




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()
UpperCAmelCase_		:				List[str]									=     logging.get_logger(__name__)




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Dict								=YolosConfig()

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

				__magic_name__	:	Any								=91
				__magic_name__	:	int								="""huggingface/label-files"""
				__magic_name__	:	int								="""coco-detection-id2label.json"""
				__magic_name__	:	Optional[int]								=json.load(open(hf_hub_download(lowerCamelCase	,     lowerCamelCase	,     repo_type="""dataset"""   )	,     """r"""   )   )
				__magic_name__	:	Optional[int]								={int(lowerCamelCase   ): v for k, v in idalabel.items()}
				__magic_name__	:	Any								=idalabel
				__magic_name__	:	Tuple								={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)
								__magic_name__	:	List[Any]								=state_dict.pop(F"blocks.{i}.attn.qkv.weight"   )
								__magic_name__	:	Tuple								=state_dict.pop(F"blocks.{i}.attn.qkv.bias"   )
								# next, add query, keys and values (in that order) to the state dict
								__magic_name__	:	List[Any]								=in_proj_weight[: config.hidden_size, :]
								__magic_name__	:	Optional[Any]								=in_proj_bias[: config.hidden_size]
								__magic_name__	:	Optional[Any]								=in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								__magic_name__	:	Any								=in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								__magic_name__	:	Any								=in_proj_weight[-config.hidden_size :, :]
								__magic_name__	:	List[Any]								=in_proj_bias[-config.hidden_size :]




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

				return name




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				for key in orig_state_dict.copy().keys():
								__magic_name__	:	List[str]								=orig_state_dict.pop(lowerCamelCase   )

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

				return orig_state_dict




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




@torch.no_grad()
def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase = False   ):
				__magic_name__	:	List[str]								=get_yolos_config(lowerCamelCase   )

				# load original state_dict
				__magic_name__	:	Union[str, Any]								=torch.load(lowerCamelCase	,     map_location="""cpu"""   )["""model"""]

				# load 🤗 model
				__magic_name__	:	int								=YolosForObjectDetection(lowerCamelCase   )
				model.eval()
				__magic_name__	:	int								=convert_state_dict(lowerCamelCase	,     lowerCamelCase   )
				model.load_state_dict(lowerCamelCase   )

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

				__magic_name__	:	Union[str, Any]								=None, None
				if yolos_name == "yolos_ti":
								__magic_name__	:	Any								=torch.tensor(
								    [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9_7_6_9, -17.7691], [-42.3281, -20.7200, -30.6294]]   )
								__magic_name__	:	str								=torch.tensor(
								    [[0.4_0_2_1, 0.0_8_3_6, 0.7_9_7_9], [0.0_1_8_4, 0.2_6_0_9, 0.0_3_6_4], [0.1_7_8_1, 0.2_0_0_4, 0.2_0_9_5]]   )
				elif yolos_name == "yolos_s_200_pre":
								__magic_name__	:	Tuple								=torch.tensor(
								    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]   )
								__magic_name__	:	List[Any]								=torch.tensor(
								    [[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]]   )
				elif yolos_name == "yolos_s_300_pre":
								__magic_name__	:	Tuple								=torch.tensor(
								    [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]   )
								__magic_name__	:	Optional[int]								=torch.tensor(
								    [[0.7_6_1_4, 0.2_3_1_6, 0.4_7_2_8], [0.7_1_6_8, 0.4_4_9_5, 0.3_8_5_5], [0.4_9_9_6, 0.1_4_6_6, 0.9_9_9_6]]   )
				elif yolos_name == "yolos_s_dWr":
								__magic_name__	:	Union[str, Any]								=torch.tensor(
								    [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]   )
								__magic_name__	:	Optional[int]								=torch.tensor(
								    [[0.5_5_8_7, 0.2_7_7_3, 0.0_6_0_5], [0.5_0_0_4, 0.3_0_1_4, 0.9_9_9_4], [0.4_9_9_9, 0.1_5_4_8, 0.9_9_9_4]]   )
				elif yolos_name == "yolos_base":
								__magic_name__	:	List[str]								=torch.tensor(
								    [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]   )
								__magic_name__	:	List[str]								=torch.tensor(
								    [[0.5_5_5_5, 0.2_7_9_4, 0.0_6_5_5], [0.9_0_4_9, 0.2_6_6_4, 0.1_8_9_4], [0.9_1_8_3, 0.1_9_8_4, 0.1_6_3_5]]   )
				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:
								__magic_name__	:	str								={
								    """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..."""   )
								__magic_name__	:	Any								=model_mapping[yolos_name]
								image_processor.push_to_hub(lowerCamelCase	,     organization="""hustvl"""   )
								model.push_to_hub(lowerCamelCase	,     organization="""hustvl"""   )


