




'''simple docstring'''




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_      :		Optional[int]							      =       '"text": ["foo", "foo"]'
a_      :		Tuple							      =       '6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'


class   __UpperCamelCase       :
       lowercase	:    Optional[Any]      						=2_00
       lowercase	:    str      						={"Content-Length": "100"}
       lowercase	:    Any      						={}






       def      lowercase__     (							self,		**lowerCAmelCase  ):



              """simple docstring"""







              return [bytes(_lowerCamelCase,		'''utf-8'''  )]





def      a_							(      *__snake_case							:			List[Any]   ,       **__snake_case							:			List[str]   )							->						str:



       """simple docstring"""
       return MockResponse()





@pytest.mark.parametrize('''urls_type'''   ,       [str, list, dict]   )
def      a_							(      __snake_case							:			Optional[Any]   ,       __snake_case							:			Union[str, Any]   ,       __snake_case							:			Tuple   )							->						int:



       """simple docstring"""
       import requests

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

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





@pytest.mark.parametrize('''paths_type'''   ,       [str, list, dict]   )
def      a_							(      __snake_case							:			Any   ,       __snake_case							:			Any   ,       __snake_case							:			Dict   )							->						int:



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





def      a_							(      __snake_case							:			List[Any]   ,       __snake_case							:			str   )							->						Union[str, Any]:



       """simple docstring"""
       assert path.endswith('''.jsonl'''   )
       for num_items, line in enumerate(__snake_case   ,       start=1   ):
              lowerCamelCase_							=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      a_							(      __snake_case							:			int   ,       __snake_case							:			List[str]   )							->						str:



       """simple docstring"""
       lowerCamelCase_							=request.getfixturevalue(__snake_case   )
       lowerCamelCase_							=DownloadManager()
       for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(__snake_case   )   ,       start=1   ):
              _test_jsonl(__snake_case   ,       __snake_case   )
       assert num_jsonl == 2





@pytest.mark.parametrize('''archive_nested_jsonl'''   ,       ['''tar_nested_jsonl_path''', '''zip_nested_jsonl_path''']   )
def      a_							(      __snake_case							:			Dict   ,       __snake_case							:			Optional[int]   )							->						int:



       """simple docstring"""
       lowerCamelCase_							=request.getfixturevalue(__snake_case   )
       lowerCamelCase_							=DownloadManager()
       for num_tar, (path, file) in enumerate(dl_manager.iter_archive(__snake_case   )   ,       start=1   ):
              for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(__snake_case   )   ,       start=1   ):
                     _test_jsonl(__snake_case   ,       __snake_case   )
       assert num_tar == 1
       assert num_jsonl == 2





def      a_							(      __snake_case							:			Dict   )							->						Optional[int]:



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












"""simple docstring"""



import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class        a     (   a_	):
    UpperCAmelCase_ :     List[Any]	     =["image_processor", "tokenizer"]
    UpperCAmelCase_ :     str	     ="AutoImageProcessor"
    UpperCAmelCase_ :     Any	     ="AutoTokenizer"







    def __init__(			self							,		_lowerCamelCase=None							,		_lowerCamelCase=None							,		**_lowerCamelCase       ):
      lowercase							    =       None
      if "feature_extractor" in kwargs:
        warnings.warn(
            'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
            ' instead.'							,		_lowerCamelCase							,		)
        lowercase							    =       kwargs.pop('feature_extractor'       )

      lowercase							    =       image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
        raise ValueError('You need to specify an `image_processor`.'       )
      if tokenizer is None:
        raise ValueError('You need to specify a `tokenizer`.'       )

      super().__init__(_lowerCamelCase							,		_lowerCamelCase       )
      lowercase							    =       self.image_processor
      lowercase							    =       False







    def __call__(			self							,		*_lowerCamelCase							,		**_lowerCamelCase       ):
      # For backward compatibility
      if self._in_target_context_manager:
        return self.current_processor(*_lowerCamelCase							,		**_lowerCamelCase       )

