'''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_	   =			get_logger(__name__)




class    a :



  """simple docstring"""
  __lowerCAmelCase				      =					"""dummy_data"""
  __lowerCAmelCase				      =					"""datasets"""
  __lowerCAmelCase				      =					False







  def __init__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_ = None   ,    snake_case_ = False   ,    snake_case_ = True   ,    snake_case_ = None   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					str		= 0
    __UpperCAmelCase:					Optional[Any]		= dataset_name
    __UpperCAmelCase:					str		= cache_dir
    __UpperCAmelCase:					Dict		= use_local_dummy_data
    __UpperCAmelCase:					str		= config
    # download_callbacks take a single url as input
    __UpperCAmelCase:					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
    __UpperCAmelCase:					Union[str, Any]		= load_existing_dummy_data

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







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    if self._dummy_file is None:
      __UpperCAmelCase:					List[str]		= self.download_dummy_data()
    return self._dummy_file







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    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    lowercase_  (							self					):

    '''simple docstring'''







    return os.path.join(self.dummy_data_folder   ,    """dummy_data.zip"""					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= (
        self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
    )

    __UpperCAmelCase:					Optional[int]		= cached_path(
        snake_case_   ,    cache_dir=self.cache_dir   ,    extract_compressed_file=snake_case_   ,    force_extract=snake_case_					)

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







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    return os.path.join(self.datasets_scripts_dir   ,    self.dataset_name   ,    self.dummy_zip_file					)







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    if self._bucket_url is None:
      __UpperCAmelCase:					List[Any]		= hf_github_url(self.dataset_name   ,    self.dummy_zip_file.replace(os.sep   ,    """/"""					)					)
    return self._bucket_url







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    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    lowercase_  (							self   ,    snake_case_   ,    *snake_case_					):

    '''simple docstring'''







    if self.load_existing_dummy_data:
      # dummy data is downloaded and tested
      __UpperCAmelCase:					Dict		= self.dummy_file
    else:
      # dummy data cannot be downloaded and only the path to dummy file is returned
      __UpperCAmelCase:					List[Any]		= self.dummy_file_name

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







  def    lowercase_  (							self   ,    snake_case_   ,    *snake_case_					):

    '''simple docstring'''







    return self.download_and_extract(snake_case_					)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    return self.download_and_extract(snake_case_					)







  def    lowercase_  (							self   ,    snake_case_   ,    *snake_case_   ,    **snake_case_					):

    '''simple docstring'''







    return path







  def    lowercase_  (							self					):

    '''simple docstring'''







    return {}







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[Any]		= {}
    for key, single_urls in data_url.items():
      for download_callback in self.download_callbacks:
        if isinstance(snake_case_   ,    snake_case_					):
          for single_url in single_urls:
            download_callback(snake_case_					)
        else:
          __UpperCAmelCase:					Tuple		= single_urls
          download_callback(snake_case_					)
            # 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(snake_case_   ,    snake_case_					):
        __UpperCAmelCase:					Tuple		= [os.path.join(snake_case_   ,    urllib.parse.quote_plus(Path(snake_case_					).name					)					) for x in single_urls]
      else:
        __UpperCAmelCase:					Dict		= single_urls
        __UpperCAmelCase:					Any		= os.path.join(snake_case_   ,    urllib.parse.quote_plus(Path(snake_case_					).name					)					)
      __UpperCAmelCase:					Dict		= value

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

    return dummy_data_dict







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







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







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    for download_callback in self.download_callbacks:
      download_callback(snake_case_					)
    # 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
    __UpperCAmelCase:					Tuple		= os.path.join(snake_case_   ,    urllib.parse.quote_plus(data_url.split("""/"""					)[-1]					)					)
    if os.path.exists(snake_case_					) 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    lowercase_  (							self					):

    '''simple docstring'''







    pass







  def    lowercase_  (							self					):

    '''simple docstring'''







    pass







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    def _iter_archive_members(snake_case_					):
      # this preserves the order of the members inside the ZIP archive
      __UpperCAmelCase:					List[Any]		= Path(self.dummy_file					).parent
      __UpperCAmelCase:					Optional[Any]		= path.relative_to(snake_case_					)
      with ZipFile(self.local_path_to_dummy_data					) as zip_file:
        __UpperCAmelCase:					List[str]		= zip_file.namelist()
      for member in members:
        if member.startswith(relative_path.as_posix()					):
          yield dummy_parent_path.joinpath(snake_case_					)

    __UpperCAmelCase:					Tuple		= Path(snake_case_					)
    __UpperCAmelCase:					List[Any]		= _iter_archive_members(snake_case_					) 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(snake_case_					).as_posix(), file_path.open("""rb"""					)







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    if not isinstance(snake_case_   ,    snake_case_					):
      __UpperCAmelCase:					str		= [paths]
    for path in paths:
      if os.path.isfile(snake_case_					):
        if os.path.basename(snake_case_					).startswith((""".""", """__""")					):
          return
        yield path
      else:
        for dirpath, dirnames, filenames in os.walk(snake_case_					):
          if os.path.basename(snake_case_					).startswith((""".""", """__""")					):
            continue
          dirnames.sort()
          for filename in sorted(snake_case_					):
            if filename.startswith((""".""", """__""")					):
              continue
            yield os.path.join(snake_case_   ,    snake_case_					)
'''simple docstring'''






import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ImageGPTImageProcessor




class    a (       unittest.TestCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_=7   ,    snake_case_=3   ,    snake_case_=18   ,    snake_case_=30   ,    snake_case_=400   ,    snake_case_=True   ,    snake_case_=None   ,    snake_case_=True   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[int]		= size if size is not None else {"""height""": 18, """width""": 18}
    __UpperCAmelCase:					Tuple		= parent
    __UpperCAmelCase:					Any		= batch_size
    __UpperCAmelCase:					str		= num_channels
    __UpperCAmelCase:					Any		= image_size
    __UpperCAmelCase:					Optional[int]		= min_resolution
    __UpperCAmelCase:					Tuple		= max_resolution
    __UpperCAmelCase:					Any		= do_resize
    __UpperCAmelCase:					Any		= size
    __UpperCAmelCase:					Optional[Any]		= do_normalize







  def    lowercase_  (							self					):

