'''simple docstring'''





import argparse

import torch

from diffusers import UNetaDConditionModel





def      __lowercase  (		lowerCamelCase      :  Optional[int]						,						lowerCamelCase      :  str						,						lowerCamelCase      :  List[str]						,						lowerCamelCase      :  List[str]=None						,						lowerCamelCase      :  Any=None						,						lowerCamelCase      :  str=None			):
 assert isinstance(a_						,						a_			), "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():
   UpperCamelCase_      :		int				=      old_checkpoint[path]
   UpperCamelCase_      :		Dict				=      old_tensor.shape[0] // 3

   UpperCamelCase_      :		Dict				=      (-1, channels) if len(old_tensor.shape			) == 3 else (-1)

   UpperCamelCase_      :		Union[str, Any]				=      old_tensor.shape[0] // config['num_head_channels'] // 3

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

   UpperCamelCase_      :		List[str]				=      query.reshape(a_			)
   UpperCamelCase_      :		Tuple				=      key.reshape(a_			)
   UpperCamelCase_      :		Tuple				=      value.reshape(a_			)

 for path in paths:
  UpperCamelCase_      :		Dict				=      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:
    UpperCamelCase_      :		Dict				=      new_path.replace(replacement['old']						,						replacement['new']			)

        # proj_attn.weight has to be converted from conv 1D to linear
  UpperCamelCase_      :		Any				=      old_checkpoint[path['old']]
  UpperCamelCase_      :		Union[str, Any]				=      ['proj_attn.weight']
  UpperCamelCase_      :		str				=      ['proj_out.weight', 'proj_in.weight']
  if any(k in new_path for k in names			):
   UpperCamelCase_      :		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:
   UpperCamelCase_      :		List[Any]				=      weight[:, :, 0]
  else:
   UpperCamelCase_      :		Optional[Any]				=      weight





def      __lowercase  (		lowerCamelCase      :  Tuple						,						lowerCamelCase      :  Any=0			):
 UpperCamelCase_      :		Union[str, Any]				=      []
 for old_item in old_list:
  UpperCamelCase_      :		List[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      __lowercase  (		lowerCamelCase      :  Optional[int]						,						lowerCamelCase      :  Dict=1			):
 if n_shave_prefix_segments >= 0:
  return ".".join(path.split('.'			)[n_shave_prefix_segments:]			)
 else:
  return ".".join(path.split('.'			)[:n_shave_prefix_segments]			)





def      __lowercase  (		lowerCamelCase      :  Union[str, Any]						,						lowerCamelCase      :  int=0			):
 UpperCamelCase_      :		Union[str, Any]				=      []
 for old_item in old_list:
  mapping.append({'old': old_item, 'new': old_item}			)

 return mapping





def      __lowercase  (		lowerCamelCase      :  List[Any]						,						lowerCamelCase      :  Any=0			):
 UpperCamelCase_      :		int				=      []
 for old_item in old_list:
  UpperCamelCase_      :		int				=      old_item.replace('in_layers.0'						,						'norm1'			)
  UpperCamelCase_      :		List[Any]				=      new_item.replace('in_layers.2'						,						'conv1'			)

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

  UpperCamelCase_      :		Tuple				=      new_item.replace('emb_layers.1'						,						'time_emb_proj'			)
  UpperCamelCase_      :		List[str]				=      new_item.replace('skip_connection'						,						'conv_shortcut'			)

  UpperCamelCase_      :		Union[str, Any]				=      shave_segments(a_						,						n_shave_prefix_segments=a_			)

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

 return mapping





def      __lowercase  (		lowerCamelCase      :  str						,						lowerCamelCase      :  str						,						lowerCamelCase      :  str=None						,						lowerCamelCase      :  Tuple=False			):
 UpperCamelCase_      :		Optional[int]				=      {}
 UpperCamelCase_      :		Dict				=      list(checkpoint.keys()			)

