





"""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 			__lowercase     (     _UpperCamelCase							):







     '''simple docstring'''




     def __init__(  self		,     _UpperCAmelCase		,     _UpperCAmelCase = None		,     _UpperCAmelCase = None		,     _UpperCAmelCase = None		,     _UpperCAmelCase = False		,     _UpperCAmelCase = False		,     _UpperCAmelCase = None		,     _UpperCAmelCase = None		,     **_UpperCAmelCase		,     ):
         super().__init__(
             _UpperCAmelCase		,     split=_UpperCAmelCase		,     features=_UpperCAmelCase		,     cache_dir=_UpperCAmelCase		,     keep_in_memory=_UpperCAmelCase		,     streaming=_UpperCAmelCase		,     num_proc=_UpperCAmelCase		,     **_UpperCAmelCase		,     )
         __a	:  List[str]         =					field
         __a	:  Union[str, Any]         =					path_or_paths if isinstance(_UpperCAmelCase		,     _UpperCAmelCase       ) else {self.split: path_or_paths}
         __a	:  List[Any]         =					Json(
             cache_dir=_UpperCAmelCase		,     data_files=_UpperCAmelCase		,     features=_UpperCAmelCase		,     field=_UpperCAmelCase		,     **_UpperCAmelCase		,     )







     def 	_lowerCamelCase						(  self       ):
         # Build iterable dataset
         if self.streaming:
             __a	:  int         =					self.builder.as_streaming_dataset(split=self.split       )
         # Build regular (map-style) dataset
         else:
             __a	:  Dict         =					None
             __a	:  Optional[int]         =					None
             __a	:  List[Any]         =					None
             __a	:  Tuple         =					None

             self.builder.download_and_prepare(
                 download_config=_UpperCAmelCase		,     download_mode=_UpperCAmelCase		,     verification_mode=_UpperCAmelCase		,     base_path=_UpperCAmelCase		,     num_proc=self.num_proc		,     )
             __a	:  Optional[int]         =					self.builder.as_dataset(
                 split=self.split		,     verification_mode=_UpperCAmelCase		,     in_memory=self.keep_in_memory       )
         return dataset






class 			__lowercase     :







     '''simple docstring'''




     def __init__(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase = None		,     _UpperCAmelCase = None		,     **_UpperCAmelCase		,     ):
         if num_proc is not None and num_proc <= 0:
             raise ValueError(f"""num_proc {num_proc} must be an integer > 0."""       )

         __a	:  Optional[int]         =					dataset
         __a	:  str         =					path_or_buf
         __a	:  Any         =					batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
         __a	:  Tuple         =					num_proc
         __a	:  List[str]         =					'''utf-8'''
         __a	:  int         =					to_json_kwargs







     def 	_lowerCamelCase						(  self       ):
         __a	:  Any         =					self.to_json_kwargs.pop('''path_or_buf'''		,     _UpperCAmelCase       )
         __a	:  int         =					self.to_json_kwargs.pop('''orient'''		,     '''records'''       )
         __a	:  List[Any]         =					self.to_json_kwargs.pop('''lines'''		,     True if orient == '''records''' else False       )
         __a	:  Dict         =					self.to_json_kwargs.pop('''index'''		,     False if orient in ['''split''', '''table'''] else True       )
         __a	:  Tuple         =					self.to_json_kwargs.pop('''compression'''		,     _UpperCAmelCase       )

         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=_UpperCAmelCase       ) as buffer:
                 __a	:  Dict         =					self._write(file_obj=_UpperCAmelCase		,     orient=_UpperCAmelCase		,     lines=_UpperCAmelCase		,     index=_UpperCAmelCase		,     **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.'''       )
             __a	:  Any         =					self._write(
                 file_obj=self.path_or_buf		,     orient=_UpperCAmelCase		,     lines=_UpperCAmelCase		,     index=_UpperCAmelCase		,     **self.to_json_kwargs       )
         return written







     def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
         __a	:  Optional[int]         =					args

         __a	:  List[Any]         =					query_table(
             table=self.dataset.data		,     key=slice(_UpperCAmelCase		,     offset + self.batch_size       )		,     indices=self.dataset._indices		,     )
         __a	:  Union[str, Any]         =					batch.to_pandas().to_json(
             path_or_buf=_UpperCAmelCase		,     orient=_UpperCAmelCase		,     lines=_UpperCAmelCase		,     index=_UpperCAmelCase		,     **_UpperCAmelCase       )
         if not json_str.endswith('''\n'''       ):
             json_str += "\n"
         return json_str.encode(self.encoding       )







     def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     **_UpperCAmelCase		,     ):
         __a	:  List[str]         =					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'''		,     ):
                 __a	:  int         =					self._batch_json((offset, orient, lines, index, to_json_kwargs)       )
                 written += file_obj.write(_UpperCAmelCase       )
         else:
             __a	:  List[str]         =					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		,     _UpperCAmelCase		,     _UpperCAmelCase       )]		,     )		,     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(_UpperCAmelCase       )

         return written






"""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, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor




class 			__lowercase     (     unittest.TestCase							):







