"""simple docstring"""


import importlib
import os

import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry

from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem

from .utils import require_lza, require_zstandard






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  assert "mock" in _fsspec_registry
  assert "bz2" in _fsspec_registry






def 			__UpperCAmelCase    (	):







  """simple docstring"""
  assert "mock" not in _fsspec_registry
  assert "bz2" in _fsspec_registry






def 			__UpperCAmelCase    (	):







  """simple docstring"""
  _UpperCAmelCase         =					"""mock-s3-bucket"""
  _UpperCAmelCase         =					f'''s3://{mock_bucket}'''
  _UpperCAmelCase         =					extract_path_from_uri(lowercase						)
  assert dataset_path.startswith("""s3://"""						) is False

  _UpperCAmelCase         =					"""./local/path"""
  _UpperCAmelCase         =					extract_path_from_uri(lowercase						)
  assert dataset_path == new_dataset_path






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					is_remote_filesystem(lowercase						)
  assert is_remote is True

  _UpperCAmelCase         =					fsspec.filesystem("""file"""						)

  _UpperCAmelCase         =					is_remote_filesystem(lowercase						)
  assert is_remote is False






@pytest.mark.parametrize("""compression_fs_class"""      ,lowercase						)
def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					{"""gzip""": gz_file, """xz""": xz_file, """zstd""": zstd_file, """bz2""": bza_file, """lz4""": lza_file}
  _UpperCAmelCase         =					input_paths[compression_fs_class.protocol]
  if input_path is None:
    _UpperCAmelCase         =					f'''for \'{compression_fs_class.protocol}\' compression protocol, '''
    if compression_fs_class.protocol == "lz4":
      reason += require_lza.kwargs["reason"]
    elif compression_fs_class.protocol == "zstd":
      reason += require_zstandard.kwargs["reason"]
    pytest.skip(lowercase						)
  _UpperCAmelCase         =					fsspec.filesystem(compression_fs_class.protocol      ,fo=lowercase						)
  assert isinstance(lowercase      ,lowercase						)
  _UpperCAmelCase         =					os.path.basename(lowercase						)
  _UpperCAmelCase         =					expected_filename[: expected_filename.rindex("""."""						)]
  assert fs.glob("""*"""						) == [expected_filename]
  with fs.open(lowercase      ,"""r"""      ,encoding="""utf-8"""						) as f, open(lowercase      ,encoding="""utf-8"""						) as expected_file:
    assert f.read() == expected_file.read()






@pytest.mark.parametrize("""protocol"""      ,["""zip""", """gzip"""]						)
def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					{"""zip""": zip_jsonl_path, """gzip""": jsonl_gz_path}
  _UpperCAmelCase         =					compressed_file_paths[protocol]
  _UpperCAmelCase         =					"""dataset.jsonl"""
  _UpperCAmelCase         =					f'''{protocol}://{member_file_path}::{compressed_file_path}'''
  _UpperCAmelCase       , *_UpperCAmelCase         =					fsspec.get_fs_token_paths(lowercase						)
  assert fs.isfile(lowercase						)
  assert not fs.isfile("""non_existing_""" + member_file_path						)






@pytest.mark.integration
def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					hf_api.dataset_info(lowercase      ,token=lowercase						)
  _UpperCAmelCase         =					HfFileSystem(repo_info=lowercase      ,token=lowercase						)
  assert sorted(hffs.glob("""*"""						)						) == [".gitattributes", "data"]
  assert hffs.isdir("""data"""						)
  assert hffs.isfile(""".gitattributes"""						) and hffs.isfile("""data/text_data.txt"""						)
  with open(lowercase						) as f:
    assert hffs.open("""data/text_data.txt"""      ,"""r"""						).read() == f.read()






def 			__UpperCAmelCase    (	):







  """simple docstring"""
  _UpperCAmelCase         =					"""bz2"""

  # Import module
  import datasets.filesystems

  # Overwrite protocol and reload
  register_implementation(lowercase      ,lowercase      ,clobber=lowercase						)
  with pytest.warns(lowercase						) as warning_info:
    importlib.reload(datasets.filesystems						)

  assert len(lowercase						) == 1
  assert (
      str(warning_info[0].message						)
      == f'''A filesystem protocol was already set for {protocol} and will be overwritten.'''
  )

