





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

from tokenizers import processors

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


if TYPE_CHECKING:
			from transformers.pipelines.conversational import Conversation

require_version("""tokenizers>=0.13.3""")

if is_sentencepiece_available():
			from .tokenization_llama import LlamaTokenizer
else:
			__UpperCamelCase:  Any   =   None

__UpperCamelCase:  List[str]   =   logging.get_logger(__name__)
__UpperCamelCase:  Dict   =   {"""vocab_file""": """tokenizer.model""", """tokenizer_file""": """tokenizer.json"""}

__UpperCamelCase:  Tuple   =   """[INST]""", """[/INST]"""
__UpperCamelCase:  Optional[int]   =   """<<SYS>>\n""", """\n<</SYS>>\n\n"""

# fmt: off
__UpperCamelCase:  List[Any]   =   """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not\
correct. If you don't know the answer to a question, please don't share false information."""


class 			__lowerCAmelCase				(					_UpperCamelCase			):


	'''simple docstring'''





	_A       	=							VOCAB_FILES_NAMES
	_A       	=							LlamaTokenizer
	_A       	=							"left"
	_A       	=							["input_ids", "attention_mask"]

	def __init__(	self:	Any,				lowerCamelCase_:	List[Any]=None,				lowerCamelCase_:	Union[str, Any]=None,				lowerCamelCase_:	List[str]=False,				lowerCamelCase_:	List[str]="<unk>",				lowerCamelCase_:	Optional[int]="<s>",				lowerCamelCase_:	Dict="</s>",				lowerCamelCase_:	str=True,				lowerCamelCase_:	Dict=False,				**lowerCamelCase_:	Union[str, Any],				):
			super().__init__(
			    vocab_file=lowerCamelCase_,				tokenizer_file=lowerCamelCase_,				clean_up_tokenization_spaces=lowerCamelCase_,				unk_token=lowerCamelCase_,				bos_token=lowerCamelCase_,				eos_token=lowerCamelCase_,				**lowerCamelCase_,				)
			lowercase__				:			Tuple            =		add_bos_token
			lowercase__				:			Dict            =		add_eos_token
			self.update_post_processor()

			lowercase__				:			int            =		vocab_file
			lowercase__				:			Optional[int]            =		False if not self.vocab_file else True

	def       snake_case__(	self:	str ):
			lowercase__				:			str            =		self.bos_token
			lowercase__				:			List[str]            =		self.bos_token_id

			lowercase__				:			List[Any]            =		self.eos_token
			lowercase__				:			List[str]            =		self.eos_token_id

			lowercase__				:			Optional[Any]            =		F"""{(bos+":0 ") * self.add_bos_token}$A:0{(" "+eos+":0") * self.add_eos_token}"""
			lowercase__				:			Optional[Any]            =		F"""{single}{(" "+bos+":1") * self.add_bos_token} $B:1{(" "+eos+":1") * self.add_eos_token}"""

			lowercase__				:			Tuple            =		[]
			if self.add_bos_token:
					special_tokens.append((bos, bos_token_id) )
			if self.add_eos_token:
					special_tokens.append((eos, eos_token_id) )
			lowercase__				:			int            =		processors.TemplateProcessing(
			    single=lowerCamelCase_,				pair=lowerCamelCase_,				special_tokens=lowerCamelCase_ )

	@property
	def       snake_case__(	self:	List[Any] ):
			return self._add_eos_token

	@property
	def       snake_case__(	self:	str ):
			return self._add_bos_token

	@add_eos_token.setter
	def       snake_case__(	self:	Union[str, Any],				lowerCamelCase_:	List[Any] ):
			lowercase__				:			Union[str, Any]            =		value
			self.update_post_processor()

	@add_bos_token.setter
	def       snake_case__(	self:	int,				lowerCamelCase_:	List[str] ):
			lowercase__				:			Union[str, Any]            =		value
			self.update_post_processor()

	def       snake_case__(	self:	List[Any],				lowerCamelCase_:	str,				lowerCamelCase_:	Optional[str] = None ):
			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(lowerCamelCase_ ):
					logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
					return
			lowercase__				:			List[str]            =		os.path.join(
			    lowerCamelCase_,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

