
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


__A				=logging.get_logger(__name__)

__A				={'''vocab_file''': '''spiece.model'''}

__A				={
    '''vocab_file''': {
        '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model''',
        '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model''',
    }
}

__A				={
    '''xlnet-base-cased''': None,
    '''xlnet-large-cased''': None,
}

# Segments (not really needed)
__A				=0
__A				=1
__A				=2
__A				=3
__A				=4




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = VOCAB_FILES_NAMES
	lowerCAmelCase__         = PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase__         = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase__         = 'left'



	def __init__(    self						,  lowercase						,  lowercase=False						,  lowercase=True						,  lowercase=False						,  lowercase="<s>"						,  lowercase="</s>"						,  lowercase="<unk>"						,  lowercase="<sep>"						,  lowercase="<pad>"						,  lowercase="<cls>"						,  lowercase="<mask>"						,  lowercase=["<eop>", "<eod>"]						,  lowercase = None						,  **lowercase						,  )				->       None:
					# Mask token behave like a normal word, i.e. include the space before it
					lowerCamelCase_						=			AddedToken(lowercase						,  lstrip=lowercase						,  rstrip=lowercase ) if isinstance(lowercase						,  lowercase ) else mask_token

					lowerCamelCase_						=			{} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    do_lower_case=lowercase						,  remove_space=lowercase						,  keep_accents=lowercase						,  bos_token=lowercase						,  eos_token=lowercase						,  unk_token=lowercase						,  sep_token=lowercase						,  pad_token=lowercase						,  cls_token=lowercase						,  mask_token=lowercase						,  additional_special_tokens=lowercase						,  sp_model_kwargs=self.sp_model_kwargs						,  **lowercase						,  )

					lowerCamelCase_						=			3

					lowerCamelCase_						=			do_lower_case
					lowerCamelCase_						=			remove_space
					lowerCamelCase_						=			keep_accents
					lowerCamelCase_						=			vocab_file

					lowerCamelCase_						=			spm.SentencePieceProcessor(**self.sp_model_kwargs )
					self.sp_model.Load(lowercase )



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       int:
					return len(self.sp_model )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					lowerCamelCase_						=			{self.convert_ids_to_tokens(lowercase ): i for i in range(self.vocab_size )}
					vocab.update(self.added_tokens_encoder )
					return vocab



	def __getstate__(    self )				->       str:
					lowerCamelCase_						=			self.__dict__.copy()
					lowerCamelCase_						=			None
					return state



	def __setstate__(    self						,  lowercase )				->       Optional[Any]:
					lowerCamelCase_						=			d

					# for backward compatibility
					if not hasattr(self						,  "sp_model_kwargs" ):
									lowerCamelCase_						=			{}

					lowerCamelCase_						=			spm.SentencePieceProcessor(**self.sp_model_kwargs )
					self.sp_model.Load(self.vocab_file )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       Any:
					if self.remove_space:
									lowerCamelCase_						=			" ".join(inputs.strip().split() )
					else:
									lowerCamelCase_						=			inputs
					lowerCamelCase_						=			outputs.replace("``"						,  "\"" ).replace("''"						,  "\"" )

					if not self.keep_accents:
									lowerCamelCase_						=			unicodedata.normalize("NFKD"						,  lowercase )
									lowerCamelCase_						=			"".join([c for c in outputs if not unicodedata.combining(lowercase )] )
					if self.do_lower_case:
									lowerCamelCase_						=			outputs.lower()

					return outputs



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       List[str]:
					lowerCamelCase_						=			self.preprocess_text(lowercase )
					lowerCamelCase_						=			self.sp_model.encode(lowercase						,  out_type=lowercase )
					lowerCamelCase_						=			[]
					for piece in pieces:
									if len(lowercase ) > 1 and piece[-1] == str("," ) and piece[-2].isdigit():
													lowerCamelCase_						=			self.sp_model.EncodeAsPieces(piece[:-1].replace(lowercase						,  "" ) )
													if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																	if len(cur_pieces[0] ) == 1:
																					lowerCamelCase_						=			cur_pieces[1:]
																	else:
																					lowerCamelCase_						=			cur_pieces[0][1:]
													cur_pieces.append(piece[-1] )
													new_pieces.extend(lowercase )
									else:
													new_pieces.append(lowercase )

					return new_pieces



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       Union[str, Any]:
					return self.sp_model.PieceToId(lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       List[Any]:
					return self.sp_model.IdToPiece(lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       Tuple:
					lowerCamelCase_						=			"".join(lowercase ).replace(lowercase						,  " " ).strip()
					return out_string



