






import argparse
import json
from pathlib import Path

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

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase			    =     logging.get_logger(__name__)

def 	__lowerCamelCase      (		snake_case__		)							->  Tuple:






						"""simple docstring"""





						if "resnet-50" in model_name:
												_SCREAMING_SNAKE_CASE     				=  ResNetConfig.from_pretrained("""microsoft/resnet-50"""		)
						elif "resnet-101" in model_name:
												_SCREAMING_SNAKE_CASE     				=  ResNetConfig.from_pretrained("""microsoft/resnet-101"""		)
						else:
												raise ValueError("""Model name should include either resnet50 or resnet101"""		)

						_SCREAMING_SNAKE_CASE     				=  DetrConfig(use_timm_backbone=UpperCAmelCase__	,backbone_config=UpperCAmelCase__		)

						# set label attributes
						_SCREAMING_SNAKE_CASE     				=  """panoptic""" in model_name
						if is_panoptic:
												_SCREAMING_SNAKE_CASE     				=  2_50
						else:
												_SCREAMING_SNAKE_CASE     				=  91
												_SCREAMING_SNAKE_CASE     				=  """huggingface/label-files"""
												_SCREAMING_SNAKE_CASE     				=  """coco-detection-id2label.json"""
												_SCREAMING_SNAKE_CASE     				=  json.load(open(hf_hub_download(UpperCAmelCase__	,UpperCAmelCase__	,repo_type="""dataset"""		)	,"""r"""		)		)
												_SCREAMING_SNAKE_CASE     				=  {int(UpperCAmelCase__		): v for k, v in idalabel.items()}
												_SCREAMING_SNAKE_CASE     				=  idalabel
												_SCREAMING_SNAKE_CASE     				=  {v: k for k, v in idalabel.items()}

						return config, is_panoptic

def 	__lowerCamelCase      (		snake_case__		)							->  Dict:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  []

						# stem
						# fmt: off
						rename_keys.append(("""backbone.0.body.conv1.weight""", """backbone.conv_encoder.model.embedder.embedder.convolution.weight""")		)
						rename_keys.append(("""backbone.0.body.bn1.weight""", """backbone.conv_encoder.model.embedder.embedder.normalization.weight""")		)
						rename_keys.append(("""backbone.0.body.bn1.bias""", """backbone.conv_encoder.model.embedder.embedder.normalization.bias""")		)
						rename_keys.append(("""backbone.0.body.bn1.running_mean""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_mean""")		)
						rename_keys.append(("""backbone.0.body.bn1.running_var""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_var""")		)
						# stages
						for stage_idx in range(len(config.backbone_config.depths		)		):
												for layer_idx in range(config.backbone_config.depths[stage_idx]		):
																		# shortcut
																		if layer_idx == 0:
																								rename_keys.append(
																								    (
																								        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight',
																								        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight',
																								    )		)
																								rename_keys.append(
																								    (
																								        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight',
																								        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight',
																								    )		)
																								rename_keys.append(
																								    (
																								        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias',
																								        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias',
																								    )		)
																								rename_keys.append(
																								    (
																								        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean',
																								        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean',
																								    )		)
																								rename_keys.append(
																								    (
																								        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var',
																								        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var',
																								    )		)
																		# 3 convs
																		for i in range(3		):
																								rename_keys.append(
																								    (
																								        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight',
																								        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight',
																								    )		)
																								rename_keys.append(
																								    (
																								        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight',
																								        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight',
																								    )		)
																								rename_keys.append(
																								    (
																								        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias',
																								        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias',
																								    )		)
																								rename_keys.append(
																								    (
																								        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean',
																								        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean',
																								    )		)
																								rename_keys.append(
																								    (
																								        F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var',
																								        F'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var',
																								    )		)
    # fmt: on