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

			UpperCAmelCase_		:				List[Any]									=     parser.parse_args()
			convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)










import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase = None	,     ):
				__magic_name__	:	Optional[int]								={}

				if train_file is not None:
								__magic_name__	:	Optional[int]								=[train_file]
				if eval_file is not None:
								__magic_name__	:	Any								=[eval_file]
				if test_file is not None:
								__magic_name__	:	int								=[test_file]

				__magic_name__	:	Any								=datasets.load_dataset("""csv"""	,     data_files=lowerCamelCase   )
				__magic_name__	:	Optional[Any]								=list(ds[list(files.keys()   )[0]].features.keys()   )
				__magic_name__	:	Optional[Any]								=features_name.pop(lowerCamelCase   )
				__magic_name__	:	str								=list(set(ds[list(files.keys()   )[0]][label_name]   )   )
				__magic_name__	:	Union[str, Any]								={label: i for i, label in enumerate(lowerCamelCase   )}
				__magic_name__	:	Dict								=tokenizer.model_input_names
				__magic_name__	:	Any								={}

				if len(lowerCamelCase   ) == 1:
								for k in files.keys():
												__magic_name__	:	Dict								=ds[k].map(
												    lambda lowerCamelCase   : tokenizer.batch_encode_plus(
												        example[features_name[0]]	,     truncation=lowerCamelCase	,     max_length=lowerCamelCase	,     padding="""max_length"""   )	,     batched=lowerCamelCase	,     )
				elif len(lowerCamelCase   ) == 2:
								for k in files.keys():
												__magic_name__	:	Optional[Any]								=ds[k].map(
												    lambda lowerCamelCase   : tokenizer.batch_encode_plus(
												        (example[features_name[0]], example[features_name[1]])	,     truncation=lowerCamelCase	,     max_length=lowerCamelCase	,     padding="""max_length"""	,     )	,     batched=lowerCamelCase	,     )

				def gen_train():
								for ex in transformed_ds[datasets.Split.TRAIN]:
												__magic_name__	:	Any								={k: v for k, v in ex.items() if k in input_names}
												__magic_name__	:	Any								=labelaid[ex[label_name]]
												yield (d, label)

				def gen_val():
								for ex in transformed_ds[datasets.Split.VALIDATION]:
												__magic_name__	:	Dict								={k: v for k, v in ex.items() if k in input_names}
												__magic_name__	:	str								=labelaid[ex[label_name]]
												yield (d, label)

				def gen_test():
								for ex in transformed_ds[datasets.Split.TEST]:
												__magic_name__	:	Union[str, Any]								={k: v for k, v in ex.items() if k in input_names}
												__magic_name__	:	Optional[int]								=labelaid[ex[label_name]]
												yield (d, label)

				__magic_name__	:	Union[str, Any]								=(
				    tf.data.Dataset.from_generator(
				        lowerCamelCase	,     ({k: tf.intaa for k in input_names}, tf.intaa)	,     ({k: tf.TensorShape([None]   ) for k in input_names}, tf.TensorShape([]   ))	,     )
				    if datasets.Split.TRAIN in transformed_ds
				    else None
				)

				if train_ds is not None:
								__magic_name__	:	Optional[Any]								=train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]   )   )   )

				__magic_name__	:	Optional[Any]								=(
				    tf.data.Dataset.from_generator(
				        lowerCamelCase	,     ({k: tf.intaa for k in input_names}, tf.intaa)	,     ({k: tf.TensorShape([None]   ) for k in input_names}, tf.TensorShape([]   ))	,     )
				    if datasets.Split.VALIDATION in transformed_ds
				    else None
				)

				if val_ds is not None:
								__magic_name__	:	Any								=val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]   )   )   )

				__magic_name__	:	Any								=(
				    tf.data.Dataset.from_generator(
				        lowerCamelCase	,     ({k: tf.intaa for k in input_names}, tf.intaa)	,     ({k: tf.TensorShape([None]   ) for k in input_names}, tf.TensorShape([]   ))	,     )
				    if datasets.Split.TEST in transformed_ds
				    else None
				)

				if test_ds is not None:
								__magic_name__	:	Optional[int]								=test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]   )   )   )

				return train_ds, val_ds, test_ds, labelaid


UpperCAmelCase_		:				int									=     logging.getLogger(__name__)




