





import argparse

import torch

from diffusers import UNetaDConditionModel







def  lowerCAmelCase__(		lowercase    :      Any				,       lowercase    :      Optional[int]				,       lowercase    :      List[str]				,       lowercase    :      Optional[int]=None				,       lowercase    :      Any=None				,       lowercase    :      Optional[Any]=None			)	->      Tuple:
  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():
      __snake_case				:			Optional[Any]            =    old_checkpoint[path]
      __snake_case				:			Tuple            =    old_tensor.shape[0] // 3

      __snake_case				:			Union[str, Any]            =    (-1, channels) if len(old_tensor.shape			) == 3 else (-1)

      __snake_case				:			Optional[Any]            =    old_tensor.shape[0] // config["num_head_channels"] // 3

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

      __snake_case				:			Optional[Any]            =    query.reshape(snake_case__			)
      __snake_case				:			Dict            =    key.reshape(snake_case__			)
      __snake_case				:			Optional[int]            =    value.reshape(snake_case__			)

  for path in paths:
    __snake_case				:			Optional[Any]            =    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:
        __snake_case				:			Optional[Any]            =    new_path.replace(replacement["old"]				,       replacement["new"]			)

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







def  lowerCAmelCase__(		lowercase    :      Optional[Any]				,       lowercase    :      int=0			)	->      Tuple:
  __snake_case				:			str            =    []
  for old_item in old_list:
    __snake_case				:			List[str]            =    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  lowerCAmelCase__(		lowercase    :      int				,       lowercase    :      Optional[int]=1			)	->      Optional[Any]:
  if n_shave_prefix_segments >= 0:
    return ".".join(path.split("."			)[n_shave_prefix_segments:]			)
  else:
    return ".".join(path.split("."			)[:n_shave_prefix_segments]			)







def  lowerCAmelCase__(		lowercase    :      Any				,       lowercase    :      Tuple=0			)	->      Dict:
  __snake_case				:			Optional[int]            =    []
  for old_item in old_list:
    mapping.append({"old": old_item, "new": old_item}			)

  return mapping







def  lowerCAmelCase__(		lowercase    :      Tuple				,       lowercase    :      List[Any]=0			)	->      str:
  __snake_case				:			Optional[Any]            =    []
  for old_item in old_list:
    __snake_case				:			Optional[Any]            =    old_item.replace("in_layers.0"				,       "norm1"			)
    __snake_case				:			Any            =    new_item.replace("in_layers.2"				,       "conv1"			)

    __snake_case				:			List[Any]            =    new_item.replace("out_layers.0"				,       "norm2"			)
    __snake_case				:			Optional[Any]            =    new_item.replace("out_layers.3"				,       "conv2"			)

    __snake_case				:			Union[str, Any]            =    new_item.replace("emb_layers.1"				,       "time_emb_proj"			)
    __snake_case				:			Tuple            =    new_item.replace("skip_connection"				,       "conv_shortcut"			)

    __snake_case				:			str            =    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  lowerCAmelCase__(		lowercase    :      Tuple				,       lowercase    :      Tuple				,       lowercase    :      List[str]=None				,       lowercase    :      Optional[int]=False			)	->      Union[str, Any]:
  __snake_case				:			Union[str, Any]            =    {}
  __snake_case				:			int            =    list(checkpoint.keys()			)

  __snake_case				:			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"			):
        __snake_case				:			Any            =    "model_ema." + "".join(key.split("."			)[1:]			)
        __snake_case				:			str            =    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:
      __snake_case				:			str            =    checkpoint.pop(snake_case__			)

  __snake_case				:			Dict            =    {}

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

  __snake_case				:			Tuple            =    unet_state_dict["input_blocks.0.0.weight"]
  __snake_case				:			Union[str, Any]            =    unet_state_dict["input_blocks.0.0.bias"]

  __snake_case				:			Optional[Any]            =    [v for v in unet_state_dict if v.startswith("input_blocks.0.1"			)]
  __snake_case				:			List[str]            =    renew_attention_paths(snake_case__			)
  __snake_case				:			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__			)

  __snake_case				:			str            =    unet_state_dict["out.0.weight"]
  __snake_case				:			str            =    unet_state_dict["out.0.bias"]
  __snake_case				:			Union[str, Any]            =    unet_state_dict["out.2.weight"]
  __snake_case				:			str            =    unet_state_dict["out.2.bias"]

