
import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
  import tensorflow as tf

  from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

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

lowercase  :					List[Any]						    =							logging.get_logger(__name__)

class __snake_case		(      enum.Enum     ):
    _a		:						Any=				0
    _a		:						Optional[Any]=				1






@add_end_docstrings(lowerCAmelCase     )
class __snake_case		(      lowerCAmelCase     ):
    _a		:						int=				"generated"
    def __init__(  self		,*snake_case		,**snake_case      ):



           '''simple docstring'''







           super().__init__(*snake_case		,**snake_case      )

           self.check_model_type(
               TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
               if self.framework == """tf"""
               else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case=None		,snake_case=None		,snake_case=None		,snake_case=None		,snake_case=None		,snake_case=None		,**snake_case		,):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      {}
           if truncation is not None:
                  lowercase							:     int       	=      truncation

           lowercase							:     Union[str, Any]       	=      generate_kwargs

           lowercase							:     List[Any]       	=      {}
           if return_tensors is not None and return_type is None:
                  lowercase							:     Any       	=      ReturnType.TENSORS if return_tensors else ReturnType.TEXT
           if return_type is not None:
                  lowercase							:     Dict       	=      return_type

           if clean_up_tokenization_spaces is not None:
                  lowercase							:     Optional[Any]       	=      clean_up_tokenization_spaces

           if stop_sequence is not None:
                  lowercase							:     Union[str, Any]       	=      self.tokenizer.encode(snake_case		,add_special_tokens=snake_case      )
                  if len(snake_case      ) > 1:
                         warnings.warn(
                             """Stopping on a multiple token sequence is not yet supported on transformers. The first token of"""
                             """ the stop sequence will be used as the stop sequence string in the interim."""      )
                  lowercase							:     List[Any]       	=      stop_sequence_ids[0]

           return preprocess_params, forward_params, postprocess_params
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           return True
    def       _SCREAMING_SNAKE_CASE       (  self		,*snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      self.model.config.prefix if self.model.config.prefix is not None else """"""
           if isinstance(args[0]		,snake_case      ):
                  if self.tokenizer.pad_token_id is None:
                         raise ValueError("""Please make sure that the tokenizer has a pad_token_id when using a batch input"""      )
                  lowercase							:     int       	=      ([prefix + arg for arg in args[0]],)
                  lowercase							:     str       	=      True

           elif isinstance(args[0]		,snake_case      ):
                  lowercase							:     Optional[int]       	=      (prefix + args[0],)
                  lowercase							:     Union[str, Any]       	=      False
           else:
                  raise ValueError(
                      f" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`"      )
           lowercase							:     Optional[int]       	=      self.tokenizer(*snake_case		,padding=snake_case		,truncation=snake_case		,return_tensors=self.framework      )
           # This is produced by tokenizers but is an invalid generate kwargs
           if "token_type_ids" in inputs:
                  del inputs["token_type_ids"]
           return inputs
    def __call__(  self		,*snake_case		,**snake_case      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      super().__call__(*snake_case		,**snake_case      )
           if (
               isinstance(args[0]		,snake_case      )
               and all(isinstance(snake_case		,snake_case      ) for el in args[0]      )
               and all(len(snake_case      ) == 1 for res in result      )
           ):
                  return [res[0] for res in result]
           return result
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=TruncationStrategy.DO_NOT_TRUNCATE		,**snake_case      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      self._parse_and_tokenize(snake_case		,truncation=snake_case		,**snake_case      )
           return inputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,**snake_case      ):



           '''simple docstring'''







           if self.framework == "pt":
                  lowercase	,			lowercase							:     Any       	=      model_inputs["""input_ids"""].shape
           elif self.framework == "tf":
                  lowercase	,			lowercase							:     Optional[Any]       	=      tf.shape(model_inputs["""input_ids"""]      ).numpy()

           lowercase							:     Union[str, Any]       	=      generate_kwargs.get("""min_length"""		,self.model.config.min_length      )
           lowercase							:     Tuple       	=      generate_kwargs.get("""max_length"""		,self.model.config.max_length      )
           self.check_inputs(snake_case		,generate_kwargs["""min_length"""]		,generate_kwargs["""max_length"""]      )
           lowercase							:     int       	=      self.model.generate(**snake_case		,**snake_case      )
           lowercase							:     Union[str, Any]       	=      output_ids.shape[0]
           if self.framework == "pt":
                  lowercase							:     Any       	=      output_ids.reshape(snake_case		,out_b // in_b		,*output_ids.shape[1:]      )
           elif self.framework == "tf":
                  lowercase							:     List[Any]       	=      tf.reshape(snake_case		,(in_b, out_b // in_b, *output_ids.shape[1:])      )
           return {"output_ids": output_ids}
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=ReturnType.TEXT		,snake_case=False      ):



