

import argparse

import torch

from diffusers import UNetaDConditionModel





def    _snake_case		(snake_case_				:    Tuple						,					snake_case_				:    Union[str, Any]						,					snake_case_				:    Union[str, Any]						,					snake_case_				:    Any=None						,					snake_case_				:    Tuple=None						,					snake_case_				:    Optional[int]=None     )       ->		Union[str, Any]:
  assert isinstance(snake_case_						,					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():
      __UpperCAmelCase  :     Dict		=    old_checkpoint[path]
      __UpperCAmelCase  :     Tuple		=    old_tensor.shape[0] // 3

      __UpperCAmelCase  :     List[str]		=    (-1, channels) if len(old_tensor.shape     ) == 3 else (-1)

      __UpperCAmelCase  :     List[str]		=    old_tensor.shape[0] // config['num_head_channels'] // 3

      __UpperCAmelCase  :     Optional[Any]		=    old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]     )
      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase  :     Optional[int]		=    old_tensor.split(channels // num_heads						,					dim=1     )

      __UpperCAmelCase  :     List[Any]		=    query.reshape(snake_case_     )
      __UpperCAmelCase  :     Tuple		=    key.reshape(snake_case_     )
      __UpperCAmelCase  :     List[str]		=    value.reshape(snake_case_     )

  for path in paths:
    __UpperCAmelCase  :     Tuple		=    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  :     Union[str, Any]		=    old_checkpoint[path['old']]
    __UpperCAmelCase  :     Dict		=    ['proj_attn.weight']
    __UpperCAmelCase  :     Any		=    ['proj_out.weight', 'proj_in.weight']
    if any(k in new_path for k in names     ):
      __UpperCAmelCase  :     Tuple		=    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  :     Optional[Any]		=    weight[:, :, 0]
    else:
      __UpperCAmelCase  :     str		=    weight





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





def    _snake_case		(snake_case_				:    str						,					snake_case_				:    Optional[int]=0     )       ->		Optional[Any]:
  __UpperCAmelCase  :     Union[str, Any]		=    []
  for old_item in old_list:
    mapping.append({'old': old_item, 'new': old_item}     )

  return mapping





def    _snake_case		(snake_case_				:    Optional[Any]						,					snake_case_				:    int=0     )       ->		int:
  __UpperCAmelCase  :     Dict		=    []
  for old_item in old_list:
    __UpperCAmelCase  :     Union[str, Any]		=    old_item.replace('in_layers.0'						,					'norm1'     )
    __UpperCAmelCase  :     Tuple		=    new_item.replace('in_layers.2'						,					'conv1'     )

    __UpperCAmelCase  :     Union[str, Any]		=    new_item.replace('out_layers.0'						,					'norm2'     )
    __UpperCAmelCase  :     Dict		=    new_item.replace('out_layers.3'						,					'conv2'     )

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

    __UpperCAmelCase  :     Optional[Any]		=    shave_segments(snake_case_						,					n_shave_prefix_segments=snake_case_     )

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

  return mapping





def    _snake_case		(snake_case_				:    int						,					snake_case_				:    int						,					snake_case_				:    Union[str, Any]=None						,					snake_case_				:    Dict=False     )       ->		Optional[Any]:
  __UpperCAmelCase  :     str		=    {}
  __UpperCAmelCase  :     List[str]		=    list(checkpoint.keys()     )

  __UpperCAmelCase  :     Dict		=    '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  :     str		=    'model_ema.' + ''.join(key.split('.'     )[1:]     )
        __UpperCAmelCase  :     Optional[int]		=    checkpoint.pop(snake_case_     )
  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(snake_case_     )

  __UpperCAmelCase  :     Dict		=    {}

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

  __UpperCAmelCase  :     Optional[Any]		=    unet_state_dict['input_blocks.0.0.weight']
  __UpperCAmelCase  :     List[Any]		=    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  :     Tuple		=    renew_attention_paths(snake_case_     )
  __UpperCAmelCase  :     Union[str, Any]		=    {'old': 'input_blocks.0.1', 'new': 'transformer_in'}
  assign_to_checkpoint(snake_case_						,					snake_case_						,					snake_case_						,					additional_replacements=[meta_path]						,					config=snake_case_     )

  __UpperCAmelCase  :     List[Any]		=    unet_state_dict['out.0.weight']
  __UpperCAmelCase  :     int		=    unet_state_dict['out.0.bias']
  __UpperCAmelCase  :     List[Any]		=    unet_state_dict['out.2.weight']
  __UpperCAmelCase  :     Tuple		=    unet_state_dict['out.2.bias']

  # Retrieves the keys for the input blocks only
  __UpperCAmelCase  :     Union[str, Any]		=    len({'.'.join(layer.split('.'     )[:2]     ) for layer in unet_state_dict if 'input_blocks' in layer}     )
  __UpperCAmelCase  :     Optional[Any]		=    {
      layer_id: [key for key in unet_state_dict if F"input_blocks.{layer_id}" in key]
      for layer_id in range(snake_case_     )
  }

