
import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_A     =  {
    'sample_size': 32,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': 10_00,
    'block_out_channels': [32, 64],
    'attention_head_dim': 8,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'scale_shift',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

_A     =  {
    'sample_size': 64,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 3,
    'num_class_embeds': 10_00,
    'block_out_channels': [1_92, 1_92 * 2, 1_92 * 3, 1_92 * 4],
    'attention_head_dim': 64,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'scale_shift',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

_A     =  {
    'sample_size': 2_56,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': None,
    'block_out_channels': [2_56, 2_56, 2_56 * 2, 2_56 * 2, 2_56 * 4, 2_56 * 4],
    'attention_head_dim': 64,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'ResnetDownsampleBlock2D',
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
        'ResnetUpsampleBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'default',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

_A     =  {
    'num_train_timesteps': 40,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

_A     =  {
    'num_train_timesteps': 2_01,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

_A     =  {
    'num_train_timesteps': 1_51,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}






def      lowerCamelCase__       (       __lowerCAmelCase						:						Dict       ):




			"""simple docstring"""


			if isinstance(A_					,							A_       ):
						return v
			if v.lower() in ("yes", "true", "t", "y", "1"):
						return True
			elif v.lower() in ("no", "false", "f", "n", "0"):
						return False
			else:
						raise argparse.ArgumentTypeError("boolean value expected"       )






def      lowerCamelCase__       (       __lowerCAmelCase						:						str					,							__lowerCAmelCase						:						Optional[Any]					,							__lowerCAmelCase						:						str					,							__lowerCAmelCase						:						List[Any]					,							__lowerCAmelCase						:						Optional[int]=False       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			checkpoint[F"""{old_prefix}.in_layers.0.weight"""]
			lowerCAmelCase_      			=			checkpoint[F"""{old_prefix}.in_layers.0.bias"""]
			lowerCAmelCase_      			=			checkpoint[F"""{old_prefix}.in_layers.2.weight"""]
			lowerCAmelCase_      			=			checkpoint[F"""{old_prefix}.in_layers.2.bias"""]
			lowerCAmelCase_      			=			checkpoint[F"""{old_prefix}.emb_layers.1.weight"""]
			lowerCAmelCase_      			=			checkpoint[F"""{old_prefix}.emb_layers.1.bias"""]
			lowerCAmelCase_      			=			checkpoint[F"""{old_prefix}.out_layers.0.weight"""]
			lowerCAmelCase_      			=			checkpoint[F"""{old_prefix}.out_layers.0.bias"""]
			lowerCAmelCase_      			=			checkpoint[F"""{old_prefix}.out_layers.3.weight"""]
			lowerCAmelCase_      			=			checkpoint[F"""{old_prefix}.out_layers.3.bias"""]

			if has_skip:
						lowerCAmelCase_      			=			checkpoint[F"""{old_prefix}.skip_connection.weight"""]
						lowerCAmelCase_      			=			checkpoint[F"""{old_prefix}.skip_connection.bias"""]

			return new_checkpoint






def      lowerCamelCase__       (       __lowerCAmelCase						:						Optional[int]					,							__lowerCAmelCase						:						List[str]					,							__lowerCAmelCase						:						List[str]					,							__lowerCAmelCase						:						List[str]					,							__lowerCAmelCase						:						List[Any]=None       ):




			"""simple docstring"""


			lowerCAmelCase_			,						lowerCAmelCase_			,						lowerCAmelCase_      			=			checkpoint[F"""{old_prefix}.qkv.weight"""].chunk(3					,							dim=0       )
			lowerCAmelCase_			,						lowerCAmelCase_			,						lowerCAmelCase_      			=			checkpoint[F"""{old_prefix}.qkv.bias"""].chunk(3					,							dim=0       )

			lowerCAmelCase_      			=			checkpoint[F"""{old_prefix}.norm.weight"""]
			lowerCAmelCase_      			=			checkpoint[F"""{old_prefix}.norm.bias"""]

