




import enum
import warnings

from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
						import tensorflow as tf


class  A		(  enum.Enum     ):
		__snake_case									=   0
		__snake_case									=   1
		__snake_case									=   2







@add_end_docstrings(__UpperCAmelCase     )
class  A		(  __UpperCAmelCase     ):
		__snake_case									=   '\n    In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The\n    voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western\n    Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision\n    and denounces one of the men as a horse thief. Although his father initially slaps him for making such an\n    accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of\n    the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,\n    begging for his blessing. <eod> </s> <eos>\n    '



		def __init__(     self,	*UpperCamelCase__,	**UpperCamelCase__   ):







								"""simple docstring"""



								super().__init__(*UpperCamelCase__,	**UpperCamelCase__   )
								self.check_model_type(
								    TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == '''tf''' else MODEL_FOR_CAUSAL_LM_MAPPING   )
								if "prefix" not in self._preprocess_params:
														# This is very specific. The logic is quite complex and needs to be done
														# as a "default".
														# It also defines both some preprocess_kwargs and generate_kwargs
														# which is why we cannot put them in their respective methods.
														lowerCAmelCase_      			=  None
														if self.model.config.prefix is not None:
																				lowerCAmelCase_      			=  self.model.config.prefix
														if prefix is None and self.model.__class__.__name__ in [
														    "XLNetLMHeadModel",
														    "TransfoXLLMHeadModel",
														    "TFXLNetLMHeadModel",
														    "TFTransfoXLLMHeadModel",
														]:
																				# For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
																				lowerCAmelCase_      			=  self.XL_PREFIX
														if prefix is not None:
																				# Recalculate some generate_kwargs linked to prefix.
																				lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_      			=  self._sanitize_parameters(prefix=UpperCamelCase__,	**self._forward_params   )
																				lowerCAmelCase_      			=  {**self._preprocess_params, **preprocess_params}
																				lowerCAmelCase_      			=  {**self._forward_params, **forward_params}



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__=None,	UpperCamelCase__=None,	UpperCamelCase__=None,	UpperCamelCase__=None,	UpperCamelCase__=None,	UpperCamelCase__=None,	UpperCamelCase__=None,	UpperCamelCase__=None,	**UpperCamelCase__,	):







								"""simple docstring"""



								lowerCAmelCase_      			=  {}
								if prefix is not None:
														lowerCAmelCase_      			=  prefix
								if prefix:
														lowerCAmelCase_      			=  self.tokenizer(
														    UpperCamelCase__,	padding=UpperCamelCase__,	add_special_tokens=UpperCamelCase__,	return_tensors=self.framework   )
														lowerCAmelCase_      			=  prefix_inputs['''input_ids'''].shape[-1]

								if handle_long_generation is not None:
														if handle_long_generation not in {"hole"}:
																				raise ValueError(
																				    f"{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected"
																				    ''' [None, \'hole\']'''   )
														lowerCAmelCase_      			=  handle_long_generation

								preprocess_params.update(UpperCamelCase__   )
								lowerCAmelCase_      			=  generate_kwargs

								lowerCAmelCase_      			=  {}
								if return_full_text is not None and return_type is None:
														if return_text is not None:
																				raise ValueError('''`return_text` is mutually exclusive with `return_full_text`'''   )
														if return_tensors is not None:
																				raise ValueError('''`return_full_text` is mutually exclusive with `return_tensors`'''   )
														lowerCAmelCase_      			=  ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
								if return_tensors is not None and return_type is None:
														if return_text is not None:
																				raise ValueError('''`return_text` is mutually exclusive with `return_tensors`'''   )
														lowerCAmelCase_      			=  ReturnType.TENSORS
								if return_type is not None:
														lowerCAmelCase_      			=  return_type
								if clean_up_tokenization_spaces is not None:
														lowerCAmelCase_      			=  clean_up_tokenization_spaces

								if stop_sequence is not None:
														lowerCAmelCase_      			=  self.tokenizer.encode(UpperCamelCase__,	add_special_tokens=UpperCamelCase__   )
														if len(UpperCamelCase__   ) > 1:
																				warnings.warn(
																				    '''Stopping on a multiple token sequence is not yet supported on transformers. The first token of'''
																				    ''' the stop sequence will be used as the stop sequence string in the interim.'''   )
														lowerCAmelCase_      			=  stop_sequence_ids[0]

								return preprocess_params, forward_params, postprocess_params



		def        SCREAMING_SNAKE_CASE__			(     self,	*UpperCamelCase__,	**UpperCamelCase__   ):







