


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase       =							logging.get_logger(__name__)
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      DPTConfig()

		if "large" in checkpoint_url:
				lowercase__            =      10_24
				lowercase__            =      40_96
				lowercase__            =      24
				lowercase__            =      16
				lowercase__            =      [5, 11, 17, 23]
				lowercase__            =      [2_56, 5_12, 10_24, 10_24]
				lowercase__            =      (1, 3_84, 3_84)

		if "ade" in checkpoint_url:
				lowercase__            =      True

				lowercase__            =      1_50
				lowercase__            =      '''huggingface/label-files'''
				lowercase__            =      '''ade20k-id2label.json'''
				lowercase__            =      json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				repo_type='''dataset'''     )     )						,				'''r'''     )     )
				lowercase__            =      {int(SCREAMING_SNAKE_CASE     ): v for k, v in idalabel.items()}
				lowercase__            =      idalabel
				lowercase__            =      {v: k for k, v in idalabel.items()}
				lowercase__            =      [1, 1_50, 4_80, 4_80]

		return config, expected_shape
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
		for k in ignore_keys:
				state_dict.pop(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     )
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		if (
		    "pretrained.model" in name
		    and "cls_token" not in name
		    and "pos_embed" not in name
		    and "patch_embed" not in name
		):
				lowercase__            =      name.replace('''pretrained.model'''						,				'''dpt.encoder'''     )
		if "pretrained.model" in name:
				lowercase__            =      name.replace('''pretrained.model'''						,				'''dpt.embeddings'''     )
		if "patch_embed" in name:
				lowercase__            =      name.replace('''patch_embed'''						,				'''patch_embeddings'''     )
		if "pos_embed" in name:
				lowercase__            =      name.replace('''pos_embed'''						,				'''position_embeddings'''     )
		if "attn.proj" in name:
				lowercase__            =      name.replace('''attn.proj'''						,				'''attention.output.dense'''     )
		if "proj" in name and "project" not in name:
				lowercase__            =      name.replace('''proj'''						,				'''projection'''     )
		if "blocks" in name:
				lowercase__            =      name.replace('''blocks'''						,				'''layer'''     )
		if "mlp.fc1" in name:
				lowercase__            =      name.replace('''mlp.fc1'''						,				'''intermediate.dense'''     )
		if "mlp.fc2" in name:
				lowercase__            =      name.replace('''mlp.fc2'''						,				'''output.dense'''     )
		if "norm1" in name:
				lowercase__            =      name.replace('''norm1'''						,				'''layernorm_before'''     )
		if "norm2" in name:
				lowercase__            =      name.replace('''norm2'''						,				'''layernorm_after'''     )
		if "scratch.output_conv" in name:
				lowercase__            =      name.replace('''scratch.output_conv'''						,				'''head'''     )
		if "scratch" in name:
				lowercase__            =      name.replace('''scratch'''						,				'''neck'''     )
		if "layer1_rn" in name:
				lowercase__            =      name.replace('''layer1_rn'''						,				'''convs.0'''     )
		if "layer2_rn" in name:
				lowercase__            =      name.replace('''layer2_rn'''						,				'''convs.1'''     )
		if "layer3_rn" in name:
				lowercase__            =      name.replace('''layer3_rn'''						,				'''convs.2'''     )
		if "layer4_rn" in name:
				lowercase__            =      name.replace('''layer4_rn'''						,				'''convs.3'''     )
		if "refinenet" in name:
				lowercase__            =      int(name[len('''neck.refinenet'''     ) : len('''neck.refinenet'''     ) + 1]     )
				# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
				lowercase__            =      name.replace(f'refinenet{layer_idx}'						,				f'fusion_stage.layers.{abs(layer_idx-4     )}'     )
		if "out_conv" in name:
				lowercase__            =      name.replace('''out_conv'''						,				'''projection'''     )
		if "resConfUnit1" in name:
				lowercase__            =      name.replace('''resConfUnit1'''						,				'''residual_layer1'''     )
		if "resConfUnit2" in name:
				lowercase__            =      name.replace('''resConfUnit2'''						,				'''residual_layer2'''     )
		if "conv1" in name:
				lowercase__            =      name.replace('''conv1'''						,				'''convolution1'''     )
		if "conv2" in name:
				lowercase__            =      name.replace('''conv2'''						,				'''convolution2'''     )
		# readout blocks
		if "pretrained.act_postprocess1.0.project.0" in name:
				lowercase__            =      name.replace('''pretrained.act_postprocess1.0.project.0'''						,				'''neck.reassemble_stage.readout_projects.0.0'''     )
		if "pretrained.act_postprocess2.0.project.0" in name:
				lowercase__            =      name.replace('''pretrained.act_postprocess2.0.project.0'''						,				'''neck.reassemble_stage.readout_projects.1.0'''     )
		if "pretrained.act_postprocess3.0.project.0" in name:
				lowercase__            =      name.replace('''pretrained.act_postprocess3.0.project.0'''						,				'''neck.reassemble_stage.readout_projects.2.0'''     )
		if "pretrained.act_postprocess4.0.project.0" in name:
				lowercase__            =      name.replace('''pretrained.act_postprocess4.0.project.0'''						,				'''neck.reassemble_stage.readout_projects.3.0'''     )
		# resize blocks
		if "pretrained.act_postprocess1.3" in name:
				lowercase__            =      name.replace('''pretrained.act_postprocess1.3'''						,				'''neck.reassemble_stage.layers.0.projection'''     )
		if "pretrained.act_postprocess1.4" in name:
				lowercase__            =      name.replace('''pretrained.act_postprocess1.4'''						,				'''neck.reassemble_stage.layers.0.resize'''     )
		if "pretrained.act_postprocess2.3" in name:
				lowercase__            =      name.replace('''pretrained.act_postprocess2.3'''						,				'''neck.reassemble_stage.layers.1.projection'''     )
		if "pretrained.act_postprocess2.4" in name:
				lowercase__            =      name.replace('''pretrained.act_postprocess2.4'''						,				'''neck.reassemble_stage.layers.1.resize'''     )
		if "pretrained.act_postprocess3.3" in name:
				lowercase__            =      name.replace('''pretrained.act_postprocess3.3'''						,				'''neck.reassemble_stage.layers.2.projection'''     )
		if "pretrained.act_postprocess4.3" in name:
				lowercase__            =      name.replace('''pretrained.act_postprocess4.3'''						,				'''neck.reassemble_stage.layers.3.projection'''     )
		if "pretrained.act_postprocess4.4" in name:
				lowercase__            =      name.replace('''pretrained.act_postprocess4.4'''						,				'''neck.reassemble_stage.layers.3.resize'''     )
		if "pretrained" in name:
				lowercase__            =      name.replace('''pretrained'''						,				'''dpt'''     )
		if "bn" in name:
				lowercase__            =      name.replace('''bn'''						,				'''batch_norm'''     )
		if "head" in name:
				lowercase__            =      name.replace('''head'''						,				'''head.head'''     )
		if "encoder.norm" in name:
				lowercase__            =      name.replace('''encoder.norm'''						,				'''layernorm'''     )
		if "auxlayer" in name:
				lowercase__            =      name.replace('''auxlayer'''						,				'''auxiliary_head.head'''     )

