import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase				)					-> int:



    '''simple docstring'''






    lowerCAmelCase				:			int    			=      UNetaDModel(**lowercase_				)
    lowerCAmelCase				:			Optional[int]    			=      checkpoint['all_modules.0.W'].data
    lowerCAmelCase				:			List[Any]    			=      checkpoint['all_modules.0.W'].data
    lowerCAmelCase				:			str    			=      checkpoint['all_modules.1.weight'].data
    lowerCAmelCase				:			List[Any]    			=      checkpoint['all_modules.1.bias'].data

    lowerCAmelCase				:			str    			=      checkpoint['all_modules.2.weight'].data
    lowerCAmelCase				:			Optional[Any]    			=      checkpoint['all_modules.2.bias'].data

    lowerCAmelCase				:			Any    			=      checkpoint['all_modules.3.weight'].data
    lowerCAmelCase				:			Optional[Any]    			=      checkpoint['all_modules.3.bias'].data

    lowerCAmelCase				:			Optional[int]    			=      checkpoint[list(checkpoint.keys()				)[-4]].data
    lowerCAmelCase				:			Any    			=      checkpoint[list(checkpoint.keys()				)[-3]].data
    lowerCAmelCase				:			Optional[Any]    			=      checkpoint[list(checkpoint.keys()				)[-2]].data
    lowerCAmelCase				:			List[str]    			=      checkpoint[list(checkpoint.keys()				)[-1]].data

    lowerCAmelCase				:			Any    			=      4

    def set_attention_weights(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase				):
        lowerCAmelCase				:			Optional[Any]    			=      old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
        lowerCAmelCase				:			Tuple    			=      old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
        lowerCAmelCase				:			Union[str, Any]    			=      old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T

        lowerCAmelCase				:			Any    			=      old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
        lowerCAmelCase				:			List[str]    			=      old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
        lowerCAmelCase				:			Any    			=      old_checkpoint[f"all_modules.{index}.NIN_2.b"].data

        lowerCAmelCase				:			Optional[Any]    			=      old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
        lowerCAmelCase				:			int    			=      old_checkpoint[f"all_modules.{index}.NIN_3.b"].data

        lowerCAmelCase				:			int    			=      old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
        lowerCAmelCase				:			Tuple    			=      old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

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

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

        lowerCAmelCase				:			Any    			=      old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
        lowerCAmelCase				:			Optional[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:
            lowerCAmelCase				:			int    			=      old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
            lowerCAmelCase				:			Dict    			=      old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data

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

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

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

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

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

    lowerCAmelCase				:			int    			=      checkpoint[f"all_modules.{module_index}.weight"].data
    lowerCAmelCase				:			int    			=      checkpoint[f"all_modules.{module_index}.bias"].data
    module_index += 1
    lowerCAmelCase				:			Tuple    			=      checkpoint[f"all_modules.{module_index}.weight"].data
    lowerCAmelCase				:			List[str]    			=      checkpoint[f"all_modules.{module_index}.bias"].data

    return new_model_architecture.state_dict()


if __name__ == "__main__":
 __A   :     int      =			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   :     Union[str, Any]      =			parser.parse_args()

 __A   :     Union[str, Any]      =			torch.load(args.checkpoint_path, map_location='''cpu''')

 with open(args.config_file) as f:
  __A   :     Union[str, Any]      =			json.loads(f.read())

 __A   :     Optional[int]      =			convert_ncsnpp_checkpoint(
     checkpoint,
     config,
 )

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

 __A   :     Any      =			UNetaDModel(**config)
 model.load_state_dict(converted_checkpoint)

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

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








def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      )					-> tuple[list[list[int]], list[list[str]]]:



