'''simple docstring'''





import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
UpperCamelCase    :			Dict						     =  logging.getLogger(__name__)






def SCREAMING_SNAKE_CASE__   (							)					->							Union[str, Any]:







     """simple docstring"""
     a :				Tuple				=      argparse.ArgumentParser(description='PyTorch Transformer Language Model'   )
     parser.add_argument('--model_name'							,     type=snake_case							,     default='transfo-xl-wt103'							,     help='pretrained model name'   )
     parser.add_argument(
         '--split'							,     type=snake_case							,     default='test'							,     choices=['all', 'valid', 'test']							,     help='which split to evaluate'   )
     parser.add_argument('--batch_size'							,     type=snake_case							,     default=10							,     help='batch size'   )
     parser.add_argument('--tgt_len'							,     type=snake_case							,     default=128							,     help='number of tokens to predict'   )
     parser.add_argument('--ext_len'							,     type=snake_case							,     default=0							,     help='length of the extended context'   )
     parser.add_argument('--mem_len'							,     type=snake_case							,     default=1_600							,     help='length of the retained previous heads'   )
     parser.add_argument('--clamp_len'							,     type=snake_case							,     default=1_000							,     help='max positional embedding index'   )
     parser.add_argument('--no_cuda'							,     action='store_true'							,     help='Do not use CUDA even though CUA is available'   )
     parser.add_argument('--work_dir'							,     type=snake_case							,     required=snake_case							,     help='path to the work_dir'   )
     parser.add_argument('--no_log'							,     action='store_true'							,     help='do not log the eval result'   )
     parser.add_argument('--same_length'							,     action='store_true'							,     help='set same length attention with masking'   )
     parser.add_argument('--server_ip'							,     type=snake_case							,     default=''							,     help='Can be used for distant debugging.'   )
     parser.add_argument('--server_port'							,     type=snake_case							,     default=''							,     help='Can be used for distant debugging.'   )
     a :				Optional[Any]				=      parser.parse_args()
     assert args.ext_len >= 0, "extended context length must be non-negative"

     if args.server_ip and args.server_port:
          # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
          import ptvsd

          print('Waiting for debugger attach'   )
          ptvsd.enable_attach(address=(args.server_ip, args.server_port)							,     redirect_output=snake_case   )
          ptvsd.wait_for_attach()

     a :				Union[str, Any]				=      torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'   )
     logger.info('device: {}'.format(snake_case   )   )

     # Load a pre-processed dataset
     # You can also build the corpus yourself using TransfoXLCorpus methods
     # The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
     # and tokenizing the dataset
     # The pre-processed corpus is a convertion (using the conversion script )
     a :				Optional[int]				=      TransfoXLCorpus.from_pretrained(args.model_name   )

     a :				int				=      corpus.get_iterator('valid'							,     args.batch_size							,     args.tgt_len							,     device=snake_case							,     ext_len=args.ext_len   )
     a :				Union[str, Any]				=      corpus.get_iterator('test'							,     args.batch_size							,     args.tgt_len							,     device=snake_case							,     ext_len=args.ext_len   )

     # Load a pre-trained model
     a :				List[str]				=      TransfoXLLMHeadModel.from_pretrained(args.model_name   )
     model.to(snake_case   )

     logger.info(
         'Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'.format(
             args.batch_size							,     args.tgt_len							,     args.ext_len							,     args.mem_len							,     args.clamp_len   )   )

     model.reset_memory_length(args.mem_len   )
     if args.clamp_len > 0:
          a :				List[str]				=      args.clamp_len
     if args.same_length:
          a :				Optional[Any]				=      True

     ###############################################################################
     # Evaluation code
     ###############################################################################
     def evaluate(snake_case    :							str   ):
          # Turn on evaluation mode which disables dropout.
          model.eval()
          a				,       a :				List[str]				=      0, 0.0
          a :				List[Any]				=      time.time()
          with torch.no_grad():
               a :				str				=      None
               for idx, (data, target, seq_len) in enumerate(snake_case   ):
                    a :				str				=      model(snake_case							,     lm_labels=snake_case							,     mems=snake_case   )
                    a				,       a				,       a :				Union[str, Any]				=      ret
                    a :				Dict				=      loss.mean()
                    total_loss += seq_len * loss.item()
                    total_len += seq_len
               a :				Tuple				=      time.time() - start_time
          logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(snake_case							,     1_000 * total_time / (idx + 1)   )   )
          return total_loss / total_len

     # Run on test data.
     if args.split == "all":
          a :				Any				=      evaluate(snake_case   )
          a :				List[str]				=      evaluate(snake_case   )
     elif args.split == "valid":
          a :				Union[str, Any]				=      evaluate(snake_case   )
          a :				Dict				=      None
     elif args.split == "test":
          a :				Tuple				=      evaluate(snake_case   )
          a :				int				=      None

     def format_log(snake_case    :							Any							,     snake_case    :							int   ):
          a :				List[Any]				=      '| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(snake_case							,     snake_case							,     math.exp(snake_case   )   )
          return log_str

     a :				Any				=      ''
     if valid_loss is not None:
          log_str += format_log(snake_case							,     'valid'   )
     if test_loss is not None:
          log_str += format_log(snake_case							,     'test'   )

     logger.info('=' * 100   )
     logger.info(snake_case   )
     logger.info('=' * 100   )


if __name__ == "__main__":
      main()








"""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)