  # Retrieves the keys for the input blocks only
  __snake_case				:			Dict            =    len({".".join(layer.split("."			)[:2]			) for layer in unet_state_dict if "input_blocks" in layer}			)
  __snake_case				:			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
  __snake_case				:			List[str]            =    len({".".join(layer.split("."			)[:2]			) for layer in unet_state_dict if "middle_block" in layer}			)
  __snake_case				:			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
  __snake_case				:			Tuple            =    len({".".join(layer.split("."			)[:2]			) for layer in unet_state_dict if "output_blocks" in layer}			)
  __snake_case				:			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__			):
    __snake_case				:			Any            =    (i - 1) // (config["layers_per_block"] + 1)
    __snake_case				:			List[Any]            =    (i - 1) % (config["layers_per_block"] + 1)

    __snake_case				:			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
    ]
    __snake_case				:			List[Any]            =    [key for key in input_blocks[i] if f"""input_blocks.{i}.1""" in key]
    __snake_case				:			List[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:
      __snake_case				:			List[str]            =    unet_state_dict.pop(
          f"""input_blocks.{i}.op.weight"""			)
      __snake_case				:			Optional[Any]            =    unet_state_dict.pop(
          f"""input_blocks.{i}.op.bias"""			)

    __snake_case				:			Optional[int]            =    renew_resnet_paths(snake_case__			)
    __snake_case				:			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__			)

    __snake_case				:			Any            =    [key for key in resnets if "temopral_conv" in key]
    __snake_case				:			List[str]            =    renew_temp_conv_paths(snake_case__			)
    __snake_case				:			Union[str, Any]            =    {
        "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__			):
      __snake_case				:			List[Any]            =    renew_attention_paths(snake_case__			)
      __snake_case				:			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__			):
      __snake_case				:			int            =    renew_attention_paths(snake_case__			)
      __snake_case				:			List[Any]            =    {
          "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__			)

  __snake_case				:			List[str]            =    middle_blocks[0]
  __snake_case				:			Optional[Any]            =    [key for key in resnet_a if "temopral_conv" in key]
  __snake_case				:			Any            =    middle_blocks[1]
  __snake_case				:			str            =    middle_blocks[2]
  __snake_case				:			str            =    middle_blocks[3]
  __snake_case				:			List[str]            =    [key for key in resnet_a if "temopral_conv" in key]

  __snake_case				:			Any            =    renew_resnet_paths(snake_case__			)
  __snake_case				:			Optional[Any]            =    {"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]			)

  __snake_case				:			int            =    renew_temp_conv_paths(snake_case__			)
  __snake_case				:			Optional[Any]            =    {"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]			)

  __snake_case				:			Optional[int]            =    renew_resnet_paths(snake_case__			)
  __snake_case				:			List[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]			)

  __snake_case				:			str            =    renew_temp_conv_paths(snake_case__			)
  __snake_case				:			Tuple            =    {"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]			)

  __snake_case				:			List[Any]            =    renew_attention_paths(snake_case__			)
  __snake_case				:			int            =    {"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__			)

  __snake_case				:			List[Any]            =    renew_attention_paths(snake_case__			)
  __snake_case				:			List[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__			):
    __snake_case				:			Dict            =    i // (config["layers_per_block"] + 1)
    __snake_case				:			List[Any]            =    i % (config["layers_per_block"] + 1)
    __snake_case				:			Tuple            =    [shave_segments(snake_case__				,       2			) for name in output_blocks[i]]
    __snake_case				:			str            =    {}

    for layer in output_block_layers:
      __snake_case				:			Dict            =    layer.split("."			)[0], shave_segments(snake_case__				,       1			)
      if layer_id in output_block_list:
        output_block_list[layer_id].append(snake_case__			)
      else:
        __snake_case				:			Optional[int]            =    [layer_name]

    if len(snake_case__			) > 1:
      __snake_case				:			int            =    [key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
      __snake_case				:			Tuple            =    [key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]
      __snake_case				:			Tuple            =    [key for key in output_blocks[i] if f"""output_blocks.{i}.2""" in key]

      __snake_case				:			Tuple            =    renew_resnet_paths(snake_case__			)
      __snake_case				:			Tuple            =    renew_resnet_paths(snake_case__			)

      __snake_case				:			str            =    {"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__			)

      __snake_case				:			Optional[int]            =    [key for key in resnets if "temopral_conv" in key]
      __snake_case				:			Dict            =    renew_temp_conv_paths(snake_case__			)
      __snake_case				:			str            =    {
          "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__			)

