


import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__UpperCamelCase				:  List[Any]      					=							{
    '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',
}

__UpperCamelCase				:  str      					=							{
    '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',
}

__UpperCamelCase				:  List[str]      					=							{
    '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',
}

__UpperCamelCase				:  str      					=							{
    'num_train_timesteps': 40,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

__UpperCamelCase				:  Union[str, Any]      					=							{
    'num_train_timesteps': 201,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

__UpperCamelCase				:  Optional[Any]      					=							{
    'num_train_timesteps': 151,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}
def      snake_case_						( __lowercase       ):
   if isinstance(__lowercase			,       __lowercase       ):
      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      snake_case_						( __lowercase			,       __lowercase			,       __lowercase			,       __lowercase			,       __lowercase=False       ):
   UpperCAmelCase_							:      Dict						=   checkpoint[F'''{old_prefix}.in_layers.0.weight''']
   UpperCAmelCase_							:      Union[str, Any]						=   checkpoint[F'''{old_prefix}.in_layers.0.bias''']
   UpperCAmelCase_							:      List[Any]						=   checkpoint[F'''{old_prefix}.in_layers.2.weight''']
   UpperCAmelCase_							:      Tuple						=   checkpoint[F'''{old_prefix}.in_layers.2.bias''']
   UpperCAmelCase_							:      Tuple						=   checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
   UpperCAmelCase_							:      Dict						=   checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
   UpperCAmelCase_							:      List[str]						=   checkpoint[F'''{old_prefix}.out_layers.0.weight''']
   UpperCAmelCase_							:      Any						=   checkpoint[F'''{old_prefix}.out_layers.0.bias''']
   UpperCAmelCase_							:      Optional[Any]						=   checkpoint[F'''{old_prefix}.out_layers.3.weight''']
   UpperCAmelCase_							:      int						=   checkpoint[F'''{old_prefix}.out_layers.3.bias''']

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

   return new_checkpoint
def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase			,       __lowercase			,       __lowercase=None       ):
   UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_							:      Optional[int]						=   checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3			,       dim=0       )
   UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_							:      List[str]						=   checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3			,       dim=0       )

   UpperCAmelCase_							:      List[Any]						=   checkpoint[F'''{old_prefix}.norm.weight''']
   UpperCAmelCase_							:      Tuple						=   checkpoint[F'''{old_prefix}.norm.bias''']

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

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

   return new_checkpoint
def      snake_case_						( __lowercase			,       __lowercase       ):
   UpperCAmelCase_							:      int						=   torch.load(__lowercase			,       map_location='''cpu'''       )
   UpperCAmelCase_							:      Optional[int]						=   {}

   UpperCAmelCase_							:      Optional[int]						=   checkpoint['''time_embed.0.weight''']
   UpperCAmelCase_							:      Any						=   checkpoint['''time_embed.0.bias''']
   UpperCAmelCase_							:      int						=   checkpoint['''time_embed.2.weight''']
   UpperCAmelCase_							:      str						=   checkpoint['''time_embed.2.bias''']

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

   UpperCAmelCase_							:      Optional[Any]						=   checkpoint['''input_blocks.0.0.weight''']
   UpperCAmelCase_							:      List[Any]						=   checkpoint['''input_blocks.0.0.bias''']

   UpperCAmelCase_							:      List[Any]						=   unet_config['''down_block_types''']
   UpperCAmelCase_							:      Tuple						=   unet_config['''layers_per_block''']
   UpperCAmelCase_							:      int						=   unet_config['''attention_head_dim''']
   UpperCAmelCase_							:      List[Any]						=   unet_config['''block_out_channels''']
   UpperCAmelCase_							:      List[Any]						=   1
   UpperCAmelCase_							:      Union[str, Any]						=   channels_list[0]

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

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

      if i != len(__lowercase       ) - 1:
         UpperCAmelCase_							:      Any						=   F'''down_blocks.{i}.downsamplers.0'''
         UpperCAmelCase_							:      str						=   F'''input_blocks.{current_layer}.0'''
         UpperCAmelCase_							:      List[str]						=   convert_resnet(__lowercase			,       __lowercase			,       __lowercase			,       __lowercase       )
         current_layer += 1

