
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()
SCREAMING_SNAKE_CASE							:Any							 =				logging.get_logger(__name__)


def 						UpperCAmelCase			(	a_						)		->	int:







				"""simple docstring"""



				__A   			= SwinConfig.from_pretrained(
				    "microsoft/swin-tiny-patch4-window7-224"		,   out_features=["stage1", "stage2", "stage3", "stage4"]						)
				__A   			= MaskFormerConfig(backbone_config=a_						)

				__A   			= "huggingface/label-files"
				if "ade20k-full" in model_name:
								# this should be ok
								__A   			= 8_4_7
								__A   			= "maskformer-ade20k-full-id2label.json"
				elif "ade" in model_name:
								# this should be ok
								__A   			= 1_5_0
								__A   			= "ade20k-id2label.json"
				elif "coco-stuff" in model_name:
								# this should be ok
								__A   			= 1_7_1
								__A   			= "maskformer-coco-stuff-id2label.json"
				elif "coco" in model_name:
								# TODO
								__A   			= 1_3_3
								__A   			= "coco-panoptic-id2label.json"
				elif "cityscapes" in model_name:
								# this should be ok
								__A   			= 1_9
								__A   			= "cityscapes-id2label.json"
				elif "vistas" in model_name:
								# this should be ok
								__A   			= 6_5
								__A   			= "mapillary-vistas-id2label.json"

				__A   			= json.load(open(hf_hub_download(a_		,   a_		,   repo_type="dataset"						)		,   "r"						)						)
				__A   			= {int(a_						): v for k, v in idalabel.items()}

				return config


def 						UpperCAmelCase			(	a_						)		->	Any:







				"""simple docstring"""



				__A   			= []
				# 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 						UpperCAmelCase			(	a_		,   a_		,   a_						)		->	List[str]:







				"""simple docstring"""



				__A   			= dct.pop(a_						)
				__A   			= val


def 						UpperCAmelCase			(	a_		,   a_						)		->	Dict:







				"""simple docstring"""



				__A   			= [int(backbone_config.embed_dim * 2**i						) for i in range(len(backbone_config.depths						)						)]
				for i in range(len(backbone_config.depths						)						):
								__A   			= 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)
												__A   			= state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''						)
												__A   			= 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
												__A   			= in_proj_weight[:dim, :]
												__A   			= in_proj_bias[: dim]
												__A   			= in_proj_weight[
												    dim : dim * 2, :
												]
												__A   			= in_proj_bias[
												    dim : dim * 2
												]
												__A   			= in_proj_weight[
												    -dim :, :
												]
												__A   			= in_proj_bias[-dim :]
												# fmt: on


def 						UpperCAmelCase			(	a_		,   a_						)		->	str:







				"""simple docstring"""



				__A   			= 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)
								__A   			= state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''						)
								__A   			= 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
								__A   			= in_proj_weight[: hidden_size, :]
								__A   			= in_proj_bias[:config.hidden_size]
								__A   			= in_proj_weight[hidden_size : hidden_size * 2, :]
								__A   			= in_proj_bias[hidden_size : hidden_size * 2]
								__A   			= in_proj_weight[-hidden_size :, :]
								__A   			= 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)
								__A   			= state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''						)
								__A   			= 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
								__A   			= in_proj_weight[: hidden_size, :]
								__A   			= in_proj_bias[:config.hidden_size]
								__A   			= in_proj_weight[hidden_size : hidden_size * 2, :]
								__A   			= in_proj_bias[hidden_size : hidden_size * 2]
								__A   			= in_proj_weight[-hidden_size :, :]
								__A   			= in_proj_bias[-hidden_size :]
				# fmt: on


def 						UpperCAmelCase			(	)		->	torch.Tensor:







				"""simple docstring"""



				__A   			= "http://images.cocodataset.org/val2017/000000039769.jpg"
				__A   			= Image.open(requests.get(a_		,   stream=a_						).raw						)
				return im


@torch.no_grad()
def 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_ = False						)		->	Union[str, Any]:







				"""simple docstring"""



				__A   			= get_maskformer_config(a_						)

				# load original state_dict
				with open(a_		,   "rb"						) as f:
								__A   			= pickle.load(a_						)
				__A   			= data["model"]

