

import argparse

import torch

from diffusers import UNetaDConditionModel







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

 """simple docstring"""






 assert isinstance(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		), "Paths should be a list of dicts containing 'old' and 'new' keys."

 # Splits the attention layers into three variables.
 if attention_paths_to_split is not None:
  for path, path_map in attention_paths_to_split.items():
   __lowerCAmelCase:       List[str]            =				old_checkpoint[path]
   __lowerCAmelCase:       Tuple            =				old_tensor.shape[0] // 3

   __lowerCAmelCase:       int            =				(-1, channels) if len(old_tensor.shape		) == 3 else (-1)

   __lowerCAmelCase:       Tuple            =				old_tensor.shape[0] // config['num_head_channels'] // 3

   __lowerCAmelCase:       List[str]            =				old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]		)
   __lowerCAmelCase:       List[Any]            =				old_tensor.split(channels // num_heads   , dim=1		)

   __lowerCAmelCase:       Optional[int]            =				query.reshape(SCREAMING_SNAKE_CASE		)
   __lowerCAmelCase:       str            =				key.reshape(SCREAMING_SNAKE_CASE		)
   __lowerCAmelCase:       List[Any]            =				value.reshape(SCREAMING_SNAKE_CASE		)

 for path in paths:
  __lowerCAmelCase:       Optional[int]            =				path['new']

  # These have already been assigned
  if attention_paths_to_split is not None and new_path in attention_paths_to_split:
   continue

  if additional_replacements is not None:
   for replacement in additional_replacements:
    __lowerCAmelCase:       int            =				new_path.replace(replacement['old']   , replacement['new']		)

        # proj_attn.weight has to be converted from conv 1D to linear
  __lowerCAmelCase:       List[str]            =				old_checkpoint[path['old']]
  __lowerCAmelCase:       str            =				['proj_attn.weight']
  __lowerCAmelCase:       Any            =				['proj_out.weight', 'proj_in.weight']
  if any(k in new_path for k in names		):
   __lowerCAmelCase:       Any            =				weight[:, :, 0]
  elif any(k in new_path for k in names_a		) and len(weight.shape		) > 2 and ".attentions." not in new_path:
   __lowerCAmelCase:       Tuple            =				weight[:, :, 0]
  else:
   __lowerCAmelCase:       Tuple            =				weight







def _a			(	SCREAMING_SNAKE_CASE		:      Any   , SCREAMING_SNAKE_CASE		:      List[Any]=0		)   ->   List[Any]:

 """simple docstring"""






 __lowerCAmelCase:       Union[str, Any]            =				[]
 for old_item in old_list:
  __lowerCAmelCase:       Any            =				old_item

  #         new_item = new_item.replace('norm.weight', 'group_norm.weight')
  #         new_item = new_item.replace('norm.bias', 'group_norm.bias')

  #         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
  #         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

  #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

  mapping.append({'old': old_item, 'new': new_item}		)

 return mapping







def _a			(	SCREAMING_SNAKE_CASE		:      int   , SCREAMING_SNAKE_CASE		:      List[str]=1		)   ->   str:

 """simple docstring"""






 if n_shave_prefix_segments >= 0:
  return ".".join(path.split('.'		)[n_shave_prefix_segments:]		)
 else:
  return ".".join(path.split('.'		)[:n_shave_prefix_segments]		)







def _a			(	SCREAMING_SNAKE_CASE		:      List[str]   , SCREAMING_SNAKE_CASE		:      Any=0		)   ->   Dict:

 """simple docstring"""






 __lowerCAmelCase:       List[Any]            =				[]
 for old_item in old_list:
  mapping.append({'old': old_item, 'new': old_item}		)

 return mapping







def _a			(	SCREAMING_SNAKE_CASE		:      str   , SCREAMING_SNAKE_CASE		:      Any=0		)   ->   Tuple:

 """simple docstring"""