      UpperCAmelCase_							:      str						=   current_channels

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

   UpperCAmelCase_							:      int						=   0
   UpperCAmelCase_							:      List[Any]						=   unet_config['''up_block_types''']

   for i, layer_type in enumerate(__lowercase       ):
      if layer_type == "ResnetUpsampleBlock2D":
         for j in range(layers_per_block + 1       ):
            UpperCAmelCase_							:      Tuple						=   F'''up_blocks.{i}.resnets.{j}'''
            UpperCAmelCase_							:      List[str]						=   F'''output_blocks.{current_layer}.0'''
            UpperCAmelCase_							:      Optional[int]						=   convert_resnet(__lowercase			,       __lowercase			,       __lowercase			,       __lowercase			,       has_skip=__lowercase       )
            current_layer += 1

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

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

   UpperCAmelCase_							:      str						=   checkpoint['''out.0.weight''']
   UpperCAmelCase_							:      Optional[Any]						=   checkpoint['''out.0.bias''']
   UpperCAmelCase_							:      Union[str, Any]						=   checkpoint['''out.2.weight''']
   UpperCAmelCase_							:      int						=   checkpoint['''out.2.bias''']

   return new_checkpoint


if __name__ == "__main__":
       __UpperCamelCase				:  Dict      					=							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.')

       __UpperCamelCase				:  str      					=							parser.parse_args()
       __UpperCamelCase				:  Optional[Any]      					=							strabool(args.class_cond)

       __UpperCamelCase				:  Optional[Any]      					=							os.path.basename(args.unet_path)
       print(F'Checkpoint: {ckpt_name}')

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

       if not args.class_cond:
              __UpperCamelCase				:  List[Any]      					=							None

       __UpperCamelCase				:  Optional[Any]      					=							con_pt_to_diffuser(args.unet_path, unet_config)

       __UpperCamelCase				:  Optional[int]      					=							UNetaDModel(**unet_config)
       image_unet.load_state_dict(converted_unet_ckpt)

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

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

       __UpperCamelCase				:  List[Any]      					=							ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
       consistency_model.save_pretrained(args.dump_path)



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class  lowerCAmelCase__(    snake_case__						):






    '''simple docstring'''





    A_  :     str			= ['image_processor', 'tokenizer']
    A_  :     int			= 'LayoutLMv2ImageProcessor'
    A_  :     str			= ('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')





    def __init__(	self				:   Union[str, Any]       ,   __snake_case				:   List[Any]=None       ,   __snake_case				:   List[str]=None       ,   **__snake_case				:   Optional[int]	):





       '''simple docstring'''
       if "feature_extractor" in kwargs:
          warnings.warn(
              '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
              ''' instead.'''       ,   __snake_case       ,   )
          UpperCAmelCase_							:      List[Any]						=   kwargs.pop('''feature_extractor'''	)

       UpperCAmelCase_							:      Optional[int]						=   image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
          raise ValueError('''You need to specify an `image_processor`.'''	)
       if tokenizer is None:
          raise ValueError('''You need to specify a `tokenizer`.'''	)

       super().__init__(__snake_case       ,   __snake_case	)





    def __call__(	self				:   List[str]       ,   __snake_case				:   Dict       ,   __snake_case				:   Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None       ,   __snake_case				:   Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None       ,   __snake_case				:   Union[List[List[int]], List[List[List[int]]]] = None       ,   __snake_case				:   Optional[Union[List[int], List[List[int]]]] = None       ,   __snake_case				:   bool = True       ,   __snake_case				:   Union[bool, str, PaddingStrategy] = False       ,   __snake_case				:   Union[bool, str, TruncationStrategy] = None       ,   __snake_case				:   Optional[int] = None       ,   __snake_case				:   int = 0       ,   __snake_case				:   Optional[int] = None       ,   __snake_case				:   Optional[bool] = None       ,   __snake_case				:   Optional[bool] = None       ,   __snake_case				:   bool = False       ,   __snake_case				:   bool = False       ,   __snake_case				:   bool = False       ,   __snake_case				:   bool = False       ,   __snake_case				:   bool = True       ,   __snake_case				:   Optional[Union[str, TensorType]] = None       ,   **__snake_case				:   Optional[int]       ,   ):





