




import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)



def 		lowerCAmelCase__    (					UpperCamelCase_				:       str ,       UpperCamelCase_				:       Tuple	)->  List[str]:
    A__		 =					XCLIPTextConfig()

    # derive patch size from model name
    A__		 =					model_name.find('''patch'''	)
    A__		 =					int(model_name[start_idx + len('''patch'''	) : start_idx + len('''patch'''	) + 2]	)
    A__		 =					XCLIPVisionConfig(patch_size=__UpperCamelCase ,       num_frames=__UpperCamelCase	)

    if "large" in model_name:
        A__		 =					7_6_8
        A__		 =					3_0_7_2
        A__		 =					1_2

        A__		 =					1_0_2_4
        A__		 =					4_0_9_6
        A__		 =					1_6
        A__		 =					2_4
        A__		 =					7_6_8
        A__		 =					3_0_7_2

    if model_name == "xclip-large-patch14-16-frames":
        A__		 =					3_3_6

    A__		 =					XCLIPConfig.from_text_vision_configs(__UpperCamelCase ,       __UpperCamelCase	)

    if "large" in model_name:
        A__		 =					7_6_8

    return config



def 		lowerCAmelCase__    (					UpperCamelCase_				:       List[Any]	)->  Optional[Any]:






    if name == "token_embedding.weight":
        A__		 =					name.replace('''token_embedding.weight''' ,       '''text_model.embeddings.token_embedding.weight'''	)
    if name == "positional_embedding":
        A__		 =					name.replace('''positional_embedding''' ,       '''text_model.embeddings.position_embedding.weight'''	)
    if "ln_1" in name:
        A__		 =					name.replace('''ln_1''' ,       '''layer_norm1'''	)
    if "ln_2" in name:
        A__		 =					name.replace('''ln_2''' ,       '''layer_norm2'''	)
    if "c_fc" in name:
        A__		 =					name.replace('''c_fc''' ,       '''fc1'''	)
    if "c_proj" in name:
        A__		 =					name.replace('''c_proj''' ,       '''fc2'''	)
    if name.startswith('''transformer.resblocks'''	):
        A__		 =					name.replace('''transformer.resblocks''' ,       '''text_model.encoder.layers'''	)
    if "attn.out_proj" in name and "message" not in name:
        A__		 =					name.replace('''attn.out_proj''' ,       '''self_attn.out_proj'''	)
    if "ln_final" in name:
        A__		 =					name.replace('''ln_final''' ,       '''text_model.final_layer_norm'''	)
    # visual encoder
    if name == "visual.class_embedding":
        A__		 =					name.replace('''visual.class_embedding''' ,       '''vision_model.embeddings.class_embedding'''	)
    if name == "visual.positional_embedding":
        A__		 =					name.replace('''visual.positional_embedding''' ,       '''vision_model.embeddings.position_embedding.weight'''	)
    if name.startswith('''visual.transformer.resblocks'''	):
        A__		 =					name.replace('''visual.transformer.resblocks''' ,       '''vision_model.encoder.layers'''	)
    if "visual.conv1" in name:
        A__		 =					name.replace('''visual.conv1''' ,       '''vision_model.embeddings.patch_embedding'''	)
    if "visual.ln_pre" in name:
        A__		 =					name.replace('''visual.ln_pre''' ,       '''vision_model.pre_layernorm'''	)
    if "visual.ln_post" in name:
        A__		 =					name.replace('''visual.ln_post''' ,       '''vision_model.post_layernorm'''	)
    if "visual.proj" in name:
        A__		 =					name.replace('''visual.proj''' ,       '''visual_projection.weight'''	)
    if "text_projection" in name:
        A__		 =					name.replace('''text_projection''' ,       '''text_projection.weight'''	)
    # things on top
    if "prompts_visual_proj" in name:
        A__		 =					name.replace('''prompts_visual_proj''' ,       '''prompts_visual_projection'''	)
    if "prompts_visual_ln" in name:
        A__		 =					name.replace('''prompts_visual_ln''' ,       '''prompts_visual_layernorm'''	)
    # mit
    if name == "mit.positional_embedding":
        A__		 =					name.replace('''positional''' ,       '''position'''	)
    if name.startswith('''mit.resblocks'''	):
        A__		 =					name.replace('''mit.resblocks''' ,       '''mit.encoder.layers'''	)
    # prompts generator
    if name.startswith('''prompts_generator.norm'''	):
        A__		 =					name.replace('''prompts_generator.norm''' ,       '''prompts_generator.layernorm'''	)

