


'''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


__snake_case											=					logging.get_logger(__name__)


def       a				(      __a			, __a			, __a       )  ->     None:



					'''simple docstring'''






					UpperCamelCase__       :Optional[Any]							 =			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()       )


__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))},
}
__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       a				(      __a			, __a       )  ->     Optional[Any]:



					'''simple docstring'''






					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(__a       )       )


def       a				(      __a			, __a       )  ->     List[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(__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			, )  ->     Tuple[PreTrainedModel, List[int], List[int]]:



					'''simple docstring'''






					UpperCamelCase__       :Optional[int]							 =			'''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__       :int							 =			AutoModelForSeqaSeqLM.from_pretrained(__a       ).eval()
					else:
										assert isinstance(__a			, __a       ), f'''teacher must be a model or string got type {type(__a       )}'''
					UpperCamelCase__       :Union[str, Any]							 =			teacher.config.to_diff_dict()

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



'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case											=					logging.get_logger(__name__)

__snake_case											=					{
    '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/config.json''',
    '''distilbert-base-uncased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/config.json''',
    '''distilbert-base-cased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json''',
    '''distilbert-base-multilingual-cased''': (
        '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'''
    ),
    '''distilbert-base-uncased-finetuned-sst-2-english''': (
        '''https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'''
    ),
}
class        lowercase     (							A__ ):

		"""simple docstring"""

		_a   			=    'distilbert'
		_a   			=    {
		    'hidden_size': 'dim',
		    'num_attention_heads': 'n_heads',
		    'num_hidden_layers': 'n_layers',
		}




		def __init__(		self   ,       UpperCamelCase_=30522   ,       UpperCamelCase_=512   ,       UpperCamelCase_=False   ,       UpperCamelCase_=6   ,       UpperCamelCase_=12   ,       UpperCamelCase_=768   ,       UpperCamelCase_=4 * 768   ,       UpperCamelCase_=0.1   ,       UpperCamelCase_=0.1   ,       UpperCamelCase_="gelu"   ,       UpperCamelCase_=0.02   ,       UpperCamelCase_=0.1   ,       UpperCamelCase_=0.2   ,       UpperCamelCase_=0   ,       **UpperCamelCase_   ,       ):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			vocab_size
							UpperCamelCase__       :Dict							 =			max_position_embeddings
							UpperCamelCase__       :str							 =			sinusoidal_pos_embds
							UpperCamelCase__       :Any							 =			n_layers
							UpperCamelCase__       :str							 =			n_heads
							UpperCamelCase__       :Tuple							 =			dim
							UpperCamelCase__       :str							 =			hidden_dim
							UpperCamelCase__       :Dict							 =			dropout
							UpperCamelCase__       :int							 =			attention_dropout
							UpperCamelCase__       :Optional[Any]							 =			activation
							UpperCamelCase__       :Optional[int]							 =			initializer_range
							UpperCamelCase__       :Union[str, Any]							 =			qa_dropout
							UpperCamelCase__       :Dict							 =			seq_classif_dropout
							super().__init__(**UpperCamelCase_   ,       pad_token_id=UpperCamelCase_							)



class        lowercase     (							A__ ):

		"""simple docstring"""

		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							if self.task == "multiple-choice":
												UpperCamelCase__       :str							 =			{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
							else:
												UpperCamelCase__       :str							 =			{0: '''batch''', 1: '''sequence'''}
							return OrderedDict(
							    [
							        ('''input_ids''', dynamic_axis),
							        ('''attention_mask''', dynamic_axis),
							    ]							)