'''simple docstring'''


from typing import Any, Dict, List, Union

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


if is_vision_available():
       from ..image_utils import load_image


if is_torch_available():
       import torch

       from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

__snake_case						  =		logging.get_logger(__name__)


__snake_case						  =		Dict[str, Any]
__snake_case						  =		List[Prediction]





@add_end_docstrings(__snake_case      )
class       _a    (		__snake_case      ):


  """simple docstring"""

  def __init__(		self      :       Union[str, Any]   ,				*lowercase_      :       Optional[int]   ,				**lowercase_      :       Dict      ):







    '''simple docstring'''


    super().__init__(*snake_case_   ,				**snake_case_      )

    if self.framework == "tf":
      raise ValueError(F"""The {self.__class__} is only available in PyTorch."""      )

    requires_backends(self   ,				"""vision"""      )
    self.check_model_type(
        dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()      )      )




  def 		lowerCamelCase__  (		self      :       Dict   ,				**lowercase_      :       int      ):







    '''simple docstring'''


    lowercase_     =       {}
    if "threshold" in kwargs:
      lowercase_     =       kwargs["""threshold"""]
    return {}, {}, postprocess_kwargs




  def __call__(		self      :       Tuple   ,				*lowercase_      :       Optional[Any]   ,				**lowercase_      :       str      ):







    '''simple docstring'''


    return super().__call__(*snake_case_   ,				**snake_case_      )




  def 		lowerCamelCase__  (		self      :       Optional[int]   ,				lowercase_      :       Optional[int]      ):







    '''simple docstring'''


    lowercase_     =       load_image(snake_case_      )
    lowercase_     =       torch.IntTensor([[image.height, image.width]]      )
    lowercase_     =       self.image_processor(images=[image]   ,				return_tensors="""pt"""      )
    if self.tokenizer is not None:
      lowercase_     =       self.tokenizer(text=inputs["""words"""]   ,				boxes=inputs["""boxes"""]   ,				return_tensors="""pt"""      )
    lowercase_     =       target_size
    return inputs




  def 		lowerCamelCase__  (		self      :       List[Any]   ,				lowercase_      :       Optional[Any]      ):







    '''simple docstring'''


    lowercase_     =       model_inputs.pop("""target_size"""      )
    lowercase_     =       self.model(**snake_case_      )
    lowercase_     =       outputs.__class__({"""target_size""": target_size, **outputs}      )
    if self.tokenizer is not None:
      lowercase_     =       model_inputs["""bbox"""]
    return model_outputs




  def 		lowerCamelCase__  (		self      :       str   ,				lowercase_      :       int   ,				lowercase_      :       Optional[Any]=0.9      ):







    '''simple docstring'''


    lowercase_     =       model_outputs["""target_size"""]
    if self.tokenizer is not None:
      # This is a LayoutLMForTokenClassification variant.
      # The OCR got the boxes and the model classified the words.
      lowercase_     ,	lowercase_     =       target_size[0].tolist()

      def unnormalize(lowercase_      :       Optional[Any]      ):
        return self._get_bounding_box(
            torch.Tensor(
                [
                    (width * bbox[0] / 1_000),
                    (height * bbox[1] / 1_000),
                    (width * bbox[2] / 1_000),
                    (height * bbox[3] / 1_000),
                ]      )      )

      lowercase_     ,	lowercase_     =       model_outputs["""logits"""].squeeze(0      ).softmax(dim=-1      ).max(dim=-1      )
      lowercase_     =       [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
      lowercase_     =       [unnormalize(snake_case_      ) for bbox in model_outputs["""bbox"""].squeeze(0      )]
      lowercase_     =       ["""score""", """label""", """box"""]
      lowercase_     =       [dict(zip(snake_case_   ,				snake_case_      )      ) for vals in zip(scores.tolist()   ,				snake_case_   ,				snake_case_      ) if vals[0] > threshold]
    else:
      # This is a regular ForObjectDetectionModel
      lowercase_     =       self.image_processor.post_process_object_detection(snake_case_   ,				snake_case_   ,				snake_case_      )
      lowercase_     =       raw_annotations[0]
      lowercase_     =       raw_annotation["""scores"""]
      lowercase_     =       raw_annotation["""labels"""]
      lowercase_     =       raw_annotation["""boxes"""]

      lowercase_     =       scores.tolist()
      lowercase_     =       [self.model.config.idalabel[label.item()] for label in labels]
      lowercase_     =       [self._get_bounding_box(snake_case_      ) for box in boxes]

      # {"scores": [...], ...} --> [{"score":x, ...}, ...]
      lowercase_     =       ["""score""", """label""", """box"""]
      lowercase_     =       [
          dict(zip(snake_case_   ,				snake_case_      )      )
          for vals in zip(raw_annotation["""scores"""]   ,				raw_annotation["""labels"""]   ,				raw_annotation["""boxes"""]      )
      ]

    return annotation





  def 		lowerCamelCase__  (		self      :       Optional[int]   ,				lowercase_      :       str      ):







    '''simple docstring'''


    if self.framework != "pt":
      raise ValueError("""The ObjectDetectionPipeline is only available in PyTorch."""      )
    lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     =       box.int().tolist()
    lowercase_     =       {
        """xmin""": xmin,
        """ymin""": ymin,
        """xmax""": xmax,
        """ymax""": ymax,
    }
    return bbox








import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


__A							:			List[Any]         =			"http://www.mocksite.com/file1.txt"
__A							:			List[Any]         =			"\"text\": [\"foo\", \"foo\"]"
__A							:			Dict         =			"6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8"







class      lowerCamelCase:
     '''simple docstring'''


