
import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__     :		Union[str, Any]        = logging.get_logger(__name__)

def 		UpperCamelCase						(   lowercase_			,			lowercase_=False )		->						List[Any]:


  '''simple docstring'''






  lowercase__	: Tuple     		=   OrderedDict()
  for key, value in state_dict.items():
    if encoder_only and not key.startswith("""head""" ):
      lowercase__	: List[str]     		=   """segformer.encoder.""" + key
    if key.startswith("""backbone""" ):
      lowercase__	: Any     		=   key.replace("""backbone"""			,			"""segformer.encoder""" )
    if "patch_embed" in key:
      # replace for example patch_embed1 by patch_embeddings.0
      lowercase__	: Tuple     		=   key[key.find("""patch_embed""" ) + len("""patch_embed""" )]
      lowercase__	: str     		=   key.replace(F'patch_embed{idx}'			,			F'patch_embeddings.{int(lowercase_ )-1}' )
    if "norm" in key:
      lowercase__	: Dict     		=   key.replace("""norm"""			,			"""layer_norm""" )
    if "segformer.encoder.layer_norm" in key:
      # replace for example layer_norm1 by layer_norm.0
      lowercase__	: int     		=   key[key.find("""segformer.encoder.layer_norm""" ) + len("""segformer.encoder.layer_norm""" )]
      lowercase__	: Any     		=   key.replace(F'layer_norm{idx}'			,			F'layer_norm.{int(lowercase_ )-1}' )
    if "layer_norm1" in key:
      lowercase__	: Union[str, Any]     		=   key.replace("""layer_norm1"""			,			"""layer_norm_1""" )
    if "layer_norm2" in key:
      lowercase__	: List[Any]     		=   key.replace("""layer_norm2"""			,			"""layer_norm_2""" )
    if "block" in key:
      # replace for example block1 by block.0
      lowercase__	: Dict     		=   key[key.find("""block""" ) + len("""block""" )]
      lowercase__	: int     		=   key.replace(F'block{idx}'			,			F'block.{int(lowercase_ )-1}' )
    if "attn.q" in key:
      lowercase__	: Tuple     		=   key.replace("""attn.q"""			,			"""attention.self.query""" )
    if "attn.proj" in key:
      lowercase__	: Tuple     		=   key.replace("""attn.proj"""			,			"""attention.output.dense""" )
    if "attn" in key:
      lowercase__	: Optional[int]     		=   key.replace("""attn"""			,			"""attention.self""" )
    if "fc1" in key:
      lowercase__	: Dict     		=   key.replace("""fc1"""			,			"""dense1""" )
    if "fc2" in key:
      lowercase__	: Optional[int]     		=   key.replace("""fc2"""			,			"""dense2""" )
    if "linear_pred" in key:
      lowercase__	: Optional[int]     		=   key.replace("""linear_pred"""			,			"""classifier""" )
    if "linear_fuse" in key:
      lowercase__	: Tuple     		=   key.replace("""linear_fuse.conv"""			,			"""linear_fuse""" )
      lowercase__	: Tuple     		=   key.replace("""linear_fuse.bn"""			,			"""batch_norm""" )
    if "linear_c" in key:
      # replace for example linear_c4 by linear_c.3
      lowercase__	: str     		=   key[key.find("""linear_c""" ) + len("""linear_c""" )]
      lowercase__	: str     		=   key.replace(F'linear_c{idx}'			,			F'linear_c.{int(lowercase_ )-1}' )
    if key.startswith("""head""" ):
      lowercase__	: Optional[int]     		=   key.replace("""head"""			,			"""classifier""" )
    lowercase__	: List[str]     		=   value

  return new_state_dict

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						Optional[int]:


  '''simple docstring'''






  for i in range(config.num_encoder_blocks ):
    for j in range(config.depths[i] ):
      # read in weights + bias of keys and values (which is a single matrix in the original implementation)
      lowercase__	: Optional[Any]     		=   state_dict.pop(F'segformer.encoder.block.{i}.{j}.attention.self.kv.weight' )
      lowercase__	: List[Any]     		=   state_dict.pop(F'segformer.encoder.block.{i}.{j}.attention.self.kv.bias' )
      # next, add keys and values (in that order) to the state dict
      lowercase__	: Any     		=   kv_weight[
          : config.hidden_sizes[i], :
      ]
      lowercase__	: List[Any]     		=   kv_bias[: config.hidden_sizes[i]]
      lowercase__	: List[Any]     		=   kv_weight[
          config.hidden_sizes[i] :, :
      ]
      lowercase__	: int     		=   kv_bias[
          config.hidden_sizes[i] :
      ]

def 		UpperCamelCase						(   )		->						str:


  '''simple docstring'''






  lowercase__	: Optional[int]     		=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
  lowercase__	: List[str]     		=   Image.open(requests.get(lowercase_			,			stream=lowercase_ ).raw )

  return image

@torch.no_grad()
def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_ )		->						List[str]:


  '''simple docstring'''






  lowercase__	: Any     		=   SegformerConfig()
  lowercase__	: Tuple     		=   False

  # set attributes based on model_name
  lowercase__	: Dict     		=   """huggingface/label-files"""
  if "segformer" in model_name:
    lowercase__	: Union[str, Any]     		=   model_name[len("""segformer.""" ) : len("""segformer.""" ) + 2]
    if "ade" in model_name:
      lowercase__	: int     		=   1_50
      lowercase__	: Optional[int]     		=   """ade20k-id2label.json"""
      lowercase__	: List[str]     		=   (1, 1_50, 1_28, 1_28)
    elif "city" in model_name:
      lowercase__	: int     		=   19
      lowercase__	: Optional[Any]     		=   """cityscapes-id2label.json"""
      lowercase__	: List[str]     		=   (1, 19, 1_28, 1_28)
    else:
      raise ValueError(F'Model {model_name} not supported' )
  elif "mit" in model_name:
    lowercase__	: List[str]     		=   True
    lowercase__	: Optional[int]     		=   model_name[4:6]
    lowercase__	: Dict     		=   10_00
    lowercase__	: Dict     		=   """imagenet-1k-id2label.json"""
    lowercase__	: List[str]     		=   (1, 10_00)
  else:
    raise ValueError(F'Model {model_name} not supported' )

  # set config attributes
  lowercase__	: Any     		=   json.load(open(hf_hub_download(lowercase_			,			lowercase_			,			repo_type="""dataset""" )			,			"""r""" ) )
  lowercase__	: Union[str, Any]     		=   {int(lowercase_ ): v for k, v in idalabel.items()}
  lowercase__	: int     		=   idalabel
  lowercase__	: Tuple     		=   {v: k for k, v in idalabel.items()}
  if size == "b0":
    pass
  elif size == "b1":
    lowercase__	: List[str]     		=   [64, 1_28, 3_20, 5_12]
    lowercase__	: Dict     		=   2_56
  elif size == "b2":
    lowercase__	: Dict     		=   [64, 1_28, 3_20, 5_12]
    lowercase__	: Dict     		=   7_68
    lowercase__	: Optional[int]     		=   [3, 4, 6, 3]
  elif size == "b3":
    lowercase__	: str     		=   [64, 1_28, 3_20, 5_12]
    lowercase__	: int     		=   7_68
    lowercase__	: Optional[Any]     		=   [3, 4, 18, 3]
  elif size == "b4":
    lowercase__	: List[Any]     		=   [64, 1_28, 3_20, 5_12]
    lowercase__	: Optional[Any]     		=   7_68
    lowercase__	: Tuple     		=   [3, 8, 27, 3]
  elif size == "b5":
    lowercase__	: List[str]     		=   [64, 1_28, 3_20, 5_12]
    lowercase__	: Tuple     		=   7_68
    lowercase__	: List[str]     		=   [3, 6, 40, 3]
  else:
    raise ValueError(F'Size {size} not supported' )

