
import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__a  			=					{
    "sample_size": 3_2,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": 1_0_0_0,
    "block_out_channels": [3_2, 6_4],
    "attention_head_dim": 8,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

__a  			=					{
    "sample_size": 6_4,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 3,
    "num_class_embeds": 1_0_0_0,
    "block_out_channels": [1_9_2, 1_9_2 * 2, 1_9_2 * 3, 1_9_2 * 4],
    "attention_head_dim": 6_4,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

__a  			=					{
    "sample_size": 2_5_6,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": None,
    "block_out_channels": [2_5_6, 2_5_6, 2_5_6 * 2, 2_5_6 * 2, 2_5_6 * 4, 2_5_6 * 4],
    "attention_head_dim": 6_4,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "default",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

__a  			=					{
    "num_train_timesteps": 4_0,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

__a  			=					{
    "num_train_timesteps": 2_0_1,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

__a  			=					{
    "num_train_timesteps": 1_5_1,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}




def 				a						(							snake_case__:					Optional[int]					):


				'''simple docstring'''
				if isinstance(snake_case__						,   snake_case__					):
								return v
				if v.lower() in ("yes", "true", "t", "y", "1"):
								return True
				elif v.lower() in ("no", "false", "f", "n", "0"):
								return False
				else:
								raise argparse.ArgumentTypeError('''boolean value expected'''					)




def 				a						(							snake_case__:					Union[str, Any]						,   snake_case__:					List[str]						,   snake_case__:					str						,   snake_case__:					Union[str, Any]						,   snake_case__:					Any=False					):


				'''simple docstring'''
				lowercase_       =					checkpoint[F'''{old_prefix}.in_layers.0.weight''']
				lowercase_       =					checkpoint[F'''{old_prefix}.in_layers.0.bias''']
				lowercase_       =					checkpoint[F'''{old_prefix}.in_layers.2.weight''']
				lowercase_       =					checkpoint[F'''{old_prefix}.in_layers.2.bias''']
				lowercase_       =					checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
				lowercase_       =					checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
				lowercase_       =					checkpoint[F'''{old_prefix}.out_layers.0.weight''']
				lowercase_       =					checkpoint[F'''{old_prefix}.out_layers.0.bias''']
				lowercase_       =					checkpoint[F'''{old_prefix}.out_layers.3.weight''']
				lowercase_       =					checkpoint[F'''{old_prefix}.out_layers.3.bias''']

				if has_skip:
								lowercase_       =					checkpoint[F'''{old_prefix}.skip_connection.weight''']
								lowercase_       =					checkpoint[F'''{old_prefix}.skip_connection.bias''']

				return new_checkpoint




def 				a						(							snake_case__:					Union[str, Any]						,   snake_case__:					List[str]						,   snake_case__:					Optional[Any]						,   snake_case__:					Optional[Any]						,   snake_case__:					List[Any]=None					):


				'''simple docstring'''
				lowercase_       =					checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3						,   dim=0					)
				lowercase_       =					checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3						,   dim=0					)

				lowercase_       =					checkpoint[F'''{old_prefix}.norm.weight''']
				lowercase_       =					checkpoint[F'''{old_prefix}.norm.bias''']

				lowercase_       =					weight_q.squeeze(-1					).squeeze(-1					)
				lowercase_       =					bias_q.squeeze(-1					).squeeze(-1					)
				lowercase_       =					weight_k.squeeze(-1					).squeeze(-1					)
				lowercase_       =					bias_k.squeeze(-1					).squeeze(-1					)
				lowercase_       =					weight_v.squeeze(-1					).squeeze(-1					)
				lowercase_       =					bias_v.squeeze(-1					).squeeze(-1					)

				lowercase_       =					(
				    checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1					).squeeze(-1					)
				)
				lowercase_       =					checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1					).squeeze(-1					)

				return new_checkpoint




def 				a						(							snake_case__:					str						,   snake_case__:					str					):


