




'''simple docstring'''


import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel
def 							UpperCAmelCase_						(		__lowerCamelCase      :      Any			,__lowerCamelCase      :      int=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 							UpperCAmelCase_						(		__lowerCamelCase      :      List[str]			,__lowerCamelCase      :      List[Any]=0				):
   lowercase_    :str					    =				[]
   for old_item in old_list:
      lowercase_    :Optional[int]					    =				old_item
      lowercase_    :Union[str, Any]					    =				new_item.replace("block."			,"resnets."				)
      lowercase_    :Any					    =				new_item.replace("conv_shorcut"			,"conv1"				)
      lowercase_    :Optional[int]					    =				new_item.replace("in_shortcut"			,"conv_shortcut"				)
      lowercase_    :Dict					    =				new_item.replace("temb_proj"			,"time_emb_proj"				)

      lowercase_    :Any					    =				shave_segments(__lowerCamelCase			,n_shave_prefix_segments=__lowerCamelCase				)

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

   return mapping
def 							UpperCAmelCase_						(		__lowerCamelCase      :      Union[str, Any]			,__lowerCamelCase      :      Optional[int]=0			,__lowerCamelCase      :      int=False				):
   lowercase_    :Tuple					    =				[]
   for old_item in old_list:
      lowercase_    :Optional[int]					    =				old_item

      # In `model.mid`, the layer is called `attn`.
      if not in_mid:
         lowercase_    :Dict					    =				new_item.replace("attn"			,"attentions"				)
      lowercase_    :Any					    =				new_item.replace(".k."			,".key."				)
      lowercase_    :Any					    =				new_item.replace(".v."			,".value."				)
      lowercase_    :int					    =				new_item.replace(".q."			,".query."				)

      lowercase_    :int					    =				new_item.replace("proj_out"			,"proj_attn"				)
      lowercase_    :Any					    =				new_item.replace("norm"			,"group_norm"				)

      lowercase_    :str					    =				shave_segments(__lowerCamelCase			,n_shave_prefix_segments=__lowerCamelCase				)
      mapping.append({"old": old_item, "new": new_item}				)

   return mapping
def 							UpperCAmelCase_						(		__lowerCamelCase      :      Tuple			,__lowerCamelCase      :      Union[str, Any]			,__lowerCamelCase      :      List[Any]			,__lowerCamelCase      :      List[Any]=None			,__lowerCamelCase      :      Tuple=None			,__lowerCamelCase      :      Dict=None				):
   assert isinstance(__lowerCamelCase			,__lowerCamelCase				), "Paths should be a list of dicts containing 'old' and 'new' keys."

   if attention_paths_to_split is not None:
      if config is None:
         raise ValueError("Please specify the config if setting 'attention_paths_to_split' to 'True'."				)

      for path, path_map in attention_paths_to_split.items():
         lowercase_    :Optional[Any]					    =				old_checkpoint[path]
         lowercase_    :List[Any]					    =				old_tensor.shape[0] // 3

         lowercase_    :Optional[Any]					    =				(-1, channels) if len(old_tensor.shape				) == 3 else (-1)

         lowercase_    :Dict					    =				old_tensor.shape[0] // config.get("num_head_channels"			,1				) // 3

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

         lowercase_    :List[str]					    =				query.reshape(__lowerCamelCase				).squeeze()
         lowercase_    :int					    =				key.reshape(__lowerCamelCase				).squeeze()
         lowercase_    :Union[str, Any]					    =				value.reshape(__lowerCamelCase				).squeeze()

   for path in paths:
      lowercase_    :str					    =				path["new"]

      if attention_paths_to_split is not None and new_path in attention_paths_to_split:
         continue

      lowercase_    :Tuple					    =				new_path.replace("down."			,"down_blocks."				)
      lowercase_    :Tuple					    =				new_path.replace("up."			,"up_blocks."				)

      if additional_replacements is not None:
         for replacement in additional_replacements:
            lowercase_    :List[str]					    =				new_path.replace(replacement["old"]			,replacement["new"]				)

      if "attentions" in new_path:
         lowercase_    :int					    =				old_checkpoint[path["old"]].squeeze()
      else:
         lowercase_    :List[Any]					    =				old_checkpoint[path["old"]]
def 							UpperCAmelCase_						(		__lowerCamelCase      :      List[str]			,__lowerCamelCase      :      Tuple				):
   lowercase_    :Optional[int]					    =				{}