  # load image processor (only resize + normalize)
  lowercase__	: List[Any]     		=   SegformerImageProcessor(
      image_scale=(5_12, 5_12)			,			keep_ratio=lowercase_			,			align=lowercase_			,			do_random_crop=lowercase_ )

  # prepare image
  lowercase__	: Union[str, Any]     		=   prepare_img()
  lowercase__	: Optional[Any]     		=   image_processor(images=lowercase_			,			return_tensors="""pt""" ).pixel_values

  logger.info(F'Converting model {model_name}...' )

  # load original state dict
  if encoder_only:
    lowercase__	: Union[str, Any]     		=   torch.load(lowercase_			,			map_location=torch.device("""cpu""" ) )
  else:
    lowercase__	: Optional[Any]     		=   torch.load(lowercase_			,			map_location=torch.device("""cpu""" ) )["""state_dict"""]

  # rename keys
  lowercase__	: List[str]     		=   rename_keys(lowercase_			,			encoder_only=lowercase_ )
  if not encoder_only:
    del state_dict["decode_head.conv_seg.weight"]
    del state_dict["decode_head.conv_seg.bias"]

  # key and value matrices need special treatment
  read_in_k_v(lowercase_			,			lowercase_ )

  # create HuggingFace model and load state dict
  if encoder_only:
    lowercase__	: List[str]     		=   False
    lowercase__	: Any     		=   SegformerForImageClassification(lowercase_ )
  else:
    lowercase__	: Optional[Any]     		=   SegformerForSemanticSegmentation(lowercase_ )
  model.load_state_dict(lowercase_ )
  model.eval()

