



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


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

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCamelCase_      			=  []
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}.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"),
    ]
)







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )			->			List[Any]:



						'''simple docstring'''






						UpperCAmelCase_					=	state_dict.pop(__UpperCAmelCase      )
						UpperCAmelCase_					=	val







def 							A     (						__UpperCAmelCase      )			->			str:



						'''simple docstring'''






						UpperCAmelCase_					=	OrderedDict()
						for key, value in state_dict.items():
												if "backbone.0.body" in key:
																		UpperCAmelCase_					=	key.replace('''backbone.0.body'''				,   '''backbone.conv_encoder.model'''      )
																		UpperCAmelCase_					=	value
												else:
																		UpperCAmelCase_					=	value

						return new_state_dict







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase=False      )			->			Any:



						'''simple docstring'''






						UpperCAmelCase_					=	''''''
						if is_panoptic:
												UpperCAmelCase_					=	'''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)
												UpperCAmelCase_					=	state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"      )
												UpperCAmelCase_					=	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
												UpperCAmelCase_					=	in_proj_weight[:256, :]
												UpperCAmelCase_					=	in_proj_bias[:256]
												UpperCAmelCase_					=	in_proj_weight[256:512, :]
												UpperCAmelCase_					=	in_proj_bias[256:512]
												UpperCAmelCase_					=	in_proj_weight[-256:, :]
												UpperCAmelCase_					=	in_proj_bias[-256:]
						# 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
												UpperCAmelCase_					=	state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"      )
												UpperCAmelCase_					=	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
												UpperCAmelCase_					=	in_proj_weight[:256, :]
												UpperCAmelCase_					=	in_proj_bias[:256]
												UpperCAmelCase_					=	in_proj_weight[256:512, :]
												UpperCAmelCase_					=	in_proj_bias[256:512]
												UpperCAmelCase_					=	in_proj_weight[-256:, :]
												UpperCAmelCase_					=	in_proj_bias[-256:]
												# read in weights + bias of input projection layer of cross-attention
												UpperCAmelCase_					=	state_dict.pop(
												    f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"      )
												UpperCAmelCase_					=	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
												UpperCAmelCase_					=	in_proj_weight_cross_attn[:256, :]
												UpperCAmelCase_					=	in_proj_bias_cross_attn[:256]
												UpperCAmelCase_					=	in_proj_weight_cross_attn[256:512, :]
												UpperCAmelCase_					=	in_proj_bias_cross_attn[256:512]
												UpperCAmelCase_					=	in_proj_weight_cross_attn[-256:, :]
												UpperCAmelCase_					=	in_proj_bias_cross_attn[-256:]







def 							A     (						)			->			Tuple:



						'''simple docstring'''






						UpperCAmelCase_					=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						UpperCAmelCase_					=	Image.open(requests.get(__UpperCAmelCase				,   stream=__UpperCAmelCase      ).raw      )

						return im







@torch.no_grad()
def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			str:



						'''simple docstring'''






						UpperCAmelCase_					=	DetrConfig()
						# set backbone and dilation attributes
						if "resnet101" in model_name:
												UpperCAmelCase_					=	'''resnet101'''
						if "dc5" in model_name:
												UpperCAmelCase_					=	True
						UpperCAmelCase_					=	'''panoptic''' in model_name
						if is_panoptic:
												UpperCAmelCase_					=	250
						else:
												UpperCAmelCase_					=	91
												UpperCAmelCase_					=	'''huggingface/label-files'''
												UpperCAmelCase_					=	'''coco-detection-id2label.json'''
												UpperCAmelCase_					=	json.load(open(hf_hub_download(__UpperCAmelCase				,   __UpperCAmelCase				,   repo_type='''dataset'''      )				,   '''r'''      )      )
												UpperCAmelCase_					=	{int(__UpperCAmelCase      ): v for k, v in idalabel.items()}
												UpperCAmelCase_					=	idalabel
												UpperCAmelCase_					=	{v: k for k, v in idalabel.items()}

						# load image processor
						UpperCAmelCase_					=	'''coco_panoptic''' if is_panoptic else '''coco_detection'''
						UpperCAmelCase_					=	DetrImageProcessor(format=__UpperCAmelCase      )

						# prepare image
						UpperCAmelCase_					=	prepare_img()
						UpperCAmelCase_					=	image_processor(images=__UpperCAmelCase				,   return_tensors='''pt'''      )
						UpperCAmelCase_					=	encoding['''pixel_values''']

