

import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


SCREAMING_SNAKE_CASE_            = logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

SCREAMING_SNAKE_CASE_            = {
    'base': AutoModel,
    'sequence-classification': AutoModelForSequenceClassification,
    'question-answering': AutoModelForQuestionAnswering,
    'pretraining': AutoModelForPreTraining,
    'token-classification': AutoModelForTokenClassification,
    'language-modeling': AutoModelWithLMHead,
    'summarization': AutoModelForSeqaSeqLM,
    'translation': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
SCREAMING_SNAKE_CASE_            = {
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
SCREAMING_SNAKE_CASE_            = sorted(arg_to_scheduler.keys())
SCREAMING_SNAKE_CASE_            = '{' + ', '.join(arg_to_scheduler_choices) + '}'

class 			a					(		pl.LightningModule			):


						def __init__(    self  ,   A_  ,   A_=None  ,   A_="base"  ,   A_=None  ,   A_=None  ,   A_=None  ,   **A_  ,   ):







										'''simple docstring'''


										super().__init__()
										# TODO: move to self.save_hyperparameters()
										# self.save_hyperparameters()
										# can also expand arguments into trainer signature for easier reading

										self.save_hyperparameters(A_		)
										_UpperCAmelCase    :						Optional[int]							=      0
										_UpperCAmelCase    :						str							=      Path(self.hparams.output_dir		)
										_UpperCAmelCase    :						Optional[Any]							=      self.hparams.cache_dir if self.hparams.cache_dir else None
										if config is None:
														_UpperCAmelCase    :						Dict							=      AutoConfig.from_pretrained(
														    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path  ,   **({"num_labels": num_labels} if num_labels is not None else {})  ,   cache_dir=A_  ,   **A_  ,   )
										else:
														_UpperCAmelCase    :						PretrainedConfig							=      config

										_UpperCAmelCase    :						int							=      ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
										for p in extra_model_params:
														if getattr(self.hparams  ,   A_  ,   A_		):
																		assert hasattr(self.config  ,   A_		), f'model config doesn\'t have a `{p}` attribute'
																		setattr(self.config  ,   A_  ,   getattr(self.hparams  ,   A_		)		)

										if tokenizer is None:
														_UpperCAmelCase    :						List[Any]							=      AutoTokenizer.from_pretrained(
														    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path  ,   cache_dir=A_  ,   )
										else:
														_UpperCAmelCase    :						PreTrainedTokenizer							=      tokenizer
										_UpperCAmelCase    :						Union[str, Any]							=      MODEL_MODES[mode]
										if model is None:
														_UpperCAmelCase    :						Union[str, Any]							=      self.model_type.from_pretrained(
														    self.hparams.model_name_or_path  ,   from_tf=bool(".ckpt" in self.hparams.model_name_or_path		)  ,   config=self.config  ,   cache_dir=A_  ,   )
										else:
														_UpperCAmelCase    :						Dict							=      model


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      self.model_type.from_pretrained(*A_  ,   **A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[int]							=      arg_to_scheduler[self.hparams.lr_scheduler]
										_UpperCAmelCase    :						Union[str, Any]							=      get_schedule_func(
										    self.opt  ,   num_warmup_steps=self.hparams.warmup_steps  ,   num_training_steps=self.total_steps()		)
										_UpperCAmelCase    :						Optional[int]							=      {"scheduler": scheduler, "interval": "step", "frequency": 1}
										return scheduler


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[int]							=      self.model
										_UpperCAmelCase    :						Optional[int]							=      ["bias", "LayerNorm.weight"]
										_UpperCAmelCase    :						Optional[int]							=      [
										    {
										        "params": [
										            p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay		)
										        ],  # check this named paramters
										        "weight_decay": self.hparams.weight_decay,
										    },
										    {
										        "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay		)],
										        "weight_decay": 0.0,
										    },
										]
										if self.hparams.adafactor:
														_UpperCAmelCase    :						int							=      Adafactor(
														    A_  ,   lr=self.hparams.learning_rate  ,   scale_parameter=A_  ,   relative_step=A_		)

										else:
														_UpperCAmelCase    :						int							=      AdamW(
														    A_  ,   lr=self.hparams.learning_rate  ,   eps=self.hparams.adam_epsilon		)
										_UpperCAmelCase    :						Union[str, Any]							=      optimizer

										_UpperCAmelCase    :						str							=      self.get_lr_scheduler()

										return [optimizer], [scheduler]


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										return self.validation_step(A_  ,   A_		)


