

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				(					__lowerCamelCase			):

   def __init__(				self	:    Optional[int]			,						lowerCamelCase__	:    NestedDataStructureLike[PathLike]			,						lowerCamelCase__	:    Optional[NamedSplit] = None			,						lowerCamelCase__	:    Optional[Features] = None			,						lowerCamelCase__	:    str = None			,						lowerCamelCase__	:    bool = False			,						lowerCamelCase__	:    bool = False			,						lowerCamelCase__	:    Optional[str] = None			,						lowerCamelCase__	:    Optional[int] = None			,						**lowerCamelCase__	:    List[str]			,						) ->			Optional[Any]:
         """simple docstring"""





         super().__init__(
             lowerCamelCase__			,						split=lowerCamelCase__			,						features=lowerCamelCase__			,						cache_dir=lowerCamelCase__			,						keep_in_memory=lowerCamelCase__			,						streaming=lowerCamelCase__			,						num_proc=lowerCamelCase__			,						**lowerCamelCase__			,						)
         A_							       =		field
         A_							       =		path_or_paths if isinstance(lowerCamelCase__			,						lowerCamelCase__       ) else {self.split: path_or_paths}
         A_							       =		Json(
             cache_dir=lowerCamelCase__			,						data_files=lowerCamelCase__			,						features=lowerCamelCase__			,						field=lowerCamelCase__			,						**lowerCamelCase__			,						)





   def     UpperCamelCase					(				self	:    Tuple       ) ->			List[Any]:
         """simple docstring"""





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

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



class    _lowercase				:

   def __init__(				self	:    str			,						lowerCamelCase__	:    Dataset			,						lowerCamelCase__	:    Union[PathLike, BinaryIO]			,						lowerCamelCase__	:    Optional[int] = None			,						lowerCamelCase__	:    Optional[int] = None			,						**lowerCamelCase__	:    List[Any]			,						) ->			Optional[int]:
         """simple docstring"""





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

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

   def     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			int:
         """simple docstring"""





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

         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=lowerCamelCase__       ) as buffer:
                     A_							       =		self._write(file_obj=lowerCamelCase__			,						orient=lowerCamelCase__			,						lines=lowerCamelCase__			,						index=lowerCamelCase__			,						**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_							       =		self._write(
                   file_obj=self.path_or_buf			,						orient=lowerCamelCase__			,						lines=lowerCamelCase__			,						index=lowerCamelCase__			,						**self.to_json_kwargs       )
         return written

   def     UpperCamelCase					(				self	:    Dict			,						lowerCamelCase__	:    Dict       ) ->			List[str]:
         """simple docstring"""





         A_       ,A_       ,A_       ,A_       ,A_							       =		args

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





   def     UpperCamelCase					(				self	:    Optional[Any]			,						lowerCamelCase__	:    BinaryIO			,						lowerCamelCase__	:    Optional[int]			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    Any			,						**lowerCamelCase__	:    Any			,						) ->			int:
         """simple docstring"""





         A_							       =		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_							       =		self._batch_json((offset, orient, lines, index, to_json_kwargs)       )
                     written += file_obj.write(lowerCamelCase__       )
         else:
               A_       ,A_							       =		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			,						lowerCamelCase__			,						lowerCamelCase__       )]			,						)			,						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(lowerCamelCase__       )

         return written








import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor

class    _lowercase				(					unittest.TestCase			):

   def __init__(				self	:    List[str]			,						lowerCamelCase__	:    List[str]			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    Dict[str, int] = None			,						lowerCamelCase__	:    int = 3_2			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    Union[int, float] = 1 / 2_5_5			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    Optional[Union[float, List[float]]] = [0.48145466, 0.4578275, 0.40821073]			,						lowerCamelCase__	:    Optional[Union[float, List[float]]] = [0.26862954, 0.26130258, 0.27577711]			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    List[str]=7			,						lowerCamelCase__	:    int=3_0			,						lowerCamelCase__	:    List[Any]=4_0_0			,						lowerCamelCase__	:    int=3			,						) ->			Optional[Any]:
         """simple docstring"""





         A_							       =		parent
         A_							       =		do_resize
         A_							       =		size if size is not None else {'''shortest_edge''': 2_8_8}
         A_							       =		size_divisor
         A_							       =		do_rescale
         A_							       =		rescale_factor
         A_							       =		do_normalize
         A_							       =		do_center_crop
         A_							       =		image_mean
         A_							       =		image_std
         A_							       =		do_pad
         A_							       =		batch_size
         A_							       =		num_channels
         A_							       =		min_resolution
         A_							       =		max_resolution

   def     UpperCamelCase					(				self	:    str       ) ->			int:
         """simple docstring"""





