


"""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()
__SCREAMING_SNAKE_CASE							:    Optional[int]		=    logging.get_logger(__name__)







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				YolosConfig:
							snake_case_           =		YolosConfig()

							# size of the architecture
							if "yolos_ti" in yolos_name:
														snake_case_           =		192
														snake_case_           =		768
														snake_case_           =		12
														snake_case_           =		3
														snake_case_           =		[800, 1_333]
														snake_case_           =		False
							elif yolos_name == "yolos_s_dWr":
														snake_case_           =		330
														snake_case_           =		14
														snake_case_           =		6
														snake_case_           =		1_320
							elif "yolos_s" in yolos_name:
														snake_case_           =		384
														snake_case_           =		1_536
														snake_case_           =		12
														snake_case_           =		6
							elif "yolos_b" in yolos_name:
														snake_case_           =		[800, 1_344]

							snake_case_           =		91
							snake_case_           =		"""huggingface/label-files"""
							snake_case_           =		"""coco-detection-id2label.json"""
							snake_case_           =		json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, repo_type="""dataset"""				)				, """r"""				)				)
							snake_case_           =		{int(_SCREAMING_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 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE = False				)    ->				Union[str, Any]:
							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 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				str:
							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 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				dict:
							for key in orig_state_dict.copy().keys():
														snake_case_           =		orig_state_dict.pop(_SCREAMING_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 						_a				(      )    ->				torch.Tensor:
							snake_case_           =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							snake_case_           =		Image.open(requests.get(_SCREAMING_SNAKE_CASE				, stream=_SCREAMING_SNAKE_CASE				).raw				)
							return im







@torch.no_grad()
def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE = False				)    ->				Any:
							snake_case_           =		get_yolos_config(_SCREAMING_SNAKE_CASE				)

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

							# load 🤗 model
							snake_case_           =		YolosForObjectDetection(_SCREAMING_SNAKE_CASE				)
							model.eval()
							snake_case_           =		convert_state_dict(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)
							model.load_state_dict(_SCREAMING_SNAKE_CASE				)

							# Check outputs on an image, prepared by YolosImageProcessor
							snake_case_           =		800 if yolos_name != """yolos_ti""" else 512
							snake_case_           =		YolosImageProcessor(format="""coco_detection"""				, size=_SCREAMING_SNAKE_CASE				)
							snake_case_           =		image_processor(images=prepare_img()				, return_tensors="""pt"""				)
							snake_case_           =		model(**_SCREAMING_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]				, _SCREAMING_SNAKE_CASE				, atol=1E-4				)
							assert torch.allclose(pred_boxes[0, :3, :3]				, _SCREAMING_SNAKE_CASE				, atol=1E-4				)

							Path(_SCREAMING_SNAKE_CASE				).mkdir(exist_ok=_SCREAMING_SNAKE_CASE				)
							print(f"""Saving model {yolos_name} to {pytorch_dump_folder_path}"""				)
							model.save_pretrained(_SCREAMING_SNAKE_CASE				)
							print(f"""Saving image processor to {pytorch_dump_folder_path}"""				)
							image_processor.save_pretrained(_SCREAMING_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(_SCREAMING_SNAKE_CASE				, organization="""hustvl"""				)
														model.push_to_hub(_SCREAMING_SNAKE_CASE				, organization="""hustvl"""				)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:    Tuple		=    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.'
				)

				__SCREAMING_SNAKE_CASE							:    List[Any]		=    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 ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE							:    Optional[Any]		=    {
    'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE							:    Tuple		=    [
				    'MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MegatronBertForCausalLM',
				    'MegatronBertForMaskedLM',
				    'MegatronBertForMultipleChoice',
				    'MegatronBertForNextSentencePrediction',
				    'MegatronBertForPreTraining',
				    'MegatronBertForQuestionAnswering',
				    'MegatronBertForSequenceClassification',
				    'MegatronBertForTokenClassification',
				    'MegatronBertModel',
				    'MegatronBertPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_megatron_bert import (
								    MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MegatronBertForCausalLM,
								    MegatronBertForMaskedLM,
								    MegatronBertForMultipleChoice,
								    MegatronBertForNextSentencePrediction,
								    MegatronBertForPreTraining,
								    MegatronBertForQuestionAnswering,
								    MegatronBertForSequenceClassification,
								    MegatronBertForTokenClassification,
								    MegatronBertModel,
								    MegatronBertPreTrainedModel,
								)

else:
				import sys

				__SCREAMING_SNAKE_CASE							:    int		=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