  # forward pass
  lowercase__	: int     		=   model(lowercase_ )
  lowercase__	: Dict     		=   outputs.logits

  # set expected_slice based on model name
  # ADE20k checkpoints
  if model_name == "segformer.b0.512x512.ade.160k":
    lowercase__	: Dict     		=   torch.tensor(
        [
            [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
            [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
            [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
        ] )
  elif model_name == "segformer.b1.512x512.ade.160k":
    lowercase__	: Any     		=   torch.tensor(
        [
            [[-7.5820, -8.7231, -8.3215], [-8.0600, -10.3529, -10.0304], [-7.5208, -9.4103, -9.6239]],
            [[-12.6918, -13.8994, -13.7137], [-13.3196, -15.7523, -15.4789], [-12.9343, -14.8757, -14.9689]],
            [[-11.1911, -11.9421, -11.3243], [-11.3342, -13.6839, -13.3581], [-10.3909, -12.1832, -12.4858]],
        ] )
  elif model_name == "segformer.b2.512x512.ade.160k":
    lowercase__	: Optional[Any]     		=   torch.tensor(
        [
            [[-11.8173, -14.3850, -16.3128], [-14.5648, -16.5804, -18.6568], [-14.7223, -15.7387, -18.4218]],
            [[-15.7290, -17.9171, -19.4423], [-18.3105, -19.9448, -21.4661], [-17.9296, -18.6497, -20.7910]],
            [[-15.0783, -17.0336, -18.2789], [-16.8771, -18.6870, -20.1612], [-16.2454, -17.1426, -19.5055]],
        ] )
  elif model_name == "segformer.b3.512x512.ade.160k":
    lowercase__	: Optional[Any]     		=   torch.tensor(
        [
            [[-9.0878, -10.2081, -10.1891], [-9.3144, -10.7941, -10.9843], [-9.2294, -10.3855, -10.5704]],
            [[-12.2316, -13.9068, -13.6102], [-12.9161, -14.3702, -14.3235], [-12.5233, -13.7174, -13.7932]],
            [[-14.6275, -15.2490, -14.9727], [-14.3400, -15.9687, -16.2827], [-14.1484, -15.4033, -15.8937]],
        ] )
  elif model_name == "segformer.b4.512x512.ade.160k":
    lowercase__	: List[str]     		=   torch.tensor(
        [
            [[-12.3144, -13.2447, -14.0802], [-13.3614, -14.5816, -15.6117], [-13.3340, -14.4433, -16.2219]],
            [[-19.2781, -20.4128, -20.7506], [-20.6153, -21.6566, -22.0998], [-19.9800, -21.0430, -22.1494]],
            [[-18.8739, -19.7804, -21.1834], [-20.1233, -21.6765, -23.2944], [-20.0315, -21.2641, -23.6944]],
        ] )
  elif model_name == "segformer.b5.640x640.ade.160k":
    lowercase__	: List[str]     		=   torch.tensor(
        [
            [[-9.5524, -12.0835, -11.7348], [-10.5229, -13.6446, -14.5662], [-9.5842, -12.8851, -13.9414]],
            [[-15.3432, -17.5323, -17.0818], [-16.3330, -18.9255, -19.2101], [-15.1340, -17.7848, -18.3971]],
            [[-12.6072, -14.9486, -14.6631], [-13.7629, -17.0907, -17.7745], [-12.7899, -16.1695, -17.1671]],
        ] )
  # Cityscapes checkpoints
  elif model_name == "segformer.b0.1024x1024.city.160k":
    lowercase__	: int     		=   torch.tensor(
        [
            [[-11.9295, -13.4057, -14.8106], [-13.3431, -14.8179, -15.3781], [-14.2836, -15.5942, -16.1588]],
            [[-11.4906, -12.8067, -13.6564], [-13.1189, -14.0500, -14.1543], [-13.8748, -14.5136, -14.8789]],
            [[0.5374, 0.1067, -0.4742], [0.1141, -0.2255, -0.7099], [-0.3000, -0.5924, -1.3105]],
        ] )
  elif model_name == "segformer.b0.512x1024.city.160k":
    lowercase__	: int     		=   torch.tensor(
        [
            [[-7.8217, -9.8767, -10.1717], [-9.4438, -10.9058, -11.4047], [-9.7939, -12.3495, -12.1079]],
            [[-7.1514, -9.5336, -10.0860], [-9.7776, -11.6822, -11.8439], [-10.1411, -12.7655, -12.8972]],
            [[0.3021, 0.0805, -0.2310], [-0.0328, -0.1605, -0.2714], [-0.1408, -0.5477, -0.6976]],
        ] )
  elif model_name == "segformer.b0.640x1280.city.160k":
    lowercase__	: int     		=   torch.tensor(
        [
            [
                [-1.1_372E01, -1.2_787E01, -1.3_477E01],
                [-1.2_536E01, -1.4_194E01, -1.4_409E01],
                [-1.3_217E01, -1.4_888E01, -1.5_327E01],
            ],
            [
                [-1.4_791E01, -1.7_122E01, -1.8_277E01],
                [-1.7_163E01, -1.9_192E01, -1.9_533E01],
                [-1.7_897E01, -1.9_991E01, -2.0_315E01],
            ],
            [
                [7.6_723E-01, 4.1_921E-01, -7.7_878E-02],
                [4.7_772E-01, 9.5_557E-03, -2.8_082E-01],
                [3.6_032E-01, -2.4_826E-01, -5.1_168E-01],
            ],
        ] )
  elif model_name == "segformer.b0.768x768.city.160k":
    lowercase__	: Optional[int]     		=   torch.tensor(
        [
            [[-9.4959, -11.3087, -11.7479], [-11.0025, -12.6540, -12.3319], [-11.4064, -13.0487, -12.9905]],
            [[-9.8905, -11.3084, -12.0854], [-11.1726, -12.7698, -12.9583], [-11.5985, -13.3278, -14.1774]],
            [[0.2213, 0.0192, -0.2466], [-0.1731, -0.4213, -0.4874], [-0.3126, -0.6541, -1.1389]],
        ] )
  elif model_name == "segformer.b1.1024x1024.city.160k":
    lowercase__	: Optional[int]     		=   torch.tensor(
        [
            [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
            [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
            [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
        ] )
  elif model_name == "segformer.b2.1024x1024.city.160k":
    lowercase__	: Optional[Any]     		=   torch.tensor(
        [
            [[-16.0976, -16.4856, -17.3962], [-16.6234, -19.0342, -19.7685], [-16.0900, -18.0661, -19.1180]],
            [[-18.4750, -18.8488, -19.5074], [-19.4030, -22.1570, -22.5977], [-19.1191, -20.8486, -22.3783]],
            [[-4.5178, -5.5037, -6.5109], [-5.0884, -7.2174, -8.0334], [-4.4156, -5.8117, -7.2970]],
        ] )
  elif model_name == "segformer.b3.1024x1024.city.160k":
    lowercase__	: Tuple     		=   torch.tensor(
        [
            [[-14.2081, -14.4732, -14.1977], [-14.5867, -16.4423, -16.6356], [-13.4441, -14.9685, -16.8696]],
            [[-14.4576, -14.7073, -15.0451], [-15.0816, -17.6237, -17.9873], [-14.4213, -16.0199, -18.5992]],
            [[-4.7349, -4.9588, -5.0966], [-4.3210, -6.9325, -7.2591], [-3.4312, -4.7484, -7.1917]],
        ] )
  elif model_name == "segformer.b4.1024x1024.city.160k":
    lowercase__	: str     		=   torch.tensor(
        [
            [[-11.7737, -11.9526, -11.3273], [-13.6692, -14.4574, -13.8878], [-13.8937, -14.6924, -15.9345]],
            [[-14.6706, -14.5330, -14.1306], [-16.1502, -16.8180, -16.4269], [-16.8338, -17.8939, -20.1746]],
            [[1.0491, 0.8289, 1.0310], [1.1044, 0.5219, 0.8055], [1.0899, 0.6926, 0.5590]],
        ] )
  elif model_name == "segformer.b5.1024x1024.city.160k":
    lowercase__	: Dict     		=   torch.tensor(
        [
            [[-12.5641, -13.4777, -13.0684], [-13.9587, -15.8983, -16.6557], [-13.3109, -15.7350, -16.3141]],
            [[-14.7074, -15.4352, -14.5944], [-16.6353, -18.1663, -18.6120], [-15.1702, -18.0329, -18.1547]],
            [[-1.7990, -2.0951, -1.7784], [-2.6397, -3.8245, -3.9686], [-1.5264, -2.8126, -2.9316]],
        ] )
  else:
    lowercase__	: Tuple     		=   logits.argmax(-1 ).item()
    print("""Predicted class:"""			,			model.config.idalabel[predicted_class_idx] )

