


from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


snake_case_  :	str							  =							logging.get_logger(__name__)

snake_case_  :	Optional[int]							  =							{
    '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json''',
    # See all MBART models at https://huggingface.co/models?filter=mbart
}

class   A__							(						UpperCamelCase__     ):
						UpperCAmelCase									=  "mbart"
						UpperCAmelCase									=  ["past_key_values"]
						UpperCAmelCase									=  {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




						def __init__(				self    :					List[str] ,			_a    :					Tuple=5_0265 ,			_a    :					List[str]=1024 ,			_a    :					List[str]=12 ,			_a    :					Tuple=4096 ,			_a    :					Optional[int]=16 ,			_a    :					Union[str, Any]=12 ,			_a    :					Optional[Any]=4096 ,			_a    :					Optional[Any]=16 ,			_a    :					Union[str, Any]=0.0 ,			_a    :					List[Any]=0.0 ,			_a    :					Dict=True ,			_a    :					List[str]=True ,			_a    :					Optional[Any]="gelu" ,			_a    :					Optional[int]=1024 ,			_a    :					str=0.1 ,			_a    :					Tuple=0.0 ,			_a    :					Tuple=0.0 ,			_a    :					List[Any]=0.02 ,			_a    :					Dict=0.0 ,			_a    :					Any=False ,			_a    :					Union[str, Any]=1 ,			_a    :					Tuple=0 ,			_a    :					List[Any]=2 ,			_a    :					List[str]=2 ,			**_a    :					Union[str, Any] ,			)							->   Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =vocab_size
										_SCREAMING_SNAKE_CASE	     =max_position_embeddings
										_SCREAMING_SNAKE_CASE	     =d_model
										_SCREAMING_SNAKE_CASE	     =encoder_ffn_dim
										_SCREAMING_SNAKE_CASE	     =encoder_layers
										_SCREAMING_SNAKE_CASE	     =encoder_attention_heads
										_SCREAMING_SNAKE_CASE	     =decoder_ffn_dim
										_SCREAMING_SNAKE_CASE	     =decoder_layers
										_SCREAMING_SNAKE_CASE	     =decoder_attention_heads
										_SCREAMING_SNAKE_CASE	     =dropout
										_SCREAMING_SNAKE_CASE	     =attention_dropout
										_SCREAMING_SNAKE_CASE	     =activation_dropout
										_SCREAMING_SNAKE_CASE	     =activation_function
										_SCREAMING_SNAKE_CASE	     =init_std
										_SCREAMING_SNAKE_CASE	     =encoder_layerdrop
										_SCREAMING_SNAKE_CASE	     =decoder_layerdrop
										_SCREAMING_SNAKE_CASE	     =classifier_dropout
										_SCREAMING_SNAKE_CASE	     =use_cache
										_SCREAMING_SNAKE_CASE	     =encoder_layers
										_SCREAMING_SNAKE_CASE	     =scale_embedding  # scale factor will be sqrt(d_model) if True
										super().__init__(
										    pad_token_id=_a ,			bos_token_id=_a ,			eos_token_id=_a ,			is_encoder_decoder=_a ,			forced_eos_token_id=_a ,			**_a ,			)

class   A__							(						UpperCamelCase__     ):




						@property
						def 							__UpperCamelCase (				self    :					int     )							->   Mapping[str, Mapping[int, str]]:


										"""simple docstring"""





										if self.task in ["default", "seq2seq-lm"]:
														_SCREAMING_SNAKE_CASE	     =OrderedDict(
														    [
														        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
														        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
														    ]     )

														if self.use_past:
																		_SCREAMING_SNAKE_CASE	     ={0: '''batch'''}
																		_SCREAMING_SNAKE_CASE	     ={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
														else:
																		_SCREAMING_SNAKE_CASE	     ={0: '''batch''', 1: '''decoder_sequence'''}
																		_SCREAMING_SNAKE_CASE	     ={0: '''batch''', 1: '''decoder_sequence'''}

