



"""simple docstring"""
import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_lowerCAmelCase    :int     =					{
    'sample_size': 32,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': 1_000,
    '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',
}

_lowerCAmelCase    :Dict     =					{
    'sample_size': 64,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 3,
    'num_class_embeds': 1_000,
    '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',
}

_lowerCAmelCase    :int     =					{
    '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',
}

_lowerCAmelCase    :Dict     =					{
    'num_train_timesteps': 40,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

_lowerCAmelCase    :int     =					{
    'num_train_timesteps': 201,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

_lowerCAmelCase    :str     =					{
    'num_train_timesteps': 151,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}





def  lowerCamelCase_						(UpperCamelCase__		:		List[str]							):
    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__		:		Optional[Any]     ,       UpperCamelCase__		:		Optional[int]     ,       UpperCamelCase__		:		List[Any]     ,       UpperCamelCase__		:		int     ,       UpperCamelCase__		:		Optional[Any]=False							):
    _UpperCAmelCase							:  Optional[int]											=    checkpoint[F'{old_prefix}.in_layers.0.weight']
    _UpperCAmelCase							:  Optional[int]											=    checkpoint[F'{old_prefix}.in_layers.0.bias']
    _UpperCAmelCase							:  str											=    checkpoint[F'{old_prefix}.in_layers.2.weight']
    _UpperCAmelCase							:  Optional[Any]											=    checkpoint[F'{old_prefix}.in_layers.2.bias']
    _UpperCAmelCase							:  Union[str, Any]											=    checkpoint[F'{old_prefix}.emb_layers.1.weight']
    _UpperCAmelCase							:  List[str]											=    checkpoint[F'{old_prefix}.emb_layers.1.bias']
    _UpperCAmelCase							:  Optional[int]											=    checkpoint[F'{old_prefix}.out_layers.0.weight']
    _UpperCAmelCase							:  Any											=    checkpoint[F'{old_prefix}.out_layers.0.bias']
    _UpperCAmelCase							:  Any											=    checkpoint[F'{old_prefix}.out_layers.3.weight']
    _UpperCAmelCase							:  Union[str, Any]											=    checkpoint[F'{old_prefix}.out_layers.3.bias']

    if has_skip:
        _UpperCAmelCase							:  Optional[int]											=    checkpoint[F'{old_prefix}.skip_connection.weight']
        _UpperCAmelCase							:  str											=    checkpoint[F'{old_prefix}.skip_connection.bias']

    return new_checkpoint





def  lowerCamelCase_						(UpperCamelCase__		:		str     ,       UpperCamelCase__		:		Dict     ,       UpperCamelCase__		:		List[Any]     ,       UpperCamelCase__		:		str     ,       UpperCamelCase__		:		Union[str, Any]=None							):
    _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  List[str]											=    checkpoint[F'{old_prefix}.qkv.weight'].chunk(3     ,       dim=0							)
    _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  List[Any]											=    checkpoint[F'{old_prefix}.qkv.bias'].chunk(3     ,       dim=0							)

    _UpperCAmelCase							:  Optional[int]											=    checkpoint[F'{old_prefix}.norm.weight']
    _UpperCAmelCase							:  List[Any]											=    checkpoint[F'{old_prefix}.norm.bias']

    _UpperCAmelCase							:  Union[str, Any]											=    weight_q.squeeze(-1							).squeeze(-1							)
    _UpperCAmelCase							:  List[Any]											=    bias_q.squeeze(-1							).squeeze(-1							)
    _UpperCAmelCase							:  Union[str, Any]											=    weight_k.squeeze(-1							).squeeze(-1							)
    _UpperCAmelCase							:  Optional[Any]											=    bias_k.squeeze(-1							).squeeze(-1							)
    _UpperCAmelCase							:  Dict											=    weight_v.squeeze(-1							).squeeze(-1							)
    _UpperCAmelCase							:  int											=    bias_v.squeeze(-1							).squeeze(-1							)

    _UpperCAmelCase							:  Tuple											=    (
        checkpoint[F'{old_prefix}.proj_out.weight'].squeeze(-1							).squeeze(-1							)
    )
    _UpperCAmelCase							:  str											=    checkpoint[F'{old_prefix}.proj_out.bias'].squeeze(-1							).squeeze(-1							)

    return new_checkpoint





def  lowerCamelCase_						(UpperCamelCase__		:		str     ,       UpperCamelCase__		:		Optional[int]							):
    _UpperCAmelCase							:  Dict											=    torch.load(UpperCamelCase__     ,       map_location='''cpu'''							)
    _UpperCAmelCase							:  str											=    {}

