

'''simple docstring'''

import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification





def    A_  ( snake_case ):
		SCREAMING_SNAKE_CASE:int						      =					SwinConfig()
		SCREAMING_SNAKE_CASE:List[str]						      =					swin_name.split("_" )

		SCREAMING_SNAKE_CASE:Any						      =					name_split[1]
		SCREAMING_SNAKE_CASE:List[str]						      =					int(name_split[4] )
		SCREAMING_SNAKE_CASE:Optional[Any]						      =					int(name_split[3][-1] )

		if model_size == "tiny":
				SCREAMING_SNAKE_CASE:Dict						      =					96
				SCREAMING_SNAKE_CASE:Optional[int]						      =					(2, 2, 6, 2)
				SCREAMING_SNAKE_CASE:Any						      =					(3, 6, 12, 24)
		elif model_size == "small":
				SCREAMING_SNAKE_CASE:List[str]						      =					96
				SCREAMING_SNAKE_CASE:List[Any]						      =					(2, 2, 18, 2)
				SCREAMING_SNAKE_CASE:Any						      =					(3, 6, 12, 24)
		elif model_size == "base":
				SCREAMING_SNAKE_CASE:Tuple						      =					128
				SCREAMING_SNAKE_CASE:Tuple						      =					(2, 2, 18, 2)
				SCREAMING_SNAKE_CASE:Dict						      =					(4, 8, 16, 32)
		else:
				SCREAMING_SNAKE_CASE:Optional[Any]						      =					192
				SCREAMING_SNAKE_CASE:Optional[int]						      =					(2, 2, 18, 2)
				SCREAMING_SNAKE_CASE:Optional[Any]						      =					(6, 12, 24, 48)

		if "in22k" in swin_name:
				SCREAMING_SNAKE_CASE:Dict						      =					21841
		else:
				SCREAMING_SNAKE_CASE:Optional[int]						      =					1000
				SCREAMING_SNAKE_CASE:Union[str, Any]						      =					'huggingface/label-files'
				SCREAMING_SNAKE_CASE:Tuple						      =					'imagenet-1k-id2label.json'
				SCREAMING_SNAKE_CASE:Optional[int]						      =					json.load(open(hf_hub_download(snake_case      ,       snake_case      ,       repo_type="dataset" )      ,       "r" ) )
				SCREAMING_SNAKE_CASE:str						      =					{int(snake_case ): v for k, v in idalabel.items()}
				SCREAMING_SNAKE_CASE:Optional[int]						      =					idalabel
				SCREAMING_SNAKE_CASE:Union[str, Any]						      =					{v: k for k, v in idalabel.items()}

		SCREAMING_SNAKE_CASE:Union[str, Any]						      =					img_size
		SCREAMING_SNAKE_CASE:Any						      =					num_classes
		SCREAMING_SNAKE_CASE:int						      =					embed_dim
		SCREAMING_SNAKE_CASE:Optional[int]						      =					depths
		SCREAMING_SNAKE_CASE:Optional[Any]						      =					num_heads
		SCREAMING_SNAKE_CASE:List[str]						      =					window_size

		return config





def    A_  ( snake_case ):
		if "patch_embed.proj" in name:
				SCREAMING_SNAKE_CASE:Optional[Any]						      =					name.replace("patch_embed.proj"      ,       "embeddings.patch_embeddings.projection" )
		if "patch_embed.norm" in name:
				SCREAMING_SNAKE_CASE:Optional[Any]						      =					name.replace("patch_embed.norm"      ,       "embeddings.norm" )
		if "layers" in name:
				SCREAMING_SNAKE_CASE:int						      =					'encoder.' + name
		if "attn.proj" in name:
				SCREAMING_SNAKE_CASE:List[Any]						      =					name.replace("attn.proj"      ,       "attention.output.dense" )
		if "attn" in name:
				SCREAMING_SNAKE_CASE:List[str]						      =					name.replace("attn"      ,       "attention.self" )
		if "norm1" in name:
				SCREAMING_SNAKE_CASE:int						      =					name.replace("norm1"      ,       "layernorm_before" )
		if "norm2" in name:
				SCREAMING_SNAKE_CASE:Union[str, Any]						      =					name.replace("norm2"      ,       "layernorm_after" )
		if "mlp.fc1" in name:
				SCREAMING_SNAKE_CASE:Optional[Any]						      =					name.replace("mlp.fc1"      ,       "intermediate.dense" )
		if "mlp.fc2" in name:
				SCREAMING_SNAKE_CASE:List[str]						      =					name.replace("mlp.fc2"      ,       "output.dense" )

		if name == "norm.weight":
				SCREAMING_SNAKE_CASE:Any						      =					'layernorm.weight'
		if name == "norm.bias":
				SCREAMING_SNAKE_CASE:List[str]						      =					'layernorm.bias'

