




import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

lowerCamelCase__					:  Dict      							=				[
    # (stable-diffusion, HF Diffusers)
    ('time_embed.0.weight', 'time_embedding.linear_1.weight'),
    ('time_embed.0.bias', 'time_embedding.linear_1.bias'),
    ('time_embed.2.weight', 'time_embedding.linear_2.weight'),
    ('time_embed.2.bias', 'time_embedding.linear_2.bias'),
    ('input_blocks.0.0.weight', 'conv_in.weight'),
    ('input_blocks.0.0.bias', 'conv_in.bias'),
    ('out.0.weight', 'conv_norm_out.weight'),
    ('out.0.bias', 'conv_norm_out.bias'),
    ('out.2.weight', 'conv_out.weight'),
    ('out.2.bias', 'conv_out.bias'),
]

lowerCamelCase__					:  Any      							=				[
    # (stable-diffusion, HF Diffusers)
    ('in_layers.0', 'norm1'),
    ('in_layers.2', 'conv1'),
    ('out_layers.0', 'norm2'),
    ('out_layers.3', 'conv2'),
    ('emb_layers.1', 'time_emb_proj'),
    ('skip_connection', 'conv_shortcut'),
]

lowerCamelCase__					:  int      							=				[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
   # loop over downblocks/upblocks

   for j in range(2):
      # loop over resnets/attentions for downblocks
      lowerCamelCase__					:  str      							=				f'''down_blocks.{i}.resnets.{j}.'''
      lowerCamelCase__					:  List[Any]      							=				f'''input_blocks.{3*i + j + 1}.0.'''
      unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

      if i < 3:
         # no attention layers in down_blocks.3
         lowerCamelCase__					:  Optional[Any]      							=				f'''down_blocks.{i}.attentions.{j}.'''
         lowerCamelCase__					:  str      							=				f'''input_blocks.{3*i + j + 1}.1.'''
         unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

   for j in range(3):
      # loop over resnets/attentions for upblocks
      lowerCamelCase__					:  List[Any]      							=				f'''up_blocks.{i}.resnets.{j}.'''
      lowerCamelCase__					:  List[Any]      							=				f'''output_blocks.{3*i + j}.0.'''
      unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

      if i > 0:
         # no attention layers in up_blocks.0
         lowerCamelCase__					:  Tuple      							=				f'''up_blocks.{i}.attentions.{j}.'''
         lowerCamelCase__					:  Dict      							=				f'''output_blocks.{3*i + j}.1.'''
         unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

   if i < 3:
      # no downsample in down_blocks.3
      lowerCamelCase__					:  Union[str, Any]      							=				f'''down_blocks.{i}.downsamplers.0.conv.'''
      lowerCamelCase__					:  Union[str, Any]      							=				f'''input_blocks.{3*(i+1)}.0.op.'''
      unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

      # no upsample in up_blocks.3
      lowerCamelCase__					:  Optional[int]      							=				f'''up_blocks.{i}.upsamplers.0.'''
      lowerCamelCase__					:  List[Any]      							=				f'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
      unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

lowerCamelCase__					:  Dict      							=				'mid_block.attentions.0.'
lowerCamelCase__					:  Any      							=				'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
   lowerCamelCase__					:  Union[str, Any]      							=				f'''mid_block.resnets.{j}.'''
   lowerCamelCase__					:  int      							=				f'''middle_block.{2*j}.'''
   unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))

def    UpperCAmelCase_				( __UpperCAmelCase		:      Tuple				)   ->					int:
      # buyer beware: this is a *brittle* function,
      # and correct output requires that all of these pieces interact in
      # the exact order in which I have arranged them.
      SCREAMING_SNAKE_CASE_      		=     {k: k for k in unet_state_dict.keys()}
      for sd_name, hf_name in unet_conversion_map:
            SCREAMING_SNAKE_CASE_      		=     sd_name
      for k, v in mapping.items():
            if "resnets" in k:
                  for sd_part, hf_part in unet_conversion_map_resnet:
                        SCREAMING_SNAKE_CASE_      		=     v.replace(__UpperCAmelCase     ,     __UpperCAmelCase				)
                  SCREAMING_SNAKE_CASE_      		=     v
      for k, v in mapping.items():
            for sd_part, hf_part in unet_conversion_map_layer:
                  SCREAMING_SNAKE_CASE_      		=     v.replace(__UpperCAmelCase     ,     __UpperCAmelCase				)
            SCREAMING_SNAKE_CASE_      		=     v
      SCREAMING_SNAKE_CASE_      		=     {v: unet_state_dict[k] for k, v in mapping.items()}
      return new_state_dict