 __lowerCAmelCase:       List[Any]            =				[]
 for old_item in old_list:
  __lowerCAmelCase:       Tuple            =				old_item.replace('in_layers.0'   , 'norm1'		)
  __lowerCAmelCase:       List[Any]            =				new_item.replace('in_layers.2'   , 'conv1'		)

  __lowerCAmelCase:       int            =				new_item.replace('out_layers.0'   , 'norm2'		)
  __lowerCAmelCase:       Optional[int]            =				new_item.replace('out_layers.3'   , 'conv2'		)

  __lowerCAmelCase:       List[Any]            =				new_item.replace('emb_layers.1'   , 'time_emb_proj'		)
  __lowerCAmelCase:       Dict            =				new_item.replace('skip_connection'   , 'conv_shortcut'		)

  __lowerCAmelCase:       Dict            =				shave_segments(SCREAMING_SNAKE_CASE   , n_shave_prefix_segments=SCREAMING_SNAKE_CASE		)

  if "temopral_conv" not in old_item:
   mapping.append({'old': old_item, 'new': new_item}		)

 return mapping







def _a			(	SCREAMING_SNAKE_CASE		:      str   , SCREAMING_SNAKE_CASE		:      Dict   , SCREAMING_SNAKE_CASE		:      Dict=None   , SCREAMING_SNAKE_CASE		:      Tuple=False		)   ->   Tuple:

 """simple docstring"""






 __lowerCAmelCase:       Any            =				{}
 __lowerCAmelCase:       Dict            =				list(checkpoint.keys()		)

 __lowerCAmelCase:       Optional[Any]            =				'model.diffusion_model.'

 # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
 if sum(k.startswith('model_ema'		) for k in keys		) > 1_00 and extract_ema:
  print(f'''Checkpoint {path} has both EMA and non-EMA weights.'''		)
  print(
      'In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA'
      ' weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.'		)
  for key in keys:
   if key.startswith('model.diffusion_model'		):
    __lowerCAmelCase:       List[Any]            =				'model_ema.' + ''.join(key.split('.'		)[1:]		)
    __lowerCAmelCase:       Tuple            =				checkpoint.pop(SCREAMING_SNAKE_CASE		)
 else:
  if sum(k.startswith('model_ema'		) for k in keys		) > 1_00:
   print(
       'In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA'
       ' weights (usually better for inference), please make sure to add the `--extract_ema` flag.'		)

  for key in keys:
   __lowerCAmelCase:       int            =				checkpoint.pop(SCREAMING_SNAKE_CASE		)

 __lowerCAmelCase:       Optional[int]            =				{}

 __lowerCAmelCase:       Any            =				unet_state_dict['time_embed.0.weight']
 __lowerCAmelCase:       Optional[int]            =				unet_state_dict['time_embed.0.bias']
 __lowerCAmelCase:       Optional[Any]            =				unet_state_dict['time_embed.2.weight']
 __lowerCAmelCase:       List[Any]            =				unet_state_dict['time_embed.2.bias']

 if config["class_embed_type"] is None:
  # No parameters to port
  ...
 elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
  __lowerCAmelCase:       List[Any]            =				unet_state_dict['label_emb.0.0.weight']
  __lowerCAmelCase:       List[str]            =				unet_state_dict['label_emb.0.0.bias']
  __lowerCAmelCase:       int            =				unet_state_dict['label_emb.0.2.weight']
  __lowerCAmelCase:       Union[str, Any]            =				unet_state_dict['label_emb.0.2.bias']
 else:
  raise NotImplementedError(f'''Not implemented `class_embed_type`: {config['class_embed_type']}'''		)

 __lowerCAmelCase:       List[Any]            =				unet_state_dict['input_blocks.0.0.weight']
 __lowerCAmelCase:       str            =				unet_state_dict['input_blocks.0.0.bias']