@dataclass
class 		__A      :
						UpperCamelCase     						=					field(metadata={"""help""": """Which column contains the label"""}			)
						UpperCamelCase     						=					field(default=UpperCamelCase__					,						metadata={"""help""": """The path of the training file"""}			)
						UpperCamelCase     						=					field(default=UpperCamelCase__					,						metadata={"""help""": """The path of the development file"""}			)
						UpperCamelCase     						=					field(default=UpperCamelCase__					,						metadata={"""help""": """The path of the test file"""}			)
						UpperCamelCase     						=					field(
						    default=128					,						metadata={
						        """help""": (
						            """The maximum total input sequence length after tokenization. Sequences longer """
						            """than this will be truncated, sequences shorter will be padded."""
						        )
						    }					,						)
						UpperCamelCase     						=					field(
						    default=UpperCamelCase__					,						metadata={"""help""": """Overwrite the cached training and evaluation sets"""}			)




@dataclass
class 		__A      :
						UpperCamelCase     						=					field(
						    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}			)
						UpperCamelCase     						=					field(
						    default=UpperCamelCase__					,						metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}			)
						UpperCamelCase     						=					field(
						    default=UpperCamelCase__					,						metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}			)
						UpperCamelCase     						=					field(default=UpperCamelCase__					,						metadata={"""help""": """Set this flag to use fast tokenization."""}			)
						# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
						# or just modify its tokenizer_config.json.
						UpperCamelCase     						=					field(
						    default=UpperCamelCase__					,						metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}					,						)




def 		lowerCAmelCase_   (	):
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.
				__magic_name__	:	List[Any]								=HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)   )
				__magic_name__				, __magic_name__				, __magic_name__	:	Union[str, Any]								=parser.parse_args_into_dataclasses()

				if (
				    os.path.exists(training_args.output_dir   )
				    and os.listdir(training_args.output_dir   )
				    and training_args.do_train
				    and not training_args.overwrite_output_dir
				):
								raise ValueError(
								    F"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
								    """ --overwrite_output_dir to overcome."""   )

				# Setup logging
				logging.basicConfig(
				    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""	,     datefmt="""%m/%d/%Y %H:%M:%S"""	,     level=logging.INFO	,     )
				logger.info(
				    F"n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1   )}, "
				    F"16-bits training: {training_args.fpaa}"   )
				logger.info(F"Training/evaluation parameters {training_args}"   )

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				__magic_name__	:	Dict								=AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path	,     cache_dir=model_args.cache_dir	,     )

				__magic_name__				, __magic_name__				, __magic_name__				, __magic_name__	:	Optional[Any]								=get_tfds(
				    train_file=data_args.train_file	,     eval_file=data_args.dev_file	,     test_file=data_args.test_file	,     tokenizer=lowerCamelCase	,     label_column_id=data_args.label_column_id	,     max_seq_length=data_args.max_seq_length	,     )

				__magic_name__	:	Any								=AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path	,     num_labels=len(lowerCamelCase   )	,     labelaid=lowerCamelCase	,     idalabel={id: label for label, id in labelaid.items()}	,     finetuning_task="""text-classification"""	,     cache_dir=model_args.cache_dir	,     )

				with training_args.strategy.scope():
								__magic_name__	:	Any								=TFAutoModelForSequenceClassification.from_pretrained(
								    model_args.model_name_or_path	,     from_pt=bool(""".bin""" in model_args.model_name_or_path   )	,     config=lowerCamelCase	,     cache_dir=model_args.cache_dir	,     )

				def compute_metrics(lowerCamelCase   ) -> Dict:
								__magic_name__	:	Tuple								=np.argmax(p.predictions	,     axis=1   )

								return {"acc": (preds == p.label_ids).mean()}

				# Initialize our Trainer
				__magic_name__	:	int								=TFTrainer(
				    model=lowerCamelCase	,     args=lowerCamelCase	,     train_dataset=lowerCamelCase	,     eval_dataset=lowerCamelCase	,     compute_metrics=lowerCamelCase	,     )

				# Training
				if training_args.do_train:
								trainer.train()
								trainer.save_model()
								tokenizer.save_pretrained(training_args.output_dir   )

				# Evaluation
				__magic_name__	:	List[str]								={}
				if training_args.do_eval:
								logger.info("""*** Evaluate ***"""   )
								__magic_name__	:	List[str]								=trainer.evaluate()
								__magic_name__	:	Optional[Any]								=os.path.join(training_args.output_dir	,     """eval_results.txt"""   )

								with open(lowerCamelCase	,     """w"""   ) as writer:
												logger.info("""***** Eval results *****"""   )

												for key, value in result.items():
																logger.info(F"  {key} = {value}"   )
																writer.write(F"{key} = {value}\n"   )

												results.update(lowerCamelCase   )

				return results


if __name__ == "__main__":
							main()




