

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

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





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					list(state_dict.keys()   )
							for name in state_dict_keys:
														snake_case_           =					state_dict.pop(UpperCamelCase__   )
														# 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'     ,							UpperCamelCase__   )
														# ffn -> feed_forward
														snake_case_           =					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'   ):
																					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       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__=None     ,							UpperCamelCase__=None     ,							UpperCamelCase__=False     ,							UpperCamelCase__=None   ):







							'''simple docstring'''






							if tokenizer_file is None:
														print('No `--tokenizer_file` provided, we will use the default tokenizer.'   )
														snake_case_           =					50277
														snake_case_           =					AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b'   )
							else:
														snake_case_           =					PreTrainedTokenizerFast(tokenizer_file=UpperCamelCase__   )
														snake_case_           =					len(UpperCamelCase__   )
							tokenizer.save_pretrained(UpperCamelCase__   )

							# 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=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
							snake_case_           =					hf_hub_download(UpperCamelCase__     ,							UpperCamelCase__   )
							snake_case_           =					torch.load(UpperCamelCase__     ,							map_location='cpu'   )
							snake_case_           =					convert_state_dict(UpperCamelCase__   )

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

							if index is not None:
														snake_case_           =					os.path.join(UpperCamelCase__     ,							UpperCamelCase__   )
														# Save the index as well
														with open(UpperCamelCase__     ,							'w'     ,							encoding='utf-8'   ) as f:
																					snake_case_           =					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.'   )
														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(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.'   )
														snake_case_           =					AutoModelForCausalLM.from_pretrained(UpperCamelCase__   )
														model.push_to_hub(UpperCamelCase__     ,							max_shard_size='2GB'   )
														tokenizer.push_to_hub(UpperCamelCase__   )


if __name__ == "__main__":
					_UpperCAmelCase	:     Union[str, Any]		       = 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	:     int		       = 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 __future__ import annotations







class 				lowercase       :




				def __init__(      self   ,     snake_case ):
											snake_case_           =					TypeError(
											    'Matrices must be formed from a list of zero or more lists containing at '
											    'least one and the same number of values, each of which must be of type '
											    'int or float.' )
											if len(snake_case ) != 0:
																		snake_case_           =					len(rows[0] )
																		if cols == 0:
																									raise error
																		for row in rows:
																									if len(snake_case ) != cols:
																																raise error
																									for value in row:
																																if not isinstance(snake_case   ,     (int, float) ):
																																							raise error
																		snake_case_           =					rows
											else:
																		snake_case_           =					[]




				def 					a							(      self ):
											return [[row[i] for row in self.rows] for i in range(len(self.rows[0] ) )]




				@property
				def 					a							(      self ):
											return len(self.rows )




				@property
				def 					a							(      self ):
											return len(self.rows[0] )




				@property
				def 					a							(      self ):
											return (self.num_rows, self.num_columns)




				@property
				def 					a							(      self ):
											return self.order[0] == self.order[1]




				def 					a							(      self ):
											snake_case_           =					[
											    [0 if column_num != row_num else 1 for column_num in range(self.num_rows )]
											    for row_num in range(self.num_rows )
											]
											return Matrix(snake_case )




				def 					a							(      self ):
											if not self.is_square:
																		return 0
											if self.order == (0, 0):
																		return 1
											if self.order == (1, 1):
																		return int(self.rows[0][0] )
											if self.order == (2, 2):
																		return int(
																		    (self.rows[0][0] * self.rows[1][1])
																		    - (self.rows[0][1] * self.rows[1][0]) )
											else:
																		return sum(
																		    self.rows[0][column] * self.cofactors().rows[0][column]
																		    for column in range(self.num_columns ) )




				def 					a							(      self ):
											return bool(self.determinant() )




				def 					a							(      self   ,     snake_case   ,     snake_case ):
											snake_case_           =					[
											    [
											        self.rows[other_row][other_column]
											        for other_column in range(self.num_columns )
											        if other_column != column
											    ]
											    for other_row in range(self.num_rows )
											    if other_row != row
											]
											return Matrix(snake_case ).determinant()




				def 					a							(      self   ,     snake_case   ,     snake_case ):
											if (row + column) % 2 == 0:
																		return self.get_minor(snake_case   ,     snake_case )
											return -1 * self.get_minor(snake_case   ,     snake_case )




				def 					a							(      self ):
											return Matrix(
											    [
											        [self.get_minor(snake_case   ,     snake_case ) for column in range(self.num_columns )]
											        for row in range(self.num_rows )
											    ] )