    _UpperCAmelCase							:  Any											=    checkpoint['''time_embed.0.weight''']
    _UpperCAmelCase							:  List[Any]											=    checkpoint['''time_embed.0.bias''']
    _UpperCAmelCase							:  Union[str, Any]											=    checkpoint['''time_embed.2.weight''']
    _UpperCAmelCase							:  str											=    checkpoint['''time_embed.2.bias''']

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

    _UpperCAmelCase							:  Dict											=    checkpoint['''input_blocks.0.0.weight''']
    _UpperCAmelCase							:  Optional[Any]											=    checkpoint['''input_blocks.0.0.bias''']

    _UpperCAmelCase							:  str											=    unet_config['''down_block_types''']
    _UpperCAmelCase							:  Any											=    unet_config['''layers_per_block''']
    _UpperCAmelCase							:  int											=    unet_config['''attention_head_dim''']
    _UpperCAmelCase							:  Dict											=    unet_config['''block_out_channels''']
    _UpperCAmelCase							:  List[Any]											=    1
    _UpperCAmelCase							:  Optional[Any]											=    channels_list[0]

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

        elif layer_type == "AttnDownBlock2D":
            for j in range(UpperCamelCase__							):
                _UpperCAmelCase							:  Union[str, Any]											=    F'down_blocks.{i}.resnets.{j}'
                _UpperCAmelCase							:  Optional[Any]											=    F'input_blocks.{current_layer}.0'
                _UpperCAmelCase							:  Tuple											=    True if j == 0 and downsample_block_has_skip else False
                _UpperCAmelCase							:  Optional[Any]											=    convert_resnet(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       has_skip=UpperCamelCase__							)
                _UpperCAmelCase							:  Optional[int]											=    F'down_blocks.{i}.attentions.{j}'
                _UpperCAmelCase							:  Dict											=    F'input_blocks.{current_layer}.1'
                _UpperCAmelCase							:  Union[str, Any]											=    convert_attention(
                    UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
                current_layer += 1

        if i != len(UpperCamelCase__							) - 1:
            _UpperCAmelCase							:  Optional[int]											=    F'down_blocks.{i}.downsamplers.0'
            _UpperCAmelCase							:  Tuple											=    F'input_blocks.{current_layer}.0'
            _UpperCAmelCase							:  Any											=    convert_resnet(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
            current_layer += 1

        _UpperCAmelCase							:  Any											=    current_channels

    # hardcoded the mid-block for now
    _UpperCAmelCase							:  Optional[int]											=    '''mid_block.resnets.0'''
    _UpperCAmelCase							:  Optional[Any]											=    '''middle_block.0'''
    _UpperCAmelCase							:  int											=    convert_resnet(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
    _UpperCAmelCase							:  int											=    '''mid_block.attentions.0'''
    _UpperCAmelCase							:  Tuple											=    '''middle_block.1'''
    _UpperCAmelCase							:  Tuple											=    convert_attention(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
    _UpperCAmelCase							:  Tuple											=    '''mid_block.resnets.1'''
    _UpperCAmelCase							:  List[Any]											=    '''middle_block.2'''
    _UpperCAmelCase							:  Tuple											=    convert_resnet(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)

    _UpperCAmelCase							:  List[str]											=    0
    _UpperCAmelCase							:  Dict											=    unet_config['''up_block_types''']

    for i, layer_type in enumerate(UpperCamelCase__							):
        if layer_type == "ResnetUpsampleBlock2D":
            for j in range(layers_per_block + 1							):
                _UpperCAmelCase							:  Union[str, Any]											=    F'up_blocks.{i}.resnets.{j}'
                _UpperCAmelCase							:  Any											=    F'output_blocks.{current_layer}.0'
                _UpperCAmelCase							:  str											=    convert_resnet(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       has_skip=UpperCamelCase__							)
                current_layer += 1

            if i != len(UpperCamelCase__							) - 1:
                _UpperCAmelCase							:  Optional[int]											=    F'up_blocks.{i}.upsamplers.0'
                _UpperCAmelCase							:  Optional[int]											=    F'output_blocks.{current_layer-1}.1'
                _UpperCAmelCase							:  Tuple											=    convert_resnet(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
        elif layer_type == "AttnUpBlock2D":
            for j in range(layers_per_block + 1							):
                _UpperCAmelCase							:  Optional[int]											=    F'up_blocks.{i}.resnets.{j}'
                _UpperCAmelCase							:  List[str]											=    F'output_blocks.{current_layer}.0'
                _UpperCAmelCase							:  Any											=    convert_resnet(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       has_skip=UpperCamelCase__							)
                _UpperCAmelCase							:  Optional[int]											=    F'up_blocks.{i}.attentions.{j}'
                _UpperCAmelCase							:  List[str]											=    F'output_blocks.{current_layer}.1'
                _UpperCAmelCase							:  Union[str, Any]											=    convert_attention(
                    UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
                current_layer += 1

            if i != len(UpperCamelCase__							) - 1:
                _UpperCAmelCase							:  Dict											=    F'up_blocks.{i}.upsamplers.0'
                _UpperCAmelCase							:  List[str]											=    F'output_blocks.{current_layer-1}.2'
                _UpperCAmelCase							:  Union[str, Any]											=    convert_resnet(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)

