



import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
a__   :					Optional[Any] 						=      logging.get_logger(__name__)

def 	_lowerCAmelCase (    A__						):
      lowercase__  =	SwinConfig.from_pretrained(
          'microsoft/swin-tiny-patch4-window7-224'    ,	out_features=['stage1', 'stage2', 'stage3', 'stage4']						)
      lowercase__  =	MaskFormerConfig(backbone_config=A__						)

      lowercase__  =	'huggingface/label-files'
      if "ade20k-full" in model_name:
            # this should be ok
            lowercase__  =	847
            lowercase__  =	'maskformer-ade20k-full-id2label.json'
      elif "ade" in model_name:
            # this should be ok
            lowercase__  =	150
            lowercase__  =	'ade20k-id2label.json'
      elif "coco-stuff" in model_name:
            # this should be ok
            lowercase__  =	171
            lowercase__  =	'maskformer-coco-stuff-id2label.json'
      elif "coco" in model_name:
            # TODO
            lowercase__  =	133
            lowercase__  =	'coco-panoptic-id2label.json'
      elif "cityscapes" in model_name:
            # this should be ok
            lowercase__  =	19
            lowercase__  =	'cityscapes-id2label.json'
      elif "vistas" in model_name:
            # this should be ok
            lowercase__  =	65
            lowercase__  =	'mapillary-vistas-id2label.json'

      lowercase__  =	json.load(open(hf_hub_download(A__    ,	A__    ,	repo_type='dataset'						)    ,	'r'						)						)
      lowercase__  =	{int(A__						): v for k, v in idalabel.items()}

      return config

def 	_lowerCAmelCase (    A__						):
      lowercase__  =	[]
      # stem
      # fmt: off
      rename_keys.append(('backbone.patch_embed.proj.weight', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight')						)
      rename_keys.append(('backbone.patch_embed.proj.bias', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias')						)
      rename_keys.append(('backbone.patch_embed.norm.weight', 'model.pixel_level_module.encoder.model.embeddings.norm.weight')						)
      rename_keys.append(('backbone.patch_embed.norm.bias', 'model.pixel_level_module.encoder.model.embeddings.norm.bias')						)
      # stages
      for i in range(len(config.backbone_config.depths						)						):
            for j in range(config.backbone_config.depths[i]						):
                  rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')						)
                  rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')						)
                  rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')						)
                  rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_index''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')						)
                  rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')						)
                  rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')						)
                  rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')						)
                  rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')						)
                  rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')						)
                  rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')						)
                  rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')						)
                  rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')						)

            if i < 3:
                  rename_keys.append((F'''backbone.layers.{i}.downsample.reduction.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight''')						)
                  rename_keys.append((F'''backbone.layers.{i}.downsample.norm.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight''')						)
                  rename_keys.append((F'''backbone.layers.{i}.downsample.norm.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias''')						)
            rename_keys.append((F'''backbone.norm{i}.weight''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.weight''')						)
            rename_keys.append((F'''backbone.norm{i}.bias''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.bias''')						)

      # FPN
      rename_keys.append(('sem_seg_head.layer_4.weight', 'model.pixel_level_module.decoder.fpn.stem.0.weight')						)
      rename_keys.append(('sem_seg_head.layer_4.norm.weight', 'model.pixel_level_module.decoder.fpn.stem.1.weight')						)
      rename_keys.append(('sem_seg_head.layer_4.norm.bias', 'model.pixel_level_module.decoder.fpn.stem.1.bias')						)
      for source_index, target_index in zip(range(3    ,	0    ,	-1						)    ,	range(0    ,	3						)						):
            rename_keys.append((F'''sem_seg_head.adapter_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight''')						)
            rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight''')						)
            rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias''')						)
            rename_keys.append((F'''sem_seg_head.layer_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight''')						)
            rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight''')						)
            rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias''')						)
      rename_keys.append(('sem_seg_head.mask_features.weight', 'model.pixel_level_module.decoder.mask_projection.weight')						)
      rename_keys.append(('sem_seg_head.mask_features.bias', 'model.pixel_level_module.decoder.mask_projection.bias')						)

