



'''simple docstring'''

import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_						)    ->   Tuple:
		_a  :   Union[str, Any]				 =							UNetaDModel(**lowerCAmelCase_						)
		_a  :   Tuple				 =							checkpoint['all_modules.0.W'].data
		_a  :   Tuple				 =							checkpoint['all_modules.0.W'].data
		_a  :   Optional[int]				 =							checkpoint['all_modules.1.weight'].data
		_a  :   str				 =							checkpoint['all_modules.1.bias'].data

		_a  :   Union[str, Any]				 =							checkpoint['all_modules.2.weight'].data
		_a  :   Dict				 =							checkpoint['all_modules.2.bias'].data

		_a  :   Any				 =							checkpoint['all_modules.3.weight'].data
		_a  :   int				 =							checkpoint['all_modules.3.bias'].data

		_a  :   Optional[Any]				 =							checkpoint[list(checkpoint.keys()						)[-4]].data
		_a  :   Tuple				 =							checkpoint[list(checkpoint.keys()						)[-3]].data
		_a  :   Union[str, Any]				 =							checkpoint[list(checkpoint.keys()						)[-2]].data
		_a  :   Dict				 =							checkpoint[list(checkpoint.keys()						)[-1]].data

		_a  :   List[str]				 =							4

		def set_attention_weights(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_						):
				_a  :   int				 =							old_checkpoint[f"""all_modules.{index}.NIN_0.W"""].data.T
				_a  :   Optional[int]				 =							old_checkpoint[f"""all_modules.{index}.NIN_1.W"""].data.T
				_a  :   Optional[Any]				 =							old_checkpoint[f"""all_modules.{index}.NIN_2.W"""].data.T

				_a  :   List[str]				 =							old_checkpoint[f"""all_modules.{index}.NIN_0.b"""].data
				_a  :   int				 =							old_checkpoint[f"""all_modules.{index}.NIN_1.b"""].data
				_a  :   Optional[int]				 =							old_checkpoint[f"""all_modules.{index}.NIN_2.b"""].data

				_a  :   int				 =							old_checkpoint[f"""all_modules.{index}.NIN_3.W"""].data.T
				_a  :   List[str]				 =							old_checkpoint[f"""all_modules.{index}.NIN_3.b"""].data

				_a  :   Any				 =							old_checkpoint[f"""all_modules.{index}.GroupNorm_0.weight"""].data
				_a  :   Optional[int]				 =							old_checkpoint[f"""all_modules.{index}.GroupNorm_0.bias"""].data

		def set_resnet_weights(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_						):
				_a  :   Any				 =							old_checkpoint[f"""all_modules.{index}.Conv_0.weight"""].data
				_a  :   List[str]				 =							old_checkpoint[f"""all_modules.{index}.Conv_0.bias"""].data
				_a  :   str				 =							old_checkpoint[f"""all_modules.{index}.GroupNorm_0.weight"""].data
				_a  :   List[str]				 =							old_checkpoint[f"""all_modules.{index}.GroupNorm_0.bias"""].data

				_a  :   Optional[Any]				 =							old_checkpoint[f"""all_modules.{index}.Conv_1.weight"""].data
				_a  :   Tuple				 =							old_checkpoint[f"""all_modules.{index}.Conv_1.bias"""].data
				_a  :   Optional[Any]				 =							old_checkpoint[f"""all_modules.{index}.GroupNorm_1.weight"""].data
				_a  :   Union[str, Any]				 =							old_checkpoint[f"""all_modules.{index}.GroupNorm_1.bias"""].data

				_a  :   int				 =							old_checkpoint[f"""all_modules.{index}.Dense_0.weight"""].data
				_a  :   Union[str, Any]				 =							old_checkpoint[f"""all_modules.{index}.Dense_0.bias"""].data

				if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
						_a  :   List[Any]				 =							old_checkpoint[f"""all_modules.{index}.Conv_2.weight"""].data
						_a  :   Optional[Any]				 =							old_checkpoint[f"""all_modules.{index}.Conv_2.bias"""].data

		for i, block in enumerate(new_model_architecture.downsample_blocks						):
				_a  :   str				 =							hasattr(lowerCAmelCase_		,    'attentions'						)
				for j in range(len(block.resnets						)						):
						set_resnet_weights(block.resnets[j]		,    lowerCAmelCase_		,    lowerCAmelCase_						)
						module_index += 1
						if has_attentions:
								set_attention_weights(block.attentions[j]		,    lowerCAmelCase_		,    lowerCAmelCase_						)
								module_index += 1

				if hasattr(lowerCAmelCase_		,    'downsamplers'						) and block.downsamplers is not None:
						set_resnet_weights(block.resnet_down		,    lowerCAmelCase_		,    lowerCAmelCase_						)
						module_index += 1
						_a  :   int				 =							checkpoint[f"""all_modules.{module_index}.Conv_0.weight"""].data
						_a  :   Tuple				 =							checkpoint[f"""all_modules.{module_index}.Conv_0.bias"""].data
						module_index += 1