  # verify logits
  if not encoder_only:
    assert logits.shape == expected_shape
    assert torch.allclose(logits[0, :3, :3, :3]			,			lowercase_			,			atol=1E-2 )

  # finally, save model and image processor
  logger.info(F'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...' )
  Path(lowercase_ ).mkdir(exist_ok=lowercase_ )
  model.save_pretrained(lowercase_ )
  image_processor.save_pretrained(lowercase_ )


if __name__ == "__main__":
     lowerCamelCase__     :		Optional[Any]        = argparse.ArgumentParser()

     parser.add_argument(
         """--model_name""",
         default="""segformer.b0.512x512.ade.160k""",
         type=str,
         help="""Name of the model you'd like to convert.""",
     )
     parser.add_argument(
         """--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file)."""
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
     )
     lowerCamelCase__     :		Optional[Any]        = parser.parse_args()
     convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)









"""simple docstring"""
import warnings
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							=  logging.get_logger(__name__)

_A							=  {
    """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/config.json""",
    # See all BART models at https://huggingface.co/models?filter=bart
}

class       lowerCamelCase	(       lowerCAmelCase__   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  'bart'
  SCREAMING_SNAKE_CASE					      =  ['past_key_values']
  SCREAMING_SNAKE_CASE					      =  {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