			lowerCAmelCase_      			=			weight_q.squeeze(-1       ).squeeze(-1       )
			lowerCAmelCase_      			=			bias_q.squeeze(-1       ).squeeze(-1       )
			lowerCAmelCase_      			=			weight_k.squeeze(-1       ).squeeze(-1       )
			lowerCAmelCase_      			=			bias_k.squeeze(-1       ).squeeze(-1       )
			lowerCAmelCase_      			=			weight_v.squeeze(-1       ).squeeze(-1       )
			lowerCAmelCase_      			=			bias_v.squeeze(-1       ).squeeze(-1       )

			lowerCAmelCase_      			=			(
			    checkpoint[F"""{old_prefix}.proj_out.weight"""].squeeze(-1       ).squeeze(-1       )
			)
			lowerCAmelCase_      			=			checkpoint[F"""{old_prefix}.proj_out.bias"""].squeeze(-1       ).squeeze(-1       )

			return new_checkpoint






def      lowerCamelCase__       (       __lowerCAmelCase						:						Tuple					,							__lowerCAmelCase						:						List[str]       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			torch.load(A_					,							map_location="cpu"       )
			lowerCAmelCase_      			=			{}

			lowerCAmelCase_      			=			checkpoint["time_embed.0.weight"]
			lowerCAmelCase_      			=			checkpoint["time_embed.0.bias"]
			lowerCAmelCase_      			=			checkpoint["time_embed.2.weight"]
			lowerCAmelCase_      			=			checkpoint["time_embed.2.bias"]

			if unet_config["num_class_embeds"] is not None:
						lowerCAmelCase_      			=			checkpoint["label_emb.weight"]

			lowerCAmelCase_      			=			checkpoint["input_blocks.0.0.weight"]
			lowerCAmelCase_      			=			checkpoint["input_blocks.0.0.bias"]

			lowerCAmelCase_      			=			unet_config["down_block_types"]
			lowerCAmelCase_      			=			unet_config["layers_per_block"]
			lowerCAmelCase_      			=			unet_config["attention_head_dim"]
			lowerCAmelCase_      			=			unet_config["block_out_channels"]
			lowerCAmelCase_      			=			1
			lowerCAmelCase_      			=			channels_list[0]

			for i, layer_type in enumerate(A_       ):
						lowerCAmelCase_      			=			channels_list[i]
						lowerCAmelCase_      			=			current_channels != prev_channels
						if layer_type == "ResnetDownsampleBlock2D":
									for j in range(A_       ):
												lowerCAmelCase_      			=			F"""down_blocks.{i}.resnets.{j}"""
												lowerCAmelCase_      			=			F"""input_blocks.{current_layer}.0"""
												lowerCAmelCase_      			=			True if j == 0 and downsample_block_has_skip else False
												lowerCAmelCase_      			=			convert_resnet(A_					,							A_					,							A_					,							A_					,							has_skip=A_       )
												current_layer += 1

						elif layer_type == "AttnDownBlock2D":
									for j in range(A_       ):
												lowerCAmelCase_      			=			F"""down_blocks.{i}.resnets.{j}"""
												lowerCAmelCase_      			=			F"""input_blocks.{current_layer}.0"""
												lowerCAmelCase_      			=			True if j == 0 and downsample_block_has_skip else False
												lowerCAmelCase_      			=			convert_resnet(A_					,							A_					,							A_					,							A_					,							has_skip=A_       )
												lowerCAmelCase_      			=			F"""down_blocks.{i}.attentions.{j}"""
												lowerCAmelCase_      			=			F"""input_blocks.{current_layer}.1"""
												lowerCAmelCase_      			=			convert_attention(
												    A_					,							A_					,							A_					,							A_					,							A_       )
												current_layer += 1

						if i != len(A_       ) - 1:
									lowerCAmelCase_      			=			F"""down_blocks.{i}.downsamplers.0"""
									lowerCAmelCase_      			=			F"""input_blocks.{current_layer}.0"""
									lowerCAmelCase_      			=			convert_resnet(A_					,							A_					,							A_					,							A_       )
									current_layer += 1

