





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

import black


a__      :     List[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.
a__      :     Any         =		'    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 ):



       """simple docstring"""





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


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       '''src/transformers'''
       shutil.rmtree(self.transformer_dir )


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case ,__snake_case=None ):



       """simple docstring"""





       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_1_9 )
       A_          =       black.format_str(__snake_case ,mode=__snake_case )
       A_          =       os.path.join(self.transformer_dir ,'''new_code.py''' )
       with open(__snake_case ,'''w''' ,newline='''\n''' ) as f:
            f.write(__snake_case )
       if overwrite_result is None:
            self.assertTrue(len(check_copies.is_copy_consistent(__snake_case ) ) == 0 )
       else:
            check_copies.is_copy_consistent(f.name ,overwrite=__snake_case )
            with open(__snake_case ,'''r''' ) as f:
                 self.assertTrue(f.read() ,__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       check_copies.find_code_in_transformers('''models.bert.modeling_bert.BertLMPredictionHead''' )
       self.assertEqual(__snake_case ,__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """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''' ,__snake_case ,)

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

       # 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''' ,__snake_case ,__snake_case ) ,)

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




  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       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(
           __snake_case ,__snake_case ,localized_readme['''format_model_list'''] )

       self.assertFalse(__snake_case )
       self.assertEqual(__snake_case ,__snake_case )

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

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

       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(
           __snake_case ,__snake_case ,localized_readme['''format_model_list'''] )

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










import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
a__      :     Any         =		logging.get_logger(__name__)

def      UpperCAmelCase_    (    _UpperCAmelCase							:List[Any]				)	->				Optional[int]:
     '''simple docstring'''


     A_          =       OrderedDict()
     for key, value in state_dict.items():
          if key.startswith('''module.encoder'''				):
               A_          =       key.replace('''module.encoder'''  ,						'''glpn.encoder'''				)
          if key.startswith('''module.decoder'''				):
               A_          =       key.replace('''module.decoder'''  ,						'''decoder.stages'''				)
          if "patch_embed" in key:
               # replace for example patch_embed1 by patch_embeddings.0
               A_          =       key[key.find('''patch_embed'''				) + len('''patch_embed'''				)]
               A_          =       key.replace(f'patch_embed{idx}'  ,						f'patch_embeddings.{int(_UpperCAmelCase				)-1}'				)
          if "norm" in key:
               A_          =       key.replace('''norm'''  ,						'''layer_norm'''				)
          if "glpn.encoder.layer_norm" in key:
               # replace for example layer_norm1 by layer_norm.0
               A_          =       key[key.find('''glpn.encoder.layer_norm'''				) + len('''glpn.encoder.layer_norm'''				)]
               A_          =       key.replace(f'layer_norm{idx}'  ,						f'layer_norm.{int(_UpperCAmelCase				)-1}'				)
          if "layer_norm1" in key:
               A_          =       key.replace('''layer_norm1'''  ,						'''layer_norm_1'''				)
          if "layer_norm2" in key:
               A_          =       key.replace('''layer_norm2'''  ,						'''layer_norm_2'''				)
          if "block" in key:
               # replace for example block1 by block.0
               A_          =       key[key.find('''block'''				) + len('''block'''				)]
               A_          =       key.replace(f'block{idx}'  ,						f'block.{int(_UpperCAmelCase				)-1}'				)
          if "attn.q" in key:
               A_          =       key.replace('''attn.q'''  ,						'''attention.self.query'''				)
          if "attn.proj" in key:
               A_          =       key.replace('''attn.proj'''  ,						'''attention.output.dense'''				)
          if "attn" in key:
               A_          =       key.replace('''attn'''  ,						'''attention.self'''				)
          if "fc1" in key:
               A_          =       key.replace('''fc1'''  ,						'''dense1'''				)
          if "fc2" in key:
               A_          =       key.replace('''fc2'''  ,						'''dense2'''				)
          if "linear_pred" in key:
               A_          =       key.replace('''linear_pred'''  ,						'''classifier'''				)
          if "linear_fuse" in key:
               A_          =       key.replace('''linear_fuse.conv'''  ,						'''linear_fuse'''				)
               A_          =       key.replace('''linear_fuse.bn'''  ,						'''batch_norm'''				)
          if "linear_c" in key:
               # replace for example linear_c4 by linear_c.3
               A_          =       key[key.find('''linear_c'''				) + len('''linear_c'''				)]
               A_          =       key.replace(f'linear_c{idx}'  ,						f'linear_c.{int(_UpperCAmelCase				)-1}'				)
          if "bot_conv" in key:
               A_          =       key.replace('''bot_conv'''  ,						'''0.convolution'''				)
          if "skip_conv1" in key:
               A_          =       key.replace('''skip_conv1'''  ,						'''1.convolution'''				)
          if "skip_conv2" in key:
               A_          =       key.replace('''skip_conv2'''  ,						'''2.convolution'''				)
          if "fusion1" in key:
               A_          =       key.replace('''fusion1'''  ,						'''1.fusion'''				)
          if "fusion2" in key:
               A_          =       key.replace('''fusion2'''  ,						'''2.fusion'''				)
          if "fusion3" in key:
               A_          =       key.replace('''fusion3'''  ,						'''3.fusion'''				)
          if "fusion" in key and "conv" in key:
               A_          =       key.replace('''conv'''  ,						'''convolutional_layer'''				)
          if key.startswith('''module.last_layer_depth'''				):
               A_          =       key.replace('''module.last_layer_depth'''  ,						'''head.head'''				)
          A_          =       value