   lowercase_    :List[Any]					    =				checkpoint["temb.dense.0.weight"]
   lowercase_    :Dict					    =				checkpoint["temb.dense.0.bias"]
   lowercase_    :Optional[int]					    =				checkpoint["temb.dense.1.weight"]
   lowercase_    :Optional[int]					    =				checkpoint["temb.dense.1.bias"]

   lowercase_    :List[Any]					    =				checkpoint["norm_out.weight"]
   lowercase_    :Dict					    =				checkpoint["norm_out.bias"]

   lowercase_    :Dict					    =				checkpoint["conv_in.weight"]
   lowercase_    :Optional[Any]					    =				checkpoint["conv_in.bias"]
   lowercase_    :Dict					    =				checkpoint["conv_out.weight"]
   lowercase_    :Union[str, Any]					    =				checkpoint["conv_out.bias"]

   lowercase_    :int					    =				len({".".join(layer.split("."				)[:2]				) for layer in checkpoint if "down" in layer}				)
   lowercase_    :List[Any]					    =				{
       layer_id: [key for key in checkpoint if F'down.{layer_id}' in key] for layer_id in range(__lowerCamelCase				)
   }

   lowercase_    :List[Any]					    =				len({".".join(layer.split("."				)[:2]				) for layer in checkpoint if "up" in layer}				)
   lowercase_    :List[Any]					    =				{layer_id: [key for key in checkpoint if F'up.{layer_id}' in key] for layer_id in range(__lowerCamelCase				)}

   for i in range(__lowerCamelCase				):
      lowercase_    :Optional[Any]					    =				(i - 1) // (config["layers_per_block"] + 1)

      if any("downsample" in layer for layer in down_blocks[i]				):
         lowercase_    :Dict					    =				checkpoint[
             F'down.{i}.downsample.op.weight'
         ]
         lowercase_    :Union[str, Any]					    =				checkpoint[F'down.{i}.downsample.op.bias']
      #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
      #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

      if any("block" in layer for layer in down_blocks[i]				):
         lowercase_    :Any					    =				len(
             {".".join(shave_segments(__lowerCamelCase			,2				).split("."				)[:2]				) for layer in down_blocks[i] if "block" in layer}				)
         lowercase_    :str					    =				{
             layer_id: [key for key in down_blocks[i] if F'block.{layer_id}' in key]
             for layer_id in range(__lowerCamelCase				)
         }

         if num_blocks > 0:
            for j in range(config["layers_per_block"]				):
               lowercase_    :List[str]					    =				renew_resnet_paths(blocks[j]				)
               assign_to_checkpoint(__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase				)

      if any("attn" in layer for layer in down_blocks[i]				):
         lowercase_    :Union[str, Any]					    =				len(
             {".".join(shave_segments(__lowerCamelCase			,2				).split("."				)[:2]				) for layer in down_blocks[i] if "attn" in layer}				)
         lowercase_    :List[Any]					    =				{
             layer_id: [key for key in down_blocks[i] if F'attn.{layer_id}' in key]
             for layer_id in range(__lowerCamelCase				)
         }

         if num_attn > 0:
            for j in range(config["layers_per_block"]				):
               lowercase_    :Union[str, Any]					    =				renew_attention_paths(attns[j]				)
               assign_to_checkpoint(__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,config=__lowerCamelCase				)

   lowercase_    :Optional[Any]					    =				[key for key in checkpoint if "mid.block_1" in key]
   lowercase_    :List[str]					    =				[key for key in checkpoint if "mid.block_2" in key]
   lowercase_    :Optional[Any]					    =				[key for key in checkpoint if "mid.attn_1" in key]