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

				# rename keys
				__A   			= 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():
								__A   			= torch.from_numpy(a_						)

				# load 🤗 model
				__A   			= MaskFormerForInstanceSegmentation(a_						)
				model.eval()

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

				__A			,							__A   			= 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
				__A   			= prepare_img()
				if "vistas" in model_name:
								__A   			= 6_5
				elif "cityscapes" in model_name:
								__A   			= 6_5_5_3_5
				else:
								__A   			= 2_5_5
				__A   			= True if "ade" in model_name else False
				__A   			= MaskFormerImageProcessor(ignore_index=a_		,   reduce_labels=a_						)

				__A   			= image_processor(a_		,   return_tensors="pt"						)

				__A   			= model(**a_						)

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

				if model_name == "maskformer-swin-tiny-ade":
								__A   			= torch.tensor(
								    [[3.6_353, -4.4_770, -2.6_065], [0.5_081, -4.2_394, -3.5_343], [2.1_909, -5.0_353, -1.9_323]]						)
				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__":
					SCREAMING_SNAKE_CASE							:int							 =				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.'
					)

					SCREAMING_SNAKE_CASE							:Optional[Any]							 =				parser.parse_args()
					convert_maskformer_checkpoint(
					    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
					)






'''simple docstring'''







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     ={
    '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 			_a  (	__a ):
       __a				:     List[Any]   				=					"""marian"""
       __a				:     Union[str, Any]   				=					["""past_key_values"""]
       __a				:     List[str]   				=					{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
       def __init__(	self						:	List[Any]   ,				lowercase						:	Union[str, Any]=58_101   ,				lowercase						:	Tuple=None   ,				lowercase						:	str=1_024   ,				lowercase						:	Optional[int]=12   ,				lowercase						:	Optional[int]=4_096   ,				lowercase						:	int=16   ,				lowercase						:	List[Any]=12   ,				lowercase						:	int=4_096   ,				lowercase						:	Optional[int]=16   ,				lowercase						:	int=0.0   ,				lowercase						:	Tuple=0.0   ,				lowercase						:	Tuple=True   ,				lowercase						:	Union[str, Any]=True   ,				lowercase						:	List[Any]="gelu"   ,				lowercase						:	Tuple=1_024   ,				lowercase						:	str=0.1   ,				lowercase						:	str=0.0   ,				lowercase						:	Optional[int]=0.0   ,				lowercase						:	Dict=0.02   ,				lowercase						:	Union[str, Any]=58_100   ,				lowercase						:	List[str]=False   ,				lowercase						:	str=58_100   ,				lowercase						:	Any=0   ,				lowercase						:	Optional[Any]=0   ,				lowercase						:	Tuple=True   ,				**lowercase						:	Optional[int]   ,				):



           '''simple docstring'''

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






class 			_a  (	__a ):
       @property
       # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
       def     A      (	self						:	int				):



           '''simple docstring'''

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

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

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



           '''simple docstring'''

           if self.task in ["default", "seq2seq-lm"]:
               UpperCAmelCase			       =		super().outputs
           else:
               UpperCAmelCase			       =		super(lowercase   ,				self				).outputs
               if self.use_past:
                   UpperCAmelCase       ,     UpperCAmelCase			       =		self.num_layers
                   for i in range(lowercase				):
                       UpperCAmelCase			       =		{0: '''batch''', 2: '''past_sequence + sequence'''}
                       UpperCAmelCase			       =		{0: '''batch''', 2: '''past_sequence + sequence'''}
           return common_outputs
       def     A      (	self						:	Dict   ,				lowercase						:	PreTrainedTokenizer   ,				lowercase						:	int = -1   ,				lowercase						:	int = -1   ,				lowercase						:	bool = False   ,				lowercase						:	Optional[TensorType] = None   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		self._generate_dummy_inputs_for_encoder_and_decoder(
               lowercase   ,				lowercase   ,				lowercase   ,				lowercase   ,				lowercase				)