# ================#
# VAE Conversion #
# ================#

lowerCamelCase__					:  str      							=				[
    # (stable-diffusion, HF Diffusers)
    ('nin_shortcut', 'conv_shortcut'),
    ('norm_out', 'conv_norm_out'),
    ('mid.attn_1.', 'mid_block.attentions.0.'),
]

for i in range(4):
   # down_blocks have two resnets
   for j in range(2):
      lowerCamelCase__					:  Union[str, Any]      							=				f'''encoder.down_blocks.{i}.resnets.{j}.'''
      lowerCamelCase__					:  Union[str, Any]      							=				f'''encoder.down.{i}.block.{j}.'''
      vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

   if i < 3:
      lowerCamelCase__					:  int      							=				f'''down_blocks.{i}.downsamplers.0.'''
      lowerCamelCase__					:  List[Any]      							=				f'''down.{i}.downsample.'''
      vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

      lowerCamelCase__					:  List[Any]      							=				f'''up_blocks.{i}.upsamplers.0.'''
      lowerCamelCase__					:  int      							=				f'''up.{3-i}.upsample.'''
      vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

   # up_blocks have three resnets
   # also, up blocks in hf are numbered in reverse from sd
   for j in range(3):
      lowerCamelCase__					:  Dict      							=				f'''decoder.up_blocks.{i}.resnets.{j}.'''
      lowerCamelCase__					:  Any      							=				f'''decoder.up.{3-i}.block.{j}.'''
      vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
   lowerCamelCase__					:  List[str]      							=				f'''mid_block.resnets.{i}.'''
   lowerCamelCase__					:  str      							=				f'''mid.block_{i+1}.'''
   vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


lowerCamelCase__					:  str      							=				[
    # (stable-diffusion, HF Diffusers)
    ('norm.', 'group_norm.'),
    ('q.', 'query.'),
    ('k.', 'key.'),
    ('v.', 'value.'),
    ('proj_out.', 'proj_attn.'),
]

def    UpperCAmelCase_				( __UpperCAmelCase		:      Tuple				)   ->					List[str]:
      # convert HF linear weights to SD conv2d weights
      return w.reshape(*w.shape     ,     1     ,     1				)

def    UpperCAmelCase_				( __UpperCAmelCase		:      List[str]				)   ->					int:
      SCREAMING_SNAKE_CASE_      		=     {k: k for k in vae_state_dict.keys()}
      for k, v in mapping.items():
            for sd_part, hf_part in vae_conversion_map:
                  SCREAMING_SNAKE_CASE_      		=     v.replace(__UpperCAmelCase     ,     __UpperCAmelCase				)
            SCREAMING_SNAKE_CASE_      		=     v
      for k, v in mapping.items():
            if "attentions" in k:
                  for sd_part, hf_part in vae_conversion_map_attn:
                        SCREAMING_SNAKE_CASE_      		=     v.replace(__UpperCAmelCase     ,     __UpperCAmelCase				)
                  SCREAMING_SNAKE_CASE_      		=     v
      SCREAMING_SNAKE_CASE_      		=     {v: vae_state_dict[k] for k, v in mapping.items()}
      SCREAMING_SNAKE_CASE_      		=     ['q', 'k', 'v', 'proj_out']
      for k, v in new_state_dict.items():
            for weight_name in weights_to_convert:
                  if f"mid.attn_1.{weight_name}.weight" in k:
                        print(f"Reshaping {k} for SD format"				)
                        SCREAMING_SNAKE_CASE_      		=     reshape_weight_for_sd(__UpperCAmelCase				)
      return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


