





'''simple docstring'''

import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# 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)
#
# 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						(     __lowerCamelCase :    Accelerator					,       __lowerCamelCase :    int = 16    )						->Optional[int]:
    _SCREAMING_SNAKE_CASE									=			AutoTokenizer.from_pretrained("""bert-base-cased"""    )
    _SCREAMING_SNAKE_CASE									=			load_dataset("""glue"""					,       """mrpc"""    )

    def tokenize_function(__lowerCamelCase :    int    ):
        # max_length=None => use the model max length (it's actually the default)
        _SCREAMING_SNAKE_CASE									=			tokenizer(examples["""sentence1"""]					,       examples["""sentence2"""]					,       truncation=__lowerCamelCase					,       max_length=__lowerCamelCase    )
        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():
        _SCREAMING_SNAKE_CASE									=			datasets.map(
            __lowerCamelCase					,       batched=__lowerCamelCase					,       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
    _SCREAMING_SNAKE_CASE									=			tokenized_datasets.rename_column("""label"""					,       """labels"""    )

    def collate_fn(__lowerCamelCase :    List[str]    ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        _SCREAMING_SNAKE_CASE									=			128 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            _SCREAMING_SNAKE_CASE									=			16
        elif accelerator.mixed_precision != "no":
            _SCREAMING_SNAKE_CASE									=			8
        else:
            _SCREAMING_SNAKE_CASE									=			None

        return tokenizer.pad(
            __lowerCamelCase					,       padding="""longest"""					,       max_length=__lowerCamelCase					,       pad_to_multiple_of=__lowerCamelCase					,       return_tensors="""pt"""					,       )

    # Instantiate dataloaders.
    _SCREAMING_SNAKE_CASE									=			DataLoader(
        tokenized_datasets["""train"""]					,       shuffle=__lowerCamelCase					,       collate_fn=__lowerCamelCase					,       batch_size=__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			DataLoader(
        tokenized_datasets["""validation"""]					,       shuffle=__lowerCamelCase					,       collate_fn=__lowerCamelCase					,       batch_size=__lowerCamelCase    )

    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						(     __lowerCamelCase :    List[str]					,       __lowerCamelCase :    Optional[int]    )						->str:
    # For testing only
    if os.environ.get("""TESTING_MOCKED_DATALOADERS"""					,       __lowerCamelCase    ) == "1":
        _SCREAMING_SNAKE_CASE									=			2
    # New Code #
    _SCREAMING_SNAKE_CASE									=			int(args.gradient_accumulation_steps    )
    # Initialize accelerator
    _SCREAMING_SNAKE_CASE									=			Accelerator(
        cpu=args.cpu					,       mixed_precision=args.mixed_precision					,       gradient_accumulation_steps=__lowerCamelCase    )
    if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
        raise NotImplementedError(
            """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"""    )
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    _SCREAMING_SNAKE_CASE									=			config["""lr"""]
    _SCREAMING_SNAKE_CASE									=			int(config["""num_epochs"""]    )
    _SCREAMING_SNAKE_CASE									=			int(config["""seed"""]    )
    _SCREAMING_SNAKE_CASE									=			int(config["""batch_size"""]    )

    _SCREAMING_SNAKE_CASE									=			evaluate.load("""glue"""					,       """mrpc"""    )

    set_seed(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			get_dataloaders(__lowerCamelCase					,       __lowerCamelCase    )
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _SCREAMING_SNAKE_CASE									=			AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""					,       return_dict=__lowerCamelCase    )

    # 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).
    _SCREAMING_SNAKE_CASE									=			model.to(accelerator.device    )

    # Instantiate optimizer
    _SCREAMING_SNAKE_CASE									=			AdamW(params=model.parameters()					,       lr=__lowerCamelCase    )

    # Instantiate scheduler
    _SCREAMING_SNAKE_CASE									=			get_linear_schedule_with_warmup(
        optimizer=__lowerCamelCase					,       num_warmup_steps=100					,       num_training_steps=(len(__lowerCamelCase    ) * 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.
    _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			accelerator.prepare(
        __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )

    # Now we train the model
    for epoch in range(__lowerCamelCase    ):
        model.train()
        for step, batch in enumerate(__lowerCamelCase    ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device    )
            # New code #
            # We use the new `accumulate` context manager to perform gradient accumulation
            # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
            with accelerator.accumulate(__lowerCamelCase    ):
                _SCREAMING_SNAKE_CASE									=			model(**__lowerCamelCase    )
                _SCREAMING_SNAKE_CASE									=			output.loss
                accelerator.backward(__lowerCamelCase    )
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

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

        _SCREAMING_SNAKE_CASE									=			metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'epoch {epoch}:'					,       __lowerCamelCase    )





def lowerCamelCase						(     )						->Any:
    _SCREAMING_SNAKE_CASE									=			argparse.ArgumentParser(description="""Simple example of training script."""    )
    parser.add_argument(
        """--mixed_precision"""					,       type=__lowerCamelCase					,       default=__lowerCamelCase					,       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."""					,       )
    # New Code #
    parser.add_argument(
        """--gradient_accumulation_steps"""					,       type=__lowerCamelCase					,       default=1					,       help="""The number of minibatches to be ran before gradients are accumulated."""					,       )
    parser.add_argument("""--cpu"""					,       action="""store_true"""					,       help="""If passed, will train on the CPU."""    )
    _SCREAMING_SNAKE_CASE									=			parser.parse_args()
    _SCREAMING_SNAKE_CASE									=			{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
    training_function(__lowerCamelCase					,       __lowerCamelCase    )


if __name__ == "__main__":
      main()