					'''simple docstring'''




					def __init__(  self		,     _UpperCAmelCase		,     _UpperCAmelCase=13		,     _UpperCAmelCase=3		,     _UpperCAmelCase=224		,     _UpperCAmelCase=30		,     _UpperCAmelCase=400		,     _UpperCAmelCase=True		,     _UpperCAmelCase=None		,     _UpperCAmelCase=True		,     _UpperCAmelCase=[0.5, 0.5, 0.5]		,     _UpperCAmelCase=[0.5, 0.5, 0.5]		,     ):
									__a	:  int         =					size if size is not None else {'''height''': 18, '''width''': 18}
									__a	:  List[Any]         =					parent
									__a	:  Dict         =					batch_size
									__a	:  Dict         =					num_channels
									__a	:  int         =					image_size
									__a	:  Optional[Any]         =					min_resolution
									__a	:  Optional[int]         =					max_resolution
									__a	:  Dict         =					do_resize
									__a	:  List[Any]         =					size
									__a	:  int         =					do_normalize
									__a	:  Optional[Any]         =					image_mean
									__a	:  int         =					image_std







					def 	_lowerCamelCase						(  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,
									}






@require_torch
@require_vision
class 			__lowercase     (     _UpperCamelCase				, unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					ViTImageProcessor if is_vision_available() else None







					def 	_lowerCamelCase						(  self       ):
									__a	:  int         =					EfficientFormerImageProcessorTester(self       )







					@property
					def 	_lowerCamelCase						(  self       ):
									return self.image_proc_tester.prepare_image_processor_dict()







					def 	_lowerCamelCase						(  self       ):
									__a	:  List[str]         =					self.image_processing_class(**self.image_processor_dict       )
									self.assertTrue(hasattr(_UpperCAmelCase		,     '''image_mean'''       )       )
									self.assertTrue(hasattr(_UpperCAmelCase		,     '''image_std'''       )       )
									self.assertTrue(hasattr(_UpperCAmelCase		,     '''do_normalize'''       )       )
									self.assertTrue(hasattr(_UpperCAmelCase		,     '''do_resize'''       )       )
									self.assertTrue(hasattr(_UpperCAmelCase		,     '''size'''       )       )







					def 	_lowerCamelCase						(  self       ):
									pass







					def 	_lowerCamelCase						(  self       ):
									# Initialize image_processor
									__a	:  Any         =					self.image_processing_class(**self.image_processor_dict       )
									# create random PIL images
									__a	:  int         =					prepare_image_inputs(self.image_proc_tester		,     equal_resolution=_UpperCAmelCase       )
									for image in image_inputs:
													self.assertIsInstance(_UpperCAmelCase		,     Image.Image       )

									# Test not batched input
									__a	:  Dict         =					image_processor(image_inputs[0]		,     return_tensors='''pt'''       ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,     (
									        1,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['''height'''],
									        self.image_proc_tester.size['''width'''],
									    )		,     )

									# Test batched
									__a	:  Any         =					image_processor(_UpperCAmelCase		,     return_tensors='''pt'''       ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,     (
									        self.image_proc_tester.batch_size,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['''height'''],
									        self.image_proc_tester.size['''width'''],
									    )		,     )







					def 	_lowerCamelCase						(  self       ):
									# Initialize image_processor
									__a	:  Tuple         =					self.image_processing_class(**self.image_processor_dict       )
									# create random numpy tensors
									__a	:  int         =					prepare_image_inputs(self.image_proc_tester		,     equal_resolution=_UpperCAmelCase		,     numpify=_UpperCAmelCase       )
									for image in image_inputs:
													self.assertIsInstance(_UpperCAmelCase		,     np.ndarray       )

									# Test not batched input
									__a	:  Optional[int]         =					image_processor(image_inputs[0]		,     return_tensors='''pt'''       ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,     (
									        1,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['''height'''],
									        self.image_proc_tester.size['''width'''],
									    )		,     )

									# Test batched
									__a	:  str         =					image_processor(_UpperCAmelCase		,     return_tensors='''pt'''       ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,     (
									        self.image_proc_tester.batch_size,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['''height'''],
									        self.image_proc_tester.size['''width'''],
									    )		,     )







					def 	_lowerCamelCase						(  self       ):
									# Initialize image_processor
									__a	:  int         =					self.image_processing_class(**self.image_processor_dict       )
									# create random PyTorch tensors
									__a	:  List[Any]         =					prepare_image_inputs(self.image_proc_tester		,     equal_resolution=_UpperCAmelCase		,     torchify=_UpperCAmelCase       )
									for image in image_inputs:
													self.assertIsInstance(_UpperCAmelCase		,     torch.Tensor       )

									# Test not batched input
									__a	:  str         =					image_processor(image_inputs[0]		,     return_tensors='''pt'''       ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,     (
									        1,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['''height'''],
									        self.image_proc_tester.size['''width'''],
									    )		,     )

									# Test batched
									__a	:  Tuple         =					image_processor(_UpperCAmelCase		,     return_tensors='''pt'''       ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,     (
									        self.image_proc_tester.batch_size,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size['''height'''],
									        self.image_proc_tester.size['''width'''],
									    )		,     )