'''simple docstring'''






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


SCREAMING_SNAKE_CASE_	   =			logging.get_logger(__name__)


if is_vision_available():
    import PIL




class    a (       __lowerCAmelCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					["""pixel_values"""]







  def __init__(							self   ,    snake_case_ = True   ,    snake_case_ = None   ,    snake_case_ = PILImageResampling.BICUBIC   ,    snake_case_ = True   ,    snake_case_ = None   ,    snake_case_ = True   ,    snake_case_ = 1 / 255   ,    snake_case_ = True   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = True   ,    **snake_case_   ,    ):

    '''simple docstring'''







    super().__init__(**snake_case_					)
    __UpperCAmelCase:					str		= size if size is not None else {"""shortest_edge""": 224}
    __UpperCAmelCase:					str		= get_size_dict(snake_case_   ,    default_to_square=snake_case_					)
    __UpperCAmelCase:					Union[str, Any]		= crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
    __UpperCAmelCase:					str		= get_size_dict(snake_case_   ,    default_to_square=snake_case_   ,    param_name="""crop_size"""					)

    __UpperCAmelCase:					Tuple		= do_resize
    __UpperCAmelCase:					Dict		= size
    __UpperCAmelCase:					Tuple		= resample
    __UpperCAmelCase:					Tuple		= do_center_crop
    __UpperCAmelCase:					Any		= crop_size
    __UpperCAmelCase:					Any		= do_rescale
    __UpperCAmelCase:					List[Any]		= rescale_factor
    __UpperCAmelCase:					Optional[Any]		= do_normalize
    __UpperCAmelCase:					Optional[int]		= image_mean if image_mean is not None else OPENAI_CLIP_MEAN
    __UpperCAmelCase:					List[str]		= image_std if image_std is not None else OPENAI_CLIP_STD
    __UpperCAmelCase:					List[Any]		= do_convert_rgb







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_ = PILImageResampling.BICUBIC   ,    snake_case_ = None   ,    **snake_case_   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= get_size_dict(snake_case_   ,    default_to_square=snake_case_					)
    if "shortest_edge" not in size:
      raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''					)
    __UpperCAmelCase:					Optional[Any]		= get_resize_output_image_size(snake_case_   ,    size=size["""shortest_edge"""]   ,    default_to_square=snake_case_					)
    return resize(snake_case_   ,    size=snake_case_   ,    resample=snake_case_   ,    data_format=snake_case_   ,    **snake_case_					)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_ = None   ,    **snake_case_   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					Dict		= get_size_dict(snake_case_					)
    if "height" not in size or "width" not in size:
      raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''					)
    return center_crop(snake_case_   ,    size=(size["""height"""], size["""width"""])   ,    data_format=snake_case_   ,    **snake_case_					)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_ = None   ,    **snake_case_   ,    ):

    '''simple docstring'''







    return rescale(snake_case_   ,    scale=snake_case_   ,    data_format=snake_case_   ,    **snake_case_					)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_ = None   ,    **snake_case_   ,    ):

    '''simple docstring'''







    return normalize(snake_case_   ,    mean=snake_case_   ,    std=snake_case_   ,    data_format=snake_case_   ,    **snake_case_					)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = ChannelDimension.FIRST   ,    **snake_case_   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[int]		= do_resize if do_resize is not None else self.do_resize
    __UpperCAmelCase:					List[Any]		= size if size is not None else self.size
    __UpperCAmelCase:					List[Any]		= get_size_dict(snake_case_   ,    param_name="""size"""   ,    default_to_square=snake_case_					)
    __UpperCAmelCase:					Union[str, Any]		= resample if resample is not None else self.resample
    __UpperCAmelCase:					Optional[Any]		= do_center_crop if do_center_crop is not None else self.do_center_crop
    __UpperCAmelCase:					Dict		= crop_size if crop_size is not None else self.crop_size
    __UpperCAmelCase:					str		= get_size_dict(snake_case_   ,    param_name="""crop_size"""   ,    default_to_square=snake_case_					)
    __UpperCAmelCase:					int		= do_rescale if do_rescale is not None else self.do_rescale
    __UpperCAmelCase:					Union[str, Any]		= rescale_factor if rescale_factor is not None else self.rescale_factor
    __UpperCAmelCase:					Optional[Any]		= do_normalize if do_normalize is not None else self.do_normalize
    __UpperCAmelCase:					Tuple		= image_mean if image_mean is not None else self.image_mean
    __UpperCAmelCase:					List[str]		= image_std if image_std is not None else self.image_std
    __UpperCAmelCase:					Union[str, Any]		= do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