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										return self.validation_end(A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[int]							=      max(1  ,   self.hparams.gpus		)  # TODO: consider num_tpu_cores
										_UpperCAmelCase    :						Any							=      self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
										return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										if stage == "test":
														_UpperCAmelCase    :						Dict							=      len(self.test_dataloader().dataset		)
										else:
														_UpperCAmelCase    :						Any							=      self.get_dataloader("train"  ,   self.hparams.train_batch_size  ,   shuffle=A_		)
														_UpperCAmelCase    :						str							=      len(self.train_dataloader().dataset		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_ = False		):







										'''simple docstring'''


										raise NotImplementedError("You must implement this for your task"		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return self.train_loader


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return self.get_dataloader("dev"  ,   self.hparams.eval_batch_size  ,   shuffle=A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return self.get_dataloader("test"  ,   self.hparams.eval_batch_size  ,   shuffle=A_		)


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										return os.path.join(
										    self.hparams.data_dir  ,   "cached_{}_{}_{}".format(
										        A_  ,   list(filter(A_  ,   self.hparams.model_name_or_path.split("/"		)		)		).pop()  ,   str(self.hparams.max_seq_length		)  ,   )  ,   )


						@pl.utilities.rank_zero_only
						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[int]							=      self.output_dir.joinpath("best_tfmr"		)
										_UpperCAmelCase    :						Any							=      self.step_count
										self.model.save_pretrained(A_		)
										self.tokenizer.save_pretrained(A_		)





						@staticmethod
						def     _UpperCAmelCase			(    A_  ,   A_		):







										'''simple docstring'''


										parser.add_argument(
										    "--model_name_or_path"  ,   default=A_  ,   type=A_  ,   required=A_  ,   help="Path to pretrained model or model identifier from huggingface.co/models"  ,   )
										parser.add_argument(
										    "--config_name"  ,   default=""  ,   type=A_  ,   help="Pretrained config name or path if not the same as model_name"		)
										parser.add_argument(
										    "--tokenizer_name"  ,   default=A_  ,   type=A_  ,   help="Pretrained tokenizer name or path if not the same as model_name"  ,   )
										parser.add_argument(
										    "--cache_dir"  ,   default=str(Path(A_		).parent / "test_run" / "cache"		)  ,   type=A_  ,   help="Where do you want to store the pre-trained models downloaded from huggingface.co"  ,   )
										parser.add_argument(
										    "--encoder_layerdrop"  ,   type=A_  ,   help="Encoder layer dropout probability (Optional). Goes into model.config"  ,   )
										parser.add_argument(
										    "--decoder_layerdrop"  ,   type=A_  ,   help="Decoder layer dropout probability (Optional). Goes into model.config"  ,   )
										parser.add_argument(
										    "--dropout"  ,   type=A_  ,   help="Dropout probability (Optional). Goes into model.config"  ,   )
										parser.add_argument(
										    "--attention_dropout"  ,   type=A_  ,   help="Attention dropout probability (Optional). Goes into model.config"  ,   )
										parser.add_argument("--learning_rate"  ,   default=5e-5  ,   type=A_  ,   help="The initial learning rate for Adam."		)
										parser.add_argument(
										    "--lr_scheduler"  ,   default="linear"  ,   choices=A_  ,   metavar=A_  ,   type=A_  ,   help="Learning rate scheduler"  ,   )
										parser.add_argument("--weight_decay"  ,   default=0.0  ,   type=A_  ,   help="Weight decay if we apply some."		)
										parser.add_argument("--adam_epsilon"  ,   default=1e-8  ,   type=A_  ,   help="Epsilon for Adam optimizer."		)
										parser.add_argument("--warmup_steps"  ,   default=0  ,   type=A_  ,   help="Linear warmup over warmup_steps."		)
										parser.add_argument("--num_workers"  ,   default=4  ,   type=A_  ,   help="kwarg passed to DataLoader"		)
										parser.add_argument("--num_train_epochs"  ,   dest="max_epochs"  ,   default=3  ,   type=A_		)
										parser.add_argument("--train_batch_size"  ,   default=32  ,   type=A_		)
										parser.add_argument("--eval_batch_size"  ,   default=32  ,   type=A_		)
										parser.add_argument("--adafactor"  ,   action="store_true"		)







class 			a					(		pl.Callback			):


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										if (
										    trainer.is_global_zero and trainer.global_rank == 0
										):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
														pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.







class 			a					(		pl.Callback			):


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										for name, param in pl_module.model.rag.named_parameters():
														if param.grad is None:
																		print(A_		)







class 			a					(		pl.Callback			):


