


"""simple docstring"""




import contextlib
import importlib
import io
import unittest

import transformers

# Try to import everything from transformers to ensure every object can be loaded.
from transformers import *  # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available


if is_torch_available():
						from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification

if is_tf_available():
						from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification

if is_flax_available():
						from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification


snake_case							:	Union[str, Any]					      =				DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

snake_case							:	Union[str, Any]					      =				'''main'''
# Default branch name
snake_case							:	Optional[Any]					      =				'''f2c752cfc5c0ab6f4bdec59acea69eefbee381c2'''
# One particular commit (not the top of `main`)
snake_case							:	Tuple					      =				'''aaaaaaa'''
# This commit does not exist, so we should 404.

snake_case							:	Dict					      =				'''d9e9f15bc825e4b2c9249e9578f884bbcb5e3684'''
# Sha-1 of config.json on the top of `main`, for checking purposes
snake_case							:	Dict					      =				'''4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3'''






@contextlib.contextmanager
def 							__lowercase       (			):
							print('Welcome!'       )
							yield
							print('Bye!'       )






@contextlib.contextmanager
def 							__lowercase       (			):
							print('Bonjour!'       )
							yield
							print('Au revoir!'       )







class 	snake_case_       (unittest.TestCase				):
					def 			lowerCamelCase__(				self       :int				) ->   Optional[Any]:
												# If the spec is missing, importlib would not be able to import the module dynamically.
												assert transformers.__spec__ is not None
												assert importlib.util.find_spec('transformers'				) is not None







class 	snake_case_       (unittest.TestCase				):
					@unittest.mock.patch('sys.stdout'      ,new_callable=io.StringIO				)
					def 			lowerCamelCase__(				self       :List[Any]      ,__snake_case       :str				) ->   Tuple:
												with ContextManagers([]				):
																			print('Transformers are awesome!'				)
												# The print statement adds a new line at the end of the output
												self.assertEqual(mock_stdout.getvalue()      ,'Transformers are awesome!\n'				)
					@unittest.mock.patch('sys.stdout'      ,new_callable=io.StringIO				)
					def 			lowerCamelCase__(				self       :List[Any]      ,__snake_case       :Union[str, Any]				) ->   str:
												with ContextManagers([context_en()]				):
																			print('Transformers are awesome!'				)
												# The output should be wrapped with an English welcome and goodbye
												self.assertEqual(mock_stdout.getvalue()      ,'Welcome!\nTransformers are awesome!\nBye!\n'				)
					@unittest.mock.patch('sys.stdout'      ,new_callable=io.StringIO				)
					def 			lowerCamelCase__(				self       :Dict      ,__snake_case       :Optional[int]				) ->   Optional[int]:
												with ContextManagers([context_fr(), context_en()]				):
																			print('Transformers are awesome!'				)
												# The output should be wrapped with an English and French welcome and goodbye
												self.assertEqual(mock_stdout.getvalue()      ,'Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n'				)
					@require_torch
					def 			lowerCamelCase__(				self       :List[str]				) ->   Any:
												self.assertEqual(find_labels(__snake_case				)      ,['labels']				)
												self.assertEqual(find_labels(__snake_case				)      ,['labels', 'next_sentence_label']				)
												self.assertEqual(find_labels(__snake_case				)      ,['start_positions', 'end_positions']				)







												class 	snake_case_       (lowerCamelCase_				):
																	pass





												self.assertEqual(find_labels(__snake_case				)      ,['labels']				)
					@require_tf
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   int:
												self.assertEqual(find_labels(__snake_case				)      ,['labels']				)
												self.assertEqual(find_labels(__snake_case				)      ,['labels', 'next_sentence_label']				)
												self.assertEqual(find_labels(__snake_case				)      ,['start_positions', 'end_positions']				)







												class 	snake_case_       (lowerCamelCase_				):
																	pass





												self.assertEqual(find_labels(__snake_case				)      ,['labels']				)



					@require_flax
					def 			lowerCamelCase__(				self       :Any				) ->   str:
												# Flax models don't have labels
												self.assertEqual(find_labels(__snake_case				)      ,[]				)
												self.assertEqual(find_labels(__snake_case				)      ,[]				)
												self.assertEqual(find_labels(__snake_case				)      ,[]				)







												class 	snake_case_       (lowerCamelCase_				):
																	pass





												self.assertEqual(find_labels(__snake_case				)      ,[]				)








import argparse
import json
from pathlib import Path

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

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
snake_case							:	Union[str, Any]					      =				logging.get_logger(__name__)

snake_case							:	Tuple					      =				torch.device('''cpu''')






def 							__lowercase       (			):
							a__					=					'http://images.cocodataset.org/val2017/000000039769.jpg'
							a__					=					Image.open(requests.get(__lowerCAmelCase ,   stream=__lowerCAmelCase       ).raw       )
							return im






def 							__lowercase       (			__lowerCAmelCase							:    List[str]       ):
							if swiftformer_name == "swiftformer_xs":
														return torch.tensor([-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0, 8.8_6_8_5E-0_1, 2.4_3_6_0E-0_1]       )

							elif swiftformer_name == "swiftformer_s":
														return torch.tensor([3.9_6_3_6E-0_1, 2.3_4_7_8E-0_1, -1.6_9_6_3E0_0, -1.7_3_8_1E0_0, -8.6_3_3_7E-0_1]       )

