


"""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()
lowerCAmelCase__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   torch.device('''cpu''')
def 					a__			(				):


  '''simple docstring'''







  lowerCAmelCase     :     List[Any]								=							"http://images.cocodataset.org/val2017/000000039769.jpg"
  lowerCAmelCase     :     Union[str, Any]								=							Image.open(requests.get(SCREAMING_SNAKE_CASE				,      stream=SCREAMING_SNAKE_CASE				).raw				)
  return im
def 					a__			(				SCREAMING_SNAKE_CASE    :    Dict				):


  '''simple docstring'''







  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 					a__			(				SCREAMING_SNAKE_CASE    :    Tuple				,      SCREAMING_SNAKE_CASE    :    List[str]				,      SCREAMING_SNAKE_CASE    :    int				):


  '''simple docstring'''







  lowerCAmelCase     :     int								=							dct.pop(SCREAMING_SNAKE_CASE				)
  lowerCAmelCase     :     List[str]								=							val
def 					a__			(				SCREAMING_SNAKE_CASE    :    Tuple				):


  '''simple docstring'''







  lowerCAmelCase     :     Union[str, Any]								=							[]
  for k in state_dict.keys():
    lowerCAmelCase     :     Optional[Any]								=							k
    if ".pwconv" in k:
      lowerCAmelCase     :     Optional[Any]								=							k_new.replace(".pwconv"				,      ".point_wise_conv"				)
    if ".dwconv" in k:
      lowerCAmelCase     :     Any								=							k_new.replace(".dwconv"				,      ".depth_wise_conv"				)
    if ".Proj." in k:
      lowerCAmelCase     :     int								=							k_new.replace(".Proj."				,      ".proj."				)
    if "patch_embed" in k_new:
      lowerCAmelCase     :     Dict								=							k_new.replace("patch_embed"				,      "swiftformer.patch_embed.patch_embedding"				)
    if "network" in k_new:
      lowerCAmelCase     :     Tuple								=							k_new.split("."				)
      if ls[2].isdigit():
        lowerCAmelCase     :     Union[str, Any]								=							"swiftformer.encoder.network." + ls[1] + ".blocks." + ls[2] + "." + ".".join(ls[3:]				)
      else:
        lowerCAmelCase     :     Tuple								=							k_new.replace("network"				,      "swiftformer.encoder.network"				)
    rename_keys.append((k, k_new)				)
  return rename_keys
@torch.no_grad()
def 					a__			(				SCREAMING_SNAKE_CASE    :    List[Any]				,      SCREAMING_SNAKE_CASE    :    Optional[Any]				,      SCREAMING_SNAKE_CASE    :    List[Any]				):


  '''simple docstring'''







  lowerCAmelCase     :     Union[str, Any]								=							SwiftFormerConfig()

  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  lowerCAmelCase     :     Dict								=							1_0_0_0
  lowerCAmelCase     :     Optional[int]								=							"huggingface/label-files"
  lowerCAmelCase     :     Optional[Any]								=							"imagenet-1k-id2label.json"
  lowerCAmelCase     :     str								=							json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      repo_type="dataset"				)				,      "r"				)				)
  lowerCAmelCase     :     str								=							{int(SCREAMING_SNAKE_CASE				): v for k, v in idalabel.items()}
  lowerCAmelCase     :     List[Any]								=							idalabel
  lowerCAmelCase     :     Union[str, Any]								=							{v: k for k, v in idalabel.items()}

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

  elif swiftformer_name == "swiftformer_s":
    lowerCAmelCase     :     Optional[int]								=							[3, 3, 9, 6]
    lowerCAmelCase     :     Union[str, Any]								=							[4_8, 6_4, 1_6_8, 2_2_4]

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

  elif swiftformer_name == "swiftformer_l3":
    lowerCAmelCase     :     List[Any]								=							[4, 4, 1_2, 6]
    lowerCAmelCase     :     str								=							[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"				):
      lowerCAmelCase     :     Tuple								=							torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE				,      map_location="cpu"				,      check_hash=SCREAMING_SNAKE_CASE				)
    else:
      lowerCAmelCase     :     Any								=							torch.load(SCREAMING_SNAKE_CASE				,      map_location="cpu"				)
  lowerCAmelCase     :     Tuple								=							checkpoint

  lowerCAmelCase     :     Optional[int]								=							create_rename_keys(SCREAMING_SNAKE_CASE				)
  for rename_key_src, rename_key_dest in rename_keys:
    rename_key(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)

