
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						:      str									= logging.get_logger(__name__)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    nn.ModuleList		,						_lowerCamelCase				:    nn.ModuleList		,						_lowerCamelCase				:    List[int])      ->					None:






							'''simple docstring'''





							__UpperCamelCase			:     List[Any]         =      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())


lowercase						:      List[str]									= {
    # 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						:      List[Any]									= {
    # 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				:    Optional[int]		,						_lowerCamelCase				:    Any)      ->					List[Any]:






							'''simple docstring'''





							try:
														__UpperCamelCase			:     str         =      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				:    Tuple		,						_lowerCamelCase				:    Optional[int])      ->					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(_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				:    int=False		,						_lowerCamelCase				:    Union[str, Any]=None		,						_lowerCamelCase				:    int=None		,						**_lowerCamelCase				:    Any		,						)      ->					Tuple[PreTrainedModel, List[int], List[int]]:






							'''simple docstring'''





							__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			:     str         =      AutoModelForSeqaSeqLM.from_pretrained(_lowerCamelCase).eval()
							else:
														assert isinstance(_lowerCamelCase		,						_lowerCamelCase), F'teacher must be a model or string got type {type(_lowerCamelCase)}'
							__UpperCamelCase			:     Dict         =      teacher.config.to_diff_dict()

							try:
														__UpperCamelCase     ,		__UpperCamelCase			:     Dict         =      teacher.config.encoder_layers, teacher.config.decoder_layers
														if e is None:
																					__UpperCamelCase			:     str         =      teacher_e
														if d is None:
																					__UpperCamelCase			:     str         =      teacher_d
														init_kwargs.update({"encoder_layers": e, "decoder_layers": d})
							except AttributeError:  # T5
														if hasattr(teacher.config		,						"num_encoder_layers"):
																					__UpperCamelCase     ,		__UpperCamelCase			:     Optional[int]         =      teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
														else:
																					__UpperCamelCase     ,		__UpperCamelCase			:     Optional[int]         =      teacher.config.num_layers, teacher.config.num_decoder_layers
														if e is None:
																					__UpperCamelCase			:     Tuple         =      teacher_e
														if d is None:
																					__UpperCamelCase			:     List[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(_lowerCamelCase)

							# Copy weights
							__UpperCamelCase			:     Tuple         =      teacher.config_class(**_lowerCamelCase)
							__UpperCamelCase			:     List[str]         =      AutoModelForSeqaSeqLM.from_config(_lowerCamelCase)
							# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
							__UpperCamelCase			:     int         =      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			:     str         =      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			:     Dict         =      {
							    "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)

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase						:      Dict									= logging.get_logger(__name__)

lowercase						:      Optional[Any]									= {
    'facebook/s2t-wav2vec2-large-en-de': (
        'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  'speech_to_text_2'
	_A			=  ['past_key_values']
	_A			=  {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}







	def __init__(      self	:Dict  ,     a	:Tuple=1_0_0_0_0  ,     a	:Optional[int]=6  ,     a	:List[str]=2_0_4_8  ,     a	:Tuple=4  ,     a	:List[Any]=0.0  ,     a	:str=True  ,     a	:Any="relu"  ,     a	:Any=2_5_6  ,     a	:Optional[int]=0.1  ,     a	:Any=0.0  ,     a	:int=0.0  ,     a	:int=0.02  ,     a	:List[Any]=2  ,     a	:Tuple=True  ,     a	:str=1  ,     a	:Optional[int]=0  ,     a	:List[Any]=2  ,     a	:Any=1_0_2_4  ,     **a	:str  ,     )	->				int:
								__UpperCamelCase			:     List[str]         =      vocab_size
								__UpperCamelCase			:     int         =      d_model
								__UpperCamelCase			:     Optional[int]         =      decoder_ffn_dim
								__UpperCamelCase			:     Any         =      decoder_layers
								__UpperCamelCase			:     Any         =      decoder_attention_heads
								__UpperCamelCase			:     Tuple         =      dropout
								__UpperCamelCase			:     Any         =      attention_dropout
								__UpperCamelCase			:     Any         =      activation_dropout
								__UpperCamelCase			:     Dict         =      activation_function
								__UpperCamelCase			:     int         =      init_std
								__UpperCamelCase			:     List[str]         =      decoder_layerdrop
								__UpperCamelCase			:     Optional[Any]         =      use_cache
								__UpperCamelCase			:     int         =      decoder_layers
								__UpperCamelCase			:     Union[str, Any]         =      scale_embedding  # scale factor will be sqrt(d_model) if True
								__UpperCamelCase			:     Dict         =      max_target_positions

								super().__init__(
								    pad_token_id=a  ,     bos_token_id=a  ,     eos_token_id=a  ,     decoder_start_token_id=a  ,     **a  ,     )