      # Transformer decoder
      for idx in range(config.decoder_config.decoder_layers						):
            # self-attention out projection
            rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight''')						)
            rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias''')						)
            # cross-attention out projection
            rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight''')						)
            rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias''')						)
            # MLP 1
            rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc1.weight''')						)
            rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc1.bias''')						)
            # MLP 2
            rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc2.weight''')						)
            rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc2.bias''')						)
            # layernorm 1 (self-attention layernorm)
            rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight''')						)
            rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias''')						)
            # layernorm 2 (cross-attention layernorm)
            rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight''')						)
            rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias''')						)
            # layernorm 3 (final layernorm)
            rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight''')						)
            rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias''')						)

      rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.weight', 'model.transformer_module.decoder.layernorm.weight')						)
      rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.bias', 'model.transformer_module.decoder.layernorm.bias')						)

      # heads on top
      rename_keys.append(('sem_seg_head.predictor.query_embed.weight', 'model.transformer_module.queries_embedder.weight')						)

      rename_keys.append(('sem_seg_head.predictor.input_proj.weight', 'model.transformer_module.input_projection.weight')						)
      rename_keys.append(('sem_seg_head.predictor.input_proj.bias', 'model.transformer_module.input_projection.bias')						)

      rename_keys.append(('sem_seg_head.predictor.class_embed.weight', 'class_predictor.weight')						)
      rename_keys.append(('sem_seg_head.predictor.class_embed.bias', 'class_predictor.bias')						)

      for i in range(3						):
            rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.weight''', F'''mask_embedder.{i}.0.weight''')						)
            rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.bias''', F'''mask_embedder.{i}.0.bias''')						)
      # fmt: on

      return rename_keys

def 	_lowerCAmelCase (    A__    ,	A__    ,	A__						):
      lowercase__  =	dct.pop(A__						)
      lowercase__  =	val

def 	_lowerCAmelCase (    A__    ,	A__						):
      lowercase__  =	[int(backbone_config.embed_dim * 2**i						) for i in range(len(backbone_config.depths						)						)]
      for i in range(len(backbone_config.depths						)						):
            lowercase__  =	num_features[i]
            for j in range(backbone_config.depths[i]						):
                  # fmt: off
                  # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
                  lowercase__  =	state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''						)
                  lowercase__  =	state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.bias'''						)
                  # next, add query, keys and values (in that order) to the state dict
                  lowercase__  =	in_proj_weight[:dim, :]
                  lowercase__  =	in_proj_bias[: dim]
                  lowercase__  =	in_proj_weight[
                      dim : dim * 2, :
                  ]
                  lowercase__  =	in_proj_bias[
                      dim : dim * 2
                  ]
                  lowercase__  =	in_proj_weight[
                      -dim :, :
                  ]
                  lowercase__  =	in_proj_bias[-dim :]
                  # fmt: on

def 	_lowerCAmelCase (    A__    ,	A__						):
      # fmt: off
      lowercase__  =	config.decoder_config.hidden_size
      for idx in range(config.decoder_config.decoder_layers						):
            # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
            lowercase__  =	state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''						)
            lowercase__  =	state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias'''						)
            # next, add query, keys and values (in that order) to the state dict
            lowercase__  =	in_proj_weight[: hidden_size, :]
            lowercase__  =	in_proj_bias[:config.hidden_size]
            lowercase__  =	in_proj_weight[hidden_size : hidden_size * 2, :]
            lowercase__  =	in_proj_bias[hidden_size : hidden_size * 2]
            lowercase__  =	in_proj_weight[-hidden_size :, :]
            lowercase__  =	in_proj_bias[-hidden_size :]
            # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
            lowercase__  =	state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''						)
            lowercase__  =	state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'''						)
            # next, add query, keys and values (in that order) to the state dict
            lowercase__  =	in_proj_weight[: hidden_size, :]
            lowercase__  =	in_proj_bias[:config.hidden_size]
            lowercase__  =	in_proj_weight[hidden_size : hidden_size * 2, :]
            lowercase__  =	in_proj_bias[hidden_size : hidden_size * 2]
            lowercase__  =	in_proj_weight[-hidden_size :, :]
            lowercase__  =	in_proj_bias[-hidden_size :]
      # fmt: on