		return name
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		for i in range(config.num_hidden_layers     ):
				# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
				lowercase__            =      state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight'     )
				lowercase__            =      state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.bias'     )
				# next, add query, keys and values (in that order) to the state dict
				lowercase__            =      in_proj_weight[: config.hidden_size, :]
				lowercase__            =      in_proj_bias[: config.hidden_size]
				lowercase__            =      in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				lowercase__            =      in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				lowercase__            =      in_proj_weight[
				    -config.hidden_size :, :
				]
				lowercase__            =      in_proj_bias[-config.hidden_size :]
def 			_a (      ):


		"""simple docstring"""




		lowercase__            =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowercase__            =      Image.open(requests.get(SCREAMING_SNAKE_CASE						,				stream=SCREAMING_SNAKE_CASE     ).raw     )
		return im
@torch.no_grad()
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__     ,		lowercase__            =      get_dpt_config(SCREAMING_SNAKE_CASE     )
		# load original state_dict from URL
		lowercase__            =      torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE						,				map_location='''cpu'''     )
		# remove certain keys
		remove_ignore_keys_(SCREAMING_SNAKE_CASE     )
		# rename keys
		for key in state_dict.copy().keys():
				lowercase__            =      state_dict.pop(SCREAMING_SNAKE_CASE     )
				lowercase__            =      val
		# read in qkv matrices
		read_in_q_k_v(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     )