lowerCamelCase__					:  str      							=				[
    # (stable-diffusion, HF Diffusers)
    ('resblocks.', 'text_model.encoder.layers.'),
    ('ln_1', 'layer_norm1'),
    ('ln_2', 'layer_norm2'),
    ('.c_fc.', '.fc1.'),
    ('.c_proj.', '.fc2.'),
    ('.attn', '.self_attn'),
    ('ln_final.', 'transformer.text_model.final_layer_norm.'),
    ('token_embedding.weight', 'transformer.text_model.embeddings.token_embedding.weight'),
    ('positional_embedding', 'transformer.text_model.embeddings.position_embedding.weight'),
]
lowerCamelCase__					:  Tuple      							=				{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
lowerCamelCase__					:  str      							=				re.compile('|'.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
lowerCamelCase__					:  Optional[int]      							=				{'q': 0, 'k': 1, 'v': 2}

def    UpperCAmelCase_				( __UpperCAmelCase		:      List[str]				)   ->					Union[str, Any]:
      SCREAMING_SNAKE_CASE_      		=     {}
      SCREAMING_SNAKE_CASE_      		=     {}
      SCREAMING_SNAKE_CASE_      		=     {}
      for k, v in text_enc_dict.items():
            if (
                k.endswith('.self_attn.q_proj.weight'				)
                or k.endswith('.self_attn.k_proj.weight'				)
                or k.endswith('.self_attn.v_proj.weight'				)
            ):
                  SCREAMING_SNAKE_CASE_      		=     k[: -len('.q_proj.weight'				)]
                  SCREAMING_SNAKE_CASE_      		=     k[-len('q_proj.weight'				)]
                  if k_pre not in capture_qkv_weight:
                        SCREAMING_SNAKE_CASE_      		=     [None, None, None]
                  SCREAMING_SNAKE_CASE_      		=     v
                  continue

            if (
                k.endswith('.self_attn.q_proj.bias'				)
                or k.endswith('.self_attn.k_proj.bias'				)
                or k.endswith('.self_attn.v_proj.bias'				)
            ):
                  SCREAMING_SNAKE_CASE_      		=     k[: -len('.q_proj.bias'				)]
                  SCREAMING_SNAKE_CASE_      		=     k[-len('q_proj.bias'				)]
                  if k_pre not in capture_qkv_bias:
                        SCREAMING_SNAKE_CASE_      		=     [None, None, None]
                  SCREAMING_SNAKE_CASE_      		=     v
                  continue

            SCREAMING_SNAKE_CASE_      		=     textenc_pattern.sub(lambda __UpperCAmelCase				: protected[re.escape(m.group(0				)				)]     ,     __UpperCAmelCase				)
            SCREAMING_SNAKE_CASE_      		=     v

      for k_pre, tensors in capture_qkv_weight.items():
            if None in tensors:
                  raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'				)
            SCREAMING_SNAKE_CASE_      		=     textenc_pattern.sub(lambda __UpperCAmelCase				: protected[re.escape(m.group(0				)				)]     ,     __UpperCAmelCase				)
            SCREAMING_SNAKE_CASE_      		=     torch.cat(__UpperCAmelCase				)

      for k_pre, tensors in capture_qkv_bias.items():
            if None in tensors:
                  raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'				)
            SCREAMING_SNAKE_CASE_      		=     textenc_pattern.sub(lambda __UpperCAmelCase				: protected[re.escape(m.group(0				)				)]     ,     __UpperCAmelCase				)
            SCREAMING_SNAKE_CASE_      		=     torch.cat(__UpperCAmelCase				)

      return new_state_dict

def    UpperCAmelCase_				( __UpperCAmelCase		:      Optional[int]				)   ->					Optional[Any]:
      return text_enc_dict


if __name__ == "__main__":
   lowerCamelCase__					:  int      							=				argparse.ArgumentParser()

   parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
   parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
   parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
   parser.add_argument(
       '--use_safetensors', action='store_true', help='Save weights use safetensors, default is ckpt.'
   )

   lowerCamelCase__					:  Any      							=				parser.parse_args()

   assert args.model_path is not None, "Must provide a model path!"

   assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

   # Path for safetensors
   lowerCamelCase__					:  Union[str, Any]      							=				osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
   lowerCamelCase__					:  Dict      							=				osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
   lowerCamelCase__					:  List[str]      							=				osp.join(args.model_path, 'text_encoder', 'model.safetensors')

