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 lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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

            __UpperCamelCase     :Tuple			  =		importlib.import_module(f""".{module_name}"""  ,			'''transformers.models'''	)
            try:
                return getattr(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
            except AttributeError:
                continue

    for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
        if getattr(SCREAMING_SNAKE_CASE  ,			'''__name__'''  ,			SCREAMING_SNAKE_CASE	) == 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     :List[str]			  =		importlib.import_module('''transformers'''	)
    if hasattr(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
        return getattr(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    return None



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE = None  ,			SCREAMING_SNAKE_CASE = False  ,			SCREAMING_SNAKE_CASE = False  ,			SCREAMING_SNAKE_CASE = None  ,			SCREAMING_SNAKE_CASE = None  ,			SCREAMING_SNAKE_CASE = None  ,			SCREAMING_SNAKE_CASE = False  ,			**SCREAMING_SNAKE_CASE  ,			):
    '''simple docstring'''







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


class 	lowerCamelCase_     :






    '''simple docstring'''






    def __init__(	self)       ->	Optional[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(__lowercase)
    def 			UpperCamelCase__   (	cls , __lowercase , **__lowercase)       ->	Union[str, Any]:
        __UpperCamelCase     :Dict			  =		kwargs.pop('''config''' , __lowercase)
        __UpperCamelCase     :List[str]			  =		kwargs.pop('''trust_remote_code''' , __lowercase)
        __UpperCamelCase     :Optional[int]			  =		True

        __UpperCamelCase  ,       __UpperCamelCase     :List[str]			  =		ImageProcessingMixin.get_image_processor_dict(__lowercase , **__lowercase)
        __UpperCamelCase     :Optional[int]			  =		config_dict.get('''image_processor_type''' , __lowercase)
        __UpperCamelCase     :Any			  =		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     :Dict			  =		config_dict.pop('''feature_extractor_type''' , __lowercase)
            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     :str			  =		feature_extractor_class.replace('''FeatureExtractor''' , '''ImageProcessor''')
            if "AutoFeatureExtractor" in config_dict.get('''auto_map''' , {}):
                __UpperCamelCase     :Optional[int]			  =		config_dict['''auto_map''']['''AutoFeatureExtractor''']
                __UpperCamelCase     :Union[str, 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(__lowercase , __lowercase):
                __UpperCamelCase     :Union[str, Any]			  =		AutoConfig.from_pretrained(__lowercase , **__lowercase)
            # It could be in `config.image_processor_type``
            __UpperCamelCase     :List[str]			  =		getattr(__lowercase , '''image_processor_type''' , __lowercase)
            if hasattr(__lowercase , '''auto_map''') and "AutoImageProcessor" in config.auto_map:
                __UpperCamelCase     :Union[str, Any]			  =		config.auto_map['''AutoImageProcessor''']

        if image_processor_class is not None:
            __UpperCamelCase     :Tuple			  =		image_processor_class_from_name(__lowercase)

        __UpperCamelCase     :Union[str, Any]			  =		image_processor_auto_map is not None
        __UpperCamelCase     :str			  =		image_processor_class is not None or type(__lowercase) in IMAGE_PROCESSOR_MAPPING
        __UpperCamelCase     :Optional[Any]			  =		resolve_trust_remote_code(
            __lowercase , __lowercase , __lowercase , __lowercase)

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

        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__   (	__lowercase , __lowercase)       ->	Union[str, Any]:
        IMAGE_PROCESSOR_MAPPING.register(__lowercase , __lowercase)






import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__lowercase							 =					16
__lowercase							 =					32



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE = 16  ,			SCREAMING_SNAKE_CASE = "bert-base-cased"	):
    '''simple docstring'''







