



# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase=0  ):





							'''simple docstring'''




							if name is None:
														_lowerCAmelCase     :							Union[str, Any]    =    None
							else:
														_lowerCAmelCase     :							Any    =    "." * max(0				, spaces - 2  ) + "# {:" + str(50 - spaces  ) + "s}"
														_lowerCAmelCase     :							Tuple    =    fmt.format(_lowerCamelCase  )

							# Print and recurse (if needed).
							if isinstance(_lowerCamelCase				, _lowerCamelCase  ):
														if msg is not None:
																					print(_lowerCamelCase  )
														for k in val.keys():
																					recursive_print(_lowerCamelCase				, val[k]				, spaces + 2  )
							elif isinstance(_lowerCamelCase				, torch.Tensor  ):
														print(_lowerCamelCase				, ":"				, val.size()  )
							else:
														print(_lowerCamelCase				, ":"				, _lowerCamelCase  )






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Dict    =    param.size()
							if checkpoint_version == 1.0:
														# version 1.0 stores [num_heads * hidden_size * num_splits, :]
														_lowerCAmelCase     :							Optional[int]    =    (num_heads, hidden_size, num_splits) + input_shape[1:]
														_lowerCAmelCase     :							Dict    =    param.view(*_lowerCamelCase  )
														_lowerCAmelCase     :							Optional[Any]    =    param.transpose(0				, 2  )
														_lowerCAmelCase     :							str    =    param.transpose(1				, 2  ).contiguous()
							elif checkpoint_version >= 2.0:
														# other versions store [num_heads * num_splits * hidden_size, :]
														_lowerCAmelCase     :							Any    =    (num_heads, num_splits, hidden_size) + input_shape[1:]
														_lowerCAmelCase     :							List[Any]    =    param.view(*_lowerCamelCase  )
														_lowerCAmelCase     :							Tuple    =    param.transpose(0				, 1  ).contiguous()
							_lowerCAmelCase     :							Optional[int]    =    param.view(*_lowerCamelCase  )
							return param






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							str    =    {}

							# old versions did not store training args
							_lowerCAmelCase     :							Optional[Any]    =    input_state_dict.get("args"				, _lowerCamelCase  )
							if ds_args is not None:
														# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
														# from pprint import pprint
														# pprint(vars(ds_args))

														_lowerCAmelCase     :							List[Any]    =    ds_args.padded_vocab_size
														_lowerCAmelCase     :							Dict    =    ds_args.max_position_embeddings
														_lowerCAmelCase     :							Any    =    ds_args.hidden_size
														_lowerCAmelCase     :							List[Any]    =    ds_args.num_layers
														_lowerCAmelCase     :							Any    =    ds_args.num_attention_heads
														_lowerCAmelCase     :							str    =    ds_args.ffn_hidden_size
														# pprint(config)

							# The number of heads.
							_lowerCAmelCase     :							int    =    config.n_head
							# The hidden_size per head.
							_lowerCAmelCase     :							List[Any]    =    config.n_embd // config.n_head
							# Megatron-LM checkpoint version
							if "checkpoint_version" in input_state_dict.keys():
														_lowerCAmelCase     :							Tuple    =    input_state_dict["checkpoint_version"]
							else:
														_lowerCAmelCase     :							List[Any]    =    0.0

							# The model.
							_lowerCAmelCase     :							Tuple    =    input_state_dict["model"]
							# The language model.
							_lowerCAmelCase     :							List[Any]    =    model["language_model"]
							# The embeddings.
							_lowerCAmelCase     :							Any    =    lm["embedding"]

							# The word embeddings.
							_lowerCAmelCase     :							Union[str, Any]    =    embeddings["word_embeddings"]["weight"]
							# Truncate the embedding table to vocab_size rows.
							_lowerCAmelCase     :							Optional[int]    =    word_embeddings[: config.vocab_size, :]
							_lowerCAmelCase     :							int    =    word_embeddings

							# The position embeddings.
							_lowerCAmelCase     :							Any    =    embeddings["position_embeddings"]["weight"]
							# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
							_lowerCAmelCase     :							List[str]    =    pos_embeddings.size(0  )
							if n_positions != config.n_positions:
														raise ValueError(
														    F"pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don't match"  )
							# Store the position embeddings.
							_lowerCAmelCase     :							int    =    pos_embeddings

							# The transformer.
							_lowerCAmelCase     :							int    =    lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

							# The regex to extract layer names.
							_lowerCAmelCase     :							Optional[int]    =    re.compile(r"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"  )