														if self.use_past:
																		self.fill_with_past_key_values_(_a ,			direction='''inputs'''     )
										elif self.task == "causal-lm":
														# TODO: figure this case out.
														_SCREAMING_SNAKE_CASE	     =OrderedDict(
														    [
														        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
														        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
														    ]     )
														if self.use_past:
																		_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =self.num_layers
																		for i in range(_a     ):
																						_SCREAMING_SNAKE_CASE	     ={0: '''batch''', 2: '''past_sequence + sequence'''}
																						_SCREAMING_SNAKE_CASE	     ={0: '''batch''', 2: '''past_sequence + sequence'''}
										else:
														_SCREAMING_SNAKE_CASE	     =OrderedDict(
														    [
														        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
														        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
														        ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
														        ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
														    ]     )

										return common_inputs




						@property
						def 							__UpperCamelCase (				self    :					List[str]     )							->   Mapping[str, Mapping[int, str]]:


										"""simple docstring"""





										if self.task in ["default", "seq2seq-lm"]:
														_SCREAMING_SNAKE_CASE	     =super().outputs
										else:
														_SCREAMING_SNAKE_CASE	     =super(_a ,			self     ).outputs
														if self.use_past:
																		_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =self.num_layers
																		for i in range(_a     ):
																						_SCREAMING_SNAKE_CASE	     ={0: '''batch''', 2: '''past_sequence + sequence'''}
																						_SCREAMING_SNAKE_CASE	     ={0: '''batch''', 2: '''past_sequence + sequence'''}
										return common_outputs




						def 							__UpperCamelCase (				self    :					Optional[Any] ,			_a    :					PreTrainedTokenizer ,			_a    :					int = -1 ,			_a    :					int = -1 ,			_a    :					bool = False ,			_a    :					Optional[TensorType] = None ,			)							->   Mapping[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    _a ,			_a ,			_a ,			_a ,			_a     )

										# Generate decoder inputs
										_SCREAMING_SNAKE_CASE	     =seq_length if not self.use_past else 1
										_SCREAMING_SNAKE_CASE	     =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    _a ,			_a ,			_a ,			_a ,			_a     )
										_SCREAMING_SNAKE_CASE	     ={f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
										_SCREAMING_SNAKE_CASE	     =dict(**_a ,			**_a     )

										if self.use_past:
														if not is_torch_available():
																		raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''     )
														else:
																		import torch
														_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =common_inputs['''input_ids'''].shape
														_SCREAMING_SNAKE_CASE	     =common_inputs['''decoder_input_ids'''].shape[1]
														_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =self.num_attention_heads
														_SCREAMING_SNAKE_CASE	     =(
														    batch,
														    num_encoder_attention_heads,
														    encoder_seq_length,
														    self._config.hidden_size // num_encoder_attention_heads,
														)
														_SCREAMING_SNAKE_CASE	     =decoder_seq_length + 3
														_SCREAMING_SNAKE_CASE	     =(
														    batch,
														    num_decoder_attention_heads,
														    decoder_past_length,
														    self._config.hidden_size // num_decoder_attention_heads,
														)

														_SCREAMING_SNAKE_CASE	     =torch.cat(
														    [common_inputs['''decoder_attention_mask'''], torch.ones(_a ,			_a     )] ,			dim=1     )

														_SCREAMING_SNAKE_CASE	     =[]
														# If the number of encoder and decoder layers are present in the model configuration, both are considered
														_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =self.num_layers
														_SCREAMING_SNAKE_CASE	     =min(_a ,			_a     )
														_SCREAMING_SNAKE_CASE	     =max(_a ,			_a     ) - min_num_layers
														_SCREAMING_SNAKE_CASE	     ='''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

														for _ in range(_a     ):
																		common_inputs["past_key_values"].append(
																		    (
																		        torch.zeros(_a     ),
																		        torch.zeros(_a     ),
																		        torch.zeros(_a     ),
																		        torch.zeros(_a     ),
																		    )     )
														# TODO: test this.
														_SCREAMING_SNAKE_CASE	     =encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
														for _ in range(_a ,			_a     ):
																		common_inputs["past_key_values"].append((torch.zeros(_a     ), torch.zeros(_a     ))     )
										return common_inputs




