




import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


snake_case_ : Optional[Any]    		=       "\\n    Text data.\n    Second line of data."

snake_case_ : Dict    		=       "file"


@pytest.fixture(scope='''session'''       )
def    A						(__A   :    Dict       )   ->      List[str]:







     """simple docstring"""


     UpperCAmelCase_          =							tmp_path_factory.mktemp('''data'''       ) / (FILE_PATH + '''.zstd''')
     UpperCAmelCase_          =							bytes(__A   ,				'''utf-8'''       )
     with zstd.open(__A   ,				'''wb'''       ) as f:
          f.write(__A       )
     return path


@pytest.fixture
def    A						(__A   :    List[str]       )   ->      Dict:







     """simple docstring"""


     with open(os.path.join(tmpfs.local_root_dir   ,				__A       )   ,				'''w'''       ) as f:
          f.write(__A       )
     return FILE_PATH


@pytest.mark.parametrize('''compression_format'''   ,				['''gzip''', '''xz''', '''zstd''']       )
def    A						(__A   :    Tuple   ,				__A   :    Any   ,				__A   :    Optional[int]   ,				__A   :    List[str]   ,				__A   :    Optional[Any]   ,				__A   :    str       )   ->      List[Any]:







     """simple docstring"""


     UpperCAmelCase_          =							{'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
     UpperCAmelCase_          =							input_paths[compression_format]
     UpperCAmelCase_          =							tmp_path / '''cache'''
     UpperCAmelCase_          =							DownloadConfig(cache_dir=__A   ,				extract_compressed_file=__A       )
     UpperCAmelCase_          =							cached_path(__A   ,				download_config=__A       )
     with open(__A       ) as f:
          UpperCAmelCase_          =							f.read()
     with open(__A       ) as f:
          UpperCAmelCase_          =							f.read()
     assert extracted_file_content == expected_file_content


@pytest.mark.parametrize('''default_extracted'''   ,				[True, False]       )
@pytest.mark.parametrize('''default_cache_dir'''   ,				[True, False]       )
def    A						(__A   :    str   ,				__A   :    Any   ,				__A   :    Dict   ,				__A   :    Union[str, Any]   ,				__A   :    Union[str, Any]       )   ->      Any:







     """simple docstring"""


     UpperCAmelCase_          =							'''custom_cache'''
     UpperCAmelCase_          =							'''custom_extracted_dir'''
     UpperCAmelCase_          =							tmp_path / '''custom_extracted_path'''
     if default_extracted:
          UpperCAmelCase_          =							('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
     else:
          monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR'''   ,				__A       )
          monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''   ,				str(__A       )       )
          UpperCAmelCase_          =							custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

     UpperCAmelCase_          =							xz_file
     UpperCAmelCase_          =							(
         DownloadConfig(extract_compressed_file=__A       )
         if default_cache_dir
         else DownloadConfig(cache_dir=tmp_path / custom_cache_dir   ,				extract_compressed_file=__A       )
     )
     UpperCAmelCase_          =							cached_path(__A   ,				download_config=__A       )
     assert Path(__A       ).parent.parts[-2:] == expected


def    A						(__A   :    Tuple       )   ->      Dict:







     """simple docstring"""


     UpperCAmelCase_          =							str(Path(__A       ).resolve()       )
     assert cached_path(__A       ) == text_file
     # relative path
     UpperCAmelCase_          =							str(Path(__A       ).resolve().relative_to(Path(os.getcwd()       )       )       )
     assert cached_path(__A       ) == text_file


def    A						(__A   :    List[Any]       )   ->      str:







     """simple docstring"""


     UpperCAmelCase_          =							str(tmp_path.resolve() / '''__missing_file__.txt'''       )
     with pytest.raises(__A       ):
          cached_path(__A       )
     # relative path
     UpperCAmelCase_          =							'''./__missing_file__.txt'''
     with pytest.raises(__A       ):
          cached_path(__A       )


def    A						(__A   :    Optional[int]       )   ->      List[str]:







     """simple docstring"""