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

			return (out_vocab_file,)





	def       snake_case__(	self:	int,				lowerCamelCase_:	"Conversation" ):
			lowercase__				:			str            =		list(conversation.iter_texts() )
			if not all([is_user for is_user, msg in dialogue[::2]] ) or not all(
			    [not is_user for is_user, msg in dialogue[1::2]] ):
					raise ValueError(
					    'The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)' )

			lowercase__				:			Optional[Any]            =		[]
			if len(conversation.past_user_inputs ) > 0:
					if not conversation.past_user_inputs[0].startswith(lowerCamelCase_ ) or E_SYS not in conversation.past_user_inputs[0]:
							lowercase__				:			str            =		(
							    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
							)
			elif not dialogue[0][1].startswith(lowerCamelCase_ ) or E_SYS not in dialogue[0][1]:
					lowercase__				:			Any            =		(dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

			dialog_tokens += sum(
			    [
			        [self.bos_token_id]
			        + self.encode(
			            F"""{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} """,				add_special_tokens=lowerCamelCase_ )
			        + [self.eos_token_id]
			        for prompt, answer in zip(dialogue[::2],				dialogue[1::2] )
			    ],				[],				)
			if not (dialogue[-1][0]):
					raise ValueError(F"""Last message must be from user, got {dialogue[-1]["role"]}""" )
			dialog_tokens += [self.bos_token_id] + self.encode(
			    F"""{B_INST} {(dialogue[-1][1]).strip()} {E_INST}""",				add_special_tokens=lowerCamelCase_ )
			return dialog_tokens










import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase:  Tuple   =   logging.get_logger(__name__)


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   str )      ->       List[Any]:




		'''simple docstring'''
		if "backbone.0.body" in orig_key:
				lowercase__				:			Optional[int]            =		orig_key.replace('backbone.0.body'      ,						'backbone.conv_encoder.model' )
		if "transformer" in orig_key:
				lowercase__				:			Any            =		orig_key.replace('transformer.'      ,						'' )
		if "norm1" in orig_key:
				if "encoder" in orig_key:
						lowercase__				:			str            =		orig_key.replace('norm1'      ,						'self_attn_layer_norm' )
				else:
						lowercase__				:			int            =		orig_key.replace('norm1'      ,						'encoder_attn_layer_norm' )
		if "norm2" in orig_key:
				if "encoder" in orig_key:
						lowercase__				:			Any            =		orig_key.replace('norm2'      ,						'final_layer_norm' )
				else:
						lowercase__				:			Optional[int]            =		orig_key.replace('norm2'      ,						'self_attn_layer_norm' )
		if "norm3" in orig_key:
				lowercase__				:			Union[str, Any]            =		orig_key.replace('norm3'      ,						'final_layer_norm' )
		if "linear1" in orig_key:
				lowercase__				:			List[str]            =		orig_key.replace('linear1'      ,						'fc1' )
		if "linear2" in orig_key:
				lowercase__				:			int            =		orig_key.replace('linear2'      ,						'fc2' )
		if "query_embed" in orig_key:
				lowercase__				:			str            =		orig_key.replace('query_embed'      ,						'query_position_embeddings' )
		if "cross_attn" in orig_key:
				lowercase__				:			int            =		orig_key.replace('cross_attn'      ,						'encoder_attn' )

		return orig_key


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Tuple )      ->       Optional[int]:




		'''simple docstring'''
		# transformer decoder self-attention layers
		for i in range(6 ):
				# read in weights + bias of input projection layer of self-attention
				lowercase__				:			int            =		state_dict.pop(f"""decoder.layers.{i}.self_attn.in_proj_weight""" )
				lowercase__				:			List[Any]            =		state_dict.pop(f"""decoder.layers.{i}.self_attn.in_proj_bias""" )
				# next, add query, keys and values (in that order) to the state dict
				lowercase__				:			str            =		in_proj_weight[:256, :]
				lowercase__				:			Tuple            =		in_proj_bias[:256]
				lowercase__				:			List[str]            =		in_proj_weight[256:512, :]
				lowercase__				:			str            =		in_proj_bias[256:512]
				lowercase__				:			Optional[int]            =		in_proj_weight[-256:, :]
				lowercase__				:			str            =		in_proj_bias[-256:]


def 			SCREAMING_SNAKE_CASE__     (					)      ->       Tuple:




		'''simple docstring'''
		lowercase__				:			str            =		'http://images.cocodataset.org/val2017/000000039769.jpg'
		lowercase__				:			Optional[int]            =		Image.open(requests.get(_lowercase      ,						stream=_lowercase ).raw )

		return im


@torch.no_grad()
def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Dict      ,						_lowercase    :   Tuple      ,						_lowercase    :   List[Any]      ,						_lowercase    :   Optional[Any]      ,						_lowercase    :   List[str]      ,						_lowercase    :   List[str]      ,						_lowercase    :   Union[str, Any]      ,						)      ->       List[str]:




		'''simple docstring'''
		lowercase__				:			List[str]            =		DeformableDetrConfig()
		# set config attributes
		if single_scale:
				lowercase__				:			List[str]            =		1
		lowercase__				:			Dict            =		dilation
		lowercase__				:			int            =		with_box_refine
		lowercase__				:			Optional[int]            =		two_stage
		# set labels
		lowercase__				:			Optional[Any]            =		91
		lowercase__				:			Any            =		'huggingface/label-files'
		lowercase__				:			List[Any]            =		'coco-detection-id2label.json'
		lowercase__				:			Any            =		json.load(open(cached_download(hf_hub_url(_lowercase      ,						_lowercase      ,						repo_type='dataset' ) )      ,						'r' ) )
		lowercase__				:			Union[str, Any]            =		{int(_lowercase ): v for k, v in idalabel.items()}
		lowercase__				:			Tuple            =		idalabel
		lowercase__				:			Optional[int]            =		{v: k for k, v in idalabel.items()}

		# load image processor
		lowercase__				:			Tuple            =		DeformableDetrImageProcessor(format='coco_detection' )

		# prepare image
		lowercase__				:			List[Any]            =		prepare_img()
		lowercase__				:			Optional[int]            =		image_processor(images=_lowercase      ,						return_tensors='pt' )
		lowercase__				:			int            =		encoding['pixel_values']

		logger.info('Converting model...' )

		# load original state dict
		lowercase__				:			Dict            =		torch.load(_lowercase      ,						map_location='cpu' )['model']
		# rename keys
		for key in state_dict.copy().keys():
				lowercase__				:			int            =		state_dict.pop(_lowercase )
				lowercase__				:			Optional[Any]            =		val
		# query, key and value matrices need special treatment
		read_in_q_k_v(_lowercase )
		# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
		lowercase__				:			List[str]            =		'model.'
		for key in state_dict.copy().keys():
				if not key.startswith('class_embed' ) and not key.startswith('bbox_embed' ):
						lowercase__				:			Optional[Any]            =		state_dict.pop(_lowercase )
						lowercase__				:			Dict            =		val
    # finally, create HuggingFace model and load state dict
		lowercase__				:			Tuple            =		DeformableDetrForObjectDetection(_lowercase )
		model.load_state_dict(_lowercase )
		model.eval()

		lowercase__				:			str            =		'cuda' if torch.cuda.is_available() else 'cpu'
		model.to(_lowercase )
		# verify our conversion
		lowercase__				:			Dict            =		model(pixel_values.to(_lowercase ) )

		lowercase__				:			List[Any]            =		torch.tensor(
		    [[-9.6_645, -4.3_449, -5.8_705], [-9.7_035, -3.8_504, -5.0_724], [-10.5_634, -5.3_379, -7.5_116]] )
		lowercase__				:			str            =		torch.tensor([[0.8_693, 0.2_289, 0.2_492], [0.3_150, 0.5_489, 0.5_845], [0.5_563, 0.7_580, 0.8_518]] )

		if single_scale:
				lowercase__				:			Any            =		torch.tensor(
				    [[-9.9_051, -4.2_541, -6.4_852], [-9.6_947, -4.0_854, -6.8_033], [-10.0_665, -5.8_470, -7.7_003]] )
				lowercase__				:			Any            =		torch.tensor([[0.7_292, 0.4_991, 0.5_532], [0.7_959, 0.2_426, 0.4_236], [0.7_582, 0.3_518, 0.4_451]] )

