



'''simple docstring'''





import argparse

import torch

from diffusers import UNetaDConditionModel

def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase=None	,_lowerCAmelCase=None	,_lowerCAmelCase=None	)				->				str:
  assert isinstance(_lowerCAmelCase	,_lowerCAmelCase	), "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(_lowerCAmelCase	)
      __lowerCamelCase	:	str   	=			key.reshape(_lowerCAmelCase	)
      __lowerCamelCase	:	List[Any]   	=			value.reshape(_lowerCAmelCase	)

  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_					(							_lowerCAmelCase	,_lowerCAmelCase=0	)				->				List[Any]:
  __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_					(							_lowerCAmelCase	,_lowerCAmelCase=1	)				->				str:






  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_					(							_lowerCAmelCase	,_lowerCAmelCase=0	)				->				Dict:
  __lowerCamelCase	:	List[Any]   	=			[]
  for old_item in old_list:
    mapping.append({'old': old_item, 'new': old_item}	)

  return mapping

def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase=0	)				->				Tuple:
  __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(_lowerCAmelCase	,n_shave_prefix_segments=_lowerCAmelCase	)

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

  return mapping

def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase=None	,_lowerCAmelCase=False	)				->				Tuple:
  __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	) > 100 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(_lowerCAmelCase	)
  else:
    if sum(k.startswith('model_ema'	) for k in keys	) > 100:
      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(_lowerCAmelCase	)

  __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(_lowerCAmelCase	)
  __lowerCamelCase	:	List[Any]   	=			{'old': 'input_blocks.0.1', 'new': 'transformer_in'}
  assign_to_checkpoint(_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,additional_replacements=[meta_path]	,config=_lowerCAmelCase	)

  __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(_lowerCAmelCase	)
  }

  # 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(_lowerCAmelCase	)
  }

  # 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(_lowerCAmelCase	)
  }

  for i in range(1	,_lowerCAmelCase	):
    __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(_lowerCAmelCase	)
    __lowerCamelCase	:	Union[str, Any]   	=			{'old': F'input_blocks.{i}.0', 'new': F'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
    assign_to_checkpoint(
        _lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,additional_replacements=[meta_path]	,config=_lowerCAmelCase	)

    __lowerCamelCase	:	List[Any]   	=			[key for key in resnets if 'temopral_conv' in key]
    __lowerCamelCase	:	Dict   	=			renew_temp_conv_paths(_lowerCAmelCase	)
    __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(
        _lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,additional_replacements=[meta_path]	,config=_lowerCAmelCase	)

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

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

  __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(_lowerCAmelCase	)
  __lowerCamelCase	:	Optional[Any]   	=			{'old': 'middle_block.0', 'new': 'mid_block.resnets.0'}
  assign_to_checkpoint(
      _lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,config=_lowerCAmelCase	,additional_replacements=[meta_path]	)

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

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

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

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

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

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

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

    if len(_lowerCAmelCase	) > 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(_lowerCAmelCase	)
      __lowerCamelCase	:	str   	=			renew_resnet_paths(_lowerCAmelCase	)

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

      __lowerCamelCase	:	Union[str, Any]   	=			[key for key in resnets if 'temopral_conv' in key]
      __lowerCamelCase	:	Dict   	=			renew_temp_conv_paths(_lowerCAmelCase	)
      __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(
          _lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,additional_replacements=[meta_path]	,config=_lowerCAmelCase	)

      __lowerCamelCase	:	Dict   	=			{k: sorted(_lowerCAmelCase	) 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(_lowerCAmelCase	) == 2:
          __lowerCamelCase	:	Any   	=			[]

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

      if len(_lowerCAmelCase	):
        __lowerCamelCase	:	int   	=			renew_attention_paths(_lowerCAmelCase	)
        __lowerCamelCase	:	int   	=			{
            'old': F'output_blocks.{i}.2',
            'new': F'up_blocks.{block_id}.temp_attentions.{layer_in_block_id}',
        }
        assign_to_checkpoint(
            _lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,additional_replacements=[meta_path]	,config=_lowerCAmelCase	)
    else:
      __lowerCamelCase	:	Dict   	=			renew_resnet_paths(_lowerCAmelCase	,n_shave_prefix_segments=1	)
      for path in resnet_0_paths:
        __lowerCamelCase	:	Dict   	=			'.'.join(['output_blocks', str(_lowerCAmelCase	), path['old']]	)
        __lowerCamelCase	:	Dict   	=			'.'.join(['up_blocks', str(_lowerCAmelCase	), 'resnets', str(_lowerCAmelCase	), 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(_lowerCAmelCase	), str(_lowerCAmelCase	), 'temopral_conv', pruned_path]	)
        __lowerCamelCase	:	List[str]   	=			'.'.join(['up_blocks', str(_lowerCAmelCase	), 'temp_convs', str(_lowerCAmelCase	), pruned_path]	)
        __lowerCamelCase	:	Any   	=			unet_state_dict[old_path]

  return new_checkpoint


if __name__ == "__main__":
   _UpperCamelCase							    =						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.')
   _UpperCamelCase							    =						parser.parse_args()

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

   _UpperCamelCase							    =						convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

