   )
									# create random PyTorch tensors
									snake_case_		     =							self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_			,							torchify=lowercase_   )
									for image in image_inputs:
														self.assertIsInstance(lowercase_			,							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.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )			,							)

									# Test batched
									snake_case_		     =							image_processing(lowercase_			,							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'''],
									    )			,							)







@require_torch
@require_vision
class 		a							(  _lowerCamelCase  ,	unittest.TestCase	):
				snake_case_           =						ChineseCLIPImageProcessor if is_vision_available() else None


				def     A_					(						self       :   Dict   ):
									snake_case_		     =							ChineseCLIPImageProcessingTester(self			,							num_channels=4			,							do_center_crop=lowercase_   )
									snake_case_		     =							3


				@property
				def     A_					(						self       :   List[str]   ):
									return self.image_processor_tester.prepare_image_processor_dict()


				def     A_					(						self       :   Optional[int]   ):
									snake_case_		     =							self.image_processing_class(**self.image_processor_dict   )
									self.assertTrue(hasattr(lowercase_			,							'''do_resize'''   )   )
									self.assertTrue(hasattr(lowercase_			,							'''size'''   )   )
									self.assertTrue(hasattr(lowercase_			,							'''do_center_crop'''   )   )
									self.assertTrue(hasattr(lowercase_			,							'''center_crop'''   )   )
									self.assertTrue(hasattr(lowercase_			,							'''do_normalize'''   )   )
									self.assertTrue(hasattr(lowercase_			,							'''image_mean'''   )   )
									self.assertTrue(hasattr(lowercase_			,							'''image_std'''   )   )
									self.assertTrue(hasattr(lowercase_			,							'''do_convert_rgb'''   )   )


				def     A_					(						self       :   Optional[int]   ):
									pass


				def     A_					(						self       :   Union[str, Any]   ):
									# Initialize image_processing
									snake_case_		     =							self.image_processing_class(**self.image_processor_dict   )
									# create random PIL images
									snake_case_		     =							self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_   )
									for image in image_inputs:
														self.assertIsInstance(lowercase_			,							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.expected_encoded_image_num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )			,							)

									# Test batched
									snake_case_		     =							image_processing(lowercase_			,							return_tensors='''pt'''   ).pixel_values
									self.assertEqual(
									    encoded_images.shape			,							(
									        self.image_processor_tester.batch_size,
									        self.expected_encoded_image_num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )			,							)







'''simple docstring'''

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

import fsspec

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






class 		a							(  _lowerCamelCase	):


				def __init__(						self       :   Optional[int]			,							lowercase_       :   NestedDataStructureLike[PathLike]			,							lowercase_       :   Optional[NamedSplit] = None			,							lowercase_       :   Optional[Features] = None			,							lowercase_       :   str = None			,							lowercase_       :   bool = False			,							lowercase_       :   bool = False			,							lowercase_       :   Optional[str] = None			,							lowercase_       :   Optional[int] = None			,							**lowercase_       :   Optional[Any]			,							):
									super().__init__(
									    lowercase_			,							split=lowercase_			,							features=lowercase_			,							cache_dir=lowercase_			,							keep_in_memory=lowercase_			,							streaming=lowercase_			,							num_proc=lowercase_			,							**lowercase_			,							)
									snake_case_		     =							field
									snake_case_		     =							path_or_paths if isinstance(lowercase_			,							lowercase_   ) else {self.split: path_or_paths}
									snake_case_		     =							Json(
									    cache_dir=lowercase_			,							data_files=lowercase_			,							features=lowercase_			,							field=lowercase_			,							**lowercase_			,							)


				def     A_					(						self       :   Optional[Any]   ):
									# Build iterable dataset
									if self.streaming:
														snake_case_		     =							self.builder.as_streaming_dataset(split=self.split   )
									# Build regular (map-style) dataset
									else:
														snake_case_		     =							None
														snake_case_		     =							None
														snake_case_		     =							None
														snake_case_		     =							None

