




"""simple docstring"""






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__						=			{
    '''169M''': 1_2,
    '''430M''': 2_4,
    '''1B5''': 2_4,
    '''3B''': 3_2,
    '''7B''': 3_2,
    '''14B''': 4_0,
}

UpperCamelCase__						=			{
    '''169M''': 7_6_8,
    '''430M''': 1_0_2_4,
    '''1B5''': 2_0_4_8,
    '''3B''': 2_5_6_0,
    '''7B''': 4_0_9_6,
    '''14B''': 5_1_2_0,
}





def 							UpperCAmelCase		(		snake_case			:					Dict    ):
					_lowerCAmelCase:Tuple									=  list(state_dict.keys()    )
					for name in state_dict_keys:
										_lowerCAmelCase:Tuple									=  state_dict.pop(snake_case    )
										# emb -> embedding
										if name.startswith('''emb.'''    ):
															_lowerCAmelCase:Dict									=  name.replace('''emb.'''      ,       '''embeddings.'''    )
										# ln_0 -> pre_ln (only present at block 0)
										if name.startswith('''blocks.0.ln0'''    ):
															_lowerCAmelCase:str									=  name.replace('''blocks.0.ln0'''      ,       '''blocks.0.pre_ln'''    )
										# att -> attention
										_lowerCAmelCase:List[str]									=  re.sub(R'''blocks\.(\d+)\.att'''      ,       R'''blocks.\1.attention'''      ,       snake_case    )
										# ffn -> feed_forward
										_lowerCAmelCase:str									=  re.sub(R'''blocks\.(\d+)\.ffn'''      ,       R'''blocks.\1.feed_forward'''      ,       snake_case    )
										# time_mix_k -> time_mix_key and reshape
										if name.endswith('''.time_mix_k'''    ):
															_lowerCAmelCase:Optional[int]									=  name.replace('''.time_mix_k'''      ,       '''.time_mix_key'''    )
										# time_mix_v -> time_mix_value and reshape
										if name.endswith('''.time_mix_v'''    ):
															_lowerCAmelCase:List[Any]									=  name.replace('''.time_mix_v'''      ,       '''.time_mix_value'''    )
										# time_mix_r -> time_mix_key and reshape
										if name.endswith('''.time_mix_r'''    ):
															_lowerCAmelCase:Optional[Any]									=  name.replace('''.time_mix_r'''      ,       '''.time_mix_receptance'''    )

										if name != "head.weight":
															_lowerCAmelCase:str									=  '''rwkv.''' + name

										_lowerCAmelCase:Tuple									=  weight
					return state_dict





def 							UpperCAmelCase		(		snake_case			:					Tuple      ,       snake_case			:					List[str]      ,       snake_case			:					Tuple      ,       snake_case			:					Any=None      ,       snake_case			:					List[str]=None      ,       snake_case			:					Dict=False      ,       snake_case			:					List[str]=None    ):
					# 1. If possible, build the tokenizer.
					if tokenizer_file is None:
										print('''No `--tokenizer_file` provided, we will use the default tokenizer.'''    )
										_lowerCAmelCase:int									=  50277
										_lowerCAmelCase:Optional[int]									=  AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b'''    )
					else:
										_lowerCAmelCase:Optional[Any]									=  PreTrainedTokenizerFast(tokenizer_file=snake_case    )
										_lowerCAmelCase:List[Any]									=  len(snake_case    )
					tokenizer.save_pretrained(snake_case    )

					# 2. Build the config
					_lowerCAmelCase:Dict									=  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:Optional[int]									=  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:List[str]									=  RwkvConfig(
					    vocab_size=snake_case      ,       num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]      ,       hidden_size=HIDEN_SIZE_MAPPING[size]      ,       )
					config.save_pretrained(snake_case    )

					# 3. Download model file then convert state_dict
					_lowerCAmelCase:Dict									=  hf_hub_download(snake_case      ,       snake_case    )
					_lowerCAmelCase:Dict									=  torch.load(snake_case      ,       map_location='''cpu'''    )
					_lowerCAmelCase:Tuple									=  convert_state_dict(snake_case    )

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

					if index is not None:
										_lowerCAmelCase:List[Any]									=  os.path.join(snake_case      ,       snake_case    )
										# Save the index as well
										with open(snake_case      ,       '''w'''      ,       encoding='''utf-8'''    ) as f:
															_lowerCAmelCase:List[Any]									=  json.dumps(snake_case      ,       indent=2      ,       sort_keys=snake_case    ) + '''\n'''
															f.write(snake_case    )

										# 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:int									=  list(shards.keys()    )

										del state_dict
										del shards
										gc.collect()

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

					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:Union[str, Any]									=  AutoModelForCausalLM.from_pretrained(snake_case    )
										model.push_to_hub(snake_case      ,       max_shard_size='''2GB'''    )
										tokenizer.push_to_hub(snake_case    )


if __name__ == "__main__":
				UpperCamelCase__						=			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__						=			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,
				)