      lowercase							    =       kwargs.pop('images'							,		_lowerCamelCase       )
      lowercase							    =       kwargs.pop('text'							,		_lowerCamelCase       )
      if len(_lowerCamelCase       ) > 0:
        lowercase							    =       args[0]
        lowercase							    =       args[1:]

      if images is None and text is None:
        raise ValueError('You need to specify either an `images` or `text` input to process.'       )

      if images is not None:
        lowercase							    =       self.image_processor(_lowerCamelCase							,		*_lowerCamelCase							,		**_lowerCamelCase       )
      if text is not None:
        lowercase							    =       self.tokenizer(_lowerCamelCase							,		**_lowerCamelCase       )

      if text is None:
        return inputs
      elif images is None:
        return encodings
      else:
        lowercase							    =       encodings['input_ids']
        return inputs







    def 			UpperCamelCase_      (			self							,		*_lowerCamelCase							,		**_lowerCamelCase       ):
      return self.tokenizer.batch_decode(*_lowerCamelCase							,		**_lowerCamelCase       )







    def 			UpperCamelCase_      (			self							,		*_lowerCamelCase							,		**_lowerCamelCase       ):
      return self.tokenizer.decode(*_lowerCamelCase							,		**_lowerCamelCase       )







    @contextmanager
    def 			UpperCamelCase_      (			self       ):
      warnings.warn(
          '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
          'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
          'your images inputs, or in a separate call.'       )
      lowercase							    =       True
      lowercase							    =       self.tokenizer
      yield
      lowercase							    =       self.image_processor
      lowercase							    =       False







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase=False							,		_lowerCamelCase=None       ):
      if added_vocab is None:
        lowercase							    =       self.tokenizer.get_added_vocab()

      lowercase							    =       {}

      while tokens:
        lowercase							    =       re.search(R'<s_(.*?)>'							,		_lowerCamelCase							,		re.IGNORECASE       )
        if start_token is None:
          break
        lowercase							    =       start_token.group(1       )
        lowercase							    =       re.search(RF'</s_{key}>'							,		_lowerCamelCase							,		re.IGNORECASE       )
        lowercase							    =       start_token.group()
        if end_token is None:
          lowercase							    =       tokens.replace(_lowerCamelCase							,		''       )
        else:
          lowercase							    =       end_token.group()
          lowercase							    =       re.escape(_lowerCamelCase       )
          lowercase							    =       re.escape(_lowerCamelCase       )
          lowercase							    =       re.search(F'{start_token_escaped}(.*?){end_token_escaped}'							,		_lowerCamelCase							,		re.IGNORECASE       )
          if content is not None:
            lowercase							    =       content.group(1       ).strip()
            if r"<s_" in content and r"</s_" in content:  # non-leaf node
              lowercase							    =       self.tokenajson(_lowerCamelCase							,		is_inner_value=_lowerCamelCase							,		added_vocab=_lowerCamelCase       )
              if value:
                if len(_lowerCamelCase       ) == 1:
                  lowercase							    =       value[0]
                lowercase							    =       value
            else:  # leaf nodes
              lowercase							    =       []
              for leaf in content.split(R'<sep/>'       ):
                lowercase							    =       leaf.strip()
                if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                  lowercase							    =       leaf[1:-2]  # for categorical special tokens
                output[key].append(_lowerCamelCase       )
              if len(output[key]       ) == 1:
                lowercase							    =       output[key][0]

          lowercase							    =       tokens[tokens.find(_lowerCamelCase       ) + len(_lowerCamelCase       ) :].strip()
          if tokens[:6] == r"<sep/>":  # non-leaf nodes
            return [output] + self.tokenajson(tokens[6:]							,		is_inner_value=_lowerCamelCase							,		added_vocab=_lowerCamelCase       )

      if len(_lowerCamelCase       ):
        return [output] if is_inner_value else output
      else:
        return [] if is_inner_value else {"text_sequence": tokens}







    @property
    def 			UpperCamelCase_      (			self       ):
      warnings.warn(
          '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'							,		_lowerCamelCase							,		)
      return self.image_processor_class







    @property
    def 			UpperCamelCase_      (			self       ):
      warnings.warn(
          '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'							,		_lowerCamelCase							,		)
      return self.image_processor