  def __init__(self							,       _lowerCamelCase=50265							,       _lowerCamelCase=1024							,       _lowerCamelCase=12							,       _lowerCamelCase=4096							,       _lowerCamelCase=16							,       _lowerCamelCase=12							,       _lowerCamelCase=4096							,       _lowerCamelCase=16							,       _lowerCamelCase=0.0							,       _lowerCamelCase=0.0							,       _lowerCamelCase="gelu"							,       _lowerCamelCase=1024							,       _lowerCamelCase=0.1							,       _lowerCamelCase=0.0							,       _lowerCamelCase=0.0							,       _lowerCamelCase=0.02							,       _lowerCamelCase=0.0							,       _lowerCamelCase=False							,       _lowerCamelCase=True							,       _lowerCamelCase=3							,       _lowerCamelCase=1							,       _lowerCamelCase=0							,       _lowerCamelCase=2							,       _lowerCamelCase=True							,       _lowerCamelCase=2							,       _lowerCamelCase=2							,       **_lowerCamelCase							,       ):


        """simple docstring"""

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

        super().__init__(
            num_labels=_lowerCamelCase							,       pad_token_id=_lowerCamelCase							,       bos_token_id=_lowerCamelCase							,       eos_token_id=_lowerCamelCase							,       is_encoder_decoder=_lowerCamelCase							,       decoder_start_token_id=_lowerCamelCase							,       forced_eos_token_id=_lowerCamelCase							,       **_lowerCamelCase							,       )

        # ensure backward compatibility for BART CNN models
        if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""							,       _lowerCamelCase	):
              UpperCAmelCase__			:							List[Any]							=    self.bos_token_id
              warnings.warn(
                  F"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
                  """The config can simply be saved and uploaded again to be fixed."""	)




class       lowerCamelCase	(       lowerCAmelCase__   ):


  '''simple docstring'''







  @property
  def  _a      (self	):


        """simple docstring"""

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

              if self.use_past:
                    UpperCAmelCase__			:							Dict							=    {0: """batch"""}
                    UpperCAmelCase__			:							Tuple							=    {0: """batch""", 1: """past_decoder_sequence + sequence"""}
              else:
                    UpperCAmelCase__			:							Dict							=    {0: """batch""", 1: """decoder_sequence"""}
                    UpperCAmelCase__			:							List[str]							=    {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.
              UpperCAmelCase__			:							str							=    OrderedDict(
                  [
                      ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                      ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                  ]	)
              if self.use_past:
                    UpperCAmelCase__					,		UpperCAmelCase__			:							List[str]							=    self.num_layers
                    for i in range(_lowerCamelCase	):
                          UpperCAmelCase__			:							Optional[int]							=    {0: """batch""", 2: """past_sequence + sequence"""}
                          UpperCAmelCase__			:							int							=    {0: """batch""", 2: """past_sequence + sequence"""}
        else:
              UpperCAmelCase__			:							Union[str, Any]							=    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
  def  _a      (self	):


        """simple docstring"""

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



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = -1							,       _lowerCamelCase = -1							,       _lowerCamelCase = False							,       _lowerCamelCase = None							,       ):


        """simple docstring"""

        UpperCAmelCase__			:							int							=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)

        # Generate decoder inputs
        UpperCAmelCase__			:							int							=    seq_length if not self.use_past else 1
        UpperCAmelCase__			:							str							=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
        UpperCAmelCase__			:							str							=    {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
        UpperCAmelCase__			:							Union[str, Any]							=    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
              UpperCAmelCase__					,		UpperCAmelCase__			:							str							=    common_inputs["""input_ids"""].shape
              UpperCAmelCase__			:							int							=    common_inputs["""decoder_input_ids"""].shape[1]
              UpperCAmelCase__					,		UpperCAmelCase__			:							List[str]							=    self.num_attention_heads
              UpperCAmelCase__			:							str							=    (
                  batch,
                  num_encoder_attention_heads,
                  encoder_seq_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )
              UpperCAmelCase__			:							Optional[int]							=    decoder_seq_length + 3
              UpperCAmelCase__			:							Any							=    (
                  batch,
                  num_decoder_attention_heads,
                  decoder_past_length,
                  self._config.hidden_size // num_decoder_attention_heads,
              )