						lowerCAmelCase_      			=			current_channels

			# hardcoded the mid-block for now
			lowerCAmelCase_      			=			"mid_block.resnets.0"
			lowerCAmelCase_      			=			"middle_block.0"
			lowerCAmelCase_      			=			convert_resnet(A_					,							A_					,							A_					,							A_       )
			lowerCAmelCase_      			=			"mid_block.attentions.0"
			lowerCAmelCase_      			=			"middle_block.1"
			lowerCAmelCase_      			=			convert_attention(A_					,							A_					,							A_					,							A_					,							A_       )
			lowerCAmelCase_      			=			"mid_block.resnets.1"
			lowerCAmelCase_      			=			"middle_block.2"
			lowerCAmelCase_      			=			convert_resnet(A_					,							A_					,							A_					,							A_       )

			lowerCAmelCase_      			=			0
			lowerCAmelCase_      			=			unet_config["up_block_types"]

			for i, layer_type in enumerate(A_       ):
						if layer_type == "ResnetUpsampleBlock2D":
									for j in range(layers_per_block + 1       ):
												lowerCAmelCase_      			=			F"""up_blocks.{i}.resnets.{j}"""
												lowerCAmelCase_      			=			F"""output_blocks.{current_layer}.0"""
												lowerCAmelCase_      			=			convert_resnet(A_					,							A_					,							A_					,							A_					,							has_skip=A_       )
												current_layer += 1

									if i != len(A_       ) - 1:
												lowerCAmelCase_      			=			F"""up_blocks.{i}.upsamplers.0"""
												lowerCAmelCase_      			=			F"""output_blocks.{current_layer-1}.1"""
												lowerCAmelCase_      			=			convert_resnet(A_					,							A_					,							A_					,							A_       )
						elif layer_type == "AttnUpBlock2D":
									for j in range(layers_per_block + 1       ):
												lowerCAmelCase_      			=			F"""up_blocks.{i}.resnets.{j}"""
												lowerCAmelCase_      			=			F"""output_blocks.{current_layer}.0"""
												lowerCAmelCase_      			=			convert_resnet(A_					,							A_					,							A_					,							A_					,							has_skip=A_       )
												lowerCAmelCase_      			=			F"""up_blocks.{i}.attentions.{j}"""
												lowerCAmelCase_      			=			F"""output_blocks.{current_layer}.1"""
												lowerCAmelCase_      			=			convert_attention(
												    A_					,							A_					,							A_					,							A_					,							A_       )
												current_layer += 1

									if i != len(A_       ) - 1:
												lowerCAmelCase_      			=			F"""up_blocks.{i}.upsamplers.0"""
												lowerCAmelCase_      			=			F"""output_blocks.{current_layer-1}.2"""
												lowerCAmelCase_      			=			convert_resnet(A_					,							A_					,							A_					,							A_       )

			lowerCAmelCase_      			=			checkpoint["out.0.weight"]
			lowerCAmelCase_      			=			checkpoint["out.0.bias"]
			lowerCAmelCase_      			=			checkpoint["out.2.weight"]
			lowerCAmelCase_      			=			checkpoint["out.2.bias"]

			return new_checkpoint


if __name__ == "__main__":
			_A     =  argparse.ArgumentParser()

			parser.add_argument("--unet_path", default=None, type=str, required=True, help="Path to the unet.pt to convert.")
			parser.add_argument(
			    "--dump_path", default=None, type=str, required=True, help="Path to output the converted UNet model."
			)
			parser.add_argument("--class_cond", default=True, type=str, help="Whether the model is class-conditional.")

			_A     =  parser.parse_args()
			_A     =  strabool(args.class_cond)

			_A     =  os.path.basename(args.unet_path)
			print(f"""Checkpoint: {ckpt_name}""")

			# Get U-Net config
			if "imagenet64" in ckpt_name:
						_A     =  IMAGENET_64_UNET_CONFIG
			elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
						_A     =  LSUN_256_UNET_CONFIG
			elif "test" in ckpt_name:
						_A     =  TEST_UNET_CONFIG
			else:
						raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")

			if not args.class_cond:
						_A     =  None

			_A     =  con_pt_to_diffuser(args.unet_path, unet_config)

