





"""simple docstring"""


import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


lowerCamelCase__				  =   logging.get_logger(__name__)

lowerCamelCase__				  =   OrderedDict(
    [
        ("align", "EfficientNetImageProcessor"),
        ("beit", "BeitImageProcessor"),
        ("bit", "BitImageProcessor"),
        ("blip", "BlipImageProcessor"),
        ("blip-2", "BlipImageProcessor"),
        ("bridgetower", "BridgeTowerImageProcessor"),
        ("chinese_clip", "ChineseCLIPImageProcessor"),
        ("clip", "CLIPImageProcessor"),
        ("clipseg", "ViTImageProcessor"),
        ("conditional_detr", "ConditionalDetrImageProcessor"),
        ("convnext", "ConvNextImageProcessor"),
        ("convnextv2", "ConvNextImageProcessor"),
        ("cvt", "ConvNextImageProcessor"),
        ("data2vec-vision", "BeitImageProcessor"),
        ("deformable_detr", "DeformableDetrImageProcessor"),
        ("deit", "DeiTImageProcessor"),
        ("deta", "DetaImageProcessor"),
        ("detr", "DetrImageProcessor"),
        ("dinat", "ViTImageProcessor"),
        ("donut-swin", "DonutImageProcessor"),
        ("dpt", "DPTImageProcessor"),
        ("efficientformer", "EfficientFormerImageProcessor"),
        ("efficientnet", "EfficientNetImageProcessor"),
        ("flava", "FlavaImageProcessor"),
        ("focalnet", "BitImageProcessor"),
        ("git", "CLIPImageProcessor"),
        ("glpn", "GLPNImageProcessor"),
        ("groupvit", "CLIPImageProcessor"),
        ("imagegpt", "ImageGPTImageProcessor"),
        ("instructblip", "BlipImageProcessor"),
        ("layoutlmv2", "LayoutLMv2ImageProcessor"),
        ("layoutlmv3", "LayoutLMv3ImageProcessor"),
        ("levit", "LevitImageProcessor"),
        ("mask2former", "Mask2FormerImageProcessor"),
        ("maskformer", "MaskFormerImageProcessor"),
        ("mgp-str", "ViTImageProcessor"),
        ("mobilenet_v1", "MobileNetV1ImageProcessor"),
        ("mobilenet_v2", "MobileNetV2ImageProcessor"),
        ("mobilevit", "MobileViTImageProcessor"),
        ("mobilevit", "MobileViTImageProcessor"),
        ("mobilevitv2", "MobileViTImageProcessor"),
        ("nat", "ViTImageProcessor"),
        ("oneformer", "OneFormerImageProcessor"),
        ("owlvit", "OwlViTImageProcessor"),
        ("perceiver", "PerceiverImageProcessor"),
        ("pix2struct", "Pix2StructImageProcessor"),
        ("poolformer", "PoolFormerImageProcessor"),
        ("regnet", "ConvNextImageProcessor"),
        ("resnet", "ConvNextImageProcessor"),
        ("sam", "SamImageProcessor"),
        ("segformer", "SegformerImageProcessor"),
        ("swiftformer", "ViTImageProcessor"),
        ("swin", "ViTImageProcessor"),
        ("swin2sr", "Swin2SRImageProcessor"),
        ("swinv2", "ViTImageProcessor"),
        ("table-transformer", "DetrImageProcessor"),
        ("timesformer", "VideoMAEImageProcessor"),
        ("tvlt", "TvltImageProcessor"),
        ("upernet", "SegformerImageProcessor"),
        ("van", "ConvNextImageProcessor"),
        ("videomae", "VideoMAEImageProcessor"),
        ("vilt", "ViltImageProcessor"),
        ("vit", "ViTImageProcessor"),
        ("vit_hybrid", "ViTHybridImageProcessor"),
        ("vit_mae", "ViTImageProcessor"),
        ("vit_msn", "ViTImageProcessor"),
        ("xclip", "CLIPImageProcessor"),
        ("yolos", "YolosImageProcessor"),
    ]
)

lowerCamelCase__				  =   _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)




def 					lowercase__    (					lowercase_			)					->		Dict:




						"""simple docstring"""

						for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
												if class_name in extractors:
																		_UpperCamelCase      :	Tuple						=       model_type_to_module_name(lowercase_			)

