




"""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      _snake_case			(				lowercase__		):
				_lowerCamelCase     :			str    =    SwinConfig()
				_lowerCamelCase     :			str    =    swin_name.split('_'		)

				_lowerCamelCase     :			Union[str, Any]    =    name_split[1]
				_lowerCamelCase     :			str    =    int(name_split[4]		)
				_lowerCamelCase     :			List[Any]    =    int(name_split[3][-1]		)

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

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

				_lowerCamelCase     :			List[str]    =    img_size
				_lowerCamelCase     :			List[Any]    =    num_classes
				_lowerCamelCase     :			Any    =    embed_dim
				_lowerCamelCase     :			Any    =    depths
				_lowerCamelCase     :			Dict    =    num_heads
				_lowerCamelCase     :			Any    =    window_size

				return config







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

				if name == "norm.weight":
								_lowerCamelCase     :			Union[str, Any]    =    'layernorm.weight'
				if name == "norm.bias":
								_lowerCamelCase     :			Any    =    'layernorm.bias'

				if "head" in name:
								_lowerCamelCase     :			Dict    =    name.replace('head'							,    'classifier'		)
				else:
								_lowerCamelCase     :			Tuple    =    'swin.' + name

				return name







def      _snake_case			(				lowercase__							,    lowercase__		):
				for key in orig_state_dict.copy().keys():
								_lowerCamelCase     :			Optional[int]    =    orig_state_dict.pop(lowercase__		)

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

												if "weight" in key:
																_lowerCamelCase     :			Union[str, Any]    =    val[:dim, :]
																_lowerCamelCase     :			int    =    val[
																    dim : dim * 2, :
																]
																_lowerCamelCase     :			int    =    val[-dim:, :]
												else:
																_lowerCamelCase     :			Optional[int]    =    val[
																    :dim
																]
																_lowerCamelCase     :			Dict    =    val[
																    dim : dim * 2
																]
																_lowerCamelCase     :			Optional[Any]    =    val[
																    -dim:
																]
								else:
												_lowerCamelCase     :			str    =    val

				return orig_state_dict







def      _snake_case			(				lowercase__							,    lowercase__		):
				_lowerCamelCase     :			List[str]    =    timm.create_model(lowercase__							,    pretrained=lowercase__		)
				timm_model.eval()

				_lowerCamelCase     :			Tuple    =    get_swin_config(lowercase__		)
				_lowerCamelCase     :			Optional[int]    =    SwinForImageClassification(lowercase__		)
				model.eval()

				_lowerCamelCase     :			int    =    convert_state_dict(timm_model.state_dict()							,    lowercase__		)
				model.load_state_dict(lowercase__		)

				_lowerCamelCase     :			Dict    =    'http://images.cocodataset.org/val2017/000000039769.jpg'

				_lowerCamelCase     :			List[str]    =    AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_'							,    '-'		)		)		)
				_lowerCamelCase     :			Optional[int]    =    Image.open(requests.get(lowercase__							,    stream=lowercase__		).raw		)
				_lowerCamelCase     :			Dict    =    image_processor(images=lowercase__							,    return_tensors='pt'		)

				_lowerCamelCase     :			Tuple    =    timm_model(inputs['pixel_values']		)
				_lowerCamelCase     :			List[Any]    =    model(**lowercase__		).logits

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

				print(f'''Saving model {swin_name} to {pytorch_dump_folder_path}'''		)
				model.save_pretrained(lowercase__		)

				print(f'''Saving image processor to {pytorch_dump_folder_path}'''		)
				image_processor.save_pretrained(lowercase__		)


if __name__ == "__main__":
			lowercase__     						=						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."""
			)

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





"""simple docstring"""






import json
import logging
import os
import sys
from time import time
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, require_torch_tpu


logging.basicConfig(level=logging.DEBUG)

lowercase__     						=						logging.getLogger()







def      _snake_case			(				lowercase__		):
				_lowerCamelCase     :			List[Any]    =    {}
				_lowerCamelCase     :			List[Any]    =    os.path.join(lowercase__							,    'all_results.json'		)
				if os.path.exists(lowercase__		):
								with open(lowercase__							,    'r'		) as f:
												_lowerCamelCase     :			List[Any]    =    json.load(lowercase__		)
				else:
								raise ValueError(f'''can\'t find {path}'''		)
				return results


lowercase__     						=						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






@require_torch_tpu
class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			def 					A_    (  self  ):
							import xla_spawn

							_lowerCamelCase     :			List[Any]    =    self.get_auto_remove_tmp_dir()
							_lowerCamelCase     :			List[Any]    =    F'''
            ./examples/pytorch/text-classification/run_glue.py
            --num_cores=8
            ./examples/pytorch/text-classification/run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --debug tpu_metrics_debug
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

							with patch.object(lowercase  ,      'argv'  ,      lowercase  ):
											_lowerCamelCase     :			Dict    =    time()
											xla_spawn.main()
											_lowerCamelCase     :			Any    =    time()

											_lowerCamelCase     :			Optional[int]    =    get_results(lowercase  )
											self.assertGreaterEqual(result['eval_accuracy']  ,      0.75  )

											# Assert that the script takes less than 500 seconds to make sure it doesn't hang.
											self.assertLess(end - start  ,      500  )






			def 					A_    (  self  ):
							import xla_spawn

							_lowerCamelCase     :			Tuple    =    '\n            ./tests/test_trainer_tpu.py\n            --num_cores=8\n            ./tests/test_trainer_tpu.py\n            '.split()
							with patch.object(lowercase  ,      'argv'  ,      lowercase  ):
											xla_spawn.main()