			_A     =  UNetaDModel(**unet_config)
			image_unet.load_state_dict(converted_unet_ckpt)

			# Get scheduler config
			if "cd" in ckpt_name or "test" in ckpt_name:
						_A     =  CD_SCHEDULER_CONFIG
			elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
						_A     =  CT_IMAGENET_64_SCHEDULER_CONFIG
			elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
						_A     =  CT_LSUN_256_SCHEDULER_CONFIG
			else:
						raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")

			_A     =  CMStochasticIterativeScheduler(**scheduler_config)

			_A     =  ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
			consistency_model.save_pretrained(args.dump_path)





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

import black


_A     =  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     =  "    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  )       ->   Any:
							lowerCAmelCase_      			=			tempfile.mkdtemp()
							os.makedirs(os.path.join(self.transformer_dir       ,       "models/bert/"  )  )
							lowerCAmelCase_      			=			self.transformer_dir
							shutil.copy(
							    os.path.join(_UpperCamelCase       ,       "src/transformers/models/bert/modeling_bert.py"  )       ,       os.path.join(self.transformer_dir       ,       "models/bert/modeling_bert.py"  )       ,       )




				def   __a		(      self  )       ->   List[Any]:
							lowerCAmelCase_      			=			"src/transformers"
							shutil.rmtree(self.transformer_dir  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase=None  )       ->   Optional[int]:
							lowerCAmelCase_      			=			comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
							if overwrite_result is not None:
										lowerCAmelCase_      			=			comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
							lowerCAmelCase_      			=			black.Mode(target_versions={black.TargetVersion.PYaa}       ,       line_length=119  )
							lowerCAmelCase_      			=			black.format_str(_UpperCamelCase       ,       mode=_UpperCamelCase  )
							lowerCAmelCase_      			=			os.path.join(self.transformer_dir       ,       "new_code.py"  )
							with open(_UpperCamelCase       ,       "w"       ,       newline="\n"  ) as f:
										f.write(_UpperCamelCase  )
							if overwrite_result is None:
										self.assertTrue(len(check_copies.is_copy_consistent(_UpperCamelCase  )  ) == 0  )
							else:
										check_copies.is_copy_consistent(f.name       ,       overwrite=_UpperCamelCase  )
										with open(_UpperCamelCase       ,       "r"  ) as f:
													self.assertTrue(f.read()       ,       _UpperCamelCase  )




				def   __a		(      self  )       ->   Any:
							lowerCAmelCase_      			=			check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"  )
							self.assertEqual(_UpperCamelCase       ,       _UpperCamelCase  )




				def   __a		(      self  )       ->   Any:
							# 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"       ,       _UpperCamelCase       ,       )

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

							# Copy consistency with a really long name
							lowerCAmelCase_      			=			"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"       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ,       )

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




				def   __a		(      self  )       ->   Any:
							lowerCAmelCase_      			=			check_copies.LOCALIZED_READMES["README_zh-hans.md"]

							lowerCAmelCase_      			=			(
							    "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."
							)
							lowerCAmelCase_      			=			(
							    "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"
							)
							lowerCAmelCase_      			=			(
							    "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"
							)

							lowerCAmelCase_			,						lowerCAmelCase_      			=			check_copies.convert_to_localized_md(
							    _UpperCamelCase       ,       _UpperCamelCase       ,       localized_readme["format_model_list"]  )

							self.assertFalse(_UpperCamelCase  )
							self.assertEqual(_UpperCamelCase       ,       _UpperCamelCase  )

							lowerCAmelCase_			,						lowerCAmelCase_      			=			check_copies.convert_to_localized_md(
							    _UpperCamelCase       ,       _UpperCamelCase       ,       localized_readme["format_model_list"]  )

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

							lowerCAmelCase_      			=			(
							    "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."
							)
							lowerCAmelCase_      			=			(
							    "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"
							)
							lowerCAmelCase_      			=			(
							    "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"
							)

							lowerCAmelCase_			,						lowerCAmelCase_      			=			check_copies.convert_to_localized_md(
							    _UpperCamelCase       ,       _UpperCamelCase       ,       localized_readme["format_model_list"]  )

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