     __magic_name__   =			200
     __magic_name__   =			{'Content-Length': '100'}
     __magic_name__   =			{}




     def        lowerCAmelCase__		(    self				,     **snake_case_							):
         return [bytes(snake_case_				,     'utf-8'							)]





def 				__lowerCAmelCase(       *_SCREAMING_SNAKE_CASE							,     **_SCREAMING_SNAKE_CASE				)							-> Optional[Any]:




    """simple docstring"""




    return MockResponse()





@pytest.mark.parametrize('urls_type'							,     [str, list, dict]				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> str:




    """simple docstring"""




    import requests

    monkeypatch.setattr(_SCREAMING_SNAKE_CASE							,     'request'							,     _SCREAMING_SNAKE_CASE				)

    _A     =							URL
    if issubclass(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        _A     =							url
    elif issubclass(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        _A     =							[url]
    elif issubclass(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        _A     =							{'train': url}
    _A     =							'dummy'
    _A     =							'downloads'
    _A     =							tmp_path
    _A     =							DownloadConfig(
        cache_dir=os.path.join(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							,     use_etag=_SCREAMING_SNAKE_CASE							,     )
    _A     =							DownloadManager(dataset_name=_SCREAMING_SNAKE_CASE							,     download_config=_SCREAMING_SNAKE_CASE				)
    _A     =							dl_manager.download(_SCREAMING_SNAKE_CASE				)
    _A     =							urls
    for downloaded_paths in [downloaded_paths]:
        if isinstance(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
            _A     =							[downloaded_paths]
            _A     =							[urls]
        elif isinstance(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
            assert "train" in downloaded_paths.keys()
            _A     =							downloaded_paths.values()
            _A     =							urls.values()
        assert downloaded_paths
        for downloaded_path, input_url in zip(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
            assert downloaded_path == dl_manager.downloaded_paths[input_url]
            _A     =							Path(_SCREAMING_SNAKE_CASE				)
            _A     =							downloaded_path.parts
            assert parts[-1] == HASH
            assert parts[-2] == cache_subdir
            assert downloaded_path.exists()
            _A     =							downloaded_path.read_text()
            assert content == CONTENT
            _A     =							downloaded_path.with_suffix('.json'				)
            assert metadata_downloaded_path.exists()
            _A     =							json.loads(metadata_downloaded_path.read_text()				)
            assert metadata_content == {"url": URL, "etag": None}





@pytest.mark.parametrize('paths_type'							,     [str, list, dict]				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Optional[Any]:




    """simple docstring"""




    _A     =							str(_SCREAMING_SNAKE_CASE				)
    if issubclass(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        _A     =							filename
    elif issubclass(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        _A     =							[filename]
    elif issubclass(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        _A     =							{'train': filename}
    _A     =							'dummy'
    _A     =							xz_file.parent
    _A     =							'extracted'
    _A     =							DownloadConfig(
        cache_dir=_SCREAMING_SNAKE_CASE							,     use_etag=_SCREAMING_SNAKE_CASE							,     )
    _A     =							DownloadManager(dataset_name=_SCREAMING_SNAKE_CASE							,     download_config=_SCREAMING_SNAKE_CASE				)
    _A     =							dl_manager.extract(_SCREAMING_SNAKE_CASE				)
    _A     =							paths
    for extracted_paths in [extracted_paths]:
        if isinstance(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
            _A     =							[extracted_paths]
            _A     =							[paths]
        elif isinstance(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
            assert "train" in extracted_paths.keys()
            _A     =							extracted_paths.values()
            _A     =							paths.values()
        assert extracted_paths
        for extracted_path, input_path in zip(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
            assert extracted_path == dl_manager.extracted_paths[input_path]
            _A     =							Path(_SCREAMING_SNAKE_CASE				)
            _A     =							extracted_path.parts
            assert parts[-1] == hash_url_to_filename(_SCREAMING_SNAKE_CASE							,     etag=_SCREAMING_SNAKE_CASE				)
            assert parts[-2] == extracted_subdir
            assert extracted_path.exists()
            _A     =							extracted_path.read_text()
            _A     =							text_file.read_text()
            assert extracted_file_content == expected_file_content





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Dict:




    """simple docstring"""




    assert path.endswith('.jsonl'				)
    for num_items, line in enumerate(_SCREAMING_SNAKE_CASE							,     start=1				):
        _A     =							json.loads(line.decode('utf-8'				)				)
        assert item.keys() == {"col_1", "col_2", "col_3"}
    assert num_items == 4





@pytest.mark.parametrize('archive_jsonl'							,     ['tar_jsonl_path', 'zip_jsonl_path']				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Optional[int]:




    """simple docstring"""




    _A     =							request.getfixturevalue(_SCREAMING_SNAKE_CASE				)
    _A     =							DownloadManager()
    for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(_SCREAMING_SNAKE_CASE				)							,     start=1				):
        _test_jsonl(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
    assert num_jsonl == 2





@pytest.mark.parametrize('archive_nested_jsonl'							,     ['tar_nested_jsonl_path', 'zip_nested_jsonl_path']				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Any:




    """simple docstring"""




    _A     =							request.getfixturevalue(_SCREAMING_SNAKE_CASE				)
    _A     =							DownloadManager()
    for num_tar, (path, file) in enumerate(dl_manager.iter_archive(_SCREAMING_SNAKE_CASE				)							,     start=1				):
        for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(_SCREAMING_SNAKE_CASE				)							,     start=1				):
            _test_jsonl(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
    assert num_tar == 1
    assert num_jsonl == 2





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> int:




    """simple docstring"""




    _A     =							DownloadManager()
    for num_file, file in enumerate(dl_manager.iter_files(_SCREAMING_SNAKE_CASE				)							,     start=1				):
        assert os.path.basename(_SCREAMING_SNAKE_CASE				) == ("test.txt" if num_file == 1 else "train.txt")
    assert num_file == 2





