

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


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  nn.ModuleList  ,      lowerCAmelCase:  nn.ModuleList  ,      lowerCAmelCase:  List[int] )   ->       None:
				_UpperCAmelCase       :		str 		=    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() )


SCREAMING_SNAKE_CASE_        =      {
    # 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))},
}
SCREAMING_SNAKE_CASE_        =      {
    # 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							(				lowerCAmelCase:  Any  ,      lowerCAmelCase:  List[str] )   ->       Dict:
				try:
								_UpperCAmelCase       :		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(lowerCAmelCase ) )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[Any]  ,      lowerCAmelCase:  Optional[Any] )   ->       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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Union[str, PreTrainedModel]  ,      lowerCAmelCase:  Union[str, Path] = "student"  ,      lowerCAmelCase:  Union[int, None] = None  ,      lowerCAmelCase:  Union[int, None] = None  ,      lowerCAmelCase:  str=False  ,      lowerCAmelCase:  Union[str, Any]=None  ,      lowerCAmelCase:  Any=None  ,      **lowerCAmelCase:  str  ,      )   ->       Tuple[PreTrainedModel, List[int], List[int]]:
				_UpperCAmelCase       :		Dict 		=    "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
								_UpperCAmelCase       :		Dict 		=    AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase ).eval()
				else:
								assert isinstance(lowerCAmelCase  ,      lowerCAmelCase ), F'teacher must be a model or string got type {type(lowerCAmelCase )}'
				_UpperCAmelCase       :		List[str] 		=    teacher.config.to_diff_dict()

				try:
								_UpperCAmelCase		,							_UpperCAmelCase       :		Any 		=    teacher.config.encoder_layers, teacher.config.decoder_layers
								if e is None:
												_UpperCAmelCase       :		int 		=    teacher_e
								if d is None:
												_UpperCAmelCase       :		Optional[Any] 		=    teacher_d
								init_kwargs.update({"encoder_layers": e, "decoder_layers": d} )
				except AttributeError:  # T5
								if hasattr(teacher.config  ,      "num_encoder_layers" ):
												_UpperCAmelCase		,							_UpperCAmelCase       :		int 		=    teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
								else:
												_UpperCAmelCase		,							_UpperCAmelCase       :		Dict 		=    teacher.config.num_layers, teacher.config.num_decoder_layers
								if e is None:
												_UpperCAmelCase       :		List[Any] 		=    teacher_e
								if d is None:
												_UpperCAmelCase       :		Optional[int] 		=    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
				_UpperCAmelCase       :		Any 		=    teacher.config_class(**lowerCAmelCase )
				_UpperCAmelCase       :		Tuple 		=    AutoModelForSeqaSeqLM.from_config(lowerCAmelCase )
				# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
				_UpperCAmelCase       :		List[Any] 		=    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
								_UpperCAmelCase		,							_UpperCAmelCase       :		Any 		=    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:
								_UpperCAmelCase       :		List[int] 		=    pick_layers_to_copy(lowerCAmelCase  ,      lowerCAmelCase )
				if d_layers_to_copy is None:
								_UpperCAmelCase       :		List[int] 		=    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}' )
				_UpperCAmelCase       :		str 		=    {
				    "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)