 __lowerCAmelCase:       Union[str, Any]            =				[v for v in unet_state_dict if v.startswith('input_blocks.0.1'		)]
 __lowerCAmelCase:       Optional[int]            =				renew_attention_paths(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       List[Any]            =				{'old': 'input_blocks.0.1', 'new': 'transformer_in'}
 assign_to_checkpoint(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , additional_replacements=[meta_path]   , config=SCREAMING_SNAKE_CASE		)

 __lowerCAmelCase:       str            =				unet_state_dict['out.0.weight']
 __lowerCAmelCase:       Optional[Any]            =				unet_state_dict['out.0.bias']
 __lowerCAmelCase:       Optional[Any]            =				unet_state_dict['out.2.weight']
 __lowerCAmelCase:       Any            =				unet_state_dict['out.2.bias']

 # Retrieves the keys for the input blocks only
 __lowerCAmelCase:       List[Any]            =				len({'.'.join(layer.split('.'		)[:2]		) for layer in unet_state_dict if 'input_blocks' in layer}		)
 __lowerCAmelCase:       Any            =				{
     layer_id: [key for key in unet_state_dict if f'''input_blocks.{layer_id}''' in key]
     for layer_id in range(SCREAMING_SNAKE_CASE		)
 }

 # Retrieves the keys for the middle blocks only
 __lowerCAmelCase:       Optional[Any]            =				len({'.'.join(layer.split('.'		)[:2]		) for layer in unet_state_dict if 'middle_block' in layer}		)
 __lowerCAmelCase:       List[Any]            =				{
     layer_id: [key for key in unet_state_dict if f'''middle_block.{layer_id}''' in key]
     for layer_id in range(SCREAMING_SNAKE_CASE		)
 }

 # Retrieves the keys for the output blocks only
 __lowerCAmelCase:       Union[str, Any]            =				len({'.'.join(layer.split('.'		)[:2]		) for layer in unet_state_dict if 'output_blocks' in layer}		)
 __lowerCAmelCase:       Optional[Any]            =				{
     layer_id: [key for key in unet_state_dict if f'''output_blocks.{layer_id}''' in key]
     for layer_id in range(SCREAMING_SNAKE_CASE		)
 }

 for i in range(1   , SCREAMING_SNAKE_CASE		):
  __lowerCAmelCase:       List[str]            =				(i - 1) // (config['layers_per_block'] + 1)
  __lowerCAmelCase:       Optional[Any]            =				(i - 1) % (config['layers_per_block'] + 1)

  __lowerCAmelCase:       str            =				[
      key for key in input_blocks[i] if f'''input_blocks.{i}.0''' in key and f'''input_blocks.{i}.0.op''' not in key
  ]
  __lowerCAmelCase:       Union[str, Any]            =				[key for key in input_blocks[i] if f'''input_blocks.{i}.1''' in key]
  __lowerCAmelCase:       List[str]            =				[key for key in input_blocks[i] if f'''input_blocks.{i}.2''' in key]

  if f'''input_blocks.{i}.op.weight''' in unet_state_dict:
   __lowerCAmelCase:       Any            =				unet_state_dict.pop(
       f'''input_blocks.{i}.op.weight'''		)
   __lowerCAmelCase:       Dict            =				unet_state_dict.pop(
       f'''input_blocks.{i}.op.bias'''		)

  __lowerCAmelCase:       Dict            =				renew_resnet_paths(SCREAMING_SNAKE_CASE		)
  __lowerCAmelCase:       Union[str, Any]            =				{'old': f'''input_blocks.{i}.0''', 'new': f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
  assign_to_checkpoint(
      SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , additional_replacements=[meta_path]   , config=SCREAMING_SNAKE_CASE		)

