



import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor







def    _snake_case (       __snake_case    )     ->     List[Any]:






      '''simple docstring'''







      UpperCAmelCase_  :	Any				      =		SwinConfig(image_size=1_9_2    )

      if "base" in model_name:
            UpperCAmelCase_  :	List[str]				      =		6
            UpperCAmelCase_  :	int				      =		1_2_8
            UpperCAmelCase_  :	Dict				      =		(2, 2, 1_8, 2)
            UpperCAmelCase_  :	Dict				      =		(4, 8, 1_6, 3_2)
      elif "large" in model_name:
            UpperCAmelCase_  :	List[str]				      =		1_2
            UpperCAmelCase_  :	Any				      =		1_9_2
            UpperCAmelCase_  :	Optional[Any]				      =		(2, 2, 1_8, 2)
            UpperCAmelCase_  :	int				      =		(6, 1_2, 2_4, 4_8)
      else:
            raise ValueError("Model not supported, only supports base and large variants"    )

      UpperCAmelCase_  :	List[str]				      =		window_size
      UpperCAmelCase_  :	Optional[Any]				      =		embed_dim
      UpperCAmelCase_  :	Optional[int]				      =		depths
      UpperCAmelCase_  :	Optional[int]				      =		num_heads

      return config







def    _snake_case (       __snake_case    )     ->     Any:






      '''simple docstring'''







      if "encoder.mask_token" in name:
            UpperCAmelCase_  :	int				      =		name.replace("encoder.mask_token"					,  "embeddings.mask_token"    )
      if "encoder.patch_embed.proj" in name:
            UpperCAmelCase_  :	Union[str, Any]				      =		name.replace("encoder.patch_embed.proj"					,  "embeddings.patch_embeddings.projection"    )
      if "encoder.patch_embed.norm" in name:
            UpperCAmelCase_  :	Optional[int]				      =		name.replace("encoder.patch_embed.norm"					,  "embeddings.norm"    )
      if "attn.proj" in name:
            UpperCAmelCase_  :	int				      =		name.replace("attn.proj"					,  "attention.output.dense"    )
      if "attn" in name:
            UpperCAmelCase_  :	Union[str, Any]				      =		name.replace("attn"					,  "attention.self"    )
      if "norm1" in name:
            UpperCAmelCase_  :	Tuple				      =		name.replace("norm1"					,  "layernorm_before"    )
      if "norm2" in name:
            UpperCAmelCase_  :	Optional[int]				      =		name.replace("norm2"					,  "layernorm_after"    )
      if "mlp.fc1" in name:
            UpperCAmelCase_  :	Optional[Any]				      =		name.replace("mlp.fc1"					,  "intermediate.dense"    )
      if "mlp.fc2" in name:
            UpperCAmelCase_  :	Tuple				      =		name.replace("mlp.fc2"					,  "output.dense"    )

      if name == "encoder.norm.weight":
            UpperCAmelCase_  :	str				      =		"layernorm.weight"
      if name == "encoder.norm.bias":
            UpperCAmelCase_  :	Dict				      =		"layernorm.bias"

      if "decoder" in name:
            pass
      else:
            UpperCAmelCase_  :	Optional[int]				      =		"swin." + name

      return name







def    _snake_case (       __snake_case					,  __snake_case    )     ->     Optional[Any]:






      '''simple docstring'''







      for key in orig_state_dict.copy().keys():
            UpperCAmelCase_  :	Union[str, Any]				      =		orig_state_dict.pop(__snake_case    )

            if "attn_mask" in key:
                  pass
            elif "qkv" in key:
                  UpperCAmelCase_  :	Optional[int]				      =		key.split("."    )
                  UpperCAmelCase_  :	Any				      =		int(key_split[2]    )
                  UpperCAmelCase_  :	Union[str, Any]				      =		int(key_split[4]    )
                  UpperCAmelCase_  :	Optional[Any]				      =		model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

                  if "weight" in key:
                        UpperCAmelCase_  :	str				      =		val[:dim, :]
                        UpperCAmelCase_  :	List[Any]				      =		val[
                            dim : dim * 2, :
                        ]
                        UpperCAmelCase_  :	Any				      =		val[-dim:, :]
                  else:
                        UpperCAmelCase_  :	Any				      =		val[
                            :dim
                        ]
                        UpperCAmelCase_  :	List[Any]				      =		val[
                            dim : dim * 2
                        ]
                        UpperCAmelCase_  :	Optional[Any]				      =		val[
                            -dim:
                        ]
            else:
                  UpperCAmelCase_  :	Any				      =		val

      return orig_state_dict







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case    )     ->     Optional[Any]:






      '''simple docstring'''







      UpperCAmelCase_  :	Dict				      =		torch.load(__snake_case					,  map_location="cpu"    )["model"]

      UpperCAmelCase_  :	Dict				      =		get_swin_config(__snake_case    )
      UpperCAmelCase_  :	Tuple				      =		SwinForMaskedImageModeling(__snake_case    )
      model.eval()