import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any )   ->       Optional[int]:
				_UpperCAmelCase       :		Optional[Any] 		=    os.path.join(args.tf_model_dir  ,      "parameters.json" )
				_UpperCAmelCase       :		List[str] 		=    json.loads(open(lowerCAmelCase ).read() )
				if not params:
								raise ValueError(
								    F'It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.' )
				if not args.output.endswith(".pt" ):
								_UpperCAmelCase       :		Union[str, Any] 		=    args.output + ".pt"
				_UpperCAmelCase       :		Dict 		=    OrderedDict()
				with tf.device("/CPU:0" ):
								_UpperCAmelCase       :		int 		=    tf.train.load_checkpoint(args.tf_model_dir )
								_UpperCAmelCase       :		int 		=    reader.get_variable_to_shape_map()
								for key_name in shapes.keys():
												_UpperCAmelCase       :		Dict 		=    reader.get_tensor(lowerCAmelCase ).astype(np.floataa )
												if key_name.endswith("/adam_m" ) or key_name.endswith("/adam_v" ):
																continue
												if key_name.startswith("pasts/" ):
																if key_name.startswith("pasts/mlp" ):
																				_UpperCAmelCase       :		int 		=    int(key_name[9] )
																elif key_name.startswith("pasts/out" ):
																				_UpperCAmelCase       :		Any 		=    8
																_UpperCAmelCase       :		str 		=    "model.sqout.%d.weight" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																_UpperCAmelCase       :		Optional[Any] 		=    vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																_UpperCAmelCase       :		str 		=    torch.tensor(lowerCAmelCase )
												elif key_name.startswith("model/moe" ):
																_UpperCAmelCase       :		List[str] 		=    int(key_name[9:].split("/" )[0] )
																if key_name.endswith("/switch_gating/kernel" ):
																				_UpperCAmelCase       :		Union[str, Any] 		=    "model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
																				_UpperCAmelCase       :		List[str] 		=    vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_UpperCAmelCase       :		Tuple 		=    torch.tensor(lowerCAmelCase )
																elif key_name.endswith("/softmlp/kernel" ):
																				_UpperCAmelCase       :		List[str] 		=    "model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
																				_UpperCAmelCase       :		List[str] 		=    vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_UpperCAmelCase       :		Union[str, Any] 		=    torch.tensor(lowerCAmelCase )
																elif key_name.endswith("/wo/kernel" ) or key_name.endswith("/wi/kernel" ):
																				_UpperCAmelCase       :		str 		=    key_name[-9:-7]
																				for i in range(16 ):
																								_UpperCAmelCase       :		Optional[int] 		=    "model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
																								_UpperCAmelCase       :		Optional[int] 		=    (
																								    vnp[i].transpose([1, 0] ).copy()
																								)  # In Mesh-Tensorflow, it is one array, so it is divided
																								_UpperCAmelCase       :		Dict 		=    torch.tensor(lowerCAmelCase )
												elif key_name.startswith("model/mlp" ):
																_UpperCAmelCase       :		Tuple 		=    int(key_name[9:].split("/" )[0] )
																if key_name.endswith("/p1/kernel" ):
																				_UpperCAmelCase       :		Any 		=    "model.blocks.%d.feed_forward.mlp.wi.weight" % player
																				_UpperCAmelCase       :		int 		=    vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_UpperCAmelCase       :		List[str] 		=    torch.tensor(lowerCAmelCase )
																elif key_name.endswith("/p1/bias" ):
																				_UpperCAmelCase       :		Optional[Any] 		=    "model.blocks.%d.feed_forward.mlp.wi.bias" % player
																				_UpperCAmelCase       :		Tuple 		=    vnp.copy()  # same because it is one dimensional
																				_UpperCAmelCase       :		Any 		=    torch.tensor(lowerCAmelCase )
																elif key_name.endswith("/p2/kernel" ):
																				_UpperCAmelCase       :		str 		=    "model.blocks.%d.feed_forward.mlp.wo.weight" % player
																				_UpperCAmelCase       :		Union[str, Any] 		=    vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_UpperCAmelCase       :		List[Any] 		=    torch.tensor(lowerCAmelCase )
																elif key_name.endswith("/p2/bias" ):
																				_UpperCAmelCase       :		int 		=    "model.blocks.%d.feed_forward.mlp.wo.bias" % player
																				_UpperCAmelCase       :		Optional[int] 		=    vnp.copy()  # same because it is one dimensional
																				_UpperCAmelCase       :		str 		=    torch.tensor(lowerCAmelCase )
												elif key_name.startswith("model/ln" ):
																_UpperCAmelCase       :		Union[str, Any] 		=    int(key_name[8:].split("/" )[0] )
																if key_name.endswith("/b" ):
																				_UpperCAmelCase       :		Union[str, Any] 		=    "model.blocks.%d.feed_forward.norm.bias" % player
																				_UpperCAmelCase       :		int 		=    vnp.copy()  # same because it is one dimensional
																				_UpperCAmelCase       :		Union[str, Any] 		=    torch.tensor(lowerCAmelCase )
																elif key_name.endswith("/g" ):
																				_UpperCAmelCase       :		Optional[Any] 		=    "model.blocks.%d.feed_forward.norm.weight" % player
																				_UpperCAmelCase       :		Union[str, Any] 		=    vnp.copy()  # same because it is one dimensional
																				_UpperCAmelCase       :		List[Any] 		=    torch.tensor(lowerCAmelCase )
												elif key_name.startswith("model/att" ):