   # Load models from safetensors if it exists, if it doesn't pytorch
   if osp.exists(unet_path):
      lowerCamelCase__					:  Any      							=				load_file(unet_path, device='cpu')
   else:
      lowerCamelCase__					:  List[str]      							=				osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
      lowerCamelCase__					:  Any      							=				torch.load(unet_path, map_location='cpu')

   if osp.exists(vae_path):
      lowerCamelCase__					:  Tuple      							=				load_file(vae_path, device='cpu')
   else:
      lowerCamelCase__					:  Any      							=				osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
      lowerCamelCase__					:  str      							=				torch.load(vae_path, map_location='cpu')

   if osp.exists(text_enc_path):
      lowerCamelCase__					:  List[str]      							=				load_file(text_enc_path, device='cpu')
   else:
      lowerCamelCase__					:  Optional[Any]      							=				osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
      lowerCamelCase__					:  int      							=				torch.load(text_enc_path, map_location='cpu')

   # Convert the UNet model
   lowerCamelCase__					:  List[str]      							=				convert_unet_state_dict(unet_state_dict)
   lowerCamelCase__					:  Union[str, Any]      							=				{'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}

   # Convert the VAE model
   lowerCamelCase__					:  List[Any]      							=				convert_vae_state_dict(vae_state_dict)
   lowerCamelCase__					:  int      							=				{'first_stage_model.' + k: v for k, v in vae_state_dict.items()}

   # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
   lowerCamelCase__					:  str      							=				'text_model.encoder.layers.22.layer_norm2.bias' in text_enc_dict

   if is_vaa_model:
      # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
      lowerCamelCase__					:  int      							=				{'transformer.' + k: v for k, v in text_enc_dict.items()}
      lowerCamelCase__					:  Union[str, Any]      							=				convert_text_enc_state_dict_vaa(text_enc_dict)
      lowerCamelCase__					:  Any      							=				{'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
   else:
      lowerCamelCase__					:  List[str]      							=				convert_text_enc_state_dict(text_enc_dict)
      lowerCamelCase__					:  Any      							=				{'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}

   # Put together new checkpoint
   lowerCamelCase__					:  Union[str, Any]      							=				{**unet_state_dict, **vae_state_dict, **text_enc_dict}
   if args.half:
      lowerCamelCase__					:  Dict      							=				{k: v.half() for k, v in state_dict.items()}

   if args.use_safetensors:
      save_file(state_dict, args.checkpoint_path)
   else:
      lowerCamelCase__					:  Optional[int]      							=				{'state_dict': state_dict}
      torch.save(state_dict, args.checkpoint_path)


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):\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 					__lowerCAmelCase	(   unittest.TestCase				):




  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=tempfile.mkdtemp()
        os.makedirs(os.path.join(self.transformer_dir				,  'models/bert/'   )   )
        _lowercase      			=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     A__	(							self   )    ->					Optional[int]:
        '''simple docstring'''






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




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase=None   )    ->					str:
        '''simple docstring'''






        _lowercase      			=comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code
        if overwrite_result is not None:
              _lowercase      			=comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
        _lowercase      			=black.Mode(target_versions={black.TargetVersion.PYaa}				,  line_length=119   )
        _lowercase      			=black.format_str(lowerCAmelCase				,  mode=lowerCAmelCase   )
        _lowercase      			=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     A__	(							self   )    ->					Dict:
        '''simple docstring'''






        _lowercase      			=check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'   )
        self.assertEqual(lowerCAmelCase				,  lowerCAmelCase   )




  def     A__	(							self   )    ->					str:
        '''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'				,  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
        _lowercase      			='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     A__	(							self   )    ->					List[str]:
        '''simple docstring'''






        _lowercase      			=check_copies.LOCALIZED_READMES['README_zh-hans.md']

        _lowercase      			=(
            '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.'
        )
        _lowercase      			=(
            '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'
        )
        _lowercase      			=(
            '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'
        )

        _lowercase			,			_lowercase      			=check_copies.convert_to_localized_md(
            lowerCAmelCase				,  lowerCAmelCase				,  localized_readme['format_model_list']   )

        self.assertFalse(lowerCAmelCase   )
        self.assertEqual(lowerCAmelCase				,  lowerCAmelCase   )

        _lowercase			,			_lowercase      			=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   )

        _lowercase      			=(
            '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.'
        )
        _lowercase      			=(
            '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'
        )
        _lowercase      			=(
            '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'
        )

        _lowercase			,			_lowercase      			=check_copies.convert_to_localized_md(
            lowerCAmelCase				,  lowerCAmelCase				,  localized_readme['format_model_list']   )

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


