
"""simple docstring"""





import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__magic_name__       =			{
    "sample_size": 32,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": 1000,
    "block_out_channels": [32, 64],
    "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",
}

__magic_name__       =			{
    "sample_size": 64,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 3,
    "num_class_embeds": 1000,
    "block_out_channels": [192, 192 * 2, 192 * 3, 192 * 4],
    "attention_head_dim": 64,
    "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",
}

__magic_name__       =			{
    "sample_size": 256,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": None,
    "block_out_channels": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    "attention_head_dim": 64,
    "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",
}

__magic_name__       =			{
    "num_train_timesteps": 40,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

__magic_name__       =			{
    "num_train_timesteps": 201,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

__magic_name__       =			{
    "num_train_timesteps": 151,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		if isinstance(UpperCamelCase_  ,				UpperCamelCase_				):
				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 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_=False				):
		__SCREAMING_SNAKE_CASE            = checkpoint[f"{old_prefix}.in_layers.0.weight"]
		__SCREAMING_SNAKE_CASE            = checkpoint[f"{old_prefix}.in_layers.0.bias"]
		__SCREAMING_SNAKE_CASE            = checkpoint[f"{old_prefix}.in_layers.2.weight"]
		__SCREAMING_SNAKE_CASE            = checkpoint[f"{old_prefix}.in_layers.2.bias"]
		__SCREAMING_SNAKE_CASE            = checkpoint[f"{old_prefix}.emb_layers.1.weight"]
		__SCREAMING_SNAKE_CASE            = checkpoint[f"{old_prefix}.emb_layers.1.bias"]
		__SCREAMING_SNAKE_CASE            = checkpoint[f"{old_prefix}.out_layers.0.weight"]
		__SCREAMING_SNAKE_CASE            = checkpoint[f"{old_prefix}.out_layers.0.bias"]
		__SCREAMING_SNAKE_CASE            = checkpoint[f"{old_prefix}.out_layers.3.weight"]
		__SCREAMING_SNAKE_CASE            = checkpoint[f"{old_prefix}.out_layers.3.bias"]

		if has_skip:
				__SCREAMING_SNAKE_CASE            = checkpoint[f"{old_prefix}.skip_connection.weight"]
				__SCREAMING_SNAKE_CASE            = checkpoint[f"{old_prefix}.skip_connection.bias"]

		return new_checkpoint



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_=None				):
		__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = checkpoint[f"{old_prefix}.qkv.weight"].chunk(3  ,				dim=0				)
		__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = checkpoint[f"{old_prefix}.qkv.bias"].chunk(3  ,				dim=0				)

		__SCREAMING_SNAKE_CASE            = checkpoint[f"{old_prefix}.norm.weight"]
		__SCREAMING_SNAKE_CASE            = checkpoint[f"{old_prefix}.norm.bias"]

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

		__SCREAMING_SNAKE_CASE            = (
		    checkpoint[f"{old_prefix}.proj_out.weight"].squeeze(-1				).squeeze(-1				)
		)
		__SCREAMING_SNAKE_CASE            = checkpoint[f"{old_prefix}.proj_out.bias"].squeeze(-1				).squeeze(-1				)

		return new_checkpoint



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = torch.load(UpperCamelCase_  ,				map_location="""cpu"""				)
		__SCREAMING_SNAKE_CASE            = {}

		__SCREAMING_SNAKE_CASE            = checkpoint["""time_embed.0.weight"""]
		__SCREAMING_SNAKE_CASE            = checkpoint["""time_embed.0.bias"""]
		__SCREAMING_SNAKE_CASE            = checkpoint["""time_embed.2.weight"""]
		__SCREAMING_SNAKE_CASE            = checkpoint["""time_embed.2.bias"""]

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

		__SCREAMING_SNAKE_CASE            = checkpoint["""input_blocks.0.0.weight"""]
		__SCREAMING_SNAKE_CASE            = checkpoint["""input_blocks.0.0.bias"""]

