



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 						snake_case  (		__a     ):



   '''simple docstring'''






   snake_case_    :       Tuple								=						["""image_processor""", """tokenizer"""]
   snake_case_    :       List[Any]								=						"""LayoutLMv2ImageProcessor"""
   snake_case_    :       Any								=						("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")

   def __init__(    self							:	Optional[int] ,						lowerCAmelCase							:	Union[str, Any]=None ,						lowerCAmelCase							:	Union[str, Any]=None ,						**lowerCAmelCase							:	str) -> Dict:

    """simple docstring"""







    if "feature_extractor" in kwargs:
     warnings.warn(
         """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
         """ instead.""" ,						a_ ,						)
     _snake_case			:     List[Any] 		=    kwargs.pop("""feature_extractor""")

    _snake_case			:     Tuple 		=    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__(a_ ,						a_)

   def __call__(    self							:	List[Any] ,						lowerCAmelCase							:	Union[str, Any] ,						lowerCAmelCase							:	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None ,						lowerCAmelCase							:	Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None ,						lowerCAmelCase							:	Union[List[List[int]], List[List[List[int]]]] = None ,						lowerCAmelCase							:	Optional[Union[List[int], List[List[int]]]] = None ,						lowerCAmelCase							:	bool = True ,						lowerCAmelCase							:	Union[bool, str, PaddingStrategy] = False ,						lowerCAmelCase							:	Union[bool, str, TruncationStrategy] = None ,						lowerCAmelCase							:	Optional[int] = None ,						lowerCAmelCase							:	int = 0 ,						lowerCAmelCase							:	Optional[int] = None ,						lowerCAmelCase							:	Optional[bool] = None ,						lowerCAmelCase							:	Optional[bool] = None ,						lowerCAmelCase							:	bool = False ,						lowerCAmelCase							:	bool = False ,						lowerCAmelCase							:	bool = False ,						lowerCAmelCase							:	bool = False ,						lowerCAmelCase							:	bool = True ,						lowerCAmelCase							:	Optional[Union[str, TensorType]] = None ,						**lowerCAmelCase							:	List[Any] ,						) -> Tuple:

    """simple docstring"""







    if self.image_processor.apply_ocr and (boxes is not None):
     raise ValueError(
         """You cannot provide bounding boxes """
         """if you initialized the image processor with apply_ocr set to True.""")

    if self.image_processor.apply_ocr and (word_labels is not None):
     raise ValueError(
         """You cannot provide word labels if you initialized the image processor with apply_ocr set to True.""")

    if return_overflowing_tokens is True and return_offsets_mapping is False:
     raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping.""")

    # first, apply the image processor
    _snake_case			:     List[Any] 		=    self.image_processor(images=a_ ,						return_tensors=a_)

    # second, apply the tokenizer
    if text is not None and self.image_processor.apply_ocr and text_pair is None:
     if isinstance(a_ ,						a_):
      _snake_case			:     Union[str, Any] 		=    [text]  # add batch dimension (as the image processor always adds a batch dimension)
     _snake_case			:     Union[str, Any] 		=    features["""words"""]

    _snake_case			:     List[Any] 		=    self.tokenizer(
        text=text if text is not None else features["""words"""] ,						text_pair=text_pair if text_pair is not None else None ,						boxes=boxes if boxes is not None else features["""boxes"""] ,						word_labels=a_ ,						add_special_tokens=a_ ,						padding=a_ ,						truncation=a_ ,						max_length=a_ ,						stride=a_ ,						pad_to_multiple_of=a_ ,						return_token_type_ids=a_ ,						return_attention_mask=a_ ,						return_overflowing_tokens=a_ ,						return_special_tokens_mask=a_ ,						return_offsets_mapping=a_ ,						return_length=a_ ,						verbose=a_ ,						return_tensors=a_ ,						**a_ ,						)

    # add pixel values
    _snake_case			:     int 		=    features.pop("""pixel_values""")
    if return_overflowing_tokens is True:
     _snake_case			:     int 		=    self.get_overflowing_images(a_ ,						encoded_inputs["""overflow_to_sample_mapping"""])
    _snake_case			:     Optional[int] 		=    images

    return encoded_inputs

   def    UpperCamelCase_     (    self							:	List[Any] ,						lowerCAmelCase							:	List[Any] ,						lowerCAmelCase							:	Union[str, Any]) -> List[Any]:

