



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__    (					UpperCamelCase_				:       str	)->  Tuple:
    for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
        if class_name in extractors:
            A__		 =					model_type_to_module_name(UpperCamelCase_	)

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

    for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
        if getattr(UpperCamelCase_ ,       '''__name__''' ,       UpperCamelCase_	) == 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(UpperCamelCase_ ,       UpperCamelCase_	):
        return getattr(UpperCamelCase_ ,       UpperCamelCase_	)

    return None



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



class     _UpperCAmelCase			:




     def __init__(				self):
         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 		snake_case_		(				cls ,						a__ ,						**a__):
         A__		 =					kwargs.pop('''config''' ,						a__)
         A__		 =					kwargs.pop('''trust_remote_code''' ,						a__)
         A__		 =					True

         A__ ,	A__		 =					ImageProcessingMixin.get_image_processor_dict(a__ ,						**a__)
         A__		 =					config_dict.get('''image_processor_type''' ,						a__)
         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''' ,						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.''')
                 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(a__ ,						a__):
                 A__		 =					AutoConfig.from_pretrained(a__ ,						**a__)
             # It could be in `config.image_processor_type``
             A__		 =					getattr(a__ ,						'''image_processor_type''' ,						a__)
             if hasattr(a__ ,						'''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(a__)

         A__		 =					image_processor_auto_map is not None
         A__		 =					image_processor_class is not None or type(a__) in IMAGE_PROCESSOR_MAPPING
         A__		 =					resolve_trust_remote_code(
             a__ ,						a__ ,						a__ ,						a__)

         if has_remote_code and trust_remote_code:
             A__		 =					get_class_from_dynamic_module(
                 a__ ,						a__ ,						**a__)
             A__		 =					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:
             A__		 =					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 		snake_case_		(				a__ ,						a__):
         IMAGE_PROCESSOR_MAPPING.register(a__ ,						a__)





import argparse
import os

# New Code #
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 import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_lowercase						  =		16
_lowercase						  =		32



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Accelerator ,       UpperCamelCase_				:       int = 1_6	)->  List[str]:
    A__		 =					AutoTokenizer.from_pretrained('''bert-base-cased'''	)
    A__		 =					load_dataset('''glue''' ,       '''mrpc'''	)

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

    # Apply the method we just defined to all the examples in all the splits of the dataset
    # starting with the main process first:
    with accelerator.main_process_first():
        A__		 =					datasets.map(
            UpperCamelCase_ ,       batched=UpperCamelCase_ ,       remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,       )

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    A__		 =					tokenized_datasets.rename_column('''label''' ,       '''labels'''	)

    def collate_fn(UpperCamelCase_				:       str	):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        A__		 =					1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            A__		 =					1_6
        elif accelerator.mixed_precision != "no":
            A__		 =					8
        else:
            A__		 =					None

        return tokenizer.pad(
            UpperCamelCase_ ,       padding='''longest''' ,       max_length=UpperCamelCase_ ,       pad_to_multiple_of=UpperCamelCase_ ,       return_tensors='''pt''' ,       )

    # Instantiate dataloaders.
    A__		 =					DataLoader(
        tokenized_datasets['''train'''] ,       shuffle=UpperCamelCase_ ,       collate_fn=UpperCamelCase_ ,       batch_size=UpperCamelCase_	)
    A__		 =					DataLoader(
        tokenized_datasets['''validation'''] ,       shuffle=UpperCamelCase_ ,       collate_fn=UpperCamelCase_ ,       batch_size=UpperCamelCase_	)

    return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
   from accelerate.test_utils.training import mocked_dataloaders

   _lowercase						  =		mocked_dataloaders  # noqa: F811



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Optional[Any] ,       UpperCamelCase_				:       Tuple	)->  Union[str, Any]:
    # For testing only
    if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,       UpperCamelCase_	) == "1":
        A__		 =					2
    # Initialize accelerator
    A__		 =					Accelerator(cpu=args.cpu ,       mixed_precision=args.mixed_precision	)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    A__		 =					config['''lr''']
    A__		 =					int(config['''num_epochs''']	)
    A__		 =					int(config['''seed''']	)
    A__		 =					int(config['''batch_size''']	)

    A__		 =					evaluate.load('''glue''' ,       '''mrpc'''	)

    # New Code #
    # We now can define an inner training loop function. It should take a batch size as the only parameter,
    # and build the dataloaders in there.
    # It also gets our decorator
    @find_executable_batch_size(starting_batch_size=UpperCamelCase_	)
    def inner_training_loop(UpperCamelCase_				:       Optional[int]	):
        # And now just move everything below under this function
        # We need to bring in the Accelerator object from earlier
        nonlocal accelerator
        # And reset all of its attributes that could hold onto any memory:
        accelerator.free_memory()

        # Then we can declare the model, optimizer, and everything else:
        set_seed(UpperCamelCase_	)

        # Instantiate the model (we build the model here so that the seed also control new weights initialization)
        A__		 =					AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' ,       return_dict=UpperCamelCase_	)

        # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
        # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
        # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
        A__		 =					model.to(accelerator.device	)

        # Instantiate optimizer
        A__		 =					AdamW(params=model.parameters() ,       lr=UpperCamelCase_	)
        A__ ,	A__		 =					get_dataloaders(UpperCamelCase_ ,       UpperCamelCase_	)

        # Instantiate scheduler
        A__		 =					get_linear_schedule_with_warmup(
            optimizer=UpperCamelCase_ ,       num_warmup_steps=1_0_0 ,       num_training_steps=(len(UpperCamelCase_	) * num_epochs) ,       )

        # 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.
        A__ ,	A__ ,	A__ ,	A__ ,	A__		 =					accelerator.prepare(
            UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_	)

        # Now we train the model
        for epoch in range(UpperCamelCase_	):
            model.train()
            for step, batch in enumerate(UpperCamelCase_	):
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device	)
                A__		 =					model(**UpperCamelCase_	)
                A__		 =					outputs.loss
                accelerator.backward(UpperCamelCase_	)
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            model.eval()
            for step, batch in enumerate(UpperCamelCase_	):
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device	)
                with torch.no_grad():
                    A__		 =					model(**UpperCamelCase_	)
                A__		 =					outputs.logits.argmax(dim=-1	)
                A__ ,	A__		 =					accelerator.gather_for_metrics((predictions, batch['''labels'''])	)
                metric.add_batch(
                    predictions=UpperCamelCase_ ,       references=UpperCamelCase_ ,       )

            A__		 =					metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f"epoch {epoch}:" ,       UpperCamelCase_	)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
    inner_training_loop()



def 		lowerCAmelCase__    (					)->  Optional[Any]:
    A__		 =					argparse.ArgumentParser(description='''Simple example of training script.'''	)
    parser.add_argument(
        '''--mixed_precision''' ,       type=UpperCamelCase_ ,       default=UpperCamelCase_ ,       choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] ,       help='''Whether to use mixed precision. Choose'''
        '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
        '''and an Nvidia Ampere GPU.''' ,       )
    parser.add_argument('''--cpu''' ,       action='''store_true''' ,       help='''If passed, will train on the CPU.'''	)
    A__		 =					parser.parse_args()
    A__		 =					{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
    training_function(UpperCamelCase_ ,       UpperCamelCase_	)


if __name__ == "__main__":
   main()
