






# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


_UpperCamelCase			:	Dict								=get_logger(__name__)






class 						UpperCAmelCase__ :
		__snake_case      :       Any     =   "dummy_data"
		__snake_case      :       Union[str, Any]     =   "datasets"
		__snake_case      :       Tuple     =   False







		def __init__(					self       ,A__       ,A__       ,A__       ,A__ = None       ,A__ = False       ,A__ = True       ,A__ = None       ,):
						_A	:				List[Any]   =      0
						_A	:				str   =      dataset_name
						_A	:				Any   =      cache_dir
						_A	:				Tuple   =      use_local_dummy_data
						_A	:				Union[str, Any]   =      config
						# download_callbacks take a single url as input
						_A	:				List[Callable]   =      download_callbacks or []
						# if False, it doesn't load existing files and it returns the paths of the dummy files relative
						# to the dummy_data zip file root
						_A	:				Tuple   =      load_existing_dummy_data

						# TODO(PVP, QL) might need to make this more general
						_A	:				Dict   =      str(A__		)
						# to be downloaded
						_A	:				Optional[Any]   =      None
						_A	:				int   =      None







		@property
		def  A__      (					self		):
						if self._dummy_file is None:
										_A	:				int   =      self.download_dummy_data()
						return self._dummy_file







		@property
		def  A__      (					self		):
						if self.config is not None:
										# structure is dummy / config_name / version_name
										return os.path.join('''dummy'''       ,self.config.name       ,self.version_name		)
						# structure is dummy / version_name
						return os.path.join('''dummy'''       ,self.version_name		)







		@property
		def  A__      (					self		):
						return os.path.join(self.dummy_data_folder       ,'''dummy_data.zip'''		)







		def  A__      (					self		):
						_A	:				Optional[Any]   =      (
						    self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
						)

						_A	:				Union[str, Any]   =      cached_path(
						    A__       ,cache_dir=self.cache_dir       ,extract_compressed_file=A__       ,force_extract=A__		)

						return os.path.join(A__       ,self.dummy_file_name		)







		@property
		def  A__      (					self		):
						return os.path.join(self.datasets_scripts_dir       ,self.dataset_name       ,self.dummy_zip_file		)







		@property
		def  A__      (					self		):
						if self._bucket_url is None:
										_A	:				Union[str, Any]   =      hf_github_url(self.dataset_name       ,self.dummy_zip_file.replace(os.sep       ,'''/'''		)		)
						return self._bucket_url







		@property
		def  A__      (					self		):
						# return full path if its a dir
						if os.path.isdir(self.dummy_file		):
										return self.dummy_file
						# else cut off path to file -> example `xsum`.
						return "/".join(self.dummy_file.replace(os.sep       ,'''/'''		).split('''/'''		)[:-1]		)







		def  A__      (					self       ,A__       ,*A__		):
						if self.load_existing_dummy_data:
										# dummy data is downloaded and tested
										_A	:				Optional[Any]   =      self.dummy_file
						else:
										# dummy data cannot be downloaded and only the path to dummy file is returned
										_A	:				str   =      self.dummy_file_name

						# special case when data_url is a dict
						if isinstance(A__       ,A__		):
										return self.create_dummy_data_dict(A__       ,A__		)
						elif isinstance(A__       ,(list, tuple)		):
										return self.create_dummy_data_list(A__       ,A__		)
						else:
										return self.create_dummy_data_single(A__       ,A__		)







		def  A__      (					self       ,A__       ,*A__		):
						return self.download_and_extract(A__		)







		def  A__      (					self       ,A__       ,A__		):
						return self.download_and_extract(A__		)







		def  A__      (					self       ,A__       ,*A__       ,**A__		):
						return path







		def  A__      (					self		):
						return {}







		def  A__      (					self       ,A__       ,A__		):
						_A	:				Union[str, Any]   =      {}
						for key, single_urls in data_url.items():
										for download_callback in self.download_callbacks:
														if isinstance(A__       ,A__		):
																		for single_url in single_urls:
																						download_callback(A__		)
														else:
																		_A	:				Dict   =      single_urls
																		download_callback(A__		)
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
										if isinstance(A__       ,A__		):
														_A	:				Union[str, Any]   =      [os.path.join(A__       ,urllib.parse.quote_plus(Path(A__		).name		)		) for x in single_urls]
										else:
														_A	:				List[str]   =      single_urls
														_A	:				List[str]   =      os.path.join(A__       ,urllib.parse.quote_plus(Path(A__		).name		)		)
										_A	:				Optional[Any]   =      value

