






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


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

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




def   __A       (  _lowercase			):

					'''simple docstring'''







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

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

										_A										=			weight
					return state_dict




def   __A       (  _lowercase       ,      _lowercase       ,      _lowercase       ,      _lowercase=None       ,      _lowercase=None       ,      _lowercase=False       ,      _lowercase=None			):

					'''simple docstring'''







					if tokenizer_file is None:
										print('''No `--tokenizer_file` provided, we will use the default tokenizer.'''			)
										_A										=			5_02_77
										_A										=			AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b'''			)
					else:
										_A										=			PreTrainedTokenizerFast(tokenizer_file=__lowerCAmelCase			)
										_A										=			len(__lowerCAmelCase			)
					tokenizer.save_pretrained(__lowerCAmelCase			)

					# 2. Build the config
					_A										=			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:
																				_A										=			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}."""			)

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

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

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

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

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

										del state_dict
										del shards
										gc.collect()

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

					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.'''			)
										_A										=			AutoModelForCausalLM.from_pretrained(__lowerCAmelCase			)
										model.push_to_hub(__lowerCAmelCase       ,      max_shard_size='''2GB'''			)
										tokenizer.push_to_hub(__lowerCAmelCase			)


if __name__ == "__main__":
				__A  			=					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.',
				)

				__A  			=					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,
				)








from datetime import datetime

import matplotlib.pyplot as plt
import torch

def        __a			(   __lowerCAmelCase   )     ->     int:
					for param in module.parameters():
										SCREAMING_SNAKE_CASE							:     List[Any]						=				False

def        __a			(   )     ->     List[str]:
					SCREAMING_SNAKE_CASE							:     List[Any]						=				'cuda' if torch.cuda.is_available() else 'cpu'
					if torch.backends.mps.is_available() and torch.backends.mps.is_built():
										SCREAMING_SNAKE_CASE							:     List[str]						=				'mps'
					if device == "mps":
										print(
										    'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
										    ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
										    ' with generations.'   )
					return device

def        __a			(   __lowerCAmelCase   )     ->     List[Any]:
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				plt.imshow(__lowerCAmelCase   )
					fig.axes.get_xaxis().set_visible(__lowerCAmelCase   )
					fig.axes.get_yaxis().set_visible(__lowerCAmelCase   )
					plt.show()



def        __a			(   )     ->     Optional[Any]:
					SCREAMING_SNAKE_CASE							:     str						=				datetime.now()
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				current_time.strftime('%H:%M:%S'   )
					return timestamp