						def 							__UpperCamelCase (				self    :					List[str] ,			_a    :					PreTrainedTokenizer ,			_a    :					int = -1 ,			_a    :					int = -1 ,			_a    :					bool = False ,			_a    :					Optional[TensorType] = None ,			)							->   Mapping[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    _a ,			_a ,			_a ,			_a ,			_a     )

										if self.use_past:
														if not is_torch_available():
																		raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''     )
														else:
																		import torch
														_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =common_inputs['''input_ids'''].shape
														# Not using the same length for past_key_values
														_SCREAMING_SNAKE_CASE	     =seqlen + 2
														_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =self.num_layers
														_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =self.num_attention_heads
														_SCREAMING_SNAKE_CASE	     =(
														    batch,
														    num_encoder_attention_heads,
														    past_key_values_length,
														    self._config.hidden_size // num_encoder_attention_heads,
														)

														_SCREAMING_SNAKE_CASE	     =common_inputs['''attention_mask'''].dtype
														_SCREAMING_SNAKE_CASE	     =torch.cat(
														    [common_inputs['''attention_mask'''], torch.ones(_a ,			_a ,			dtype=_a     )] ,			dim=1     )
														_SCREAMING_SNAKE_CASE	     =[
														    (torch.zeros(_a     ), torch.zeros(_a     )) for _ in range(_a     )
														]
										return common_inputs




						def 							__UpperCamelCase (				self    :					Optional[Any] ,			_a    :					PreTrainedTokenizer ,			_a    :					int = -1 ,			_a    :					int = -1 ,			_a    :					bool = False ,			_a    :					Optional[TensorType] = None ,			)							->   Mapping[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =compute_effective_axis_dimension(
										    _a ,			fixed_dimension=OnnxConfig.default_fixed_batch ,			num_token_to_add=0     )

										# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
										_SCREAMING_SNAKE_CASE	     =tokenizer.num_special_tokens_to_add(_a     )
										_SCREAMING_SNAKE_CASE	     =compute_effective_axis_dimension(
										    _a ,			fixed_dimension=OnnxConfig.default_fixed_sequence ,			num_token_to_add=_a     )

										# Generate dummy inputs according to compute batch and sequence
										_SCREAMING_SNAKE_CASE	     =[''' '''.join([tokenizer.unk_token]     ) * seq_length] * batch_size
										_SCREAMING_SNAKE_CASE	     =dict(tokenizer(_a ,			return_tensors=_a     )     )
										return common_inputs




						def 							__UpperCamelCase (				self    :					List[str] ,			_a    :					PreTrainedTokenizer ,			_a    :					int = -1 ,			_a    :					int = -1 ,			_a    :					bool = False ,			_a    :					Optional[TensorType] = None ,			)							->   Mapping[str, Any]:


										"""simple docstring"""





										if self.task in ["default", "seq2seq-lm"]:
														_SCREAMING_SNAKE_CASE	     =self._generate_dummy_inputs_for_default_and_seqaseq_lm(
														    _a ,			batch_size=_a ,			seq_length=_a ,			is_pair=_a ,			framework=_a     )

										elif self.task == "causal-lm":
														_SCREAMING_SNAKE_CASE	     =self._generate_dummy_inputs_for_causal_lm(
														    _a ,			batch_size=_a ,			seq_length=_a ,			is_pair=_a ,			framework=_a     )
										else:
														_SCREAMING_SNAKE_CASE	     =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
														    _a ,			batch_size=_a ,			seq_length=_a ,			is_pair=_a ,			framework=_a     )

										return common_inputs




						def 							__UpperCamelCase (				self    :					Union[str, Any] ,			_a    :					Optional[Any] ,			_a    :					List[str] ,			_a    :					Tuple ,			_a    :					Any     )							->   Optional[Any]:


