


import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification




def    A_(			A   ):
	UpperCAmelCase_								=     SwinvaConfig()
	UpperCAmelCase_								=     swinva_name.split("""_"""   )

	UpperCAmelCase_								=     name_split[1]
	if "to" in name_split[3]:
		UpperCAmelCase_								=     int(name_split[3][-3:]   )
	else:
		UpperCAmelCase_								=     int(name_split[3]   )
	if "to" in name_split[2]:
		UpperCAmelCase_								=     int(name_split[2][-2:]   )
	else:
		UpperCAmelCase_								=     int(name_split[2][6:]   )

	if model_size == "tiny":
		UpperCAmelCase_								=     96
		UpperCAmelCase_								=     (2, 2, 6, 2)
		UpperCAmelCase_								=     (3, 6, 12, 24)
	elif model_size == "small":
		UpperCAmelCase_								=     96
		UpperCAmelCase_								=     (2, 2, 18, 2)
		UpperCAmelCase_								=     (3, 6, 12, 24)
	elif model_size == "base":
		UpperCAmelCase_								=     128
		UpperCAmelCase_								=     (2, 2, 18, 2)
		UpperCAmelCase_								=     (4, 8, 16, 32)
	else:
		UpperCAmelCase_								=     192
		UpperCAmelCase_								=     (2, 2, 18, 2)
		UpperCAmelCase_								=     (6, 12, 24, 48)

	if "to" in swinva_name:
		UpperCAmelCase_								=     (12, 12, 12, 6)

	if ("22k" in swinva_name) and ("to" not in swinva_name):
		UpperCAmelCase_								=     21841
		UpperCAmelCase_								=     """huggingface/label-files"""
		UpperCAmelCase_								=     """imagenet-22k-id2label.json"""
		UpperCAmelCase_								=     json.load(open(hf_hub_download(A				,      A				,      repo_type="""dataset"""   )				,      """r"""   )   )
		UpperCAmelCase_								=     {int(A   ): v for k, v in idalabel.items()}
		UpperCAmelCase_								=     idalabel
		UpperCAmelCase_								=     {v: k for k, v in idalabel.items()}

	else:
		UpperCAmelCase_								=     1000
		UpperCAmelCase_								=     """huggingface/label-files"""
		UpperCAmelCase_								=     """imagenet-1k-id2label.json"""
		UpperCAmelCase_								=     json.load(open(hf_hub_download(A				,      A				,      repo_type="""dataset"""   )				,      """r"""   )   )
		UpperCAmelCase_								=     {int(A   ): v for k, v in idalabel.items()}
		UpperCAmelCase_								=     idalabel
		UpperCAmelCase_								=     {v: k for k, v in idalabel.items()}

	UpperCAmelCase_								=     img_size
	UpperCAmelCase_								=     num_classes
	UpperCAmelCase_								=     embed_dim
	UpperCAmelCase_								=     depths
	UpperCAmelCase_								=     num_heads
	UpperCAmelCase_								=     window_size

	return config




def    A_(			A   ):
	if "patch_embed.proj" in name:
		UpperCAmelCase_								=     name.replace("""patch_embed.proj"""				,      """embeddings.patch_embeddings.projection"""   )
	if "patch_embed.norm" in name:
		UpperCAmelCase_								=     name.replace("""patch_embed.norm"""				,      """embeddings.norm"""   )
	if "layers" in name:
		UpperCAmelCase_								=     """encoder.""" + name
	if "attn.proj" in name:
		UpperCAmelCase_								=     name.replace("""attn.proj"""				,      """attention.output.dense"""   )
	if "attn" in name:
		UpperCAmelCase_								=     name.replace("""attn"""				,      """attention.self"""   )
	if "norm1" in name:
		UpperCAmelCase_								=     name.replace("""norm1"""				,      """layernorm_before"""   )
	if "norm2" in name:
		UpperCAmelCase_								=     name.replace("""norm2"""				,      """layernorm_after"""   )
	if "mlp.fc1" in name:
		UpperCAmelCase_								=     name.replace("""mlp.fc1"""				,      """intermediate.dense"""   )
	if "mlp.fc2" in name:
		UpperCAmelCase_								=     name.replace("""mlp.fc2"""				,      """output.dense"""   )
	if "q_bias" in name:
		UpperCAmelCase_								=     name.replace("""q_bias"""				,      """query.bias"""   )
	if "k_bias" in name:
		UpperCAmelCase_								=     name.replace("""k_bias"""				,      """key.bias"""   )
	if "v_bias" in name:
		UpperCAmelCase_								=     name.replace("""v_bias"""				,      """value.bias"""   )
	if "cpb_mlp" in name:
		UpperCAmelCase_								=     name.replace("""cpb_mlp"""				,      """continuous_position_bias_mlp"""   )
	if name == "norm.weight":
		UpperCAmelCase_								=     """layernorm.weight"""
	if name == "norm.bias":
		UpperCAmelCase_								=     """layernorm.bias"""

	if "head" in name:
		UpperCAmelCase_								=     name.replace("""head"""				,      """classifier"""   )
	else:
		UpperCAmelCase_								=     """swinv2.""" + name

	return name




def    A_(			A				,      A   ):
	for key in orig_state_dict.copy().keys():
		UpperCAmelCase_								=     orig_state_dict.pop(A   )

		if "mask" in key:
			continue
		elif "qkv" in key:
			UpperCAmelCase_								=     key.split("""."""   )
			UpperCAmelCase_								=     int(key_split[1]   )
			UpperCAmelCase_								=     int(key_split[3]   )
			UpperCAmelCase_								=     model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