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

  # Retrieves the keys for the output blocks only
  __UpperCAmelCase  :     Optional[int]		=    len({'.'.join(layer.split('.'     )[:2]     ) for layer in unet_state_dict if 'output_blocks' in layer}     )
  __UpperCAmelCase  :     List[str]		=    {
      layer_id: [key for key in unet_state_dict if F"output_blocks.{layer_id}" in key]
      for layer_id in range(snake_case_     )
  }

  for i in range(1						,					snake_case_     ):
    __UpperCAmelCase  :     Optional[int]		=    (i - 1) // (config['layers_per_block'] + 1)
    __UpperCAmelCase  :     Union[str, Any]		=    (i - 1) % (config['layers_per_block'] + 1)

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

    __UpperCAmelCase  :     Optional[int]		=    renew_resnet_paths(snake_case_     )
    __UpperCAmelCase  :     List[str]		=    {'old': F"input_blocks.{i}.0", 'new': F"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
    assign_to_checkpoint(
        snake_case_						,					snake_case_						,					snake_case_						,					additional_replacements=[meta_path]						,					config=snake_case_     )

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

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

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

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

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

  __UpperCAmelCase  :     Union[str, Any]		=    renew_temp_conv_paths(snake_case_     )
  __UpperCAmelCase  :     List[str]		=    {'old': 'middle_block.0.temopral_conv', 'new': 'mid_block.temp_convs.0'}
  assign_to_checkpoint(
      snake_case_						,					snake_case_						,					snake_case_						,					config=snake_case_						,					additional_replacements=[meta_path]     )

  __UpperCAmelCase  :     Union[str, Any]		=    renew_resnet_paths(snake_case_     )
  __UpperCAmelCase  :     Any		=    {'old': 'middle_block.3', 'new': 'mid_block.resnets.1'}
  assign_to_checkpoint(
      snake_case_						,					snake_case_						,					snake_case_						,					config=snake_case_						,					additional_replacements=[meta_path]     )

  __UpperCAmelCase  :     int		=    renew_temp_conv_paths(snake_case_     )
  __UpperCAmelCase  :     Optional[int]		=    {'old': 'middle_block.3.temopral_conv', 'new': 'mid_block.temp_convs.1'}
  assign_to_checkpoint(
      snake_case_						,					snake_case_						,					snake_case_						,					config=snake_case_						,					additional_replacements=[meta_path]     )

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

  __UpperCAmelCase  :     Optional[Any]		=    renew_attention_paths(snake_case_     )
  __UpperCAmelCase  :     Optional[Any]		=    {'old': 'middle_block.2', 'new': 'mid_block.temp_attentions.0'}
  assign_to_checkpoint(
      snake_case_						,					snake_case_						,					snake_case_						,					additional_replacements=[meta_path]						,					config=snake_case_     )

  for i in range(snake_case_     ):
    __UpperCAmelCase  :     Union[str, Any]		=    i // (config['layers_per_block'] + 1)
    __UpperCAmelCase  :     List[str]		=    i % (config['layers_per_block'] + 1)
    __UpperCAmelCase  :     Dict		=    [shave_segments(snake_case_						,					2     ) for name in output_blocks[i]]
    __UpperCAmelCase  :     List[Any]		=    {}

    for layer in output_block_layers:
      __UpperCAmelCase, __UpperCAmelCase  :     str		=    layer.split('.'     )[0], shave_segments(snake_case_						,					1     )
      if layer_id in output_block_list:
        output_block_list[layer_id].append(snake_case_     )
      else:
        __UpperCAmelCase  :     Dict		=    [layer_name]

    if len(snake_case_     ) > 1:
      __UpperCAmelCase  :     List[Any]		=    [key for key in output_blocks[i] if F"output_blocks.{i}.0" in key]
      __UpperCAmelCase  :     Dict		=    [key for key in output_blocks[i] if F"output_blocks.{i}.1" in key]
      __UpperCAmelCase  :     Optional[int]		=    [key for key in output_blocks[i] if F"output_blocks.{i}.2" in key]

      __UpperCAmelCase  :     Optional[Any]		=    renew_resnet_paths(snake_case_     )
      __UpperCAmelCase  :     Any		=    renew_resnet_paths(snake_case_     )

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

      __UpperCAmelCase  :     Union[str, Any]		=    [key for key in resnets if 'temopral_conv' in key]
      __UpperCAmelCase  :     Optional[Any]		=    renew_temp_conv_paths(snake_case_     )
      __UpperCAmelCase  :     Optional[Any]		=    {
          'old': F"output_blocks.{i}.0.temopral_conv",
          'new': F"up_blocks.{block_id}.temp_convs.{layer_in_block_id}",
      }
      assign_to_checkpoint(
          snake_case_						,					snake_case_						,					snake_case_						,					additional_replacements=[meta_path]						,					config=snake_case_     )

