





"""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()
a_			=      logging.get_logger(__name__)







def 		__lowercase					(       snake_case_  :  str						)       ->YolosConfig:



				'''simple docstring'''







				__A   :						Dict									=					YolosConfig()

				# size of the architecture
				if "yolos_ti" in yolos_name:
								__A   :						str									=					192
								__A   :						int									=					768
								__A   :						Optional[int]									=					12
								__A   :						Tuple									=					3
								__A   :						Optional[int]									=					[800, 1333]
								__A   :						Any									=					False
				elif yolos_name == "yolos_s_dWr":
								__A   :						Optional[Any]									=					330
								__A   :						List[Any]									=					14
								__A   :						Union[str, Any]									=					6
								__A   :						List[Any]									=					1320
				elif "yolos_s" in yolos_name:
								__A   :						int									=					384
								__A   :						Dict									=					1536
								__A   :						Any									=					12
								__A   :						Union[str, Any]									=					6
				elif "yolos_b" in yolos_name:
								__A   :						str									=					[800, 1344]

				__A   :						List[str]									=					91
				__A   :						Any									=					"huggingface/label-files"
				__A   :						Optional[int]									=					"coco-detection-id2label.json"
				__A   :						Tuple									=					json.load(open(hf_hub_download(_lowerCamelCase    ,_lowerCamelCase    ,repo_type='''dataset'''						)    ,'''r'''						)						)
				__A   :						Dict									=					{int(_lowerCamelCase						): v for k, v in idalabel.items()}
				__A   :						Any									=					idalabel
				__A   :						List[str]									=					{v: k for k, v in idalabel.items()}

				return config







def 		__lowercase					(       snake_case_  :  dict    ,snake_case_  :  YolosConfig    ,snake_case_  :  bool = 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   :						List[str]									=					state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""						)
								__A   :						Union[str, Any]									=					state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""						)
								# next, add query, keys and values (in that order) to the state dict
								__A   :						List[str]									=					in_proj_weight[: config.hidden_size, :]
								__A   :						str									=					in_proj_bias[: config.hidden_size]
								__A   :						str									=					in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								__A   :						Any									=					in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								__A   :						Optional[int]									=					in_proj_weight[-config.hidden_size :, :]
								__A   :						int									=					in_proj_bias[-config.hidden_size :]







def 		__lowercase					(       snake_case_  :  str						)       ->str:



				'''simple docstring'''







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

				return name







def 		__lowercase					(       snake_case_  :  dict    ,snake_case_  :  YolosForObjectDetection						)       ->dict:



				'''simple docstring'''







				for key in orig_state_dict.copy().keys():
								__A   :						List[str]									=					orig_state_dict.pop(_lowerCamelCase						)

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

				return orig_state_dict







def 		__lowercase					(       )       ->torch.Tensor:



				'''simple docstring'''







				__A   :						str									=					"http://images.cocodataset.org/val2017/000000039769.jpg"
				__A   :						Optional[Any]									=					Image.open(requests.get(_lowerCamelCase    ,stream=_lowerCamelCase						).raw						)
				return im







@torch.no_grad()
def 		__lowercase					(       snake_case_  :  str    ,snake_case_  :  str    ,snake_case_  :  str    ,snake_case_  :  bool = False						)       ->Any:



				'''simple docstring'''







				__A   :						Optional[int]									=					get_yolos_config(_lowerCamelCase						)

				# load original state_dict
				__A   :						Any									=					torch.load(_lowerCamelCase    ,map_location='''cpu'''						)["model"]

				# load 🤗 model
				__A   :						List[Any]									=					YolosForObjectDetection(_lowerCamelCase						)
				model.eval()
				__A   :						Optional[int]									=					convert_state_dict(_lowerCamelCase    ,_lowerCamelCase						)
				model.load_state_dict(_lowerCamelCase						)

				# Check outputs on an image, prepared by YolosImageProcessor
				__A   :						List[str]									=					800 if yolos_name != "yolos_ti" else 512
				__A   :						Optional[int]									=					YolosImageProcessor(format='''coco_detection'''    ,size=_lowerCamelCase						)
				__A   :						Any									=					image_processor(images=prepare_img()    ,return_tensors='''pt'''						)
				__A   :						Union[str, Any]									=					model(**_lowerCamelCase						)
				__A   :						List[Any]									=					outputs.logits, outputs.pred_boxes

				__A   :						Optional[int]									=					None, None
				if yolos_name == "yolos_ti":
								__A   :						Dict									=					torch.tensor(
								    [[-39.5_022, -11.9_820, -17.6_888], [-29.9_574, -9.9_769, -17.7_691], [-42.3_281, -20.7_200, -30.6_294]]						)
								__A   :						str									=					torch.tensor(
								    [[0.4_021, 0.0_836, 0.7_979], [0.0_184, 0.2_609, 0.0_364], [0.1_781, 0.2_004, 0.2_095]]						)
				elif yolos_name == "yolos_s_200_pre":
								__A   :						Optional[int]									=					torch.tensor(
								    [[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]]						)
								__A   :						Union[str, Any]									=					torch.tensor(
								    [[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]]						)
				elif yolos_name == "yolos_s_300_pre":
								__A   :						str									=					torch.tensor(
								    [[-36.2_220, -14.4_385, -23.5_457], [-35.6_970, -14.7_583, -21.3_935], [-31.5_939, -13.6_042, -16.8_049]]						)
								__A   :						Union[str, Any]									=					torch.tensor(
								    [[0.7_614, 0.2_316, 0.4_728], [0.7_168, 0.4_495, 0.3_855], [0.4_996, 0.1_466, 0.9_996]]						)
				elif yolos_name == "yolos_s_dWr":
								__A   :						int									=					torch.tensor(
								    [[-42.8_668, -24.1_049, -41.1_690], [-34.7_456, -14.1_274, -24.9_194], [-33.7_898, -12.1_946, -25.6_495]]						)
								__A   :						int									=					torch.tensor(
								    [[0.5_587, 0.2_773, 0.0_605], [0.5_004, 0.3_014, 0.9_994], [0.4_999, 0.1_548, 0.9_994]]						)
				elif yolos_name == "yolos_base":
								__A   :						Any									=					torch.tensor(
								    [[-40.6_064, -24.3_084, -32.6_447], [-55.1_990, -30.7_719, -35.5_877], [-51.4_311, -33.3_507, -35.6_462]]						)
								__A   :						int									=					torch.tensor(
								    [[0.5_555, 0.2_794, 0.0_655], [0.9_049, 0.2_664, 0.1_894], [0.9_183, 0.1_984, 0.1_635]]						)
				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:
								__A   :						List[Any]									=					{
								    "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   :						Tuple									=					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)











"""simple docstring"""



from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


a_			=      {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      ["""GPTNeoXTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """GPTNeoXForCausalLM""",
           """GPTNeoXForQuestionAnswering""",
           """GPTNeoXForSequenceClassification""",
           """GPTNeoXForTokenClassification""",
           """GPTNeoXLayer""",
           """GPTNeoXModel""",
           """GPTNeoXPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gpt_neox import (
                  GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTNeoXForCausalLM,
                  GPTNeoXForQuestionAnswering,
                  GPTNeoXForSequenceClassification,
                  GPTNeoXForTokenClassification,
                  GPTNeoXLayer,
                  GPTNeoXModel,
                  GPTNeoXPreTrainedModel,
              )


else:
       import sys

       a_			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