  # load HuggingFace model
  lowerCAmelCase     :     Union[str, Any]								=							SwiftFormerForImageClassification(SCREAMING_SNAKE_CASE				).eval()
  hf_model.load_state_dict(SCREAMING_SNAKE_CASE				)

  # prepare test inputs
  lowerCAmelCase     :     List[Any]								=							prepare_img()
  lowerCAmelCase     :     Dict								=							ViTImageProcessor.from_pretrained("preprocessor_config"				)
  lowerCAmelCase     :     int								=							processor(images=SCREAMING_SNAKE_CASE				,      return_tensors="pt"				)

  # compare outputs from both models
  lowerCAmelCase     :     int								=							get_expected_output(SCREAMING_SNAKE_CASE				)
  lowerCAmelCase     :     Tuple								=							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]				,      SCREAMING_SNAKE_CASE				,      atol=1E-3				)

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


if __name__ == "__main__":
     lowerCAmelCase__      				=   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.''')

     lowerCAmelCase__      				=   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


__lowerCAmelCase							:	Any			=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.
__lowerCAmelCase							:	Optional[int]			="    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n"






class    UpperCAmelCase	(       unittest.TestCase				):
 def      UpperCAmelCase_							(				self      :Tuple							)->							Union[str, Any]:
     A__				=			tempfile.mkdtemp()
     os.makedirs(os.path.join(self.transformer_dir						,       "models/bert/"							)							)
     A__				=			self.transformer_dir
     shutil.copy(
         os.path.join(lowercase_						,       "src/transformers/models/bert/modeling_bert.py"							)						,       os.path.join(self.transformer_dir						,       "models/bert/modeling_bert.py"							)						,       )
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							Tuple:
     A__				=			"src/transformers"
     shutil.rmtree(self.transformer_dir							)
 def      UpperCAmelCase_							(				self      :List[Any]						,       lowercase_      :List[str]						,       lowercase_      :Union[str, Any]						,       lowercase_      :int						,       lowercase_      :Tuple=None							)->							Optional[Any]:
     A__				=			comment + F"\nclass {class_name}(nn.Module):\n" + class_code
     if overwrite_result is not None:
         A__				=			comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result
     A__				=			black.Mode(target_versions={black.TargetVersion.PYaa}						,       line_length=1_19							)
     A__				=			black.format_str(lowercase_						,       mode=lowercase_							)
     A__				=			os.path.join(self.transformer_dir						,       "new_code.py"							)
     with open(lowercase_						,       "w"						,       newline="\n"							) as f:
         f.write(lowercase_							)
     if overwrite_result is None:
         self.assertTrue(len(check_copies.is_copy_consistent(lowercase_							)							) == 0							)
     else:
         check_copies.is_copy_consistent(f.name						,       overwrite=lowercase_							)
         with open(lowercase_						,       "r"							) as f:
             self.assertTrue(f.read()						,       lowercase_							)
 def      UpperCAmelCase_							(				self      :str							)->							Optional[Any]:
     A__				=			check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"							)
     self.assertEqual(lowercase_						,       lowercase_							)
 def      UpperCAmelCase_							(				self      :List[str]							)->							Optional[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"						,       lowercase_						,       )

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

     # Copy consistency with a really long name
     A__				=			"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"						,       lowercase_						,       lowercase_							)						,       )

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



 def      UpperCAmelCase_							(				self      :Dict							)->							Any:
     A__				=			check_copies.LOCALIZED_READMES["README_zh-hans.md"]

     A__				=			(
         "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."
     )
     A__				=			(
         "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"
     )
     A__				=			(
         "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"
     )

     A__,	A__				=			check_copies.convert_to_localized_md(
         lowercase_						,       lowercase_						,       localized_readme["format_model_list"]							)

     self.assertFalse(lowercase_							)
     self.assertEqual(lowercase_						,       lowercase_							)

     A__,	A__				=			check_copies.convert_to_localized_md(
         lowercase_						,       lowercase_						,       localized_readme["format_model_list"]							)

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

     A__				=			(
         "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."
     )
     A__				=			(
         "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"
     )
     A__				=			(
         "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"
     )

     A__,	A__				=			check_copies.convert_to_localized_md(
         lowercase_						,       lowercase_						,       localized_readme["format_model_list"]							)

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