		if single_scale and dilation:
				lowercase__				:			Optional[int]            =		torch.tensor(
				    [[-8.9_652, -4.1_074, -5.6_635], [-9.0_596, -4.9_447, -6.6_075], [-10.1_178, -4.5_275, -6.2_671]] )
				lowercase__				:			Dict            =		torch.tensor([[0.7_665, 0.4_130, 0.4_769], [0.8_364, 0.1_841, 0.3_391], [0.6_261, 0.3_895, 0.7_978]] )

		if with_box_refine:
				lowercase__				:			List[Any]            =		torch.tensor(
				    [[-8.8_895, -5.4_187, -6.8_153], [-8.4_706, -6.1_668, -7.6_184], [-9.0_042, -5.5_359, -6.9_141]] )
				lowercase__				:			int            =		torch.tensor([[0.7_828, 0.2_208, 0.4_323], [0.0_892, 0.5_996, 0.1_319], [0.5_524, 0.6_389, 0.8_914]] )

		if with_box_refine and two_stage:
				lowercase__				:			Union[str, Any]            =		torch.tensor(
				    [[-6.7_108, -4.3_213, -6.3_777], [-8.9_014, -6.1_799, -6.7_240], [-6.9_315, -4.4_735, -6.2_298]] )
				lowercase__				:			List[Any]            =		torch.tensor([[0.2_583, 0.5_499, 0.4_683], [0.7_652, 0.9_068, 0.4_882], [0.5_490, 0.2_763, 0.0_564]] )

		print('Logits:'      ,						outputs.logits[0, :3, :3] )

		assert torch.allclose(outputs.logits[0, :3, :3]      ,						expected_logits.to(_lowercase )      ,						atol=1e-4 )
		assert torch.allclose(outputs.pred_boxes[0, :3, :3]      ,						expected_boxes.to(_lowercase )      ,						atol=1e-4 )

		print('Everything ok!' )

		# Save model and image processor
		logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""" )
		Path(_lowercase ).mkdir(exist_ok=_lowercase )
		model.save_pretrained(_lowercase )
		image_processor.save_pretrained(_lowercase )

		# Push to hub
		if push_to_hub:
				lowercase__				:			Any            =		'deformable-detr'
				model_name += "-single-scale" if single_scale else ""
				model_name += "-dc5" if dilation else ""
				model_name += "-with-box-refine" if with_box_refine else ""
				model_name += "-two-stage" if two_stage else ""
				print('Pushing model to hub...' )
				model.push_to_hub(repo_path_or_name=_lowercase      ,						organization='nielsr'      ,						commit_message='Add model' )


if __name__ == "__main__":
			__UpperCamelCase:  List[str]   =   argparse.ArgumentParser()

			parser.add_argument(
			    """--checkpoint_path""",
			    type=str,
			    default="""/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth""",
			    help="""Path to Pytorch checkpoint (.pth file) you'd like to convert.""",
			)
			parser.add_argument("""--single_scale""", action="""store_true""", help="""Whether to set config.num_features_levels = 1.""")
			parser.add_argument("""--dilation""", action="""store_true""", help="""Whether to set config.dilation=True.""")
			parser.add_argument("""--with_box_refine""", action="""store_true""", help="""Whether to set config.with_box_refine=True.""")
			parser.add_argument("""--two_stage""", action="""store_true""", help="""Whether to set config.two_stage=True.""")
			parser.add_argument(
			    """--pytorch_dump_folder_path""",
			    default=None,
			    type=str,
			    required=True,
			    help="""Path to the folder to output PyTorch model.""",
			)
			parser.add_argument(
			    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
			)
			__UpperCamelCase:  List[str]   =   parser.parse_args()
			convert_deformable_detr_checkpoint(
			    args.checkpoint_path,
			    args.single_scale,
			    args.dilation,
			    args.with_box_refine,
			    args.two_stage,
			    args.pytorch_dump_folder_path,
			    args.push_to_hub,
			)


