
'''simple docstring'''

from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCAmelCase__      :       Any 		=						logging.get_logger(__name__)






class       A   (		lowerCAmelCase__			):
	snake_case__					:List[str]         =   "mt5"
	snake_case__					:int         =   ["past_key_values"]





	def __init__(    self	:				str					,							__magic_name__	:				Dict=250112					,							__magic_name__	:				List[Any]=512					,							__magic_name__	:				Union[str, Any]=64					,							__magic_name__	:				Optional[Any]=1024					,							__magic_name__	:				List[Any]=8					,							__magic_name__	:				Optional[Any]=None					,							__magic_name__	:				Tuple=6					,							__magic_name__	:				str=32					,							__magic_name__	:				Optional[Any]=128					,							__magic_name__	:				Any=0.1					,							__magic_name__	:				str=1E-6					,							__magic_name__	:				Union[str, Any]=1.0					,							__magic_name__	:				Optional[Any]="gated-gelu"					,							__magic_name__	:				List[Any]=True					,							__magic_name__	:				int=True					,							__magic_name__	:				List[str]="T5Tokenizer"					,							__magic_name__	:				Any=False					,							__magic_name__	:				Optional[int]=0					,							__magic_name__	:				List[str]=1					,							__magic_name__	:				Dict=0					,							**__magic_name__	:				Dict					,							):


				"""simple docstring"""

				super().__init__(
				    is_encoder_decoder=a__					,							tokenizer_class=a__					,							tie_word_embeddings=a__					,							pad_token_id=a__					,							eos_token_id=a__					,							decoder_start_token_id=a__					,							**a__					,							)
				lowerCAmelCase__									=							vocab_size
				lowerCAmelCase__									=							d_model
				lowerCAmelCase__									=							d_kv
				lowerCAmelCase__									=							d_ff
				lowerCAmelCase__									=							num_layers
				lowerCAmelCase__									=							(
				    num_decoder_layers if num_decoder_layers is not None else self.num_layers
				)  # default = symmetry
				lowerCAmelCase__									=							num_heads
				lowerCAmelCase__									=							relative_attention_num_buckets
				lowerCAmelCase__									=							relative_attention_max_distance
				lowerCAmelCase__									=							dropout_rate
				lowerCAmelCase__									=							layer_norm_epsilon
				lowerCAmelCase__									=							initializer_factor
				lowerCAmelCase__									=							feed_forward_proj
				lowerCAmelCase__									=							use_cache

				lowerCAmelCase__									=							self.feed_forward_proj.split("-"							)
				lowerCAmelCase__									=							act_info[-1]
				lowerCAmelCase__									=							act_info[0] == "gated"

				if len(a__							) > 1 and act_info[0] != "gated" or len(a__							) > 2:
							raise ValueError(
							    f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
							    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
							    "\'gated-gelu\' or \'relu\'"							)

				# for backwards compatibility
				if feed_forward_proj == "gated-gelu":
							lowerCAmelCase__									=							"gelu_new"





	@property
	def 							__SCREAMING_SNAKE_CASE     (    self	:				Optional[int]							):


				"""simple docstring"""

				return self.d_model





	@property
	def 							__SCREAMING_SNAKE_CASE     (    self	:				Optional[int]							):


				"""simple docstring"""

				return self.num_heads







	@property
	def 							__SCREAMING_SNAKE_CASE     (    self	:				Optional[int]							):


				"""simple docstring"""

				return self.num_layers






class       A   (		lowerCAmelCase__			):





	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
	def 							__SCREAMING_SNAKE_CASE     (    self	:				List[Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							{
				    "input_ids": {0: "batch", 1: "encoder_sequence"},
				    "attention_mask": {0: "batch", 1: "encoder_sequence"},
				}
				if self.use_past:
							lowerCAmelCase__									=							"past_encoder_sequence + sequence"
							lowerCAmelCase__									=							{0: "batch"}
							lowerCAmelCase__									=							{0: "batch", 1: "past_decoder_sequence + sequence"}
				else:
							lowerCAmelCase__									=							{0: "batch", 1: "decoder_sequence"}
							lowerCAmelCase__									=							{0: "batch", 1: "decoder_sequence"}

				if self.use_past:
							self.fill_with_past_key_values_(a__					,							direction="inputs"							)

				return common_inputs





	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
	def 							__SCREAMING_SNAKE_CASE     (    self	:				str							):


				"""simple docstring"""

				return 13







	@property
	def 							__SCREAMING_SNAKE_CASE     (    self	:				Tuple							):


				"""simple docstring"""

				return 5E-4












"""simple docstring"""


import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Tuple						,  snake_case   :					List[Any]		)->		str:
       _lowerCamelCase    	=					UNetaDModel(**snake_case		)
       _lowerCamelCase    	=					checkpoint['all_modules.0.W'].data
       _lowerCamelCase    	=					checkpoint['all_modules.0.W'].data
       _lowerCamelCase    	=					checkpoint['all_modules.1.weight'].data
       _lowerCamelCase    	=					checkpoint['all_modules.1.bias'].data

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

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

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

       _lowerCamelCase    	=					4

       def set_attention_weights(snake_case   :					List[Any]						,  snake_case   :					Any						,  snake_case   :					List[Any]		):
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_0.W'].data.T
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_1.W'].data.T
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_2.W'].data.T

              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_0.b'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_1.b'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_2.b'].data

              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_3.W'].data.T
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_3.b'].data

              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_0.weight'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_0.bias'].data

       def set_resnet_weights(snake_case   :					Tuple						,  snake_case   :					Optional[int]						,  snake_case   :					Tuple		):
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_0.weight'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_0.bias'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_0.weight'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_0.bias'].data

              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_1.weight'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_1.bias'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_1.weight'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_1.bias'].data

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

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

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

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

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

              if hasattr(snake_case						,  'resnet_up'		) and block.resnet_up is not None:
                     _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.weight'].data
                     _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.bias'].data
                     module_index += 1
                     _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.weight'].data
                     _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.bias'].data
                     module_index += 1
                     set_resnet_weights(block.resnet_up						,  snake_case						,  snake_case		)
                     module_index += 1

       _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.weight'].data
       _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.bias'].data
       module_index += 1
       _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.weight'].data
       _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.bias'].data

       return new_model_architecture.state_dict()


if __name__ == "__main__":
      A_     :      Any								=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.""",
      )

      A_     :      int								=parser.parse_args()

      A_     :      Dict								=torch.load(args.checkpoint_path, map_location="""cpu""")

      with open(args.config_file) as f:
            A_     :      List[str]								=json.loads(f.read())

      A_     :      str								=convert_ncsnpp_checkpoint(
          checkpoint,
          config,
      )

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

      A_     :      str								=UNetaDModel(**config)
      model.load_state_dict(converted_checkpoint)

      try:
            A_     :      List[Any]								=ScoreSdeVeScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

            A_     :      Optional[Any]								=ScoreSdeVePipeline(unet=model, scheduler=scheduler)
            pipe.save_pretrained(args.dump_path)
      except:  # noqa: E722
            model.save_pretrained(args.dump_path)