																_UpperCAmelCase       :		Optional[Any] 		=    int(key_name[9:].split("/" )[0] )
																if key_name.endswith("/qkv/kernel" ):
																				_UpperCAmelCase       :		List[Any] 		=    vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																				_UpperCAmelCase       :		Dict 		=    state[:, 0, :, :]
																				_UpperCAmelCase       :		List[Any] 		=    state[:, 1, :, :]
																				_UpperCAmelCase       :		Tuple 		=    state[:, 2, :, :]
																				_UpperCAmelCase       :		List[str] 		=    (
																				    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]] )
																				    .transpose([1, 0] )
																				    .copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				_UpperCAmelCase       :		int 		=    (
																				    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]] )
																				    .transpose([1, 0] )
																				    .copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				_UpperCAmelCase       :		Tuple 		=    (
																				    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]] )
																				    .transpose([1, 0] )
																				    .copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				_UpperCAmelCase       :		int 		=    "model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
																				_UpperCAmelCase       :		Any 		=    torch.tensor(lowerCAmelCase )
																				_UpperCAmelCase       :		List[Any] 		=    "model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
																				_UpperCAmelCase       :		Any 		=    torch.tensor(lowerCAmelCase )
																				_UpperCAmelCase       :		Union[str, Any] 		=    "model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
																				_UpperCAmelCase       :		str 		=    torch.tensor(lowerCAmelCase )
																elif key_name.endswith("/o/kernel" ):
																				_UpperCAmelCase       :		Dict 		=    "model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
																				_UpperCAmelCase       :		Tuple 		=    (
																				    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]] ).transpose([1, 0] ).copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				_UpperCAmelCase       :		List[str] 		=    torch.tensor(lowerCAmelCase )
												elif key_name.startswith("model/an" ):
																_UpperCAmelCase       :		Optional[int] 		=    int(key_name[8:].split("/" )[0] )
																if key_name.endswith("/b" ):
																				_UpperCAmelCase       :		Any 		=    "model.blocks.%d.self_attn.norm.bias" % player
																				_UpperCAmelCase       :		Any 		=    vnp.copy()  # same because it is one dimensional
																				_UpperCAmelCase       :		Dict 		=    torch.tensor(lowerCAmelCase )
																elif key_name.endswith("/g" ):
																				_UpperCAmelCase       :		Dict 		=    "model.blocks.%d.self_attn.norm.weight" % player
																				_UpperCAmelCase       :		Any 		=    vnp.copy()  # same because it is one dimensional
																				_UpperCAmelCase       :		Tuple 		=    torch.tensor(lowerCAmelCase )
												elif (
												    key_name.startswith("model/wte" )
												    or key_name.startswith("model/wpe" )
												    or key_name.startswith("model/ete" )
												):
																_UpperCAmelCase       :		Optional[Any] 		=    {"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
																    key_name[-3:]
																]
																_UpperCAmelCase       :		Dict 		=    "model.%s.weight" % nlayer
																_UpperCAmelCase       :		Optional[Any] 		=    vnp.copy()  # same in embedded
																_UpperCAmelCase       :		List[Any] 		=    torch.tensor(lowerCAmelCase )
																if key_name.startswith("model/wte" ):
																				_UpperCAmelCase       :		List[str] 		=    "lm_head.weight"
																				_UpperCAmelCase       :		Optional[int] 		=    vnp.copy()  # same in embedded
																				_UpperCAmelCase       :		List[str] 		=    torch.tensor(lowerCAmelCase )
												elif key_name.startswith("model/wob" ):
																_UpperCAmelCase       :		Dict 		=    "final_logits_bias"
																_UpperCAmelCase       :		Optional[Any] 		=    vnp.copy()  # same in embedded
																_UpperCAmelCase       :		str 		=    state.reshape((1, -1) )
																_UpperCAmelCase       :		Dict 		=    torch.tensor(lowerCAmelCase )
												elif key_name == "model/dense/kernel":
																_UpperCAmelCase       :		List[Any] 		=    "model.last_project.weight"
																_UpperCAmelCase       :		int 		=    vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																_UpperCAmelCase       :		str 		=    torch.tensor(lowerCAmelCase )
												elif key_name == "model/dense_1/bias":
																_UpperCAmelCase       :		List[Any] 		=    "model.last_project.bias"
																_UpperCAmelCase       :		Any 		=    vnp.copy()  # same because it is one dimensional
																_UpperCAmelCase       :		Any 		=    torch.tensor(lowerCAmelCase )
				torch.save(lowerCAmelCase  ,      args.output )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_        =      argparse.ArgumentParser(
					    description='model converter.', formatter_class=argparse.ArgumentDefaultsHelpFormatter
					)
					parser.add_argument('--tf_model_dir', metavar='PATH', type=str, required=True, help='import model')
					parser.add_argument('--output', metavar='PATH', type=str, required=True, help='output model')
					SCREAMING_SNAKE_CASE_        =      parser.parse_args()
					convert_tf_gptsan_to_pt(args)