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

        # Clear attentions as they have been attributed above.
        if len(snake_case__			) == 2:
          __snake_case				:			Dict            =    []

      if len(snake_case__			):
        __snake_case				:			int            =    renew_attention_paths(snake_case__			)
        __snake_case				:			List[Any]            =    {
            "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__			):
        __snake_case				:			Optional[Any]            =    renew_attention_paths(snake_case__			)
        __snake_case				:			Any            =    {
            "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:
      __snake_case				:			Dict            =    renew_resnet_paths(snake_case__				,       n_shave_prefix_segments=1			)
      for path in resnet_0_paths:
        __snake_case				:			Union[str, Any]            =    ".".join(["output_blocks", str(snake_case__			), path["old"]]			)
        __snake_case				:			Tuple            =    ".".join(["up_blocks", str(snake_case__			), "resnets", str(snake_case__			), path["new"]]			)
        __snake_case				:			int            =    unet_state_dict[old_path]

      __snake_case				:			int            =    [l for l in output_block_layers if "temopral_conv" in l]
      for path in temopral_conv_paths:
        __snake_case				:			Tuple            =    path.split("temopral_conv."			)[-1]
        __snake_case				:			Dict            =    ".".join(["output_blocks", str(snake_case__			), str(snake_case__			), "temopral_conv", pruned_path]			)
        __snake_case				:			Optional[int]            =    ".".join(["up_blocks", str(snake_case__			), "temp_convs", str(snake_case__			), pruned_path]			)
        __snake_case				:			Optional[int]            =    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 --








from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


_UpperCamelCase    =	logging.get_logger(__name__)

_UpperCamelCase    =	{
    '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json''',
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}




class 			_lowerCamelCase	(      a      ):



      """simple docstring"""






      UpperCAmelCase_  : Tuple				      ="blenderbot"
      UpperCAmelCase_  : Any				      =["past_key_values"]
      UpperCAmelCase_  : Optional[int]				      ={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







      def __init__(	self			,  UpperCAmelCase=8008			,  UpperCAmelCase=128			,  UpperCAmelCase=2			,  UpperCAmelCase=10240			,  UpperCAmelCase=32			,  UpperCAmelCase=24			,  UpperCAmelCase=10240			,  UpperCAmelCase=32			,  UpperCAmelCase=0.0			,  UpperCAmelCase=0.0			,  UpperCAmelCase=True			,  UpperCAmelCase=True			,  UpperCAmelCase="gelu"			,  UpperCAmelCase=2560			,  UpperCAmelCase=0.1			,  UpperCAmelCase=0.0			,  UpperCAmelCase=0.0			,  UpperCAmelCase=0.02			,  UpperCAmelCase=1			,  UpperCAmelCase=False			,  UpperCAmelCase=0			,  UpperCAmelCase=1			,  UpperCAmelCase=2			,  UpperCAmelCase=3			,  UpperCAmelCase=2			,  **UpperCAmelCase			,  )    ->				List[Any]:






        '''simple docstring'''






        __snake_case				:			List[Any]            =    vocab_size
        __snake_case				:			List[str]            =    max_position_embeddings
        __snake_case				:			str            =    d_model
        __snake_case				:			int            =    encoder_ffn_dim
        __snake_case				:			Dict            =    encoder_layers
        __snake_case				:			Any            =    encoder_attention_heads
        __snake_case				:			Optional[int]            =    decoder_ffn_dim
        __snake_case				:			int            =    decoder_layers
        __snake_case				:			Dict            =    decoder_attention_heads
        __snake_case				:			Optional[int]            =    dropout
        __snake_case				:			Optional[Any]            =    attention_dropout
        __snake_case				:			Dict            =    activation_dropout
        __snake_case				:			Any            =    activation_function
        __snake_case				:			List[Any]            =    init_std
        __snake_case				:			Any            =    encoder_layerdrop
        __snake_case				:			List[Any]            =    decoder_layerdrop
        __snake_case				:			Any            =    use_cache
        __snake_case				:			Optional[int]            =    encoder_layers
        __snake_case				:			Optional[int]            =    scale_embedding  # scale factor will be sqrt(d_model) if True

        super().__init__(
            pad_token_id=UpperCAmelCase			,  bos_token_id=UpperCAmelCase			,  eos_token_id=UpperCAmelCase			,  is_encoder_decoder=UpperCAmelCase			,  decoder_start_token_id=UpperCAmelCase			,  encoder_no_repeat_ngram_size=UpperCAmelCase			,  forced_eos_token_id=UpperCAmelCase			,  **UpperCAmelCase			,  )




class 			_lowerCamelCase	(      a      ):



      """simple docstring"""