    _UpperCAmelCase							:  Tuple											=    checkpoint['''out.0.weight''']
    _UpperCAmelCase							:  Any											=    checkpoint['''out.0.bias''']
    _UpperCAmelCase							:  Any											=    checkpoint['''out.2.weight''']
    _UpperCAmelCase							:  str											=    checkpoint['''out.2.bias''']

    return new_checkpoint


if __name__ == "__main__":
  _lowerCAmelCase    :Union[str, Any]     =					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.')

  _lowerCAmelCase    :Optional[int]     =					parser.parse_args()
  _lowerCAmelCase    :List[Any]     =					strabool(args.class_cond)

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

  # Get U-Net config
  if "imagenet64" in ckpt_name:
    _lowerCAmelCase    :Union[str, Any]     =					IMAGENET_64_UNET_CONFIG
  elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
    _lowerCAmelCase    :List[str]     =					LSUN_256_UNET_CONFIG
  elif "test" in ckpt_name:
    _lowerCAmelCase    :int     =					TEST_UNET_CONFIG
  else:
    raise ValueError(f"Checkpoint type {ckpt_name} is not currently supported.")

  if not args.class_cond:
    _lowerCAmelCase    :int     =					None

  _lowerCAmelCase    :List[Any]     =					con_pt_to_diffuser(args.unet_path, unet_config)

  _lowerCAmelCase    :Dict     =					UNetaDModel(**unet_config)
  image_unet.load_state_dict(converted_unet_ckpt)

  # Get scheduler config
  if "cd" in ckpt_name or "test" in ckpt_name:
    _lowerCAmelCase    :Optional[int]     =					CD_SCHEDULER_CONFIG
  elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
    _lowerCAmelCase    :int     =					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)):
    _lowerCAmelCase    :Tuple     =					CT_LSUN_256_SCHEDULER_CONFIG
  else:
    raise ValueError(f"Checkpoint type {ckpt_name} is not currently supported.")

  _lowerCAmelCase    :List[str]     =					CMStochasticIterativeScheduler(**scheduler_config)

  _lowerCAmelCase    :int     =					ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
  consistency_model.save_pretrained(args.dump_path)







"""simple docstring"""
import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class 							_UpperCAmelCase							( a		,a		,unittest.TestCase      ):





     '''simple docstring'''



     a__									=IFImgaImgSuperResolutionPipeline
     a__									=TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
     a__									=TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''original_image'''}      )
     a__									=PipelineTesterMixin.required_optional_params - {'''latents'''}





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         return self._get_superresolution_dummy_components()





     def  __lowerCAmelCase	(					self       ,	A       ,	A=0		)							->					Union[str, Any]:
         if str(A		).startswith('''mps'''		):
             _UpperCAmelCase							:  Any											=    torch.manual_seed(A		)
         else:
             _UpperCAmelCase							:  int											=    torch.Generator(device=A		).manual_seed(A		)

         _UpperCAmelCase							:  str											=    floats_tensor((1, 3, 3_2, 3_2)       ,	rng=random.Random(A		)		).to(A		)
         _UpperCAmelCase							:  Dict											=    floats_tensor((1, 3, 1_6, 1_6)       ,	rng=random.Random(A		)		).to(A		)

         _UpperCAmelCase							:  List[Any]											=    {
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''image''': image,
             '''original_image''': original_image,
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }

         return inputs





     @unittest.skipIf(
         torch_device != '''cuda''' or not is_xformers_available()       ,	reason='''XFormers attention is only available with CUDA and `xformers` installed'''       ,	)
     def  __lowerCAmelCase	(					self		)							->					List[Any]:
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3		)





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         self._test_save_load_optional_components()





     @unittest.skipIf(torch_device != '''cuda'''       ,	reason='''float16 requires CUDA'''		)
     def  __lowerCAmelCase	(					self		)							->					Optional[Any]:
         # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
         super().test_save_load_floataa(expected_max_diff=1E-1		)





     def  __lowerCAmelCase	(					self		)							->					int:
         self._test_attention_slicing_forward_pass(expected_max_diff=1E-2		)





     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         self._test_save_load_local()





     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         self._test_inference_batch_single_identical(
             expected_max_diff=1E-2       ,	)