 UpperCamelCase_      :		List[str]				=      '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'			):
    UpperCamelCase_      :		Any				=      'model_ema.' + ''.join(key.split('.'			)[1:]			)
    UpperCamelCase_      :		Optional[int]				=      checkpoint.pop(a_			)
 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:
   UpperCamelCase_      :		int				=      checkpoint.pop(a_			)

 UpperCamelCase_      :		Tuple				=      {}

 UpperCamelCase_      :		Optional[int]				=      unet_state_dict['time_embed.0.weight']
 UpperCamelCase_      :		str				=      unet_state_dict['time_embed.0.bias']
 UpperCamelCase_      :		int				=      unet_state_dict['time_embed.2.weight']
 UpperCamelCase_      :		str				=      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":
  UpperCamelCase_      :		Union[str, Any]				=      unet_state_dict['label_emb.0.0.weight']
  UpperCamelCase_      :		str				=      unet_state_dict['label_emb.0.0.bias']
  UpperCamelCase_      :		Optional[int]				=      unet_state_dict['label_emb.0.2.weight']
  UpperCamelCase_      :		int				=      unet_state_dict['label_emb.0.2.bias']
 else:
  raise NotImplementedError(F"Not implemented `class_embed_type`: {config['class_embed_type']}"			)

 UpperCamelCase_      :		Union[str, Any]				=      unet_state_dict['input_blocks.0.0.weight']
 UpperCamelCase_      :		Dict				=      unet_state_dict['input_blocks.0.0.bias']

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

 UpperCamelCase_      :		Any				=      unet_state_dict['out.0.weight']
 UpperCamelCase_      :		str				=      unet_state_dict['out.0.bias']
 UpperCamelCase_      :		Dict				=      unet_state_dict['out.2.weight']
 UpperCamelCase_      :		Optional[int]				=      unet_state_dict['out.2.bias']

 # Retrieves the keys for the input blocks only
 UpperCamelCase_      :		int				=      len({'.'.join(layer.split('.'			)[:2]			) for layer in unet_state_dict if 'input_blocks' in layer}			)
 UpperCamelCase_      :		Dict				=      {
     layer_id: [key for key in unet_state_dict if F"input_blocks.{layer_id}" in key]
     for layer_id in range(a_			)
 }

 # Retrieves the keys for the middle blocks only
 UpperCamelCase_      :		Optional[int]				=      len({'.'.join(layer.split('.'			)[:2]			) for layer in unet_state_dict if 'middle_block' in layer}			)
 UpperCamelCase_      :		Any				=      {
     layer_id: [key for key in unet_state_dict if F"middle_block.{layer_id}" in key]
     for layer_id in range(a_			)
 }

 # Retrieves the keys for the output blocks only
 UpperCamelCase_      :		Dict				=      len({'.'.join(layer.split('.'			)[:2]			) for layer in unet_state_dict if 'output_blocks' in layer}			)
 UpperCamelCase_      :		List[Any]				=      {
     layer_id: [key for key in unet_state_dict if F"output_blocks.{layer_id}" in key]
     for layer_id in range(a_			)
 }

 for i in range(1						,						a_			):
  UpperCamelCase_      :		Optional[int]				=      (i - 1) // (config['layers_per_block'] + 1)
  UpperCamelCase_      :		List[str]				=      (i - 1) % (config['layers_per_block'] + 1)

  UpperCamelCase_      :		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
  ]
  UpperCamelCase_      :		Union[str, Any]				=      [key for key in input_blocks[i] if F"input_blocks.{i}.1" in key]
  UpperCamelCase_      :		Optional[int]				=      [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:
   UpperCamelCase_      :		Any				=      unet_state_dict.pop(
       F"input_blocks.{i}.op.weight"			)
   UpperCamelCase_      :		Optional[int]				=      unet_state_dict.pop(
       F"input_blocks.{i}.op.bias"			)

  UpperCamelCase_      :		Union[str, Any]				=      renew_resnet_paths(a_			)
  UpperCamelCase_      :		int				=      {'old': F"input_blocks.{i}.0", 'new': F"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
  assign_to_checkpoint(
      a_						,						a_						,						a_						,						additional_replacements=[meta_path]						,						config=a_			)