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

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

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

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

  return new_checkpoint


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

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

      _SCREAMING_SNAKE_CASE          = convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

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








from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


_SCREAMING_SNAKE_CASE          = logging.get_logger(__name__)


class     lowerCamelCase_   ( _lowerCAmelCase):
 _lowerCAmelCase       		=  '''mt5'''
 _lowerCAmelCase       		=  ['''past_key_values''']




 def __init__( self     ,     _UpperCAmelCase=250_112     ,     _UpperCAmelCase=512     ,     _UpperCAmelCase=64     ,     _UpperCAmelCase=1_024     ,     _UpperCAmelCase=8     ,     _UpperCAmelCase=None     ,     _UpperCAmelCase=6     ,     _UpperCAmelCase=32     ,     _UpperCAmelCase=128     ,     _UpperCAmelCase=0.1     ,     _UpperCAmelCase=1E-6     ,     _UpperCAmelCase=1.0     ,     _UpperCAmelCase="gated-gelu"     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase="T5Tokenizer"     ,     _UpperCAmelCase=False     ,     _UpperCAmelCase=0     ,     _UpperCAmelCase=1     ,     _UpperCAmelCase=0     ,     **_UpperCAmelCase     ,     ):
   super().__init__(
       is_encoder_decoder=_UpperCAmelCase     ,     tokenizer_class=_UpperCAmelCase     ,     tie_word_embeddings=_UpperCAmelCase     ,     pad_token_id=_UpperCAmelCase     ,     eos_token_id=_UpperCAmelCase     ,     decoder_start_token_id=_UpperCAmelCase     ,     **_UpperCAmelCase     ,     )
   __UpperCAmelCase  :     Dict		=    vocab_size
   __UpperCAmelCase  :     List[str]		=    d_model
   __UpperCAmelCase  :     str		=    d_kv
   __UpperCAmelCase  :     List[str]		=    d_ff
   __UpperCAmelCase  :     int		=    num_layers
   __UpperCAmelCase  :     List[Any]		=    (
       num_decoder_layers if num_decoder_layers is not None else self.num_layers
   )  # default = symmetry
   __UpperCAmelCase  :     List[Any]		=    num_heads
   __UpperCAmelCase  :     Tuple		=    relative_attention_num_buckets
   __UpperCAmelCase  :     Optional[int]		=    relative_attention_max_distance
   __UpperCAmelCase  :     Dict		=    dropout_rate
   __UpperCAmelCase  :     Union[str, Any]		=    layer_norm_epsilon
   __UpperCAmelCase  :     Tuple		=    initializer_factor
   __UpperCAmelCase  :     Union[str, Any]		=    feed_forward_proj
   __UpperCAmelCase  :     Optional[Any]		=    use_cache

   __UpperCAmelCase  :     Any		=    self.feed_forward_proj.split('-'       )
   __UpperCAmelCase  :     Dict		=    act_info[-1]
   __UpperCAmelCase  :     Any		=    act_info[0] == 'gated'

   if len(_UpperCAmelCase       ) > 1 and act_info[0] != "gated" or len(_UpperCAmelCase       ) > 2:
     raise ValueError(
         F"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
         'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
         '\'gated-gelu\' or \'relu\''       )

   # for backwards compatibility
   if feed_forward_proj == "gated-gelu":
     __UpperCAmelCase  :     List[str]		=    'gelu_new'




 @property
 def 						a					( self       ):
   return self.d_model




 @property
 def 						a					( self       ):
   return self.num_heads




 @property
 def 						a					( self       ):
   return self.num_layers



class     lowerCamelCase_   ( _lowerCAmelCase):




 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
 def 						a					( self       ):
   __UpperCAmelCase  :     Optional[int]		=    {
       'input_ids': {0: 'batch', 1: 'encoder_sequence'},
       'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
   }
   if self.use_past:
     __UpperCAmelCase  :     Optional[Any]		=    'past_encoder_sequence + sequence'
     __UpperCAmelCase  :     Tuple		=    {0: 'batch'}
     __UpperCAmelCase  :     List[Any]		=    {0: 'batch', 1: 'past_decoder_sequence + sequence'}
   else:
     __UpperCAmelCase  :     Tuple		=    {0: 'batch', 1: 'decoder_sequence'}
     __UpperCAmelCase  :     int		=    {0: 'batch', 1: 'decoder_sequence'}

   if self.use_past:
     self.fill_with_past_key_values_(_UpperCAmelCase     ,     direction='inputs'       )

   return common_inputs




 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
 def 						a					( self       ):
   return 13




 @property
 def 						a					( self       ):
   return 5E-4





