



"""simple docstring"""


import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

__A   :							str										=   [
    "cross_validation.py",
    "gradient_accumulation.py",
    "local_sgd.py",
    "multi_process_metrics.py",
    "memory.py",
    "automatic_gradient_accumulation.py",
    "fsdp_with_peak_mem_tracking.py",
    "deepspeed_with_config_support.py",
    "megatron_lm_gpt_pretraining.py",
]
class 			_a (					unittest.TestCase):
			"""simple docstring"""



			def  lowercase__						(		self     :    Union[str, Any]				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    bool				,   __UpperCamelCase     :    str = None				,   __UpperCamelCase     :    list = None   )->int:
							_UpperCAmelCase					=    None
							_UpperCAmelCase					=    os.path.abspath(os.path.join('''examples'''				,   '''by_feature'''   )   )
							_UpperCAmelCase					=    os.path.abspath('''examples'''   )
							for item in os.listdir(__UpperCamelCase   ):
											if item not in EXCLUDE_EXAMPLES:
															_UpperCAmelCase					=    os.path.join(__UpperCamelCase				,   __UpperCamelCase   )
															if os.path.isfile(__UpperCamelCase   ) and ".py" in item_path:
																			with self.subTest(
																			    tested_script=__UpperCamelCase				,   feature_script=__UpperCamelCase				,   tested_section='''main()''' if parser_only else '''training_function()'''				,   ):
																							_UpperCAmelCase					=    compare_against_test(
																							    os.path.join(__UpperCamelCase				,   __UpperCamelCase   )				,   __UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase   )
																							_UpperCAmelCase					=    '''\n'''.join(__UpperCamelCase   )
																							if special_strings is not None:
																											for string in special_strings:
																															_UpperCAmelCase					=    diff.replace(__UpperCamelCase				,   ''''''   )
																							self.assertEqual(__UpperCamelCase				,   ''''''   )



			def  lowercase__						(		self     :    Tuple   )->Any:
							self.one_complete_example('''complete_nlp_example.py'''				,   __UpperCamelCase   )
							self.one_complete_example('''complete_nlp_example.py'''				,   __UpperCamelCase   )



			def  lowercase__						(		self     :    Optional[Any]   )->int:
							_UpperCAmelCase					=    os.path.abspath(os.path.join('''examples'''				,   '''cv_example.py'''   )   )
							_UpperCAmelCase					=    [
							    ''' ''' * 1_6 + '''{\n\n''',
							    ''' ''' * 2_0 + '''"accuracy": eval_metric["accuracy"],\n\n''',
							    ''' ''' * 2_0 + '''"f1": eval_metric["f1"],\n\n''',
							    ''' ''' * 2_0 + '''"train_loss": total_loss.item() / len(train_dataloader),\n\n''',
							    ''' ''' * 2_0 + '''"epoch": epoch,\n\n''',
							    ''' ''' * 1_6 + '''},\n\n''',
							    ''' ''' * 1_6 + '''step=epoch,\n''',
							    ''' ''' * 1_2,
							    ''' ''' * 8 + '''for step, batch in enumerate(active_dataloader):\n''',
							]
							self.one_complete_example('''complete_cv_example.py'''				,   __UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase   )
							self.one_complete_example('''complete_cv_example.py'''				,   __UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase   )



@mock.patch.dict(os.environ			,							{"""TESTING_MOCKED_DATALOADERS""": """1"""})
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	False



			@classmethod
			def  lowercase__						(		cls     :    Optional[int]   )->Optional[Any]:
							super().setUpClass()
							_UpperCAmelCase					=    tempfile.mkdtemp()
							_UpperCAmelCase					=    os.path.join(cls._tmpdir				,   '''default_config.yml'''   )

							write_basic_config(save_location=cls.configPath   )
							_UpperCAmelCase					=    ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]



			@classmethod
			def  lowercase__						(		cls     :    Dict   )->Any:
							super().tearDownClass()
							shutil.rmtree(cls._tmpdir   )



			def  lowercase__						(		self     :    Optional[int]   )->Any:
							_UpperCAmelCase					=    F'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps epoch\n        --output_dir {self.tmpdir}\n        '.split()
							run_command(self._launch_args + testargs   )
							self.assertTrue(os.path.exists(os.path.join(self.tmpdir				,   '''epoch_0'''   )   )   )



			def  lowercase__						(		self     :    Optional[int]   )->Optional[int]:
							_UpperCAmelCase					=    F'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps 1\n        --output_dir {self.tmpdir}\n        '.split()
							_UpperCAmelCase					=    run_command(self._launch_args + testargs   )
							self.assertTrue(os.path.exists(os.path.join(self.tmpdir				,   '''step_2'''   )   )   )



			def  lowercase__						(		self     :    Optional[Any]   )->List[Any]:
							_UpperCAmelCase					=    F'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir				,   "epoch_0"   )}\n        '.split()
							_UpperCAmelCase					=    run_command(self._launch_args + testargs				,   return_stdout=__UpperCamelCase   )
							self.assertNotIn('''epoch 0:'''				,   __UpperCamelCase   )
							self.assertIn('''epoch 1:'''				,   __UpperCamelCase   )



			def  lowercase__						(		self     :    List[str]   )->str:
							_UpperCAmelCase					=    F'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir				,   "step_2"   )}\n        '.split()
							_UpperCAmelCase					=    run_command(self._launch_args + testargs				,   return_stdout=__UpperCamelCase   )
							if torch.cuda.is_available():
											_UpperCAmelCase					=    torch.cuda.device_count()
							else:
											_UpperCAmelCase					=    1
							if num_processes > 1:
											self.assertNotIn('''epoch 0:'''				,   __UpperCamelCase   )
											self.assertIn('''epoch 1:'''				,   __UpperCamelCase   )
							else:
											self.assertIn('''epoch 0:'''				,   __UpperCamelCase   )
											self.assertIn('''epoch 1:'''				,   __UpperCamelCase   )



