

import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
_SCREAMING_SNAKE_CASE					:  Dict             =      logging.getLogger(__name__)
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   np.argmax(_A				,		axis=1    )
     return np.sum(outputs == labels    )
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     with open(_A				,		encoding='''utf_8'''    ) as f:
          SCREAMING_SNAKE_CASE__   =   csv.reader(_A    )
          SCREAMING_SNAKE_CASE__   =   []
          next(_A    )  # skip the first line
          for line in tqdm(_A    ):
               output.append((''' '''.join(line[1:5]    ), line[5], line[6], int(line[-1]    ) - 1)    )
     return output
def     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A				,		_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   []
     for dataset in encoded_datasets:
          SCREAMING_SNAKE_CASE__   =   len(_A    )
          SCREAMING_SNAKE_CASE__   =   np.zeros((n_batch, 2, input_len)				,		dtype=np.intaa    )
          SCREAMING_SNAKE_CASE__   =   np.zeros((n_batch, 2)				,		dtype=np.intaa    )
          SCREAMING_SNAKE_CASE__   =   np.full((n_batch, 2, input_len)				,		fill_value=-1_00				,		dtype=np.intaa    )
          SCREAMING_SNAKE_CASE__   =   np.zeros((n_batch,)				,		dtype=np.intaa    )
          for (
              i,
              (story, conta, conta, mc_label),
          ) in enumerate(_A    ):
               SCREAMING_SNAKE_CASE__   =   [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
               SCREAMING_SNAKE_CASE__   =   [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
               SCREAMING_SNAKE_CASE__   =   with_conta
               SCREAMING_SNAKE_CASE__   =   with_conta
               SCREAMING_SNAKE_CASE__   =   len(_A    ) - 1
               SCREAMING_SNAKE_CASE__   =   len(_A    ) - 1
               SCREAMING_SNAKE_CASE__   =   with_conta
               SCREAMING_SNAKE_CASE__   =   with_conta
               SCREAMING_SNAKE_CASE__   =   mc_label
          SCREAMING_SNAKE_CASE__   =   (input_ids, mc_token_ids, lm_labels, mc_labels)
          tensor_datasets.append(tuple(torch.tensor(_A    ) for t in all_inputs    )    )
     return tensor_datasets
def     UpperCAmelCase_       (						):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   argparse.ArgumentParser()
     parser.add_argument('''--model_name'''				,		type=_A				,		default='''openai-gpt'''				,		help='''pretrained model name'''    )
     parser.add_argument('''--do_train'''				,		action='''store_true'''				,		help='''Whether to run training.'''    )
     parser.add_argument('''--do_eval'''				,		action='''store_true'''				,		help='''Whether to run eval on the dev set.'''    )
     parser.add_argument(
         '''--output_dir'''				,		default=_A				,		type=_A				,		required=_A				,		help='''The output directory where the model predictions and checkpoints will be written.'''				,		)
     parser.add_argument('''--train_dataset'''				,		type=_A				,		default=''''''    )
     parser.add_argument('''--eval_dataset'''				,		type=_A				,		default=''''''    )
     parser.add_argument('''--seed'''				,		type=_A				,		default=42    )
     parser.add_argument('''--num_train_epochs'''				,		type=_A				,		default=3    )
     parser.add_argument('''--train_batch_size'''				,		type=_A				,		default=8    )
     parser.add_argument('''--eval_batch_size'''				,		type=_A				,		default=16    )
     parser.add_argument('''--adam_epsilon'''				,		default=1e-8				,		type=_A				,		help='''Epsilon for Adam optimizer.'''    )
     parser.add_argument('''--max_grad_norm'''				,		type=_A				,		default=1    )
     parser.add_argument(
         '''--max_steps'''				,		default=-1				,		type=_A				,		help=(
             '''If > 0: set total number of training                         steps to perform. Override num_train_epochs.'''
         )				,		)
     parser.add_argument(
         '''--gradient_accumulation_steps'''				,		type=_A				,		default=1				,		help='''Number of updates steps to accumulate before                        performing a backward/update pass.'''				,		)
     parser.add_argument('''--learning_rate'''				,		type=_A				,		default=6.25e-5    )
     parser.add_argument('''--warmup_steps'''				,		default=0				,		type=_A				,		help='''Linear warmup over warmup_steps.'''    )
     parser.add_argument('''--lr_schedule'''				,		type=_A				,		default='''warmup_linear'''    )
     parser.add_argument('''--weight_decay'''				,		type=_A				,		default=0.0_1    )
     parser.add_argument('''--lm_coef'''				,		type=_A				,		default=0.9    )
     parser.add_argument('''--n_valid'''				,		type=_A				,		default=3_74    )