  __lowerCAmelCase:       List[Any]            =				[key for key in resnets if 'temopral_conv' in key]
  __lowerCAmelCase:       Dict            =				renew_temp_conv_paths(SCREAMING_SNAKE_CASE		)
  __lowerCAmelCase:       Optional[int]            =				{
      'old': f'''input_blocks.{i}.0.temopral_conv''',
      'new': f'''down_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
  }
  assign_to_checkpoint(
      SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , additional_replacements=[meta_path]   , config=SCREAMING_SNAKE_CASE		)

  if len(SCREAMING_SNAKE_CASE		):
   __lowerCAmelCase:       Tuple            =				renew_attention_paths(SCREAMING_SNAKE_CASE		)
   __lowerCAmelCase:       List[str]            =				{'old': f'''input_blocks.{i}.1''', 'new': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}'''}
   assign_to_checkpoint(
       SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , additional_replacements=[meta_path]   , config=SCREAMING_SNAKE_CASE		)

  if len(SCREAMING_SNAKE_CASE		):
   __lowerCAmelCase:       Any            =				renew_attention_paths(SCREAMING_SNAKE_CASE		)
   __lowerCAmelCase:       Any            =				{
       'old': f'''input_blocks.{i}.2''',
       'new': f'''down_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
   }
   assign_to_checkpoint(
       SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , additional_replacements=[meta_path]   , config=SCREAMING_SNAKE_CASE		)

 __lowerCAmelCase:       List[Any]            =				middle_blocks[0]
 __lowerCAmelCase:       List[Any]            =				[key for key in resnet_a if 'temopral_conv' in key]
 __lowerCAmelCase:       int            =				middle_blocks[1]
 __lowerCAmelCase:       Dict            =				middle_blocks[2]
 __lowerCAmelCase:       str            =				middle_blocks[3]
 __lowerCAmelCase:       List[Any]            =				[key for key in resnet_a if 'temopral_conv' in key]

 __lowerCAmelCase:       Optional[int]            =				renew_resnet_paths(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Optional[Any]            =				{'old': 'middle_block.0', 'new': 'mid_block.resnets.0'}
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , config=SCREAMING_SNAKE_CASE   , additional_replacements=[meta_path]		)

 __lowerCAmelCase:       Tuple            =				renew_temp_conv_paths(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       List[str]            =				{'old': 'middle_block.0.temopral_conv', 'new': 'mid_block.temp_convs.0'}
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , config=SCREAMING_SNAKE_CASE   , additional_replacements=[meta_path]		)

 __lowerCAmelCase:       Dict            =				renew_resnet_paths(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       str            =				{'old': 'middle_block.3', 'new': 'mid_block.resnets.1'}
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , config=SCREAMING_SNAKE_CASE   , additional_replacements=[meta_path]		)

 __lowerCAmelCase:       Union[str, Any]            =				renew_temp_conv_paths(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Dict            =				{'old': 'middle_block.3.temopral_conv', 'new': 'mid_block.temp_convs.1'}
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , config=SCREAMING_SNAKE_CASE   , additional_replacements=[meta_path]		)

 __lowerCAmelCase:       List[Any]            =				renew_attention_paths(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Union[str, Any]            =				{'old': 'middle_block.1', 'new': 'mid_block.attentions.0'}
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , additional_replacements=[meta_path]   , config=SCREAMING_SNAKE_CASE		)

 __lowerCAmelCase:       Tuple            =				renew_attention_paths(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Tuple            =				{'old': 'middle_block.2', 'new': 'mid_block.temp_attentions.0'}
 assign_to_checkpoint(
     SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , additional_replacements=[meta_path]   , config=SCREAMING_SNAKE_CASE		)

 for i in range(SCREAMING_SNAKE_CASE		):
  __lowerCAmelCase:       int            =				i // (config['layers_per_block'] + 1)
  __lowerCAmelCase:       str            =				i % (config['layers_per_block'] + 1)
  __lowerCAmelCase:       str            =				[shave_segments(SCREAMING_SNAKE_CASE   , 2		) for name in output_blocks[i]]
  __lowerCAmelCase:       Optional[Any]            =				{}

  for layer in output_block_layers:
   __lowerCAmelCase:       int            =				layer.split('.'		)[0], shave_segments(SCREAMING_SNAKE_CASE   , 1		)
   if layer_id in output_block_list:
    output_block_list[layer_id].append(SCREAMING_SNAKE_CASE		)
   else:
    __lowerCAmelCase:       Any            =				[layer_name]

  if len(SCREAMING_SNAKE_CASE		) > 1:
   __lowerCAmelCase:       Optional[int]            =				[key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
   __lowerCAmelCase:       str            =				[key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]
   __lowerCAmelCase:       str            =				[key for key in output_blocks[i] if f'''output_blocks.{i}.2''' in key]