def 	_lowerCAmelCase (    ):
      lowercase__  =	'http://images.cocodataset.org/val2017/000000039769.jpg'
      lowercase__  =	Image.open(requests.get(A__    ,	stream=A__						).raw						)
      return im

@torch.no_grad()
def 	_lowerCAmelCase (    A__    ,	A__    ,	A__    ,	A__ = False						):
      lowercase__  =	get_maskformer_config(A__						)

      # load original state_dict
      with open(A__    ,	'rb'						) as f:
            lowercase__  =	pickle.load(A__						)
      lowercase__  =	data['model']

      # for name, param in state_dict.items():
      #     print(name, param.shape)

      # rename keys
      lowercase__  =	create_rename_keys(A__						)
      for src, dest in rename_keys:
            rename_key(A__    ,	A__    ,	A__						)
      read_in_swin_q_k_v(A__    ,	config.backbone_config						)
      read_in_decoder_q_k_v(A__    ,	A__						)

      # update to torch tensors
      for key, value in state_dict.items():
            lowercase__  =	torch.from_numpy(A__						)

      # load 🤗 model
      lowercase__  =	MaskFormerForInstanceSegmentation(A__						)
      model.eval()

      for name, param in model.named_parameters():
            print(A__    ,	param.shape						)

      lowercase__,						lowercase__  =	model.load_state_dict(A__    ,	strict=A__						)
      assert missing_keys == [
          "model.pixel_level_module.encoder.model.layernorm.weight",
          "model.pixel_level_module.encoder.model.layernorm.bias",
      ]
      assert len(A__						) == 0, F'''Unexpected keys: {unexpected_keys}'''

      # verify results
      lowercase__  =	prepare_img()
      if "vistas" in model_name:
            lowercase__  =	65
      elif "cityscapes" in model_name:
            lowercase__  =	65_535
      else:
            lowercase__  =	255
      lowercase__  =	True if 'ade' in model_name else False
      lowercase__  =	MaskFormerImageProcessor(ignore_index=A__    ,	reduce_labels=A__						)

      lowercase__  =	image_processor(A__    ,	return_tensors='pt'						)

      lowercase__  =	model(**A__						)

      print('Logits:'    ,	outputs.class_queries_logits[0, :3, :3]						)

      if model_name == "maskformer-swin-tiny-ade":
            lowercase__  =	torch.tensor(
                [[3.63_53, -4.47_70, -2.60_65], [0.50_81, -4.23_94, -3.53_43], [2.19_09, -5.03_53, -1.93_23]]						)
      assert torch.allclose(outputs.class_queries_logits[0, :3, :3]    ,	A__    ,	atol=1E-4						)
      print('Looks ok!'						)

      if pytorch_dump_folder_path is not None:
            print(F'''Saving model and image processor to {pytorch_dump_folder_path}'''						)
            Path(A__						).mkdir(exist_ok=A__						)
            model.save_pretrained(A__						)
            image_processor.save_pretrained(A__						)

      if push_to_hub:
            print('Pushing model and image processor to the hub...'						)
            model.push_to_hub(F'''nielsr/{model_name}'''						)
            image_processor.push_to_hub(F'''nielsr/{model_name}'''						)


if __name__ == "__main__":
       a__   :					Dict 						=      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--model_name",
           default="maskformer-swin-tiny-ade",
           type=str,
           help=("Name of the MaskFormer model you'd like to convert",),
       )
       parser.add_argument(
           "--checkpoint_path",
           default="/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl",
           type=str,
           help="Path to the original state dict (.pth file).",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
       )
       parser.add_argument(
           "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
       )

       a__   :					str 						=      parser.parse_args()
       convert_maskformer_checkpoint(
           args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
       )