  UpperCamelCase_      :		int				=      [key for key in resnets if 'temopral_conv' in key]
  UpperCamelCase_      :		List[str]				=      renew_temp_conv_paths(a_			)
  UpperCamelCase_      :		int				=      {
      'old': F"input_blocks.{i}.0.temopral_conv",
      'new': F"down_blocks.{block_id}.temp_convs.{layer_in_block_id}",
  }
  assign_to_checkpoint(
      a_						,						a_						,						a_						,						additional_replacements=[meta_path]						,						config=a_			)

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

  if len(a_			):
   UpperCamelCase_      :		str				=      renew_attention_paths(a_			)
   UpperCamelCase_      :		Dict				=      {
       'old': F"input_blocks.{i}.2",
       'new': F"down_blocks.{block_id}.temp_attentions.{layer_in_block_id}",
   }
   assign_to_checkpoint(
       a_						,						a_						,						a_						,						additional_replacements=[meta_path]						,						config=a_			)

 UpperCamelCase_      :		int				=      middle_blocks[0]
 UpperCamelCase_      :		Optional[int]				=      [key for key in resnet_a if 'temopral_conv' in key]
 UpperCamelCase_      :		Optional[Any]				=      middle_blocks[1]
 UpperCamelCase_      :		Tuple				=      middle_blocks[2]
 UpperCamelCase_      :		Any				=      middle_blocks[3]
 UpperCamelCase_      :		str				=      [key for key in resnet_a if 'temopral_conv' in key]

 UpperCamelCase_      :		Dict				=      renew_resnet_paths(a_			)
 UpperCamelCase_      :		int				=      {'old': 'middle_block.0', 'new': 'mid_block.resnets.0'}
 assign_to_checkpoint(
     a_						,						a_						,						a_						,						config=a_						,						additional_replacements=[meta_path]			)

 UpperCamelCase_      :		Optional[int]				=      renew_temp_conv_paths(a_			)
 UpperCamelCase_      :		int				=      {'old': 'middle_block.0.temopral_conv', 'new': 'mid_block.temp_convs.0'}
 assign_to_checkpoint(
     a_						,						a_						,						a_						,						config=a_						,						additional_replacements=[meta_path]			)

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

 UpperCamelCase_      :		Any				=      renew_temp_conv_paths(a_			)
 UpperCamelCase_      :		Any				=      {'old': 'middle_block.3.temopral_conv', 'new': 'mid_block.temp_convs.1'}
 assign_to_checkpoint(
     a_						,						a_						,						a_						,						config=a_						,						additional_replacements=[meta_path]			)

 UpperCamelCase_      :		Optional[int]				=      renew_attention_paths(a_			)
 UpperCamelCase_      :		int				=      {'old': 'middle_block.1', 'new': 'mid_block.attentions.0'}
 assign_to_checkpoint(
     a_						,						a_						,						a_						,						additional_replacements=[meta_path]						,						config=a_			)

 UpperCamelCase_      :		Union[str, Any]				=      renew_attention_paths(a_			)
 UpperCamelCase_      :		Optional[int]				=      {'old': 'middle_block.2', 'new': 'mid_block.temp_attentions.0'}
 assign_to_checkpoint(
     a_						,						a_						,						a_						,						additional_replacements=[meta_path]						,						config=a_			)

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

  for layer in output_block_layers:
   UpperCamelCase_,				UpperCamelCase_      :		Union[str, Any]				=      layer.split('.'			)[0], shave_segments(a_						,						1			)
   if layer_id in output_block_list:
    output_block_list[layer_id].append(a_			)
   else:
    UpperCamelCase_      :		Any				=      [layer_name]

  if len(a_			) > 1:
   UpperCamelCase_      :		Any				=      [key for key in output_blocks[i] if F"output_blocks.{i}.0" in key]
   UpperCamelCase_      :		Optional[int]				=      [key for key in output_blocks[i] if F"output_blocks.{i}.1" in key]
   UpperCamelCase_      :		Union[str, Any]				=      [key for key in output_blocks[i] if F"output_blocks.{i}.2" in key]

