
"""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


_lowercase							      = logging.get_logger(__name__)






def 					_snake_case (						snake_case__      :   nn.ModuleList		,					snake_case__      :   nn.ModuleList		,					snake_case__      :   List[int]   ):
	A        =      nn.ModuleList([src_layers[i] for i in layers_to_copy]   )
	assert len(__lowercase   ) == len(__lowercase   ), F'{len(__lowercase   )} != {len(__lowercase   )}'
	dest_layers.load_state_dict(layers_to_copy.state_dict()   )


_lowercase							      = {
    # 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))},
}
_lowercase							      = {
    # 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 					_snake_case (						snake_case__      :   Optional[Any]		,					snake_case__      :   List[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(__lowercase   )   )






def 					_snake_case (						snake_case__      :   Dict		,					snake_case__      :   Optional[Any]   ):
	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(__lowercase   )   )
	elif n_student == 1:
		return [n_teacher - 1]
	else:
		return LAYERS_TO_SUPERVISE[n_teacher][n_student]






def 					_snake_case (						snake_case__      :   Union[str, PreTrainedModel]		,					snake_case__      :   Union[str, Path] = "student"		,					snake_case__      :   Union[int, None] = None		,					snake_case__      :   Union[int, None] = None		,					snake_case__      :   List[str]=False		,					snake_case__      :   Tuple=None		,					snake_case__      :   str=None		,					**snake_case__      :   Any		,					):
	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(__lowercase		,					__lowercase   ):
		AutoTokenizer.from_pretrained(__lowercase   ).save_pretrained(__lowercase   )  # purely for convenience
		A        =      AutoModelForSeqaSeqLM.from_pretrained(__lowercase   ).eval()
	else:
		assert isinstance(__lowercase		,					__lowercase   ), F'teacher must be a model or string got type {type(__lowercase   )}'
	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(__lowercase   )

	# Copy weights
	A        =      teacher.config_class(**__lowercase   )
	A        =      AutoModelForSeqaSeqLM.from_config(__lowercase   )
	# 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=__lowercase   )
	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(__lowercase   )   ), list(range(__lowercase   )   )
		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(__lowercase   )
		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(__lowercase		,					__lowercase   )
	if d_layers_to_copy is None:
		A        =      pick_layers_to_copy(__lowercase		,					__lowercase   )

	try:
		if hasattr(
		    __lowercase		,					'prophetnet'   ):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
			copy_layers(teacher.prophetnet.encoder.layers		,					student.prophetnet.encoder.layers		,					__lowercase   )
			copy_layers(teacher.prophetnet.decoder.layers		,					student.prophetnet.decoder.layers		,					__lowercase   )
		else:
			copy_layers(teacher.model.encoder.layers		,					student.model.encoder.layers		,					__lowercase   )
			copy_layers(teacher.model.decoder.layers		,					student.model.decoder.layers		,					__lowercase   )
	except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
		copy_layers(teacher.encoder.block		,					student.encoder.block		,					__lowercase   )
		copy_layers(teacher.decoder.block		,					student.decoder.block		,					__lowercase   )
	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(__lowercase   )
	# 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 os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_pegasus import PegasusTokenizer
else:
			_lowerCamelCase       :				int       =			None


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


_lowerCamelCase       :				Tuple       =			'''▁'''

_lowerCamelCase       :				Optional[Any]       =			{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCamelCase       :				Any       =			{
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''},
    '''tokenizer_file''': {
        '''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'''
    },
}

_lowerCamelCase       :				Optional[int]       =			{
    '''google/pegasus-xsum''': 512,
}




