




'''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


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

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


def      UpperCamelCase_(	snake_case							:       Union[str, Any]     ):

							'''simple docstring'''






							snake_case_						=      list(state_dict.keys()     )
							for name in state_dict_keys:
														snake_case_						=      state_dict.pop(snake_case     )
														# 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"							,				snake_case     )
														# ffn -> feed_forward
														snake_case_						=      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"     ):
																					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_(	snake_case							:       List[Any]							,				snake_case							:       str							,				snake_case							:       Optional[Any]							,				snake_case							:       str=None							,				snake_case							:       Union[str, Any]=None							,				snake_case							:       Any=False							,				snake_case							:       Tuple=None     ):

							'''simple docstring'''






							if tokenizer_file is None:
														print("No `--tokenizer_file` provided, we will use the default tokenizer."     )
														snake_case_						=      5_0_2_7_7
														snake_case_						=      AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"     )
							else:
														snake_case_						=      PreTrainedTokenizerFast(tokenizer_file=snake_case     )
														snake_case_						=      len(snake_case     )
							tokenizer.save_pretrained(snake_case     )

							# 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=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
							snake_case_						=      hf_hub_download(snake_case							,				snake_case     )
							snake_case_						=      torch.load(snake_case							,				map_location="cpu"     )
							snake_case_						=      convert_state_dict(snake_case     )

							# 4. Split in shards and save
							snake_case_					,      snake_case_						=      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:
														snake_case_						=      os.path.join(snake_case							,				snake_case     )
														# Save the index as well
														with open(snake_case							,				"w"							,				encoding="utf-8"     ) as f:
																					snake_case_						=      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."     )
														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(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."     )
														snake_case_						=      AutoModelForCausalLM.from_pretrained(snake_case     )
														model.push_to_hub(snake_case							,				max_shard_size="2GB"     )
														tokenizer.push_to_hub(snake_case     )


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:   Optional[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.",
		)

		_SCREAMING_SNAKE_CASE							:   Dict      		= 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,
		)











import warnings
from pathlib import Path
from typing import List, Tuple, Union

import fire
from torch import nn

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging


UpperCAmelCase						:   Any			  =     logging.get_logger(__name__)







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      None:
					__A	:						int       =				nn.ModuleList([src_layers[i] for i in layers_to_copy]  )
					assert len(a  ) == len(a  ), F"""{len(a  )} != {len(a  )}"""
					dest_layers.load_state_dict(layers_to_copy.state_dict()  )


UpperCAmelCase						:   List[Any]			  =     {
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    12: {
        1: [0],  # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
        2: [0, 6],
        3: [0, 6, 11],
        4: [0, 4, 8, 11],
        6: [0, 2, 4, 7, 9, 11],
        9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
        12: list(range(12)),
    },
    16: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 15],
        3: [0, 8, 15],
        4: [0, 5, 10, 15],
        6: [0, 3, 6, 9, 12, 15],
        8: [0, 2, 4, 6, 8, 10, 12, 15],
        9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
        12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
        16: list(range(16)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
UpperCAmelCase						:   Optional[int]			  =     {
    # maps  num layers in student -> which teacher layers to copy.
    6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
    12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
    16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      Dict:
					try:
										__A	:						int       =				LAYERS_TO_COPY[n_teacher][n_student]
										return val
					except KeyError:
										if n_student != n_teacher:
															warnings.warn(
															    F"""no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first"""
															    F""" {n_student}"""  )
										return list(range(a  )  )