"""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 MobileViTImageProcessor


class 				a (						unittest.TestCase      ):


    def __init__(    self		:     Dict      , __lowerCAmelCase		:     Tuple      , __lowerCAmelCase		:     Optional[Any]=7      , __lowerCAmelCase		:     Optional[Any]=3      , __lowerCAmelCase		:     Optional[Any]=18      , __lowerCAmelCase		:     str=30      , __lowerCAmelCase		:     List[str]=400      , __lowerCAmelCase		:     Union[str, Any]=True      , __lowerCAmelCase		:     str=None      , __lowerCAmelCase		:     Optional[int]=True      , __lowerCAmelCase		:     int=None      , __lowerCAmelCase		:     List[str]=True      , ):
      _UpperCAmelCase         =					size if size is not None else {"""shortest_edge""": 20}
      _UpperCAmelCase         =					crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
      _UpperCAmelCase         =					parent
      _UpperCAmelCase         =					batch_size
      _UpperCAmelCase         =					num_channels
      _UpperCAmelCase         =					image_size
      _UpperCAmelCase         =					min_resolution
      _UpperCAmelCase         =					max_resolution
      _UpperCAmelCase         =					do_resize
      _UpperCAmelCase         =					size
      _UpperCAmelCase         =					do_center_crop
      _UpperCAmelCase         =					crop_size
      _UpperCAmelCase         =					do_flip_channel_order


    def    lowerCAmelCase_     (    self		:     List[str]    ):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_flip_channel_order": self.do_flip_channel_order,
      }



@require_torch
@require_vision
class 				a (						lowerCAmelCase_		,							unittest.TestCase      ):
    _snake_case   :     Optional[int]   							=							MobileViTImageProcessor if is_vision_available() else None


    def    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      _UpperCAmelCase         =					MobileViTImageProcessingTester(self    )


    @property
    def    lowerCAmelCase_     (    self		:     Tuple    ):
      return self.image_processor_tester.prepare_image_processor_dict()


    def    lowerCAmelCase_     (    self		:     Dict    ):
      _UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """do_resize"""    )    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """size"""    )    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """do_center_crop"""    )    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """center_crop"""    )    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """do_flip_channel_order"""    )    )


    def    lowerCAmelCase_     (    self		:     Union[str, Any]    ):
      _UpperCAmelCase         =					self.image_processing_class.from_dict(self.image_processor_dict    )
      self.assertEqual(image_processor.size      , {"""shortest_edge""": 20}    )
      self.assertEqual(image_processor.crop_size      , {"""height""": 18, """width""": 18}    )

      _UpperCAmelCase         =					self.image_processing_class.from_dict(self.image_processor_dict      , size=42      , crop_size=84    )
      self.assertEqual(image_processor.size      , {"""shortest_edge""": 42}    )
      self.assertEqual(image_processor.crop_size      , {"""height""": 84, """width""": 84}    )


    def    lowerCAmelCase_     (    self		:     List[str]    ):
      pass


    def    lowerCAmelCase_     (    self		:     Dict    ):
      # Initialize image_processing
      _UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict    )
      # create random PIL images
      _UpperCAmelCase         =					prepare_image_inputs(self.image_processor_tester      , equal_resolution=__lowerCAmelCase    )
      for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase      , Image.Image    )

      # Test not batched input
      _UpperCAmelCase         =					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.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )

      # Test batched
      _UpperCAmelCase         =					image_processing(__lowerCAmelCase      , 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.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )


    def    lowerCAmelCase_     (    self		:     str    ):
      # Initialize image_processing
      _UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict    )
      # create random numpy tensors
      _UpperCAmelCase         =					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
      _UpperCAmelCase         =					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.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )

      # Test batched
      _UpperCAmelCase         =					image_processing(__lowerCAmelCase      , 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.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )


    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      # Initialize image_processing
      _UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict    )
      # create random PyTorch tensors
      _UpperCAmelCase         =					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
      _UpperCAmelCase         =					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.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )

      # Test batched
      _UpperCAmelCase         =					image_processing(__lowerCAmelCase      , 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.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )
