




'''simple docstring'''


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()
_lowercase  =			logging.get_logger(__name__)

_lowercase  =			torch.device("""cpu""")
def 						lowerCamelCase__   (				):
						__snake_case						 =   'http://images.cocodataset.org/val2017/000000039769.jpg'
						__snake_case						 =   Image.open(requests.get(a    ,	stream=a							).raw							)
						return im
def 						lowerCamelCase__   (				a							):
						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 						lowerCamelCase__   (				a    ,	a    ,	a							):
						__snake_case						 =   dct.pop(a							)
						__snake_case						 =   val
def 						lowerCamelCase__   (				a							):
						__snake_case						 =   []
						for k in state_dict.keys():
												__snake_case						 =   k
												if ".pwconv" in k:
																		__snake_case						 =   k_new.replace('.pwconv'    ,	'.point_wise_conv'							)
												if ".dwconv" in k:
																		__snake_case						 =   k_new.replace('.dwconv'    ,	'.depth_wise_conv'							)
												if ".Proj." in k:
																		__snake_case						 =   k_new.replace('.Proj.'    ,	'.proj.'							)
												if "patch_embed" in k_new:
																		__snake_case						 =   k_new.replace('patch_embed'    ,	'swiftformer.patch_embed.patch_embedding'							)
												if "network" in k_new:
																		__snake_case						 =   k_new.split('.'							)
																		if ls[2].isdigit():
																								__snake_case						 =   'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]							)
																		else:
																								__snake_case						 =   k_new.replace('network'    ,	'swiftformer.encoder.network'							)
												rename_keys.append((k, k_new)							)
						return rename_keys
@torch.no_grad()
def 						lowerCamelCase__   (				a    ,	a    ,	a							):
						__snake_case						 =   SwiftFormerConfig()

						# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
						__snake_case						 =   1000
						__snake_case						 =   'huggingface/label-files'
						__snake_case						 =   'imagenet-1k-id2label.json'
						__snake_case						 =   json.load(open(hf_hub_download(a    ,	a    ,	repo_type='dataset'							)    ,	'r'							)							)
						__snake_case						 =   {int(a							): v for k, v in idalabel.items()}
						__snake_case						 =   idalabel
						__snake_case						 =   {v: k for k, v in idalabel.items()}

						# size of the architecture
						if swiftformer_name == "swiftformer_xs":
												__snake_case						 =   [3, 3, 6, 4]
												__snake_case						 =   [48, 56, 112, 220]

						elif swiftformer_name == "swiftformer_s":
												__snake_case						 =   [3, 3, 9, 6]
												__snake_case						 =   [48, 64, 168, 224]

						elif swiftformer_name == "swiftformer_l1":
												__snake_case						 =   [4, 3, 10, 5]
												__snake_case						 =   [48, 96, 192, 384]

						elif swiftformer_name == "swiftformer_l3":
												__snake_case						 =   [4, 4, 12, 6]
												__snake_case						 =   [64, 128, 320, 512]

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

						__snake_case						 =   create_rename_keys(a							)
						for rename_key_src, rename_key_dest in rename_keys:
												rename_key(a    ,	a    ,	a							)

						# load HuggingFace model
						__snake_case						 =   SwiftFormerForImageClassification(a							).eval()
						hf_model.load_state_dict(a							)

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

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

						assert hf_logits.shape == torch.Size([1, 1000]							)
						assert torch.allclose(hf_logits[0, 0:5]    ,	a    ,	atol=1E-3							)

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


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

	_lowercase  =			parser.parse_args()
	convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)







'''simple docstring'''


import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_lowercase  =			os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
_lowercase  =			"""    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
"""
class 						a_				(			unittest.TestCase						):




	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   tempfile.mkdtemp()
							os.makedirs(os.path.join(self.transformer_dir       ,		'models/bert/'      )      )
							__snake_case						 =   self.transformer_dir
							shutil.copy(
							    os.path.join(__lowerCAmelCase       ,		'src/transformers/models/bert/modeling_bert.py'      )       ,		os.path.join(self.transformer_dir       ,		'models/bert/modeling_bert.py'      )       ,		)




	def   lowercase__   (       self     :  Optional[Any]      ):
							__snake_case						 =   'src/transformers'
							shutil.rmtree(self.transformer_dir      )