           '''simple docstring'''







           lowercase							:     int       	=      []
           for output_ids in model_outputs["output_ids"][0]:
                  if return_type == ReturnType.TENSORS:
                         lowercase							:     Union[str, Any]       	=      {f"{self.return_name}_token_ids": output_ids}
                  elif return_type == ReturnType.TEXT:
                         lowercase							:     Tuple       	=      {
                             f"{self.return_name}_text": self.tokenizer.decode(
                                 snake_case		,skip_special_tokens=snake_case		,clean_up_tokenization_spaces=snake_case		,)
                         }
                  records.append(snake_case      )
           return records






@add_end_docstrings(lowerCAmelCase     )
class __snake_case		(      lowerCAmelCase     ):
    _a		:						Optional[int]=				"summary"
    def __call__(  self		,*snake_case		,**snake_case      ):



           '''simple docstring'''







           return super().__call__(*snake_case		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           if max_length < min_length:
                  logger.warning(f"Your min_length={min_length} must be inferior than your max_length={max_length}."      )

           if input_length < max_length:
                  logger.warning(
                      f"Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is "
                      """a summarization task, where outputs shorter than the input are typically wanted, you might """
                      f"consider decreasing max_length manually, e.g. summarizer('...', max_length={input_length//2})"      )






@add_end_docstrings(lowerCAmelCase     )
class __snake_case		(      lowerCAmelCase     ):
    _a		:						Tuple=				"translation"
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           if input_length > 0.9 * max_length:
                  logger.warning(
                      f"Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider "
                      """increasing your max_length manually, e.g. translator('...', max_length=400)"""      )
           return True
    def       _SCREAMING_SNAKE_CASE       (  self		,*snake_case		,snake_case=TruncationStrategy.DO_NOT_TRUNCATE		,snake_case=None		,snake_case=None      ):



           '''simple docstring'''







           if getattr(self.tokenizer		,"""_build_translation_inputs"""		,snake_case      ):
                  return self.tokenizer._build_translation_inputs(
                      *snake_case		,return_tensors=self.framework		,truncation=snake_case		,src_lang=snake_case		,tgt_lang=snake_case      )
           else:
                  return super()._parse_and_tokenize(*snake_case		,truncation=snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case=None		,snake_case=None		,**snake_case      ):



           '''simple docstring'''







           lowercase	,			lowercase	,			lowercase							:     List[Any]       	=      super()._sanitize_parameters(**snake_case      )
           if src_lang is not None:
                  lowercase							:     int       	=      src_lang
           if tgt_lang is not None:
                  lowercase							:     str       	=      tgt_lang
           if src_lang is None and tgt_lang is None:
                  # Backward compatibility, direct arguments use is preferred.
                  lowercase							:     List[str]       	=      kwargs.get("""task"""		,self.task      )
                  lowercase							:     Tuple       	=      task.split("""_"""      )
                  if task and len(snake_case      ) == 4:
                         # translation, XX, to YY
                         lowercase							:     Any       	=      items[1]
                         lowercase							:     List[str]       	=      items[3]
           return preprocess_params, forward_params, postprocess_params
    def __call__(  self		,*snake_case		,**snake_case      ):



           '''simple docstring'''







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


import os
import zipfile

import pytest

from datasets.utils.extract import (
    BzipaExtractor,
    Extractor,
    GzipExtractor,
    LzaExtractor,
    SevenZipExtractor,
    TarExtractor,
    XzExtractor,
    ZipExtractor,
    ZstdExtractor,
)

from .utils import require_lza, require_pyazr, require_zstandard







@pytest.mark.parametrize(
    """compression_format, is_archive"""					,	[
        ("""7z""", True),
        ("""bz2""", False),
        ("""gzip""", False),
        ("""lz4""", False),
        ("""tar""", True),
        ("""xz""", False),
        ("""zip""", True),
        ("""zstd""", False),
    ]					,	)
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	) ->    Any:
       lowercase							:     Dict       	=      {
           """7z""": (seven_zip_file, SevenZipExtractor),
           """bz2""": (bza_file, BzipaExtractor),
           """gzip""": (gz_file, GzipExtractor),
           """lz4""": (lza_file, LzaExtractor),
           """tar""": (tar_file, TarExtractor),
           """xz""": (xz_file, XzExtractor),
           """zip""": (zip_file, ZipExtractor),
           """zstd""": (zstd_file, ZstdExtractor),
       }
       lowercase	,			lowercase							:     Optional[Any]       	=      input_paths_and_base_extractors[compression_format]
       if input_path is None:
              lowercase							:     Dict       	=      f"for '{compression_format}' compression_format, "
              if compression_format == "7z":
                     reason += require_pyazr.kwargs["reason"]
              elif compression_format == "lz4":
                     reason += require_lza.kwargs["reason"]
              elif compression_format == "zstd":
                     reason += require_zstandard.kwargs["reason"]
              pytest.skip(SCREAMING_SNAKE_CASE__				)
       assert base_extractor.is_extractable(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Any       	=      tmp_path / ("""extracted""" if is_archive else """extracted.txt""")
       base_extractor.extract(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       if is_archive:
              assert output_path.is_dir()
              for file_path in output_path.iterdir():
                     assert file_path.name == text_file.name
                     lowercase							:     str       	=      file_path.read_text(encoding="""utf-8"""				)
       else:
              lowercase							:     Optional[Any]       	=      output_path.read_text(encoding="""utf-8"""				)
       lowercase							:     Tuple       	=      text_file.read_text(encoding="""utf-8"""				)
       assert extracted_file_content == expected_file_content