    return name



def 		lowerCAmelCase__    (					UpperCamelCase_				:       str ,       UpperCamelCase_				:       Tuple	)->  Union[str, Any]:






    for key in orig_state_dict.copy().keys():
        A__		 =					orig_state_dict.pop(__UpperCamelCase	)

        if "attn.in_proj" in key:
            A__		 =					key.split('''.'''	)
            if key.startswith('''visual'''	):
                A__		 =					key_split[3]
                A__		 =					config.vision_config.hidden_size
                if "message_attn" in key:
                    if "weight" in key:
                        A__		 =					val[
                            :dim, :
                        ]
                        A__		 =					val[
                            dim : dim * 2, :
                        ]
                        A__		 =					val[
                            -dim:, :
                        ]
                    else:
                        A__		 =					val[
                            :dim
                        ]
                        A__		 =					val[
                            dim : dim * 2
                        ]
                        A__		 =					val[
                            -dim:
                        ]
                else:
                    if "weight" in key:
                        A__		 =					val[
                            :dim, :
                        ]
                        A__		 =					val[
                            dim : dim * 2, :
                        ]
                        A__		 =					val[
                            -dim:, :
                        ]
                    else:
                        A__		 =					val[:dim]
                        A__		 =					val[
                            dim : dim * 2
                        ]
                        A__		 =					val[-dim:]
            elif key.startswith('''mit'''	):
                A__		 =					key_split[2]
                A__		 =					config.vision_config.mit_hidden_size
                if "weight" in key:
                    A__		 =					val[:dim, :]
                    A__		 =					val[dim : dim * 2, :]
                    A__		 =					val[-dim:, :]
                else:
                    A__		 =					val[:dim]
                    A__		 =					val[dim : dim * 2]
                    A__		 =					val[-dim:]
            else:
                A__		 =					key_split[2]
                A__		 =					config.text_config.hidden_size
                if "weight" in key:
                    A__		 =					val[:dim, :]
                    A__		 =					val[
                        dim : dim * 2, :
                    ]
                    A__		 =					val[-dim:, :]
                else:
                    A__		 =					val[:dim]
                    A__		 =					val[
                        dim : dim * 2
                    ]
                    A__		 =					val[-dim:]
        else:
            A__		 =					rename_key(__UpperCamelCase	)
            if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
                A__		 =					val.T
            A__		 =					val

    return orig_state_dict



def 		lowerCAmelCase__    (					UpperCamelCase_				:       List[str]	)->  Dict:






    if num_frames == 8:
        A__		 =					"""eating_spaghetti_8_frames.npy"""
    elif num_frames == 1_6:
        A__		 =					"""eating_spaghetti.npy"""
    elif num_frames == 3_2:
        A__		 =					"""eating_spaghetti_32_frames.npy"""
    A__		 =					hf_hub_download(
        repo_id='''hf-internal-testing/spaghetti-video''' ,       filename=__UpperCamelCase ,       repo_type='''dataset''' ,       )
    A__		 =					np.load(__UpperCamelCase	)
    return list(__UpperCamelCase	)