    __UpperCAmelCase:					Optional[int]		= make_list_of_images(snake_case_					)

    if not valid_images(snake_case_					):
      raise ValueError(
          """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
          """torch.Tensor, tf.Tensor or jax.ndarray."""					)

    if do_resize and size is None:
      raise ValueError("""Size must be specified if do_resize is True."""					)

    if do_center_crop and crop_size is None:
      raise ValueError("""Crop size must be specified if do_center_crop is True."""					)

    if do_rescale and rescale_factor is None:
      raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

    if do_normalize and (image_mean is None or image_std is None):
      raise ValueError("""Image mean and std must be specified if do_normalize is True."""					)

    # PIL RGBA images are converted to RGB
    if do_convert_rgb:
      __UpperCAmelCase:					Optional[int]		= [convert_to_rgb(snake_case_					) for image in images]

    # All transformations expect numpy arrays.
    __UpperCAmelCase:					Dict		= [to_numpy_array(snake_case_					) for image in images]

    if do_resize:
      __UpperCAmelCase:					Union[str, Any]		= [self.resize(image=snake_case_   ,    size=snake_case_   ,    resample=snake_case_					) for image in images]

    if do_center_crop:
      __UpperCAmelCase:					List[Any]		= [self.center_crop(image=snake_case_   ,    size=snake_case_					) for image in images]

    if do_rescale:
      __UpperCAmelCase:					Optional[int]		= [self.rescale(image=snake_case_   ,    scale=snake_case_					) for image in images]

    if do_normalize:
      __UpperCAmelCase:					List[Any]		= [self.normalize(image=snake_case_   ,    mean=snake_case_   ,    std=snake_case_					) for image in images]

    __UpperCAmelCase:					Optional[Any]		= [to_channel_dimension_format(snake_case_   ,    snake_case_					) for image in images]

    __UpperCAmelCase:					List[str]		= {"""pixel_values""": images}
    return BatchFeature(data=snake_case_   ,    tensor_type=snake_case_					)
'''simple docstring'''






import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links






def  UpperCamelCase__					(					_lowercase    :				Union[str, Any]   ,    _lowercase    :				List[Any]=7     )							->	Union[str, Any]:
  __UpperCAmelCase:					Optional[int]		= None
  if token is not None:
    __UpperCAmelCase:					List[Any]		= {"""Accept""": """application/vnd.github+json""", """Authorization""": F'''Bearer {token}'''}

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

  __UpperCAmelCase:					Optional[int]		= 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}'''

  __UpperCAmelCase:					List[Any]		= requests.get(_lowercase   ,    headers=_lowercase     ).json()

  return result["workflow_runs"]






def  UpperCamelCase__					(					_lowercase    :				Optional[int]     )							->	List[str]:
  __UpperCAmelCase:					str		= get_daily_ci_runs(_lowercase     )
  __UpperCAmelCase:					str		= None
  for workflow_run in workflow_runs:
    if workflow_run["status"] == "completed":
      __UpperCAmelCase:					Optional[int]		= workflow_run["""id"""]
      break

  return workflow_run_id






def  UpperCamelCase__					(					_lowercase    :				Optional[int]   ,    _lowercase    :				List[str]   ,    _lowercase    :				List[str]     )							->	Dict:
  __UpperCAmelCase:					List[Any]		= get_last_daily_ci_runs(_lowercase     )
  if workflow_run_id is not None:
    __UpperCAmelCase:					List[Any]		= get_artifacts_links(worflow_run_id=_lowercase   ,    token=_lowercase     )
    for artifact_name in artifact_names:
      if artifact_name in artifacts_links:
        __UpperCAmelCase:					str		= artifacts_links[artifact_name]
        download_artifact(
            artifact_name=_lowercase   ,    artifact_url=_lowercase   ,    output_dir=_lowercase   ,    token=_lowercase     )







def  UpperCamelCase__					(					_lowercase    :				str   ,    _lowercase    :				Tuple   ,    _lowercase    :				Tuple     )							->	int:
  get_last_daily_ci_artifacts(_lowercase   ,    _lowercase   ,    _lowercase     )

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

  return results