





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__   : Union[str, Any]					  = {
    """169M""": 7_68,
    """430M""": 10_24,
    """1B5""": 20_48,
    """3B""": 25_60,
    """7B""": 40_96,
    """14B""": 51_20,
}




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

		if name != "head.weight":
			UpperCAmelCase_								=     """rwkv.""" + name

		UpperCAmelCase_								=     weight
	return state_dict




def    A_(			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."""   )
		UpperCAmelCase_								=     50277
		UpperCAmelCase_								=     AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b"""   )
	else:
		UpperCAmelCase_								=     PreTrainedTokenizerFast(tokenizer_file=A   )
		UpperCAmelCase_								=     len(A   )
	tokenizer.save_pretrained(A   )

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

	UpperCAmelCase_								=     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
	UpperCAmelCase_								=     hf_hub_download(A				,      A   )
	UpperCAmelCase_								=     torch.load(A				,      map_location="""cpu"""   )
	UpperCAmelCase_								=     convert_state_dict(A   )

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

	if index is not None:
		UpperCAmelCase_								=     os.path.join(A				,      A   )
		# Save the index as well
		with open(A				,      """w"""				,      encoding="""utf-8"""   ) as f:
			UpperCAmelCase_								=     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."""   )
		UpperCAmelCase_								=     list(shards.keys()   )

		del state_dict
		del shards
		gc.collect()

		for shard_file in shard_files:
			UpperCAmelCase_								=     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."""   )
		UpperCAmelCase_								=     AutoModelForCausalLM.from_pretrained(A   )
		model.push_to_hub(A				,      max_shard_size="""2GB"""   )
		tokenizer.push_to_hub(A   )


if __name__ == "__main__":
				UpperCamelCase__   : Optional[int]					  = 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__   : List[str]					  = 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__   : Tuple					  = logging.get_logger(__name__)




def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     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__   : str					  = {
    # 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__   : Any					  = {
    # 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    A_(			A				,      A   ):
	try:
		UpperCAmelCase_								=     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    A_(			A				,      A   ):
	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    A_(			A				,      A = "student"				,      A = None				,      A = None				,      A=False				,      A=None				,      A=None				,      **A				,      ):
	UpperCAmelCase_								=     """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
		UpperCAmelCase_								=     AutoModelForSeqaSeqLM.from_pretrained(A   ).eval()
	else:
		assert isinstance(A				,      A   ), f"""teacher must be a model or string got type {type(A   )}"""
	UpperCAmelCase_								=     teacher.config.to_diff_dict()

	try:
		UpperCAmelCase_						,	UpperCAmelCase_								=     teacher.config.encoder_layers, teacher.config.decoder_layers
		if e is None:
			UpperCAmelCase_								=     teacher_e
		if d is None:
			UpperCAmelCase_								=     teacher_d
		init_kwargs.update({"""encoder_layers""": e, """decoder_layers""": d}   )
	except AttributeError:  # T5
		if hasattr(teacher.config				,      """num_encoder_layers"""   ):
			UpperCAmelCase_						,	UpperCAmelCase_								=     teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
		else:
			UpperCAmelCase_						,	UpperCAmelCase_								=     teacher.config.num_layers, teacher.config.num_decoder_layers
		if e is None:
			UpperCAmelCase_								=     teacher_e
		if d is None:
			UpperCAmelCase_								=     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
	UpperCAmelCase_								=     teacher.config_class(**A   )
	UpperCAmelCase_								=     AutoModelForSeqaSeqLM.from_config(A   )
	# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
	UpperCAmelCase_								=     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
		UpperCAmelCase_						,	UpperCAmelCase_								=     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:
		UpperCAmelCase_								=     pick_layers_to_copy(A				,      A   )
	if d_layers_to_copy is None:
		UpperCAmelCase_								=     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}"""   )
	UpperCAmelCase_								=     {
	    """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)