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

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


a__   :					int 						=      logging.get_logger(__name__)

a__   :					int 						=      {
    "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json",
    # See all Marian models at https://huggingface.co/models?filter=marian
}



class    UpperCAmelCase__(				lowerCamelCase						):

   '''simple docstring'''




   A     :	Union[str, Any]												=     "marian"
   A     :	Dict												=     ["past_key_values"]
   A     :	List[Any]												=     {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


   def __init__(    self	:      Any      ,					lowerCAmelCase	:      List[Any]=5_81_01      ,					lowerCAmelCase	:      Optional[int]=None      ,					lowerCAmelCase	:      List[str]=10_24      ,					lowerCAmelCase	:      int=12      ,					lowerCAmelCase	:      Union[str, Any]=40_96      ,					lowerCAmelCase	:      int=16      ,					lowerCAmelCase	:      Dict=12      ,					lowerCAmelCase	:      Optional[int]=40_96      ,					lowerCAmelCase	:      str=16      ,					lowerCAmelCase	:      List[Any]=0.0      ,					lowerCAmelCase	:      Optional[Any]=0.0      ,					lowerCAmelCase	:      List[str]=True      ,					lowerCAmelCase	:      Union[str, Any]=True      ,					lowerCAmelCase	:      List[Any]="gelu"      ,					lowerCAmelCase	:      int=10_24      ,					lowerCAmelCase	:      Optional[int]=0.1      ,					lowerCAmelCase	:      Optional[int]=0.0      ,					lowerCAmelCase	:      Tuple=0.0      ,					lowerCAmelCase	:      List[Any]=0.02      ,					lowerCAmelCase	:      Union[str, Any]=5_81_00      ,					lowerCAmelCase	:      str=False      ,					lowerCAmelCase	:      Optional[Any]=5_81_00      ,					lowerCAmelCase	:      Union[str, Any]=0      ,					lowerCAmelCase	:      Optional[Any]=0      ,					lowerCAmelCase	:      int=True      ,					**lowerCAmelCase	:      List[str]      ,					)						->					Dict:




         """simple docstring"""

         lowercase__  =	vocab_size
         lowercase__  =	decoder_vocab_size or vocab_size
         lowercase__  =	max_position_embeddings
         lowercase__  =	d_model
         lowercase__  =	encoder_ffn_dim
         lowercase__  =	encoder_layers
         lowercase__  =	encoder_attention_heads
         lowercase__  =	decoder_ffn_dim
         lowercase__  =	decoder_layers
         lowercase__  =	decoder_attention_heads
         lowercase__  =	dropout
         lowercase__  =	attention_dropout
         lowercase__  =	activation_dropout
         lowercase__  =	activation_function
         lowercase__  =	init_std
         lowercase__  =	encoder_layerdrop
         lowercase__  =	decoder_layerdrop
         lowercase__  =	use_cache
         lowercase__  =	encoder_layers
         lowercase__  =	scale_embedding  # scale factor will be sqrt(d_model) if True
         lowercase__  =	share_encoder_decoder_embeddings
         super().__init__(
             pad_token_id=lowerCAmelCase      ,					eos_token_id=lowerCAmelCase      ,					is_encoder_decoder=lowerCAmelCase      ,					decoder_start_token_id=lowerCAmelCase      ,					forced_eos_token_id=lowerCAmelCase      ,					**lowerCAmelCase      ,					)



class    UpperCAmelCase__(				lowerCamelCase						):

   '''simple docstring'''




