'''simple docstring'''





import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


SCREAMING_SNAKE_CASE_:     Tuple  ={
    'sample_size': 32,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': 10_00,
    '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',
}

SCREAMING_SNAKE_CASE_:     Union[str, Any]  ={
    'sample_size': 64,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 3,
    'num_class_embeds': 10_00,
    'block_out_channels': [1_92, 1_92 * 2, 1_92 * 3, 1_92 * 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',
}

SCREAMING_SNAKE_CASE_:     Tuple  ={
    'sample_size': 2_56,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': None,
    'block_out_channels': [2_56, 2_56, 2_56 * 2, 2_56 * 2, 2_56 * 4, 2_56 * 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',
}

SCREAMING_SNAKE_CASE_:     Any  ={
    'num_train_timesteps': 40,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

SCREAMING_SNAKE_CASE_:     List[str]  ={
    'num_train_timesteps': 2_01,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

SCREAMING_SNAKE_CASE_:     List[Any]  ={
    'num_train_timesteps': 1_51,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}





def 	lowerCAmelCase_				(					snake_case_       : Optional[int]			)    ->      Optional[Any]:


     '''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 	lowerCAmelCase_				(					snake_case_       : Optional[Any]					,    snake_case_       : Optional[Any]					,    snake_case_       : Any					,    snake_case_       : Optional[int]					,    snake_case_       : Dict=False			)    ->      Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_           =   checkpoint[f"""{old_prefix}.in_layers.0.weight"""]
     UpperCAmelCase_           =   checkpoint[f"""{old_prefix}.in_layers.0.bias"""]
     UpperCAmelCase_           =   checkpoint[f"""{old_prefix}.in_layers.2.weight"""]
     UpperCAmelCase_           =   checkpoint[f"""{old_prefix}.in_layers.2.bias"""]
     UpperCAmelCase_           =   checkpoint[f"""{old_prefix}.emb_layers.1.weight"""]
     UpperCAmelCase_           =   checkpoint[f"""{old_prefix}.emb_layers.1.bias"""]
     UpperCAmelCase_           =   checkpoint[f"""{old_prefix}.out_layers.0.weight"""]
     UpperCAmelCase_           =   checkpoint[f"""{old_prefix}.out_layers.0.bias"""]
     UpperCAmelCase_           =   checkpoint[f"""{old_prefix}.out_layers.3.weight"""]
     UpperCAmelCase_           =   checkpoint[f"""{old_prefix}.out_layers.3.bias"""]

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

     return new_checkpoint





def 	lowerCAmelCase_				(					snake_case_       : List[Any]					,    snake_case_       : Optional[int]					,    snake_case_       : int					,    snake_case_       : Any					,    snake_case_       : Any=None			)    ->      List[str]:


     '''simple docstring'''




     UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_           =   checkpoint[f"""{old_prefix}.qkv.weight"""].chunk(3					,    dim=0			)
     UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_           =   checkpoint[f"""{old_prefix}.qkv.bias"""].chunk(3					,    dim=0			)

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

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

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

     return new_checkpoint





def 	lowerCAmelCase_				(					snake_case_       : str					,    snake_case_       : int			)    ->      Dict:


     '''simple docstring'''




     UpperCAmelCase_           =   torch.load(snake_case_					,    map_location="cpu"			)
     UpperCAmelCase_           =   {}

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

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

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

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

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

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

          UpperCAmelCase_           =   current_channels

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

     UpperCAmelCase_           =   0
     UpperCAmelCase_           =   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			):
                    UpperCAmelCase_           =   f"""up_blocks.{i}.resnets.{j}"""
                    UpperCAmelCase_           =   f"""output_blocks.{current_layer}.0"""
                    UpperCAmelCase_           =   convert_resnet(snake_case_					,    snake_case_					,    snake_case_					,    snake_case_					,    has_skip=snake_case_			)
                    current_layer += 1

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

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

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

     return new_checkpoint


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:     Tuple  =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.')