@pytest.mark.parametrize(
    """compression_format, is_archive"""					,	[
        ("""7z""", True),
        ("""bz2""", False),
        ("""gzip""", False),
        ("""lz4""", False),
        ("""tar""", True),
        ("""xz""", False),
        ("""zip""", True),
        ("""zstd""", False),
    ]					,	)
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	) ->    Dict:
       lowercase							:     str       	=      {
           """7z""": seven_zip_file,
           """bz2""": bza_file,
           """gzip""": gz_file,
           """lz4""": lza_file,
           """tar""": tar_file,
           """xz""": xz_file,
           """zip""": zip_file,
           """zstd""": zstd_file,
       }
       lowercase							:     Optional[Any]       	=      input_paths[compression_format]
       if input_path is None:
              lowercase							:     int       	=      f"for '{compression_format}' compression_format, "
              if compression_format == "7z":
                     reason += require_pyazr.kwargs["reason"]
              elif compression_format == "lz4":
                     reason += require_lza.kwargs["reason"]
              elif compression_format == "zstd":
                     reason += require_zstandard.kwargs["reason"]
              pytest.skip(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Union[str, Any]       	=      Extractor.infer_extractor_format(SCREAMING_SNAKE_CASE__				)
       assert extractor_format is not None
       lowercase							:     Any       	=      tmp_path / ("""extracted""" if is_archive else """extracted.txt""")
       Extractor.extract(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       if is_archive:
              assert output_path.is_dir()
              for file_path in output_path.iterdir():
                     assert file_path.name == text_file.name
                     lowercase							:     Dict       	=      file_path.read_text(encoding="""utf-8"""				)
       else:
              lowercase							:     int       	=      output_path.read_text(encoding="""utf-8"""				)
       lowercase							:     Optional[Any]       	=      text_file.read_text(encoding="""utf-8"""				)
       assert extracted_file_content == expected_file_content







@pytest.fixture
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Dict:
       import tarfile

       lowercase							:     Tuple       	=      tmp_path / """data_dot_dot"""
       directory.mkdir()
       lowercase							:     str       	=      directory / """tar_file_with_dot_dot.tar"""
       with tarfile.TarFile(SCREAMING_SNAKE_CASE__					,	"""w"""				) as f:
              f.add(SCREAMING_SNAKE_CASE__					,	arcname=os.path.join(""".."""					,	text_file.name				)				)
       return path







@pytest.fixture
def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    List[str]:
       import tarfile

       lowercase							:     Tuple       	=      tmp_path / """data_sym_link"""
       directory.mkdir()
       lowercase							:     int       	=      directory / """tar_file_with_sym_link.tar"""
       os.symlink(""".."""					,	directory / """subdir"""					,	target_is_directory=SCREAMING_SNAKE_CASE__				)
       with tarfile.TarFile(SCREAMING_SNAKE_CASE__					,	"""w"""				) as f:
              f.add(str(directory / """subdir"""				)					,	arcname="""subdir"""				)  # str required by os.readlink on Windows and Python < 3.8
       return path







@pytest.mark.parametrize(
    """insecure_tar_file, error_log"""					,	[("""tar_file_with_dot_dot""", """illegal path"""), ("""tar_file_with_sym_link""", """Symlink""")]					,	)
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Optional[Any]:
       lowercase							:     List[Any]       	=      {
           """tar_file_with_dot_dot""": tar_file_with_dot_dot,
           """tar_file_with_sym_link""": tar_file_with_sym_link,
       }
       lowercase							:     Optional[int]       	=      insecure_tar_files[insecure_tar_file]
       lowercase							:     List[str]       	=      tmp_path / """extracted"""
       TarExtractor.extract(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       assert caplog.text
       for record in caplog.records:
              assert record.levelname == "ERROR"
              assert error_log in record.msg







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    Optional[int]:
       # We should have less false positives than zipfile.is_zipfile
       # We do that by checking only the magic number
       lowercase							:     Any       	=      tmpdir / """not_a_zip_file"""
       # From: https://github.com/python/cpython/pull/5053
       lowercase							:     str       	=      (
           B"""\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00"""
           B"""\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6'\x00\x00\x00\x15I"""
           B"""DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07"""
           B"""\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82"""
       )
       with not_a_zip_file.open("""wb"""				) as f:
              f.write(SCREAMING_SNAKE_CASE__				)
       assert zipfile.is_zipfile(str(SCREAMING_SNAKE_CASE__				)				)  # is a false positive for `zipfile`
       assert not ZipExtractor.is_extractable(SCREAMING_SNAKE_CASE__				)  # but we're right