																		_UpperCamelCase      :	Dict						=       importlib.import_module(F'''.{module_name}'''		,"transformers.models"			)
																		try:
																								return getattr(lowercase_		,lowercase_			)
																		except AttributeError:
																								continue

						for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
												if getattr(lowercase_		,"__name__"		,lowercase_			) == class_name:
																		return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
						_UpperCamelCase      :	Tuple						=       importlib.import_module("transformers"			)
						if hasattr(lowercase_		,lowercase_			):
												return getattr(lowercase_		,lowercase_			)

						return None




def 					lowercase__    (					lowercase_		,lowercase_ = None		,lowercase_ = False		,lowercase_ = False		,lowercase_ = None		,lowercase_ = None		,lowercase_ = None		,lowercase_ = False		,**lowercase_		,)					->		Tuple:




						"""simple docstring"""

						_UpperCamelCase      :	Tuple						=       get_file_from_repo(
						    lowercase_		,lowercase_		,cache_dir=lowercase_		,force_download=lowercase_		,resume_download=lowercase_		,proxies=lowercase_		,use_auth_token=lowercase_		,revision=lowercase_		,local_files_only=lowercase_		,)
						if resolved_config_file is None:
												logger.info(
												    "Could not locate the image processor configuration file, will try to use the model config instead."			)
												return {}

						with open(lowercase_		,encoding="utf-8"			) as reader:
												return json.load(lowercase_			)



class      __SCREAMING_SNAKE_CASE    :







		'''simple docstring'''


		def __init__(		self   :       int	)		->				List[Any]:
								raise EnvironmentError(
								    "AutoImageProcessor is designed to be instantiated "
								    "using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method."	)

		@classmethod
		@replace_list_option_in_docstrings(__a	)
		def    __SCREAMING_SNAKE_CASE				(		cls   :       str					,   __a   :       Tuple					,   **__a   :       Optional[int]	)		->				Optional[Any]:
								_UpperCamelCase      :	Tuple						=       kwargs.pop("config"					,   __a	)
								_UpperCamelCase      :	List[str]						=       kwargs.pop("trust_remote_code"					,   __a	)
								_UpperCamelCase      :	Dict						=       True

								_UpperCamelCase      :	Union[str, Any]						=       ImageProcessingMixin.get_image_processor_dict(__a					,   **__a	)
								_UpperCamelCase      :	Optional[int]						=       config_dict.get("image_processor_type"					,   __a	)
								_UpperCamelCase      :	List[Any]						=       None
								if "AutoImageProcessor" in config_dict.get("auto_map"					,   {}	):
														_UpperCamelCase      :	List[str]						=       config_dict["auto_map"]["AutoImageProcessor"]

								# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
								# and if so, infer the image processor class from there.
								if image_processor_class is None and image_processor_auto_map is None:
														_UpperCamelCase      :	Dict						=       config_dict.pop("feature_extractor_type"					,   __a	)
														if feature_extractor_class is not None:
																				logger.warning(
																				    "Could not find image processor class in the image processor config or the model config. Loading"
																				    " based on pattern matching with the model's feature extractor configuration."	)
																				_UpperCamelCase      :	List[str]						=       feature_extractor_class.replace("FeatureExtractor"					,   "ImageProcessor"	)
														if "AutoFeatureExtractor" in config_dict.get("auto_map"					,   {}	):
																				_UpperCamelCase      :	Tuple						=       config_dict["auto_map"]["AutoFeatureExtractor"]
																				_UpperCamelCase      :	Optional[Any]						=       feature_extractor_auto_map.replace("FeatureExtractor"					,   "ImageProcessor"	)
																				logger.warning(
																				    "Could not find image processor auto map in the image processor config or the model config."
																				    " Loading based on pattern matching with the model's feature extractor configuration."	)

        # If we don't find the image processor class in the image processor config, let's try the model config.
								if image_processor_class is None and image_processor_auto_map is None:
														if not isinstance(__a					,   __a	):
																				_UpperCamelCase      :	Optional[int]						=       AutoConfig.from_pretrained(__a					,   **__a	)
														# It could be in `config.image_processor_type``
														_UpperCamelCase      :	Dict						=       getattr(__a					,   "image_processor_type"					,   __a	)
														if hasattr(__a					,   "auto_map"	) and "AutoImageProcessor" in config.auto_map:
																				_UpperCamelCase      :	Tuple						=       config.auto_map["AutoImageProcessor"]

								if image_processor_class is not None:
														_UpperCamelCase      :	Optional[int]						=       image_processor_class_from_name(__a	)