								"""simple docstring"""



								if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
														kwargs.update({'''add_space_before_punct_symbol''': True}   )

								return super()._parse_and_tokenize(*UpperCamelCase__,	**UpperCamelCase__   )



		def __call__(     self,	UpperCamelCase__,	**UpperCamelCase__   ):







								"""simple docstring"""



								return super().__call__(UpperCamelCase__,	**UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__="",	UpperCamelCase__=None,	**UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.tokenizer(
								    prefix + prompt_text,	padding=UpperCamelCase__,	add_special_tokens=UpperCamelCase__,	return_tensors=self.framework   )
								lowerCAmelCase_      			=  prompt_text

								if handle_long_generation == "hole":
														lowerCAmelCase_      			=  inputs['''input_ids'''].shape[-1]
														if "max_new_tokens" in generate_kwargs:
																				lowerCAmelCase_      			=  generate_kwargs['''max_new_tokens''']
														else:
																				lowerCAmelCase_      			=  generate_kwargs.get('''max_length''',	self.model.config.max_length   ) - cur_len
																				if new_tokens < 0:
																										raise ValueError('''We cannot infer how many new tokens are expected'''   )
														if cur_len + new_tokens > self.tokenizer.model_max_length:
																				lowerCAmelCase_      			=  self.tokenizer.model_max_length - new_tokens
																				if keep_length <= 0:
																										raise ValueError(
																										    '''We cannot use `hole` to handle this generation the number of desired tokens exceeds the'''
																										    ''' models max length'''   )

																				lowerCAmelCase_      			=  inputs['''input_ids'''][:, -keep_length:]
																				if "attention_mask" in inputs:
																										lowerCAmelCase_      			=  inputs['''attention_mask'''][:, -keep_length:]

								return inputs



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	**UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  model_inputs['''input_ids''']
								lowerCAmelCase_      			=  model_inputs.get('''attention_mask''',	UpperCamelCase__   )
								# Allow empty prompts
								if input_ids.shape[1] == 0:
														lowerCAmelCase_      			=  None
														lowerCAmelCase_      			=  None
														lowerCAmelCase_      			=  1
								else:
														lowerCAmelCase_      			=  input_ids.shape[0]
								lowerCAmelCase_      			=  model_inputs.pop('''prompt_text'''   )

								# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
								# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
								lowerCAmelCase_      			=  generate_kwargs.pop('''prefix_length''',	0   )
								if prefix_length > 0:
														lowerCAmelCase_      			=  '''max_new_tokens''' in generate_kwargs or (
														    '''generation_config''' in generate_kwargs
														    and generate_kwargs['''generation_config'''].max_new_tokens is not None
														)
														if not has_max_new_tokens:
																				lowerCAmelCase_      			=  generate_kwargs.get('''max_length'''   ) or self.model.config.max_length
																				generate_kwargs["max_length"] += prefix_length
														lowerCAmelCase_      			=  '''min_new_tokens''' in generate_kwargs or (
														    '''generation_config''' in generate_kwargs
														    and generate_kwargs['''generation_config'''].min_new_tokens is not None
														)
														if not has_min_new_tokens and "min_length" in generate_kwargs:
																				generate_kwargs["min_length"] += prefix_length

        # BS x SL
								lowerCAmelCase_      			=  self.model.generate(input_ids=UpperCamelCase__,	attention_mask=UpperCamelCase__,	**UpperCamelCase__   )
								lowerCAmelCase_      			=  generated_sequence.shape[0]
								if self.framework == "pt":
														lowerCAmelCase_      			=  generated_sequence.reshape(UpperCamelCase__,	out_b // in_b,	*generated_sequence.shape[1:]   )
								elif self.framework == "tf":
														lowerCAmelCase_      			=  tf.reshape(UpperCamelCase__,	(in_b, out_b // in_b, *generated_sequence.shape[1:])   )
								return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}