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = False						,  lowercase = None						,  lowercase = True						,  **lowercase						,  )				->       str:
					lowerCamelCase_						=			kwargs.pop("use_source_tokenizer"						,  lowercase )

					lowerCamelCase_						=			self.convert_ids_to_tokens(lowercase						,  skip_special_tokens=lowercase )

					# To avoid mixing byte-level and unicode for byte-level BPT
					# we need to build string separately for added tokens and byte-level tokens
					# cf. https://github.com/huggingface/transformers/issues/1133
					lowerCamelCase_						=			[]
					lowerCamelCase_						=			[]
					for token in filtered_tokens:
									if skip_special_tokens and token in self.all_special_ids:
													continue
									if token in self.added_tokens_encoder:
													if current_sub_text:
																	sub_texts.append(self.convert_tokens_to_string(lowercase ) )
																	lowerCamelCase_						=			[]
													sub_texts.append(lowercase )
									else:
													current_sub_text.append(lowercase )
					if current_sub_text:
									sub_texts.append(self.convert_tokens_to_string(lowercase ) )

					# Mimic the behavior of the Rust tokenizer:
					# By default, there are no spaces between special tokens
					lowerCamelCase_						=			"".join(lowercase )

					lowerCamelCase_						=			(
					    clean_up_tokenization_spaces
					    if clean_up_tokenization_spaces is not None
					    else self.clean_up_tokenization_spaces
					)
					if clean_up_tokenization_spaces:
									lowerCamelCase_						=			self.clean_up_tokenization(lowercase )
									return clean_text
					else:
									return text



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None )				->       List[int]:
					lowerCamelCase_						=			[self.sep_token_id]
					lowerCamelCase_						=			[self.cls_token_id]
					if token_ids_a is None:
									return token_ids_a + sep + cls
					return token_ids_a + sep + token_ids_a + sep + cls



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None						,  lowercase = False )				->       List[int]:

					if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=lowercase						,  token_ids_a=lowercase						,  already_has_special_tokens=lowercase )

					if token_ids_a is not None:
									return ([0] * len(lowercase )) + [1] + ([0] * len(lowercase )) + [1, 1]
					return ([0] * len(lowercase )) + [1, 1]



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None )				->       List[int]:
					lowerCamelCase_						=			[self.sep_token_id]
					lowerCamelCase_						=			[2]

					if token_ids_a is None:
									return len(token_ids_a + sep ) * [0] + cls_segment_id
					return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None )				->       Tuple[str]:
					if not os.path.isdir(lowercase ):
									logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
									return
					lowerCamelCase_						=			os.path.join(
					    lowercase						,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

					if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase ) and os.path.isfile(self.vocab_file ):
									copyfile(self.vocab_file						,  lowercase )
					elif not os.path.isfile(self.vocab_file ):
									with open(lowercase						,  "wb" ) as fi:
													lowerCamelCase_						=			self.sp_model.serialized_model_proto()
													fi.write(lowercase )

					return (out_vocab_file,)




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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


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





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				lowerCamelCase_						=			YolosConfig()

				# size of the architecture
				if "yolos_ti" in yolos_name:
								lowerCamelCase_						=			1_9_2
								lowerCamelCase_						=			7_6_8
								lowerCamelCase_						=			1_2
								lowerCamelCase_						=			3
								lowerCamelCase_						=			[8_0_0, 1_3_3_3]
								lowerCamelCase_						=			False
				elif yolos_name == "yolos_s_dWr":
								lowerCamelCase_						=			3_3_0
								lowerCamelCase_						=			1_4
								lowerCamelCase_						=			6
								lowerCamelCase_						=			1_3_2_0
				elif "yolos_s" in yolos_name:
								lowerCamelCase_						=			3_8_4
								lowerCamelCase_						=			1_5_3_6
								lowerCamelCase_						=			1_2
								lowerCamelCase_						=			6
				elif "yolos_b" in yolos_name:
								lowerCamelCase_						=			[8_0_0, 1_3_4_4]

				lowerCamelCase_						=			9_1
				lowerCamelCase_						=			"huggingface/label-files"
				lowerCamelCase_						=			"coco-detection-id2label.json"
				lowerCamelCase_						=			json.load(open(hf_hub_download(lowerCamelCase__	, lowerCamelCase__	, repo_type="dataset"    )	, "r"    )    )
				lowerCamelCase_						=			{int(lowerCamelCase__    ): v for k, v in idalabel.items()}
				lowerCamelCase_						=			idalabel
				lowerCamelCase_						=			{v: k for k, v in idalabel.items()}

