

'''simple docstring'''
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     =  '\\n    Text data.\n    Second line of data.'

_snake_case     =  'file'







@pytest.fixture(scope="session"     )
def 					_A		(	snake_case     )			->	List[Any]:
   _lowercase			:      List[Any]			     =   tmp_path_factory.mktemp("data"     ) / (FILE_PATH + ".zstd")
   _lowercase			:      str			     =   bytes(A__		,							"utf-8"     )
   with zstd.open(A__		,							"wb"     ) as f:
      f.write(A__     )
   return path







@pytest.fixture
def 					_A		(	snake_case     )			->	Optional[Any]:
   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		(	snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case     )			->	List[str]:
   _lowercase			:      List[str]			     =   {"gzip": gz_file, "xz": xz_file, "zstd": zstd_path}
   _lowercase			:      List[Any]			     =   input_paths[compression_format]
   _lowercase			:      int			     =   tmp_path / "cache"
   _lowercase			:      Optional[int]			     =   DownloadConfig(cache_dir=A__		,							extract_compressed_file=A__     )
   _lowercase			:      str			     =   cached_path(A__		,							download_config=A__     )
   with open(A__     ) as f:
      _lowercase			:      Any			     =   f.read()
   with open(A__     ) as f:
      _lowercase			:      List[Any]			     =   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		(	snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case     )			->	List[str]:
   _lowercase			:      Optional[Any]			     =   "custom_cache"
   _lowercase			:      str			     =   "custom_extracted_dir"
   _lowercase			:      Dict			     =   tmp_path / "custom_extracted_path"
   if default_extracted:
      _lowercase			:      Dict			     =   ("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__     )     )
      _lowercase			:      List[Any]			     =   custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

   _lowercase			:      str			     =   xz_file
   _lowercase			:      Optional[Any]			     =   (
       DownloadConfig(extract_compressed_file=A__     )
       if default_cache_dir
       else DownloadConfig(cache_dir=tmp_path / custom_cache_dir		,							extract_compressed_file=A__     )
   )
   _lowercase			:      Tuple			     =   cached_path(A__		,							download_config=A__     )
   assert Path(A__     ).parent.parts[-2:] == expected







def 					_A		(	snake_case     )			->	Any:
   # absolute path
   _lowercase			:      Union[str, Any]			     =   str(Path(A__     ).resolve()     )
   assert cached_path(A__     ) == text_file
   # relative path
   _lowercase			:      Dict			     =   str(Path(A__     ).resolve().relative_to(Path(os.getcwd()     )     )     )
   assert cached_path(A__     ) == text_file







def 					_A		(	snake_case     )			->	int:
   # absolute path
   _lowercase			:      Dict			     =   str(tmp_path.resolve() / "__missing_file__.txt"     )
   with pytest.raises(A__     ):
      cached_path(A__     )
   # relative path
   _lowercase			:      Any			     =   "./__missing_file__.txt"
   with pytest.raises(A__     ):
      cached_path(A__     )







def 					_A		(	snake_case     )			->	Any:
   _lowercase			:      Union[str, Any]			     =   get_from_cache(F'''tmp://{tmpfs_file}'''     )
   with open(A__     ) as f:
      _lowercase			:      Optional[Any]			     =   f.read()
   assert output_file_content == FILE_CONTENT







@patch("datasets.config.HF_DATASETS_OFFLINE"		,							A__     )
def 					_A		(	)			->	Optional[Any]:
   with pytest.raises(A__     ):
      cached_path("https://huggingface.co"     )







@patch("datasets.config.HF_DATASETS_OFFLINE"		,							A__     )
def 					_A		(	snake_case     )			->	Optional[Any]:
   _lowercase			:      List[Any]			     =   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		(	snake_case     )			->	Optional[int]:
   _lowercase			:      str			     =   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		(	snake_case     )			->	Optional[Any]:
   _lowercase			:      List[str]			     =   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"     )














"""simple docstring"""
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
			from PIL import Image

			from ..image_utils import load_image

if is_tf_available():
			import tensorflow as tf

			from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
			from ..tf_utils import stable_softmax

if is_torch_available():
			from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

lowerCamelCase_					=      logging.get_logger(__name__)







@add_end_docstrings(__A     )
class 			UpperCamelCase_       (__A     ):





	def __init__(      self					: int	,	*lowerCAmelCase_					: Tuple	,	**lowerCAmelCase_					: List[str]						)						->			Optional[Any]:
				super().__init__(*lowerCAmelCase_	,	**lowerCAmelCase_						)
				requires_backends(self	,	"vision"						)
				self.check_model_type(
				    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
				    if self.framework == "tf"
				    else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING						)





	def      _SCREAMING_SNAKE_CASE      (      self					: str	,	lowerCAmelCase_					: Optional[int]=None						)						->			List[Any]:
				UpperCAmelCase_     : str			     =					{}
				if top_k is not None:
							UpperCAmelCase_     : List[str]			     =					top_k
				return {}, {}, postprocess_params





	def __call__(      self					: str	,	lowerCAmelCase_					: Union[str, List[str], "Image.Image", List["Image.Image"]]	,	**lowerCAmelCase_					: Any						)						->			Tuple:
				return super().__call__(lowerCAmelCase_	,	**lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: str	,	lowerCAmelCase_					: str						)						->			Any:
				UpperCAmelCase_     : Tuple			     =					load_image(lowerCAmelCase_						)
				UpperCAmelCase_     : Dict			     =					self.image_processor(images=lowerCAmelCase_	,	return_tensors=self.framework						)
				return model_inputs





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]	,	lowerCAmelCase_					: Dict						)						->			str:
				UpperCAmelCase_     : Any			     =					self.model(**lowerCAmelCase_						)
				return model_outputs





	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple	,	lowerCAmelCase_					: Union[str, Any]	,	lowerCAmelCase_					: Optional[int]=5						)						->			Any:
				if top_k > self.model.config.num_labels:
							UpperCAmelCase_     : int			     =					self.model.config.num_labels

				if self.framework == "pt":
							UpperCAmelCase_     : str			     =					model_outputs.logits.softmax(-1						)[0]
							UpperCAmelCase_     ,	UpperCAmelCase_     : Tuple			     =					probs.topk(lowerCAmelCase_						)
				elif self.framework == "tf":
							UpperCAmelCase_     : str			     =					stable_softmax(model_outputs.logits	,	axis=-1						)[0]
							UpperCAmelCase_     : Union[str, Any]			     =					tf.math.top_k(lowerCAmelCase_	,	k=lowerCAmelCase_						)
							UpperCAmelCase_     ,	UpperCAmelCase_     : List[Any]			     =					topk.values.numpy(), topk.indices.numpy()
				else:
							raise ValueError(f"""Unsupported framework: {self.framework}"""						)

				UpperCAmelCase_     : int			     =					scores.tolist()
				UpperCAmelCase_     : Optional[Any]			     =					ids.tolist()
				return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCAmelCase_	,	lowerCAmelCase_						)]