     parser.add_argument('''--server_ip'''				,		type=_A				,		default=''''''				,		help='''Can be used for distant debugging.'''    )
     parser.add_argument('''--server_port'''				,		type=_A				,		default=''''''				,		help='''Can be used for distant debugging.'''    )
     SCREAMING_SNAKE_CASE__   =   parser.parse_args()
     print(_A    )

     if args.server_ip and args.server_port:
          # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
          import ptvsd

          print('''Waiting for debugger attach'''    )
          ptvsd.enable_attach(address=(args.server_ip, args.server_port)				,		redirect_output=_A    )
          ptvsd.wait_for_attach()

     random.seed(args.seed    )
     np.random.seed(args.seed    )
     torch.manual_seed(args.seed    )
     torch.cuda.manual_seed_all(args.seed    )

     SCREAMING_SNAKE_CASE__   =   torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''    )
     SCREAMING_SNAKE_CASE__   =   torch.cuda.device_count()
     logger.info('''device: {}, n_gpu {}'''.format(_A				,		_A    )    )

     if not args.do_train and not args.do_eval:
          raise ValueError('''At least one of `do_train` or `do_eval` must be True.'''    )

     if not os.path.exists(args.output_dir    ):
          os.makedirs(args.output_dir    )

     # Load tokenizer and model
     # This loading functions also add new tokens and embeddings called `special tokens`
     # These new embeddings will be fine-tuned on the RocStories dataset
     SCREAMING_SNAKE_CASE__   =   ['''_start_''', '''_delimiter_''', '''_classify_''']
     SCREAMING_SNAKE_CASE__   =   OpenAIGPTTokenizer.from_pretrained(args.model_name    )
     tokenizer.add_tokens(_A    )
     SCREAMING_SNAKE_CASE__   =   tokenizer.convert_tokens_to_ids(_A    )
     SCREAMING_SNAKE_CASE__   =   OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name    )
     model.resize_token_embeddings(len(_A    )    )
     model.to(_A    )

     # Load and encode the datasets
     def tokenize_and_encode(_A    ):
          if isinstance(_A				,		_A    ):
               return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(_A    )    )
          elif isinstance(_A				,		_A    ):
               return obj
          return [tokenize_and_encode(_A    ) for o in obj]

     logger.info('''Encoding dataset...'''    )
     SCREAMING_SNAKE_CASE__   =   load_rocstories_dataset(args.train_dataset    )
     SCREAMING_SNAKE_CASE__   =   load_rocstories_dataset(args.eval_dataset    )
     SCREAMING_SNAKE_CASE__   =   (train_dataset, eval_dataset)
     SCREAMING_SNAKE_CASE__   =   tokenize_and_encode(_A    )

     # Compute the max input length for the Transformer
     SCREAMING_SNAKE_CASE__   =   model.config.n_positions // 2 - 2
     SCREAMING_SNAKE_CASE__   =   max(
         len(story[:max_length]    ) + max(len(conta[:max_length]    )				,		len(conta[:max_length]    )    ) + 3
         for dataset in encoded_datasets
         for story, conta, conta, _ in dataset    )
     SCREAMING_SNAKE_CASE__   =   min(_A				,		model.config.n_positions    )  # Max size of input for the pre-trained model

     # Prepare inputs tensors and dataloaders
     SCREAMING_SNAKE_CASE__   =   pre_process_datasets(_A				,		_A				,		_A				,		*_A    )
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   tensor_datasets[0], tensor_datasets[1]

     SCREAMING_SNAKE_CASE__   =   TensorDataset(*_A    )
     SCREAMING_SNAKE_CASE__   =   RandomSampler(_A    )
     SCREAMING_SNAKE_CASE__   =   DataLoader(_A				,		sampler=_A				,		batch_size=args.train_batch_size    )