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







class 		a							:


				def __init__(						self       :   Dict			,							lowercase_       :   Dataset			,							lowercase_       :   Union[PathLike, BinaryIO]			,							lowercase_       :   Optional[int] = None			,							lowercase_       :   Optional[int] = None			,							**lowercase_       :   List[str]			,							):
									if num_proc is not None and num_proc <= 0:
														raise ValueError(F"num_proc {num_proc} must be an integer > 0."   )

									snake_case_		     =							dataset
									snake_case_		     =							path_or_buf
									snake_case_		     =							batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
									snake_case_		     =							num_proc
									snake_case_		     =							'''utf-8'''
									snake_case_		     =							to_json_kwargs


				def     A_					(						self       :   str   ):
									snake_case_		     =							self.to_json_kwargs.pop('''path_or_buf'''			,							lowercase_   )
									snake_case_		     =							self.to_json_kwargs.pop('''orient'''			,							'''records'''   )
									snake_case_		     =							self.to_json_kwargs.pop('''lines'''			,							True if orient == '''records''' else False   )
									snake_case_		     =							self.to_json_kwargs.pop('''index'''			,							False if orient in ['''split''', '''table'''] else True   )
									snake_case_		     =							self.to_json_kwargs.pop('''compression'''			,							lowercase_   )

									if compression not in [None, "infer", "gzip", "bz2", "xz"]:
														raise NotImplementedError(F"`datasets` currently does not support {compression} compression"   )

									if isinstance(self.path_or_buf			,							(str, bytes, os.PathLike)   ):
														with fsspec.open(self.path_or_buf			,							'''wb'''			,							compression=lowercase_   ) as buffer:
																			snake_case_		     =							self._write(file_obj=lowercase_			,							orient=lowercase_			,							lines=lowercase_			,							index=lowercase_			,							**self.to_json_kwargs   )
									else:
														if compression:
																			raise NotImplementedError(
																			    F"The compression parameter is not supported when writing to a buffer, but compression={compression}"
																			    ''' was passed. Please provide a local path instead.'''   )
														snake_case_		     =							self._write(
														    file_obj=self.path_or_buf			,							orient=lowercase_			,							lines=lowercase_			,							index=lowercase_			,							**self.to_json_kwargs   )
									return written


				def     A_					(						self       :   List[str]			,							lowercase_       :   Dict   ):
									snake_case_     ,snake_case_     ,snake_case_     ,snake_case_     ,snake_case_		     =							args

									snake_case_		     =							query_table(
									    table=self.dataset.data			,							key=slice(lowercase_			,							offset + self.batch_size   )			,							indices=self.dataset._indices			,							)
									snake_case_		     =							batch.to_pandas().to_json(
									    path_or_buf=lowercase_			,							orient=lowercase_			,							lines=lowercase_			,							index=lowercase_			,							**lowercase_   )
									if not json_str.endswith('''\n'''   ):
														json_str += "\n"
									return json_str.encode(self.encoding   )


				def     A_					(						self       :   Optional[int]			,							lowercase_       :   BinaryIO			,							lowercase_       :   List[Any]			,							lowercase_       :   str			,							lowercase_       :   int			,							**lowercase_       :   Optional[int]			,							):
									snake_case_		     =							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 json from Arrow format'''			,							):
																			snake_case_		     =							self._batch_json((offset, orient, lines, index, to_json_kwargs)   )
																			written += file_obj.write(lowercase_   )
									else:
														snake_case_     ,snake_case_		     =							len(self.dataset   ), self.batch_size
														with multiprocessing.Pool(self.num_proc   ) as pool:
																			for json_str in logging.tqdm(
																			    pool.imap(
																			        self._batch_json			,							[(offset, orient, lines, index, to_json_kwargs) for offset in range(0			,							lowercase_			,							lowercase_   )]			,							)			,							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 json from Arrow format'''			,							):
																								written += file_obj.write(lowercase_   )

									return written