				return config





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__ = False    ):
				for i in range(config.num_hidden_layers    ):
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								lowerCamelCase_						=			state_dict.pop(F'blocks.{i}.attn.qkv.weight'    )
								lowerCamelCase_						=			state_dict.pop(F'blocks.{i}.attn.qkv.bias'    )
								# next, add query, keys and values (in that order) to the state dict
								lowerCamelCase_						=			in_proj_weight[: config.hidden_size, :]
								lowerCamelCase_						=			in_proj_bias[: config.hidden_size]
								lowerCamelCase_						=			in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								lowerCamelCase_						=			in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								lowerCamelCase_						=			in_proj_weight[-config.hidden_size :, :]
								lowerCamelCase_						=			in_proj_bias[-config.hidden_size :]





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				if "backbone" in name:
								lowerCamelCase_						=			name.replace("backbone"	, "vit"    )
				if "cls_token" in name:
								lowerCamelCase_						=			name.replace("cls_token"	, "embeddings.cls_token"    )
				if "det_token" in name:
								lowerCamelCase_						=			name.replace("det_token"	, "embeddings.detection_tokens"    )
				if "mid_pos_embed" in name:
								lowerCamelCase_						=			name.replace("mid_pos_embed"	, "encoder.mid_position_embeddings"    )
				if "pos_embed" in name:
								lowerCamelCase_						=			name.replace("pos_embed"	, "embeddings.position_embeddings"    )
				if "patch_embed.proj" in name:
								lowerCamelCase_						=			name.replace("patch_embed.proj"	, "embeddings.patch_embeddings.projection"    )
				if "blocks" in name:
								lowerCamelCase_						=			name.replace("blocks"	, "encoder.layer"    )
				if "attn.proj" in name:
								lowerCamelCase_						=			name.replace("attn.proj"	, "attention.output.dense"    )
				if "attn" in name:
								lowerCamelCase_						=			name.replace("attn"	, "attention.self"    )
				if "norm1" in name:
								lowerCamelCase_						=			name.replace("norm1"	, "layernorm_before"    )
				if "norm2" in name:
								lowerCamelCase_						=			name.replace("norm2"	, "layernorm_after"    )
				if "mlp.fc1" in name:
								lowerCamelCase_						=			name.replace("mlp.fc1"	, "intermediate.dense"    )
				if "mlp.fc2" in name:
								lowerCamelCase_						=			name.replace("mlp.fc2"	, "output.dense"    )
				if "class_embed" in name:
								lowerCamelCase_						=			name.replace("class_embed"	, "class_labels_classifier"    )
				if "bbox_embed" in name:
								lowerCamelCase_						=			name.replace("bbox_embed"	, "bbox_predictor"    )
				if "vit.norm" in name:
								lowerCamelCase_						=			name.replace("vit.norm"	, "vit.layernorm"    )

				return name





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				for key in orig_state_dict.copy().keys():
								lowerCamelCase_						=			orig_state_dict.pop(lowerCamelCase__    )

								if "qkv" in key:
												lowerCamelCase_						=			key.split("."    )
												lowerCamelCase_						=			int(key_split[2]    )
												lowerCamelCase_						=			model.vit.encoder.layer[layer_num].attention.attention.all_head_size
												if "weight" in key:
																lowerCamelCase_						=			val[:dim, :]
																lowerCamelCase_						=			val[
																    dim : dim * 2, :
																]
																lowerCamelCase_						=			val[-dim:, :]
												else:
																lowerCamelCase_						=			val[:dim]
																lowerCamelCase_						=			val[dim : dim * 2]
																lowerCamelCase_						=			val[-dim:]
								else:
												lowerCamelCase_						=			val

				return orig_state_dict





def 			lowerCamelCase_ (				):
				lowerCamelCase_						=			"http://images.cocodataset.org/val2017/000000039769.jpg"
				lowerCamelCase_						=			Image.open(requests.get(lowerCamelCase__	, stream=lowerCamelCase__    ).raw    )
				return im





@torch.no_grad()
def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__ = False    ):
				lowerCamelCase_						=			get_yolos_config(lowerCamelCase__    )

				# load original state_dict
				lowerCamelCase_						=			torch.load(lowerCamelCase__	, map_location="cpu"    )["model"]

				# load 🤗 model
				lowerCamelCase_						=			YolosForObjectDetection(lowerCamelCase__    )
				model.eval()
				lowerCamelCase_						=			convert_state_dict(lowerCamelCase__	, lowerCamelCase__    )
				model.load_state_dict(lowerCamelCase__    )