		if "head" in name:
				SCREAMING_SNAKE_CASE:Union[str, Any]						      =					name.replace("head"      ,       "classifier" )
		else:
				SCREAMING_SNAKE_CASE:Union[str, Any]						      =					'swin.' + name

		return name





def    A_  ( snake_case      ,       snake_case ):
		for key in orig_state_dict.copy().keys():
				SCREAMING_SNAKE_CASE:str						      =					orig_state_dict.pop(snake_case )

				if "mask" in key:
						continue
				elif "qkv" in key:
						SCREAMING_SNAKE_CASE:Union[str, Any]						      =					key.split("." )
						SCREAMING_SNAKE_CASE:Any						      =					int(key_split[1] )
						SCREAMING_SNAKE_CASE:Optional[int]						      =					int(key_split[3] )
						SCREAMING_SNAKE_CASE:Optional[int]						      =					model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

						if "weight" in key:
								SCREAMING_SNAKE_CASE:List[Any]						      =					val[:dim, :]
								SCREAMING_SNAKE_CASE:Any						      =					val[
								    dim : dim * 2, :
								]
								SCREAMING_SNAKE_CASE:str						      =					val[-dim:, :]
						else:
								SCREAMING_SNAKE_CASE:Dict						      =					val[
								    :dim
								]
								SCREAMING_SNAKE_CASE:List[str]						      =					val[
								    dim : dim * 2
								]
								SCREAMING_SNAKE_CASE:Tuple						      =					val[
								    -dim:
								]
				else:
						SCREAMING_SNAKE_CASE:Dict						      =					val

		return orig_state_dict





def    A_  ( snake_case      ,       snake_case ):
		SCREAMING_SNAKE_CASE:Dict						      =					timm.create_model(snake_case      ,       pretrained=snake_case )
		timm_model.eval()

		SCREAMING_SNAKE_CASE:Any						      =					get_swin_config(snake_case )
		SCREAMING_SNAKE_CASE:Optional[int]						      =					SwinForImageClassification(snake_case )
		model.eval()

		SCREAMING_SNAKE_CASE:List[Any]						      =					convert_state_dict(timm_model.state_dict()      ,       snake_case )
		model.load_state_dict(snake_case )

		SCREAMING_SNAKE_CASE:Optional[Any]						      =					'http://images.cocodataset.org/val2017/000000039769.jpg'

		SCREAMING_SNAKE_CASE:int						      =					AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"      ,       "-" ) ) )
		SCREAMING_SNAKE_CASE:Dict						      =					Image.open(requests.get(snake_case      ,       stream=snake_case ).raw )
		SCREAMING_SNAKE_CASE:str						      =					image_processor(images=snake_case      ,       return_tensors="pt" )

		SCREAMING_SNAKE_CASE:Optional[Any]						      =					timm_model(inputs["pixel_values"] )
		SCREAMING_SNAKE_CASE:Optional[int]						      =					model(**snake_case ).logits

		assert torch.allclose(snake_case      ,       snake_case      ,       atol=1e-3 )

		print(F'''Saving model {swin_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 __name__ == "__main__":
				A_       						=							argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--swin_name",
				    default="swin_tiny_patch4_window7_224",
				    type=str,
				    help="Name of the Swin 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."
				)

				A_       						=							parser.parse_args()
				convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)









'''simple docstring'''

import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


A_       						=							"true"





def    A_  ( snake_case      ,       snake_case=82      ,       snake_case=16 ):
  set_seed(42 )
  SCREAMING_SNAKE_CASE:Optional[int]						      =					RegressionModel()
  SCREAMING_SNAKE_CASE:int						      =					deepcopy(snake_case )
  SCREAMING_SNAKE_CASE:List[str]						      =					RegressionDataset(length=snake_case )
  SCREAMING_SNAKE_CASE:int						      =					DataLoader(snake_case      ,       batch_size=snake_case )
  model.to(accelerator.device )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Dict						      =					accelerator.prepare(snake_case      ,       snake_case )
  return model, ddp_model, dataloader





def    A_  ( snake_case      ,       snake_case=False ):
  SCREAMING_SNAKE_CASE:List[Any]						      =					AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased" )
  SCREAMING_SNAKE_CASE:Dict						      =					load_dataset("glue"      ,       "mrpc"      ,       split="validation" )

  def tokenize_function(snake_case ):
    SCREAMING_SNAKE_CASE:Optional[int]						      =					tokenizer(examples["sentence1"]      ,       examples["sentence2"]      ,       truncation=snake_case      ,       max_length=snake_case )
    return outputs

  with accelerator.main_process_first():
    SCREAMING_SNAKE_CASE:int						      =					dataset.map(
        snake_case      ,       batched=snake_case      ,       remove_columns=["idx", "sentence1", "sentence2"]      ,       )