				def 					a							(      self ):
											return Matrix(
											    [
											        [
											            self.minors().rows[row][column]
											            if (row + column) % 2 == 0
											            else self.minors().rows[row][column] * -1
											            for column in range(self.minors().num_columns )
											        ]
											        for row in range(self.minors().num_rows )
											    ] )




				def 					a							(      self ):
											snake_case_           =					[
											    [self.cofactors().rows[column][row] for column in range(self.num_columns )]
											    for row in range(self.num_rows )
											]
											return Matrix(snake_case )




				def 					a							(      self ):
											snake_case_           =					self.determinant()
											if not determinant:
																		raise TypeError('Only matrices with a non-zero determinant have an inverse' )
											return self.adjugate() * (1 / determinant)




				def __repr__(      self ):
											return str(self.rows )




				def __str__(      self ):
											if self.num_rows == 0:
																		return "[]"
											if self.num_rows == 1:
																		return "[[" + ". ".join(str(self.rows[0] ) ) + "]]"
											return (
											    "["
											    + "\n ".join(
											        [
											            '[' + '. '.join([str(snake_case ) for value in row] ) + '.]'
											            for row in self.rows
											        ] )
											    + "]"
											)




				def 					a							(      self   ,     snake_case   ,     snake_case = None ):
											snake_case_           =					TypeError('Row must be a list containing all ints and/or floats' )
											if not isinstance(snake_case   ,     snake_case ):
																		raise type_error
											for value in row:
																		if not isinstance(snake_case   ,     (int, float) ):
																									raise type_error
											if len(snake_case ) != self.num_columns:
																		raise ValueError(
																		    'Row must be equal in length to the other rows in the matrix' )
											if position is None:
																		self.rows.append(snake_case )
											else:
																		snake_case_           =					self.rows[0:position] + [row] + self.rows[position:]




				def 					a							(      self   ,     snake_case   ,     snake_case = None ):
											snake_case_           =					TypeError(
											    'Column must be a list containing all ints and/or floats' )
											if not isinstance(snake_case   ,     snake_case ):
																		raise type_error
											for value in column:
																		if not isinstance(snake_case   ,     (int, float) ):
																									raise type_error
											if len(snake_case ) != self.num_rows:
																		raise ValueError(
																		    'Column must be equal in length to the other columns in the matrix' )
											if position is None:
																		snake_case_           =					[self.rows[i] + [column[i]] for i in range(self.num_rows )]
											else:
																		snake_case_           =					[
																		    self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
																		    for i in range(self.num_rows )
																		]




				def __eq__(      self   ,     snake_case ):
											if not isinstance(snake_case   ,     snake_case ):
																		return NotImplemented
											return self.rows == other.rows




				def __ne__(      self   ,     snake_case ):
											return not self == other




				def __neg__(      self ):
											return self * -1




				def __add__(      self   ,     snake_case ):
											if self.order != other.order:
																		raise ValueError('Addition requires matrices of the same order' )
											return Matrix(
											    [
											        [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns )]
											        for i in range(self.num_rows )
											    ] )




				def __sub__(      self   ,     snake_case ):
											if self.order != other.order:
																		raise ValueError('Subtraction requires matrices of the same order' )
											return Matrix(
											    [
											        [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns )]
											        for i in range(self.num_rows )
											    ] )




				def __mul__(      self   ,     snake_case ):
											if isinstance(snake_case   ,     (int, float) ):
																		return Matrix(
																		    [[int(element * other ) for element in row] for row in self.rows] )
											elif isinstance(snake_case   ,     snake_case ):
																		if self.num_columns != other.num_rows:
																									raise ValueError(
																									    'The number of columns in the first matrix must '
																									    'be equal to the number of rows in the second' )
																		return Matrix(
																		    [
																		        [Matrix.dot_product(snake_case   ,     snake_case ) for column in other.columns()]
																		        for row in self.rows
																		    ] )
											else:
																		raise TypeError(
																		    'A Matrix can only be multiplied by an int, float, or another matrix' )




				def __pow__(      self   ,     snake_case ):
											if not isinstance(snake_case   ,     snake_case ):
																		raise TypeError('A Matrix can only be raised to the power of an int' )
											if not self.is_square:
																		raise ValueError('Only square matrices can be raised to a power' )
											if other == 0:
																		return self.identity()
											if other < 0:
																		if self.is_invertable():
																									return self.inverse() ** (-other)
																		raise ValueError(
																		    'Only invertable matrices can be raised to a negative power' )
											snake_case_           =					self
											for _ in range(other - 1 ):
																		result *= self
											return result




				@classmethod
				def 					a							(      cls   ,     snake_case   ,     snake_case ):
											return sum(row[i] * column[i] for i in range(len(snake_case ) ) )


if __name__ == "__main__":
					import doctest

					doctest.testmod()