     SCREAMING_SNAKE_CASE__   =   TensorDataset(*_A    )
     SCREAMING_SNAKE_CASE__   =   SequentialSampler(_A    )
     SCREAMING_SNAKE_CASE__   =   DataLoader(_A				,		sampler=_A				,		batch_size=args.eval_batch_size    )

     # Prepare optimizer
     if args.do_train:
          if args.max_steps > 0:
               SCREAMING_SNAKE_CASE__   =   args.max_steps
               SCREAMING_SNAKE_CASE__   =   args.max_steps // (len(_A    ) // args.gradient_accumulation_steps) + 1
          else:
               SCREAMING_SNAKE_CASE__   =   len(_A    ) // args.gradient_accumulation_steps * args.num_train_epochs

          SCREAMING_SNAKE_CASE__   =   list(model.named_parameters()    )
          SCREAMING_SNAKE_CASE__   =   ['''bias''', '''LayerNorm.bias''', '''LayerNorm.weight''']
          SCREAMING_SNAKE_CASE__   =   [
              {
                  '''params''': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay    )],
                  '''weight_decay''': args.weight_decay,
              },
              {'''params''': [p for n, p in param_optimizer if any(nd in n for nd in no_decay    )], '''weight_decay''': 0.0},
          ]
          SCREAMING_SNAKE_CASE__   =   AdamW(_A				,		lr=args.learning_rate				,		eps=args.adam_epsilon    )
          SCREAMING_SNAKE_CASE__   =   get_linear_schedule_with_warmup(
              _A				,		num_warmup_steps=args.warmup_steps				,		num_training_steps=_A    )

     if args.do_train:
          SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   0, 0, None
          model.train()
          for _ in trange(int(args.num_train_epochs    )				,		desc='''Epoch'''    ):
               SCREAMING_SNAKE_CASE__   =   0
               SCREAMING_SNAKE_CASE__   =   0
               SCREAMING_SNAKE_CASE__   =   tqdm(_A				,		desc='''Training'''    )
               for step, batch in enumerate(_A    ):
                    SCREAMING_SNAKE_CASE__   =   tuple(t.to(_A    ) for t in batch    )
                    SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   batch
                    SCREAMING_SNAKE_CASE__   =   model(_A				,		mc_token_ids=_A				,		lm_labels=_A				,		mc_labels=_A    )
                    SCREAMING_SNAKE_CASE__   =   args.lm_coef * losses[0] + losses[1]
                    loss.backward()
                    optimizer.step()
                    scheduler.step()
                    optimizer.zero_grad()
                    tr_loss += loss.item()
                    SCREAMING_SNAKE_CASE__   =   (
                        loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
                    )
                    nb_tr_steps += 1
                    SCREAMING_SNAKE_CASE__   =   '''Training loss: {:.2e} lr: {:.2e}'''.format(_A				,		scheduler.get_lr()[0]    )

    # Save a trained model
     if args.do_train:
          # Save a trained model, configuration and tokenizer
          SCREAMING_SNAKE_CASE__   =   model.module if hasattr(_A				,		'''module'''    ) else model  # Only save the model itself

          # If we save using the predefined names, we can load using `from_pretrained`
          SCREAMING_SNAKE_CASE__   =   os.path.join(args.output_dir				,		_A    )
          SCREAMING_SNAKE_CASE__   =   os.path.join(args.output_dir				,		_A    )

          torch.save(model_to_save.state_dict()				,		_A    )
          model_to_save.config.to_json_file(_A    )
          tokenizer.save_vocabulary(args.output_dir    )

          # Load a trained model and vocabulary that you have fine-tuned
          SCREAMING_SNAKE_CASE__   =   OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir    )
          SCREAMING_SNAKE_CASE__   =   OpenAIGPTTokenizer.from_pretrained(args.output_dir    )
          model.to(_A    )

     if args.do_eval:
          model.eval()
          SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   0, 0
          SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   0, 0
          for batch in tqdm(_A				,		desc='''Evaluating'''    ):
               SCREAMING_SNAKE_CASE__   =   tuple(t.to(_A    ) for t in batch    )
               SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   batch
               with torch.no_grad():
                    SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   model(
                        _A				,		mc_token_ids=_A				,		lm_labels=_A				,		mc_labels=_A    )

