

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()
lowercase_       		=  logging.get_logger(__name__)
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]      )							->      YolosConfig:





    '''simple docstring'''






    A__          =   YolosConfig()

    # size of the architecture
    if "yolos_ti" in yolos_name:
        A__          =   192
        A__          =   768
        A__          =   12
        A__          =   3
        A__          =   [800, 1333]
        A__          =   False
    elif yolos_name == "yolos_s_dWr":
        A__          =   330
        A__          =   14
        A__          =   6
        A__          =   1320
    elif "yolos_s" in yolos_name:
        A__          =   384
        A__          =   1536
        A__          =   12
        A__          =   6
    elif "yolos_b" in yolos_name:
        A__          =   [800, 1344]

    A__          =   91
    A__          =   'huggingface/label-files'
    A__          =   'coco-detection-id2label.json'
    A__          =   json.load(open(hf_hub_download(snake_case__    ,			snake_case__    ,			repo_type='dataset'      )    ,			'r'      )      )
    A__          =   {int(snake_case__      ): v for k, v in idalabel.items()}
    A__          =   idalabel
    A__          =   {v: k for k, v in idalabel.items()}

    return config
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	Optional[Any] = False      )							->      int:





    '''simple docstring'''






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





    '''simple docstring'''






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

    return name
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Tuple      )							->      dict:





    '''simple docstring'''






    for key in orig_state_dict.copy().keys():
        A__          =   orig_state_dict.pop(snake_case__      )

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

    return orig_state_dict
def 						_snake_case(    )							->      torch.Tensor:





    '''simple docstring'''






    A__          =   'http://images.cocodataset.org/val2017/000000039769.jpg'
    A__          =   Image.open(requests.get(snake_case__    ,			stream=snake_case__      ).raw      )
    return im
@torch.no_grad()
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any] = False      )							->      Optional[int]:





    '''simple docstring'''






    A__          =   get_yolos_config(snake_case__      )

    # load original state_dict
    A__          =   torch.load(snake_case__    ,			map_location='cpu'      )['model']

    # load 🤗 model
    A__          =   YolosForObjectDetection(snake_case__      )
    model.eval()
    A__          =   convert_state_dict(snake_case__    ,			snake_case__      )
    model.load_state_dict(snake_case__      )

    # Check outputs on an image, prepared by YolosImageProcessor
    A__          =   800 if yolos_name != 'yolos_ti' else 512
    A__          =   YolosImageProcessor(format='coco_detection'    ,			size=snake_case__      )
    A__          =   image_processor(images=prepare_img()    ,			return_tensors='pt'      )
    A__          =   model(**snake_case__      )
    A__      ,						A__          =   outputs.logits, outputs.pred_boxes

    A__      ,						A__          =   None, None
    if yolos_name == "yolos_ti":
        A__          =   torch.tensor(
            [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]      )
        A__          =   torch.tensor(
            [[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]]      )
    elif yolos_name == "yolos_s_200_pre":
        A__          =   torch.tensor(
            [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]      )
        A__          =   torch.tensor(
            [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]      )
    elif yolos_name == "yolos_s_300_pre":
        A__          =   torch.tensor(
            [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]      )
        A__          =   torch.tensor(
            [[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]]      )
    elif yolos_name == "yolos_s_dWr":
        A__          =   torch.tensor(
            [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]      )
        A__          =   torch.tensor(
            [[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]]      )
    elif yolos_name == "yolos_base":
        A__          =   torch.tensor(
            [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]      )
        A__          =   torch.tensor(
            [[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]]      )
    else:
        raise ValueError(f'Unknown yolos_name: {yolos_name}'      )

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

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

    if push_to_hub:
        A__          =   {
            '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...'      )
        A__          =   model_mapping[yolos_name]
        image_processor.push_to_hub(snake_case__    ,			organization='hustvl'      )
        model.push_to_hub(snake_case__    ,			organization='hustvl'      )


if __name__ == "__main__":
     lowercase_       		=  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."
     )

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







"""simple docstring"""




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

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


if is_sentencepiece_available():
					from .tokenization_xlnet import XLNetTokenizer
else:
					lowerCAmelCase							:			int     							=							None


lowerCAmelCase							:			Tuple     							=							logging.get_logger(__name__)

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

lowerCAmelCase							:			Union[str, Any]     							=							{
    """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""",
    },
    """tokenizer_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json""",
    },
}

lowerCAmelCase							:			Optional[int]     							=							{
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

lowerCAmelCase							:			Union[str, Any]     							=							"""▁"""

# Segments (not really needed)
lowerCAmelCase							:			str     							=							0
lowerCAmelCase							:			Optional[int]     							=							1
lowerCAmelCase							:			Tuple     							=							2
lowerCAmelCase							:			Optional[Any]     							=							3
lowerCAmelCase							:			List[Any]     							=							4






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''
				__UpperCamelCase			  =						VOCAB_FILES_NAMES
				__UpperCamelCase			  =						PRETRAINED_VOCAB_FILES_MAP
				__UpperCamelCase			  =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__UpperCamelCase			  =						"left"
				__UpperCamelCase			  =						XLNetTokenizer





				def __init__(       self		, _a=None		, _a=None		, _a=False		, _a=True		, _a=False		, _a="<s>"		, _a="</s>"		, _a="<unk>"		, _a="<sep>"		, _a="<pad>"		, _a="<cls>"		, _a="<mask>"		, _a=["<eop>", "<eod>"]		, **_a		, ):





							"""simple docstring"""
							# Mask token behave like a normal word, i.e. include the space before it
							lowerCamelCase			      =					AddedToken(_a		, lstrip=_a		, rstrip=_a ) if isinstance(_a		, _a ) else mask_token

							super().__init__(
							    vocab_file=_a		, tokenizer_file=_a		, do_lower_case=_a		, remove_space=_a		, keep_accents=_a		, bos_token=_a		, eos_token=_a		, unk_token=_a		, sep_token=_a		, pad_token=_a		, cls_token=_a		, mask_token=_a		, additional_special_tokens=_a		, **_a		, )

							lowerCamelCase			      =					3
							lowerCamelCase			      =					do_lower_case
							lowerCamelCase			      =					remove_space
							lowerCamelCase			      =					keep_accents
							lowerCamelCase			      =					vocab_file
							lowerCamelCase			      =					False if not self.vocab_file else True





				def    _lowerCAmelCase				(       self		, _a		, _a = None ):





							"""simple docstring"""
							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    _lowerCAmelCase				(       self		, _a		, _a = None ):





							"""simple docstring"""
							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    _lowerCAmelCase				(       self		, _a		, _a = 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
							lowerCamelCase			      =					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,)