         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "size_divisor": self.size_divisor,
         }





   def     UpperCamelCase					(				self	:    List[str]			,						lowerCamelCase__	:    Optional[Any]			,						lowerCamelCase__	:    int=False       ) ->			Dict:
         """simple docstring"""





         if not batched:
               A_							       =		self.size['''shortest_edge''']
               A_							       =		image_inputs[0]
               if isinstance(lowerCamelCase__			,						Image.Image       ):
                     A_       ,A_							       =		image.size
               else:
                     A_       ,A_							       =		image.shape[1], image.shape[2]
               A_							       =		size / min(lowerCamelCase__			,						lowerCamelCase__       )
               if h < w:
                     A_       ,A_							       =		size, scale * w
               else:
                     A_       ,A_							       =		scale * h, size

               A_							       =		int((1_3_3_3 / 8_0_0) * size       )
               if max(lowerCamelCase__			,						lowerCamelCase__       ) > max_size:
                     A_							       =		max_size / max(lowerCamelCase__			,						lowerCamelCase__       )
                     A_							       =		newh * scale
                     A_							       =		neww * scale

               A_       ,A_							       =		int(newh + 0.5       ), int(neww + 0.5       )
               A_       ,A_							       =		(
                   newh // self.size_divisor * self.size_divisor,
                   neww // self.size_divisor * self.size_divisor,
               )

         else:
               A_							       =		[]
               for image in image_inputs:
                     A_       ,A_							       =		self.get_expected_values([image]       )
                     expected_values.append((expected_height, expected_width)       )
               A_							       =		max(lowerCamelCase__			,						key=lambda lowerCamelCase__       : item[0]       )[0]
               A_							       =		max(lowerCamelCase__			,						key=lambda lowerCamelCase__       : item[1]       )[1]

         return expected_height, expected_width



@require_torch
@require_vision
class    _lowercase				(					__lowerCamelCase,unittest.TestCase			):
   _lowercase     :       List[str]  					=    BridgeTowerImageProcessor if is_vision_available() else None

   def     UpperCamelCase					(				self	:    int       ) ->			Tuple:
         """simple docstring"""





         A_							       =		BridgeTowerImageProcessingTester(self       )

   @property
   def     UpperCamelCase					(				self	:    str       ) ->			Optional[int]:
         """simple docstring"""





         return self.image_processor_tester.prepare_image_processor_dict()

   def     UpperCamelCase					(				self	:    Tuple       ) ->			List[Any]:
         """simple docstring"""





         A_							       =		self.image_processing_class(**self.image_processor_dict       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''image_mean'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''image_std'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''do_normalize'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''do_resize'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''size'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''size_divisor'''       )       )

   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			str:
         """simple docstring"""





         pass

   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			Union[str, Any]:
         """simple docstring"""





         A_							       =		self.image_processing_class(**self.image_processor_dict       )
         # create random PIL images
         A_							       =		prepare_image_inputs(self.image_processor_tester			,						equal_resolution=lowerCamelCase__       )
         for image in image_inputs:
               self.assertIsInstance(lowerCamelCase__			,						Image.Image       )

         # Test not batched input
         A_							       =		image_processing(image_inputs[0]			,						return_tensors='''pt'''       ).pixel_values

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

         # Test batched
         A_							       =		image_processing(lowerCamelCase__			,						return_tensors='''pt'''       ).pixel_values

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__			,						batched=lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )			,						)

   def     UpperCamelCase					(				self	:    str       ) ->			Optional[int]:
         """simple docstring"""





         A_							       =		self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         A_							       =		prepare_image_inputs(self.image_processor_tester			,						equal_resolution=lowerCamelCase__			,						numpify=lowerCamelCase__       )
         for image in image_inputs:
               self.assertIsInstance(lowerCamelCase__			,						np.ndarray       )

         # Test not batched input
         A_							       =		image_processing(image_inputs[0]			,						return_tensors='''pt'''       ).pixel_values

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

         # Test batched
         A_							       =		image_processing(lowerCamelCase__			,						return_tensors='''pt'''       ).pixel_values

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__			,						batched=lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )			,						)





   def     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			int:
         """simple docstring"""





         A_							       =		self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         A_							       =		prepare_image_inputs(self.image_processor_tester			,						equal_resolution=lowerCamelCase__			,						torchify=lowerCamelCase__       )
         for image in image_inputs:
               self.assertIsInstance(lowerCamelCase__			,						torch.Tensor       )

         # Test not batched input
         A_							       =		image_processing(image_inputs[0]			,						return_tensors='''pt'''       ).pixel_values

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

         # Test batched
         A_							       =		image_processing(lowerCamelCase__			,						return_tensors='''pt'''       ).pixel_values

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__			,						batched=lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )			,						)