			@slow
			def  lowercase__						(		self     :    Dict   )->List[Any]:
							_UpperCAmelCase					=    '''
        examples/by_feature/cross_validation.py
        --num_folds 2
        '''.split()
							with mock.patch.dict(os.environ				,   {'''TESTING_MOCKED_DATALOADERS''': '''0'''}   ):
											_UpperCAmelCase					=    run_command(self._launch_args + testargs				,   return_stdout=__UpperCamelCase   )
											_UpperCAmelCase					=    re.findall('''({.+})'''				,   __UpperCamelCase   )
											_UpperCAmelCase					=    [r for r in results if '''accuracy''' in r][-1]
											_UpperCAmelCase					=    ast.literal_eval(__UpperCamelCase   )
											self.assertGreaterEqual(results['''accuracy''']				,   0.7_5   )



			def  lowercase__						(		self     :    Any   )->List[Any]:
							_UpperCAmelCase					=    ['''examples/by_feature/multi_process_metrics.py''']
							run_command(self._launch_args + testargs   )



			@require_trackers
			@mock.patch.dict(os.environ				,   {'''WANDB_MODE''': '''offline'''}   )
			def  lowercase__						(		self     :    Optional[int]   )->Union[str, Any]:
							with tempfile.TemporaryDirectory() as tmpdir:
											_UpperCAmelCase					=    F'\n            examples/by_feature/tracking.py\n            --with_tracking\n            --project_dir {tmpdir}\n            '.split()
											run_command(self._launch_args + testargs   )
											self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase				,   '''tracking'''   )   )   )



			def  lowercase__						(		self     :    Dict   )->Dict:
							_UpperCAmelCase					=    ['''examples/by_feature/gradient_accumulation.py''']
							run_command(self._launch_args + testargs   )



			def  lowercase__						(		self     :    Union[str, Any]   )->Tuple:
							_UpperCAmelCase					=    ['''examples/by_feature/local_sgd.py''']
							run_command(self._launch_args + testargs   )








"""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  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[Any]				):






				'''simple docstring'''



				_UpperCAmelCase					=    SwinConfig()
				_UpperCAmelCase					=    swin_name.split('''_'''				)

				_UpperCAmelCase					=    name_split[1]
				_UpperCAmelCase					=    int(name_split[4]				)
				_UpperCAmelCase					=    int(name_split[3][-1]				)

				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 "in22k" in swin_name:
								_UpperCAmelCase					=    2_1841
				else:
								_UpperCAmelCase					=    1000
								_UpperCAmelCase					=    '''huggingface/label-files'''
								_UpperCAmelCase					=    '''imagenet-1k-id2label.json'''
								_UpperCAmelCase					=    json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     repo_type='''dataset'''				)					,     '''r'''				)				)
								_UpperCAmelCase					=    {int(_SCREAMING_SNAKE_CASE				): 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  lowercase	(      _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				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 name == "norm.weight":
								_UpperCAmelCase					=    '''layernorm.weight'''
				if name == "norm.bias":
								_UpperCAmelCase					=    '''layernorm.bias'''

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

				return name







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Union[str, Any]					,     _SCREAMING_SNAKE_CASE   :						Dict				):






				'''simple docstring'''



				for key in orig_state_dict.copy().keys():
								_UpperCAmelCase					=    orig_state_dict.pop(_SCREAMING_SNAKE_CASE				)

								if "mask" in key:
												continue
								elif "qkv" in key:
												_UpperCAmelCase					=    key.split('''.'''				)
												_UpperCAmelCase					=    int(key_split[1]				)
												_UpperCAmelCase					=    int(key_split[3]				)
												_UpperCAmelCase					=    model.swin.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  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				_UpperCAmelCase					=    timm.create_model(_SCREAMING_SNAKE_CASE					,     pretrained=_SCREAMING_SNAKE_CASE				)
				timm_model.eval()

				_UpperCAmelCase					=    get_swin_config(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    SwinForImageClassification(_SCREAMING_SNAKE_CASE				)
				model.eval()

				_UpperCAmelCase					=    convert_state_dict(timm_model.state_dict()					,     _SCREAMING_SNAKE_CASE				)
				model.load_state_dict(_SCREAMING_SNAKE_CASE				)

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

				_UpperCAmelCase					=    AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_'''					,     '''-'''				)				)				)
				_UpperCAmelCase					=    Image.open(requests.get(_SCREAMING_SNAKE_CASE					,     stream=_SCREAMING_SNAKE_CASE				).raw				)
				_UpperCAmelCase					=    image_processor(images=_SCREAMING_SNAKE_CASE					,     return_tensors='''pt'''				)

				_UpperCAmelCase					=    timm_model(inputs['''pixel_values''']				)
				_UpperCAmelCase					=    model(**_SCREAMING_SNAKE_CASE				).logits

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

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

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


if __name__ == "__main__":
					__A   :							int										=   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   :							Tuple										=   parser.parse_args()
					convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)