      @property
      def   UpperCAmelCase						(	self				)    ->				Mapping[str, Mapping[int, str]]:






        '''simple docstring'''






        if self.task in ["default", "seq2seq-lm"]:
          __snake_case				:			Any            =    OrderedDict(
              [
                  ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                  ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
              ]				)
          if self.use_past:
            __snake_case				:			List[str]            =    {0: "batch"}
            __snake_case				:			Optional[Any]            =    {0: "batch", 1: "past_decoder_sequence + sequence"}
          else:
            __snake_case				:			int            =    {0: "batch", 1: "decoder_sequence"}
            __snake_case				:			Optional[int]            =    {0: "batch", 1: "decoder_sequence"}
          if self.use_past:
            self.fill_with_past_key_values_(UpperCAmelCase			,  direction="inputs"				)
        elif self.task == "causal-lm":
          __snake_case				:			List[str]            =    OrderedDict(
              [
                  ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                  ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
              ]				)
          if self.use_past:
            __snake_case      ,				__snake_case				:			List[Any]            =    self.num_layers
            for i in range(UpperCAmelCase				):
              __snake_case				:			int            =    {0: "batch", 2: "past_sequence + sequence"}
              __snake_case				:			Tuple            =    {0: "batch", 2: "past_sequence + sequence"}
        else:
          __snake_case				:			Dict            =    OrderedDict(
              [
                  ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                  ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                  ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
                  ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
              ]				)

        return common_inputs







      @property
      # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
      def   UpperCAmelCase						(	self				)    ->				Mapping[str, Mapping[int, str]]:






        '''simple docstring'''






        if self.task in ["default", "seq2seq-lm"]:
          __snake_case				:			Union[str, Any]            =    super().outputs
        else:
          __snake_case				:			Dict            =    super(UpperCAmelCase			,  self				).outputs
          if self.use_past:
            __snake_case      ,				__snake_case				:			Any            =    self.num_layers
            for i in range(UpperCAmelCase				):
              __snake_case				:			Optional[int]            =    {0: "batch", 2: "past_sequence + sequence"}
              __snake_case				:			Tuple            =    {0: "batch", 2: "past_sequence + sequence"}
        return common_outputs







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase = -1			,  UpperCAmelCase = -1			,  UpperCAmelCase = False			,  UpperCAmelCase = None			,  )    ->				Mapping[str, Any]:






        '''simple docstring'''






        __snake_case				:			int            =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)
        # Generate decoder inputs
        __snake_case				:			List[Any]            =    seq_length if not self.use_past else 1
        __snake_case				:			Tuple            =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)
        __snake_case				:			Tuple            =    {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
        __snake_case				:			Dict            =    dict(**UpperCAmelCase			,  **UpperCAmelCase				)

        if self.use_past:
          if not is_torch_available():
            raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."				)
          else:
            import torch
          __snake_case      ,				__snake_case				:			int            =    common_inputs["input_ids"].shape
          __snake_case				:			Union[str, Any]            =    common_inputs["decoder_input_ids"].shape[1]
          __snake_case      ,				__snake_case				:			Optional[Any]            =    self.num_attention_heads
          __snake_case				:			Tuple            =    (
              batch,
              num_encoder_attention_heads,
              encoder_seq_length,
              self._config.hidden_size // num_encoder_attention_heads,
          )
          __snake_case				:			Any            =    decoder_seq_length
          __snake_case				:			List[Any]            =    (
              batch,
              num_decoder_attention_heads,
              decoder_past_length,
              self._config.hidden_size // num_decoder_attention_heads,
          )
          __snake_case				:			Any            =    torch.cat(
              [common_inputs["decoder_attention_mask"], torch.ones(UpperCAmelCase			,  UpperCAmelCase				)]			,  dim=1				)
          __snake_case				:			Any            =    []
          __snake_case      ,				__snake_case				:			Tuple            =    self.num_layers

          for _ in range(UpperCAmelCase				):
            common_inputs["past_key_values"].append(
                (
                    torch.zeros(UpperCAmelCase				),
                    torch.zeros(UpperCAmelCase				),
                    torch.zeros(UpperCAmelCase				),
                    torch.zeros(UpperCAmelCase				),
                )				)
        return common_inputs







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase = -1			,  UpperCAmelCase = -1			,  UpperCAmelCase = False			,  UpperCAmelCase = None			,  )    ->				Mapping[str, Any]:






        '''simple docstring'''