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

	return orig_state_dict




def    A_(			A				,      A   ):
	UpperCAmelCase_								=     timm.create_model(A				,      pretrained=A   )
	timm_model.eval()

	UpperCAmelCase_								=     get_swinva_config(A   )
	UpperCAmelCase_								=     SwinvaForImageClassification(A   )
	model.eval()

	UpperCAmelCase_								=     convert_state_dict(timm_model.state_dict()				,      A   )
	model.load_state_dict(A   )

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

	UpperCAmelCase_								=     AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swinva_name.replace("""_"""				,      """-"""   )   )   )
	UpperCAmelCase_								=     Image.open(requests.get(A				,      stream=A   ).raw   )
	UpperCAmelCase_								=     image_processor(images=A				,      return_tensors="""pt"""   )

	UpperCAmelCase_								=     timm_model(inputs["""pixel_values"""]   )
	UpperCAmelCase_								=     model(**A   ).logits

	assert torch.allclose(A				,      A				,      atol=1E-3   )

	print(f"""Saving model {swinva_name} to {pytorch_dump_folder_path}"""   )
	model.save_pretrained(A   )

	print(f"""Saving image processor to {pytorch_dump_folder_path}"""   )
	image_processor.save_pretrained(A   )

	model.push_to_hub(
	    repo_path_or_name=Path(A				,      A   )				,      organization="""nandwalritik"""				,      commit_message="""Add model"""				,      )


if __name__ == "__main__":
				UpperCamelCase__   : Tuple					  = argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    """--swinv2_name""",
				    default="""swinv2_tiny_patch4_window8_256""",
				    type=str,
				    help="""Name of the Swinv2 timm model you'd like to convert.""",
				)
				parser.add_argument(
				    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
				)

				UpperCamelCase__   : Tuple					  = parser.parse_args()
				convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)









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
#
########################################################################


UpperCamelCase__   : Union[str, Any]					  = 16
UpperCamelCase__   : Tuple					  = 32




def    A_(			A				,      A = 16   ):
	UpperCAmelCase_								=     AutoTokenizer.from_pretrained("""bert-base-cased"""   )
	UpperCAmelCase_								=     load_dataset("""glue"""				,      """mrpc"""   )

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

	def collate_fn(A   ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		UpperCAmelCase_								=     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":
			UpperCAmelCase_								=     16
		elif accelerator.mixed_precision != "no":
			UpperCAmelCase_								=     8
		else:
			UpperCAmelCase_								=     None

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

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

	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

				UpperCamelCase__   : str					  = mocked_dataloaders  # noqa: F811




def    A_(			A				,      A   ):
	# For testing only
	if os.environ.get("""TESTING_MOCKED_DATALOADERS"""				,      A   ) == "1":
		UpperCAmelCase_								=     2
	# New Code #
	UpperCAmelCase_								=     int(args.gradient_accumulation_steps   )
	# Initialize accelerator
	UpperCAmelCase_								=     Accelerator(
	    cpu=args.cpu				,      mixed_precision=args.mixed_precision				,      gradient_accumulation_steps=A   )
	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
	UpperCAmelCase_								=     config["""lr"""]
	UpperCAmelCase_								=     int(config["""num_epochs"""]   )
	UpperCAmelCase_								=     int(config["""seed"""]   )
	UpperCAmelCase_								=     int(config["""batch_size"""]   )

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

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

	# 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_								=     model.to(accelerator.device   )

	# Instantiate optimizer
	UpperCAmelCase_								=     AdamW(params=model.parameters()				,      lr=A   )

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

	# Now we train the model
	for epoch in range(A   ):
		model.train()
		for step, batch in enumerate(A   ):
			# 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(A   ):
				UpperCAmelCase_								=     model(**A   )
				UpperCAmelCase_								=     output.loss
				accelerator.backward(A   )
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

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

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




def    A_(			):
	UpperCAmelCase_								=     argparse.ArgumentParser(description="""Simple example of training script."""   )
	parser.add_argument(
	    """--mixed_precision"""				,      type=A				,      default=A				,      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=A				,      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."""   )
	UpperCAmelCase_								=     parser.parse_args()
	UpperCAmelCase_								=     {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(A				,      A   )


if __name__ == "__main__":
				main()