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      List[int]:
					if n_student > n_teacher:
										raise ValueError(F"""Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}"""  )
					elif n_teacher == n_student:
										return list(range(a  )  )
					elif n_student == 1:
										return [n_teacher - 1]
					else:
										return LAYERS_TO_SUPERVISE[n_teacher][n_student]







def  _SCREAMING_SNAKE_CASE				(   a					,						a = "student"					,						a = None					,						a = None					,						a=False					,						a=None					,						a=None					,						**a					,						)      ->      Tuple[PreTrainedModel, List[int], List[int]]:
					__A	:						List[str]       =				'encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher.'
					assert (e is not None) or (d is not None), _msg
					if isinstance(a					,						a  ):
										AutoTokenizer.from_pretrained(a  ).save_pretrained(a  )  # purely for convenience
										__A	:						Optional[int]       =				AutoModelForSeqaSeqLM.from_pretrained(a  ).eval()
					else:
										assert isinstance(a					,						a  ), F"""teacher must be a model or string got type {type(a  )}"""
					__A	:						int       =				teacher.config.to_diff_dict()

					try:
										__A    ,				__A	:						List[Any]       =				teacher.config.encoder_layers, teacher.config.decoder_layers
										if e is None:
															__A	:						str       =				teacher_e
										if d is None:
															__A	:						List[Any]       =				teacher_d
										init_kwargs.update({'encoder_layers': e, 'decoder_layers': d}  )
					except AttributeError:  # T5
										if hasattr(teacher.config					,						'num_encoder_layers'  ):
															__A    ,				__A	:						List[Any]       =				teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
										else:
															__A    ,				__A	:						Optional[int]       =				teacher.config.num_layers, teacher.config.num_decoder_layers
										if e is None:
															__A	:						int       =				teacher_e
										if d is None:
															__A	:						Optional[Any]       =				teacher_d
										if hasattr(teacher.config					,						'num_encoder_layers'  ):
															init_kwargs.update({'num_encoder_layers': e, 'num_decoder_layers': d}  )
										else:
															init_kwargs.update({'num_layers': e, 'num_decoder_layers': d}  )

    # Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
					init_kwargs.update(a  )

					# Copy weights
					__A	:						Dict       =				teacher.config_class(**a  )
					__A	:						int       =				AutoModelForSeqaSeqLM.from_config(a  )
					# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
					__A	:						Any       =				student.load_state_dict(teacher.state_dict()					,						strict=a  )
					assert info.missing_keys == [], info.missing_keys  # every student key should have a teacher keys.

					if copy_first_teacher_layers:  # Our copying is done. We just log and save
										__A    ,				__A	:						Optional[int]       =				list(range(a  )  ), list(range(a  )  )
										logger.info(
										    F"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to"""
										    F""" {save_path}"""  )
										student.save_pretrained(a  )
										return student, e_layers_to_copy, d_layers_to_copy

					# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
					if e_layers_to_copy is None:
										__A	:						List[int]       =				pick_layers_to_copy(a					,						a  )
					if d_layers_to_copy is None:
										__A	:						List[int]       =				pick_layers_to_copy(a					,						a  )

					try:
										if hasattr(
										    a					,						'prophetnet'  ):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
															copy_layers(teacher.prophetnet.encoder.layers					,						student.prophetnet.encoder.layers					,						a  )
															copy_layers(teacher.prophetnet.decoder.layers					,						student.prophetnet.decoder.layers					,						a  )
										else:
															copy_layers(teacher.model.encoder.layers					,						student.model.encoder.layers					,						a  )
															copy_layers(teacher.model.decoder.layers					,						student.model.decoder.layers					,						a  )
					except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
										copy_layers(teacher.encoder.block					,						student.encoder.block					,						a  )
										copy_layers(teacher.decoder.block					,						student.decoder.block					,						a  )
					logger.info(
					    F"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}"""  )
					__A	:						Optional[int]       =				{
					    'teacher_type': teacher.config.model_type,
					    'copied_encoder_layers': e_layers_to_copy,
					    'copied_decoder_layers': d_layers_to_copy,
					}
					student.save_pretrained(a  )
					# Save information about copying for easier reproducibility

					return student, e_layers_to_copy, d_layers_to_copy


if __name__ == "__main__":
				fire.Fire(create_student_by_copying_alternating_layers)