				# Check outputs on an image, prepared by YolosImageProcessor
				lowerCamelCase_						=			8_0_0 if yolos_name != "yolos_ti" else 5_1_2
				lowerCamelCase_						=			YolosImageProcessor(format="coco_detection"	, size=lowerCamelCase__    )
				lowerCamelCase_						=			image_processor(images=prepare_img()	, return_tensors="pt"    )
				lowerCamelCase_						=			model(**lowerCamelCase__    )
				lowerCamelCase_	,    lowerCamelCase_						=			outputs.logits, outputs.pred_boxes

				lowerCamelCase_	,    lowerCamelCase_						=			None, None
				if yolos_name == "yolos_ti":
								lowerCamelCase_						=			torch.tensor(
								    [[-39.50_22, -11.98_20, -17.68_88], [-29.95_74, -9.97_69, -17.76_91], [-42.32_81, -20.72_00, -30.62_94]]    )
								lowerCamelCase_						=			torch.tensor(
								    [[0.40_21, 0.08_36, 0.79_79], [0.01_84, 0.26_09, 0.03_64], [0.17_81, 0.20_04, 0.20_95]]    )
				elif yolos_name == "yolos_s_200_pre":
								lowerCamelCase_						=			torch.tensor(
								    [[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]]    )
								lowerCamelCase_						=			torch.tensor(
								    [[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]]    )
				elif yolos_name == "yolos_s_300_pre":
								lowerCamelCase_						=			torch.tensor(
								    [[-36.22_20, -14.43_85, -23.54_57], [-35.69_70, -14.75_83, -21.39_35], [-31.59_39, -13.60_42, -16.80_49]]    )
								lowerCamelCase_						=			torch.tensor(
								    [[0.76_14, 0.23_16, 0.47_28], [0.71_68, 0.44_95, 0.38_55], [0.49_96, 0.14_66, 0.99_96]]    )
				elif yolos_name == "yolos_s_dWr":
								lowerCamelCase_						=			torch.tensor(
								    [[-42.86_68, -24.10_49, -41.16_90], [-34.74_56, -14.12_74, -24.91_94], [-33.78_98, -12.19_46, -25.64_95]]    )
								lowerCamelCase_						=			torch.tensor(
								    [[0.55_87, 0.27_73, 0.06_05], [0.50_04, 0.30_14, 0.99_94], [0.49_99, 0.15_48, 0.99_94]]    )
				elif yolos_name == "yolos_base":
								lowerCamelCase_						=			torch.tensor(
								    [[-40.60_64, -24.30_84, -32.64_47], [-55.19_90, -30.77_19, -35.58_77], [-51.43_11, -33.35_07, -35.64_62]]    )
								lowerCamelCase_						=			torch.tensor(
								    [[0.55_55, 0.27_94, 0.06_55], [0.90_49, 0.26_64, 0.18_94], [0.91_83, 0.19_84, 0.16_35]]    )
				else:
								raise ValueError(F'Unknown yolos_name: {yolos_name}'    )

				assert torch.allclose(logits[0, :3, :3]	, lowerCamelCase__	, atol=1e-4    )
				assert torch.allclose(pred_boxes[0, :3, :3]	, lowerCamelCase__	, atol=1e-4    )

				Path(lowerCamelCase__    ).mkdir(exist_ok=lowerCamelCase__    )
				print(F'Saving model {yolos_name} to {pytorch_dump_folder_path}'    )
				model.save_pretrained(lowerCamelCase__    )
				print(F'Saving image processor to {pytorch_dump_folder_path}'    )
				image_processor.save_pretrained(lowerCamelCase__    )

				if push_to_hub:
								lowerCamelCase_						=			{
								    "yolos_ti": "yolos-tiny",
								    "yolos_s_200_pre": "yolos-small",
								    "yolos_s_300_pre": "yolos-small-300",
								    "yolos_s_dWr": "yolos-small-dwr",
								    "yolos_base": "yolos-base",
								}

								print("Pushing to the hub..."    )
								lowerCamelCase_						=			model_mapping[yolos_name]
								image_processor.push_to_hub(lowerCamelCase__	, organization="hustvl"    )
								model.push_to_hub(lowerCamelCase__	, organization="hustvl"    )


if __name__ == "__main__":
			__A				=argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--yolos_name''',
			    default='''yolos_s_200_pre''',
			    type=str,
			    help=(
			        '''Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\','''
			        ''' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.'''
			    ),
			)
			parser.add_argument(
			    '''--checkpoint_path''', default=None, type=str, help='''Path to the original state dict (.pth file).'''
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
			)
			parser.add_argument(
			    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
			)

			__A				=parser.parse_args()
			convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)