   UpperCamelCase_      :		Optional[Any]				=      renew_resnet_paths(a_			)
   UpperCamelCase_      :		List[Any]				=      renew_resnet_paths(a_			)

   UpperCamelCase_      :		List[Any]				=      {'old': F"output_blocks.{i}.0", 'new': F"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
   assign_to_checkpoint(
       a_						,						a_						,						a_						,						additional_replacements=[meta_path]						,						config=a_			)

   UpperCamelCase_      :		List[Any]				=      [key for key in resnets if 'temopral_conv' in key]
   UpperCamelCase_      :		int				=      renew_temp_conv_paths(a_			)
   UpperCamelCase_      :		int				=      {
       'old': F"output_blocks.{i}.0.temopral_conv",
       'new': F"up_blocks.{block_id}.temp_convs.{layer_in_block_id}",
   }
   assign_to_checkpoint(
       a_						,						a_						,						a_						,						additional_replacements=[meta_path]						,						config=a_			)

   UpperCamelCase_      :		str				=      {k: sorted(a_			) for k, v in output_block_list.items()}
   if ["conv.bias", "conv.weight"] in output_block_list.values():
    UpperCamelCase_      :		str				=      list(output_block_list.values()			).index(['conv.bias', 'conv.weight']			)
    UpperCamelCase_      :		List[Any]				=      unet_state_dict[
        F"output_blocks.{i}.{index}.conv.weight"
    ]
    UpperCamelCase_      :		Optional[Any]				=      unet_state_dict[
        F"output_blocks.{i}.{index}.conv.bias"
    ]

    # Clear attentions as they have been attributed above.
    if len(a_			) == 2:
     UpperCamelCase_      :		List[str]				=      []

   if len(a_			):
    UpperCamelCase_      :		Dict				=      renew_attention_paths(a_			)
    UpperCamelCase_      :		int				=      {
        'old': F"output_blocks.{i}.1",
        'new': F"up_blocks.{block_id}.attentions.{layer_in_block_id}",
    }
    assign_to_checkpoint(
        a_						,						a_						,						a_						,						additional_replacements=[meta_path]						,						config=a_			)

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

   UpperCamelCase_      :		Union[str, Any]				=      [l for l in output_block_layers if 'temopral_conv' in l]
   for path in temopral_conv_paths:
    UpperCamelCase_      :		List[str]				=      path.split('temopral_conv.'			)[-1]
    UpperCamelCase_      :		Union[str, Any]				=      '.'.join(['output_blocks', str(a_			), str(a_			), 'temopral_conv', pruned_path]			)
    UpperCamelCase_      :		int				=      '.'.join(['up_blocks', str(a_			), 'temp_convs', str(a_			), pruned_path]			)
    UpperCamelCase_      :		Tuple				=      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 unittest

from cer import CER


a_					      = CER()





class 	_lowercase		(      unittest.TestCase ):

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int    )     ->		str:

  """simple docstring"""





  UpperCamelCase_      :		List[Any]				=      ['White House']
  UpperCamelCase_      :		Optional[Any]				=      ['white house']
  # S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
  UpperCamelCase_      :		int				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.1818181818    ) < 1e-6    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Union[str, Any]    )     ->		List[Any]:

  """simple docstring"""





  UpperCamelCase_      :		str				=      ['were wolf']
  UpperCamelCase_      :		str				=      ['werewolf']
  # S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
  UpperCamelCase_      :		List[str]				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.1111111    ) < 1e-6    )

  UpperCamelCase_      :		Tuple				=      ['werewolf']
  UpperCamelCase_      :		Union[str, Any]				=      ['weae     wolf']
  # S = 1, D = 1, I = 0, N = 8, CER = 0.25
  UpperCamelCase_      :		Optional[int]				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.25    ) < 1e-6    )

  # consecutive whitespaces case 1
  UpperCamelCase_      :		Optional[int]				=      ['were wolf']
  UpperCamelCase_      :		Dict				=      ['were               wolf']
  # S = 0, D = 0, I = 0, N = 9, CER = 0
  UpperCamelCase_      :		Optional[int]				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.0    ) < 1e-6    )