							# The simple map of names for "automated" rules.
							_lowerCAmelCase     :							int    =    {
							    "attention.dense": ".attn.c_proj.",
							    "self_attention.dense": ".attn.c_proj.",
							    "mlp.dense_h_to_4h": ".mlp.c_fc.",
							    "mlp.dense_4h_to_h": ".mlp.c_proj.",
							}

							# Extract the layers.
							for key, val in transformer.items():
														# Match the name.
														_lowerCAmelCase     :							Optional[Any]    =    layer_re.match(_lowerCamelCase  )

														# Stop if that's not a layer
														if m is None:
																					break

														# The index of the layer.
														_lowerCAmelCase     :							Optional[Any]    =    int(m.group(1  )  )
														# The name of the operation.
														_lowerCAmelCase     :							List[str]    =    m.group(2  )
														# Is it a weight or a bias?
														_lowerCAmelCase     :							List[Any]    =    m.group(3  )

														# The name of the layer.
														_lowerCAmelCase     :							str    =    F"transformer.h.{layer_idx}"

														# For layernorm(s), simply store the layer norm.
														if op_name.endswith("layernorm"  ):
																					_lowerCAmelCase     :							Optional[Any]    =    "ln_1" if op_name.startswith("input"  ) else "ln_2"
																					_lowerCAmelCase     :							List[Any]    =    val

														# Transpose the QKV matrix.
														elif (
														    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
														) and weight_or_bias == "weight":
																					# Insert a tensor of 1x1xDxD bias.
																					_lowerCAmelCase     :							Any    =    torch.tril(torch.ones((n_positions, n_positions)				, dtype=torch.floataa  )  ).view(
																					    1				, 1				, _lowerCamelCase				, _lowerCamelCase  )
																					_lowerCAmelCase     :							Dict    =    causal_mask

																					# Insert a "dummy" tensor for masked_bias.
																					_lowerCAmelCase     :							Tuple    =    torch.tensor(-1e4				, dtype=torch.floataa  )
																					_lowerCAmelCase     :							Tuple    =    masked_bias

																					_lowerCAmelCase     :							List[Any]    =    fix_query_key_value_ordering(_lowerCamelCase				, _lowerCamelCase				, 3				, _lowerCamelCase				, _lowerCamelCase  )
																					# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
																					_lowerCAmelCase     :							Dict    =    out_val.transpose(0				, 1  ).contiguous()
																					# Store.
																					_lowerCAmelCase     :							List[Any]    =    out_val

														# Transpose the bias.
														elif (
														    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
														) and weight_or_bias == "bias":
																					_lowerCAmelCase     :							Optional[int]    =    fix_query_key_value_ordering(_lowerCamelCase				, _lowerCamelCase				, 3				, _lowerCamelCase				, _lowerCamelCase  )
																					# Store. No change of shape.
																					_lowerCAmelCase     :							Tuple    =    out_val

														# Transpose the weights.
														elif weight_or_bias == "weight":
																					_lowerCAmelCase     :							Any    =    megatron_to_transformers[op_name]
																					_lowerCAmelCase     :							str    =    val.transpose(0				, 1  )

														# Copy the bias.
														elif weight_or_bias == "bias":
																					_lowerCAmelCase     :							Tuple    =    megatron_to_transformers[op_name]
																					_lowerCAmelCase     :							Dict    =    val

    # DEBUG.
							assert config.n_layer == layer_idx + 1

							# The final layernorm.
							_lowerCAmelCase     :							Dict    =    transformer["final_layernorm.weight"]
							_lowerCAmelCase     :							List[str]    =    transformer["final_layernorm.bias"]

							# For LM head, transformers' wants the matrix to weight embeddings.
							_lowerCAmelCase     :							Union[str, Any]    =    word_embeddings

							# It should be done!
							return output_state_dict






def 				A				(					):





							'''simple docstring'''




							_lowerCAmelCase     :							Optional[Any]    =    argparse.ArgumentParser()
							parser.add_argument("--print-checkpoint-structure"				, action="store_true"  )
							parser.add_argument(
							    "path_to_checkpoint"				, type=_lowerCamelCase				, help="Path to the checkpoint file (.zip archive or direct .pt file)"				, )
							parser.add_argument(
							    "--config_file"				, default=""				, type=_lowerCamelCase				, help="An optional config json file describing the pre-trained model."				, )
							_lowerCAmelCase     :							Union[str, Any]    =    parser.parse_args()