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      trainer.lr_schedulers[0]["scheduler"]
										_UpperCAmelCase    :						Optional[int]							=      {f'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()		)}
										pl_module.logger.log_metrics(A_		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										rank_zero_info("***** Validation results *****"		)
										_UpperCAmelCase    :						Optional[Any]							=      trainer.callback_metrics
										# Log results
										for key in sorted(A_		):
														if key not in ["log", "progress_bar"]:
																		rank_zero_info("{} = {}\n".format(A_  ,   str(metrics[key]		)		)		)





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										rank_zero_info("***** Test results *****"		)
										_UpperCAmelCase    :						str							=      trainer.callback_metrics
										# Log and save results to file
										_UpperCAmelCase    :						str							=      os.path.join(pl_module.hparams.output_dir  ,   "test_results.txt"		)
										with open(A_  ,   "w"		) as writer:
														for key in sorted(A_		):
																		if key not in ["log", "progress_bar"]:
																						rank_zero_info("{} = {}\n".format(A_  ,   str(metrics[key]		)		)		)
																						writer.write("{} = {}\n".format(A_  ,   str(metrics[key]		)		)		)







def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[int]					, lowerCAmelCase:					Tuple	)				->    None:
				#  To allow all pl args uncomment the following line
				#  parser = pl.Trainer.add_argparse_args(parser)
				parser.add_argument(
				    "--output_dir"					, default=str(Path(lowerCAmelCase	).parent / "test_run" / "model_checkpoints"	)					, type=lowerCAmelCase					, help="The output directory where the model predictions and checkpoints will be written."					, )
				parser.add_argument(
				    "--fp16"					, action="store_true"					, help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"					, )

				parser.add_argument(
				    "--fp16_opt_level"					, type=lowerCAmelCase					, default="O2"					, help=(
				        "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
				        "See details at https://nvidia.github.io/apex/amp.html"
				    )					, )
				parser.add_argument("--n_tpu_cores"					, dest="tpu_cores"					, type=lowerCAmelCase	)
				parser.add_argument("--max_grad_norm"					, dest="gradient_clip_val"					, default=1.0					, type=lowerCAmelCase					, help="Max gradient norm"	)
				parser.add_argument("--do_train"					, action="store_true"					, help="Whether to run training."	)
				parser.add_argument("--do_predict"					, action="store_true"					, help="Whether to run predictions on the test set."	)
				parser.add_argument(
				    "--gradient_accumulation_steps"					, dest="accumulate_grad_batches"					, type=lowerCAmelCase					, default=1					, help="Number of updates steps to accumulate before performing a backward/update pass."					, )
				parser.add_argument("--seed"					, type=lowerCAmelCase					, default=42					, help="random seed for initialization"	)
				parser.add_argument(
				    "--data_dir"					, default=str(Path(lowerCAmelCase	).parent / "test_run" / "dummy-train-data"	)					, type=lowerCAmelCase					, help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."					, )





def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					BaseTransformer					, lowerCAmelCase:					argparse.Namespace					, lowerCAmelCase:					Any=None					, lowerCAmelCase:					Dict=True					, lowerCAmelCase:					List[str]=[]					, lowerCAmelCase:					Optional[int]=None					, lowerCAmelCase:					List[str]=None					, **lowerCAmelCase:					int					, )				->    Any:
				pl.seed_everything(args.seed	)

				# init model
				_UpperCAmelCase    :						int							=      Path(model.hparams.output_dir	)
				odir.mkdir(exist_ok=lowerCAmelCase	)

				# add custom checkpoints
				if checkpoint_callback is None:
								_UpperCAmelCase    :						int							=      pl.callbacks.ModelCheckpoint(
								    filepath=args.output_dir					, prefix="checkpoint"					, monitor="val_loss"					, mode="min"					, save_top_k=1	)
				if early_stopping_callback:
								extra_callbacks.append(lowerCAmelCase	)
				if logging_callback is None:
								_UpperCAmelCase    :						Optional[Any]							=      LoggingCallback()

				_UpperCAmelCase    :						List[str]							=      {}

				if args.fpaa:
								_UpperCAmelCase    :						Union[str, Any]							=      16

				if args.gpus > 1:
								_UpperCAmelCase    :						Optional[int]							=      "auto"
								_UpperCAmelCase    :						int							=      "ddp"

				_UpperCAmelCase    :						Optional[Any]							=      args.accumulate_grad_batches
				_UpperCAmelCase    :						str							=      None
				_UpperCAmelCase    :						Dict							=      "auto"

				_UpperCAmelCase    :						Optional[int]							=      pl.Trainer.from_argparse_args(
				    lowerCAmelCase					, weights_summary=lowerCAmelCase					, callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]					, logger=lowerCAmelCase					, val_check_interval=1					, num_sanity_val_steps=2					, **lowerCAmelCase					, )

				if args.do_train:
								trainer.fit(lowerCAmelCase	)

				else:
								print("RAG modeling tests with new set functions successfuly executed!"	)
				return trainer





