



'''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,
)


lowercase							       =			logging.get_logger(__name__)

lowercase							       =			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'''),
    ]
)

lowercase							       =			_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
          if class_name in extractors:
               a_       		=model_type_to_module_name(lowercase__       )

               a_       		=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.
     a_       		=importlib.import_module("transformers"       )
     if hasattr(lowercase__       ,	lowercase__       ):
          return getattr(lowercase__       ,	lowercase__       )

     return None






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__ = None       ,	lowercase__ = False       ,	lowercase__ = False       ,	lowercase__ = None       ,	lowercase__ = None       ,	lowercase__ = None       ,	lowercase__ = False       ,	**lowercase__       ,	):


     '''simple docstring'''







     a_       		=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 	UpperCAmelCase	:


 '''simple docstring'''




 def __init__(   self) ->      Tuple:



      """simple docstring"""







      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(lowerCAmelCase_)
 def       lowercase_  (   cls							,  lowerCAmelCase_							,  **lowerCAmelCase_) ->      Optional[Any]:



      """simple docstring"""







      a_       		=kwargs.pop("config"							,  lowerCAmelCase_)
      a_       		=kwargs.pop("trust_remote_code"							,  lowerCAmelCase_)
      a_       		=True

      a_		,						a_       		=ImageProcessingMixin.get_image_processor_dict(lowerCAmelCase_							,  **lowerCAmelCase_)
      a_       		=config_dict.get("image_processor_type"							,  lowerCAmelCase_)
      a_       		=None
      if "AutoImageProcessor" in config_dict.get("auto_map"							,  {}):
           a_       		=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:
           a_       		=config_dict.pop("feature_extractor_type"							,  lowerCAmelCase_)
           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.")
                a_       		=feature_extractor_class.replace("FeatureExtractor"							,  "ImageProcessor")
           if "AutoFeatureExtractor" in config_dict.get("auto_map"							,  {}):
                a_       		=config_dict["auto_map"]["AutoFeatureExtractor"]
                a_       		=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(lowerCAmelCase_							,  lowerCAmelCase_):
                a_       		=AutoConfig.from_pretrained(lowerCAmelCase_							,  **lowerCAmelCase_)
           # It could be in `config.image_processor_type``
           a_       		=getattr(lowerCAmelCase_							,  "image_processor_type"							,  lowerCAmelCase_)
           if hasattr(lowerCAmelCase_							,  "auto_map") and "AutoImageProcessor" in config.auto_map:
                a_       		=config.auto_map["AutoImageProcessor"]

      if image_processor_class is not None:
           a_       		=image_processor_class_from_name(lowerCAmelCase_)

      a_       		=image_processor_auto_map is not None
      a_       		=image_processor_class is not None or type(lowerCAmelCase_) in IMAGE_PROCESSOR_MAPPING
      a_       		=resolve_trust_remote_code(
          lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_)

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

      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       lowercase_  (   lowerCAmelCase_							,  lowerCAmelCase_) ->      Union[str, Any]:



      """simple docstring"""







      IMAGE_PROCESSOR_MAPPING.register(lowerCAmelCase_							,  lowerCAmelCase_)







'''simple docstring'''


from __future__ import annotations

lowercase							       =			[]






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     for i in range(len(lowercase__       )       ):
          if board[row][i] == 1:
               return False
     for i in range(len(lowercase__       )       ):
          if board[i][column] == 1:
               return False
     for i, j in zip(range(lowercase__       ,	-1       ,	-1       )       ,	range(lowercase__       ,	-1       ,	-1       )       ):
          if board[i][j] == 1:
               return False
     for i, j in zip(range(lowercase__       ,	-1       ,	-1       )       ,	range(lowercase__       ,	len(lowercase__       )       )       ):
          if board[i][j] == 1:
               return False
     return True






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     if row >= len(lowercase__       ):
          solution.append(lowercase__       )
          printboard(lowercase__       )
          print()
          return True
     for i in range(len(lowercase__       )       ):
          if is_safe(lowercase__       ,	lowercase__       ,	lowercase__       ):
               a_       		=1
               solve(lowercase__       ,	row + 1       )
               a_       		=0
     return False






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     for i in range(len(lowercase__       )       ):
          for j in range(len(lowercase__       )       ):
               if board[i][j] == 1:
                    print("Q"       ,	end=" "       )
               else:
                    print("."       ,	end=" "       )
          print()


# n=int(input("The no. of queens"))
lowercase							       =			8
lowercase							       =			[[0 for i in range(n)] for j in range(n)]
solve(board, 0)

print('''The total no. of solutions are :''', len(solution))