		__SCREAMING_SNAKE_CASE            = unet_config["""down_block_types"""]
		__SCREAMING_SNAKE_CASE            = unet_config["""layers_per_block"""]
		__SCREAMING_SNAKE_CASE            = unet_config["""attention_head_dim"""]
		__SCREAMING_SNAKE_CASE            = unet_config["""block_out_channels"""]
		__SCREAMING_SNAKE_CASE            = 1
		__SCREAMING_SNAKE_CASE            = channels_list[0]

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

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

				if i != len(UpperCamelCase_				) - 1:
						__SCREAMING_SNAKE_CASE            = f"down_blocks.{i}.downsamplers.0"
						__SCREAMING_SNAKE_CASE            = f"input_blocks.{current_layer}.0"
						__SCREAMING_SNAKE_CASE            = convert_resnet(UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				)
						current_layer += 1

				__SCREAMING_SNAKE_CASE            = current_channels

		# hardcoded the mid-block for now
		__SCREAMING_SNAKE_CASE            = """mid_block.resnets.0"""
		__SCREAMING_SNAKE_CASE            = """middle_block.0"""
		__SCREAMING_SNAKE_CASE            = convert_resnet(UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = """mid_block.attentions.0"""
		__SCREAMING_SNAKE_CASE            = """middle_block.1"""
		__SCREAMING_SNAKE_CASE            = convert_attention(UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = """mid_block.resnets.1"""
		__SCREAMING_SNAKE_CASE            = """middle_block.2"""
		__SCREAMING_SNAKE_CASE            = convert_resnet(UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				)

		__SCREAMING_SNAKE_CASE            = 0
		__SCREAMING_SNAKE_CASE            = unet_config["""up_block_types"""]

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

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

						if i != len(UpperCamelCase_				) - 1:
								__SCREAMING_SNAKE_CASE            = f"up_blocks.{i}.upsamplers.0"
								__SCREAMING_SNAKE_CASE            = f"output_blocks.{current_layer-1}.2"
								__SCREAMING_SNAKE_CASE            = convert_resnet(UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				)

		__SCREAMING_SNAKE_CASE            = checkpoint["""out.0.weight"""]
		__SCREAMING_SNAKE_CASE            = checkpoint["""out.0.bias"""]
		__SCREAMING_SNAKE_CASE            = checkpoint["""out.2.weight"""]
		__SCREAMING_SNAKE_CASE            = checkpoint["""out.2.bias"""]

		return new_checkpoint


if __name__ == "__main__":
							__magic_name__       =			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.")

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

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

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

							if not args.class_cond:
														__magic_name__       =			None

							__magic_name__       =			con_pt_to_diffuser(args.unet_path, unet_config)

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

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

							__magic_name__       =			CMStochasticIterativeScheduler(**scheduler_config)

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







'''simple docstring'''



import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
  import jax







@require_flax
class 						lowerCAmelCase__   (      a ,					unittest.TestCase					):



 """simple docstring"""



 lowerCAmelCase__  					=      FlaxAutoencoderKL
 @property
 def       UpperCAmelCase__		(		self					:       Tuple			)  -> List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      4
      __SCREAMING_SNAKE_CASE     =      3
      __SCREAMING_SNAKE_CASE     =      (32, 32)

      __SCREAMING_SNAKE_CASE     =      jax.random.PRNGKey(0			)
      __SCREAMING_SNAKE_CASE     =      jax.random.uniform(__SCREAMING_SNAKE_CASE					,							((batch_size, num_channels) + sizes)			)

      return {"sample": image, "prng_key": prng_key}


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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      {
          """block_out_channels""": [32, 64],
          """in_channels""": 3,
          """out_channels""": 3,
          """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
          """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
          """latent_channels""": 4,
      }
      __SCREAMING_SNAKE_CASE     =      self.dummy_input
      return init_dict, inputs_dict