		# load HuggingFace model
		lowercase__            =      DPTForSemanticSegmentation(SCREAMING_SNAKE_CASE     ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(SCREAMING_SNAKE_CASE     )
		model.load_state_dict(SCREAMING_SNAKE_CASE     )
		model.eval()

		# Check outputs on an image
		lowercase__            =      4_80 if '''ade''' in checkpoint_url else 3_84
		lowercase__            =      DPTImageProcessor(size=SCREAMING_SNAKE_CASE     )

		lowercase__            =      prepare_img()
		lowercase__            =      image_processor(SCREAMING_SNAKE_CASE						,				return_tensors='''pt'''     )

		# forward pass
		lowercase__            =      model(**SCREAMING_SNAKE_CASE     ).logits if '''ade''' in checkpoint_url else model(**SCREAMING_SNAKE_CASE     ).predicted_depth

		# Assert logits
		lowercase__            =      torch.tensor([[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]]     )
		if "ade" in checkpoint_url:
				lowercase__            =      torch.tensor([[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]]     )
		assert outputs.shape == torch.Size(SCREAMING_SNAKE_CASE     )
		assert (
		    torch.allclose(outputs[0, 0, :3, :3]						,				SCREAMING_SNAKE_CASE						,				atol=1E-4     )
		    if "ade" in checkpoint_url
		    else torch.allclose(outputs[0, :3, :3]						,				SCREAMING_SNAKE_CASE     )
		)

		Path(SCREAMING_SNAKE_CASE     ).mkdir(exist_ok=SCREAMING_SNAKE_CASE     )
		print(f'Saving model to {pytorch_dump_folder_path}'     )
		model.save_pretrained(SCREAMING_SNAKE_CASE     )
		print(f'Saving image processor to {pytorch_dump_folder_path}'     )
		image_processor.save_pretrained(SCREAMING_SNAKE_CASE     )

		if push_to_hub:
				print('''Pushing model to hub...'''     )
				model.push_to_hub(
				    repo_path_or_name=Path(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     )						,				organization='''nielsr'''						,				commit_message='''Add model'''						,				use_temp_dir=SCREAMING_SNAKE_CASE						,				)
				image_processor.push_to_hub(
				    repo_path_or_name=Path(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     )						,				organization='''nielsr'''						,				commit_message='''Add image processor'''						,				use_temp_dir=SCREAMING_SNAKE_CASE						,				)


if __name__ == "__main__":
			lowerCAmelCase       =							argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--checkpoint_url',
			    default='https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt',
			    type=str,
			    help='URL of the original DPT checkpoint you\'d like to convert.',
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path',
			    default=None,
			    type=str,
			    required=True,
			    help='Path to the output PyTorch model directory.',
			)
			parser.add_argument(
			    '--push_to_hub',
			    action='store_true',
			)
			parser.add_argument(
			    '--model_name',
			    default='dpt-large',
			    type=str,
			    help='Name of the model, in case you\'re pushing to the hub.',
			)

			lowerCAmelCase       =							parser.parse_args()
			convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)







"""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


A:   Tuple								=		logging.get_logger(__name__)

A:   List[Any]								=		{
    "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   int						=						'blenderbot-small'
   __lowerCAmelCase  :   Any						=						['past_key_values']
   __lowerCAmelCase  :   str						=						{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE=50265      ,     _SCREAMING_SNAKE_CASE=512      ,     _SCREAMING_SNAKE_CASE=8      ,     _SCREAMING_SNAKE_CASE=2048      ,     _SCREAMING_SNAKE_CASE=16      ,     _SCREAMING_SNAKE_CASE=8      ,     _SCREAMING_SNAKE_CASE=2048      ,     _SCREAMING_SNAKE_CASE=16      ,     _SCREAMING_SNAKE_CASE=0.0      ,     _SCREAMING_SNAKE_CASE=0.0      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE="gelu"      ,     _SCREAMING_SNAKE_CASE=512      ,     _SCREAMING_SNAKE_CASE=0.1      ,     _SCREAMING_SNAKE_CASE=0.0      ,     _SCREAMING_SNAKE_CASE=0.0      ,     _SCREAMING_SNAKE_CASE=0.02      ,     _SCREAMING_SNAKE_CASE=1      ,     _SCREAMING_SNAKE_CASE=False      ,     _SCREAMING_SNAKE_CASE=0      ,     _SCREAMING_SNAKE_CASE=1      ,     _SCREAMING_SNAKE_CASE=2      ,     _SCREAMING_SNAKE_CASE=2      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      str:





    '''simple docstring'''




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

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



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):

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





    '''simple docstring'''




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

     if self.use_past:
      UpperCAmelCase	:					int	=	{0: """batch"""}
      UpperCAmelCase	:					List[Any]	=	{0: """batch""", 1: """past_decoder_sequence + sequence"""}
     else:
      UpperCAmelCase	:					Optional[Any]	=	{0: """batch""", 1: """decoder_sequence"""}
      UpperCAmelCase	:					Any	=	{0: """batch""", 1: """decoder_sequence"""}

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

    return common_inputs

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





    '''simple docstring'''




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

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





    '''simple docstring'''




    UpperCAmelCase	:					Dict	=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
        _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)

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

    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	:					int	=	common_inputs["""input_ids"""].shape
     UpperCAmelCase	:					Any	=	common_inputs["""decoder_input_ids"""].shape[1]
     UpperCAmelCase	,		UpperCAmelCase	:					str	=	self.num_attention_heads
     UpperCAmelCase	:					Union[str, Any]	=	(
         batch,
         num_encoder_attention_heads,
         encoder_seq_length,
         self._config.hidden_size // num_encoder_attention_heads,
     )
     UpperCAmelCase	:					Union[str, Any]	=	decoder_seq_length + 3
     UpperCAmelCase	:					Union[str, Any]	=	(
         batch,
         num_decoder_attention_heads,
         decoder_past_length,
         self._config.hidden_size // num_decoder_attention_heads,
     )

     UpperCAmelCase	:					Optional[Any]	=	torch.cat(
         [common_inputs["""decoder_attention_mask"""], torch.ones(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)]      ,     dim=1						)

     UpperCAmelCase	:					List[str]	=	[]
     # If the number of encoder and decoder layers are present in the model configuration, both are considered
     UpperCAmelCase	,		UpperCAmelCase	:					List[Any]	=	self.num_layers
     UpperCAmelCase	:					Optional[Any]	=	min(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)
     UpperCAmelCase	:					Optional[Any]	=	max(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						) - min_num_layers
     UpperCAmelCase	:					Optional[int]	=	"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

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

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





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
        _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)

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

     UpperCAmelCase	:					List[Any]	=	common_inputs["""attention_mask"""].dtype
     UpperCAmelCase	:					Any	=	torch.cat(
         [common_inputs["""attention_mask"""], torch.ones(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     dtype=_SCREAMING_SNAKE_CASE						)]      ,     dim=1						)
     UpperCAmelCase	:					List[str]	=	[
         (torch.zeros(_SCREAMING_SNAKE_CASE						), torch.zeros(_SCREAMING_SNAKE_CASE						)) for _ in range(_SCREAMING_SNAKE_CASE						)
     ]
    return common_inputs

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





    '''simple docstring'''




    UpperCAmelCase	:					str	=	compute_effective_axis_dimension(
        _SCREAMING_SNAKE_CASE      ,     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	:					List[str]	=	tokenizer.num_special_tokens_to_add(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					int	=	compute_effective_axis_dimension(
        _SCREAMING_SNAKE_CASE      ,     fixed_dimension=OnnxConfig.default_fixed_sequence      ,     num_token_to_add=_SCREAMING_SNAKE_CASE						)

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

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





    '''simple docstring'''




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

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

    return common_inputs




   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      List[Any]:





    '''simple docstring'''




    if self.task in ["default", "seq2seq-lm"]:
     UpperCAmelCase	:					List[Any]	=	super()._flatten_past_key_values_(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)
    else:
     UpperCAmelCase	:					Dict	=	super(_SCREAMING_SNAKE_CASE      ,     self						)._flatten_past_key_values_(
         _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)