   # Mid new 2
   lowercase_    :Dict					    =				renew_resnet_paths(__lowerCamelCase				)
   assign_to_checkpoint(
       __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]			,)

   lowercase_    :Union[str, Any]					    =				renew_resnet_paths(__lowerCamelCase				)
   assign_to_checkpoint(
       __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]			,)

   lowercase_    :List[str]					    =				renew_attention_paths(__lowerCamelCase			,in_mid=__lowerCamelCase				)
   assign_to_checkpoint(
       __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]			,)

   for i in range(__lowerCamelCase				):
      lowercase_    :Any					    =				num_up_blocks - 1 - i

      if any("upsample" in layer for layer in up_blocks[i]				):
         lowercase_    :int					    =				checkpoint[
             F'up.{i}.upsample.conv.weight'
         ]
         lowercase_    :Union[str, Any]					    =				checkpoint[F'up.{i}.upsample.conv.bias']

      if any("block" in layer for layer in up_blocks[i]				):
         lowercase_    :Tuple					    =				len(
             {".".join(shave_segments(__lowerCamelCase			,2				).split("."				)[:2]				) for layer in up_blocks[i] if "block" in layer}				)
         lowercase_    :List[str]					    =				{
             layer_id: [key for key in up_blocks[i] if F'block.{layer_id}' in key] for layer_id in range(__lowerCamelCase				)
         }

         if num_blocks > 0:
            for j in range(config["layers_per_block"] + 1				):
               lowercase_    :Optional[Any]					    =				{"old": F'up_blocks.{i}', "new": F'up_blocks.{block_id}'}
               lowercase_    :Dict					    =				renew_resnet_paths(blocks[j]				)
               assign_to_checkpoint(__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,additional_replacements=[replace_indices]				)

      if any("attn" in layer for layer in up_blocks[i]				):
         lowercase_    :Tuple					    =				len(
             {".".join(shave_segments(__lowerCamelCase			,2				).split("."				)[:2]				) for layer in up_blocks[i] if "attn" in layer}				)
         lowercase_    :List[str]					    =				{
             layer_id: [key for key in up_blocks[i] if F'attn.{layer_id}' in key] for layer_id in range(__lowerCamelCase				)
         }

         if num_attn > 0:
            for j in range(config["layers_per_block"] + 1				):
               lowercase_    :Optional[Any]					    =				{"old": F'up_blocks.{i}', "new": F'up_blocks.{block_id}'}
               lowercase_    :str					    =				renew_attention_paths(attns[j]				)
               assign_to_checkpoint(__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,additional_replacements=[replace_indices]				)

   lowercase_    :List[Any]					    =				{k.replace("mid_new_2"			,"mid_block"				): v for k, v in new_checkpoint.items()}
   return new_checkpoint
def 							UpperCAmelCase_						(		__lowerCamelCase      :      Optional[Any]			,__lowerCamelCase      :      Any				):
   lowercase_    :List[Any]					    =				{}

   lowercase_    :Any					    =				checkpoint["encoder.norm_out.weight"]
   lowercase_    :Optional[int]					    =				checkpoint["encoder.norm_out.bias"]

   lowercase_    :Dict					    =				checkpoint["encoder.conv_in.weight"]
   lowercase_    :Tuple					    =				checkpoint["encoder.conv_in.bias"]
   lowercase_    :Tuple					    =				checkpoint["encoder.conv_out.weight"]
   lowercase_    :Optional[int]					    =				checkpoint["encoder.conv_out.bias"]

   lowercase_    :Any					    =				checkpoint["decoder.norm_out.weight"]
   lowercase_    :int					    =				checkpoint["decoder.norm_out.bias"]

   lowercase_    :Optional[Any]					    =				checkpoint["decoder.conv_in.weight"]
   lowercase_    :Union[str, Any]					    =				checkpoint["decoder.conv_in.bias"]
   lowercase_    :str					    =				checkpoint["decoder.conv_out.weight"]
   lowercase_    :str					    =				checkpoint["decoder.conv_out.bias"]

   lowercase_    :str					    =				len({".".join(layer.split("."				)[:3]				) for layer in checkpoint if "down" in layer}				)
   lowercase_    :int					    =				{
       layer_id: [key for key in checkpoint if F'down.{layer_id}' in key] for layer_id in range(__lowerCamelCase				)
   }

   lowercase_    :int					    =				len({".".join(layer.split("."				)[:3]				) for layer in checkpoint if "up" in layer}				)
   lowercase_    :List[str]					    =				{layer_id: [key for key in checkpoint if F'up.{layer_id}' in key] for layer_id in range(__lowerCamelCase				)}

   for i in range(__lowerCamelCase				):
      lowercase_    :Optional[int]					    =				(i - 1) // (config["layers_per_block"] + 1)