										"""simple docstring"""





										if self.task in ["default", "seq2seq-lm"]:
														_SCREAMING_SNAKE_CASE	     =super()._flatten_past_key_values_(_a ,			_a ,			_a ,			_a     )
										else:
														_SCREAMING_SNAKE_CASE	     =super(_a ,			self     )._flatten_past_key_values_(
														    _a ,			_a ,			_a ,			_a     )






'''simple docstring'''



import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel




def       _lowerCAmelCase  ( lowerCamelCase_	:     Tuple						,				lowerCamelCase_	:     List[Any] ):
			__lowercase   =							UNetaDModel(**lowerCamelCase_ )
			__lowercase   =							checkpoint['''all_modules.0.W'''].data
			__lowercase   =							checkpoint['''all_modules.0.W'''].data
			__lowercase   =							checkpoint['''all_modules.1.weight'''].data
			__lowercase   =							checkpoint['''all_modules.1.bias'''].data

			__lowercase   =							checkpoint['''all_modules.2.weight'''].data
			__lowercase   =							checkpoint['''all_modules.2.bias'''].data

			__lowercase   =							checkpoint['''all_modules.3.weight'''].data
			__lowercase   =							checkpoint['''all_modules.3.bias'''].data

			__lowercase   =							checkpoint[list(checkpoint.keys() )[-4]].data
			__lowercase   =							checkpoint[list(checkpoint.keys() )[-3]].data
			__lowercase   =							checkpoint[list(checkpoint.keys() )[-2]].data
			__lowercase   =							checkpoint[list(checkpoint.keys() )[-1]].data

			__lowercase   =							4

			def set_attention_weights(lowerCamelCase_	:     Tuple						,				lowerCamelCase_	:     int						,				lowerCamelCase_	:     str ):
						__lowercase   =							old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
						__lowercase   =							old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
						__lowercase   =							old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T

						__lowercase   =							old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
						__lowercase   =							old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
						__lowercase   =							old_checkpoint[f"all_modules.{index}.NIN_2.b"].data

						__lowercase   =							old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
						__lowercase   =							old_checkpoint[f"all_modules.{index}.NIN_3.b"].data

						__lowercase   =							old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
						__lowercase   =							old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

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

						__lowercase   =							old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data
						__lowercase   =							old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data
						__lowercase   =							old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data
						__lowercase   =							old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data

						__lowercase   =							old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
						__lowercase   =							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:
									__lowercase   =							old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
									__lowercase   =							old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data

			for i, block in enumerate(new_model_architecture.downsample_blocks ):
						__lowercase   =							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
									__lowercase   =							checkpoint[f"all_modules.{module_index}.Conv_0.weight"].data
									__lowercase   =							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 ):
						__lowercase   =							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:
									__lowercase   =							checkpoint[f"all_modules.{module_index}.weight"].data
									__lowercase   =							checkpoint[f"all_modules.{module_index}.bias"].data
									module_index += 1
									__lowercase   =							checkpoint[f"all_modules.{module_index}.weight"].data
									__lowercase   =							checkpoint[f"all_modules.{module_index}.bias"].data
									module_index += 1
									set_resnet_weights(block.resnet_up						,				lowerCamelCase_						,				lowerCamelCase_ )
									module_index += 1

			__lowercase   =							checkpoint[f"all_modules.{module_index}.weight"].data
			__lowercase   =							checkpoint[f"all_modules.{module_index}.bias"].data
			module_index += 1
			__lowercase   =							checkpoint[f"all_modules.{module_index}.weight"].data
			__lowercase   =							checkpoint[f"all_modules.{module_index}.bias"].data

			return new_model_architecture.state_dict()


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE    =			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.''',
	)

	_SCREAMING_SNAKE_CASE    =			parser.parse_args()

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

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

	_SCREAMING_SNAKE_CASE    =			convert_ncsnpp_checkpoint(
	    checkpoint,
	    config,
	)

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

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

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

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