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_            = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            = 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_            = _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					str	)				->    Tuple:
				for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
								if class_name in extractors:
												_UpperCAmelCase    :						Union[str, Any]							=      model_type_to_module_name(lowerCAmelCase	)

												_UpperCAmelCase    :						Optional[int]							=      importlib.import_module(F'.{module_name}'					, "transformers.models"	)
												try:
																return getattr(lowerCAmelCase					, lowerCAmelCase	)
												except AttributeError:
																continue

				for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
								if getattr(lowerCAmelCase					, "__name__"					, lowerCAmelCase	) == 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    :						Any							=      importlib.import_module("transformers"	)
				if hasattr(lowerCAmelCase					, lowerCAmelCase	):
								return getattr(lowerCAmelCase					, lowerCAmelCase	)

				return None

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Union[str, os.PathLike]					, lowerCAmelCase:					Optional[Union[str, os.PathLike]] = None					, lowerCAmelCase:					bool = False					, lowerCAmelCase:					bool = False					, lowerCAmelCase:					Optional[Dict[str, str]] = None					, lowerCAmelCase:					Optional[Union[bool, str]] = None					, lowerCAmelCase:					Optional[str] = None					, lowerCAmelCase:					bool = False					, **lowerCAmelCase:					List[Any]					, )				->    Any:
				_UpperCAmelCase    :						List[Any]							=      get_file_from_repo(
				    lowerCAmelCase					, lowerCAmelCase					, cache_dir=lowerCAmelCase					, force_download=lowerCAmelCase					, resume_download=lowerCAmelCase					, proxies=lowerCAmelCase					, use_auth_token=lowerCAmelCase					, revision=lowerCAmelCase					, local_files_only=lowerCAmelCase					, )
				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(lowerCAmelCase					, encoding="utf-8"	) as reader:
								return json.load(lowerCAmelCase	)





class 			a					:


						def __init__(    self		):







										'''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(A_		)
						def     _UpperCAmelCase			(    cls  ,   A_  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      kwargs.pop("config"  ,   A_		)
										_UpperCAmelCase    :						Dict							=      kwargs.pop("trust_remote_code"  ,   A_		)
										_UpperCAmelCase    :						str							=      True

										_UpperCAmelCase						,							_UpperCAmelCase    :						Tuple							=      ImageProcessingMixin.get_image_processor_dict(A_  ,   **A_		)
										_UpperCAmelCase    :						Any							=      config_dict.get("image_processor_type"  ,   A_		)
										_UpperCAmelCase    :						str							=      None
										if "AutoImageProcessor" in config_dict.get("auto_map"  ,   {}		):
														_UpperCAmelCase    :						int							=      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    :						Any							=      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    :						Optional[Any]							=      feature_extractor_class.replace("FeatureExtractor"  ,   "ImageProcessor"		)
														if "AutoFeatureExtractor" in config_dict.get("auto_map"  ,   {}		):
																		_UpperCAmelCase    :						Optional[Any]							=      config_dict["auto_map"]["AutoFeatureExtractor"]
																		_UpperCAmelCase    :						List[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    :						Any							=      AutoConfig.from_pretrained(A_  ,   **A_		)
														# It could be in `config.image_processor_type``
														_UpperCAmelCase    :						Optional[Any]							=      getattr(A_  ,   "image_processor_type"  ,   A_		)
														if hasattr(A_  ,   "auto_map"		) and "AutoImageProcessor" in config.auto_map:
																		_UpperCAmelCase    :						Any							=      config.auto_map["AutoImageProcessor"]

										if image_processor_class is not None:
														_UpperCAmelCase    :						List[str]							=      image_processor_class_from_name(A_		)

										_UpperCAmelCase    :						Optional[Any]							=      image_processor_auto_map is not None
										_UpperCAmelCase    :						Any							=      image_processor_class is not None or type(A_		) in IMAGE_PROCESSOR_MAPPING
										_UpperCAmelCase    :						List[Any]							=      resolve_trust_remote_code(
										    A_  ,   A_  ,   A_  ,   A_		)

										if has_remote_code and trust_remote_code:
														_UpperCAmelCase    :						Optional[int]							=      get_class_from_dynamic_module(
														    A_  ,   A_  ,   **A_		)
														_UpperCAmelCase    :						Optional[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     _UpperCAmelCase			(    A_  ,   A_		):







										'''simple docstring'''


										IMAGE_PROCESSOR_MAPPING.register(A_  ,   A_		)