        __snake_case				:			List[Any]            =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)

        if self.use_past:
          if not is_torch_available():
            raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."				)
          else:
            import torch
          __snake_case      ,				__snake_case				:			List[Any]            =    common_inputs["input_ids"].shape
          __snake_case				:			Any            =    seqlen
          __snake_case      ,				__snake_case				:			Dict            =    self.num_layers
          __snake_case      ,				__snake_case				:			int            =    self.num_attention_heads
          __snake_case				:			str            =    (
              batch,
              num_encoder_attention_heads,
              past_key_values_length,
              self._config.hidden_size // num_encoder_attention_heads,
          )
          __snake_case				:			Dict            =    common_inputs["attention_mask"].dtype
          __snake_case				:			Optional[Any]            =    torch.cat(
              [common_inputs["attention_mask"], torch.ones(UpperCAmelCase			,  UpperCAmelCase			,  dtype=UpperCAmelCase				)]			,  dim=1				)
          __snake_case				:			str            =    [
              (torch.zeros(UpperCAmelCase				), torch.zeros(UpperCAmelCase				)) for _ in range(UpperCAmelCase				)
          ]
        return common_inputs







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase = -1			,  UpperCAmelCase = -1			,  UpperCAmelCase = False			,  UpperCAmelCase = None			,  )    ->				Mapping[str, Any]:






        '''simple docstring'''






        __snake_case				:			Tuple            =    compute_effective_axis_dimension(
            UpperCAmelCase			,  fixed_dimension=OnnxConfig.default_fixed_batch			,  num_token_to_add=0				)

        # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
        __snake_case				:			str            =    tokenizer.num_special_tokens_to_add(UpperCAmelCase				)
        __snake_case				:			Any            =    compute_effective_axis_dimension(
            UpperCAmelCase			,  fixed_dimension=OnnxConfig.default_fixed_sequence			,  num_token_to_add=UpperCAmelCase				)

        # Generate dummy inputs according to compute batch and sequence
        __snake_case				:			Tuple            =    [" ".join([tokenizer.unk_token]				) * seq_length] * batch_size
        __snake_case				:			str            =    dict(tokenizer(UpperCAmelCase			,  return_tensors=UpperCAmelCase				)				)
        return common_inputs







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase = -1			,  UpperCAmelCase = -1			,  UpperCAmelCase = False			,  UpperCAmelCase = None			,  )    ->				Mapping[str, Any]:






        '''simple docstring'''






        if self.task in ["default", "seq2seq-lm"]:
          __snake_case				:			Any            =    self._generate_dummy_inputs_for_default_and_seqaseq_lm(
              UpperCAmelCase			,  batch_size=UpperCAmelCase			,  seq_length=UpperCAmelCase			,  is_pair=UpperCAmelCase			,  framework=UpperCAmelCase				)

        elif self.task == "causal-lm":
          __snake_case				:			Union[str, Any]            =    self._generate_dummy_inputs_for_causal_lm(
              UpperCAmelCase			,  batch_size=UpperCAmelCase			,  seq_length=UpperCAmelCase			,  is_pair=UpperCAmelCase			,  framework=UpperCAmelCase				)
        else:
          __snake_case				:			Dict            =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              UpperCAmelCase			,  batch_size=UpperCAmelCase			,  seq_length=UpperCAmelCase			,  is_pair=UpperCAmelCase			,  framework=UpperCAmelCase				)

        return common_inputs







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				int:






        '''simple docstring'''






        if self.task in ["default", "seq2seq-lm"]:
          __snake_case				:			str            =    super()._flatten_past_key_values_(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)
        else:
          __snake_case				:			int            =    super(UpperCAmelCase			,  self				)._flatten_past_key_values_(
              UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				Any:






        '''simple docstring'''






        if direction not in ["inputs", "outputs"]:
          raise ValueError(F"""direction must either be \"inputs\" or \"outputs\", but {direction} was given"""				)

        __snake_case				:			Tuple            =    "past_key_values" if direction == "inputs" else "present"
        __snake_case      ,				__snake_case				:			Any            =    self.num_layers

        __snake_case				:			int            =    "past_encoder_sequence"
        __snake_case				:			int            =    "past_decoder_sequence" if direction == "inputs" else "past_decoder_sequence + sequence"

        for i in range(UpperCAmelCase				):
          __snake_case				:			Any            =    {0: "batch", 2: decoder_sequence}
          __snake_case				:			List[Any]            =    {0: "batch", 2: decoder_sequence}
          __snake_case				:			Tuple            =    {0: "batch", 2: encoder_sequence}
          __snake_case				:			int            =    {0: "batch", 2: encoder_sequence}

