






"""simple docstring"""






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

import black


UpperCAmelCase_   : Optional[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.
UpperCAmelCase_   : List[Any]   			= """    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 							lowerCAmelCase__				(			unittest.TestCase ):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE (						self     :  int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		tempfile.mkdtemp()
          os.makedirs(os.path.join(self.transformer_dir						,	'''models/bert/'''))
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		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 _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		'''src/transformers'''
          shutil.rmtree(self.transformer_dir)
      def _SCREAMING_SNAKE_CASE (						self     :  Dict						,	lowercase_     :  Dict						,	lowercase_     :  str						,	lowercase_     :  int						,	lowercase_     :  Tuple=None):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		comment + F'\nclass {class_name}(nn.Module):\n' + class_code
          if overwrite_result is not None:
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		comment + F'\nclass {class_name}(nn.Module):\n' + overwrite_result
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		black.Mode(target_versions={black.TargetVersion.PYaa}						,	line_length=119)
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		black.format_str(lowercase_						,	mode=lowercase_)
          SCREAMING_SNAKE_CASE_      :  Any    						=		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 _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  str    						=		check_copies.find_code_in_transformers('''models.bert.modeling_bert.BertLMPredictionHead''')
          self.assertEqual(lowercase_						,	lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          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
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		'''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 _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		check_copies.LOCALIZED_READMES['''README_zh-hans.md''']

          SCREAMING_SNAKE_CASE_      :  Any    						=		(
              '''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.'''
          )
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		(
              '''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'''
          )
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		(
              '''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'''
          )

          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  List[str]    						=		check_copies.convert_to_localized_md(
              lowercase_						,	lowercase_						,	localized_readme['''format_model_list'''])

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

          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		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_)

          SCREAMING_SNAKE_CASE_      :  Dict    						=		(
              '''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.'''
          )
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		(
              '''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'''
          )
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		(
              '''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'''
          )

          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  List[str]    						=		check_copies.convert_to_localized_md(
              lowercase_						,	lowercase_						,	localized_readme['''format_model_list'''])

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







'''simple docstring'''





import argparse
import json
from pathlib import Path

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

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__a          =		logging.get_logger(__name__)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase=False		)		-> str:
      snake_case__  :    Union[str, Any]    						=      []
      for i in range(config.num_hidden_layers		):
            # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
            rename_keys.append((f"blocks.{i}.norm1.weight", f"deit.encoder.layer.{i}.layernorm_before.weight")		)
            rename_keys.append((f"blocks.{i}.norm1.bias", f"deit.encoder.layer.{i}.layernorm_before.bias")		)
            rename_keys.append((f"blocks.{i}.attn.proj.weight", f"deit.encoder.layer.{i}.attention.output.dense.weight")		)
            rename_keys.append((f"blocks.{i}.attn.proj.bias", f"deit.encoder.layer.{i}.attention.output.dense.bias")		)
            rename_keys.append((f"blocks.{i}.norm2.weight", f"deit.encoder.layer.{i}.layernorm_after.weight")		)
            rename_keys.append((f"blocks.{i}.norm2.bias", f"deit.encoder.layer.{i}.layernorm_after.bias")		)
            rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"deit.encoder.layer.{i}.intermediate.dense.weight")		)
            rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"deit.encoder.layer.{i}.intermediate.dense.bias")		)
            rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"deit.encoder.layer.{i}.output.dense.weight")		)
            rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"deit.encoder.layer.{i}.output.dense.bias")		)

      # projection layer + position embeddings
      rename_keys.extend(
          [
              ("""cls_token""", """deit.embeddings.cls_token"""),
              ("""dist_token""", """deit.embeddings.distillation_token"""),
              ("""patch_embed.proj.weight""", """deit.embeddings.patch_embeddings.projection.weight"""),
              ("""patch_embed.proj.bias""", """deit.embeddings.patch_embeddings.projection.bias"""),
              ("""pos_embed""", """deit.embeddings.position_embeddings"""),
          ]		)

      if base_model:
            # layernorm + pooler
            rename_keys.extend(
                [
                    ("""norm.weight""", """layernorm.weight"""),
                    ("""norm.bias""", """layernorm.bias"""),
                    ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
                    ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
                ]		)

            # if just the base model, we should remove "deit" from all keys that start with "deit"
            snake_case__  :    List[Any]    						=      [(pair[0], pair[1][4:]) if pair[1].startswith("""deit"""		) else pair for pair in rename_keys]
      else:
            # layernorm + classification heads
            rename_keys.extend(
                [
                    ("""norm.weight""", """deit.layernorm.weight"""),
                    ("""norm.bias""", """deit.layernorm.bias"""),
                    ("""head.weight""", """cls_classifier.weight"""),
                    ("""head.bias""", """cls_classifier.bias"""),
                    ("""head_dist.weight""", """distillation_classifier.weight"""),
                    ("""head_dist.bias""", """distillation_classifier.bias"""),
                ]		)

