





"""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


lowerCamelCase__ :       List[str]						     =			logging.get_logger(__name__)





def __A						(   a_ :							nn.ModuleList		,		a_ :							nn.ModuleList		,		a_ :							List[int]     )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     str				= nn.ModuleList([src_layers[i] for i in layers_to_copy]     )
		assert len(snake_case_     ) == len(snake_case_     ), F"{len(snake_case_     )} != {len(snake_case_     )}"
		dest_layers.load_state_dict(layers_to_copy.state_dict()     )


lowerCamelCase__ :       Dict						     =			{
    # 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))},
}
lowerCamelCase__ :       Dict						     =			{
    # 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_ :							Any		,		a_ :							Optional[Any]     )->							Optional[int]:




		'''simple docstring'''






		try:
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= 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(snake_case_     )     )





def __A						(   a_ :							List[str]		,		a_ :							str     )->							int:




		'''simple docstring'''






		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(snake_case_     )     )
		elif n_student == 1:
				return [n_teacher - 1]
		else:
				return LAYERS_TO_SUPERVISE[n_teacher][n_student]





def __A						(   a_ :							Union[str, PreTrainedModel]		,		a_ :							Union[str, Path] = "student"		,		a_ :							Union[int, None] = None		,		a_ :							Union[int, None] = None		,		a_ :							Optional[Any]=False		,		a_ :							Union[str, Any]=None		,		a_ :							Optional[Any]=None		,		**a_ :							Tuple		,		)->							Any:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     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(snake_case_		,		snake_case_     ):
				AutoTokenizer.from_pretrained(snake_case_     ).save_pretrained(snake_case_     )  # purely for convenience
				SCREAMING_SNAKE_CASE       :     str				= AutoModelForSeqaSeqLM.from_pretrained(snake_case_     ).eval()
		else:
				assert isinstance(snake_case_		,		snake_case_     ), F"teacher must be a model or string got type {type(snake_case_     )}"
		SCREAMING_SNAKE_CASE       :     Dict				= teacher.config.to_diff_dict()

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

		# Copy weights
		SCREAMING_SNAKE_CASE       :     Any				= teacher.config_class(**snake_case_     )
		SCREAMING_SNAKE_CASE       :     Optional[int]				= AutoModelForSeqaSeqLM.from_config(snake_case_     )
		# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
		SCREAMING_SNAKE_CASE       :     Tuple				= student.load_state_dict(teacher.state_dict()		,		strict=snake_case_     )
		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
				SCREAMING_SNAKE_CASE       :     List[Any]				= list(range(snake_case_     )     ), list(range(snake_case_     )     )
				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(snake_case_     )
				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:
				SCREAMING_SNAKE_CASE       :     List[int]				= pick_layers_to_copy(snake_case_		,		snake_case_     )
		if d_layers_to_copy is None:
				SCREAMING_SNAKE_CASE       :     List[int]				= pick_layers_to_copy(snake_case_		,		snake_case_     )

		try:
				if hasattr(
				    snake_case_		,		'''prophetnet'''     ):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
						copy_layers(teacher.prophetnet.encoder.layers		,		student.prophetnet.encoder.layers		,		snake_case_     )
						copy_layers(teacher.prophetnet.decoder.layers		,		student.prophetnet.decoder.layers		,		snake_case_     )
				else:
						copy_layers(teacher.model.encoder.layers		,		student.model.encoder.layers		,		snake_case_     )
						copy_layers(teacher.model.decoder.layers		,		student.model.decoder.layers		,		snake_case_     )
		except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
				copy_layers(teacher.encoder.block		,		student.encoder.block		,		snake_case_     )
				copy_layers(teacher.decoder.block		,		student.decoder.block		,		snake_case_     )
		logger.info(
		    F"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}"     )
		SCREAMING_SNAKE_CASE       :     str				= {
		    "teacher_type": teacher.config.model_type,
		    "copied_encoder_layers": e_layers_to_copy,
		    "copied_decoder_layers": d_layers_to_copy,
		}
		student.save_pretrained(snake_case_     )
		# 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 json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase__ :       List[str]						     =			logging.get_logger(__name__)

lowerCamelCase__ :       Optional[int]						     =			{"vocab_file": "vocab.json"}

lowerCamelCase__ :       Dict						     =			{
    "vocab_file": {
        "mgp-str": "https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json",
    }
}

lowerCamelCase__ :       Optional[Any]						     =			{"mgp-str": 27}




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  VOCAB_FILES_NAMES
	UpperCamelCase				=  PRETRAINED_VOCAB_FILES_MAP
	UpperCamelCase				=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES





	def __init__(						self    :int		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :Optional[int]="[GO]"		, lowerCamelCase_    :int="[GO]"		, lowerCamelCase_    :str="[s]"		, lowerCamelCase_    :Dict="[GO]"		, **lowerCamelCase_    :List[str]					)							->			Tuple:







			'''simple docstring'''




			super().__init__(
			    unk_token=lowerCamelCase_		, bos_token=lowerCamelCase_		, eos_token=lowerCamelCase_		, pad_token=lowerCamelCase_		, **lowerCamelCase_		, )

			with open(lowerCamelCase_		, encoding='''utf-8'''					) as vocab_handle:
					SCREAMING_SNAKE_CASE       :     int				= json.load(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= {v: k for k, v in self.vocab.items()}





	@property
	def   __lowerCAmelCase (						self    :int					)							->			Dict:







			'''simple docstring'''




			return len(self.vocab					)





	def   __lowerCAmelCase (						self    :List[str]					)							->			Dict:







			'''simple docstring'''




			return dict(self.vocab		, **self.added_tokens_encoder					)





	def   __lowerCAmelCase (						self    :List[str]		, lowerCamelCase_    :Optional[Any]					)							->			List[str]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= []
			for s in text:
					char_tokens.extend(lowerCamelCase_					)
			return char_tokens





	def   __lowerCAmelCase (						self    :Dict		, lowerCamelCase_    :Tuple					)							->			Optional[int]:







			'''simple docstring'''




			return self.vocab.get(lowerCamelCase_		, self.vocab.get(self.unk_token					)					)





	def   __lowerCAmelCase (						self    :List[str]		, lowerCamelCase_    :Dict					)							->			Optional[int]:







			'''simple docstring'''




			return self.decoder.get(lowerCamelCase_					)







	def   __lowerCAmelCase (						self    :Optional[int]		, lowerCamelCase_    :str		, lowerCamelCase_    :Optional[str] = None					)							->			Tuple[str]:







			'''simple docstring'''




			if not os.path.isdir(lowerCamelCase_					):
					logger.error('''Vocabulary path ({}) should be a directory'''.format(lowerCamelCase_					)					)
					return
			SCREAMING_SNAKE_CASE       :     List[Any]				= os.path.join(
			    lowerCamelCase_		, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

			with open(lowerCamelCase_		, '''w'''		, encoding='''utf-8'''					) as f:
					f.write(json.dumps(self.vocab		, indent=2		, sort_keys=lowerCamelCase_		, ensure_ascii=lowerCamelCase_					) + '''\n'''					)

			return (vocab_file,)





