





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 (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
_SCREAMING_SNAKE_CASE 		=						[]
for i in range(6):
						# 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}.cross_attn.out_proj.weight''',
						        F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
						    )
						)
						rename_keys.append(
						    (
						        F'''transformer.decoder.layers.{i}.cross_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'''))

						# q, k, v projections in self/cross-attention in decoder for conditional DETR
						rename_keys.append(
						    (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', F'''decoder.layers.{i}.sa_qcontent_proj.weight''')
						)
						rename_keys.append(
						    (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', F'''decoder.layers.{i}.sa_kcontent_proj.weight''')
						)
						rename_keys.append(
						    (F'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', F'''decoder.layers.{i}.sa_qpos_proj.weight''')
						)
						rename_keys.append(
						    (F'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', F'''decoder.layers.{i}.sa_kpos_proj.weight''')
						)
						rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.weight''', F'''decoder.layers.{i}.sa_v_proj.weight'''))
						rename_keys.append(
						    (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', F'''decoder.layers.{i}.ca_qcontent_proj.weight''')
						)
						# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
						rename_keys.append(
						    (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', F'''decoder.layers.{i}.ca_kcontent_proj.weight''')
						)
						rename_keys.append(
						    (F'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', F'''decoder.layers.{i}.ca_kpos_proj.weight''')
						)
						rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.weight''', F'''decoder.layers.{i}.ca_v_proj.weight'''))
						rename_keys.append(
						    (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', F'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
						)

						rename_keys.append(
						    (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', F'''decoder.layers.{i}.sa_qcontent_proj.bias''')
						)
						rename_keys.append(
						    (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', F'''decoder.layers.{i}.sa_kcontent_proj.bias''')
						)
						rename_keys.append((F'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', F'''decoder.layers.{i}.sa_qpos_proj.bias'''))
						rename_keys.append((F'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', F'''decoder.layers.{i}.sa_kpos_proj.bias'''))
						rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.bias''', F'''decoder.layers.{i}.sa_v_proj.bias'''))
						rename_keys.append(
						    (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', F'''decoder.layers.{i}.ca_qcontent_proj.bias''')
						)
						# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
						rename_keys.append(
						    (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', F'''decoder.layers.{i}.ca_kcontent_proj.bias''')
						)
						rename_keys.append((F'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', F'''decoder.layers.{i}.ca_kpos_proj.bias'''))
						rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.bias''', F'''decoder.layers.{i}.ca_v_proj.bias'''))
						rename_keys.append(
						    (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', F'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
						)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
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"""),
        ("""transformer.decoder.ref_point_head.layers.0.weight""", """decoder.ref_point_head.layers.0.weight"""),
        ("""transformer.decoder.ref_point_head.layers.0.bias""", """decoder.ref_point_head.layers.0.bias"""),
        ("""transformer.decoder.ref_point_head.layers.1.weight""", """decoder.ref_point_head.layers.1.weight"""),
        ("""transformer.decoder.ref_point_head.layers.1.bias""", """decoder.ref_point_head.layers.1.bias"""),
        ("""transformer.decoder.query_scale.layers.0.weight""", """decoder.query_scale.layers.0.weight"""),
        ("""transformer.decoder.query_scale.layers.0.bias""", """decoder.query_scale.layers.0.bias"""),
        ("""transformer.decoder.query_scale.layers.1.weight""", """decoder.query_scale.layers.1.weight"""),
        ("""transformer.decoder.query_scale.layers.1.bias""", """decoder.query_scale.layers.1.bias"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.weight""", """decoder.layers.0.ca_qpos_proj.weight"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.bias""", """decoder.layers.0.ca_qpos_proj.bias"""),
    ]
)


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				):
	snake_case_						:     str     =		state_dict.pop(__a				)
	snake_case_						:     Optional[Any]     =		val


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     List[str]     =		OrderedDict()
	for key, value in state_dict.items():
		if "backbone.0.body" in key:
			snake_case_						:     str     =		key.replace('backbone.0.body'				,	'backbone.conv_encoder.model'				)
			snake_case_						:     List[Any]     =		value
		else:
			snake_case_						:     Optional[int]     =		value

	return new_state_dict


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a=False				):
	snake_case_						:     Optional[Any]     =		''
	if is_panoptic:
		snake_case_						:     Dict     =		'conditional_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)
		snake_case_						:     int     =		state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""				)
		snake_case_						:     str     =		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
		snake_case_						:     List[Any]     =		in_proj_weight[:2_56, :]
		snake_case_						:     List[str]     =		in_proj_bias[:2_56]
		snake_case_						:     Dict     =		in_proj_weight[2_56:5_12, :]
		snake_case_						:     Union[str, Any]     =		in_proj_bias[2_56:5_12]
		snake_case_						:     str     =		in_proj_weight[-2_56:, :]
		snake_case_						:     List[str]     =		in_proj_bias[-2_56:]


