



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()
__A             =logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__A             =[]
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							: Optional[int]			,       _UpperCAmelCase							: Any			,       _UpperCAmelCase							: Any						):







		'''simple docstring'''





		__UpperCAmelCase   :					Optional[int]													=     state_dict.pop(_UpperCAmelCase						)
		__UpperCAmelCase   :					Optional[Any]													=     val

def 	a						(				_UpperCAmelCase							: int						):







		'''simple docstring'''





		__UpperCAmelCase   :					List[Any]													=     OrderedDict()
		for key, value in state_dict.items():
				if "backbone.0.body" in key:
						__UpperCAmelCase   :					List[Any]													=     key.replace('''backbone.0.body'''			,       '''backbone.conv_encoder.model'''						)
						__UpperCAmelCase   :					Dict													=     value
				else:
						__UpperCAmelCase   :					str													=     value

		return new_state_dict

def 	a						(				_UpperCAmelCase							: Optional[int]			,       _UpperCAmelCase							: Any=False						):







		'''simple docstring'''





		__UpperCAmelCase   :					List[str]													=     ''''''
		if is_panoptic:
				__UpperCAmelCase   :					int													=     '''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   :					Union[str, Any]													=     state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'						)
				__UpperCAmelCase   :					Any													=     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   :					str													=     in_proj_weight[:2_56, :]
				__UpperCAmelCase   :					Union[str, Any]													=     in_proj_bias[:2_56]
				__UpperCAmelCase   :					List[Any]													=     in_proj_weight[2_56:5_12, :]
				__UpperCAmelCase   :					str													=     in_proj_bias[2_56:5_12]
				__UpperCAmelCase   :					int													=     in_proj_weight[-2_56:, :]
				__UpperCAmelCase   :					int													=     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
				__UpperCAmelCase   :					Optional[Any]													=     state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'						)
				__UpperCAmelCase   :					Tuple													=     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   :					str													=     in_proj_weight[:2_56, :]
				__UpperCAmelCase   :					Optional[int]													=     in_proj_bias[:2_56]
				__UpperCAmelCase   :					Any													=     in_proj_weight[2_56:5_12, :]
				__UpperCAmelCase   :					List[str]													=     in_proj_bias[2_56:5_12]
				__UpperCAmelCase   :					Tuple													=     in_proj_weight[-2_56:, :]
				__UpperCAmelCase   :					Union[str, Any]													=     in_proj_bias[-2_56:]
				# read in weights + bias of input projection layer of cross-attention
				__UpperCAmelCase   :					str													=     state_dict.pop(
				    f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'						)
				__UpperCAmelCase   :					List[str]													=     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   :					int													=     in_proj_weight_cross_attn[:2_56, :]
				__UpperCAmelCase   :					List[Any]													=     in_proj_bias_cross_attn[:2_56]
				__UpperCAmelCase   :					List[Any]													=     in_proj_weight_cross_attn[2_56:5_12, :]
				__UpperCAmelCase   :					Union[str, Any]													=     in_proj_bias_cross_attn[2_56:5_12]
				__UpperCAmelCase   :					Optional[int]													=     in_proj_weight_cross_attn[-2_56:, :]
				__UpperCAmelCase   :					Any													=     in_proj_bias_cross_attn[-2_56:]

def 	a						(				):







		'''simple docstring'''





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

		return im

@torch.no_grad()
def 	a						(				_UpperCAmelCase							: int			,       _UpperCAmelCase							: str						):







		'''simple docstring'''





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

		# load image processor
		__UpperCAmelCase   :					Tuple													=     '''coco_panoptic''' if is_panoptic else '''coco_detection'''
		__UpperCAmelCase   :					Optional[Any]													=     DetrImageProcessor(format=_UpperCAmelCase						)

		# prepare image
		__UpperCAmelCase   :					Dict													=     prepare_img()
		__UpperCAmelCase   :					List[Any]													=     image_processor(images=_UpperCAmelCase			,       return_tensors='''pt'''						)
		__UpperCAmelCase   :					Optional[Any]													=     encoding['''pixel_values''']