						for i in range(config.encoder_layers		):
												# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
												rename_keys.append(
												    (
												        F'transformer.encoder.layers.{i}.self_attn.out_proj.weight',
												        F'encoder.layers.{i}.self_attn.out_proj.weight',
												    )		)
												rename_keys.append(
												    (F'transformer.encoder.layers.{i}.self_attn.out_proj.bias', F'encoder.layers.{i}.self_attn.out_proj.bias')		)
												rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'encoder.layers.{i}.fc1.weight')		)
												rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'encoder.layers.{i}.fc1.bias')		)
												rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'encoder.layers.{i}.fc2.weight')		)
												rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'encoder.layers.{i}.fc2.bias')		)
												rename_keys.append(
												    (F'transformer.encoder.layers.{i}.norm1.weight', F'encoder.layers.{i}.self_attn_layer_norm.weight')		)
												rename_keys.append(
												    (F'transformer.encoder.layers.{i}.norm1.bias', F'encoder.layers.{i}.self_attn_layer_norm.bias')		)
												rename_keys.append(
												    (F'transformer.encoder.layers.{i}.norm2.weight', F'encoder.layers.{i}.final_layer_norm.weight')		)
												rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'encoder.layers.{i}.final_layer_norm.bias')		)
												# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
												rename_keys.append(
												    (
												        F'transformer.decoder.layers.{i}.self_attn.out_proj.weight',
												        F'decoder.layers.{i}.self_attn.out_proj.weight',
												    )		)
												rename_keys.append(
												    (F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'decoder.layers.{i}.self_attn.out_proj.bias')		)
												rename_keys.append(
												    (
												        F'transformer.decoder.layers.{i}.multihead_attn.out_proj.weight',
												        F'decoder.layers.{i}.encoder_attn.out_proj.weight',
												    )		)
												rename_keys.append(
												    (
												        F'transformer.decoder.layers.{i}.multihead_attn.out_proj.bias',
												        F'decoder.layers.{i}.encoder_attn.out_proj.bias',
												    )		)
												rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'decoder.layers.{i}.fc1.weight')		)
												rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'decoder.layers.{i}.fc1.bias')		)
												rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'decoder.layers.{i}.fc2.weight')		)
												rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'decoder.layers.{i}.fc2.bias')		)
												rename_keys.append(
												    (F'transformer.decoder.layers.{i}.norm1.weight', F'decoder.layers.{i}.self_attn_layer_norm.weight')		)
												rename_keys.append(
												    (F'transformer.decoder.layers.{i}.norm1.bias', F'decoder.layers.{i}.self_attn_layer_norm.bias')		)
												rename_keys.append(
												    (F'transformer.decoder.layers.{i}.norm2.weight', F'decoder.layers.{i}.encoder_attn_layer_norm.weight')		)
												rename_keys.append(
												    (F'transformer.decoder.layers.{i}.norm2.bias', F'decoder.layers.{i}.encoder_attn_layer_norm.bias')		)
												rename_keys.append(
												    (F'transformer.decoder.layers.{i}.norm3.weight', F'decoder.layers.{i}.final_layer_norm.weight')		)
												rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'decoder.layers.{i}.final_layer_norm.bias')		)

						# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
						rename_keys.extend(
						    [
						        ("""input_proj.weight""", """input_projection.weight"""),
						        ("""input_proj.bias""", """input_projection.bias"""),
						        ("""query_embed.weight""", """query_position_embeddings.weight"""),
						        ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
						        ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
						        ("""class_embed.weight""", """class_labels_classifier.weight"""),
						        ("""class_embed.bias""", """class_labels_classifier.bias"""),
						        ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
						        ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
						        ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
						        ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
						        ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
						        ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
						    ]		)

						return rename_keys

def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__		)							->  str:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  state_dict.pop(UpperCAmelCase__		)
						_SCREAMING_SNAKE_CASE     				=  val

def 	__lowerCamelCase      (		snake_case__	,snake_case__=False		)							->  Dict:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  """"""
						if is_panoptic:
												_SCREAMING_SNAKE_CASE     				=  """detr."""

						# first: transformer encoder
						for i in range(6		):
												# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
												_SCREAMING_SNAKE_CASE     				=  state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'		)
												_SCREAMING_SNAKE_CASE     				=  state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'		)
												# next, add query, keys and values (in that order) to the state dict
												_SCREAMING_SNAKE_CASE     				=  in_proj_weight[:2_56, :]
												_SCREAMING_SNAKE_CASE     				=  in_proj_bias[:2_56]
												_SCREAMING_SNAKE_CASE     				=  in_proj_weight[2_56:5_12, :]
												_SCREAMING_SNAKE_CASE     				=  in_proj_bias[2_56:5_12]
												_SCREAMING_SNAKE_CASE     				=  in_proj_weight[-2_56:, :]
												_SCREAMING_SNAKE_CASE     				=  in_proj_bias[-2_56:]
						# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
						for i in range(6		):
												# read in weights + bias of input projection layer of self-attention
												_SCREAMING_SNAKE_CASE     				=  state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'		)
												_SCREAMING_SNAKE_CASE     				=  state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'		)
												# next, add query, keys and values (in that order) to the state dict
												_SCREAMING_SNAKE_CASE     				=  in_proj_weight[:2_56, :]
												_SCREAMING_SNAKE_CASE     				=  in_proj_bias[:2_56]
												_SCREAMING_SNAKE_CASE     				=  in_proj_weight[2_56:5_12, :]
												_SCREAMING_SNAKE_CASE     				=  in_proj_bias[2_56:5_12]
												_SCREAMING_SNAKE_CASE     				=  in_proj_weight[-2_56:, :]
												_SCREAMING_SNAKE_CASE     				=  in_proj_bias[-2_56:]
												# read in weights + bias of input projection layer of cross-attention
												_SCREAMING_SNAKE_CASE     				=  state_dict.pop(
												    F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'		)
												_SCREAMING_SNAKE_CASE     				=  state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'		)
												# next, add query, keys and values (in that order) of cross-attention to the state dict
												_SCREAMING_SNAKE_CASE     				=  in_proj_weight_cross_attn[:2_56, :]
												_SCREAMING_SNAKE_CASE     				=  in_proj_bias_cross_attn[:2_56]
												_SCREAMING_SNAKE_CASE     				=  in_proj_weight_cross_attn[2_56:5_12, :]
												_SCREAMING_SNAKE_CASE     				=  in_proj_bias_cross_attn[2_56:5_12]
												_SCREAMING_SNAKE_CASE     				=  in_proj_weight_cross_attn[-2_56:, :]
												_SCREAMING_SNAKE_CASE     				=  in_proj_bias_cross_attn[-2_56:]