      if any("downsample" in layer for layer in down_blocks[i]				):
         lowercase_    :Tuple					    =				checkpoint[
             F'encoder.down.{i}.downsample.conv.weight'
         ]
         lowercase_    :List[str]					    =				checkpoint[
             F'encoder.down.{i}.downsample.conv.bias'
         ]

      if any("block" in layer for layer in down_blocks[i]				):
         lowercase_    :Optional[Any]					    =				len(
             {".".join(shave_segments(__lowerCamelCase			,3				).split("."				)[:3]				) for layer in down_blocks[i] if "block" in layer}				)
         lowercase_    :List[str]					    =				{
             layer_id: [key for key in down_blocks[i] if F'block.{layer_id}' in key]
             for layer_id in range(__lowerCamelCase				)
         }

         if num_blocks > 0:
            for j in range(config["layers_per_block"]				):
               lowercase_    :Dict					    =				renew_resnet_paths(blocks[j]				)
               assign_to_checkpoint(__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase				)

      if any("attn" in layer for layer in down_blocks[i]				):
         lowercase_    :str					    =				len(
             {".".join(shave_segments(__lowerCamelCase			,3				).split("."				)[:3]				) for layer in down_blocks[i] if "attn" in layer}				)
         lowercase_    :Optional[Any]					    =				{
             layer_id: [key for key in down_blocks[i] if F'attn.{layer_id}' in key]
             for layer_id in range(__lowerCamelCase				)
         }

         if num_attn > 0:
            for j in range(config["layers_per_block"]				):
               lowercase_    :str					    =				renew_attention_paths(attns[j]				)
               assign_to_checkpoint(__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,config=__lowerCamelCase				)

   lowercase_    :str					    =				[key for key in checkpoint if "mid.block_1" in key]
   lowercase_    :Union[str, Any]					    =				[key for key in checkpoint if "mid.block_2" in key]
   lowercase_    :Union[str, Any]					    =				[key for key in checkpoint if "mid.attn_1" in key]

   # Mid new 2
   lowercase_    :Union[str, Any]					    =				renew_resnet_paths(__lowerCamelCase				)
   assign_to_checkpoint(
       __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]			,)

   lowercase_    :Optional[int]					    =				renew_resnet_paths(__lowerCamelCase				)
   assign_to_checkpoint(
       __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]			,)

   lowercase_    :str					    =				renew_attention_paths(__lowerCamelCase			,in_mid=__lowerCamelCase				)
   assign_to_checkpoint(
       __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]			,)

   for i in range(__lowerCamelCase				):
      lowercase_    :int					    =				num_up_blocks - 1 - i

      if any("upsample" in layer for layer in up_blocks[i]				):
         lowercase_    :Dict					    =				checkpoint[
             F'decoder.up.{i}.upsample.conv.weight'
         ]
         lowercase_    :Optional[int]					    =				checkpoint[
             F'decoder.up.{i}.upsample.conv.bias'
         ]

      if any("block" in layer for layer in up_blocks[i]				):
         lowercase_    :Tuple					    =				len(
             {".".join(shave_segments(__lowerCamelCase			,3				).split("."				)[:3]				) for layer in up_blocks[i] if "block" in layer}				)
         lowercase_    :int					    =				{
             layer_id: [key for key in up_blocks[i] if F'block.{layer_id}' in key] for layer_id in range(__lowerCamelCase				)
         }

         if num_blocks > 0:
            for j in range(config["layers_per_block"] + 1				):
               lowercase_    :Optional[Any]					    =				{"old": F'up_blocks.{i}', "new": F'up_blocks.{block_id}'}
               lowercase_    :int					    =				renew_resnet_paths(blocks[j]				)
               assign_to_checkpoint(__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,additional_replacements=[replace_indices]				)

      if any("attn" in layer for layer in up_blocks[i]				):
         lowercase_    :Any					    =				len(
             {".".join(shave_segments(__lowerCamelCase			,3				).split("."				)[:3]				) for layer in up_blocks[i] if "attn" in layer}				)
         lowercase_    :int					    =				{
             layer_id: [key for key in up_blocks[i] if F'attn.{layer_id}' in key] for layer_id in range(__lowerCamelCase				)
         }

         if num_attn > 0:
            for j in range(config["layers_per_block"] + 1				):
               lowercase_    :int					    =				{"old": F'up_blocks.{i}', "new": F'up_blocks.{block_id}'}
               lowercase_    :Optional[Any]					    =				renew_attention_paths(attns[j]				)
               assign_to_checkpoint(__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,additional_replacements=[replace_indices]				)