								_UpperCamelCase      :	Any						=       image_processor_auto_map is not None
								_UpperCamelCase      :	str						=       image_processor_class is not None or type(__a	) in IMAGE_PROCESSOR_MAPPING
								_UpperCamelCase      :	int						=       resolve_trust_remote_code(
								    __a					,   __a					,   __a					,   __a	)

								if has_remote_code and trust_remote_code:
														_UpperCamelCase      :	Dict						=       get_class_from_dynamic_module(
														    __a					,   __a					,   **__a	)
														_UpperCamelCase      :	int						=       kwargs.pop("code_revision"					,   __a	)
														if os.path.isdir(__a	):
																				image_processor_class.register_for_auto_class()
														return image_processor_class.from_dict(__a					,   **__a	)
								elif image_processor_class is not None:
														return image_processor_class.from_dict(__a					,   **__a	)
								# Last try: we use the IMAGE_PROCESSOR_MAPPING.
								elif type(__a	) in IMAGE_PROCESSOR_MAPPING:
														_UpperCamelCase      :	Optional[int]						=       IMAGE_PROCESSOR_MAPPING[type(__a	)]
														return image_processor_class.from_dict(__a					,   **__a	)

								raise ValueError(
								    F'''Unrecognized image processor in {pretrained_model_name_or_path}. Should have a '''
								    F'''`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following '''
								    F'''`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()	)}'''	)


		@staticmethod
		def    __SCREAMING_SNAKE_CASE				(		__a   :       List[str]					,   __a   :       Optional[int]	)		->				str:
								IMAGE_PROCESSOR_MAPPING.register(__a					,   __a	)







"""simple docstring"""


import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links




def 					lowercase__    (					lowercase_		,lowercase_=7			)					->		Tuple:




      """simple docstring"""

      _UpperCamelCase      :	Optional[int]						=       None
      if token is not None:
            _UpperCamelCase      :	Optional[Any]						=       {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}

      # The id of a workflow (not of a workflow run)
      _UpperCamelCase      :	Any						=       "636036"

      _UpperCamelCase      :	Tuple						=       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      :	Dict						=       requests.get(lowercase_		,headers=lowercase_			).json()

      return result["workflow_runs"]




def 					lowercase__    (					lowercase_			)					->		List[str]:




      """simple docstring"""

      _UpperCamelCase      :	List[Any]						=       get_daily_ci_runs(lowercase_			)
      _UpperCamelCase      :	Tuple						=       None
      for workflow_run in workflow_runs:
            if workflow_run["status"] == "completed":
                  _UpperCamelCase      :	Union[str, Any]						=       workflow_run["id"]
                  break

      return workflow_run_id




def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_			)					->		Optional[int]:




      """simple docstring"""

      _UpperCamelCase      :	str						=       get_last_daily_ci_runs(lowercase_			)
      if workflow_run_id is not None:
            _UpperCamelCase      :	int						=       get_artifacts_links(worflow_run_id=lowercase_		,token=lowercase_			)
            for artifact_name in artifact_names:
                  if artifact_name in artifacts_links:
                        _UpperCamelCase      :	Dict						=       artifacts_links[artifact_name]
                        download_artifact(
                            artifact_name=lowercase_		,artifact_url=lowercase_		,output_dir=lowercase_		,token=lowercase_			)




def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_			)					->		int:




      """simple docstring"""

      get_last_daily_ci_artifacts(lowercase_		,lowercase_		,lowercase_			)

      _UpperCamelCase      :	Dict						=       {}
      for artifact_name in artifact_names:
            _UpperCamelCase      :	Union[str, Any]						=       os.path.join(lowercase_		,F'''{artifact_name}.zip'''			)
            if os.path.isfile(lowercase_			):
                  _UpperCamelCase      :	int						=       {}
                  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      :	int						=       f.read().decode("UTF-8"			)

      return results