   __lowerCAmelCase:       List[str]            =				renew_resnet_paths(SCREAMING_SNAKE_CASE		)
   __lowerCAmelCase:       str            =				renew_resnet_paths(SCREAMING_SNAKE_CASE		)

   __lowerCAmelCase:       List[str]            =				{'old': f'''output_blocks.{i}.0''', 'new': f'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
   assign_to_checkpoint(
       SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , additional_replacements=[meta_path]   , config=SCREAMING_SNAKE_CASE		)

   __lowerCAmelCase:       Union[str, Any]            =				[key for key in resnets if 'temopral_conv' in key]
   __lowerCAmelCase:       Dict            =				renew_temp_conv_paths(SCREAMING_SNAKE_CASE		)
   __lowerCAmelCase:       Dict            =				{
       'old': f'''output_blocks.{i}.0.temopral_conv''',
       'new': f'''up_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
   }
   assign_to_checkpoint(
       SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , additional_replacements=[meta_path]   , config=SCREAMING_SNAKE_CASE		)

   __lowerCAmelCase:       Dict            =				{k: sorted(SCREAMING_SNAKE_CASE		) for k, v in output_block_list.items()}
   if ["conv.bias", "conv.weight"] in output_block_list.values():
    __lowerCAmelCase:       List[Any]            =				list(output_block_list.values()		).index(['conv.bias', 'conv.weight']		)
    __lowerCAmelCase:       List[str]            =				unet_state_dict[
        f'''output_blocks.{i}.{index}.conv.weight'''
    ]
    __lowerCAmelCase:       Tuple            =				unet_state_dict[
        f'''output_blocks.{i}.{index}.conv.bias'''
    ]

    # Clear attentions as they have been attributed above.
    if len(SCREAMING_SNAKE_CASE		) == 2:
     __lowerCAmelCase:       Any            =				[]

   if len(SCREAMING_SNAKE_CASE		):
    __lowerCAmelCase:       List[str]            =				renew_attention_paths(SCREAMING_SNAKE_CASE		)
    __lowerCAmelCase:       Tuple            =				{
        'old': f'''output_blocks.{i}.1''',
        'new': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
    }
    assign_to_checkpoint(
        SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , additional_replacements=[meta_path]   , config=SCREAMING_SNAKE_CASE		)

   if len(SCREAMING_SNAKE_CASE		):
    __lowerCAmelCase:       int            =				renew_attention_paths(SCREAMING_SNAKE_CASE		)
    __lowerCAmelCase:       int            =				{
        'old': f'''output_blocks.{i}.2''',
        'new': f'''up_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
    }
    assign_to_checkpoint(
        SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , additional_replacements=[meta_path]   , config=SCREAMING_SNAKE_CASE		)
  else:
   __lowerCAmelCase:       Dict            =				renew_resnet_paths(SCREAMING_SNAKE_CASE   , n_shave_prefix_segments=1		)
   for path in resnet_0_paths:
    __lowerCAmelCase:       Dict            =				'.'.join(['output_blocks', str(SCREAMING_SNAKE_CASE		), path['old']]		)
    __lowerCAmelCase:       Dict            =				'.'.join(['up_blocks', str(SCREAMING_SNAKE_CASE		), 'resnets', str(SCREAMING_SNAKE_CASE		), path['new']]		)
    __lowerCAmelCase:       int            =				unet_state_dict[old_path]