def 		lowerCAmelCase__    (					UpperCamelCase_				:       List[Any] ,       UpperCamelCase_				:       List[str]=None ,       UpperCamelCase_				:       Any=False	)->  Tuple:
    A__		 =					{
        # fully supervised kinetics-400 checkpoints
        """xclip-base-patch32""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth""",
        """xclip-base-patch32-16-frames""": (
            """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"""
        ),
        """xclip-base-patch16""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth""",
        """xclip-base-patch16-16-frames""": (
            """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"""
        ),
        """xclip-large-patch14""": """https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb""",
        """xclip-large-patch14-16-frames""": """https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f""",
        # fully supervised kinetics-600 checkpoints
        """xclip-base-patch16-kinetics-600""": (
            """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"""
        ),
        """xclip-base-patch16-kinetics-600-16-frames""": (
            """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"""
        ),
        """xclip-large-patch14-kinetics-600""": """https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be""",
        # few shot
        """xclip-base-patch16-hmdb-2-shot""": (
            """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"""
        ),
        """xclip-base-patch16-hmdb-4-shot""": (
            """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"""
        ),
        """xclip-base-patch16-hmdb-8-shot""": (
            """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"""
        ),
        """xclip-base-patch16-hmdb-16-shot""": (
            """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"""
        ),
        """xclip-base-patch16-ucf-2-shot""": (
            """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"""
        ),
        """xclip-base-patch16-ucf-4-shot""": (
            """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"""
        ),
        """xclip-base-patch16-ucf-8-shot""": (
            """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"""
        ),
        """xclip-base-patch16-ucf-16-shot""": (
            """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"""
        ),
        # zero shot
        """xclip-base-patch16-zero-shot""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth""",
    }

    A__		 =					model_to_url[model_name]
    A__		 =					8
    if "16-frames" in model_name:
        A__		 =					1_6
    elif "shot" in model_name:
        A__		 =					3_2

    A__		 =					get_xclip_config(__UpperCamelCase ,       __UpperCamelCase	)
    A__		 =					XCLIPModel(__UpperCamelCase	)
    model.eval()

    if "drive" in checkpoint_url:
        A__		 =					"""pytorch_model.bin"""
        gdown.cached_download(__UpperCamelCase ,       __UpperCamelCase ,       quiet=__UpperCamelCase	)
        A__		 =					torch.load(__UpperCamelCase ,       map_location='''cpu'''	)["""model"""]
    else:
        A__		 =					torch.hub.load_state_dict_from_url(__UpperCamelCase	)["""model"""]

    A__		 =					convert_state_dict(__UpperCamelCase ,       __UpperCamelCase	)

    A__		 =					XCLIPModel(__UpperCamelCase	)
    A__		 =					model.load_state_dict(__UpperCamelCase ,       strict=__UpperCamelCase	)
    assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
    model.eval()

    A__		 =					3_3_6 if model_name == """xclip-large-patch14-16-frames""" else 2_2_4
    A__		 =					VideoMAEImageProcessor(size=__UpperCamelCase	)
    A__		 =					CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''	)
    A__		 =					CLIPTokenizerFast.from_pretrained('''openai/clip-vit-base-patch32'''	)
    A__		 =					XCLIPProcessor(image_processor=__UpperCamelCase ,       tokenizer=__UpperCamelCase	)

    A__		 =					prepare_video(__UpperCamelCase	)
    A__		 =					processor(
        text=['''playing sports''', '''eating spaghetti''', '''go shopping'''] ,       videos=__UpperCamelCase ,       return_tensors='''pt''' ,       padding=__UpperCamelCase	)

    print('''Shape of pixel values:''' ,       inputs.pixel_values.shape	)

    with torch.no_grad():
        A__		 =					model(**__UpperCamelCase	)

