
'''simple docstring'''

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_					(_a      ):




       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				,				)
         _lowerCAmelCase    :					Any							=   path_or_paths if isinstance(__A				,				__A					) else {self.split: path_or_paths}
         _lowerCAmelCase    :					Union[str, Any]							=   Csv(
             cache_dir=__A				,				data_files=__A				,				features=__A				,				**__A				,				)







       def 							__UpperCamelCase    (       self					):





         if self.streaming:
           _lowerCAmelCase    :					Optional[int]							=   self.builder.as_streaming_dataset(split=self.split					)
         # Build regular (map-style) dataset
         else:
           _lowerCAmelCase    :					Any							=   None
           _lowerCAmelCase    :					Optional[int]							=   None
           _lowerCAmelCase    :					Tuple							=   None
           _lowerCAmelCase    :					List[str]							=   None

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



class 					a_					:




       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.'					)

         _lowerCAmelCase    :					List[Any]							=   dataset
         _lowerCAmelCase    :					Optional[Any]							=   path_or_buf
         _lowerCAmelCase    :					Any							=   batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
         _lowerCAmelCase    :					List[str]							=   num_proc
         _lowerCAmelCase    :					Dict							=   "utf-8"
         _lowerCAmelCase    :					Optional[int]							=   to_csv_kwargs




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Union[str, Any]							=   self.to_csv_kwargs.pop("""path_or_buf"""				,				__A					)
         _lowerCAmelCase    :					Dict							=   self.to_csv_kwargs.pop("""header"""				,				__A					)
         _lowerCAmelCase    :					int							=   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:
             _lowerCAmelCase    :					Optional[Any]							=   self._write(file_obj=__A				,				header=__A				,				index=__A				,				**self.to_csv_kwargs					)
         else:
           _lowerCAmelCase    :					Any							=   self._write(file_obj=self.path_or_buf				,				header=__A				,				index=__A				,				**self.to_csv_kwargs					)
         return written




       def 							__UpperCamelCase    (       self				,				snake_case_					):
         _lowerCAmelCase    :					List[str]							=   args

         _lowerCAmelCase    :					Dict							=   query_table(
             table=self.dataset.data				,				key=slice(__A				,				offset + self.batch_size					)				,				indices=self.dataset._indices				,				)
         _lowerCAmelCase    :					Union[str, Any]							=   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 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_				,				snake_case_				,				**snake_case_					):
         _lowerCAmelCase    :					List[Any]							=   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"""				,				):
             _lowerCAmelCase    :					int							=   self._batch_csv((offset, header, index, to_csv_kwargs)					)
             written += file_obj.write(__A					)

         else:
           _lowerCAmelCase    :					List[Any]							=   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




'''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 ConvNextImageProcessor


class 					a_					(unittest.TestCase      ):




       def __init__(       self				,				snake_case_				,				snake_case_=7				,				snake_case_=3				,				snake_case_=1_8				,				snake_case_=3_0				,				snake_case_=4_0_0				,				snake_case_=True				,				snake_case_=None				,				snake_case_=0.875				,				snake_case_=True				,				snake_case_=[0.5, 0.5, 0.5]				,				snake_case_=[0.5, 0.5, 0.5]				,				):
         _lowerCAmelCase    :					List[str]							=   size if size is not None else {"""shortest_edge""": 2_0}
         _lowerCAmelCase    :					Tuple							=   parent
         _lowerCAmelCase    :					List[str]							=   batch_size
         _lowerCAmelCase    :					List[str]							=   num_channels
         _lowerCAmelCase    :					Tuple							=   image_size
         _lowerCAmelCase    :					Union[str, Any]							=   min_resolution
         _lowerCAmelCase    :					Tuple							=   max_resolution
         _lowerCAmelCase    :					List[Any]							=   do_resize
         _lowerCAmelCase    :					Dict							=   size
         _lowerCAmelCase    :					str							=   crop_pct
         _lowerCAmelCase    :					List[str]							=   do_normalize
         _lowerCAmelCase    :					Any							=   image_mean
         _lowerCAmelCase    :					Any							=   image_std







       def 							__UpperCamelCase    (       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_pct": self.crop_pct,
         }



@require_torch
@require_vision
class 					a_					(_a							,     unittest.TestCase      ):
       __lowerCAmelCase      :			Dict      = ConvNextImageProcessor if is_vision_available() else None




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					int							=   ConvNextImageProcessingTester(self					)




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




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					List[Any]							=   self.image_processing_class(**self.image_processor_dict					)
         self.assertTrue(hasattr(snake_case_				,				"""do_resize"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""size"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""crop_pct"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""do_normalize"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""image_mean"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""image_std"""					)					)




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Any							=   self.image_processing_class.from_dict(self.image_processor_dict					)
         self.assertEqual(image_processor.size				,				{"""shortest_edge""": 2_0}					)

         _lowerCAmelCase    :					List[Any]							=   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					):
         pass




       def 							__UpperCamelCase    (       self					):
         # Initialize image_processing
         _lowerCAmelCase    :					str							=   self.image_processing_class(**self.image_processor_dict					)
         # create random PIL images
         _lowerCAmelCase    :					str							=   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=snake_case_					)
         for image in image_inputs:
           self.assertIsInstance(snake_case_				,				Image.Image					)

         # Test not batched input
         _lowerCAmelCase    :					Optional[int]							=   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
         _lowerCAmelCase    :					Optional[Any]							=   image_processing(snake_case_				,				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					):
         # Initialize image_processing
         _lowerCAmelCase    :					int							=   self.image_processing_class(**self.image_processor_dict					)
         # create random numpy tensors
         _lowerCAmelCase    :					Any							=   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=snake_case_				,				numpify=snake_case_					)
         for image in image_inputs:
           self.assertIsInstance(snake_case_				,				np.ndarray					)

         # Test not batched input
         _lowerCAmelCase    :					Optional[Any]							=   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
         _lowerCAmelCase    :					List[str]							=   image_processing(snake_case_				,				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					):
         # Initialize image_processing
         _lowerCAmelCase    :					Tuple							=   self.image_processing_class(**self.image_processor_dict					)
         # create random PyTorch tensors
         _lowerCAmelCase    :					List[str]							=   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=snake_case_				,				torchify=snake_case_					)
         for image in image_inputs:
           self.assertIsInstance(snake_case_				,				torch.Tensor					)

         # Test not batched input
         _lowerCAmelCase    :					List[str]							=   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
         _lowerCAmelCase    :					Optional[int]							=   image_processing(snake_case_				,				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"""],
             )				,				)