      return rename_keys





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase=False		)		-> Union[str, Any]:
      for i in range(config.num_hidden_layers		):
            if base_model:
                  snake_case__  :    Tuple    						=      """"""
            else:
                  snake_case__  :    Dict    						=      """deit."""
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            snake_case__  :    Optional[Any]    						=      state_dict.pop(f"blocks.{i}.attn.qkv.weight"		)
            snake_case__  :    Tuple    						=      state_dict.pop(f"blocks.{i}.attn.qkv.bias"		)
            # next, add query, keys and values (in that order) to the state dict
            snake_case__  :    Any    						=      in_proj_weight[
                : config.hidden_size, :
            ]
            snake_case__  :    Optional[int]    						=      in_proj_bias[: config.hidden_size]
            snake_case__  :    Any    						=      in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            snake_case__  :    str    						=      in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            snake_case__  :    List[str]    						=      in_proj_weight[
                -config.hidden_size :, :
            ]
            snake_case__  :    Tuple    						=      in_proj_bias[-config.hidden_size :]





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> int:
      snake_case__  :    str    						=      dct.pop(_lowerCAmelCase		)
      snake_case__  :    Tuple    						=      val





def 						__snake_case(				)		-> Tuple:
      snake_case__  :    Union[str, Any]    						=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
      snake_case__  :    Optional[int]    						=      Image.open(requests.get(_lowerCAmelCase     ,   stream=_lowerCAmelCase		).raw		)
      return im





@torch.no_grad()
def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> str:
      snake_case__  :    Optional[int]    						=      DeiTConfig()
      # all deit models have fine-tuned heads
      snake_case__  :    Union[str, Any]    						=      False
      # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
      snake_case__  :    int    						=      1_000
      snake_case__  :    Any    						=      """huggingface/label-files"""
      snake_case__  :    Optional[Any]    						=      """imagenet-1k-id2label.json"""
      snake_case__  :    Tuple    						=      json.load(open(hf_hub_download(_lowerCAmelCase     ,   _lowerCAmelCase     ,   repo_type="""dataset"""		)     ,   """r"""		)		)
      snake_case__  :    List[Any]    						=      {int(_lowerCAmelCase		): v for k, v in idalabel.items()}
      snake_case__  :    List[Any]    						=      idalabel
      snake_case__  :    List[str]    						=      {v: k for k, v in idalabel.items()}
      snake_case__  :    Tuple    						=      int(deit_name[-6:-4]		)
      snake_case__  :    Optional[Any]    						=      int(deit_name[-3:]		)
      # size of the architecture
      if deit_name[9:].startswith("""tiny"""		):
            snake_case__  :    Tuple    						=      192
            snake_case__  :    Union[str, Any]    						=      768
            snake_case__  :    Tuple    						=      12
            snake_case__  :    Union[str, Any]    						=      3
      elif deit_name[9:].startswith("""small"""		):
            snake_case__  :    str    						=      384
            snake_case__  :    Any    						=      1_536
            snake_case__  :    str    						=      12
            snake_case__  :    int    						=      6
      if deit_name[9:].startswith("""base"""		):
            pass
      elif deit_name[4:].startswith("""large"""		):
            snake_case__  :    Union[str, Any]    						=      1_024
            snake_case__  :    Any    						=      4_096
            snake_case__  :    List[Any]    						=      24
            snake_case__  :    Tuple    						=      16

      # load original model from timm
      snake_case__  :    List[Any]    						=      timm.create_model(_lowerCAmelCase     ,   pretrained=_lowerCAmelCase		)
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      snake_case__  :    Optional[Any]    						=      timm_model.state_dict()
      snake_case__  :    Optional[int]    						=      create_rename_keys(_lowerCAmelCase     ,   _lowerCAmelCase		)
      for src, dest in rename_keys:
            rename_key(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)
      read_in_q_k_v(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)

      # load HuggingFace model
      snake_case__  :    Optional[Any]    						=      DeiTForImageClassificationWithTeacher(_lowerCAmelCase		).eval()
      model.load_state_dict(_lowerCAmelCase		)

      # Check outputs on an image, prepared by DeiTImageProcessor
      snake_case__  :    List[Any]    						=      int(
          (256 / 224) * config.image_size		)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
      snake_case__  :    Optional[Any]    						=      DeiTImageProcessor(size=_lowerCAmelCase     ,   crop_size=config.image_size		)
      snake_case__  :    str    						=      image_processor(images=prepare_img()     ,   return_tensors="""pt"""		)
      snake_case__  :    Optional[Any]    						=      encoding["""pixel_values"""]
      snake_case__  :    Tuple    						=      model(_lowerCAmelCase		)

      snake_case__  :    Optional[int]    						=      timm_model(_lowerCAmelCase		)
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(_lowerCAmelCase     ,   outputs.logits     ,   atol=1e-3		)

      Path(_lowerCAmelCase		).mkdir(exist_ok=_lowerCAmelCase		)
      print(f"Saving model {deit_name} to {pytorch_dump_folder_path}"		)
      model.save_pretrained(_lowerCAmelCase		)
      print(f"Saving image processor to {pytorch_dump_folder_path}"		)
      image_processor.save_pretrained(_lowerCAmelCase		)


if __name__ == "__main__":
       __a          =		argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--deit_name",
           default="vit_deit_base_distilled_patch16_224",
           type=str,
           help="Name of the DeiT 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          =		parser.parse_args()
       convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)
