"""simple docstring"""



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		:		Optional[Any]														=							logging.get_logger(__name__)





def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							YolosConfig()

     # size of the architecture
     if "yolos_ti" in yolos_name:
          __snake_case				=							1_92
          __snake_case				=							7_68
          __snake_case				=							12
          __snake_case				=							3
          __snake_case				=							[8_00, 13_33]
          __snake_case				=							False
     elif yolos_name == "yolos_s_dWr":
          __snake_case				=							3_30
          __snake_case				=							14
          __snake_case				=							6
          __snake_case				=							13_20
     elif "yolos_s" in yolos_name:
          __snake_case				=							3_84
          __snake_case				=							15_36
          __snake_case				=							12
          __snake_case				=							6
     elif "yolos_b" in yolos_name:
          __snake_case				=							[8_00, 13_44]

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

     return config





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case = 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)
          __snake_case				=							state_dict.pop(f"blocks.{i}.attn.qkv.weight")
          __snake_case				=							state_dict.pop(f"blocks.{i}.attn.qkv.bias")
          # next, add query, keys and values (in that order) to the state dict
          __snake_case				=							in_proj_weight[: config.hidden_size, :]
          __snake_case				=							in_proj_bias[: config.hidden_size]
          __snake_case				=							in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          __snake_case				=							in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          __snake_case				=							in_proj_weight[-config.hidden_size :, :]
          __snake_case				=							in_proj_bias[-config.hidden_size :]





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

     return name





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
     for key in orig_state_dict.copy().keys():
          __snake_case				=							orig_state_dict.pop(snake_case)

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

     return orig_state_dict





def    SCREAMING_SNAKE_CASE       (     ):
     __snake_case				=							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     __snake_case				=							Image.open(requests.get(snake_case,      stream=snake_case).raw)
     return im





@torch.no_grad()
def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case,      snake_case = False):
     __snake_case				=							get_yolos_config(snake_case)

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

     # load 🤗 model
     __snake_case				=							YolosForObjectDetection(snake_case)
     model.eval()
     __snake_case				=							convert_state_dict(snake_case,      snake_case)
     model.load_state_dict(snake_case)

     # Check outputs on an image, prepared by YolosImageProcessor
     __snake_case				=							8_00 if yolos_name != '''yolos_ti''' else 5_12
     __snake_case				=							YolosImageProcessor(format='''coco_detection''',      size=snake_case)
     __snake_case				=							image_processor(images=prepare_img(),      return_tensors='''pt''')
     __snake_case				=							model(**snake_case)
     __snake_case		,	__snake_case				=							outputs.logits, outputs.pred_boxes

     __snake_case		,	__snake_case				=							None, None
     if yolos_name == "yolos_ti":
          __snake_case				=							torch.tensor(
              [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]])
          __snake_case				=							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":
          __snake_case				=							torch.tensor(
              [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]])
          __snake_case				=							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":
          __snake_case				=							torch.tensor(
              [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]])
          __snake_case				=							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":
          __snake_case				=							torch.tensor(
              [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]])
          __snake_case				=							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":
          __snake_case				=							torch.tensor(
              [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]])
          __snake_case				=							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:
          __snake_case				=							{
              '''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...''')
          __snake_case				=							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		:		int														=							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		:		Any														=							parser.parse_args()
       convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
"""simple docstring"""



import re





def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							re.compile(R'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''')
     if match := re.search(snake_case,      snake_case):
          return match.string == phone
     return False


if __name__ == "__main__":
       print(indian_phone_validator("+918827897895"))