def 							SCREAMING_SNAKE_CASE__  (      ):
	snake_case_						:     Optional[int]     =		'http://images.cocodataset.org/val2017/000000039769.jpg'
	snake_case_						:     List[Any]     =		Image.open(requests.get(__a				,	stream=__a				).raw				)

	return im


@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				):
	snake_case_						:     List[str]     =		ConditionalDetrConfig()
	# set backbone and dilation attributes
	if "resnet101" in model_name:
		snake_case_						:     Tuple     =		'resnet101'
	if "dc5" in model_name:
		snake_case_						:     int     =		True
	snake_case_						:     Union[str, Any]     =		'panoptic' in model_name
	if is_panoptic:
		snake_case_						:     Optional[int]     =		2_50
	else:
		snake_case_						:     Union[str, Any]     =		91
		snake_case_						:     str     =		'huggingface/label-files'
		snake_case_						:     Optional[Any]     =		'coco-detection-id2label.json'
		snake_case_						:     Optional[Any]     =		json.load(open(hf_hub_download(__a				,	__a				,	repo_type='dataset'				)				,	'r'				)				)
		snake_case_						:     Dict     =		{int(__a				): v for k, v in idalabel.items()}
		snake_case_						:     List[str]     =		idalabel
		snake_case_						:     Union[str, Any]     =		{v: k for k, v in idalabel.items()}

	# load image processor
	snake_case_						:     Any     =		'coco_panoptic' if is_panoptic else 'coco_detection'
	snake_case_						:     List[Any]     =		ConditionalDetrImageProcessor(format=__a				)

	# prepare image
	snake_case_						:     Any     =		prepare_img()
	snake_case_						:     List[Any]     =		image_processor(images=__a				,	return_tensors='pt'				)
	snake_case_						:     Tuple     =		encoding['pixel_values']

	logger.info(f"""Converting model {model_name}..."""				)

	# load original model from torch hub
	snake_case_						:     Tuple     =		torch.hub.load('DeppMeng/ConditionalDETR'				,	__a				,	pretrained=__a				).eval()
	snake_case_						:     str     =		conditional_detr.state_dict()
	# rename keys
	for src, dest in rename_keys:
		if is_panoptic:
			snake_case_						:     Optional[int]     =		'conditional_detr.' + src
		rename_key(__a				,	__a				,	__a				)
	snake_case_						:     List[str]     =		rename_backbone_keys(__a				)
	# query, key and value matrices need special treatment
	read_in_q_k_v(__a				,	is_panoptic=__a				)
	# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
	snake_case_						:     List[str]     =		'conditional_detr.model.' if is_panoptic else 'model.'
	for key in state_dict.copy().keys():
		if is_panoptic:
			if (
			    key.startswith('conditional_detr'				)
			    and not key.startswith('class_labels_classifier'				)
			    and not key.startswith('bbox_predictor'				)
			):
				snake_case_						:     Dict     =		state_dict.pop(__a				)
				snake_case_						:     List[Any]     =		val
			elif "class_labels_classifier" in key or "bbox_predictor" in key:
				snake_case_						:     Any     =		state_dict.pop(__a				)
				snake_case_						:     str     =		val
			elif key.startswith('bbox_attention'				) or key.startswith('mask_head'				):
				continue
			else:
				snake_case_						:     str     =		state_dict.pop(__a				)
				snake_case_						:     Dict     =		val
		else:
			if not key.startswith('class_labels_classifier'				) and not key.startswith('bbox_predictor'				):
				snake_case_						:     List[Any]     =		state_dict.pop(__a				)
				snake_case_						:     Union[str, Any]     =		val
    # finally, create HuggingFace model and load state dict
	snake_case_						:     Optional[Any]     =		ConditionalDetrForSegmentation(__a				) if is_panoptic else ConditionalDetrForObjectDetection(__a				)
	model.load_state_dict(__a				)
	model.eval()
	model.push_to_hub(repo_id=__a				,	organization='DepuMeng'				,	commit_message='Add model'				)
	# verify our conversion
	snake_case_						:     Optional[Any]     =		conditional_detr(__a				)
	snake_case_						:     Optional[int]     =		model(__a				)
	assert torch.allclose(outputs.logits				,	original_outputs['pred_logits']				,	atol=1E-4				)
	assert torch.allclose(outputs.pred_boxes				,	original_outputs['pred_boxes']				,	atol=1E-4				)
	if is_panoptic:
		assert torch.allclose(outputs.pred_masks				,	original_outputs['pred_masks']				,	atol=1E-4				)

