


"""simple docstring"""


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


a_       							=    logging.get_logger(__name__)






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase   )	->	None:
				_A					  =			nn.ModuleList([src_layers[i] for i in layers_to_copy]   )
				assert len(lowerCamelCase__   ) == len(lowerCamelCase__   ), f"""{len(lowerCamelCase__   )} != {len(lowerCamelCase__   )}"""
				dest_layers.load_state_dict(layers_to_copy.state_dict()   )


a_       							=    {
    # 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))},
}
a_       							=    {
    # 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__ (				__lowercase     ,			__lowercase   )	->	Union[str, Any]:
				try:
								_A					  =			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(lowerCamelCase__   )   )






def      a__ (				__lowercase     ,			__lowercase   )	->	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(lowerCamelCase__   )   )
				elif n_student == 1:
								return [n_teacher - 1]
				else:
								return LAYERS_TO_SUPERVISE[n_teacher][n_student]






def      a__ (				__lowercase     ,			__lowercase = "student"     ,			__lowercase = None     ,			__lowercase = None     ,			__lowercase=False     ,			__lowercase=None     ,			__lowercase=None     ,			**__lowercase     ,			)	->	Tuple[PreTrainedModel, List[int], List[int]]:
				_A					  =			"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(lowerCamelCase__     ,			lowerCamelCase__   ):
								AutoTokenizer.from_pretrained(lowerCamelCase__   ).save_pretrained(lowerCamelCase__   )  # purely for convenience
								_A					  =			AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__   ).eval()
				else:
								assert isinstance(lowerCamelCase__     ,			lowerCamelCase__   ), f"""teacher must be a model or string got type {type(lowerCamelCase__   )}"""
				_A					  =			teacher.config.to_diff_dict()

				try:
								_A  ,		_A					  =			teacher.config.encoder_layers, teacher.config.decoder_layers
								if e is None:
												_A					  =			teacher_e
								if d is None:
												_A					  =			teacher_d
								init_kwargs.update({"encoder_layers": e, "decoder_layers": d}   )
				except AttributeError:  # T5
								if hasattr(teacher.config     ,			"num_encoder_layers"   ):
												_A  ,		_A					  =			teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
								else:
												_A  ,		_A					  =			teacher.config.num_layers, teacher.config.num_decoder_layers
								if e is None:
												_A					  =			teacher_e
								if d is None:
												_A					  =			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(lowerCamelCase__   )

				# Copy weights
				_A					  =			teacher.config_class(**lowerCamelCase__   )
				_A					  =			AutoModelForSeqaSeqLM.from_config(lowerCamelCase__   )
				# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
				_A					  =			student.load_state_dict(teacher.state_dict()     ,			strict=lowerCamelCase__   )
				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					  =			list(range(lowerCamelCase__   )   ), list(range(lowerCamelCase__   )   )
								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(lowerCamelCase__   )
								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					  =			pick_layers_to_copy(lowerCamelCase__     ,			lowerCamelCase__   )
				if d_layers_to_copy is None:
								_A					  =			pick_layers_to_copy(lowerCamelCase__     ,			lowerCamelCase__   )

				try:
								if hasattr(
								    lowerCamelCase__     ,			"prophetnet"   ):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
												copy_layers(teacher.prophetnet.encoder.layers     ,			student.prophetnet.encoder.layers     ,			lowerCamelCase__   )
												copy_layers(teacher.prophetnet.decoder.layers     ,			student.prophetnet.decoder.layers     ,			lowerCamelCase__   )
								else:
												copy_layers(teacher.model.encoder.layers     ,			student.model.encoder.layers     ,			lowerCamelCase__   )
												copy_layers(teacher.model.decoder.layers     ,			student.model.decoder.layers     ,			lowerCamelCase__   )
				except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
								copy_layers(teacher.encoder.block     ,			student.encoder.block     ,			lowerCamelCase__   )
								copy_layers(teacher.decoder.block     ,			student.decoder.block     ,			lowerCamelCase__   )
				logger.info(
				    f"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}"""   )
				_A					  =			{
				    "teacher_type": teacher.config.model_type,
				    "copied_encoder_layers": e_layers_to_copy,
				    "copied_decoder_layers": d_layers_to_copy,
				}
				student.save_pretrained(lowerCamelCase__   )
				# 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)



"""simple docstring"""


import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
		a_       							=    argparse.ArgumentParser(
		    description=(
		        "Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"
		        " Distillation"
		    )
		)
		parser.add_argument("--model_type", default="roberta", choices=["roberta", "gpt2"])
		parser.add_argument("--model_name", default="roberta-large", type=str)
		parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_roberta_048131723.pth", type=str)
		parser.add_argument("--vocab_transform", action="store_true")
		a_       							=    parser.parse_args()

		if args.model_type == "roberta":
				a_       							=    RobertaForMaskedLM.from_pretrained(args.model_name)
				a_       							=    "roberta"
		elif args.model_type == "gpt2":
				a_       							=    GPTaLMHeadModel.from_pretrained(args.model_name)
				a_       							=    "transformer"

		a_       							=    model.state_dict()
		a_       							=    {}

		# Embeddings #
		if args.model_type == "gpt2":
				for param_name in ["wte.weight", "wpe.weight"]:
						a_       							=    state_dict[f'''{prefix}.{param_name}''']
		else:
				for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
						a_       							=    f'''{prefix}.embeddings.{w}.weight'''
						a_       							=    state_dict[param_name]
				for w in ["weight", "bias"]:
						a_       							=    f'''{prefix}.embeddings.LayerNorm.{w}'''
						a_       							=    state_dict[param_name]

    # Transformer Blocks #
		a_       							=    0
		for teacher_idx in [0, 2, 4, 7, 9, 11]:
				if args.model_type == "gpt2":
						for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
								for w in ["weight", "bias"]:
										a_       							=    state_dict[
										    f'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
										]
						a_       							=    state_dict[f'''{prefix}.h.{teacher_idx}.attn.bias''']
				else:
						for layer in [
						    "attention.self.query",
						    "attention.self.key",
						    "attention.self.value",
						    "attention.output.dense",
						    "attention.output.LayerNorm",
						    "intermediate.dense",
						    "output.dense",
						    "output.LayerNorm",
						]:
								for w in ["weight", "bias"]:
										a_       							=    state_dict[
										    f'''{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'''
										]
				std_idx += 1

		# Language Modeling Head ###s
		if args.model_type == "roberta":
				for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
						a_       							=    state_dict[f'''{layer}''']
				if args.vocab_transform:
						for w in ["weight", "bias"]:
								a_       							=    state_dict[f'''lm_head.dense.{w}''']
								a_       							=    state_dict[f'''lm_head.layer_norm.{w}''']
		elif args.model_type == "gpt2":
				for w in ["weight", "bias"]:
						a_       							=    state_dict[f'''{prefix}.ln_f.{w}''']
				a_       							=    state_dict["lm_head.weight"]

		print(f'''N layers selected for distillation: {std_idx}''')
		print(f'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

		print(f'''Save transferred checkpoint to {args.dump_checkpoint}.''')
		torch.save(compressed_sd, args.dump_checkpoint)