"""simple docstring"""



# 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


SCREAMING_SNAKE_CASE__:Tuple			=	get_logger(__name__)






class 				snake_case__     :
 _snake_case   :			Optional[Any]           =		"""dummy_data"""
 _snake_case   :			str           =		"""datasets"""
 _snake_case   :			List[Any]           =		False


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						lowerCamelCase = False  ,						lowerCamelCase = True  ,						lowerCamelCase = None  ,						):
        __a     				=  0
        __a     				=  dataset_name
        __a     				=  cache_dir
        __a     				=  use_local_dummy_data
        __a     				=  config
        # download_callbacks take a single url as input
        __a     				=  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     				=  load_existing_dummy_data

        # TODO(PVP, QL) might need to make this more general
        __a     				=  str(lowerCamelCase		)
        # to be downloaded
        __a     				=  None
        __a     				=  None


 @property
 def 		a__ (				self		):
        if self._dummy_file is None:
               __a     				=  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     				=  (
            self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
        )

        __a     				=  cached_path(
            lowerCamelCase  ,						cache_dir=self.cache_dir  ,						extract_compressed_file=lowerCamelCase  ,						force_extract=lowerCamelCase		)

        return os.path.join(lowerCamelCase  ,						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     				=  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  ,						lowerCamelCase  ,						*lowerCamelCase		):
        if self.load_existing_dummy_data:
               # dummy data is downloaded and tested
               __a     				=  self.dummy_file
        else:
               # dummy data cannot be downloaded and only the path to dummy file is returned
               __a     				=  self.dummy_file_name

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


 def 		a__ (				self  ,						lowerCamelCase  ,						*lowerCamelCase		):
        return self.download_and_extract(lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        return self.download_and_extract(lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        return path


 def 		a__ (				self		):
        return {}


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  {}
        for key, single_urls in data_url.items():
               for download_callback in self.download_callbacks:
                      if isinstance(lowerCamelCase  ,						lowerCamelCase		):
                             for single_url in single_urls:
                                    download_callback(lowerCamelCase		)
                      else:
                             __a     				=  single_urls
                             download_callback(lowerCamelCase		)
            # 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(lowerCamelCase  ,						lowerCamelCase		):
                      __a     				=  [os.path.join(lowerCamelCase  ,						urllib.parse.quote_plus(Path(lowerCamelCase		).name		)		) for x in single_urls]
               else:
                      __a     				=  single_urls
                      __a     				=  os.path.join(lowerCamelCase  ,						urllib.parse.quote_plus(Path(lowerCamelCase		).name		)		)
               __a     				=  value

        # make sure that values are unique
        if all(isinstance(lowerCamelCase  ,						lowerCamelCase		) 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     				=  {key: value + key for key, value in dummy_data_dict.items()}

        return dummy_data_dict


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  []
        # trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
        __a     				=  all(bool(re.findall("[0-9]{3,}-of-[0-9]{3,}"  ,						lowerCamelCase		)		) for url in data_url		)
        __a     				=  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     				=  [data_url[0]] * len(lowerCamelCase		)
        for single_url in data_url:
               for download_callback in self.download_callbacks:
                      download_callback(lowerCamelCase		)
               # 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     				=  os.path.join(lowerCamelCase  ,						urllib.parse.quote_plus(single_url.split("/"		)[-1]		)		)
               dummy_data_list.append(lowerCamelCase		)
        return dummy_data_list


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        for download_callback in self.download_callbacks:
               download_callback(lowerCamelCase		)
        # 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     				=  os.path.join(lowerCamelCase  ,						urllib.parse.quote_plus(data_url.split("/"		)[-1]		)		)
        if os.path.exists(lowerCamelCase		) 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  ,						lowerCamelCase		):
        def _iter_archive_members(lowerCamelCase		):
               # this preserves the order of the members inside the ZIP archive
               __a     				=  Path(self.dummy_file		).parent
               __a     				=  path.relative_to(lowerCamelCase		)
               with ZipFile(self.local_path_to_dummy_data		) as zip_file:
                      __a     				=  zip_file.namelist()
               for member in members:
                      if member.startswith(relative_path.as_posix()		):
                             yield dummy_parent_path.joinpath(lowerCamelCase		)