    # Verify outputs
    A__		 =					outputs.logits_per_video
    A__		 =					logits_per_video.softmax(dim=1	)
    print('''Probs:''' ,       __UpperCamelCase	)
    # kinetics-400
    if model_name == "xclip-base-patch32":
        A__		 =					torch.tensor([[0.0019, 0.9951, 0.0030]]	)
    elif model_name == "xclip-base-patch32-16-frames":
        A__		 =					torch.tensor([[7.0999E-04, 9.9883E-01, 4.5580E-04]]	)
    elif model_name == "xclip-base-patch16":
        A__		 =					torch.tensor([[0.0083, 0.9681, 0.0236]]	)
    elif model_name == "xclip-base-patch16-16-frames":
        A__		 =					torch.tensor([[7.6937E-04, 9.9728E-01, 1.9473E-03]]	)
    elif model_name == "xclip-large-patch14":
        A__		 =					torch.tensor([[0.0062, 0.9864, 0.0075]]	)
    elif model_name == "xclip-large-patch14-16-frames":
        A__		 =					torch.tensor([[3.3877E-04, 9.9937E-01, 2.8888E-04]]	)
    # kinetics-600
    elif model_name == "xclip-base-patch16-kinetics-600":
        A__		 =					torch.tensor([[0.0555, 0.8914, 0.0531]]	)
    elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
        A__		 =					torch.tensor([[3.8554E-04, 9.9929E-01, 3.2754E-04]]	)
    elif model_name == "xclip-large-patch14-kinetics-600":
        A__		 =					torch.tensor([[0.0036, 0.9920, 0.0045]]	)
    # few shot
    elif model_name == "xclip-base-patch16-hmdb-2-shot":
        A__		 =					torch.tensor([[7.1890E-06, 9.9994E-01, 5.6559E-05]]	)
    elif model_name == "xclip-base-patch16-hmdb-4-shot":
        A__		 =					torch.tensor([[1.0320E-05, 9.9993E-01, 6.2435E-05]]	)
    elif model_name == "xclip-base-patch16-hmdb-8-shot":
        A__		 =					torch.tensor([[4.1377E-06, 9.9990E-01, 9.8386E-05]]	)
    elif model_name == "xclip-base-patch16-hmdb-16-shot":
        A__		 =					torch.tensor([[4.1347E-05, 9.9962E-01, 3.3411E-04]]	)
    elif model_name == "xclip-base-patch16-ucf-2-shot":
        A__		 =					torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]]	)
    elif model_name == "xclip-base-patch16-ucf-4-shot":
        A__		 =					torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]]	)
    elif model_name == "xclip-base-patch16-ucf-8-shot":
        A__		 =					torch.tensor([[0.0027, 0.9904, 0.0070]]	)
    elif model_name == "xclip-base-patch16-ucf-16-shot":
        A__		 =					torch.tensor([[9.8219E-04, 9.9593E-01, 3.0863E-03]]	)
    # zero shot
    elif model_name == "xclip-base-patch16-zero-shot":
        A__		 =					torch.tensor([[3.5082E-04, 9.9785E-01, 1.7966E-03]]	)
    else:
        raise ValueError(f"Model name {model_name} not supported"	)
    assert torch.allclose(__UpperCamelCase ,       __UpperCamelCase ,       atol=1E-3	)
    print('''Looks ok!'''	)

    if pytorch_dump_folder_path is not None:
        print(f"Saving model {model_name} to {pytorch_dump_folder_path}"	)
        model.save_pretrained(__UpperCamelCase	)

    if push_to_hub:
        print('''Pushing model, processor and slow tokenizer files to the hub...'''	)
        model.push_to_hub(__UpperCamelCase ,       organization='''nielsr'''	)
        processor.push_to_hub(__UpperCamelCase ,       organization='''nielsr'''	)
        slow_tokenizer.push_to_hub(__UpperCamelCase ,       organization='''nielsr'''	)


if __name__ == "__main__":
   _lowercase						  =		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--model_name",
       default="xclip-base-patch32",
       type=str,
       help="Name of the model.",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
   )
   parser.add_argument(
       "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
   )

   _lowercase						  =		parser.parse_args()
   convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





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()