     return new_state_dict

def      UpperCAmelCase_    (    _UpperCAmelCase							:List[Any]  ,						_UpperCAmelCase							:Union[str, Any]				)	->				Tuple:
     '''simple docstring'''


     for i in range(config.num_encoder_blocks				):
          for j in range(config.depths[i]				):
               # read in weights + bias of keys and values (which is a single matrix in the original implementation)
               A_          =       state_dict.pop(f'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'				)
               A_          =       state_dict.pop(f'glpn.encoder.block.{i}.{j}.attention.self.kv.bias'				)
               # next, add keys and values (in that order) to the state dict
               A_          =       kv_weight[
                   : config.hidden_sizes[i], :
               ]
               A_          =       kv_bias[: config.hidden_sizes[i]]
               A_          =       kv_weight[
                   config.hidden_sizes[i] :, :
               ]
               A_          =       kv_bias[config.hidden_sizes[i] :]

def      UpperCAmelCase_    (    )	->				str:
     '''simple docstring'''


     A_          =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     A_          =       Image.open(requests.get(_UpperCAmelCase  ,						stream=_UpperCAmelCase				).raw				)

     return image

@torch.no_grad()
def      UpperCAmelCase_    (    _UpperCAmelCase							:Tuple  ,						_UpperCAmelCase							:List[Any]  ,						_UpperCAmelCase							:Union[str, Any]=False  ,						_UpperCAmelCase							:Any=None				)	->				Optional[int]:
     '''simple docstring'''


     A_          =       GLPNConfig(hidden_sizes=[64, 128, 320, 512]  ,						decoder_hidden_size=64  ,						depths=[3, 8, 27, 3]				)

     # load image processor (only resize + rescale)
     A_          =       GLPNImageProcessor()

     # prepare image
     A_          =       prepare_img()
     A_          =       image_processor(images=_UpperCAmelCase  ,						return_tensors='''pt'''				).pixel_values

     logger.info('''Converting model...'''				)

     # load original state dict
     A_          =       torch.load(_UpperCAmelCase  ,						map_location=torch.device('''cpu'''				)				)

     # rename keys
     A_          =       rename_keys(_UpperCAmelCase				)

     # key and value matrices need special treatment
     read_in_k_v(_UpperCAmelCase  ,						_UpperCAmelCase				)

     # create HuggingFace model and load state dict
     A_          =       GLPNForDepthEstimation(_UpperCAmelCase				)
     model.load_state_dict(_UpperCAmelCase				)
     model.eval()

     # forward pass
     A_          =       model(_UpperCAmelCase				)
     A_          =       outputs.predicted_depth

     # verify output
     if model_name is not None:
          if "nyu" in model_name:
               A_          =       torch.tensor(
                   [[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]]				)
          elif "kitti" in model_name:
               A_          =       torch.tensor(
                   [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]				)
          else:
               raise ValueError(f'Unknown model name: {model_name}'				)

          A_          =       torch.Size([1, 480, 640]				)

          assert predicted_depth.shape == expected_shape
          assert torch.allclose(predicted_depth[0, :3, :3]  ,						_UpperCAmelCase  ,						atol=1e-4				)
          print('''Looks ok!'''				)

     # finally, push to hub if required
     if push_to_hub:
          logger.info('''Pushing model and image processor to the hub...'''				)
          model.push_to_hub(
              repo_path_or_name=Path(_UpperCAmelCase  ,						_UpperCAmelCase				)  ,						organization='''nielsr'''  ,						commit_message='''Add model'''  ,						use_temp_dir=_UpperCAmelCase  ,						)
          image_processor.push_to_hub(
              repo_path_or_name=Path(_UpperCAmelCase  ,						_UpperCAmelCase				)  ,						organization='''nielsr'''  ,						commit_message='''Add image processor'''  ,						use_temp_dir=_UpperCAmelCase  ,						)


if __name__ == "__main__":
      a__      :     Dict         =		argparse.ArgumentParser()

      parser.add_argument(
          '--checkpoint_path',
          default=None,
          type=str,
          help='Path to the original PyTorch checkpoint (.pth file).',
      )
      parser.add_argument(
          '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
      )
      parser.add_argument(
          '--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
      )
      parser.add_argument(
          '--model_name',
          default='glpn-kitti',
          type=str,
          help='Name of the model in case you\'re pushing to the hub.',
      )
      a__      :     int         =		parser.parse_args()
      convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)



