

from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {
    'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
    'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/config.json',
    'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/config.json',
    'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/config.json',
    'bert-base-multilingual-uncased': 'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json',
    'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json',
    'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/config.json',
    'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/config.json',
    'bert-large-uncased-whole-word-masking': (
        'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json'
    ),
    'bert-large-cased-whole-word-masking': (
        'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json'
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json'
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json'
    ),
    'bert-base-cased-finetuned-mrpc': 'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json',
    'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json',
    'bert-base-german-dbmdz-uncased': 'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json',
    'cl-tohoku/bert-base-japanese': 'https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json',
    'cl-tohoku/bert-base-japanese-whole-word-masking': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json'
    ),
    'cl-tohoku/bert-base-japanese-char': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json'
    ),
    'cl-tohoku/bert-base-japanese-char-whole-word-masking': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json'
    ),
    'TurkuNLP/bert-base-finnish-cased-v1': (
        'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json'
    ),
    'TurkuNLP/bert-base-finnish-uncased-v1': (
        'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json'
    ),
    'wietsedv/bert-base-dutch-cased': 'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json',
    # See all BERT models at https://huggingface.co/models?filter=bert
}

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			"bert"


						def __init__(       self						,					A_=30522						,					A_=768						,					A_=12						,					A_=12						,					A_=3072						,					A_="gelu"						,					A_=0.1						,					A_=0.1						,					A_=512						,					A_=2						,					A_=0.02						,					A_=1e-12						,					A_=0						,					A_="absolute"						,					A_=True						,					A_=None						,					**A_						,					):







										'''simple docstring'''


										super().__init__(pad_token_id=A_						,					**A_	)

										_UpperCAmelCase       :		int 		=    vocab_size
										_UpperCAmelCase       :		List[Any] 		=    hidden_size
										_UpperCAmelCase       :		Any 		=    num_hidden_layers
										_UpperCAmelCase       :		Optional[Any] 		=    num_attention_heads
										_UpperCAmelCase       :		Tuple 		=    hidden_act
										_UpperCAmelCase       :		str 		=    intermediate_size
										_UpperCAmelCase       :		List[str] 		=    hidden_dropout_prob
										_UpperCAmelCase       :		Optional[int] 		=    attention_probs_dropout_prob
										_UpperCAmelCase       :		List[Any] 		=    max_position_embeddings
										_UpperCAmelCase       :		Optional[Any] 		=    type_vocab_size
										_UpperCAmelCase       :		int 		=    initializer_range
										_UpperCAmelCase       :		List[Any] 		=    layer_norm_eps
										_UpperCAmelCase       :		List[Any] 		=    position_embedding_type
										_UpperCAmelCase       :		str 		=    use_cache
										_UpperCAmelCase       :		Dict 		=    classifier_dropout







class 				a  (	UpperCAmelCase				):


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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





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)