    '''simple docstring'''







    return {
        # here we create 2 clusters for the sake of simplicity
        "clusters": np.asarray(
            [
                [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
                [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
            ]					),
        "do_resize": self.do_resize,
        "size": self.size,
        "do_normalize": self.do_normalize,
    }




@require_torch
@require_vision
class    a (       __lowerCAmelCase						,  unittest.TestCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					ImageGPTImageProcessor if is_vision_available() else None







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					str		= ImageGPTImageProcessingTester(self					)







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    return self.image_processor_tester.prepare_image_processor_dict()







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Tuple		= self.image_processing_class(**self.image_processor_dict					)
    self.assertTrue(hasattr(snake_case_   ,    """clusters"""					)					)
    self.assertTrue(hasattr(snake_case_   ,    """do_resize"""					)					)
    self.assertTrue(hasattr(snake_case_   ,    """size"""					)					)
    self.assertTrue(hasattr(snake_case_   ,    """do_normalize"""					)					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= self.image_processing_class.from_dict(self.image_processor_dict					)
    self.assertEqual(image_processor.size   ,    {"""height""": 18, """width""": 18}					)

    __UpperCAmelCase:					int		= self.image_processing_class.from_dict(self.image_processor_dict   ,    size=42					)
    self.assertEqual(image_processor.size   ,    {"""height""": 42, """width""": 42}					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					int		= self.image_processing_class(**self.image_processor_dict					)
    __UpperCAmelCase:					str		= json.loads(image_processor.to_json_string()					)
    for key, value in self.image_processor_dict.items():
      if key == "clusters":
        self.assertTrue(np.array_equal(snake_case_   ,    obj[key]					)					)
      else:
        self.assertEqual(obj[key]   ,    snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= self.image_processing_class(**self.image_processor_dict					)

    with tempfile.TemporaryDirectory() as tmpdirname:
      __UpperCAmelCase:					List[Any]		= os.path.join(snake_case_   ,    """image_processor.json"""					)
      image_processor_first.to_json_file(snake_case_					)
      __UpperCAmelCase:					List[str]		= self.image_processing_class.from_json_file(snake_case_					).to_dict()

    __UpperCAmelCase:					str		= image_processor_first.to_dict()
    for key, value in image_processor_first.items():
      if key == "clusters":
        self.assertTrue(np.array_equal(snake_case_   ,    image_processor_second[key]					)					)
      else:
        self.assertEqual(image_processor_first[key]   ,    snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= self.image_processing_class(**self.image_processor_dict					)

    with tempfile.TemporaryDirectory() as tmpdirname:
      image_processor_first.save_pretrained(snake_case_					)
      __UpperCAmelCase:					Any		= self.image_processing_class.from_pretrained(snake_case_					).to_dict()

    __UpperCAmelCase:					str		= image_processor_first.to_dict()
    for key, value in image_processor_first.items():
      if key == "clusters":
        self.assertTrue(np.array_equal(snake_case_   ,    image_processor_second[key]					)					)
      else:
        self.assertEqual(image_processor_first[key]   ,    snake_case_					)







  @unittest.skip("""ImageGPT requires clusters at initialization"""					)
  def    lowercase_  (							self					):

    '''simple docstring'''







    pass






def  UpperCamelCase__					(					)							->	List[str]:
  __UpperCAmelCase:					Any		= load_dataset("""hf-internal-testing/fixtures_image_utils"""   ,    split="""test"""     )

  __UpperCAmelCase:					List[Any]		= Image.open(dataset[4]["""file"""]     )
  __UpperCAmelCase:					Dict		= Image.open(dataset[5]["""file"""]     )

  __UpperCAmelCase:					Dict		= [imagea, imagea]

  return images







@require_vision
@require_torch
class    a (       unittest.TestCase				):



  """simple docstring"""







  @slow
  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""					)

    __UpperCAmelCase:					Dict		= prepare_images()

    # test non-batched
    __UpperCAmelCase:					Tuple		= image_processing(images[0]   ,    return_tensors="""pt"""					)

    self.assertIsInstance(encoding.input_ids   ,    torch.LongTensor					)
    self.assertEqual(encoding.input_ids.shape   ,    (1, 1024)					)

    __UpperCAmelCase:					int		= [306, 191, 191]
    self.assertEqual(encoding.input_ids[0, :3].tolist()   ,    snake_case_					)

    # test batched
    __UpperCAmelCase:					Union[str, Any]		= image_processing(snake_case_   ,    return_tensors="""pt"""					)

    self.assertIsInstance(encoding.input_ids   ,    torch.LongTensor					)
    self.assertEqual(encoding.input_ids.shape   ,    (2, 1024)					)

    __UpperCAmelCase:					Tuple		= [303, 13, 13]
    self.assertEqual(encoding.input_ids[1, -3:].tolist()   ,    snake_case_					)