       '''simple docstring'''
       # verify input
       if self.image_processor.apply_ocr and (boxes is not None):
          raise ValueError(
              '''You cannot provide bounding boxes '''
              '''if you initialized the image processor with apply_ocr set to True.'''	)

       if self.image_processor.apply_ocr and (word_labels is not None):
          raise ValueError(
              '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''	)

       if return_overflowing_tokens is True and return_offsets_mapping is False:
          raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''	)

       # first, apply the image processor
       UpperCAmelCase_							:      Tuple						=   self.image_processor(images=__snake_case       ,   return_tensors=__snake_case	)

       # second, apply the tokenizer
       if text is not None and self.image_processor.apply_ocr and text_pair is None:
          if isinstance(__snake_case       ,   __snake_case	):
             UpperCAmelCase_							:      Optional[Any]						=   [text]  # add batch dimension (as the image processor always adds a batch dimension)
          UpperCAmelCase_							:      Any						=   features['''words''']

       UpperCAmelCase_							:      str						=   self.tokenizer(
           text=text if text is not None else features['''words''']       ,   text_pair=text_pair if text_pair is not None else None       ,   boxes=boxes if boxes is not None else features['''boxes''']       ,   word_labels=__snake_case       ,   add_special_tokens=__snake_case       ,   padding=__snake_case       ,   truncation=__snake_case       ,   max_length=__snake_case       ,   stride=__snake_case       ,   pad_to_multiple_of=__snake_case       ,   return_token_type_ids=__snake_case       ,   return_attention_mask=__snake_case       ,   return_overflowing_tokens=__snake_case       ,   return_special_tokens_mask=__snake_case       ,   return_offsets_mapping=__snake_case       ,   return_length=__snake_case       ,   verbose=__snake_case       ,   return_tensors=__snake_case       ,   **__snake_case       ,   )

       # add pixel values
       UpperCAmelCase_							:      List[str]						=   features.pop('''pixel_values'''	)
       if return_overflowing_tokens is True:
          UpperCAmelCase_							:      Optional[int]						=   self.get_overflowing_images(__snake_case       ,   encoded_inputs['''overflow_to_sample_mapping''']	)
       UpperCAmelCase_							:      List[Any]						=   images

       return encoded_inputs





    def 					_lowerCamelCase (	self				:   Union[str, Any]       ,   __snake_case				:   str       ,   __snake_case				:   List[Any]	):





       '''simple docstring'''
       # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
       UpperCAmelCase_							:      List[str]						=   []
       for sample_idx in overflow_to_sample_mapping:
          images_with_overflow.append(images[sample_idx]	)

       if len(__snake_case	) != len(__snake_case	):
          raise ValueError(
              '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
              f''' {len(__snake_case	)} and {len(__snake_case	)}'''	)

       return images_with_overflow





    def 					_lowerCamelCase (	self				:   List[Any]       ,   *__snake_case				:   Optional[int]       ,   **__snake_case				:   List[Any]	):





       '''simple docstring'''
       return self.tokenizer.batch_decode(*__snake_case       ,   **__snake_case	)





    def 					_lowerCamelCase (	self				:   str       ,   *__snake_case				:   Optional[Any]       ,   **__snake_case				:   Union[str, Any]	):





       '''simple docstring'''
       return self.tokenizer.decode(*__snake_case       ,   **__snake_case	)





    @property
    def 					_lowerCamelCase (	self				:   Optional[Any]	):





       '''simple docstring'''
       return ["input_ids", "bbox", "attention_mask", "image"]





    @property
    def 					_lowerCamelCase (	self				:   List[str]	):





       '''simple docstring'''
       warnings.warn(
           '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''       ,   __snake_case       ,   )
       return self.image_processor_class





    @property
    def 					_lowerCamelCase (	self				:   Dict	):





       '''simple docstring'''
       warnings.warn(
           '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''       ,   __snake_case       ,   )
       return self.image_processor