def 	__lowerCamelCase      (		)							->  List[str]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
						_SCREAMING_SNAKE_CASE     				=  Image.open(requests.get(UpperCAmelCase__	,stream=UpperCAmelCase__		).raw		)

						return im

@torch.no_grad()
def 	__lowerCamelCase      (		snake_case__	,snake_case__=None	,snake_case__=False		)							->  Dict:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  get_detr_config(UpperCAmelCase__		)

						# load original model from torch hub
						_SCREAMING_SNAKE_CASE     				=  {
						    """detr-resnet-50""": """detr_resnet50""",
						    """detr-resnet-101""": """detr_resnet101""",
						}
						logger.info(F'Converting model {model_name}...'		)
						_SCREAMING_SNAKE_CASE     				=  torch.hub.load("""facebookresearch/detr"""	,model_name_to_original_name[model_name]	,pretrained=UpperCAmelCase__		).eval()
						_SCREAMING_SNAKE_CASE     				=  detr.state_dict()
						# rename keys
						for src, dest in create_rename_keys(UpperCAmelCase__		):
												if is_panoptic:
																		_SCREAMING_SNAKE_CASE     				=  """detr.""" + src
												rename_key(UpperCAmelCase__	,UpperCAmelCase__	,UpperCAmelCase__		)
						# query, key and value matrices need special treatment
						read_in_q_k_v(UpperCAmelCase__	,is_panoptic=UpperCAmelCase__		)
						# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
						_SCREAMING_SNAKE_CASE     				=  """detr.model.""" if is_panoptic else """model."""
						for key in state_dict.copy().keys():
												if is_panoptic:
																		if (
																		    key.startswith("""detr"""		)
																		    and not key.startswith("""class_labels_classifier"""		)
																		    and not key.startswith("""bbox_predictor"""		)
																		):
																								_SCREAMING_SNAKE_CASE     				=  state_dict.pop(UpperCAmelCase__		)
																								_SCREAMING_SNAKE_CASE     				=  val
																		elif "class_labels_classifier" in key or "bbox_predictor" in key:
																								_SCREAMING_SNAKE_CASE     				=  state_dict.pop(UpperCAmelCase__		)
																								_SCREAMING_SNAKE_CASE     				=  val
																		elif key.startswith("""bbox_attention"""		) or key.startswith("""mask_head"""		):
																								continue
																		else:
																								_SCREAMING_SNAKE_CASE     				=  state_dict.pop(UpperCAmelCase__		)
																								_SCREAMING_SNAKE_CASE     				=  val
												else:
																		if not key.startswith("""class_labels_classifier"""		) and not key.startswith("""bbox_predictor"""		):
																								_SCREAMING_SNAKE_CASE     				=  state_dict.pop(UpperCAmelCase__		)
																								_SCREAMING_SNAKE_CASE     				=  val

    # finally, create HuggingFace model and load state dict
						_SCREAMING_SNAKE_CASE     				=  DetrForSegmentation(UpperCAmelCase__		) if is_panoptic else DetrForObjectDetection(UpperCAmelCase__		)
						model.load_state_dict(UpperCAmelCase__		)
						model.eval()

						# verify our conversion on an image
						_SCREAMING_SNAKE_CASE     				=  """coco_panoptic""" if is_panoptic else """coco_detection"""
						_SCREAMING_SNAKE_CASE     				=  DetrImageProcessor(format=UpperCAmelCase__		)

						_SCREAMING_SNAKE_CASE     				=  processor(images=prepare_img()	,return_tensors="""pt"""		)
						_SCREAMING_SNAKE_CASE     				=  encoding["""pixel_values"""]