   lowercase_    :Optional[Any]					    =				{k.replace("mid_new_2"			,"mid_block"				): v for k, v in new_checkpoint.items()}
   lowercase_    :List[Any]					    =				checkpoint["quant_conv.weight"]
   lowercase_    :Dict					    =				checkpoint["quant_conv.bias"]
   if "quantize.embedding.weight" in checkpoint:
      lowercase_    :Tuple					    =				checkpoint["quantize.embedding.weight"]
   lowercase_    :List[str]					    =				checkpoint["post_quant_conv.weight"]
   lowercase_    :Dict					    =				checkpoint["post_quant_conv.bias"]

   return new_checkpoint


if __name__ == "__main__":
 lowerCAmelCase      :  Any      							=argparse.ArgumentParser()

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

 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help='''The config json file corresponding to the architecture.''',
 )

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

 lowerCAmelCase      :  Union[str, Any]      							=parser.parse_args()
 lowerCAmelCase      :  str      							=torch.load(args.checkpoint_path)

 with open(args.config_file) as f:
  lowerCAmelCase      :  List[str]      							=json.loads(f.read())

 # unet case
 lowerCAmelCase      :  Optional[int]      							={key.split('''.''')[0] for key in checkpoint.keys()}
 if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
  lowerCAmelCase      :  int      							=convert_vq_autoenc_checkpoint(checkpoint, config)
 else:
  lowerCAmelCase      :  Optional[Any]      							=convert_ddpm_checkpoint(checkpoint, config)

 if "ddpm" in config:
  del config["ddpm"]

 if config["_class_name"] == "VQModel":
  lowerCAmelCase      :  List[str]      							=VQModel(**config)
  model.load_state_dict(converted_checkpoint)
  model.save_pretrained(args.dump_path)
 elif config["_class_name"] == "AutoencoderKL":
  lowerCAmelCase      :  Optional[Any]      							=AutoencoderKL(**config)
  model.load_state_dict(converted_checkpoint)
  model.save_pretrained(args.dump_path)
 else:
  lowerCAmelCase      :  Union[str, Any]      							=UNetaDModel(**config)
  model.load_state_dict(converted_checkpoint)

  lowerCAmelCase      :  Dict      							=DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

  lowerCAmelCase      :  str      							=DDPMPipeline(unet=model, scheduler=scheduler)
  pipe.save_pretrained(args.dump_path)









'''simple docstring'''


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


lowerCAmelCase      :  str      							=logging.get_logger(__name__)

lowerCAmelCase      :  Union[str, Any]      							={
    '''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    a_     (       _lowerCAmelCase							):
     __A  				=    "blenderbot"
     __A  				=    ["past_key_values"]
     __A  				=    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}





     def __init__(				self	:      Dict			,   lowercase	:      List[Any]=8_008			,   lowercase	:      List[str]=128			,   lowercase	:      Optional[int]=2			,   lowercase	:      Optional[int]=10_240			,   lowercase	:      Tuple=32			,   lowercase	:      int=24			,   lowercase	:      Dict=10_240			,   lowercase	:      Any=32			,   lowercase	:      Optional[int]=0.0			,   lowercase	:      Dict=0.0			,   lowercase	:      int=True			,   lowercase	:      Tuple=True			,   lowercase	:      List[str]="gelu"			,   lowercase	:      int=2_560			,   lowercase	:      Optional[Any]=0.1			,   lowercase	:      Any=0.0			,   lowercase	:      List[str]=0.0			,   lowercase	:      Optional[Any]=0.02			,   lowercase	:      Tuple=1			,   lowercase	:      Union[str, Any]=False			,   lowercase	:      str=0			,   lowercase	:      List[str]=1			,   lowercase	:      Optional[Any]=2			,   lowercase	:      Union[str, Any]=3			,   lowercase	:      List[str]=2			,   **lowercase	:      Optional[Any]			,   ):





        """simple docstring"""




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

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







class    a_     (       _lowerCAmelCase							):





     @property
     def 							lowercase__   (				self	:      Union[str, Any]		):





        """simple docstring"""




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





        """simple docstring"""