				'''simple docstring'''
				lowercase_       =					torch.load(snake_case__						,   map_location='''cpu'''					)
				lowercase_       =					{}

				lowercase_       =					checkpoint['time_embed.0.weight']
				lowercase_       =					checkpoint['time_embed.0.bias']
				lowercase_       =					checkpoint['time_embed.2.weight']
				lowercase_       =					checkpoint['time_embed.2.bias']

				if unet_config["num_class_embeds"] is not None:
								lowercase_       =					checkpoint['label_emb.weight']

				lowercase_       =					checkpoint['input_blocks.0.0.weight']
				lowercase_       =					checkpoint['input_blocks.0.0.bias']

				lowercase_       =					unet_config['down_block_types']
				lowercase_       =					unet_config['layers_per_block']
				lowercase_       =					unet_config['attention_head_dim']
				lowercase_       =					unet_config['block_out_channels']
				lowercase_       =					1
				lowercase_       =					channels_list[0]

				for i, layer_type in enumerate(snake_case__					):
								lowercase_       =					channels_list[i]
								lowercase_       =					current_channels != prev_channels
								if layer_type == "ResnetDownsampleBlock2D":
												for j in range(snake_case__					):
																lowercase_       =					F'''down_blocks.{i}.resnets.{j}'''
																lowercase_       =					F'''input_blocks.{current_layer}.0'''
																lowercase_       =					True if j == 0 and downsample_block_has_skip else False
																lowercase_       =					convert_resnet(snake_case__						,   snake_case__						,   snake_case__						,   snake_case__						,   has_skip=snake_case__					)
																current_layer += 1

								elif layer_type == "AttnDownBlock2D":
												for j in range(snake_case__					):
																lowercase_       =					F'''down_blocks.{i}.resnets.{j}'''
																lowercase_       =					F'''input_blocks.{current_layer}.0'''
																lowercase_       =					True if j == 0 and downsample_block_has_skip else False
																lowercase_       =					convert_resnet(snake_case__						,   snake_case__						,   snake_case__						,   snake_case__						,   has_skip=snake_case__					)
																lowercase_       =					F'''down_blocks.{i}.attentions.{j}'''
																lowercase_       =					F'''input_blocks.{current_layer}.1'''
																lowercase_       =					convert_attention(
																    snake_case__						,   snake_case__						,   snake_case__						,   snake_case__						,   snake_case__					)
																current_layer += 1

								if i != len(snake_case__					) - 1:
												lowercase_       =					F'''down_blocks.{i}.downsamplers.0'''
												lowercase_       =					F'''input_blocks.{current_layer}.0'''
												lowercase_       =					convert_resnet(snake_case__						,   snake_case__						,   snake_case__						,   snake_case__					)
												current_layer += 1

								lowercase_       =					current_channels

				# hardcoded the mid-block for now
				lowercase_       =					'mid_block.resnets.0'
				lowercase_       =					'middle_block.0'
				lowercase_       =					convert_resnet(snake_case__						,   snake_case__						,   snake_case__						,   snake_case__					)
				lowercase_       =					'mid_block.attentions.0'
				lowercase_       =					'middle_block.1'
				lowercase_       =					convert_attention(snake_case__						,   snake_case__						,   snake_case__						,   snake_case__						,   snake_case__					)
				lowercase_       =					'mid_block.resnets.1'
				lowercase_       =					'middle_block.2'
				lowercase_       =					convert_resnet(snake_case__						,   snake_case__						,   snake_case__						,   snake_case__					)

				lowercase_       =					0
				lowercase_       =					unet_config['up_block_types']

				for i, layer_type in enumerate(snake_case__					):
								if layer_type == "ResnetUpsampleBlock2D":
												for j in range(layers_per_block + 1					):
																lowercase_       =					F'''up_blocks.{i}.resnets.{j}'''
																lowercase_       =					F'''output_blocks.{current_layer}.0'''
																lowercase_       =					convert_resnet(snake_case__						,   snake_case__						,   snake_case__						,   snake_case__						,   has_skip=snake_case__					)
																current_layer += 1