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	_UpperCAmelCase       :		int       =					VOCAB_FILES_NAMES
	_UpperCAmelCase       :		Union[str, Any]       =					PRETRAINED_VOCAB_FILES_MAP
	_UpperCAmelCase       :		Union[str, Any]       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_UpperCAmelCase       :		Any       =					PegasusTokenizer
	_UpperCAmelCase       :		Dict       =					["input_ids", "attention_mask"]



	def __init__(  self	:    Tuple			,  lowercase	:    str=None			,  lowercase	:    Any=None			,  lowercase	:    List[Any]="<pad>"			,  lowercase	:    List[Any]="</s>"			,  lowercase	:    Tuple="<unk>"			,  lowercase	:    Any="<mask_2>"			,  lowercase	:    List[str]="<mask_1>"			,  lowercase	:    List[Any]=None			,  lowercase	:    Dict=103			,  **lowercase	:    Optional[Any]			,  ):


				'''simple docstring'''


				_snake_case		=			offset

				if additional_special_tokens is not None:
							if not isinstance(lowercase			,  lowercase	):
										raise TypeError(
										    f'''additional_special_tokens should be of type {type(lowercase	)}, but is'''
										    f''' {type(lowercase	)}'''	)

							_snake_case		=			(
							    ([mask_token_sent] + additional_special_tokens)
							    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
							    else additional_special_tokens
							)
							# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
							additional_special_tokens_extended += [
							    f'''<unk_{i}>''' for i in range(len(lowercase	)			,  self.offset - 1	)
							]

							if len(set(lowercase	)	) != len(lowercase	):
										raise ValueError(
										    'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
										    f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''	)
							_snake_case		=			additional_special_tokens_extended
				else:
							_snake_case		=			[mask_token_sent] if mask_token_sent is not None else []
							additional_special_tokens += [f'''<unk_{i}>''' for i in range(2			,  self.offset	)]

				super().__init__(
				    lowercase			,  tokenizer_file=lowercase			,  pad_token=lowercase			,  eos_token=lowercase			,  unk_token=lowercase			,  mask_token=lowercase			,  mask_token_sent=lowercase			,  offset=lowercase			,  additional_special_tokens=lowercase			,  **lowercase			,  )
				_snake_case		=			vocab_file
				_snake_case		=			False if not self.vocab_file else True



	def  A							(  self	:    List[str]			,  lowercase	:    Optional[int]	):


				'''simple docstring'''


				_snake_case		=			set(self.all_special_ids	)  # call it once instead of inside list comp
				all_special_ids.remove(self.unk_token_id	)  # <unk> is only sometimes special

				if all_special_ids != set(range(len(self.additional_special_tokens	) + 3	)	):
							raise ValueError(
							    'There should be 3 special tokens: mask_token, pad_token, and eos_token +'
							    f''' {len(self.additional_special_tokens	)} additional_special_tokens, but got {all_special_ids}'''	)

				return [1 if x in all_special_ids else 0 for x in seq]



	def  A							(  self	:    List[Any]			,  lowercase	:    List			,  lowercase	:    Optional[List] = None			,  lowercase	:    bool = False	):


				'''simple docstring'''


				if already_has_special_tokens:
							return self._special_token_mask(lowercase	)
				elif token_ids_a is None:
							return self._special_token_mask(lowercase	) + [1]
				else:
							return self._special_token_mask(token_ids_a + token_ids_a	) + [1]



	def  A							(  self	:    Any			,  lowercase	:    Tuple			,  lowercase	:    Any=None	):


				'''simple docstring'''


				if token_ids_a is None:
							return token_ids_a + [self.eos_token_id]
				# We don't expect to process pairs, but leave the pair logic for API consistency
				return token_ids_a + token_ids_a + [self.eos_token_id]





	def  A							(  self	:    int			,  lowercase	:    str			,  lowercase	:    Optional[str] = None	):


				'''simple docstring'''


				if not self.can_save_slow_tokenizer:
							raise ValueError(
							    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
							    'tokenizer.'	)

				if not os.path.isdir(lowercase	):
							logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
							return
				_snake_case		=			os.path.join(
				    lowercase			,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

				if os.path.abspath(self.vocab_file	) != os.path.abspath(lowercase	):
							copyfile(self.vocab_file			,  lowercase	)

				return (out_vocab_file,)