   __lowerCAmelCase:       str            =				[l for l in output_block_layers if 'temopral_conv' in l]
   for path in temopral_conv_paths:
    __lowerCAmelCase:       Optional[Any]            =				path.split('temopral_conv.'		)[-1]
    __lowerCAmelCase:       str            =				'.'.join(['output_blocks', str(SCREAMING_SNAKE_CASE		), str(SCREAMING_SNAKE_CASE		), 'temopral_conv', pruned_path]		)
    __lowerCAmelCase:       List[str]            =				'.'.join(['up_blocks', str(SCREAMING_SNAKE_CASE		), 'temp_convs', str(SCREAMING_SNAKE_CASE		), pruned_path]		)
    __lowerCAmelCase:       Any            =				unet_state_dict[old_path]

 return new_checkpoint


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

  parser.add_argument(
      '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
  )
  parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
  _a			=       parser.parse_args()

  _a			=       torch.load(args.checkpoint_path, map_location='''cpu''')
  _a			=       UNetaDConditionModel()

  _a			=       convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

  _a			=       set(unet.state_dict().keys()) - set(converted_ckpt.keys())
  _a			=       set(converted_ckpt.keys()) - set(unet.state_dict().keys())

  assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

  # load state_dict
  unet.load_state_dict(converted_ckpt)

  unet.save_pretrained(args.dump_path)

  # -- finish converting the unet --









import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
  from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import PoolFormerImageProcessor






class 							A_			(		snake_case__ ):


     def   UpperCAmelCase  (							self      :		int )							->     Tuple:
      __lowerCAmelCase:       Optional[Any]            =				self.config_class(**self.inputs_dict )
      self.parent.assertTrue(hasattr(UpperCAmelCase      ,  'hidden_sizes' ) )
      self.parent.assertTrue(hasattr(UpperCAmelCase      ,  'num_encoder_blocks' ) )







class 							A_			:


     def __init__(							self      :		Optional[int]      ,  UpperCAmelCase      :		Any      ,  UpperCAmelCase      :		List[str]=1_3      ,  UpperCAmelCase      :		Optional[Any]=6_4      ,  UpperCAmelCase      :		Union[str, Any]=3      ,  UpperCAmelCase      :		Optional[Any]=4      ,  UpperCAmelCase      :		Any=[2, 2, 2, 2]      ,  UpperCAmelCase      :		Optional[Any]=[8, 4, 2, 1]      ,  UpperCAmelCase      :		str=[1_6, 3_2, 6_4, 1_2_8]      ,  UpperCAmelCase      :		str=[1, 4, 8, 1_6]      ,  UpperCAmelCase      :		Optional[Any]=False      ,  UpperCAmelCase      :		str=True      ,  UpperCAmelCase      :		Optional[int]="gelu"      ,  UpperCAmelCase      :		Union[str, Any]=0.1      ,  UpperCAmelCase      :		Optional[Any]=0.02      ,  UpperCAmelCase      :		List[Any]=3      ,  UpperCAmelCase      :		Optional[int]=None      ,  )							->     Any:
      __lowerCAmelCase:       List[str]            =				parent
      __lowerCAmelCase:       Union[str, Any]            =				batch_size
      __lowerCAmelCase:       Union[str, Any]            =				image_size
      __lowerCAmelCase:       str            =				num_channels
      __lowerCAmelCase:       Tuple            =				num_encoder_blocks
      __lowerCAmelCase:       Optional[int]            =				sr_ratios
      __lowerCAmelCase:       Union[str, Any]            =				depths
      __lowerCAmelCase:       int            =				hidden_sizes
      __lowerCAmelCase:       List[Any]            =				downsampling_rates
      __lowerCAmelCase:       List[Any]            =				is_training
      __lowerCAmelCase:       Union[str, Any]            =				use_labels
      __lowerCAmelCase:       Union[str, Any]            =				hidden_act
      __lowerCAmelCase:       Any            =				hidden_dropout_prob
      __lowerCAmelCase:       List[Any]            =				initializer_range
      __lowerCAmelCase:       Dict            =				num_labels
      __lowerCAmelCase:       List[Any]            =				scope


     def   UpperCAmelCase  (							self      :		Optional[int] )							->     List[str]:
      __lowerCAmelCase:       Dict            =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