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

           if self.use_past:
               if not is_torch_available():
                   raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''				)
               else:
                   import torch
               UpperCAmelCase       ,     UpperCAmelCase			       =		common_inputs['''input_ids'''].shape
               UpperCAmelCase			       =		common_inputs['''decoder_input_ids'''].shape[1]
               UpperCAmelCase       ,     UpperCAmelCase			       =		self.num_attention_heads
               UpperCAmelCase			       =		(
                   batch,
                   num_encoder_attention_heads,
                   encoder_seq_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )
               UpperCAmelCase			       =		decoder_seq_length + 3
               UpperCAmelCase			       =		(
                   batch,
                   num_decoder_attention_heads,
                   decoder_past_length,
                   self._config.hidden_size // num_decoder_attention_heads,
               )

               UpperCAmelCase			       =		torch.cat(
                   [common_inputs['''decoder_attention_mask'''], torch.ones(lowercase   ,				lowercase				)]   ,				dim=1				)

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

               for _ in range(lowercase				):
                   common_inputs["past_key_values"].append(
                       (
                           torch.zeros(lowercase				),
                           torch.zeros(lowercase				),
                           torch.zeros(lowercase				),
                           torch.zeros(lowercase				),
                       )				)
               # TODO: test this.
               UpperCAmelCase			       =		encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
               for _ in range(lowercase   ,				lowercase				):
                   common_inputs["past_key_values"].append((torch.zeros(lowercase				), torch.zeros(lowercase				))				)
           return common_inputs
       def     A      (	self						:	int   ,				lowercase						:	PreTrainedTokenizer   ,				lowercase						:	int = -1   ,				lowercase						:	int = -1   ,				lowercase						:	bool = False   ,				lowercase						:	Optional[TensorType] = None   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		self._generate_dummy_inputs_for_encoder_and_decoder(
               lowercase   ,				lowercase   ,				lowercase   ,				lowercase   ,				lowercase				)

           if self.use_past:
               if not is_torch_available():
                   raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''				)
               else:
                   import torch
               UpperCAmelCase       ,     UpperCAmelCase			       =		common_inputs['''input_ids'''].shape
               # Not using the same length for past_key_values
               UpperCAmelCase			       =		seqlen + 2
               UpperCAmelCase       ,     UpperCAmelCase			       =		self.num_layers
               UpperCAmelCase       ,     UpperCAmelCase			       =		self.num_attention_heads
               UpperCAmelCase			       =		(
                   batch,
                   num_encoder_attention_heads,
                   past_key_values_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )

               UpperCAmelCase			       =		common_inputs['''attention_mask'''].dtype
               UpperCAmelCase			       =		torch.cat(
                   [common_inputs['''attention_mask'''], torch.ones(lowercase   ,				lowercase   ,				dtype=lowercase				)]   ,				dim=1				)
               UpperCAmelCase			       =		[
                   (torch.zeros(lowercase				), torch.zeros(lowercase				)) for _ in range(lowercase				)
               ]
           return common_inputs
       def     A      (	self						:	str   ,				lowercase						:	PreTrainedTokenizer   ,				lowercase						:	int = -1   ,				lowercase						:	int = -1   ,				lowercase						:	bool = False   ,				lowercase						:	Optional[TensorType] = None   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		compute_effective_axis_dimension(
               lowercase   ,				fixed_dimension=OnnxConfig.default_fixed_batch   ,				num_token_to_add=0				)

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

           # Generate dummy inputs according to compute batch and sequence
           UpperCAmelCase			       =		[''' '''.join([tokenizer.unk_token]				) * seq_length] * batch_size
           UpperCAmelCase			       =		dict(tokenizer(lowercase   ,				return_tensors=lowercase				)				)
           return common_inputs
       def     A      (	self						:	List[str]   ,				lowercase						:	PreTrainedTokenizer   ,				lowercase						:	int = -1   ,				lowercase						:	int = -1   ,				lowercase						:	bool = False   ,				lowercase						:	Optional[TensorType] = None   ,				):



           '''simple docstring'''

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

           else:
               UpperCAmelCase			       =		self._generate_dummy_inputs_for_causal_lm(
                   lowercase   ,				batch_size=lowercase   ,				seq_length=lowercase   ,				is_pair=lowercase   ,				framework=lowercase				)

           return common_inputs
       def     A      (	self						:	List[Any]   ,				lowercase						:	Any   ,				lowercase						:	Tuple   ,				lowercase						:	Any   ,				lowercase						:	Any				):



           '''simple docstring'''

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







       @property
       def     A      (	self						:	Any				):



           '''simple docstring'''

           return 1E-4