    '''simple docstring'''






    lowerCAmelCase				:			List[Any]    			=      list(_UpperCAmelCase				)
    lowerCAmelCase				:			Union[str, Any]    			=      list(_UpperCAmelCase				)
    lowerCAmelCase				:			Optional[int]    			=      len(_UpperCAmelCase				)
    lowerCAmelCase				:			List[Any]    			=      len(_UpperCAmelCase				)

    lowerCAmelCase				:			str    			=      [
        [0 for _ in range(len_destination_seq + 1				)] for _ in range(len_source_seq + 1				)
    ]
    lowerCAmelCase				:			str    			=      [
        ['0' for _ in range(len_destination_seq + 1				)] for _ in range(len_source_seq + 1				)
    ]

    for i in range(1,      len_source_seq + 1				):
        lowerCAmelCase				:			int    			=      i * delete_cost
        lowerCAmelCase				:			List[str]    			=      f"D{source_seq[i - 1]:c}"

    for i in range(1,      len_destination_seq + 1				):
        lowerCAmelCase				:			Tuple    			=      i * insert_cost
        lowerCAmelCase				:			Union[str, Any]    			=      f"I{destination_seq[i - 1]:c}"

    for i in range(1,      len_source_seq + 1				):
        for j in range(1,      len_destination_seq + 1				):
            if source_seq[i - 1] == destination_seq[j - 1]:
                lowerCAmelCase				:			str    			=      costs[i - 1][j - 1] + copy_cost
                lowerCAmelCase				:			Tuple    			=      f"C{source_seq[i - 1]:c}"
            else:
                lowerCAmelCase				:			Optional[int]    			=      costs[i - 1][j - 1] + replace_cost
                lowerCAmelCase				:			Optional[int]    			=      f"R{source_seq[i - 1]:c}" + str(destination_seq[j - 1]				)

            if costs[i - 1][j] + delete_cost < costs[i][j]:
                lowerCAmelCase				:			List[str]    			=      costs[i - 1][j] + delete_cost
                lowerCAmelCase				:			Optional[int]    			=      f"D{source_seq[i - 1]:c}"

            if costs[i][j - 1] + insert_cost < costs[i][j]:
                lowerCAmelCase				:			Union[str, Any]    			=      costs[i][j - 1] + insert_cost
                lowerCAmelCase				:			str    			=      f"I{destination_seq[j - 1]:c}"

    return costs, ops
def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase				)					-> list[str]:



    '''simple docstring'''






    if i == 0 and j == 0:
        return []
    else:
        if ops[i][j][0] == "C" or ops[i][j][0] == "R":
            lowerCAmelCase				:			List[str]    			=      assemble_transformation(_UpperCAmelCase,      i - 1,      j - 1				)
            seq.append(ops[i][j]				)
            return seq
        elif ops[i][j][0] == "D":
            lowerCAmelCase				:			int    			=      assemble_transformation(_UpperCAmelCase,      i - 1,      _UpperCAmelCase				)
            seq.append(ops[i][j]				)
            return seq
        else:
            lowerCAmelCase				:			Tuple    			=      assemble_transformation(_UpperCAmelCase,      _UpperCAmelCase,      j - 1				)
            seq.append(ops[i][j]				)
            return seq


if __name__ == "__main__":
 __A     ,      __A   :     List[str]      =			compute_transform_tables('''Python''', '''Algorithms''', -1, 1, 2, 2)

 __A   :     Optional[int]      =			len(operations)
 __A   :     Optional[Any]      =			len(operations[0])
 __A   :     Any      =			assemble_transformation(operations, m - 1, n - 1)

 __A   :     int      =			list('''Python''')
 __A   :     Tuple      =			0
 __A   :     Tuple      =			0

 with open('''min_cost.txt''', '''w''') as file:
  for op in sequence:
   print(''''''.join(string))

   if op[0] == "C":
    file.write('''%-16s''' % '''Copy %c''' % op[1])
    file.write('''\t\t\t''' + ''''''.join(string))
    file.write('''\r\n''')

    cost -= 1
   elif op[0] == "R":
    __A   :     Any      =			op[2]

    file.write('''%-16s''' % ('''Replace %c''' % op[1] + ''' with ''' + str(op[2])))
    file.write('''\t\t''' + ''''''.join(string))
    file.write('''\r\n''')

    cost += 1
   elif op[0] == "D":
    string.pop(i)

    file.write('''%-16s''' % '''Delete %c''' % op[1])
    file.write('''\t\t\t''' + ''''''.join(string))
    file.write('''\r\n''')

    cost += 2
   else:
    string.insert(i, op[1])

    file.write('''%-16s''' % '''Insert %c''' % op[1])
    file.write('''\t\t\t''' + ''''''.join(string))
    file.write('''\r\n''')

    cost += 2

   i += 1

  print(''''''.join(string))
  print('''Cost: ''', cost)

  file.write('''\r\nMinimum cost: ''' + str(cost))