"""simple docstring"""






import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__						=			logging.get_logger(__name__)





def 							UpperCAmelCase		(		snake_case			:					Optional[int]    ):
					_lowerCAmelCase:List[Any]									=  MobileNetVaConfig(layer_norm_eps=0.0_01    )

					if "_quant" in model_name:
										raise ValueError('''Quantized models are not supported.'''    )

					_lowerCAmelCase:List[Any]									=  re.match(R'''^mobilenet_v1_([^_]*)_([^_]*)$'''      ,       snake_case    )
					if matches:
										_lowerCAmelCase:str									=  float(matches[1]    )
										_lowerCAmelCase:str									=  int(matches[2]    )

					# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
					# the usual 1000. The first class (index 0) is "background".
					_lowerCAmelCase:int									=  1001
					_lowerCAmelCase:Union[str, Any]									=  '''imagenet-1k-id2label.json'''
					_lowerCAmelCase:Optional[int]									=  '''huggingface/label-files'''
					_lowerCAmelCase:List[str]									=  json.load(open(hf_hub_download(snake_case      ,       snake_case      ,       repo_type='''dataset'''    )      ,       '''r'''    )    )
					_lowerCAmelCase:Union[str, Any]									=  {int(snake_case    ) + 1: v for k, v in idalabel.items()}
					_lowerCAmelCase:Optional[Any]									=  '''background'''
					_lowerCAmelCase:Optional[Any]									=  idalabel
					_lowerCAmelCase:List[Any]									=  {v: k for k, v in idalabel.items()}

					return config





def 							UpperCAmelCase		(		):
					_lowerCAmelCase:Tuple									=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					_lowerCAmelCase:Optional[Any]									=  Image.open(requests.get(snake_case      ,       stream=snake_case    ).raw    )
					return im





@torch.no_grad()
def 							UpperCAmelCase		(		snake_case			:					Any      ,       snake_case			:					Union[str, Any]      ,       snake_case			:					List[Any]      ,       snake_case			:					Any=False    ):
					_lowerCAmelCase:List[str]									=  get_mobilenet_va_config(snake_case    )

					# Load 🤗 model
					_lowerCAmelCase:Any									=  MobileNetVaForImageClassification(snake_case    ).eval()

					# Load weights from TensorFlow checkpoint
					load_tf_weights_in_mobilenet_va(snake_case      ,       snake_case      ,       snake_case    )

					# Check outputs on an image, prepared by MobileNetV1ImageProcessor
					_lowerCAmelCase:Dict									=  MobileNetVaImageProcessor(
					    crop_size={'''width''': config.image_size, '''height''': config.image_size}      ,       size={'''shortest_edge''': config.image_size + 32}      ,       )
					_lowerCAmelCase:List[str]									=  image_processor(images=prepare_img()      ,       return_tensors='''pt'''    )
					_lowerCAmelCase:List[str]									=  model(**snake_case    )
					_lowerCAmelCase:int									=  outputs.logits

					assert logits.shape == (1, 1001)

					if model_name == "mobilenet_v1_1.0_224":
										_lowerCAmelCase:Union[str, Any]									=  torch.tensor([-4.17_39, -1.12_33, 3.12_05]    )
					elif model_name == "mobilenet_v1_0.75_192":
										_lowerCAmelCase:Any									=  torch.tensor([-3.94_40, -2.31_41, -0.33_33]    )
					else:
										_lowerCAmelCase:Dict									=  None

					if expected_logits is not None:
										assert torch.allclose(logits[0, :3]      ,       snake_case      ,       atol=1e-4    )

					Path(snake_case    ).mkdir(exist_ok=snake_case    )
					print(F'Saving model {model_name} to {pytorch_dump_folder_path}'    )
					model.save_pretrained(snake_case    )
					print(F'Saving image processor to {pytorch_dump_folder_path}'    )
					image_processor.save_pretrained(snake_case    )

					if push_to_hub:
										print('''Pushing to the hub...'''    )
										_lowerCAmelCase:List[str]									=  '''google/''' + model_name
										image_processor.push_to_hub(snake_case    )
										model.push_to_hub(snake_case    )


if __name__ == "__main__":
				UpperCamelCase__						=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--model_name''',
				    default='''mobilenet_v1_1.0_224''',
				    type=str,
				    help='''Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<depth>_<size>\'.''',
				)
				parser.add_argument(
				    '''--checkpoint_path''', required=True, type=str, help='''Path to the original TensorFlow checkpoint (.ckpt file).'''
				)
				parser.add_argument(
				    '''--pytorch_dump_folder_path''', required=True, 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__						=			parser.parse_args()
				convert_movilevit_checkpoint(
				    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
				)