  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					tokenized_datasets.rename_column("label"      ,       "labels" )

  def collate_fn(snake_case ):
    if use_longest:
      return tokenizer.pad(snake_case      ,       padding="longest"      ,       return_tensors="pt" )
    return tokenizer.pad(snake_case      ,       padding="max_length"      ,       max_length=128      ,       return_tensors="pt" )

  return DataLoader(snake_case      ,       shuffle=snake_case      ,       collate_fn=snake_case      ,       batch_size=16 )





def    A_  ( snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:Optional[int]						      =					Accelerator(dispatch_batches=snake_case      ,       split_batches=snake_case )
  SCREAMING_SNAKE_CASE:List[Any]						      =					get_dataloader(snake_case      ,       not dispatch_batches )
  SCREAMING_SNAKE_CASE:Tuple						      =					AutoModelForSequenceClassification.from_pretrained(
      "hf-internal-testing/mrpc-bert-base-cased"      ,       return_dict=snake_case )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:List[str]						      =					accelerator.prepare(snake_case      ,       snake_case )
  return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator





def    A_  ( snake_case      ,       snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:str						      =					[]
  for batch in dataloader:
    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					batch.values()
    with torch.no_grad():
      SCREAMING_SNAKE_CASE:int						      =					model(snake_case )
      SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:List[str]						      =					accelerator.gather_for_metrics((logit, target) )
      logits_and_targets.append((logit, target) )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[], []
  for logit, targ in logits_and_targets:
    logits.append(snake_case )
    targs.append(snake_case )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Optional[Any]						      =					torch.cat(snake_case ), torch.cat(snake_case )
  return logits, targs





def    A_  ( snake_case      ,       snake_case=82      ,       snake_case=False      ,       snake_case=False      ,       snake_case=16 ):
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:int						      =					get_basic_setup(snake_case      ,       snake_case      ,       snake_case )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Optional[Any]						      =					generate_predictions(snake_case      ,       snake_case      ,       snake_case )
  assert (
      len(snake_case ) == num_samples
  ), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(snake_case )}'''





def    A_  ( snake_case = False      ,       snake_case = False ):
  SCREAMING_SNAKE_CASE:List[Any]						      =					evaluate.load("glue"      ,       "mrpc" )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Dict						      =					get_mrpc_setup(snake_case      ,       snake_case )
  # First do baseline
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					setup["no"]
  model.to(snake_case )
  model.eval()
  for batch in dataloader:
    batch.to(snake_case )
    with torch.inference_mode():
      SCREAMING_SNAKE_CASE:Dict						      =					model(**snake_case )
    SCREAMING_SNAKE_CASE:Any						      =					outputs.logits.argmax(dim=-1 )
    metric.add_batch(predictions=snake_case      ,       references=batch["labels"] )
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					metric.compute()

  # Then do distributed
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					setup["ddp"]
  model.eval()
  for batch in dataloader:
    with torch.inference_mode():
      SCREAMING_SNAKE_CASE:Any						      =					model(**snake_case )
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					outputs.logits.argmax(dim=-1 )
    SCREAMING_SNAKE_CASE:List[str]						      =					batch["labels"]
    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:int						      =					accelerator.gather_for_metrics((preds, references) )
    metric.add_batch(predictions=snake_case      ,       references=snake_case )
  SCREAMING_SNAKE_CASE:Any						      =					metric.compute()

  for key in "accuracy f1".split():
    assert math.isclose(
        baseline[key]      ,       distributed[key] ), F'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''





def    A_  ( ):
  SCREAMING_SNAKE_CASE:Optional[int]						      =					Accelerator(split_batches=snake_case      ,       dispatch_batches=snake_case )
  if accelerator.is_local_main_process:
    datasets.utils.logging.set_verbosity_warning()
    transformers.utils.logging.set_verbosity_warning()
  else:
    datasets.utils.logging.set_verbosity_error()
    transformers.utils.logging.set_verbosity_error()
  # These are a bit slower so they should only be ran on the GPU or TPU
  if torch.cuda.is_available() or is_tpu_available():
    if accelerator.is_local_main_process:
      print("**Testing gather_for_metrics**" )
    for split_batches in [True, False]:
      for dispatch_batches in [True, False]:
        if accelerator.is_local_main_process:
          print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`''' )
        test_mrpc(snake_case      ,       snake_case )
        accelerator.state._reset_state()
  if accelerator.is_local_main_process:
    print("**Test torch metrics**" )
  for split_batches in [True, False]:
    for dispatch_batches in [True, False]:
      SCREAMING_SNAKE_CASE:Optional[Any]						      =					Accelerator(split_batches=snake_case      ,       dispatch_batches=snake_case )
      if accelerator.is_local_main_process:
        print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99''' )
      test_torch_metrics(snake_case      ,       99 )
      accelerator.state._reset_state()
  if accelerator.is_local_main_process:
    print("**Test last batch is not dropped when perfectly divisible**" )
  SCREAMING_SNAKE_CASE:List[str]						      =					Accelerator()
  test_torch_metrics(snake_case      ,       512 )
  accelerator.state._reset_state()





def    A_  ( snake_case ):
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
    main()