      UpperCAmelCase_  :	Any				      =		convert_state_dict(__snake_case					,  __snake_case    )
      model.load_state_dict(__snake_case    )

      UpperCAmelCase_  :	Tuple				      =		"http://images.cocodataset.org/val2017/000000039769.jpg"

      UpperCAmelCase_  :	int				      =		ViTImageProcessor(size={"height": 1_9_2, "width": 1_9_2}    )
      UpperCAmelCase_  :	Tuple				      =		Image.open(requests.get(__snake_case					,  stream=__snake_case    ).raw    )
      UpperCAmelCase_  :	str				      =		image_processor(images=__snake_case					,  return_tensors="pt"    )

      with torch.no_grad():
            UpperCAmelCase_  :	List[str]				      =		model(**__snake_case    ).logits

      print(outputs.keys()    )
      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(__snake_case    )

            print(F"""Saving image processor to {pytorch_dump_folder_path}"""    )
            image_processor.save_pretrained(__snake_case    )

      if push_to_hub:
            print(F"""Pushing model and image processor for {model_name} to hub"""    )
            model.push_to_hub(F"""microsoft/{model_name}"""    )
            image_processor.push_to_hub(F"""microsoft/{model_name}"""    )


if __name__ == "__main__":
   __lowerCamelCase         =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--model_name''',
       default='''swin-base-simmim-window6-192''',
       type=str,
       choices=['''swin-base-simmim-window6-192''', '''swin-large-simmim-window12-192'''],
       help='''Name of the Swin SimMIM model you\'d like to convert.''',
   )
   parser.add_argument(
       '''--checkpoint_path''',
       default='''/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth''',
       type=str,
       help='''Path to the original PyTorch checkpoint (.pth file).''',
   )
   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.'''
   )

   __lowerCamelCase         =			parser.parse_args()
   convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)





import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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,
# specifically showcasing how to perform Cross Validation,
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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
#
########################################################################


__lowerCamelCase         =			16
__lowerCamelCase         =			32







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case = 1_6    )     ->     Tuple:






      '''simple docstring'''







      UpperCAmelCase_  :	int				      =		AutoTokenizer.from_pretrained("bert-base-cased"    )
      UpperCAmelCase_  :	List[Any]				      =		DatasetDict(
          {
              "train": dataset["train"].select(__snake_case    ),
              "validation": dataset["train"].select(__snake_case    ),
              "test": dataset["validation"],
          }    )

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

      def collate_fn(__snake_case    ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            UpperCAmelCase_  :	Dict				      =		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":
                  UpperCAmelCase_  :	Optional[Any]				      =		1_6
            elif accelerator.mixed_precision != "no":
                  UpperCAmelCase_  :	str				      =		8
            else:
                  UpperCAmelCase_  :	Dict				      =		None

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

      # Instantiate dataloaders.
      UpperCAmelCase_  :	List[str]				      =		DataLoader(
          tokenized_datasets["train"]					,  shuffle=__snake_case					,  collate_fn=__snake_case					,  batch_size=__snake_case    )
      UpperCAmelCase_  :	Union[str, Any]				      =		DataLoader(
          tokenized_datasets["validation"]					,  shuffle=__snake_case					,  collate_fn=__snake_case					,  batch_size=__snake_case    )

      UpperCAmelCase_  :	str				      =		DataLoader(
          tokenized_datasets["test"]					,  shuffle=__snake_case					,  collate_fn=__snake_case					,  batch_size=__snake_case    )

      return train_dataloader, eval_dataloader, test_dataloader







def    _snake_case (       __snake_case					,  __snake_case    )     ->     List[str]:






      '''simple docstring'''







      UpperCAmelCase_  :	Tuple				      =		[]
      # Download the dataset
      UpperCAmelCase_  :	Optional[int]				      =		load_dataset("glue"					,  "mrpc"    )
      # Create our splits
      UpperCAmelCase_  :	Optional[Any]				      =		StratifiedKFold(n_splits=int(args.num_folds    )    )
      # Initialize accelerator
      UpperCAmelCase_  :	Tuple				      =		Accelerator(cpu=args.cpu					,  mixed_precision=args.mixed_precision    )
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      UpperCAmelCase_  :	Optional[int]				      =		config["lr"]
      UpperCAmelCase_  :	Dict				      =		int(config["num_epochs"]    )
      UpperCAmelCase_  :	Union[str, Any]				      =		int(config["seed"]    )
      UpperCAmelCase_  :	Optional[Any]				      =		int(config["batch_size"]    )

      UpperCAmelCase_  :	Optional[Any]				      =		evaluate.load("glue"					,  "mrpc"    )

      # If the batch size is too big we use gradient accumulation
      UpperCAmelCase_  :	Optional[int]				      =		1
      if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
            UpperCAmelCase_  :	int				      =		batch_size // MAX_GPU_BATCH_SIZE
            UpperCAmelCase_  :	List[str]				      =		MAX_GPU_BATCH_SIZE

      set_seed(__snake_case    )