												if i != len(snake_case__					) - 1:
																lowercase_       =					F'''up_blocks.{i}.upsamplers.0'''
																lowercase_       =					F'''output_blocks.{current_layer-1}.1'''
																lowercase_       =					convert_resnet(snake_case__						,   snake_case__						,   snake_case__						,   snake_case__					)
								elif layer_type == "AttnUpBlock2D":
												for j in range(layers_per_block + 1					):
																lowercase_       =					F'''up_blocks.{i}.resnets.{j}'''
																lowercase_       =					F'''output_blocks.{current_layer}.0'''
																lowercase_       =					convert_resnet(snake_case__						,   snake_case__						,   snake_case__						,   snake_case__						,   has_skip=snake_case__					)
																lowercase_       =					F'''up_blocks.{i}.attentions.{j}'''
																lowercase_       =					F'''output_blocks.{current_layer}.1'''
																lowercase_       =					convert_attention(
																    snake_case__						,   snake_case__						,   snake_case__						,   snake_case__						,   snake_case__					)
																current_layer += 1

												if i != len(snake_case__					) - 1:
																lowercase_       =					F'''up_blocks.{i}.upsamplers.0'''
																lowercase_       =					F'''output_blocks.{current_layer-1}.2'''
																lowercase_       =					convert_resnet(snake_case__						,   snake_case__						,   snake_case__						,   snake_case__					)

				lowercase_       =					checkpoint['out.0.weight']
				lowercase_       =					checkpoint['out.0.bias']
				lowercase_       =					checkpoint['out.2.weight']
				lowercase_       =					checkpoint['out.2.bias']

				return new_checkpoint


if __name__ == "__main__":
						__a  			=					argparse.ArgumentParser()

						parser.add_argument('--unet_path', default=None, type=str, required=True, help='Path to the unet.pt to convert.')
						parser.add_argument(
						    '--dump_path', default=None, type=str, required=True, help='Path to output the converted UNet model.'
						)
						parser.add_argument('--class_cond', default=True, type=str, help='Whether the model is class-conditional.')

						__a  			=					parser.parse_args()
						__a  			=					strabool(args.class_cond)

						__a  			=					os.path.basename(args.unet_path)
						print(f"Checkpoint: {ckpt_name}")

						# Get U-Net config
						if "imagenet64" in ckpt_name:
												__a  			=					IMAGENET_64_UNET_CONFIG
						elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
												__a  			=					LSUN_256_UNET_CONFIG
						elif "test" in ckpt_name:
												__a  			=					TEST_UNET_CONFIG
						else:
												raise ValueError(f"Checkpoint type {ckpt_name} is not currently supported.")

						if not args.class_cond:
												__a  			=					None

						__a  			=					con_pt_to_diffuser(args.unet_path, unet_config)

						__a  			=					UNetaDModel(**unet_config)
						image_unet.load_state_dict(converted_unet_ckpt)

						# Get scheduler config
						if "cd" in ckpt_name or "test" in ckpt_name:
												__a  			=					CD_SCHEDULER_CONFIG
						elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
												__a  			=					CT_IMAGENET_64_SCHEDULER_CONFIG
						elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
												__a  			=					CT_LSUN_256_SCHEDULER_CONFIG
						else:
												raise ValueError(f"Checkpoint type {ckpt_name} is not currently supported.")

						__a  			=					CMStochasticIterativeScheduler(**scheduler_config)

						__a  			=					ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
						consistency_model.save_pretrained(args.dump_path)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


a_   :int        =      {
    "configuration_longt5": ["LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongT5Config", "LongT5OnnxConfig"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :List[str]        =      [
     "LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST",
     "LongT5EncoderModel",
     "LongT5ForConditionalGeneration",
     "LongT5Model",
     "LongT5PreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :int        =      [
     "FlaxLongT5ForConditionalGeneration",
     "FlaxLongT5Model",
     "FlaxLongT5PreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_longta import (
      LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
      LongTaEncoderModel,
      LongTaForConditionalGeneration,
      LongTaModel,
      LongTaPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_longta import (
      FlaxLongTaForConditionalGeneration,
      FlaxLongTaModel,
      FlaxLongTaPreTrainedModel,
  )


else:
 import sys

 a_   :Optional[int]        =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






