






'''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()
__lowerCAmelCase       		=      logging.get_logger(__name__)





def 		_UpperCAmelCase   (							__A							:   str      ):
				a_		:  Union[str, Any]											=      YolosConfig()

				# size of the architecture
				if "yolos_ti" in yolos_name:
								a_		:  Dict											=      1_92
								a_		:  int											=      7_68
								a_		:  Dict											=      12
								a_		:  List[str]											=      3
								a_		:  Union[str, Any]											=      [8_00, 13_33]
								a_		:  Optional[int]											=      False
				elif yolos_name == "yolos_s_dWr":
								a_		:  Tuple											=      3_30
								a_		:  List[str]											=      14
								a_		:  Any											=      6
								a_		:  List[str]											=      13_20
				elif "yolos_s" in yolos_name:
								a_		:  int											=      3_84
								a_		:  Any											=      15_36
								a_		:  int											=      12
								a_		:  List[Any]											=      6
				elif "yolos_b" in yolos_name:
								a_		:  Any											=      [8_00, 13_44]

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

				return config





def 		_UpperCAmelCase   (							__A							:   dict						,		__A							:   YolosConfig						,		__A							:   bool = 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)
								a_		:  Optional[Any]											=      state_dict.pop(f'blocks.{i}.attn.qkv.weight'      )
								a_		:  Optional[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[Any]											=      in_proj_weight[: config.hidden_size, :]
								a_		:  List[Any]											=      in_proj_bias[: config.hidden_size]
								a_		:  int											=      in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								a_		:  List[Any]											=      in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								a_		:  List[str]											=      in_proj_weight[-config.hidden_size :, :]
								a_		:  List[Any]											=      in_proj_bias[-config.hidden_size :]





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

				return name





def 		_UpperCAmelCase   (							__A							:   dict						,		__A							:   YolosForObjectDetection      ):
				for key in orig_state_dict.copy().keys():
								a_		:  Tuple											=      orig_state_dict.pop(__A      )

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

				return orig_state_dict





def 		_UpperCAmelCase   (							):
				a_		:  Any											=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				a_		:  Tuple											=      Image.open(requests.get(__A						,		stream=__A      ).raw      )
				return im





@torch.no_grad()
def 		_UpperCAmelCase   (							__A							:   str						,		__A							:   str						,		__A							:   str						,		__A							:   bool = False      ):
				a_		:  str											=      get_yolos_config(__A      )

				# load original state_dict
				a_		:  Tuple											=      torch.load(__A						,		map_location='''cpu'''      )['''model''']

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

				# Check outputs on an image, prepared by YolosImageProcessor
				a_		:  Dict											=      8_00 if yolos_name != '''yolos_ti''' else 5_12
				a_		:  List[str]											=      YolosImageProcessor(format='''coco_detection'''						,		size=__A      )
				a_		:  List[Any]											=      image_processor(images=prepare_img()						,		return_tensors='''pt'''      )
				a_		:  Dict											=      model(**__A      )
				a_				,			a_		:  Union[str, Any]											=      outputs.logits, outputs.pred_boxes

				a_				,			a_		:  List[Any]											=      None, None
				if yolos_name == "yolos_ti":
								a_		:  Union[str, Any]											=      torch.tensor(
								    [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]      )
								a_		:  List[str]											=      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":
								a_		:  Union[str, Any]											=      torch.tensor(
								    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]      )
								a_		:  Optional[int]											=      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":
								a_		:  Union[str, Any]											=      torch.tensor(
								    [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]      )
								a_		:  Optional[int]											=      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":
								a_		:  Any											=      torch.tensor(
								    [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]      )
								a_		:  Any											=      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":
								a_		:  int											=      torch.tensor(
								    [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]      )
								a_		:  Tuple											=      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]						,		__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:
								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(__A						,		organization='''hustvl'''      )
								model.push_to_hub(__A						,		organization='''hustvl'''      )


if __name__ == "__main__":
		__lowerCAmelCase       		=      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.'
		)

		__lowerCAmelCase       		=      parser.parse_args()
		convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)












'''simple docstring'''







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
		from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


__lowerCAmelCase       		=      logging.get_logger(__name__)

__lowerCAmelCase       		=      {
    'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/resolve/main/config.json',
    'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/config.json',
    'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json',
    'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json',
    'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/config.json',
    'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json',
}





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				"bloom"
			snake_case__  =				["past_key_values"]
			snake_case__  =				{
			    "num_hidden_layers": "n_layer",
			    "num_attention_heads": "n_head",
			}