						# make sure that values are unique
						if all(isinstance(A__       ,A__		) for i in dummy_data_dict.values()		) and len(set(dummy_data_dict.values()		)		) < len(
						    dummy_data_dict.values()		):
										# append key to value to make its name unique
										_A	:				Optional[Any]   =      {key: value + key for key, value in dummy_data_dict.items()}

						return dummy_data_dict







		def  A__      (					self       ,A__       ,A__		):
						_A	:				Dict   =      []
						# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
						_A	:				Optional[Any]   =      all(bool(re.findall('''[0-9]{3,}-of-[0-9]{3,}'''       ,A__		)		) for url in data_url		)
						_A	:				List[str]   =      all(
						    url.startswith('''https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed'''		) for url in data_url		)
						if data_url and (is_tf_records or is_pubmed_records):
										_A	:				Dict   =      [data_url[0]] * len(A__		)
						for single_url in data_url:
										for download_callback in self.download_callbacks:
														download_callback(A__		)
										# we force the name of each key to be the last file / folder name of the url path
										# if the url has arguments, we need to encode them with urllib.parse.quote_plus
										_A	:				List[Any]   =      os.path.join(A__       ,urllib.parse.quote_plus(single_url.split('''/'''		)[-1]		)		)
										dummy_data_list.append(A__		)
						return dummy_data_list







		def  A__      (					self       ,A__       ,A__		):
						for download_callback in self.download_callbacks:
										download_callback(A__		)
						# we force the name of each key to be the last file / folder name of the url path
						# if the url has arguments, we need to encode them with urllib.parse.quote_plus
						_A	:				List[str]   =      os.path.join(A__       ,urllib.parse.quote_plus(data_url.split('''/'''		)[-1]		)		)
						if os.path.exists(A__		) or not self.load_existing_dummy_data:
										return value
						else:
										# Backward compatibility, maybe deprecate at one point.
										# For many datasets with single url calls to dl_manager.download_and_extract,
										# the dummy_data.zip file is actually the zipped downloaded file
										# while now we expected the dummy_data.zip file to be a directory containing
										# the downloaded file.
										return path_to_dummy_data







		def  A__      (					self		):
						pass







		def  A__      (					self		):
						pass







		def  A__      (					self       ,A__		):
						def _iter_archive_members(A__		):
										# this preserves the order of the members inside the ZIP archive
										_A	:				int   =      Path(self.dummy_file		).parent
										_A	:				int   =      path.relative_to(A__		)
										with ZipFile(self.local_path_to_dummy_data		) as zip_file:
														_A	:				Any   =      zip_file.namelist()
										for member in members:
														if member.startswith(relative_path.as_posix()		):
																		yield dummy_parent_path.joinpath(A__		)

						_A	:				Optional[Any]   =      Path(A__		)
						_A	:				str   =      _iter_archive_members(A__		) if self.use_local_dummy_data else path.rglob('''*'''		)
						for file_path in file_paths:
										if file_path.is_file() and not file_path.name.startswith(('''.''', '''__''')		):
														yield file_path.relative_to(A__		).as_posix(), file_path.open('''rb'''		)







		def  A__      (					self       ,A__		):
						if not isinstance(A__       ,A__		):
										_A	:				Optional[int]   =      [paths]
						for path in paths:
										if os.path.isfile(A__		):
														if os.path.basename(A__		).startswith(('''.''', '''__''')		):
																		return
														yield path
										else:
														for dirpath, dirnames, filenames in os.walk(A__		):
																		if os.path.basename(A__		).startswith(('''.''', '''__''')		):
																						continue
																		dirnames.sort()
																		for filename in sorted(A__		):
																						if filename.startswith(('''.''', '''__''')		):
																										continue
																						yield os.path.join(A__       ,A__		)











from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_UpperCamelCase			:	Any								=logging.get_logger(__name__)


if is_vision_available():
	import PIL






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Union[str, Any]     =   ["pixel_values"]







		def __init__(					self       ,A__ = True       ,A__ = None       ,A__ = PILImageResampling.BICUBIC       ,A__ = True       ,A__ = None       ,A__ = True       ,A__ = 1 / 255       ,A__ = True       ,A__ = None       ,A__ = None       ,A__ = True       ,**A__       ,):
						super().__init__(**A__		)
						_A	:				List[str]   =      size if size is not None else {'''shortest_edge''': 224}
						_A	:				int   =      get_size_dict(A__       ,default_to_square=A__		)
						_A	:				Optional[int]   =      crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
						_A	:				Dict   =      get_size_dict(A__       ,default_to_square=A__       ,param_name='''crop_size'''		)