		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__=ReturnType.FULL_TEXT,	UpperCamelCase__=True   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  model_outputs['''generated_sequence'''][0]
								lowerCAmelCase_      			=  model_outputs['''input_ids''']
								lowerCAmelCase_      			=  model_outputs['''prompt_text''']
								lowerCAmelCase_      			=  generated_sequence.numpy().tolist()
								lowerCAmelCase_      			=  []
								for sequence in generated_sequence:
														if return_type == ReturnType.TENSORS:
																				lowerCAmelCase_      			=  {'''generated_token_ids''': sequence}
														elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
																				# Decode text
																				lowerCAmelCase_      			=  self.tokenizer.decode(
																				    UpperCamelCase__,	skip_special_tokens=UpperCamelCase__,	clean_up_tokenization_spaces=UpperCamelCase__,	)

																				# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
																				if input_ids is None:
																										lowerCAmelCase_      			=  0
																				else:
																										lowerCAmelCase_      			=  len(
																										    self.tokenizer.decode(
																										        input_ids[0],	skip_special_tokens=UpperCamelCase__,	clean_up_tokenization_spaces=UpperCamelCase__,	)   )

																				if return_type == ReturnType.FULL_TEXT:
																										lowerCAmelCase_      			=  prompt_text + text[prompt_length:]
																				else:
																										lowerCAmelCase_      			=  text[prompt_length:]

																				lowerCAmelCase_      			=  {'''generated_text''': all_text}
														records.append(UpperCamelCase__   )

								return records










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 __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  YolosConfig()

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

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

						return config





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





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

						return name





def __UpperCamelCase			(       _A ,  _A  ):
						for key in orig_state_dict.copy().keys():
												lowerCAmelCase_      			=  orig_state_dict.pop(_A  )

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

						return orig_state_dict





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





@torch.no_grad()
def __UpperCamelCase			(       _A ,  _A ,  _A ,  _A = False  ):
						lowerCAmelCase_      			=  get_yolos_config(_A  )

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

						# load 🤗 model
						lowerCAmelCase_      			=  YolosForObjectDetection(_A  )
						model.eval()
						lowerCAmelCase_      			=  convert_state_dict(_A ,  _A  )
						model.load_state_dict(_A  )

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

						lowerCAmelCase_			,					lowerCAmelCase_      			=  None, None
						if yolos_name == "yolos_ti":
												lowerCAmelCase_      			=  torch.tensor(
												    [[-3_9.5_0_2_2, -1_1.9_8_2_0, -1_7.6_8_8_8], [-2_9.9_5_7_4, -9.9_7_6_9, -1_7.7_6_9_1], [-4_2.3_2_8_1, -2_0.7_2_0_0, -3_0.6_2_9_4]]  )
												lowerCAmelCase_      			=  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":
												lowerCAmelCase_      			=  torch.tensor(
												    [[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]]  )
												lowerCAmelCase_      			=  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":
												lowerCAmelCase_      			=  torch.tensor(
												    [[-3_6.2_2_2_0, -1_4.4_3_8_5, -2_3.5_4_5_7], [-3_5.6_9_7_0, -1_4.7_5_8_3, -2_1.3_9_3_5], [-3_1.5_9_3_9, -1_3.6_0_4_2, -1_6.8_0_4_9]]  )
												lowerCAmelCase_      			=  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":
												lowerCAmelCase_      			=  torch.tensor(
												    [[-4_2.8_6_6_8, -2_4.1_0_4_9, -4_1.1_6_9_0], [-3_4.7_4_5_6, -1_4.1_2_7_4, -2_4.9_1_9_4], [-3_3.7_8_9_8, -1_2.1_9_4_6, -2_5.6_4_9_5]]  )
												lowerCAmelCase_      			=  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":
												lowerCAmelCase_      			=  torch.tensor(
												    [[-4_0.6_0_6_4, -2_4.3_0_8_4, -3_2.6_4_4_7], [-5_5.1_9_9_0, -3_0.7_7_1_9, -3_5.5_8_7_7], [-5_1.4_3_1_1, -3_3.3_5_0_7, -3_5.6_4_6_2]]  )
												lowerCAmelCase_      			=  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] ,  _A ,  atol=1E-4  )
						assert torch.allclose(pred_boxes[0, :3, :3] ,  _A ,  atol=1E-4  )

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

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