        __a     				=  Path(lowerCamelCase		)
        __a     				=  _iter_archive_members(lowerCamelCase		) 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(lowerCamelCase		).as_posix(), file_path.open("rb"		)






 def 		a__ (				self  ,						lowerCamelCase		):
        if not isinstance(lowerCamelCase  ,						lowerCamelCase		):
               __a     				=  [paths]
        for path in paths:
               if os.path.isfile(lowerCamelCase		):
                      if os.path.basename(lowerCamelCase		).startswith((".", "__")		):
                             return
                      yield path
               else:
                      for dirpath, dirnames, filenames in os.walk(lowerCamelCase		):
                             if os.path.basename(lowerCamelCase		).startswith((".", "__")		):
                                    continue
                             dirnames.sort()
                             for filename in sorted(lowerCamelCase		):
                                    if filename.startswith((".", "__")		):
                                           continue
                                    yield os.path.join(lowerCamelCase  ,						lowerCamelCase		)

"""simple docstring"""



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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


SCREAMING_SNAKE_CASE__:Tuple			=	logging.get_logger(__name__)






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			Optional[Any]           =		["""pixel_values"""]


 def __init__(				self  ,						lowerCamelCase = True  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = PILImageResampling.BILINEAR  ,						lowerCamelCase = True  ,						lowerCamelCase = 1 / 255  ,						lowerCamelCase = True  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        super().__init__(**lowerCamelCase		)
        __a     				=  size if size is not None else {"shortest_edge": 384}
        __a     				=  get_size_dict(lowerCamelCase  ,						default_to_square=lowerCamelCase		)

        __a     				=  do_resize
        __a     				=  size
        # Default value set here for backwards compatibility where the value in config is None
        __a     				=  crop_pct if crop_pct is not None else 224 / 256
        __a     				=  resample
        __a     				=  do_rescale
        __a     				=  rescale_factor
        __a     				=  do_normalize
        __a     				=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        __a     				=  image_std if image_std is not None else IMAGENET_STANDARD_STD


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = PILImageResampling.BICUBIC  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        __a     				=  get_size_dict(lowerCamelCase  ,						default_to_square=lowerCamelCase		)
        if "shortest_edge" not in size:
               raise ValueError(F"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"		)
        __a     				=  size["shortest_edge"]

        if shortest_edge < 384:
               # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
               __a     				=  int(shortest_edge / crop_pct		)
               __a     				=  get_resize_output_image_size(lowerCamelCase  ,						size=lowerCamelCase  ,						default_to_square=lowerCamelCase		)
               __a     				=  resize(image=lowerCamelCase  ,						size=lowerCamelCase  ,						resample=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)
               # then crop to (shortest_edge, shortest_edge)
               return center_crop(image=lowerCamelCase  ,						size=(shortest_edge, shortest_edge)  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)
        else:
               # warping (no cropping) when evaluated at 384 or larger
               return resize(
                   lowerCamelCase  ,						size=(shortest_edge, shortest_edge)  ,						resample=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)


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


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






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = ChannelDimension.FIRST  ,						**lowerCamelCase  ,						):
        __a     				=  do_resize if do_resize is not None else self.do_resize
        __a     				=  crop_pct if crop_pct is not None else self.crop_pct
        __a     				=  resample if resample is not None else self.resample
        __a     				=  do_rescale if do_rescale is not None else self.do_rescale
        __a     				=  rescale_factor if rescale_factor is not None else self.rescale_factor
        __a     				=  do_normalize if do_normalize is not None else self.do_normalize
        __a     				=  image_mean if image_mean is not None else self.image_mean
        __a     				=  image_std if image_std is not None else self.image_std

        __a     				=  size if size is not None else self.size
        __a     				=  get_size_dict(lowerCamelCase  ,						default_to_square=lowerCamelCase		)

        __a     				=  make_list_of_images(lowerCamelCase		)

        if not valid_images(lowerCamelCase		):
               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 or resample is None:
               raise ValueError("Size and resample must be specified if do_resize is True."		)

        if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
               raise ValueError("crop_pct must be specified if size < 384."		)

        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."		)

        # All transformations expect numpy arrays.
        __a     				=  [to_numpy_array(lowerCamelCase		) for image in images]

        if do_resize:
               __a     				=  [self.resize(image=lowerCamelCase  ,						size=lowerCamelCase  ,						crop_pct=lowerCamelCase  ,						resample=lowerCamelCase		) for image in images]

        if do_rescale:
               __a     				=  [self.rescale(image=lowerCamelCase  ,						scale=lowerCamelCase		) for image in images]

        if do_normalize:
               __a     				=  [self.normalize(image=lowerCamelCase  ,						mean=lowerCamelCase  ,						std=lowerCamelCase		) for image in images]

        __a     				=  [to_channel_dimension_format(lowerCamelCase  ,						lowerCamelCase		) for image in images]

        __a     				=  {"pixel_values": images}
        return BatchFeature(data=lowerCamelCase  ,						tensor_type=lowerCamelCase		)