	# Save model and image processor
	logger.info(f"""Saving PyTorch 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 __name__ == "__main__":
						_SCREAMING_SNAKE_CASE 		=						argparse.ArgumentParser()

						parser.add_argument(
						    """--model_name""",
						    default="""conditional_detr_resnet50""",
						    type=str,
						    help="""Name of the CONDITIONAL_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."""
						)
						_SCREAMING_SNAKE_CASE 		=						parser.parse_args()
						convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)








import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				,	__a				,	__a				):
	# Initialise PyTorch model.
	# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
	# TapasConfig to False.

	# initialize configuration from json file
	snake_case_						:     Optional[int]     =		TapasConfig.from_json_file(__a				)
	# set absolute/relative position embeddings parameter
	snake_case_						:     List[Any]     =		reset_position_index_per_cell

	# set remaining parameters of TapasConfig as well as the model based on the task
	if task == "SQA":
		snake_case_						:     int     =		TapasForQuestionAnswering(config=__a				)
	elif task == "WTQ":
		# run_task_main.py hparams
		snake_case_						:     Optional[int]     =		4
		snake_case_						:     List[str]     =		True
		# hparam_utils.py hparams
		snake_case_						:     Optional[Any]     =		0.664694
		snake_case_						:     Dict     =		0.207951
		snake_case_						:     Tuple     =		0.121194
		snake_case_						:     Dict     =		True
		snake_case_						:     int     =		True
		snake_case_						:     int     =		False
		snake_case_						:     str     =		0.0352513

		snake_case_						:     int     =		TapasForQuestionAnswering(config=__a				)
	elif task == "WIKISQL_SUPERVISED":
		# run_task_main.py hparams
		snake_case_						:     int     =		4
		snake_case_						:     Optional[int]     =		False
		# hparam_utils.py hparams
		snake_case_						:     str     =		36.4519
		snake_case_						:     Optional[Any]     =		0.903421
		snake_case_						:     List[Any]     =		222.088
		snake_case_						:     Optional[int]     =		True
		snake_case_						:     Optional[Any]     =		True
		snake_case_						:     str     =		True
		snake_case_						:     int     =		0.763141

		snake_case_						:     str     =		TapasForQuestionAnswering(config=__a				)
	elif task == "TABFACT":
		snake_case_						:     List[Any]     =		TapasForSequenceClassification(config=__a				)
	elif task == "MLM":
		snake_case_						:     Optional[int]     =		TapasForMaskedLM(config=__a				)
	elif task == "INTERMEDIATE_PRETRAINING":
		snake_case_						:     Tuple     =		TapasModel(config=__a				)
	else:
		raise ValueError(f"""Task {task} not supported."""				)

	print(f"""Building PyTorch model from configuration: {config}"""				)
	# Load weights from tf checkpoint
	load_tf_weights_in_tapas(__a				,	__a				,	__a				)

	# Save pytorch-model (weights and configuration)
	print(f"""Save PyTorch model to {pytorch_dump_path}"""				)
	model.save_pretrained(__a				)

	# Save tokenizer files
	print(f"""Save tokenizer files to {pytorch_dump_path}"""				)
	snake_case_						:     str     =		TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt'				,	model_max_length=5_12				)
	tokenizer.save_pretrained(__a				)

	print('Used relative position embeddings:'				,	model.config.reset_position_index_per_cell				)


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE 		=						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--task""", default="""SQA""", type=str, help="""Model task for which to convert a checkpoint. Defaults to SQA."""
						)
						parser.add_argument(
						    """--reset_position_index_per_cell""",
						    default=False,
						    action="""store_true""",
						    help="""Whether to use relative position embeddings or not. Defaults to True.""",
						)
						parser.add_argument(
						    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
						)
						parser.add_argument(
						    """--tapas_config_file""",
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        """The config json file corresponding to the pre-trained TAPAS model. \n"""
						        """This specifies the model architecture."""
						    ),
						)
						parser.add_argument(
						    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
						)
						_SCREAMING_SNAKE_CASE 		=						parser.parse_args()
						convert_tf_checkpoint_to_pytorch(
						    args.task,
						    args.reset_position_index_per_cell,
						    args.tf_checkpoint_path,
						    args.tapas_config_file,
						    args.pytorch_dump_path,
						)