						_SCREAMING_SNAKE_CASE     				=  detr(UpperCAmelCase__		)
						_SCREAMING_SNAKE_CASE     				=  model(UpperCAmelCase__		)

						assert torch.allclose(outputs.logits	,original_outputs["""pred_logits"""]	,atol=1e-3		)
						assert torch.allclose(outputs.pred_boxes	,original_outputs["""pred_boxes"""]	,atol=1e-3		)
						if is_panoptic:
												assert torch.allclose(outputs.pred_masks	,original_outputs["""pred_masks"""]	,atol=1e-4		)
						print("""Looks ok!"""		)

						if pytorch_dump_folder_path is not None:
												# Save model and image processor
												logger.info(F'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'		)
												Path(UpperCAmelCase__		).mkdir(exist_ok=UpperCAmelCase__		)
												model.save_pretrained(UpperCAmelCase__		)
												processor.save_pretrained(UpperCAmelCase__		)

						if push_to_hub:
												# Upload model and image processor to the hub
												logger.info("""Uploading PyTorch model and image processor to the hub..."""		)
												model.push_to_hub(F'nielsr/{model_name}'		)
												processor.push_to_hub(F'nielsr/{model_name}'		)


if __name__ == "__main__":
		UpperCamelCase			    =     argparse.ArgumentParser()

		parser.add_argument(
		    '''--model_name''',
		    default='''detr-resnet-50''',
		    type=str,
		    choices=['''detr-resnet-50''', '''detr-resnet-101'''],
		    help='''Name of the DETR model you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
		)
		parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the model to the hub or not.''')
		UpperCamelCase			    =     parser.parse_args()
		convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool


if is_vision_available():
		from PIL import Image



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				Dict									=							"naver-clova-ix/donut-base-finetuned-docvqa"
			__snake_case    :				Dict									=							(
			    "This is a tool that answers a question about an document (pdf). It takes an input named `document` which "
			    "should be the document containing the information, as well as a `question` that is the question about the "
			    "document. It returns a text that contains the answer to the question."
			)
			__snake_case    :				Dict									=							"document_qa"
			__snake_case    :				Any									=							AutoProcessor
			__snake_case    :				int									=							VisionEncoderDecoderModel

			__snake_case    :				Union[str, Any]									=							["image", "text"]
			__snake_case    :				Optional[int]									=							["text"]







			def __init__(     self:  List[Any]				,     *UpperCAmelCase_:  Optional[int]				,     **UpperCAmelCase_:  List[str]					):







									'''simple docstring'''




									if not is_vision_available():
															raise ValueError("""Pillow must be installed to use the DocumentQuestionAnsweringTool."""					)

									super().__init__(*UpperCAmelCase_				,     **UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[int]				,     UpperCAmelCase_:  "Image"				,     UpperCAmelCase_:  str					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  """<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
									_SCREAMING_SNAKE_CASE     				=  task_prompt.replace("""{user_input}"""				,     UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  self.pre_processor.tokenizer(
									    UpperCAmelCase_				,     add_special_tokens=UpperCAmelCase_				,     return_tensors="""pt"""					).input_ids
									_SCREAMING_SNAKE_CASE     				=  self.pre_processor(UpperCAmelCase_				,     return_tensors="""pt"""					).pixel_values

									return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}







			def 							UpperCamelCase				(     self:  Dict				,     UpperCAmelCase_:  str					):







									'''simple docstring'''




									return self.model.generate(
									    inputs["""pixel_values"""].to(self.device					)				,     decoder_input_ids=inputs["""decoder_input_ids"""].to(self.device					)				,     max_length=self.model.decoder.config.max_position_embeddings				,     early_stopping=UpperCAmelCase_				,     pad_token_id=self.pre_processor.tokenizer.pad_token_id				,     eos_token_id=self.pre_processor.tokenizer.eos_token_id				,     use_cache=UpperCAmelCase_				,     num_beams=1				,     bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]				,     return_dict_in_generate=UpperCAmelCase_				,     ).sequences







			def 							UpperCamelCase				(     self:  Dict				,     UpperCAmelCase_:  Tuple					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.pre_processor.batch_decode(UpperCAmelCase_					)[0]
									_SCREAMING_SNAKE_CASE     				=  sequence.replace(self.pre_processor.tokenizer.eos_token				,     """"""					)
									_SCREAMING_SNAKE_CASE     				=  sequence.replace(self.pre_processor.tokenizer.pad_token				,     """"""					)
									_SCREAMING_SNAKE_CASE     				=  re.sub(R"""<.*?>"""				,     """"""				,     UpperCAmelCase_				,     count=1					).strip()  # remove first task start token
									_SCREAMING_SNAKE_CASE     				=  self.pre_processor.tokenajson(UpperCAmelCase_					)

									return sequence["answer"]

