





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase__						:			Dict									=logging.get_logger(__name__)

UpperCAmelCase__						:			int									={
    '''hustvl/yolos-small''': '''https://huggingface.co/hustvl/yolos-small/resolve/main/config.json''',
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}

class       __A     (						a    ):
				__A      =				"""yolos"""


				def __init__(		self   , UpperCAmelCase_=768   , UpperCAmelCase_=12   , UpperCAmelCase_=12   , UpperCAmelCase_=3072   , UpperCAmelCase_="gelu"   , UpperCAmelCase_=0.0   , UpperCAmelCase_=0.0   , UpperCAmelCase_=0.0_2   , UpperCAmelCase_=1E-12   , UpperCAmelCase_=[512, 864]   , UpperCAmelCase_=16   , UpperCAmelCase_=3   , UpperCAmelCase_=True   , UpperCAmelCase_=100   , UpperCAmelCase_=True   , UpperCAmelCase_=False   , UpperCAmelCase_=1   , UpperCAmelCase_=5   , UpperCAmelCase_=2   , UpperCAmelCase_=5   , UpperCAmelCase_=2   , UpperCAmelCase_=0.1   , **UpperCAmelCase_   , ):
								super().__init__(**UpperCAmelCase_							)

								lowerCamelCase	 =hidden_size
								lowerCamelCase	 =num_hidden_layers
								lowerCamelCase	 =num_attention_heads
								lowerCamelCase	 =intermediate_size
								lowerCamelCase	 =hidden_act
								lowerCamelCase	 =hidden_dropout_prob
								lowerCamelCase	 =attention_probs_dropout_prob
								lowerCamelCase	 =initializer_range
								lowerCamelCase	 =layer_norm_eps
								lowerCamelCase	 =image_size
								lowerCamelCase	 =patch_size
								lowerCamelCase	 =num_channels
								lowerCamelCase	 =qkv_bias
								lowerCamelCase	 =num_detection_tokens
								lowerCamelCase	 =use_mid_position_embeddings
								lowerCamelCase	 =auxiliary_loss
								# Hungarian matcher
								lowerCamelCase	 =class_cost
								lowerCamelCase	 =bbox_cost
								lowerCamelCase	 =giou_cost
								# Loss coefficients
								lowerCamelCase	 =bbox_loss_coefficient
								lowerCamelCase	 =giou_loss_coefficient
								lowerCamelCase	 =eos_coefficient




class       __A     (						a    ):
				__A      =				version.parse("""1.11"""    )


				@property
				def _snake_case       (		self							):
								return OrderedDict(
								    [
								        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
								    ]							)


				@property
				def _snake_case       (		self							):
								return 1E-4


				@property
				def _snake_case       (		self							):
								return 12












import argparse
import gc
import json
import os
import re

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint


UpperCAmelCase__						:			Optional[int]									={
    '''169M''': 12,
    '''430M''': 24,
    '''1B5''': 24,
    '''3B''': 32,
    '''7B''': 32,
    '''14B''': 40,
}

UpperCAmelCase__						:			str									={
    '''169M''': 7_68,
    '''430M''': 10_24,
    '''1B5''': 20_48,
    '''3B''': 25_60,
    '''7B''': 40_96,
    '''14B''': 51_20,
}





def      _lowercase					(						_UpperCAmelCase				)						->  Tuple:
				lowerCamelCase	 =list(state_dict.keys()				)
				for name in state_dict_keys:
								lowerCamelCase	 =state_dict.pop(_UpperCAmelCase				)
								# emb -> embedding
								if name.startswith("""emb."""				):
												lowerCamelCase	 =name.replace("""emb."""			,      """embeddings."""				)
								# ln_0 -> pre_ln (only present at block 0)
								if name.startswith("""blocks.0.ln0"""				):
												lowerCamelCase	 =name.replace("""blocks.0.ln0"""			,      """blocks.0.pre_ln"""				)
								# att -> attention
								lowerCamelCase	 =re.sub(r"""blocks\.(\d+)\.att"""			,      r"""blocks.\1.attention"""			,      _UpperCAmelCase				)
								# ffn -> feed_forward
								lowerCamelCase	 =re.sub(r"""blocks\.(\d+)\.ffn"""			,      r"""blocks.\1.feed_forward"""			,      _UpperCAmelCase				)
								# time_mix_k -> time_mix_key and reshape
								if name.endswith(""".time_mix_k"""				):
												lowerCamelCase	 =name.replace(""".time_mix_k"""			,      """.time_mix_key"""				)
								# time_mix_v -> time_mix_value and reshape
								if name.endswith(""".time_mix_v"""				):
												lowerCamelCase	 =name.replace(""".time_mix_v"""			,      """.time_mix_value"""				)
								# time_mix_r -> time_mix_key and reshape
								if name.endswith(""".time_mix_r"""				):
												lowerCamelCase	 =name.replace(""".time_mix_r"""			,      """.time_mix_receptance"""				)

								if name != "head.weight":
												lowerCamelCase	 ="""rwkv.""" + name

								lowerCamelCase	 =weight
				return state_dict





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase=None			,      _UpperCAmelCase=None			,      _UpperCAmelCase=False			,      _UpperCAmelCase=None				)						->  Tuple:
				# 1. If possible, build the tokenizer.
				if tokenizer_file is None:
								print("""No `--tokenizer_file` provided, we will use the default tokenizer."""				)
								lowerCamelCase	 =5_02_77
								lowerCamelCase	 =AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b"""				)
				else:
								lowerCamelCase	 =PreTrainedTokenizerFast(tokenizer_file=_UpperCAmelCase				)
								lowerCamelCase	 =len(_UpperCAmelCase				)
				tokenizer.save_pretrained(_UpperCAmelCase				)