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

						# load original model from torch hub
						UpperCAmelCase_					=	torch.hub.load('''facebookresearch/detr'''				,   __UpperCAmelCase				,   pretrained=__UpperCAmelCase      ).eval()
						UpperCAmelCase_					=	detr.state_dict()
						# rename keys
						for src, dest in rename_keys:
												if is_panoptic:
																		UpperCAmelCase_					=	'''detr.''' + src
												rename_key(__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )
						UpperCAmelCase_					=	rename_backbone_keys(__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
						UpperCAmelCase_					=	'''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'''      )
																		):
																								UpperCAmelCase_					=	state_dict.pop(__UpperCAmelCase      )
																								UpperCAmelCase_					=	val
																		elif "class_labels_classifier" in key or "bbox_predictor" in key:
																								UpperCAmelCase_					=	state_dict.pop(__UpperCAmelCase      )
																								UpperCAmelCase_					=	val
																		elif key.startswith('''bbox_attention'''      ) or key.startswith('''mask_head'''      ):
																								continue
																		else:
																								UpperCAmelCase_					=	state_dict.pop(__UpperCAmelCase      )
																								UpperCAmelCase_					=	val
												else:
																		if not key.startswith('''class_labels_classifier'''      ) and not key.startswith('''bbox_predictor'''      ):
																								UpperCAmelCase_					=	state_dict.pop(__UpperCAmelCase      )
																								UpperCAmelCase_					=	val
    # finally, create HuggingFace model and load state dict
						UpperCAmelCase_					=	DetrForSegmentation(__UpperCAmelCase      ) if is_panoptic else DetrForObjectDetection(__UpperCAmelCase      )
						model.load_state_dict(__UpperCAmelCase      )
						model.eval()
						# verify our conversion
						UpperCAmelCase_					=	detr(__UpperCAmelCase      )
						UpperCAmelCase_					=	model(__UpperCAmelCase      )
						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(__UpperCAmelCase      ).mkdir(exist_ok=__UpperCAmelCase      )
						model.save_pretrained(__UpperCAmelCase      )
						image_processor.save_pretrained(__UpperCAmelCase      )


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

							parser.add_argument(
							    "--model_name", default="detr_resnet50", type=str, 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."
							)
							UpperCamelCase_      			=  parser.parse_args()
							convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)










import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase







@require_tokenizers
class 				a_    (						unittest.TestCase							):
						UpperCamelCase__ :		Any										=EsmTokenizer

						def       __a			(       self      :Optional[int])						->   Optional[int]:
												super().setUp()
												UpperCAmelCase_					=	tempfile.mkdtemp()
												# fmt: off
												UpperCAmelCase_					=	["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
												# fmt: on
												UpperCAmelCase_					=	os.path.join(self.tmpdirname  ,		VOCAB_FILES_NAMES['''vocab_file'''])
												with open(self.vocab_file  ,		'''w'''  ,		encoding='''utf-8''') as vocab_writer:
																		vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]))

						def       __a			(       self      :List[str]  ,		**_lowercase      :Union[str, Any])						->   List[PreTrainedTokenizerBase]:
												return [self.get_tokenizer(**_lowercase)]

						def       __a			(       self      :Optional[int]  ,		**_lowercase      :List[str])						->   PreTrainedTokenizer:
												return self.tokenizer_class.from_pretrained(self.tmpdirname  ,		**_lowercase)

						def       __a			(       self      :int)						->   Any:
												UpperCAmelCase_					=	self.tokenizer_class(self.vocab_file)

												UpperCAmelCase_					=	tokenizer.tokenize('''LAGVS''')
												self.assertListEqual(_lowercase  ,		['''L''', '''A''', '''G''', '''V''', '''S'''])
												self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase)  ,		[4, 5, 6, 7, 8])

						def       __a			(       self      :List[Any])						->   Optional[int]:
												UpperCAmelCase_					=	self.tokenizer_class(self.vocab_file)

												UpperCAmelCase_					=	'''LAGVS'''
												self.assertListEqual(tokenizer.encode(_lowercase)  ,		[0, 4, 5, 6, 7, 8, 2])

						def       __a			(       self      :Tuple)						->   Union[str, Any]:
												UpperCAmelCase_					=	self.tokenizer_class(self.vocab_file)

												UpperCAmelCase_					=	['''LAGVS''', '''WCB''']
												UpperCAmelCase_					=	tokenizer(_lowercase  ,		padding=_lowercase)['''input_ids''']

												self.assertListEqual(_lowercase  ,		[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]])

						def       __a			(       self      :str)						->   Any:
												UpperCAmelCase_					=	self.tokenizer_class(self.vocab_file)

												UpperCAmelCase_					=	['''LAGVS''', '''WCB''']
												UpperCAmelCase_					=	tokenizer(_lowercase  ,		padding=_lowercase)['''input_ids''']

												self.assertListEqual(_lowercase  ,		[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]])




						def       __a			(       self      :Dict)						->   Optional[int]:
												UpperCAmelCase_					=	self.get_tokenizers(fast=_lowercase)
												for tokenizer in tokenizers:
																		with self.subTest(f"{tokenizer.__class__.__name__}"):
																								UpperCAmelCase_					=	'''<unk>'''
																								UpperCAmelCase_					=	'''<mask>'''

																								UpperCAmelCase_					=	tokenizer.tokenize(_lowercase)
																								UpperCAmelCase_					=	tokenizer.tokenize(_lowercase)

																								self.assertEqual(len(_lowercase)  ,		1)
																								self.assertEqual(len(_lowercase)  ,		1)
																								self.assertEqual(token_a[0]  ,		_lowercase)
																								self.assertEqual(token_a[0]  ,		_lowercase)





