






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


lowercase    				=   {
    "169M": 12,
    "430M": 24,
    "1B5": 24,
    "3B": 32,
    "7B": 32,
    "14B": 40,
}

lowercase    				=   {
    "169M": 768,
    "430M": 1024,
    "1B5": 2048,
    "3B": 2560,
    "7B": 4096,
    "14B": 5120,
}





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

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

														snake_case_           =				weight
							return state_dict





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

							# 2. Build the config
							snake_case_           =				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:
																												snake_case_           =				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}.''')

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

							# 3. Download model file then convert state_dict
							snake_case_           =				hf_hub_download(a_				,	a_)
							snake_case_           =				torch.load(a_				,	map_location='cpu')
							snake_case_           =				convert_state_dict(a_)

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

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

														# 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.')
														snake_case_           =				list(shards.keys())

														del state_dict
														del shards
														gc.collect()

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

							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.')
														snake_case_           =				AutoModelForCausalLM.from_pretrained(a_)
														model.push_to_hub(a_				,	max_shard_size='2GB')
														tokenizer.push_to_hub(a_)


if __name__ == "__main__":
	lowercase    				=   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.",
	)

	lowercase    				=   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,
	)













lowercase    				=   {
    "A": ".-", "B": "-...", "C": "-.-.", "D": "-..", "E": ".", "F": "..-.", "G": "--.",
    "H": "....", "I": "..", "J": ".---", "K": "-.-", "L": ".-..", "M": "--", "N": "-.",
    "O": "---", "P": ".--.", "Q": "--.-", "R": ".-.", "S": "...", "T": "-", "U": "..-",
    "V": "...-", "W": ".--", "X": "-..-", "Y": "-.--", "Z": "--..", "1": ".----",
    "2": "..---", "3": "...--", "4": "....-", "5": ".....", "6": "-....", "7": "--...",
    "8": "---..", "9": "----.", "0": "-----", "&": ".-...", "@": ".--.-.",
    ":": "---...", ",": "--..--", ".": ".-.-.-", "'": ".----.", "\"": ".-..-.",
    "?": "..--..", "/": "-..-.", "=": "-...-", "+": ".-.-.", "-": "-....-",
    "(": "-.--.", ")": "-.--.-", "!": "-.-.--", " ": "/"
}  # Exclamation mark is not in ITU-R recommendation
# fmt: on
lowercase    				=   {value: key for key, value in MORSE_CODE_DICT.items()}





def 		__UpperCAmelCase     (	a_):
							return " ".join(MORSE_CODE_DICT[char] for char in message.upper())





def 		__UpperCAmelCase     (	a_):
							return "".join(REVERSE_DICT[char] for char in message.split())





def 		__UpperCAmelCase     (	):
							snake_case_           =				'Morse code here!'
							print(a_)
							snake_case_           =				encrypt(a_)
							print(a_)
							snake_case_           =				decrypt(a_)
							print(a_)


if __name__ == "__main__":
	main()