     UpperCAmelCase_          =							get_from_cache(F"""tmp://{tmpfs_file}"""       )
     with open(__A       ) as f:
          UpperCAmelCase_          =							f.read()
     assert output_file_content == FILE_CONTENT


@patch('''datasets.config.HF_DATASETS_OFFLINE'''   ,				__A       )
def    A						()   ->      Any:







     """simple docstring"""


     with pytest.raises(__A       ):
          cached_path('''https://huggingface.co'''       )


@patch('''datasets.config.HF_DATASETS_OFFLINE'''   ,				__A       )
def    A						(__A   :    Optional[int]       )   ->      str:







     """simple docstring"""


     UpperCAmelCase_          =							tmp_path_factory.mktemp('''data'''       ) / '''file.html'''
     with pytest.raises(__A       ):
          http_get('''https://huggingface.co'''   ,				temp_file=__A       )
     with pytest.raises(__A       ):
          http_head('''https://huggingface.co'''       )


@patch('''datasets.config.HF_DATASETS_OFFLINE'''   ,				__A       )
def    A						(__A   :    List[str]       )   ->      List[str]:







     """simple docstring"""


     UpperCAmelCase_          =							tmp_path_factory.mktemp('''data'''       ) / '''file.html'''
     with pytest.raises(__A       ):
          ftp_get('''ftp://huggingface.co'''   ,				temp_file=__A       )
     with pytest.raises(__A       ):
          ftp_head('''ftp://huggingface.co'''       )


@patch('''datasets.config.HF_DATASETS_OFFLINE'''   ,				__A       )
def    A						(__A   :    Optional[int]       )   ->      List[str]:







     """simple docstring"""


     UpperCAmelCase_          =							tmp_path_factory.mktemp('''data'''       ) / '''file.html'''
     with pytest.raises(__A       ):
          fsspec_get('''s3://huggingface.co'''   ,				temp_file=__A       )
     with pytest.raises(__A       ):
          fsspec_head('''s3://huggingface.co'''       )












import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image
else:







    class __snake_case			:







       @staticmethod
       def lowerCamelCase						(      *_snake_case		:						Optional[int]					,						**_snake_case		:						int):

            """simple docstring"""


            pass


def    A						(__A   :    Image       )   ->      str:







     """simple docstring"""


     UpperCAmelCase_          =							hashlib.mda(image.tobytes()       )
     return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class __snake_case			(			unittest.TestCase		):
   UpperCAmelCase__  :    Tuple          =  MODEL_FOR_DEPTH_ESTIMATION_MAPPING







   def lowerCamelCase						(      self		:						Tuple					,						_snake_case		:						Optional[Any]					,						_snake_case		:						Optional[Any]					,						_snake_case		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							DepthEstimationPipeline(model=_snake_case					,						image_processor=_snake_case)
        return depth_estimator, [
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
        ]







   def lowerCamelCase						(      self		:						str					,						_snake_case		:						Optional[int]					,						_snake_case		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
        self.assertEqual({'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)}					,						_snake_case)
        import datasets

        UpperCAmelCase_          =							datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''					,						'''image'''					,						split='''test''')
        UpperCAmelCase_          =							depth_estimator(
            [
                Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''),
                '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                # RGBA
                dataset[0]['''file'''],
                # LA
                dataset[1]['''file'''],
                # L
                dataset[2]['''file'''],
            ])
        self.assertEqual(
            [
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
                {'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)},
            ]					,						_snake_case					,						)







   @require_tf
   @unittest.skip('''Depth estimation is not implemented in TF''')
   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


        pass







   @slow
   @require_torch
   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							'''Intel/dpt-large'''
        UpperCAmelCase_          =							pipeline('''depth-estimation'''					,						model=_snake_case)
        UpperCAmelCase_          =							depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''')
        UpperCAmelCase_          =							hashimage(outputs['''depth'''])

        # This seems flaky.
        # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
        self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item())					,						2_9.3_0_4)
        self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item())					,						2.6_6_2)







   @require_torch
   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT''')