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











'''simple docstring'''





import unittest

from diffusers import __version__
from diffusers.utils import deprecate






class  lowerCamelCase_   (    unittest.TestCase					):
  """simple docstring"""




  a_							 =""".""".join([str(int(__version__.split("""."""					)[0]					) + 1					)] + __version__.split("""."""					)[1:]					)
  a_							 ="""0.0.1"""




  def 						_lowercase					(			self				:     Optional[Any]       ) -> int:
    __lowerCamelCase	:	Any   	=			{'deprecated_arg': 4}

    with self.assertWarns(_a       ) as warning:
      __lowerCamelCase	:	Optional[int]   	=			deprecate('deprecated_arg'      ,     self.higher_version      ,     'message'      ,     take_from=_a       )

    assert output == 4
    assert (
        str(warning.warning       )
        == f'The `deprecated_arg` argument is deprecated and will be removed in version {self.higher_version}.'
        " message"
    )




  def 						_lowercase					(			self				:     Union[str, Any]       ) -> Optional[int]:
    __lowerCamelCase	:	int   	=			{'deprecated_arg': 4}

    with self.assertWarns(_a       ) as warning:
      __lowerCamelCase	:	Optional[Any]   	=			deprecate(('deprecated_arg', self.higher_version, 'message')      ,     take_from=_a       )

    assert output == 4
    assert (
        str(warning.warning       )
        == f'The `deprecated_arg` argument is deprecated and will be removed in version {self.higher_version}.'
        " message"
    )




  def 						_lowercase					(			self				:     int       ) -> Optional[Any]:
    __lowerCamelCase	:	Optional[int]   	=			{'deprecated_arg_1': 4, 'deprecated_arg_2': 8}
    with self.assertWarns(_a       ) as warning:
      __lowerCamelCase	,__lowerCamelCase	:	Any   	=			deprecate(
          ('deprecated_arg_1', self.higher_version, 'Hey')      ,     ('deprecated_arg_2', self.higher_version, 'Hey')      ,     take_from=_a      ,     )
    assert output_a == 4
    assert output_a == 8
    assert (
        str(warning.warnings[0].message       )
        == "The `deprecated_arg_1` argument is deprecated and will be removed in version"
        f' {self.higher_version}. Hey'
    )
    assert (
        str(warning.warnings[1].message       )
        == "The `deprecated_arg_2` argument is deprecated and will be removed in version"
        f' {self.higher_version}. Hey'
    )




  def 						_lowercase					(			self				:     str       ) -> Optional[Any]:
    __lowerCamelCase	:	Optional[int]   	=			{'deprecated_arg': 4}

    with self.assertRaises(_a       ) as error:
      deprecate(('wrong_arg', self.higher_version, 'message')      ,     take_from=_a       )

    assert "test_deprecate_function_incorrect_arg in" in str(error.exception       )
    assert "line" in str(error.exception       )
    assert "got an unexpected keyword argument `deprecated_arg`" in str(error.exception       )




  def 						_lowercase					(			self				:     Optional[Any]       ) -> Union[str, Any]:
    with self.assertWarns(_a       ) as warning:
      deprecate(('deprecated_arg', self.higher_version, 'message')       )

    assert (
        str(warning.warning       )
        == f'`deprecated_arg` is deprecated and will be removed in version {self.higher_version}. message'
    )




  def 						_lowercase					(			self				:     Optional[Any]       ) -> Union[str, Any]:
    with self.assertWarns(_a       ) as warning:
      deprecate(
          ('deprecated_arg_1', self.higher_version, 'Hey')      ,     ('deprecated_arg_2', self.higher_version, 'Hey')      ,     )
    assert (
        str(warning.warnings[0].message       )
        == f'`deprecated_arg_1` is deprecated and will be removed in version {self.higher_version}. Hey'
    )
    assert (
        str(warning.warnings[1].message       )
        == f'`deprecated_arg_2` is deprecated and will be removed in version {self.higher_version}. Hey'
    )




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






    class  lowerCamelCase_   :
      """simple docstring"""




      a_							 =5

    with self.assertWarns(_a       ) as warning:
      __lowerCamelCase	:	int   	=			deprecate(('arg', self.higher_version, 'message')      ,     take_from=Args()       )

    assert arg == 5
    assert (
        str(warning.warning       )
        == f'The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message'
    )




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






    class  lowerCamelCase_   :
      """simple docstring"""




      a_							 =5
      a_							 =7

    with self.assertWarns(_a       ) as warning:
      __lowerCamelCase	,__lowerCamelCase	:	List[str]   	=			deprecate(
          ('arg', self.higher_version, 'message')      ,     ('foo', self.higher_version, 'message')      ,     ('does not exist', self.higher_version, 'message')      ,     take_from=Args()      ,     )

    assert arg_a == 5
    assert arg_a == 7
    assert (
        str(warning.warning       )
        == f'The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message'
    )
    assert (
        str(warning.warnings[0].message       )
        == f'The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message'
    )
    assert (
        str(warning.warnings[1].message       )
        == f'The `foo` attribute is deprecated and will be removed in version {self.higher_version}. message'
    )




  def 						_lowercase					(			self				:     Union[str, Any]       ) -> List[str]:
    __lowerCamelCase	:	Union[str, Any]   	=			{'deprecated_arg': 4}

    with self.assertRaises(_a       ) as error:
      deprecate(('wrong_arg', self.lower_version, 'message')      ,     take_from=_a       )

    assert (
        str(error.exception       )
        == "The deprecation tuple ('wrong_arg', '0.0.1', 'message') should be removed since diffusers' version"
        f' {__version__} is >= {self.lower_version}'
    )




  def 						_lowercase					(			self				:     Optional[int]       ) -> str:
    with self.assertWarns(_a       ) as warning:
      deprecate(('deprecated_arg', self.higher_version, 'This message is better!!!')      ,     standard_warn=_a       )

    assert str(warning.warning       ) == "This message is better!!!"




  def 						_lowercase					(			self				:     List[Any]       ) -> Union[str, Any]:
    with self.assertWarns(_a       ) as warning:
      deprecate(('deprecated_arg', self.higher_version, 'This message is better!!!')      ,     standard_warn=_a       )
    assert str(warning.warning       ) == "This message is better!!!"
    assert "diffusers/tests/others/test_utils.py" in warning.filename