				# 2. Build the config
				lowerCamelCase	 =list(NUM_HIDDEN_LAYERS_MAPPING.keys()				)
				if size is None:
								# Try to infer size from the checkpoint name
								for candidate in possible_sizes:
												if candidate in checkpoint_file:
																lowerCamelCase	 =candidate
																break
								if size is None:
												raise ValueError("""Could not infer the size, please provide it with the `--size` argument."""				)
				if size not in possible_sizes:
								raise ValueError(F"""`size` should be one of {possible_sizes}, got {size}."""				)

				lowerCamelCase	 =RwkvConfig(
				    vocab_size=_UpperCAmelCase			,      num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]			,      hidden_size=HIDEN_SIZE_MAPPING[size]			,      )
				config.save_pretrained(_UpperCAmelCase				)

				# 3. Download model file then convert state_dict
				lowerCamelCase	 =hf_hub_download(_UpperCAmelCase			,      _UpperCAmelCase				)
				lowerCamelCase	 =torch.load(_UpperCAmelCase			,      map_location="""cpu"""				)
				lowerCamelCase	 =convert_state_dict(_UpperCAmelCase				)

				# 4. Split in shards and save
				lowerCamelCase ,  lowerCamelCase	 =shard_checkpoint(_UpperCAmelCase				)
				for shard_file, shard in shards.items():
								torch.save(_UpperCAmelCase			,      os.path.join(_UpperCAmelCase			,      _UpperCAmelCase				)				)

				if index is not None:
								lowerCamelCase	 =os.path.join(_UpperCAmelCase			,      _UpperCAmelCase				)
								# Save the index as well
								with open(_UpperCAmelCase			,      """w"""			,      encoding="""utf-8"""				) as f:
												lowerCamelCase	 =json.dumps(_UpperCAmelCase			,      indent=2			,      sort_keys=_UpperCAmelCase				) + """\n"""
												f.write(_UpperCAmelCase				)

								# 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
								print(
								    """Cleaning up shards. This may error with an OOM error, it this is the case don't worry you still have converted the model."""				)
								lowerCamelCase	 =list(shards.keys()				)

								del state_dict
								del shards
								gc.collect()

								for shard_file in shard_files:
												lowerCamelCase	 =torch.load(os.path.join(_UpperCAmelCase			,      _UpperCAmelCase				)				)
												torch.save({k: v.cpu().clone() for k, v in state_dict.items()}			,      os.path.join(_UpperCAmelCase			,      _UpperCAmelCase				)				)

				del state_dict
				gc.collect()

				if push_to_hub:
								if model_name is None:
												raise ValueError("""Please provide a `model_name` to push the model to the Hub."""				)
								lowerCamelCase	 =AutoModelForCausalLM.from_pretrained(_UpperCAmelCase				)
								model.push_to_hub(_UpperCAmelCase			,      max_shard_size="""2GB"""				)
								tokenizer.push_to_hub(_UpperCAmelCase				)


if __name__ == "__main__":
							UpperCAmelCase__						:			Dict									=argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--repo_id''', default=None, type=str, required=True, help='''Repo ID from which to pull the checkpoint.'''
							)
							parser.add_argument(
							    '''--checkpoint_file''', default=None, type=str, required=True, help='''Name of the checkpoint file in the repo.'''
							)
							parser.add_argument(
							    '''--output_dir''', default=None, type=str, required=True, help='''Where to save the converted model.'''
							)
							parser.add_argument(
							    '''--tokenizer_file''',
							    default=None,
							    type=str,
							    help='''Path to the tokenizer file to use (if not provided, only the model is converted).''',
							)
							parser.add_argument(
							    '''--size''',
							    default=None,
							    type=str,
							    help='''Size of the model. Will be inferred from the `checkpoint_file` if not passed.''',
							)
							parser.add_argument(
							    '''--push_to_hub''',
							    action='''store_true''',
							    help='''Push to the Hub the converted model.''',
							)
							parser.add_argument(
							    '''--model_name''',
							    default=None,
							    type=str,
							    help='''Name of the pushed model on the Hub, including the username / organization.''',
							)

							UpperCAmelCase__						:			List[Any]									=parser.parse_args()
							convert_rmkv_checkpoint_to_hf_format(
							    args.repo_id,
							    args.checkpoint_file,
							    args.output_dir,
							    size=args.size,
							    tokenizer_file=args.tokenizer_file,
							    push_to_hub=args.push_to_hub,
							    model_name=args.model_name,
							)