      # New Code #
      # Create our folds:
      UpperCAmelCase_  :	int				      =		kfold.split(np.zeros(datasets["train"].num_rows    )					,  datasets["train"]["label"]    )
      UpperCAmelCase_  :	Dict				      =		[]
      # Iterate over them
      for i, (train_idxs, valid_idxs) in enumerate(__snake_case    ):
            UpperCAmelCase_      ,      UpperCAmelCase_      ,      UpperCAmelCase_  :	str				      =		get_fold_dataloaders(
                __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  )
            # Instantiate the model (we build the model here so that the seed also control new weights initialization)
            UpperCAmelCase_  :	Optional[int]				      =		AutoModelForSequenceClassification.from_pretrained("bert-base-cased"					,  return_dict=__snake_case    )

            # 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).
            UpperCAmelCase_  :	Union[str, Any]				      =		model.to(accelerator.device    )

            # Instantiate optimizer
            UpperCAmelCase_  :	Tuple				      =		AdamW(params=model.parameters()					,  lr=__snake_case    )

            # Instantiate scheduler
            UpperCAmelCase_  :	str				      =		get_linear_schedule_with_warmup(
                optimizer=__snake_case					,  num_warmup_steps=1_0_0					,  num_training_steps=(len(__snake_case    ) * num_epochs) // gradient_accumulation_steps					,  )

            # 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_  :	Union[str, Any]				      =		accelerator.prepare(
                __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case    )

            # Now we train the model
            for epoch in range(__snake_case    ):
                  model.train()
                  for step, batch in enumerate(__snake_case    ):
                        # We could avoid this line since we set the accelerator with `device_placement=True`.
                        batch.to(accelerator.device    )
                        UpperCAmelCase_  :	Optional[Any]				      =		model(**__snake_case    )
                        UpperCAmelCase_  :	Dict				      =		outputs.loss
                        UpperCAmelCase_  :	List[Any]				      =		loss / gradient_accumulation_steps
                        accelerator.backward(__snake_case    )
                        if step % gradient_accumulation_steps == 0:
                              optimizer.step()
                              lr_scheduler.step()
                              optimizer.zero_grad()

                  model.eval()
                  for step, batch in enumerate(__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_  :	str				      =		model(**__snake_case    )
                        UpperCAmelCase_  :	Optional[int]				      =		outputs.logits.argmax(dim=-1    )
                        UpperCAmelCase_      ,      UpperCAmelCase_  :	List[Any]				      =		accelerator.gather_for_metrics((predictions, batch["labels"])    )
                        metric.add_batch(
                            predictions=__snake_case					,  references=__snake_case					,  )

                  UpperCAmelCase_  :	Optional[Any]				      =		metric.compute()
                  # Use accelerator.print to print only on the main process.
                  accelerator.print(F"""epoch {epoch}:"""					,  __snake_case    )

            # New Code #
            # We also run predictions on the test set at the very end
            UpperCAmelCase_  :	Union[str, Any]				      =		[]
            for step, batch in enumerate(__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_  :	Tuple				      =		model(**__snake_case    )
                  UpperCAmelCase_  :	Optional[int]				      =		outputs.logits
                  UpperCAmelCase_      ,      UpperCAmelCase_  :	Optional[int]				      =		accelerator.gather_for_metrics((predictions, batch["labels"])    )
                  fold_predictions.append(predictions.cpu()    )
                  if i == 0:
                        # We need all of the test predictions
                        test_references.append(references.cpu()    )
        # Use accelerator.print to print only on the main process.
            test_predictions.append(torch.cat(__snake_case					,  dim=0    )    )
            # We now need to release all our memory and get rid of the current model, optimizer, etc
            accelerator.free_memory()
      # New Code #
      # Finally we check the accuracy of our folded results:
      UpperCAmelCase_  :	Union[str, Any]				      =		torch.cat(__snake_case					,  dim=0    )
      UpperCAmelCase_  :	str				      =		torch.stack(__snake_case					,  dim=0    ).sum(dim=0    ).div(int(args.num_folds    )    ).argmax(dim=-1    )
      UpperCAmelCase_  :	List[str]				      =		metric.compute(predictions=__snake_case					,  references=__snake_case    )
      accelerator.print("Average test metrics from all folds:"					,  __snake_case    )







def    _snake_case (       )     ->     str:






      '''simple docstring'''







      UpperCAmelCase_  :	Union[str, Any]				      =		argparse.ArgumentParser(description="Simple example of training script."    )
      parser.add_argument(
          "--mixed_precision"					,  type=__snake_case					,  default=__snake_case					,  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."    )
      # New Code #
      parser.add_argument("--num_folds"					,  type=__snake_case					,  default=3					,  help="The number of splits to perform across the dataset"    )
      UpperCAmelCase_  :	Any				      =		parser.parse_args()
      UpperCAmelCase_  :	List[Any]				      =		{"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
      training_function(__snake_case					,  __snake_case    )


if __name__ == "__main__":
   main()