    """simple docstring"""







    _snake_case			:     Optional[int] 		=    []
    for sample_idx in overflow_to_sample_mapping:
     images_with_overflow.append(images[sample_idx])

    if len(a_) != len(a_):
     raise ValueError(
         """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
         F''' {len(a_)} and {len(a_)}''')

    return images_with_overflow

   def    UpperCamelCase_     (    self							:	Union[str, Any] ,						*lowerCAmelCase							:	List[str] ,						**lowerCAmelCase							:	Dict) -> Dict:

    """simple docstring"""







    return self.tokenizer.batch_decode(*a_ ,						**a_)

   def    UpperCamelCase_     (    self							:	List[str] ,						*lowerCAmelCase							:	Tuple ,						**lowerCAmelCase							:	Dict) -> List[Any]:

    """simple docstring"""







    return self.tokenizer.decode(*a_ ,						**a_)

   @property
   def    UpperCamelCase_     (    self							:	List[str]) -> List[str]:

    """simple docstring"""







    return ["input_ids", "bbox", "attention_mask", "image"]

   @property
   def    UpperCamelCase_     (    self							:	int) -> Union[str, Any]:

    """simple docstring"""







    warnings.warn(
        """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" ,						a_ ,						)
    return self.image_processor_class


   @property
   def    UpperCamelCase_     (    self							:	Dict) -> Optional[int]:

    """simple docstring"""







    warnings.warn(
        """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" ,						a_ ,						)
    return self.image_processor






"""simple docstring"""


import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links


def 					UpperCAmelCase__       (snake_case__							:					Optional[int]		,   snake_case__							:					Any=7    ):
  """simple docstring"""

  _snake_case							:		Any								=  None
  if token is not None:
    _snake_case							:		Any								=  {"""Accept""": """application/vnd.github+json""", """Authorization""": F"Bearer {token}"}

  # The id of a workflow (not of a workflow run)
  _snake_case							:		List[str]								=  """636036"""

  _snake_case							:		Union[str, Any]								=  F"https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"
  # On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
  url += F"?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"

  _snake_case							:		str								=  requests.get(snake_case__		,   headers=snake_case__    ).json()

  return result["workflow_runs"]


def 					UpperCAmelCase__       (snake_case__							:					Optional[Any]    ):
  """simple docstring"""

  _snake_case							:		str								=  get_daily_ci_runs(snake_case__    )
  _snake_case							:		str								=  None
  for workflow_run in workflow_runs:
    if workflow_run["status"] == "completed":
      _snake_case							:		List[str]								=  workflow_run["""id"""]
      break

  return workflow_run_id


def 					UpperCAmelCase__       (snake_case__							:					str		,   snake_case__							:					Union[str, Any]		,   snake_case__							:					Optional[int]    ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  get_last_daily_ci_runs(snake_case__    )
  if workflow_run_id is not None:
    _snake_case							:		Optional[Any]								=  get_artifacts_links(worflow_run_id=snake_case__		,   token=snake_case__    )
    for artifact_name in artifact_names:
      if artifact_name in artifacts_links:
        _snake_case							:		Optional[int]								=  artifacts_links[artifact_name]
        download_artifact(
            artifact_name=snake_case__		,   artifact_url=snake_case__		,   output_dir=snake_case__		,   token=snake_case__    )






def 					UpperCAmelCase__       (snake_case__							:					Union[str, Any]		,   snake_case__							:					List[str]		,   snake_case__							:					int    ):
  """simple docstring"""

  get_last_daily_ci_artifacts(snake_case__		,   snake_case__		,   snake_case__    )

  _snake_case							:		int								=  {}
  for artifact_name in artifact_names:
    _snake_case							:		int								=  os.path.join(snake_case__		,   F"{artifact_name}.zip"    )
    if os.path.isfile(snake_case__    ):
      _snake_case							:		Tuple								=  {}
      with zipfile.ZipFile(snake_case__    ) as z:
        for filename in z.namelist():
          if not os.path.isdir(snake_case__    ):
            # read the file
            with z.open(snake_case__    ) as f:
              _snake_case							:		Any								=  f.read().decode("""UTF-8"""    )

  return results