						_A	:				List[str]   =      do_resize
						_A	:				List[str]   =      size
						_A	:				Tuple   =      resample
						_A	:				List[Any]   =      do_center_crop
						_A	:				Dict   =      crop_size
						_A	:				Any   =      do_rescale
						_A	:				Optional[Any]   =      rescale_factor
						_A	:				Dict   =      do_normalize
						_A	:				Optional[int]   =      image_mean if image_mean is not None else OPENAI_CLIP_MEAN
						_A	:				Optional[int]   =      image_std if image_std is not None else OPENAI_CLIP_STD
						_A	:				Any   =      do_convert_rgb







		def  A__      (					self       ,A__       ,A__       ,A__ = PILImageResampling.BICUBIC       ,A__ = None       ,**A__       ,):
						_A	:				List[Any]   =      get_size_dict(A__       ,default_to_square=A__		)
						if "shortest_edge" not in size:
										raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""		)
						_A	:				Any   =      get_resize_output_image_size(A__       ,size=size['''shortest_edge''']       ,default_to_square=A__		)
						return resize(A__       ,size=A__       ,resample=A__       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__       ,A__ = None       ,**A__       ,):
						_A	:				Union[str, Any]   =      get_size_dict(A__		)
						if "height" not in size or "width" not in size:
										raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""		)
						return center_crop(A__       ,size=(size['''height'''], size['''width'''])       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__       ,A__ = None       ,**A__       ,):
						return rescale(A__       ,scale=A__       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__       ,A__       ,A__ = None       ,**A__       ,):
						return normalize(A__       ,mean=A__       ,std=A__       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = ChannelDimension.FIRST       ,**A__       ,):
						_A	:				str   =      do_resize if do_resize is not None else self.do_resize
						_A	:				List[str]   =      size if size is not None else self.size
						_A	:				List[str]   =      get_size_dict(A__       ,param_name='''size'''       ,default_to_square=A__		)
						_A	:				Optional[Any]   =      resample if resample is not None else self.resample
						_A	:				int   =      do_center_crop if do_center_crop is not None else self.do_center_crop
						_A	:				Any   =      crop_size if crop_size is not None else self.crop_size
						_A	:				int   =      get_size_dict(A__       ,param_name='''crop_size'''       ,default_to_square=A__		)
						_A	:				int   =      do_rescale if do_rescale is not None else self.do_rescale
						_A	:				List[str]   =      rescale_factor if rescale_factor is not None else self.rescale_factor
						_A	:				Tuple   =      do_normalize if do_normalize is not None else self.do_normalize
						_A	:				Union[str, Any]   =      image_mean if image_mean is not None else self.image_mean
						_A	:				int   =      image_std if image_std is not None else self.image_std
						_A	:				Optional[Any]   =      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

						_A	:				Optional[Any]   =      make_list_of_images(A__		)

						if not valid_images(A__		):
										raise ValueError(
										    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
										    '''torch.Tensor, tf.Tensor or jax.ndarray.'''		)

						if do_resize and size is None:
										raise ValueError('''Size must be specified if do_resize is True.'''		)

						if do_center_crop and crop_size is None:
										raise ValueError('''Crop size must be specified if do_center_crop is True.'''		)

						if do_rescale and rescale_factor is None:
										raise ValueError('''Rescale factor must be specified if do_rescale is True.'''		)

						if do_normalize and (image_mean is None or image_std is None):
										raise ValueError('''Image mean and std must be specified if do_normalize is True.'''		)

						# PIL RGBA images are converted to RGB
						if do_convert_rgb:
										_A	:				int   =      [convert_to_rgb(A__		) for image in images]

						# All transformations expect numpy arrays.
						_A	:				int   =      [to_numpy_array(A__		) for image in images]

						if do_resize:
										_A	:				List[str]   =      [self.resize(image=A__       ,size=A__       ,resample=A__		) for image in images]

						if do_center_crop:
										_A	:				Union[str, Any]   =      [self.center_crop(image=A__       ,size=A__		) for image in images]

						if do_rescale:
										_A	:				Tuple   =      [self.rescale(image=A__       ,scale=A__		) for image in images]

						if do_normalize:
										_A	:				Dict   =      [self.normalize(image=A__       ,mean=A__       ,std=A__		) for image in images]

						_A	:				Dict   =      [to_channel_dimension_format(A__       ,A__		) for image in images]

						_A	:				Dict   =      {'''pixel_values''': images}
						return BatchFeature(data=A__       ,tensor_type=A__		)