							# Extract the basename.
							_lowerCAmelCase     :							List[Any]    =    os.path.dirname(args.path_to_checkpoint  )

							# Load the model.
							# the .zip is very optional, let's keep it for backward compatibility
							print(F"Extracting PyTorch state dictionary from {args.path_to_checkpoint}"  )
							if args.path_to_checkpoint.endswith(".zip"  ):
														with zipfile.ZipFile(args.path_to_checkpoint				, "r"  ) as checkpoint:
																					with checkpoint.open("release/mp_rank_00/model_optim_rng.pt"  ) as pytorch_dict:
																												_lowerCAmelCase     :							int    =    torch.load(_lowerCamelCase				, map_location="cpu"  )
							else:
														_lowerCAmelCase     :							List[Any]    =    torch.load(args.path_to_checkpoint				, map_location="cpu"  )

							_lowerCAmelCase     :							Dict    =    input_state_dict.get("args"				, _lowerCamelCase  )

							# Read the config, or default to the model released by NVIDIA.
							if args.config_file == "":
														if ds_args is not None:
																					if ds_args.bias_gelu_fusion:
																												_lowerCAmelCase     :							Union[str, Any]    =    "gelu_fast"
																					elif ds_args.openai_gelu:
																												_lowerCAmelCase     :							int    =    "gelu_new"
																					else:
																												_lowerCAmelCase     :							Dict    =    "gelu"
														else:
																					# in the very early days this used to be "gelu_new"
																					_lowerCAmelCase     :							Dict    =    "gelu_new"

														# Spell out all parameters in case the defaults change.
														_lowerCAmelCase     :							Union[str, Any]    =    GPTaConfig(
														    vocab_size=50_257				, n_positions=1_024				, n_embd=1_024				, n_layer=24				, n_head=16				, n_inner=4_096				, activation_function=_lowerCamelCase				, resid_pdrop=0.1				, embd_pdrop=0.1				, attn_pdrop=0.1				, layer_norm_epsilon=1e-5				, initializer_range=0.02				, summary_type="cls_index"				, summary_use_proj=_lowerCamelCase				, summary_activation=_lowerCamelCase				, summary_proj_to_labels=_lowerCamelCase				, summary_first_dropout=0.1				, scale_attn_weights=_lowerCamelCase				, use_cache=_lowerCamelCase				, bos_token_id=50_256				, eos_token_id=50_256				, )
							else:
														_lowerCAmelCase     :							str    =    GPTaConfig.from_json_file(args.config_file  )

							_lowerCAmelCase     :							Dict    =    ["GPT2LMHeadModel"]

							# Convert.
							print("Converting"  )
							_lowerCAmelCase     :							Tuple    =    convert_megatron_checkpoint(_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  )

							# Print the structure of converted state dict.
							if args.print_checkpoint_structure:
														recursive_print(_lowerCamelCase				, _lowerCamelCase  )

							# Add tokenizer class info to config
							# see https://github.com/huggingface/transformers/issues/13906)
							if ds_args is not None:
														_lowerCAmelCase     :							Optional[int]    =    ds_args.tokenizer_type
														if tokenizer_type == "GPT2BPETokenizer":
																					_lowerCAmelCase     :							Optional[int]    =    "gpt2"
														elif tokenizer_type == "PretrainedFromHF":
																					_lowerCAmelCase     :							Union[str, Any]    =    ds_args.tokenizer_name_or_path
														else:
																					raise ValueError(F"Unrecognized tokenizer_type {tokenizer_type}"  )
							else:
														_lowerCAmelCase     :							str    =    "gpt2"

							_lowerCAmelCase     :							Union[str, Any]    =    AutoTokenizer.from_pretrained(_lowerCamelCase  )
							_lowerCAmelCase     :							Dict    =    type(_lowerCamelCase  ).__name__
							_lowerCAmelCase     :							List[str]    =    tokenizer_class

							# Store the config to file.
							print("Saving config"  )
							config.save_pretrained(_lowerCamelCase  )

							# Save tokenizer based on args
							print(F"Adding {tokenizer_class} tokenizer files"  )
							tokenizer.save_pretrained(_lowerCamelCase  )

							# Store the state_dict to file.
							_lowerCAmelCase     :							List[str]    =    os.path.join(_lowerCamelCase				, "pytorch_model.bin"  )
							print(F"Saving checkpoint to \"{output_checkpoint_file}\""  )
							torch.save(_lowerCamelCase				, _lowerCamelCase  )


####################################################################################################

if __name__ == "__main__":
						main()

####################################################################################################






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)