    __UpperCamelCase     :List[str]			  =		AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Any			  =		load_dataset('''glue'''  ,			'''mrpc'''	)

    def tokenize_function(SCREAMING_SNAKE_CASE	):
        # max_length=None => use the model max length (it's actually the default)
        __UpperCamelCase     :int			  =		tokenizer(examples['''sentence1''']  ,			examples['''sentence2''']  ,			truncation=SCREAMING_SNAKE_CASE  ,			max_length=SCREAMING_SNAKE_CASE	)
        return outputs

    # Apply the method we just defined to all the examples in all the splits of the dataset
    __UpperCamelCase     :Tuple			  =		datasets.map(
        SCREAMING_SNAKE_CASE  ,			batched=SCREAMING_SNAKE_CASE  ,			remove_columns=['''idx''', '''sentence1''', '''sentence2''']  ,			load_from_cache_file=SCREAMING_SNAKE_CASE	)

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    __UpperCamelCase     :List[str]			  =		tokenized_datasets.rename_column('''label'''  ,			'''labels'''	)

    def collate_fn(SCREAMING_SNAKE_CASE	):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        if accelerator.distributed_type == DistributedType.TPU:
            return tokenizer.pad(SCREAMING_SNAKE_CASE  ,			padding='''max_length'''  ,			max_length=128  ,			return_tensors='''pt'''	)
        return tokenizer.pad(SCREAMING_SNAKE_CASE  ,			padding='''longest'''  ,			return_tensors='''pt'''	)

    # Instantiate dataloaders.
    __UpperCamelCase     :Union[str, Any]			  =		DataLoader(
        tokenized_datasets['''train''']  ,			shuffle=SCREAMING_SNAKE_CASE  ,			collate_fn=SCREAMING_SNAKE_CASE  ,			batch_size=SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Dict			  =		DataLoader(
        tokenized_datasets['''validation''']  ,			shuffle=SCREAMING_SNAKE_CASE  ,			collate_fn=SCREAMING_SNAKE_CASE  ,			batch_size=SCREAMING_SNAKE_CASE	)

    return train_dataloader, eval_dataloader



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[Any]			  =		Accelerator()

    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    __UpperCamelCase     :int			  =		config['''lr''']
    __UpperCamelCase     :str			  =		int(config['''num_epochs''']	)
    __UpperCamelCase     :Any			  =		int(config['''seed''']	)
    __UpperCamelCase     :Dict			  =		int(config['''batch_size''']	)
    __UpperCamelCase     :Optional[Any]			  =		args.model_name_or_path

    set_seed(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		get_dataloaders(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    __UpperCamelCase     :Any			  =		AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE  ,			return_dict=SCREAMING_SNAKE_CASE	)

    # Instantiate optimizer
    __UpperCamelCase     :List[str]			  =		(
        AdamW
        if accelerator.state.deepspeed_plugin is None
        or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
        else DummyOptim
    )
    __UpperCamelCase     :Optional[Any]			  =		optimizer_cls(params=model.parameters()  ,			lr=SCREAMING_SNAKE_CASE	)

    if accelerator.state.deepspeed_plugin is not None:
        __UpperCamelCase     :Dict			  =		accelerator.state.deepspeed_plugin.deepspeed_config[
            '''gradient_accumulation_steps'''
        ]
    else:
        __UpperCamelCase     :Dict			  =		1
    __UpperCamelCase     :Tuple			  =		(len(SCREAMING_SNAKE_CASE	) * num_epochs) // gradient_accumulation_steps

    # Instantiate scheduler
    if (
        accelerator.state.deepspeed_plugin is None
        or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
    ):
        __UpperCamelCase     :str			  =		get_linear_schedule_with_warmup(
            optimizer=SCREAMING_SNAKE_CASE  ,			num_warmup_steps=0  ,			num_training_steps=SCREAMING_SNAKE_CASE  ,			)
    else:
        __UpperCamelCase     :Dict			  =		DummyScheduler(SCREAMING_SNAKE_CASE  ,			total_num_steps=SCREAMING_SNAKE_CASE  ,			warmup_num_steps=0	)