              UpperCAmelCase__			:							str							=    torch.cat(
                  [common_inputs["""decoder_attention_mask"""], torch.ones(_lowerCamelCase							,       _lowerCamelCase	)]							,       dim=1	)

              UpperCAmelCase__			:							List[str]							=    []
              # If the number of encoder and decoder layers are present in the model configuration, both are considered
              UpperCAmelCase__					,		UpperCAmelCase__			:							Dict							=    self.num_layers
              UpperCAmelCase__			:							Union[str, Any]							=    min(_lowerCamelCase							,       _lowerCamelCase	)
              UpperCAmelCase__			:							str							=    max(_lowerCamelCase							,       _lowerCamelCase	) - min_num_layers
              UpperCAmelCase__			:							Dict							=    """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.
              UpperCAmelCase__			:							Dict							=    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  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = -1							,       _lowerCamelCase = -1							,       _lowerCamelCase = False							,       _lowerCamelCase = None							,       ):


        """simple docstring"""

        UpperCAmelCase__			:							Any							=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            _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
              UpperCAmelCase__					,		UpperCAmelCase__			:							Optional[int]							=    common_inputs["""input_ids"""].shape
              # Not using the same length for past_key_values
              UpperCAmelCase__			:							int							=    seqlen + 2
              UpperCAmelCase__					,		UpperCAmelCase__			:							Optional[int]							=    self.num_layers
              UpperCAmelCase__					,		UpperCAmelCase__			:							str							=    self.num_attention_heads
              UpperCAmelCase__			:							Tuple							=    (
                  batch,
                  num_encoder_attention_heads,
                  past_key_values_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )

              UpperCAmelCase__			:							Optional[int]							=    common_inputs["""attention_mask"""].dtype
              UpperCAmelCase__			:							List[str]							=    torch.cat(
                  [common_inputs["""attention_mask"""], torch.ones(_lowerCamelCase							,       _lowerCamelCase							,       dtype=_lowerCamelCase	)]							,       dim=1	)
              UpperCAmelCase__			:							Tuple							=    [
                  (torch.zeros(_lowerCamelCase	), torch.zeros(_lowerCamelCase	)) for _ in range(_lowerCamelCase	)
              ]
        return common_inputs



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = -1							,       _lowerCamelCase = -1							,       _lowerCamelCase = False							,       _lowerCamelCase = None							,       ):


        """simple docstring"""

        UpperCAmelCase__			:							Tuple							=    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
        UpperCAmelCase__			:							Optional[int]							=    tokenizer.num_special_tokens_to_add(_lowerCamelCase	)
        UpperCAmelCase__			:							int							=    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
        UpperCAmelCase__			:							List[Any]							=    [""" """.join([tokenizer.unk_token]	) * seq_length] * batch_size
        UpperCAmelCase__			:							Optional[int]							=    dict(tokenizer(_lowerCamelCase							,       return_tensors=_lowerCamelCase	)	)
        return common_inputs



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = -1							,       _lowerCamelCase = -1							,       _lowerCamelCase = False							,       _lowerCamelCase = None							,       ):


        """simple docstring"""

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

        elif self.task == "causal-lm":
              UpperCAmelCase__			:							Optional[int]							=    self._generate_dummy_inputs_for_causal_lm(
                  _lowerCamelCase							,       batch_size=_lowerCamelCase							,       seq_length=_lowerCamelCase							,       is_pair=_lowerCamelCase							,       framework=_lowerCamelCase	)
        else:
              UpperCAmelCase__			:							Dict							=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                  _lowerCamelCase							,       batch_size=_lowerCamelCase							,       seq_length=_lowerCamelCase							,       is_pair=_lowerCamelCase							,       framework=_lowerCamelCase	)

        return common_inputs




  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        if self.task in ["default", "seq2seq-lm"]:
              UpperCAmelCase__			:							Union[str, Any]							=    super()._flatten_past_key_values_(_lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
        else:
              UpperCAmelCase__			:							Optional[int]							=    super(_lowerCamelCase							,       self	)._flatten_past_key_values_(
                  _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)

