



"""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


A:     int										=	logging.get_logger(__name__)

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


A:     Tuple										=	{
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    1_2: {
        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, 1_1],
        4: [0, 4, 8, 1_1],
        6: [0, 2, 4, 7, 9, 1_1],
        9: [0, 1, 2, 4, 5, 7, 9, 1_0, 1_1],
        1_2: list(range(1_2)),
    },
    1_6: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 1_5],
        3: [0, 8, 1_5],
        4: [0, 5, 1_0, 1_5],
        6: [0, 3, 6, 9, 1_2, 1_5],
        8: [0, 2, 4, 6, 8, 1_0, 1_2, 1_5],
        9: [0, 1, 3, 5, 7, 9, 1_1, 1_3, 1_5],
        1_2: [0, 1, 2, 3, 4, 5, 6, 7, 9, 1_1, 1_3, 1_5],
        1_6: list(range(1_6)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
A:     Optional[int]										=	{
    # 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]},
    1_2: {1: [1_1], 2: [5, 1_1], 3: [3, 7, 1_1], 6: [1, 3, 5, 8, 1_0, 1_1]},
    1_6: {1: [1_5], 4: [4, 9, 1_2, 1_5], 8: [1, 3, 5, 7, 9, 1_1, 1_3, 1_5]},
}

def    _snake_case		(		UpperCamelCase		:	Dict    ,	UpperCamelCase		:	Optional[int]		):
 try:
  UpperCAmelCase			:      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(UpperCamelCase		)		)

def    _snake_case		(		UpperCamelCase		:	Any    ,	UpperCamelCase		:	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(UpperCamelCase		)		)
 elif n_student == 1:
  return [n_teacher - 1]
 else:
  return LAYERS_TO_SUPERVISE[n_teacher][n_student]

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

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

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

 try:
  if hasattr(
      UpperCamelCase    ,	"""prophetnet"""		):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
   copy_layers(teacher.prophetnet.encoder.layers    ,	student.prophetnet.encoder.layers    ,	UpperCamelCase		)
   copy_layers(teacher.prophetnet.decoder.layers    ,	student.prophetnet.decoder.layers    ,	UpperCamelCase		)
  else:
   copy_layers(teacher.model.encoder.layers    ,	student.model.encoder.layers    ,	UpperCamelCase		)
   copy_layers(teacher.model.decoder.layers    ,	student.model.decoder.layers    ,	UpperCamelCase		)
 except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
  copy_layers(teacher.encoder.block    ,	student.encoder.block    ,	UpperCamelCase		)
  copy_layers(teacher.decoder.block    ,	student.decoder.block    ,	UpperCamelCase		)
 logger.info(
     F"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}"		)
 UpperCAmelCase			:      int			=			{
     """teacher_type""": teacher.config.model_type,
     """copied_encoder_layers""": e_layers_to_copy,
     """copied_decoder_layers""": d_layers_to_copy,
 }
 student.save_pretrained(UpperCamelCase		)
 # 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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


A:     Union[str, Any]										=	{
    "configuration_speecht5": [
        "SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP",
        "SpeechT5Config",
        "SpeechT5HifiGanConfig",
    ],
    "feature_extraction_speecht5": ["SpeechT5FeatureExtractor"],
    "processing_speecht5": ["SpeechT5Processor"],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:     List[str]										=	["SpeechT5Tokenizer"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:     Dict										=	[
          "SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST",
          "SpeechT5ForSpeechToText",
          "SpeechT5ForSpeechToSpeech",
          "SpeechT5ForTextToSpeech",
          "SpeechT5Model",
          "SpeechT5PreTrainedModel",
          "SpeechT5HifiGan",
      ]

if TYPE_CHECKING:
      from .configuration_speechta import (
          SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
          SpeechTaConfig,
          SpeechTaHifiGanConfig,
      )
      from .feature_extraction_speechta import SpeechTaFeatureExtractor
      from .processing_speechta import SpeechTaProcessor

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_speechta import SpeechTaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speechta import (
                SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechTaForSpeechToSpeech,
                SpeechTaForSpeechToText,
                SpeechTaForTextToSpeech,
                SpeechTaHifiGan,
                SpeechTaModel,
                SpeechTaPreTrainedModel,
            )

else:
      import sys

      A:     Dict										=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