import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification

def 			lowercase_				(			_lowerCamelCase							:       int):
    lowercase__ :   int    					=				[]
    embed.append(
        (
            f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
            f'''stage{idx}.patch_embed.proj.weight''',
        ))
    embed.append(
        (
            f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
            f'''stage{idx}.patch_embed.proj.bias''',
        ))
    embed.append(
        (
            f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
            f'''stage{idx}.patch_embed.norm.weight''',
        ))
    embed.append(
        (
            f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
            f'''stage{idx}.patch_embed.norm.bias''',
        ))
    return embed

def 			lowercase_				(			_lowerCamelCase							:       Optional[int]		,	_lowerCamelCase							:       int):
    lowercase__ :   Optional[Any]    					=				[]
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
            f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
            f'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
            f'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
            f'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
            f'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
        ))
    attention_weights.append(
        (
            f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
            f'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
        ))
    attention_weights.append(
        (f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.weight'''))
    attention_weights.append(
        (f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.bias'''))
    attention_weights.append(
        (f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.weight'''))
    attention_weights.append(
        (f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.bias'''))
    attention_weights.append(
        (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', f'''stage{idx}.blocks.{cnt}.norm1.weight'''))
    attention_weights.append(
        (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', f'''stage{idx}.blocks.{cnt}.norm1.bias'''))
    attention_weights.append(
        (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', f'''stage{idx}.blocks.{cnt}.norm2.weight'''))
    attention_weights.append(
        (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', f'''stage{idx}.blocks.{cnt}.norm2.bias'''))
    return attention_weights

def 			lowercase_				(			_lowerCamelCase							:       Optional[int]):
    lowercase__ :   Tuple    					=				[]
    token.append((f'''cvt.encoder.stages.{idx}.cls_token''', "stage2.cls_token"))
    return token

def 			lowercase_				(			):
    lowercase__ :   List[str]    					=				[]
    head.append(("layernorm.weight", "norm.weight"))
    head.append(("layernorm.bias", "norm.bias"))
    head.append(("classifier.weight", "head.weight"))
    head.append(("classifier.bias", "head.bias"))
    return head

def 			lowercase_				(			_lowerCamelCase							:       Union[str, Any]		,	_lowerCamelCase							:       Optional[int]		,	_lowerCamelCase							:       Optional[int]		,	_lowerCamelCase							:       Union[str, Any]):
    lowercase__ :   Optional[Any]    					=				"imagenet-1k-id2label.json"
    lowercase__ :   List[str]    					=				1000

    lowercase__ :   Dict    					=				"huggingface/label-files"
    lowercase__ :   List[Any]    					=				num_labels
    lowercase__ :   Tuple    					=				json.load(open(cached_download(hf_hub_url(_lowerCamelCase		,	_lowerCamelCase		,	repo_type="dataset"))		,	"r"))
    lowercase__ :   Tuple    					=				{int(_lowerCamelCase): v for k, v in idalabel.items()}

    lowercase__ :   Any    					=				idalabel
    lowercase__ :   List[Any]    					=				{v: k for k, v in idalabel.items()}

    lowercase__ :   Optional[int]    					=				CvtConfig(num_labels=_lowerCamelCase		,	idalabel=_lowerCamelCase		,	labelaid=_lowerCamelCase)

    # For depth size 13 (13 = 1+2+10)
    if cvt_model.rsplit("/"		,	1)[-1][4:6] == "13":
        lowercase__ :   Any    					=				[1, 2, 10]

    # For depth size 21 (21 = 1+4+16)
    elif cvt_model.rsplit("/"		,	1)[-1][4:6] == "21":
        lowercase__ :   Tuple    					=				[1, 4, 16]

    # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
    else:
        lowercase__ :   Union[str, Any]    					=				[2, 2, 20]
        lowercase__ :   Optional[Any]    					=				[3, 12, 16]
        lowercase__ :   Optional[Any]    					=				[192, 768, 1024]

    lowercase__ :   Union[str, Any]    					=				CvtForImageClassification(_lowerCamelCase)
    lowercase__ :   Tuple    					=				AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k")
    lowercase__ :   int    					=				image_size
    lowercase__ :   Dict    					=				torch.load(_lowerCamelCase		,	map_location=torch.device("cpu"))

    lowercase__ :   Any    					=				OrderedDict()
    lowercase__ :   int    					=				[]

    for idx in range(len(config.depth)):
        if config.cls_token[idx]:
            lowercase__ :   Dict    					=				list_of_state_dict + cls_token(_lowerCamelCase)
        lowercase__ :   List[str]    					=				list_of_state_dict + embeddings(_lowerCamelCase)
        for cnt in range(config.depth[idx]):
            lowercase__ :   Any    					=				list_of_state_dict + attention(_lowerCamelCase		,	_lowerCamelCase)

    lowercase__ :   List[str]    					=				list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(_lowerCamelCase)
    for i in range(len(_lowerCamelCase)):
        lowercase__ :   Dict    					=				original_weights[list_of_state_dict[i][1]]

    model.load_state_dict(_lowerCamelCase)
    model.save_pretrained(_lowerCamelCase)
    image_processor.save_pretrained(_lowerCamelCase)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
      UpperCamelCase  							=						argparse.ArgumentParser()
      parser.add_argument(
          '''--cvt_model''',
          default='''cvt-w24''',
          type=str,
          help='''Name of the cvt model you\'d like to convert.''',
      )
      parser.add_argument(
          '''--image_size''',
          default=384,
          type=int,
          help='''Input Image Size''',
      )
      parser.add_argument(
          '''--cvt_file_name''',
          default=R'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
          type=str,
          help='''Input Image Size''',
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
      )

      UpperCamelCase  							=						parser.parse_args()
      convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)