      __lowerCAmelCase:       str            =				None
      if self.use_labels:
       __lowerCAmelCase:       int            =				ids_tensor([self.batch_size, self.image_size, self.image_size]      ,  self.num_labels )

      __lowerCAmelCase:       Dict            =				PoolFormerConfig(
          image_size=self.image_size      ,  num_channels=self.num_channels      ,  num_encoder_blocks=self.num_encoder_blocks      ,  depths=self.depths      ,  hidden_sizes=self.hidden_sizes      ,  hidden_act=self.hidden_act      ,  hidden_dropout_prob=self.hidden_dropout_prob      ,  initializer_range=self.initializer_range      ,  )

      return config, pixel_values, labels


     def   UpperCAmelCase  (							self      :		str      ,  UpperCAmelCase      :		Tuple      ,  UpperCAmelCase      :		int      ,  UpperCAmelCase      :		List[Any] )							->     str:
      __lowerCAmelCase:       Any            =				PoolFormerModel(config=UpperCAmelCase )
      model.to(UpperCAmelCase )
      model.eval()
      __lowerCAmelCase:       List[str]            =				model(UpperCAmelCase )
      __lowerCAmelCase:       Union[str, Any]            =				self.image_size // 32.0
      self.parent.assertEqual(
          result.last_hidden_state.shape      ,  (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width) )


     def   UpperCAmelCase  (							self      :		Any )							->     int:
      __lowerCAmelCase:       List[str]            =				self.prepare_config_and_inputs()
      __lowerCAmelCase     ,							__lowerCAmelCase     ,							__lowerCAmelCase:       Union[str, Any]            =				config_and_inputs
      __lowerCAmelCase:       List[str]            =				{'pixel_values': pixel_values}
      return config, inputs_dict







@require_torch
class 							A_			(		snake_case__							,     snake_case__							,     unittest.TestCase ):
     _lowercase      :					Optional[Any]       =     (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
     _lowercase      :					List[Any]       =     (
         {'feature-extraction': PoolFormerModel, 'image-classification': PoolFormerForImageClassification}
         if is_torch_available()
         else {}
     )

     _lowercase      :					Tuple       =     False
     _lowercase      :					Tuple       =     False
     _lowercase      :					List[Any]       =     False
     _lowercase      :					Any       =     False
     _lowercase      :					List[Any]       =     False


     def   UpperCAmelCase  (							self      :		str )							->     Any:
      __lowerCAmelCase:       int            =				PoolFormerModelTester(self )
      __lowerCAmelCase:       int            =				PoolFormerConfigTester(self      ,  config_class=UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		Optional[Any] )							->     int:
      self.config_tester.run_common_tests()


     def   UpperCAmelCase  (							self      :		int )							->     int:
      __lowerCAmelCase:       Union[str, Any]            =				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCAmelCase )


     @unittest.skip('PoolFormer does not use inputs_embeds' )
     def   UpperCAmelCase  (							self      :		Optional[int] )							->     Union[str, Any]:
      pass


     @unittest.skip('PoolFormer does not have get_input_embeddings method and get_output_embeddings methods' )
     def   UpperCAmelCase  (							self      :		int )							->     List[str]:
      pass


     def   UpperCAmelCase  (							self      :		List[str] )							->     List[str]:
      def check_hidden_states_output(UpperCAmelCase      :		List[str]      ,  UpperCAmelCase      :		int      ,  UpperCAmelCase      :		List[Any] ):
       __lowerCAmelCase:       Union[str, Any]            =				model_class(UpperCAmelCase )
       model.to(UpperCAmelCase )
       model.eval()

       with torch.no_grad():
        __lowerCAmelCase:       Dict            =				model(**self._prepare_for_class(UpperCAmelCase      ,  UpperCAmelCase ) )