							elif swiftformer_name == "swiftformer_l1":
														return torch.tensor([-4.2_7_6_8E-0_1, -4.7_4_2_9E-0_1, -1.0_8_9_7E0_0, -1.0_2_4_8E0_0, 3.5_5_2_3E-0_2]       )

							elif swiftformer_name == "swiftformer_l3":
														return torch.tensor([-2.5_3_3_0E-0_1, 2.4_2_1_1E-0_1, -6.0_1_8_5E-0_1, -8.2_7_8_9E-0_1, -6.0_4_4_6E-0_2]       )






def 							__lowercase       (			__lowerCAmelCase							:    List[Any] ,   __lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    str       ):
							a__					=					dct.pop(__lowerCAmelCase       )
							a__					=					val






def 							__lowercase       (			__lowerCAmelCase							:    int       ):
							a__					=					[]
							for k in state_dict.keys():
														a__					=					k
														if ".pwconv" in k:
																					a__					=					k_new.replace('.pwconv' ,   '.point_wise_conv'       )
														if ".dwconv" in k:
																					a__					=					k_new.replace('.dwconv' ,   '.depth_wise_conv'       )
														if ".Proj." in k:
																					a__					=					k_new.replace('.Proj.' ,   '.proj.'       )
														if "patch_embed" in k_new:
																					a__					=					k_new.replace('patch_embed' ,   'swiftformer.patch_embed.patch_embedding'       )
														if "network" in k_new:
																					a__					=					k_new.split('.'       )
																					if ls[2].isdigit():
																												a__					=					'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]       )
																					else:
																												a__					=					k_new.replace('network' ,   'swiftformer.encoder.network'       )
														rename_keys.append((k, k_new)       )
							return rename_keys






@torch.no_grad()
def 							__lowercase       (			__lowerCAmelCase							:    Optional[Any] ,   __lowerCAmelCase							:    str ,   __lowerCAmelCase							:    str       ):
							a__					=					SwiftFormerConfig()

							# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
							a__					=					1_0_0_0
							a__					=					'huggingface/label-files'
							a__					=					'imagenet-1k-id2label.json'
							a__					=					json.load(open(hf_hub_download(__lowerCAmelCase ,   __lowerCAmelCase ,   repo_type='dataset'       ) ,   'r'       )       )
							a__					=					{int(__lowerCAmelCase       ): v for k, v in idalabel.items()}
							a__					=					idalabel
							a__					=					{v: k for k, v in idalabel.items()}

							# size of the architecture
							if swiftformer_name == "swiftformer_xs":
														a__					=					[3, 3, 6, 4]
														a__					=					[4_8, 5_6, 1_1_2, 2_2_0]

							elif swiftformer_name == "swiftformer_s":
														a__					=					[3, 3, 9, 6]
														a__					=					[4_8, 6_4, 1_6_8, 2_2_4]

							elif swiftformer_name == "swiftformer_l1":
														a__					=					[4, 3, 1_0, 5]
														a__					=					[4_8, 9_6, 1_9_2, 3_8_4]

							elif swiftformer_name == "swiftformer_l3":
														a__					=					[4, 4, 1_2, 6]
														a__					=					[6_4, 1_2_8, 3_2_0, 5_1_2]

							# load state_dict of original model, remove and rename some keys
							if original_ckpt:
														if original_ckpt.startswith('https'       ):
																					a__					=					torch.hub.load_state_dict_from_url(__lowerCAmelCase ,   map_location='cpu' ,   check_hash=__lowerCAmelCase       )
														else:
																					a__					=					torch.load(__lowerCAmelCase ,   map_location='cpu'       )
							a__					=					checkpoint

							a__					=					create_rename_keys(__lowerCAmelCase       )
							for rename_key_src, rename_key_dest in rename_keys:
														rename_key(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )

							# load HuggingFace model
							a__					=					SwiftFormerForImageClassification(__lowerCAmelCase       ).eval()
							hf_model.load_state_dict(__lowerCAmelCase       )

							# prepare test inputs
							a__					=					prepare_img()
							a__					=					ViTImageProcessor.from_pretrained('preprocessor_config'       )
							a__					=					processor(images=__lowerCAmelCase ,   return_tensors='pt'       )

							# compare outputs from both models
							a__					=					get_expected_output(__lowerCAmelCase       )
							a__					=					hf_model(inputs['pixel_values']       ).logits

							assert hf_logits.shape == torch.Size([1, 1_0_0_0]       )
							assert torch.allclose(hf_logits[0, 0:5] ,   __lowerCAmelCase ,   atol=1E-3       )

							Path(__lowerCAmelCase       ).mkdir(exist_ok=__lowerCAmelCase       )
							print(F'Saving model {swiftformer_name} to {pytorch_dump_folder_path}'       )
							hf_model.save_pretrained(__lowerCAmelCase       )


if __name__ == "__main__":
						snake_case							:	int					      =				argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--swiftformer_name''',
						    default='''swiftformer_xs''',
						    choices=['''swiftformer_xs''', '''swiftformer_s''', '''swiftformer_l1''', '''swiftformer_l3'''],
						    type=str,
						    help='''Name of the SwiftFormer model you\'d like to convert.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''',
						    default='''./converted_outputs/''',
						    type=str,
						    help='''Path to the output PyTorch model directory.''',
						)
						parser.add_argument('''--original_ckpt''', default=None, type=str, help='''Path to the original model checkpoint.''')

						snake_case							:	Optional[int]					      =				parser.parse_args()
						convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)