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




         """simple docstring"""

         if self.task in ["default", "seq2seq-lm"]:
               lowercase__  =	OrderedDict(
                   [
                       ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                       ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                   ])

               if self.use_past:
                     lowercase__  =	{0: 'batch'}
                     lowercase__  =	{0: 'batch', 1: 'past_decoder_sequence + sequence'}
               else:
                     lowercase__  =	{0: 'batch', 1: 'decoder_sequence'}
                     lowercase__  =	{0: 'batch', 1: 'decoder_sequence'}

               if self.use_past:
                     self.fill_with_past_key_values_(lowerCAmelCase      ,					direction='inputs')
         elif self.task == "causal-lm":
               # TODO: figure this case out.
               lowercase__  =	OrderedDict(
                   [
                       ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                       ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                   ])
               if self.use_past:
                     lowercase__,						lowercase__  =	self.num_layers
                     for i in range(lowerCAmelCase):
                           lowercase__  =	{0: 'batch', 2: 'past_sequence + sequence'}
                           lowercase__  =	{0: 'batch', 2: 'past_sequence + sequence'}
         else:
               lowercase__  =	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	:      Any)						->					Mapping[str, Mapping[int, str]]:




         """simple docstring"""

         if self.task in ["default", "seq2seq-lm"]:
               lowercase__  =	super().outputs
         else:
               lowercase__  =	super(lowerCAmelCase      ,					self).outputs
               if self.use_past:
                     lowercase__,						lowercase__  =	self.num_layers
                     for i in range(lowerCAmelCase):
                           lowercase__  =	{0: 'batch', 2: 'past_sequence + sequence'}
                           lowercase__  =	{0: 'batch', 2: 'past_sequence + sequence'}
         return common_outputs


   def       UpperCAmelCase		(    self	:      Optional[int]      ,					lowerCAmelCase	:      PreTrainedTokenizer      ,					lowerCAmelCase	:      int = -1      ,					lowerCAmelCase	:      int = -1      ,					lowerCAmelCase	:      bool = False      ,					lowerCAmelCase	:      Optional[TensorType] = None      ,					)						->					Mapping[str, Any]:




         """simple docstring"""

         lowercase__  =	self._generate_dummy_inputs_for_encoder_and_decoder(
             lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)

         # Generate decoder inputs
         lowercase__  =	seq_length if not self.use_past else 1
         lowercase__  =	self._generate_dummy_inputs_for_encoder_and_decoder(
             lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)
         lowercase__  =	{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
         lowercase__  =	dict(**lowerCAmelCase      ,					**lowerCAmelCase)

         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__  =	common_inputs['input_ids'].shape
               lowercase__  =	common_inputs['decoder_input_ids'].shape[1]
               lowercase__,						lowercase__  =	self.num_attention_heads
               lowercase__  =	(
                   batch,
                   num_encoder_attention_heads,
                   encoder_seq_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )
               lowercase__  =	decoder_seq_length + 3
               lowercase__  =	(
                   batch,
                   num_decoder_attention_heads,
                   decoder_past_length,
                   self._config.hidden_size // num_decoder_attention_heads,
               )

               lowercase__  =	torch.cat(
                   [common_inputs['decoder_attention_mask'], torch.ones(lowerCAmelCase      ,					lowerCAmelCase)]      ,					dim=1)

               lowercase__  =	[]
               # If the number of encoder and decoder layers are present in the model configuration, both are considered
               lowercase__,						lowercase__  =	self.num_layers
               lowercase__  =	min(lowerCAmelCase      ,					lowerCAmelCase)
               lowercase__  =	max(lowerCAmelCase      ,					lowerCAmelCase) - min_num_layers
               lowercase__  =	'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

               for _ in range(lowerCAmelCase):
                     common_inputs["past_key_values"].append(
                         (
                             torch.zeros(lowerCAmelCase),
                             torch.zeros(lowerCAmelCase),
                             torch.zeros(lowerCAmelCase),
                             torch.zeros(lowerCAmelCase),
                         ))
               # TODO: test this.
               lowercase__  =	encoder_shape if remaining_side_name == 'encoder' else decoder_shape
               for _ in range(lowerCAmelCase      ,					lowerCAmelCase):
                     common_inputs["past_key_values"].append((torch.zeros(lowerCAmelCase), torch.zeros(lowerCAmelCase)))
         return common_inputs


   def       UpperCAmelCase		(    self	:      str      ,					lowerCAmelCase	:      PreTrainedTokenizer      ,					lowerCAmelCase	:      int = -1      ,					lowerCAmelCase	:      int = -1      ,					lowerCAmelCase	:      bool = False      ,					lowerCAmelCase	:      Optional[TensorType] = None      ,					)						->					Mapping[str, Any]:




         """simple docstring"""

         lowercase__  =	self._generate_dummy_inputs_for_encoder_and_decoder(
             lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)

         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__  =	common_inputs['input_ids'].shape
               # Not using the same length for past_key_values
               lowercase__  =	seqlen + 2
               lowercase__,						lowercase__  =	self.num_layers
               lowercase__,						lowercase__  =	self.num_attention_heads
               lowercase__  =	(
                   batch,
                   num_encoder_attention_heads,
                   past_key_values_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )

               lowercase__  =	common_inputs['attention_mask'].dtype
               lowercase__  =	torch.cat(
                   [common_inputs['attention_mask'], torch.ones(lowerCAmelCase      ,					lowerCAmelCase      ,					dtype=lowerCAmelCase)]      ,					dim=1)
               lowercase__  =	[
                   (torch.zeros(lowerCAmelCase), torch.zeros(lowerCAmelCase)) for _ in range(lowerCAmelCase)
               ]
         return common_inputs


   def       UpperCAmelCase		(    self	:      Dict      ,					lowerCAmelCase	:      PreTrainedTokenizer      ,					lowerCAmelCase	:      int = -1      ,					lowerCAmelCase	:      int = -1      ,					lowerCAmelCase	:      bool = False      ,					lowerCAmelCase	:      Optional[TensorType] = None      ,					)						->					Mapping[str, Any]:




         """simple docstring"""

         lowercase__  =	compute_effective_axis_dimension(
             lowerCAmelCase      ,					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__  =	tokenizer.num_special_tokens_to_add(lowerCAmelCase)
         lowercase__  =	compute_effective_axis_dimension(
             lowerCAmelCase      ,					fixed_dimension=OnnxConfig.default_fixed_sequence      ,					num_token_to_add=lowerCAmelCase)

         # Generate dummy inputs according to compute batch and sequence
         lowercase__  =	[' '.join([tokenizer.unk_token]) * seq_length] * batch_size
         lowercase__  =	dict(tokenizer(lowerCAmelCase      ,					return_tensors=lowerCAmelCase))
         return common_inputs


   def       UpperCAmelCase		(    self	:      str      ,					lowerCAmelCase	:      PreTrainedTokenizer      ,					lowerCAmelCase	:      int = -1      ,					lowerCAmelCase	:      int = -1      ,					lowerCAmelCase	:      bool = False      ,					lowerCAmelCase	:      Optional[TensorType] = None      ,					)						->					Mapping[str, Any]:




         """simple docstring"""

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

         else:
               lowercase__  =	self._generate_dummy_inputs_for_causal_lm(
                   lowerCAmelCase      ,					batch_size=lowerCAmelCase      ,					seq_length=lowerCAmelCase      ,					is_pair=lowerCAmelCase      ,					framework=lowerCAmelCase)

         return common_inputs


   def       UpperCAmelCase		(    self	:      List[Any]      ,					lowerCAmelCase	:      Optional[int]      ,					lowerCAmelCase	:      Optional[Any]      ,					lowerCAmelCase	:      str      ,					lowerCAmelCase	:      str)						->					Optional[int]:




         """simple docstring"""

         if self.task in ["default", "seq2seq-lm"]:
               lowercase__  =	super()._flatten_past_key_values_(lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)
         else:
               lowercase__  =	super(lowerCAmelCase      ,					self)._flatten_past_key_values_(
                   lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)



   @property
   def       UpperCAmelCase		(    self	:      Optional[int])						->					float:




         """simple docstring"""

         return 1E-4