       __lowerCAmelCase:       int            =				outputs.hidden_states

       __lowerCAmelCase:       Optional[int]            =				self.model_tester.num_encoder_blocks
       self.assertEqual(len(UpperCAmelCase )      ,  UpperCAmelCase )

       # verify the first hidden states (first block)
       self.assertListEqual(
           list(hidden_states[0].shape[-3:] )      ,  [
               self.model_tester.hidden_sizes[0],
               self.model_tester.image_size // 4,
               self.model_tester.image_size // 4,
           ]      ,  )

      __lowerCAmelCase     ,							__lowerCAmelCase:       Dict            =				self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __lowerCAmelCase:       Optional[int]            =				True
       check_hidden_states_output(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase )

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       __lowerCAmelCase:       Optional[Any]            =				True

       check_hidden_states_output(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		List[Any] )							->     Optional[int]:
      if not self.model_tester.is_training:
       return

      __lowerCAmelCase     ,							__lowerCAmelCase:       Union[str, Any]            =				self.model_tester.prepare_config_and_inputs_for_common()
      __lowerCAmelCase:       Optional[int]            =				True

      for model_class in self.all_model_classes:
       if model_class in get_values(UpperCAmelCase ):
        continue
       __lowerCAmelCase:       str            =				model_class(UpperCAmelCase )
       model.to(UpperCAmelCase )
       model.train()
       __lowerCAmelCase:       Any            =				self._prepare_for_class(UpperCAmelCase      ,  UpperCAmelCase      ,  return_labels=UpperCAmelCase )
       __lowerCAmelCase:       str            =				model(**UpperCAmelCase ).loss
       loss.backward()


     def   UpperCAmelCase  (							self      :		Dict )							->     Tuple:
      __lowerCAmelCase     ,							__lowerCAmelCase:       Union[str, Any]            =				self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __lowerCAmelCase:       Union[str, Any]            =				model_class(UpperCAmelCase )
       __lowerCAmelCase:       Dict            =				inspect.signature(model.forward )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       __lowerCAmelCase:       List[str]            =				[*signature.parameters.keys()]

       __lowerCAmelCase:       List[Any]            =				['pixel_values']
       self.assertListEqual(arg_names[:1]      ,  UpperCAmelCase )


     @slow
     def   UpperCAmelCase  (							self      :		int )							->     str:
      for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __lowerCAmelCase:       List[str]            =				PoolFormerModel.from_pretrained(UpperCAmelCase )
       self.assertIsNotNone(UpperCAmelCase )







def _a			(	)   ->   Any:

 """simple docstring"""






 __lowerCAmelCase:       Optional[Any]            =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
 return image






@require_torch
class 							A_			(		unittest.TestCase ):


     @slow
     def   UpperCAmelCase  (							self      :		Optional[Any] )							->     int:
      __lowerCAmelCase:       Optional[int]            =				PoolFormerImageProcessor()
      __lowerCAmelCase:       Any            =				PoolFormerForImageClassification.from_pretrained('sail/poolformer_s12' ).to(UpperCAmelCase )

      __lowerCAmelCase:       int            =				image_processor(images=prepare_img()      ,  return_tensors='pt' ).to(UpperCAmelCase )

      # forward pass
      with torch.no_grad():
       __lowerCAmelCase:       List[Any]            =				model(**UpperCAmelCase )

      # verify the logits
      __lowerCAmelCase:       List[Any]            =				torch.Size((1, 1_0_0_0) )
      self.assertEqual(outputs.logits.shape      ,  UpperCAmelCase )

      __lowerCAmelCase:       Union[str, Any]            =				torch.tensor([-0.6113, 0.1685, -0.0492] ).to(UpperCAmelCase )
      self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,  UpperCAmelCase      ,  atol=1E-4 ) )





