






import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


__A          = (30, 487)
__A          = (36, 1024)
__A          = (100, 100)
__A          = 100

__A          = datasets.Features(
    {'text': ArrayaD(SHAPE_TEST_1, dtype='float32'), 'image': ArrayaD(SHAPE_TEST_2, dtype='float32')}
)

__A       ,     __A          = os.path.split(__file__)
__A          = os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))




@get_duration
def    __A    (   _lowercase    ,	_lowercase    ,	_lowercase       ):

					'''simple docstring'''







					with ArrowWriter(features=_lowercase    ,	path=os.path.join(_lowercase    ,	'''beta.arrow'''       )       ) as writer:
										for key, record in dummy_data:
															_A									=       my_features.encode_example(_lowercase       )
															writer.write(_lowercase       )
										_A							,_A									=       writer.finalize()




@get_duration
def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       datasets.Dataset.from_file(
					    filename=os.path.join(_lowercase    ,	'''beta.arrow'''       )    ,	info=datasets.DatasetInfo(features=_lowercase       )       )
					for _ in dataset:
										pass




@get_duration
def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       datasets.Dataset.from_file(
					    filename=os.path.join(_lowercase    ,	'''beta.arrow'''       )    ,	info=datasets.DatasetInfo(features=_lowercase       )       )
					dataset.set_format('''numpy'''       )
					for _ in dataset:
										pass




@get_duration
def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       10
					_A									=       datasets.Dataset.from_file(
					    filename=os.path.join(_lowercase    ,	'''beta.arrow'''       )    ,	info=datasets.DatasetInfo(features=_lowercase       )       )
					for i in range(0    ,	len(_lowercase       )    ,	_lowercase       ):
										_A									=       dataset[i : i + batch_size]




@get_duration
def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       10
					_A									=       datasets.Dataset.from_file(
					    filename=os.path.join(_lowercase    ,	'''beta.arrow'''       )    ,	info=datasets.DatasetInfo(features=_lowercase       )       )
					dataset.set_format('''numpy'''       )
					for i in range(0    ,	len(_lowercase       )    ,	_lowercase       ):
										_A									=       dataset[i : i + batch_size]




@get_duration
def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       datasets.Dataset.from_file(
					    filename=os.path.join(_lowercase    ,	'''beta.arrow'''       )    ,	info=datasets.DatasetInfo(features=_lowercase       )       )
					for col in feats:
										_A									=       dataset[col]




@get_duration
def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       datasets.Dataset.from_file(
					    filename=os.path.join(_lowercase    ,	'''beta.arrow'''       )    ,	info=datasets.DatasetInfo(features=_lowercase       )       )
					dataset.set_format('''numpy'''       )
					for col in feats:
										_A									=       dataset[col]




def    __A    (   ):

					'''simple docstring'''







					_A									=       {}
					_A									=       (
					    read_unformated,
					    read_formatted_as_numpy,
					    read_batch_unformated,
					    read_batch_formatted_as_numpy,
					    read_col_unformated,
					    read_col_formatted_as_numpy,
					)
					with tempfile.TemporaryDirectory() as tmp_dir:
										_A									=       datasets.Features({'''image''': ArrayaD(_lowercase    ,	dtype='''float32'''       )}       )
										_A									=       generate_examples(features=_lowercase    ,	num_examples=_lowercase       )
										_A									=       write(_lowercase    ,	_lowercase    ,	_lowercase       )
										for read_func in read_functions:
															_A									=       read_func(_lowercase    ,	_lowercase       )

					with tempfile.TemporaryDirectory() as tmp_dir:
										# don't use fixed length for fair comparison
										# feats = datasets.Features(
										#     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
										# )
										_A									=       datasets.Features({'''image''': datasets.Sequence(datasets.Sequence(datasets.Value('''float32'''       )       )       )}       )
										_A									=       generate_examples(
										    features=_lowercase    ,	num_examples=_lowercase    ,	seq_shapes={'''image''': SPEED_TEST_SHAPE}       )
										_A									=       write(_lowercase    ,	_lowercase    ,	_lowercase       )
										for read_func in read_functions:
															_A									=       read_func(_lowercase    ,	_lowercase       )

					with tempfile.TemporaryDirectory() as tmp_dir:
										# don't use fixed length for fair comparison
										# feats = datasets.Features(
										#     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
										# )
										_A									=       datasets.Features({'''image''': datasets.Sequence(datasets.Value('''float32'''       )       )}       )
										_A									=       generate_examples(
										    features=_lowercase    ,	num_examples=_lowercase    ,	seq_shapes={'''image''': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}    ,	)
										_A									=       write(_lowercase    ,	_lowercase    ,	_lowercase       )
										for read_func in read_functions:
															_A									=       read_func(_lowercase    ,	_lowercase       )

					with open(_lowercase    ,	'''wb'''       ) as f:
										f.write(json.dumps(_lowercase       ).encode('''utf-8'''       )       )


if __name__ == "__main__":  # useful to run the profiler
				benchmark_array_xd()










"""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)