  # consecutive whitespaces case 2
  UpperCamelCase_      :		Optional[Any]				=      ['were   wolf']
  UpperCamelCase_      :		int				=      ['were               wolf']
  # S = 0, D = 0, I = 0, N = 9, CER = 0
  UpperCamelCase_      :		str				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.0    ) < 1e-6    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str    )     ->		str:

  """simple docstring"""





  UpperCamelCase_      :		Tuple				=      ['werewolf']
  UpperCamelCase_      :		int				=      ['weaewolf']
  # S = 1, D = 0, I = 0, N = 8, CER = 0.125
  UpperCamelCase_      :		Optional[int]				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.125    ) < 1e-6    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]    )     ->		Optional[int]:

  """simple docstring"""





  UpperCamelCase_      :		List[str]				=      ['werewolf']
  UpperCamelCase_      :		Dict				=      ['wereawolf']
  # S = 0, D = 1, I = 0, N = 8, CER = 0.125
  UpperCamelCase_      :		str				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.125    ) < 1e-6    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any    )     ->		str:

  """simple docstring"""





  UpperCamelCase_      :		List[str]				=      ['werewolf']
  UpperCamelCase_      :		Any				=      ['wereolf']
  # S = 0, D = 0, I = 1, N = 8, CER = 0.125
  UpperCamelCase_      :		Optional[Any]				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.125    ) < 1e-6    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int    )     ->		Optional[int]:

  """simple docstring"""





  UpperCamelCase_      :		Any				=      ['werewolf']
  UpperCamelCase_      :		str				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertEqual(snake_case     ,   0.0    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int    )     ->		Optional[int]:

  """simple docstring"""





  UpperCamelCase_      :		Union[str, Any]				=      ['werewolf', 'I am your father']
  UpperCamelCase_      :		Optional[int]				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertEqual(snake_case     ,   0.0    )

  UpperCamelCase_      :		Any				=      ['werewolf', 'I am your father', 'doge']
  UpperCamelCase_      :		List[str]				=      ['werxwolf', 'I       am your father', 'doge']
  # S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
  UpperCamelCase_      :		Dict				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.03571428    ) < 1e-6    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[str]    )     ->		Dict:

  """simple docstring"""





  UpperCamelCase_      :		Any				=      ['My hovercraft', 'is full of eels']
  UpperCamelCase_      :		Union[str, Any]				=      ['My hovercraft is full', ' of eels']
  # S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
  # whitespace at the front of " of eels" will be strip during preporcessing
  # so need to insert 2 whitespaces
  UpperCamelCase_      :		Optional[int]				=      cer.compute(predictions=snake_case     ,   references=snake_case     ,   concatenate_texts=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.071428    ) < 1e-6    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[Any]    )     ->		Optional[int]:

  """simple docstring"""





  UpperCamelCase_      :		Dict				=      ['我能吞下玻璃而不伤身体']
  UpperCamelCase_      :		int				=      [' 能吞虾玻璃而 不霜身体啦']
  # S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
  UpperCamelCase_      :		Dict				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.4545454545    ) < 1e-6    )

  UpperCamelCase_      :		Union[str, Any]				=      ['我能吞下玻璃', '而不伤身体']
  UpperCamelCase_      :		int				=      ['我    能 吞 下 玻 璃', '而不伤身体']
  # S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
  UpperCamelCase_      :		Any				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.454545454545    ) < 1e-6    )

  UpperCamelCase_      :		Any				=      ['我能吞下玻璃而不伤身体']
  UpperCamelCase_      :		int				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertFalse(snake_case     ,   0.0    )


 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[Any]    )     ->		int:

  """simple docstring"""





  UpperCamelCase_      :		str				=      ['']
  UpperCamelCase_      :		Optional[Any]				=      ['Hypothesis']
  with self.assertRaises(snake_case    ):
   cer.compute(predictions=snake_case     ,   references=snake_case    )


if __name__ == "__main__":
   unittest.main()