    SCREAMING_SNAKE_CASE_:     Union[str, Any]  =parser.parse_args()
    SCREAMING_SNAKE_CASE_:     Optional[Any]  =strabool(args.class_cond)

    SCREAMING_SNAKE_CASE_:     List[Any]  =os.path.basename(args.unet_path)
    print(f"Checkpoint: {ckpt_name}")

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

    if not args.class_cond:
        SCREAMING_SNAKE_CASE_:     Union[str, Any]  =None

    SCREAMING_SNAKE_CASE_:     Any  =con_pt_to_diffuser(args.unet_path, unet_config)

    SCREAMING_SNAKE_CASE_:     str  =UNetaDModel(**unet_config)
    image_unet.load_state_dict(converted_unet_ckpt)

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

    SCREAMING_SNAKE_CASE_:     Union[str, Any]  =CMStochasticIterativeScheduler(**scheduler_config)

    SCREAMING_SNAKE_CASE_:     Union[str, Any]  =ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
    consistency_model.save_pretrained(args.dump_path)








import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


lowerCamelCase											=       logging.getLogger()




@unittest.skip('Temporarily disable the doc tests.'     )
@require_torch
@require_tf
@slow
class A  (   unittest.TestCase     ):






      def        lowerCamelCase       (					self						:     Dict			,   lowercase_						:     Path			,   lowercase_						:     Union[str, None] = None			,   lowercase_						:     Union[List[str], None] = None			,   lowercase_						:     Union[str, List[str], None] = None			,   lowercase_						:     bool = True			,   )					->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase       :		Optional[Any]		=[file for file in os.listdir(lowercase_ ) if os.path.isfile(os.path.join(lowercase_			,   lowercase_ ) )]

             if identifier is not None:
                    _lowerCamelCase       :		Any		=[file for file in files if identifier in file]

             if n_identifier is not None:
                    if isinstance(lowercase_			,   lowercase_ ):
                           for n_ in n_identifier:
                                  _lowerCamelCase       :		Any		=[file for file in files if n_ not in file]
                    else:
                           _lowerCamelCase       :		List[Any]		=[file for file in files if n_identifier not in file]

             _lowerCamelCase       :		List[Any]		=ignore_files or []
             ignore_files.append('__init__.py' )
             _lowerCamelCase       :		List[Any]		=[file for file in files if file not in ignore_files]

             for file in files:
                    # Open all files
                    print('Testing'			,   lowercase_ )

                    if only_modules:
                           _lowerCamelCase       :		Optional[int]		=file.split('.' )[0]
                           try:
                                  _lowerCamelCase       :		Any		=getattr(lowercase_			,   lowercase_ )
                                  _lowerCamelCase       :		Optional[int]		=doctest.DocTestSuite(lowercase_ )
                                  _lowerCamelCase       :		List[Any]		=unittest.TextTestRunner().run(lowercase_ )
                                  self.assertIs(len(result.failures )			,   0 )
                           except AttributeError:
                                  logger.info(F'''{module_identifier} is not a module.''' )
                    else:
                           _lowerCamelCase       :		int		=doctest.testfile(str('..' / directory / file )			,   optionflags=doctest.ELLIPSIS )
                           self.assertIs(result.failed			,   0 )






      def        lowerCamelCase       (					self						:     Any )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		int		=Path('src/transformers' )
             _lowerCamelCase       :		Optional[Any]		='modeling'
             _lowerCamelCase       :		List[str]		=[
                 'modeling_ctrl.py',
                 'modeling_tf_ctrl.py',
             ]
             self.analyze_directory(lowercase_			,   identifier=lowercase_			,   ignore_files=lowercase_ )






      def        lowerCamelCase       (					self						:     List[str] )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Optional[int]		=Path('src/transformers' )
             _lowerCamelCase       :		Union[str, Any]		='tokenization'
             self.analyze_directory(lowercase_			,   identifier=lowercase_ )






      def        lowerCamelCase       (					self						:     Optional[int] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		str		=Path('src/transformers' )
             _lowerCamelCase       :		Any		='configuration'
             self.analyze_directory(lowercase_			,   identifier=lowercase_ )






      def        lowerCamelCase       (					self						:     List[str] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		=Path('src/transformers' )
             _lowerCamelCase       :		int		=['configuration', 'modeling', 'tokenization']
             self.analyze_directory(lowercase_			,   n_identifier=lowercase_ )






      def        lowerCamelCase       (					self						:     Optional[Any] )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		str		=Path('docs/source' )
             _lowerCamelCase       :		Dict		=['favicon.ico']
             self.analyze_directory(lowercase_			,   ignore_files=lowercase_			,   only_modules=lowercase_ )