	def   lowercase__   (       self     :  List[Any]       ,		__lowerCAmelCase     :  Optional[int]       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  List[str]=None      ):
							__snake_case						 =   comment + F'\nclass {class_name}(nn.Module):\n' + class_code
							if overwrite_result is not None:
													__snake_case						 =   comment + F'\nclass {class_name}(nn.Module):\n' + overwrite_result
							__snake_case						 =   black.Mode(target_versions={black.TargetVersion.PYaa}       ,		line_length=1_1_9      )
							__snake_case						 =   black.format_str(__lowerCAmelCase       ,		mode=__lowerCAmelCase      )
							__snake_case						 =   os.path.join(self.transformer_dir       ,		'new_code.py'      )
							with open(__lowerCAmelCase       ,		'w'       ,		newline='\n'      ) as f:
													f.write(__lowerCAmelCase      )
							if overwrite_result is None:
													self.assertTrue(len(check_copies.is_copy_consistent(__lowerCAmelCase      )      ) == 0      )
							else:
													check_copies.is_copy_consistent(f.name       ,		overwrite=__lowerCAmelCase      )
													with open(__lowerCAmelCase       ,		'r'      ) as f:
																			self.assertTrue(f.read()       ,		__lowerCAmelCase      )




	def   lowercase__   (       self     :  Tuple      ):
							__snake_case						 =   check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'      )
							self.assertEqual(__lowerCAmelCase       ,		__lowerCAmelCase      )




	def   lowercase__   (       self     :  int      ):
							# Base copy consistency
							self.check_copy_consistency(
							    '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'       ,		'BertLMPredictionHead'       ,		REFERENCE_CODE + '\n'       ,		)

							# With no empty line at the end
							self.check_copy_consistency(
							    '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'       ,		'BertLMPredictionHead'       ,		__lowerCAmelCase       ,		)

							# Copy consistency with rename
							self.check_copy_consistency(
							    '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'       ,		'TestModelLMPredictionHead'       ,		re.sub('Bert'       ,		'TestModel'       ,		__lowerCAmelCase      )       ,		)

							# Copy consistency with a really long name
							__snake_case						 =   'TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
							self.check_copy_consistency(
							    F'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}'       ,		F'{long_class_name}LMPredictionHead'       ,		re.sub('Bert'       ,		__lowerCAmelCase       ,		__lowerCAmelCase      )       ,		)

							# Copy consistency with overwrite
							self.check_copy_consistency(
							    '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'       ,		'TestModelLMPredictionHead'       ,		__lowerCAmelCase       ,		overwrite_result=re.sub('Bert'       ,		'TestModel'       ,		__lowerCAmelCase      )       ,		)




	def   lowercase__   (       self     :  List[str]      ):
							__snake_case						 =   check_copies.LOCALIZED_READMES['README_zh-hans.md']

							__snake_case						 =   (
							    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
							    ' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
							    ' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
							    ' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1.'
							    ' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),'
							    ' released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
							    ' lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same'
							    ' method has been applied to compress GPT2 into'
							    ' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
							    ' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
							    ' Multilingual BERT into'
							    ' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
							    ' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**'
							    ' (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders'
							    ' as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang'
							    ' Luong, Quoc V. Le, Christopher D. Manning.'
							)
							__snake_case						 =   (
							    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
							    ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
							    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
							    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
							)
							__snake_case						 =   (
							    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
							    ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
							    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
							    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1.'
							    ' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文'
							    ' [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
							    ' lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same'
							    ' method has been applied to compress GPT2 into'
							    ' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
							    ' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
							    ' Multilingual BERT into'
							    ' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
							    ' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自'
							    ' Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather'
							    ' than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,'
							    ' Christopher D. Manning 发布。\n'
							)

							__snake_case ,       __snake_case						 =   check_copies.convert_to_localized_md(
							    __lowerCAmelCase       ,		__lowerCAmelCase       ,		localized_readme['format_model_list']      )

							self.assertFalse(__lowerCAmelCase      )
							self.assertEqual(__lowerCAmelCase       ,		__lowerCAmelCase      )

							__snake_case ,       __snake_case						 =   check_copies.convert_to_localized_md(
							    __lowerCAmelCase       ,		__lowerCAmelCase       ,		localized_readme['format_model_list']      )

							# Check whether the number of models is equal to README.md after conversion.
							self.assertTrue(__lowerCAmelCase      )

							__snake_case						 =   (
							    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
							    ' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
							    ' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
							    ' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.'
							)
							__snake_case						 =   (
							    '1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and'
							    ' the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
							    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
							    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
							)
							__snake_case						 =   (
							    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
							    ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
							    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
							    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
							)

							__snake_case ,       __snake_case						 =   check_copies.convert_to_localized_md(
							    __lowerCAmelCase       ,		__lowerCAmelCase       ,		localized_readme['format_model_list']      )

							# Check if the model link is synchronized.
							self.assertEqual(__lowerCAmelCase       ,		__lowerCAmelCase      )