    # Prepare everything
    # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
    # prepare method.
    __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :int			  =		accelerator.prepare(
        SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    # We need to keep track of how many total steps we have iterated over
    __UpperCamelCase     :List[Any]			  =		0
    # We also need to keep track of the stating epoch so files are named properly
    __UpperCamelCase     :Dict			  =		0

    # Now we train the model
    __UpperCamelCase     :Any			  =		evaluate.load('''glue'''  ,			'''mrpc'''	)
    __UpperCamelCase     :Union[str, Any]			  =		0
    __UpperCamelCase     :Optional[int]			  =		{}
    for epoch in range(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
        model.train()
        for step, batch in enumerate(SCREAMING_SNAKE_CASE	):
            __UpperCamelCase     :Optional[Any]			  =		model(**SCREAMING_SNAKE_CASE	)
            __UpperCamelCase     :Tuple			  =		outputs.loss
            __UpperCamelCase     :str			  =		loss / gradient_accumulation_steps
            accelerator.backward(SCREAMING_SNAKE_CASE	)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1

        model.eval()
        __UpperCamelCase     :Any			  =		0
        for step, batch in enumerate(SCREAMING_SNAKE_CASE	):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device	)
            with torch.no_grad():
                __UpperCamelCase     :Any			  =		model(**SCREAMING_SNAKE_CASE	)
            __UpperCamelCase     :Optional[int]			  =		outputs.logits.argmax(dim=-1	)
            # It is slightly faster to call this once, than multiple times
            __UpperCamelCase  ,       __UpperCamelCase     :List[Any]			  =		accelerator.gather(
                (predictions, batch['''labels'''])	)  # If we are in a multiprocess environment, the last batch has duplicates
            if accelerator.use_distributed:
                if step == len(SCREAMING_SNAKE_CASE	) - 1:
                    __UpperCamelCase     :List[str]			  =		predictions[: len(eval_dataloader.dataset	) - samples_seen]
                    __UpperCamelCase     :Optional[int]			  =		references[: len(eval_dataloader.dataset	) - samples_seen]
                else:
                    samples_seen += references.shape[0]
            metric.add_batch(
                predictions=SCREAMING_SNAKE_CASE  ,			references=SCREAMING_SNAKE_CASE  ,			)

        __UpperCamelCase     :Dict			  =		metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(f"""epoch {epoch}:"""  ,			SCREAMING_SNAKE_CASE	)
        __UpperCamelCase     :str			  =		eval_metric['''accuracy''']

        if best_performance < eval_metric["accuracy"]:
            __UpperCamelCase     :int			  =		eval_metric['''accuracy''']

    if args.performance_lower_bound is not None:
        assert (
            args.performance_lower_bound <= best_performance
        ), f"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
        with open(os.path.join(args.output_dir  ,			'''all_results.json'''	)  ,			'''w'''	) as f:
            json.dump(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''	)
    parser.add_argument(
        '''--model_name_or_path'''  ,			type=SCREAMING_SNAKE_CASE  ,			default='''bert-base-cased'''  ,			help='''Path to pretrained model or model identifier from huggingface.co/models.'''  ,			required=SCREAMING_SNAKE_CASE  ,			)
    parser.add_argument(
        '''--output_dir'''  ,			type=SCREAMING_SNAKE_CASE  ,			default='''.'''  ,			help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''  ,			)
    parser.add_argument(
        '''--performance_lower_bound'''  ,			type=SCREAMING_SNAKE_CASE  ,			default=SCREAMING_SNAKE_CASE  ,			help='''Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'''  ,			)
    parser.add_argument(
        '''--num_epochs'''  ,			type=SCREAMING_SNAKE_CASE  ,			default=3  ,			help='''Number of train epochs.'''  ,			)
    __UpperCamelCase     :List[str]			  =		parser.parse_args()
    __UpperCamelCase     :Tuple			  =		{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
    training_function(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
    main()