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

		# load original model from torch hub
		__UpperCAmelCase   :					Tuple													=     torch.hub.load('''facebookresearch/detr'''			,       _UpperCAmelCase			,       pretrained=_UpperCAmelCase						).eval()
		__UpperCAmelCase   :					Optional[int]													=     detr.state_dict()
		# rename keys
		for src, dest in rename_keys:
				if is_panoptic:
						__UpperCAmelCase   :					Dict													=     '''detr.''' + src
				rename_key(_UpperCAmelCase			,       _UpperCAmelCase			,       _UpperCAmelCase						)
		__UpperCAmelCase   :					Union[str, Any]													=     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   :					Union[str, Any]													=     '''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   :					int													=     state_dict.pop(_UpperCAmelCase						)
								__UpperCAmelCase   :					Tuple													=     val
						elif "class_labels_classifier" in key or "bbox_predictor" in key:
								__UpperCAmelCase   :					str													=     state_dict.pop(_UpperCAmelCase						)
								__UpperCAmelCase   :					List[str]													=     val
						elif key.startswith('''bbox_attention'''						) or key.startswith('''mask_head'''						):
								continue
						else:
								__UpperCAmelCase   :					Tuple													=     state_dict.pop(_UpperCAmelCase						)
								__UpperCAmelCase   :					Union[str, Any]													=     val
				else:
						if not key.startswith('''class_labels_classifier'''						) and not key.startswith('''bbox_predictor'''						):
								__UpperCAmelCase   :					List[Any]													=     state_dict.pop(_UpperCAmelCase						)
								__UpperCAmelCase   :					List[str]													=     val
    # finally, create HuggingFace model and load state dict
		__UpperCAmelCase   :					str													=     DetrForSegmentation(_UpperCAmelCase						) if is_panoptic else DetrForObjectDetection(_UpperCAmelCase						)
		model.load_state_dict(_UpperCAmelCase						)
		model.eval()
		# verify our conversion
		__UpperCAmelCase   :					Optional[int]													=     detr(_UpperCAmelCase						)
		__UpperCAmelCase   :					Dict													=     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__":
				__A             =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."
				)
				__A             =parser.parse_args()
				convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)










import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_reformer import ReformerTokenizer
else:
				__A             =None


__A             =logging.get_logger(__name__)


__A             ="▁"

__A             ={"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__A             ={
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

__A             ={
    "google/reformer-crime-and-punishment": 5_2_4_2_8_8,
}

class 		UpperCAmelCase__      (      __UpperCamelCase       ):




			'''simple docstring'''






			UpperCamelCase      						=	VOCAB_FILES_NAMES
			UpperCamelCase      						=	PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase      						=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase      						=	["""input_ids""", """attention_mask"""]
			UpperCamelCase      						=	ReformerTokenizer




			def __init__(       self							:			Tuple	,	a_							:			Optional[int]=None	,	a_							:			int=None	,	a_							:			Dict="</s>"	,	a_							:			Dict="<unk>"	,	a_							:			List[Any]=[]	,	**a_							:			int	,	):
					'''simple docstring'''





					super().__init__(
					    a_	,	tokenizer_file=a_	,	eos_token=a_	,	unk_token=a_	,	additional_special_tokens=a_	,	**a_	,	)

					__UpperCAmelCase   :					Optional[Any]													=     vocab_file
					__UpperCAmelCase   :					Optional[Any]													=     False if not self.vocab_file else True




			def 			snake_case__					(       self							:			Dict	,	a_							:			str	,	a_							:			Optional[str] = None			):
					'''simple docstring'''





					if not self.can_save_slow_tokenizer:
							raise ValueError(
							    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
							    '''tokenizer.'''			)

					if not os.path.isdir(a_			):
							logger.error(F'Vocabulary path ({save_directory}) should be a directory'			)
							return
					__UpperCAmelCase   :					Tuple													=     os.path.join(
					    a_	,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

					if os.path.abspath(self.vocab_file			) != os.path.abspath(a_			):
							copyfile(self.vocab_file	,	a_			)

					return (out_vocab_file,)