		set_resnet_weights(new_model_architecture.mid_block.resnets[0]		,    lowerCAmelCase_		,    lowerCAmelCase_						)
		module_index += 1
		set_attention_weights(new_model_architecture.mid_block.attentions[0]		,    lowerCAmelCase_		,    lowerCAmelCase_						)
		module_index += 1
		set_resnet_weights(new_model_architecture.mid_block.resnets[1]		,    lowerCAmelCase_		,    lowerCAmelCase_						)
		module_index += 1

		for i, block in enumerate(new_model_architecture.up_blocks						):
				_a  :   Optional[Any]				 =							hasattr(lowerCAmelCase_		,    'attentions'						)
				for j in range(len(block.resnets						)						):
						set_resnet_weights(block.resnets[j]		,    lowerCAmelCase_		,    lowerCAmelCase_						)
						module_index += 1
				if has_attentions:
						set_attention_weights(
						    block.attentions[0]		,    lowerCAmelCase_		,    lowerCAmelCase_						)  # why can there only be a single attention layer for up?
						module_index += 1

				if hasattr(lowerCAmelCase_		,    'resnet_up'						) and block.resnet_up is not None:
						_a  :   Dict				 =							checkpoint[f"""all_modules.{module_index}.weight"""].data
						_a  :   Optional[int]				 =							checkpoint[f"""all_modules.{module_index}.bias"""].data
						module_index += 1
						_a  :   Optional[int]				 =							checkpoint[f"""all_modules.{module_index}.weight"""].data
						_a  :   Any				 =							checkpoint[f"""all_modules.{module_index}.bias"""].data
						module_index += 1
						set_resnet_weights(block.resnet_up		,    lowerCAmelCase_		,    lowerCAmelCase_						)
						module_index += 1

		_a  :   List[str]				 =							checkpoint[f"""all_modules.{module_index}.weight"""].data
		_a  :   str				 =							checkpoint[f"""all_modules.{module_index}.bias"""].data
		module_index += 1
		_a  :   Union[str, Any]				 =							checkpoint[f"""all_modules.{module_index}.weight"""].data
		_a  :   Union[str, Any]				 =							checkpoint[f"""all_modules.{module_index}.bias"""].data

		return new_model_architecture.state_dict()


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

							parser.add_argument(
							    '''--checkpoint_path''',
							    default='''/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin''',
							    type=str,
							    required=False,
							    help='''Path to the checkpoint to convert.''',
							)

							parser.add_argument(
							    '''--config_file''',
							    default='''/Users/arthurzucker/Work/diffusers/ArthurZ/config.json''',
							    type=str,
							    required=False,
							    help='''The config json file corresponding to the architecture.''',
							)

							parser.add_argument(
							    '''--dump_path''',
							    default='''/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt''',
							    type=str,
							    required=False,
							    help='''Path to the output model.''',
							)

							__lowerCAmelCase		    =     parser.parse_args()

							__lowerCAmelCase		    =     torch.load(args.checkpoint_path, map_location='''cpu''')

							with open(args.config_file) as f:
														__lowerCAmelCase		    =     json.loads(f.read())

							__lowerCAmelCase		    =     convert_ncsnpp_checkpoint(
							    checkpoint,
							    config,
							)

							if "sde" in config:
														del config["sde"]

							__lowerCAmelCase		    =     UNetaDModel(**config)
							model.load_state_dict(converted_checkpoint)

							try:
														__lowerCAmelCase		    =     ScoreSdeVeScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

														__lowerCAmelCase		    =     ScoreSdeVePipeline(unet=model, scheduler=scheduler)
														pipe.save_pretrained(args.dump_path)
							except:  # noqa: E722
														model.save_pretrained(args.dump_path)









'''simple docstring'''

from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest

@require_tokenizers
class 			__magic_name__		(					_UpperCamelCase     ):
							lowerCAmelCase :					int            =      SqueezeBertTokenizer
							lowerCAmelCase :					int            =      SqueezeBertTokenizerFast
							lowerCAmelCase :					List[Any]            =      True







							def 							__lowercase    (							self      : List[str]			,**_UpperCAmelCase      : Tuple	):
									return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname			,**_UpperCAmelCase	)







							@slow
							def 							__lowercase    (							self      : Tuple	):
									_a  :   Optional[int]				 =							SqueezeBertTokenizer.from_pretrained('squeezebert/squeezebert-mnli-headless'	)

									_a  :   int				 =							tokenizer.encode('sequence builders'			,add_special_tokens=_UpperCAmelCase	)
									_a  :   List[Any]				 =							tokenizer.encode('multi-sequence build'			,add_special_tokens=_UpperCAmelCase	)

									_a  :   Union[str, Any]				 =							tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase	)
									_a  :   Dict				 =							tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase			,_UpperCAmelCase	)

									assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
									assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
									    tokenizer.sep_token_id
									]