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





     def 							lowercase__   (				self	:      Tuple			,   lowercase	:      PreTrainedTokenizer			,   lowercase	:      int = -1			,   lowercase	:      int = -1			,   lowercase	:      bool = False			,   lowercase	:      Optional[TensorType] = None			,   ):





        """simple docstring"""




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

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

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





     def 							lowercase__   (				self	:      Optional[Any]			,   lowercase	:      PreTrainedTokenizer			,   lowercase	:      int = -1			,   lowercase	:      int = -1			,   lowercase	:      bool = False			,   lowercase	:      Optional[TensorType] = None			,   ):





        """simple docstring"""




        lowercase_    :str					    =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase			,   lowercase			,   lowercase			,   lowercase			,   lowercase		)

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





     def 							lowercase__   (				self	:      Dict			,   lowercase	:      PreTrainedTokenizer			,   lowercase	:      int = -1			,   lowercase	:      int = -1			,   lowercase	:      bool = False			,   lowercase	:      Optional[TensorType] = None			,   ):





        """simple docstring"""




        lowercase_    :Optional[int]					    =				compute_effective_axis_dimension(
            lowercase			,   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
        lowercase_    :Optional[Any]					    =				tokenizer.num_special_tokens_to_add(lowercase		)
        lowercase_    :int					    =				compute_effective_axis_dimension(
            lowercase			,   fixed_dimension=OnnxConfig.default_fixed_sequence			,   num_token_to_add=lowercase		)

        # Generate dummy inputs according to compute batch and sequence
        lowercase_    :int					    =				[" ".join([tokenizer.unk_token]		) * seq_length] * batch_size
        lowercase_    :Dict					    =				dict(tokenizer(lowercase			,   return_tensors=lowercase		)		)
        return common_inputs





     def 							lowercase__   (				self	:      Optional[Any]			,   lowercase	:      PreTrainedTokenizer			,   lowercase	:      int = -1			,   lowercase	:      int = -1			,   lowercase	:      bool = False			,   lowercase	:      Optional[TensorType] = None			,   ):





        """simple docstring"""




        if self.task in ["default", "seq2seq-lm"]:
           lowercase_    :str					    =				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
               lowercase			,   batch_size=lowercase			,   seq_length=lowercase			,   is_pair=lowercase			,   framework=lowercase		)

        elif self.task == "causal-lm":
           lowercase_    :Optional[Any]					    =				self._generate_dummy_inputs_for_causal_lm(
               lowercase			,   batch_size=lowercase			,   seq_length=lowercase			,   is_pair=lowercase			,   framework=lowercase		)
        else:
           lowercase_    :List[str]					    =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               lowercase			,   batch_size=lowercase			,   seq_length=lowercase			,   is_pair=lowercase			,   framework=lowercase		)

        return common_inputs





     def 							lowercase__   (				self	:      Optional[int]			,   lowercase	:      str			,   lowercase	:      Union[str, Any]			,   lowercase	:      int			,   lowercase	:      List[Any]		):





        """simple docstring"""




        if self.task in ["default", "seq2seq-lm"]:
           lowercase_    :Optional[int]					    =				super()._flatten_past_key_values_(lowercase			,   lowercase			,   lowercase			,   lowercase		)
        else:
           lowercase_    :Tuple					    =				super(lowercase			,   self		)._flatten_past_key_values_(
               lowercase			,   lowercase			,   lowercase			,   lowercase		)







     def 							lowercase__   (				self	:      List[Any]			,   lowercase	:      Mapping[str, Mapping[int, str]]			,   lowercase	:      str		):





        """simple docstring"""




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

        lowercase_    :Any					    =				"past_key_values" if direction == "inputs" else "present"
        lowercase_    ,							lowercase_    :Optional[int]					    =				self.num_layers

        lowercase_    :str					    =				"past_encoder_sequence"
        lowercase_    :Tuple					    =				"past_decoder_sequence" if direction == "inputs" else "past_decoder_sequence + sequence"

        for i in range(lowercase		):
           lowercase_    :Dict					    =				{0: "batch", 2: decoder_sequence}
           lowercase_    :Optional[int]					    =				{0: "batch", 2: decoder_sequence}
           lowercase_    :Optional[int]					    =				{0: "batch", 2: encoder_sequence}
           lowercase_    :int					    =				{0: "batch", 2: encoder_sequence}