			def __init__( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			int=25_0880  ,						__SCREAMING_SNAKE_CASE      :			Dict=64  ,						__SCREAMING_SNAKE_CASE      :			Tuple=2  ,						__SCREAMING_SNAKE_CASE      :			int=8  ,						__SCREAMING_SNAKE_CASE      :			Any=1e-5  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=0.02  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]=True  ,						__SCREAMING_SNAKE_CASE      :			int=1  ,						__SCREAMING_SNAKE_CASE      :			Any=2  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=False  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=0.0  ,						__SCREAMING_SNAKE_CASE      :			str=0.0  ,						__SCREAMING_SNAKE_CASE      :			List[Any]=1  ,						__SCREAMING_SNAKE_CASE      :			List[str]=False  ,						**__SCREAMING_SNAKE_CASE      :			str  ,						)			->			Any:
							a_		:  Optional[int]											=      vocab_size
							# Backward compatibility with n_embed kwarg
							a_		:  Any											=      kwargs.pop('''n_embed'''  ,						__SCREAMING_SNAKE_CASE					)
							a_		:  Optional[int]											=      hidden_size if n_embed is None else n_embed
							a_		:  int											=      n_layer
							a_		:  str											=      n_head
							a_		:  Optional[int]											=      layer_norm_epsilon
							a_		:  Dict											=      initializer_range
							a_		:  List[str]											=      use_cache
							a_		:  Dict											=      pretraining_tp
							a_		:  Optional[Any]											=      apply_residual_connection_post_layernorm
							a_		:  Optional[Any]											=      hidden_dropout
							a_		:  List[str]											=      attention_dropout

							a_		:  Dict											=      bos_token_id
							a_		:  Optional[int]											=      eos_token_id
							a_		:  Any											=      slow_but_exact

							super().__init__(bos_token_id=__SCREAMING_SNAKE_CASE  ,						eos_token_id=__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE					)





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				version.parse("1.12"       )






			def __init__( self      :			Tuple  ,						__SCREAMING_SNAKE_CASE      :			PretrainedConfig  ,						__SCREAMING_SNAKE_CASE      :			str = "default"  ,						__SCREAMING_SNAKE_CASE      :			List[PatchingSpec] = None  ,						__SCREAMING_SNAKE_CASE      :			bool = False  ,						)			->			Optional[Any]:
							super().__init__(__SCREAMING_SNAKE_CASE  ,						task=__SCREAMING_SNAKE_CASE  ,						patching_specs=__SCREAMING_SNAKE_CASE  ,						use_past=__SCREAMING_SNAKE_CASE					)
							if not getattr(self._config  ,						'''pad_token_id'''  ,						__SCREAMING_SNAKE_CASE					):
											# TODO: how to do that better?
											a_		:  Tuple											=      0






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			Mapping[str, Mapping[int, str]]:
							a_		:  Optional[Any]											=      OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}					)
							if self.use_past:
											# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
											self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE  ,						direction='''inputs'''  ,						inverted_values_shape=__SCREAMING_SNAKE_CASE					)
											a_		:  Union[str, Any]											=      {0: '''batch''', 1: '''past_sequence + sequence'''}
							else:
											a_		:  Union[str, Any]											=      {0: '''batch''', 1: '''sequence'''}

							return common_inputs






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			int:
							return self._config.n_layer






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			int:
							return self._config.n_head






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			float:
							return 1e-3






			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			"PreTrainedTokenizer"  ,						__SCREAMING_SNAKE_CASE      :			int = -1  ,						__SCREAMING_SNAKE_CASE      :			int = -1  ,						__SCREAMING_SNAKE_CASE      :			bool = False  ,						__SCREAMING_SNAKE_CASE      :			Optional["TensorType"] = None  ,						)			->			Mapping[str, Any]:
							a_		:  Dict											=      super(__SCREAMING_SNAKE_CASE  ,						self					).generate_dummy_inputs(
							    __SCREAMING_SNAKE_CASE  ,						batch_size=__SCREAMING_SNAKE_CASE  ,						seq_length=__SCREAMING_SNAKE_CASE  ,						is_pair=__SCREAMING_SNAKE_CASE  ,						framework=__SCREAMING_SNAKE_CASE					)

							# We need to order the input in the way they appears in the forward()
							a_		:  Union[str, Any]											=      OrderedDict({'''input_ids''': common_inputs['''input_ids''']}					)

							# Need to add the past_keys
							if self.use_past:
											if not is_torch_available():
															raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''					)
											else:
															import torch

															a_				,			a_		:  Any											=      common_inputs['''input_ids'''].shape
															# Not using the same length for past_key_values
															a_		:  str											=      seqlen + 2
															a_		:  Any											=      self._config.hidden_size // self.num_attention_heads
															a_		:  Optional[int]											=      (
															    batch * self.num_attention_heads,
															    head_dim,
															    past_key_values_length,
															)
															a_		:  Any											=      (
															    batch * self.num_attention_heads,
															    past_key_values_length,
															    head_dim,
															)
															a_		:  List[str]											=      [
															    (torch.zeros(__SCREAMING_SNAKE_CASE					), torch.zeros(__SCREAMING_SNAKE_CASE					)) for _ in range(self.num_layers					)
															]

							a_		:  Union[str, Any]											=      common_inputs['''attention_mask''']
							if self.use_past:
											a_		:  Optional[int]											=      ordered_inputs['''attention_mask'''].dtype
											a_		:  List[Any]											=      torch.cat(
											    [ordered_inputs['''attention_mask'''], torch.ones(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE  ,						dtype=__SCREAMING_SNAKE_CASE					)]  ,						dim=1					)

							return ordered_inputs







			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]					)			->			int:
							return 13