               SCREAMING_SNAKE_CASE__   =   mc_logits.detach().cpu().numpy()
               SCREAMING_SNAKE_CASE__   =   mc_labels.to('''cpu'''    ).numpy()
               SCREAMING_SNAKE_CASE__   =   accuracy(_A				,		_A    )

               eval_loss += mc_loss.mean().item()
               eval_accuracy += tmp_eval_accuracy

               nb_eval_examples += input_ids.size(0    )
               nb_eval_steps += 1

          SCREAMING_SNAKE_CASE__   =   eval_loss / nb_eval_steps
          SCREAMING_SNAKE_CASE__   =   eval_accuracy / nb_eval_examples
          SCREAMING_SNAKE_CASE__   =   tr_loss / nb_tr_steps if args.do_train else None
          SCREAMING_SNAKE_CASE__   =   {'''eval_loss''': eval_loss, '''eval_accuracy''': eval_accuracy, '''train_loss''': train_loss}

          SCREAMING_SNAKE_CASE__   =   os.path.join(args.output_dir				,		'''eval_results.txt'''    )
          with open(_A				,		'''w'''    ) as writer:
               logger.info('''***** Eval results *****'''    )
               for key in sorted(result.keys()    ):
                    logger.info('''  %s = %s'''				,		_A				,		str(result[key]    )    )
                    writer.write('''%s = %s\n''' % (key, str(result[key]    ))    )


if __name__ == "__main__":
       main()





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,
)


_SCREAMING_SNAKE_CASE					:  List[str]             =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:  Tuple             =      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'''),
    ]
)

_SCREAMING_SNAKE_CASE					:  str             =      _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
          if class_name in extractors:
               SCREAMING_SNAKE_CASE__   =   model_type_to_module_name(_A    )

               SCREAMING_SNAKE_CASE__   =   importlib.import_module(F'''.{module_name}'''				,		'''transformers.models'''    )
               try:
                    return getattr(_A				,		_A    )
               except AttributeError:
                    continue

     for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
          if getattr(_A				,		'''__name__'''				,		_A    ) == 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.
     SCREAMING_SNAKE_CASE__   =   importlib.import_module('''transformers'''    )
     if hasattr(_A				,		_A    ):
          return getattr(_A				,		_A    )

     return None
def     UpperCAmelCase_       (						_A				,		_A = None				,		_A = False				,		_A = False				,		_A = None				,		_A = None				,		_A = None				,		_A = False				,		**_A				,		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   get_file_from_repo(
         _A				,		_A				,		cache_dir=_A				,		force_download=_A				,		resume_download=_A				,		proxies=_A				,		use_auth_token=_A				,		revision=_A				,		local_files_only=_A				,		)
     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(_A				,		encoding='''utf-8'''    ) as reader:
          return json.load(_A    )







class 	UpperCAmelCase__		:






  """simple docstring"""


  def __init__(							self						:		List[Any] )		->							int:
       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						:		Optional[int]     ,       __lowerCamelCase						:		Any     ,       **__lowerCamelCase						:		Tuple )		->							List[str]:
       SCREAMING_SNAKE_CASE__   =   kwargs.pop('''config'''     ,       __lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   kwargs.pop('''trust_remote_code'''     ,       __lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   True

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

       if image_processor_class is not None:
            SCREAMING_SNAKE_CASE__   =   image_processor_class_from_name(__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   image_processor_auto_map is not None
       SCREAMING_SNAKE_CASE__   =   image_processor_class is not None or type(__lowerCamelCase ) in IMAGE_PROCESSOR_MAPPING
       SCREAMING_SNAKE_CASE__   =   resolve_trust_remote_code(
           __lowerCamelCase     ,       __lowerCamelCase     ,       __lowerCamelCase     ,       __lowerCamelCase )

       if has_remote_code and trust_remote_code:
            SCREAMING_SNAKE_CASE__   =   get_class_from_dynamic_module(
                __lowerCamelCase     ,       __lowerCamelCase     ,       **__lowerCamelCase )
            SCREAMING_SNAKE_CASE__   =   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:
            SCREAMING_SNAKE_CASE__   =   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						:		Dict     ,       __lowerCamelCase						:		Optional[int] )		->							str:
       IMAGE_PROCESSOR_MAPPING.register(__lowerCamelCase     ,       __lowerCamelCase )


