
"""simple docstring"""




import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


A__      :	str          =    logging.get_logger(__name__)

A__      :	str          =    {
    """nvidia/segformer-b0-finetuned-ade-512-512""": (
        """https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"""
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							'segformer'



     def __init__(      self ,			__UpperCamelCase=3 ,			__UpperCamelCase=4 ,			__UpperCamelCase=[2, 2, 2, 2] ,			__UpperCamelCase=[8, 4, 2, 1] ,			__UpperCamelCase=[32, 64, 1_60, 2_56] ,			__UpperCamelCase=[7, 3, 3, 3] ,			__UpperCamelCase=[4, 2, 2, 2] ,			__UpperCamelCase=[1, 2, 5, 8] ,			__UpperCamelCase=[4, 4, 4, 4] ,			__UpperCamelCase="gelu" ,			__UpperCamelCase=0.0 ,			__UpperCamelCase=0.0 ,			__UpperCamelCase=0.1 ,			__UpperCamelCase=0.02 ,			__UpperCamelCase=0.1 ,			__UpperCamelCase=1E-6 ,			__UpperCamelCase=2_56 ,			__UpperCamelCase=2_55 ,			**__UpperCamelCase ,			)->					Optional[int]:
          super().__init__(**__UpperCamelCase  )

          if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
               warnings.warn(
                   "Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"
                   " removed, as the behaviour will default to that of reshape_last_stage = True." ,			__UpperCamelCase ,			)

          UpperCAmelCase__							: Optional[int] 							=						num_channels
          UpperCAmelCase__							: Union[str, Any] 							=						num_encoder_blocks
          UpperCAmelCase__							: Tuple 							=						depths
          UpperCAmelCase__							: List[str] 							=						sr_ratios
          UpperCAmelCase__							: Union[str, Any] 							=						hidden_sizes
          UpperCAmelCase__							: Any 							=						patch_sizes
          UpperCAmelCase__							: List[Any] 							=						strides
          UpperCAmelCase__							: Optional[int] 							=						mlp_ratios
          UpperCAmelCase__							: Tuple 							=						num_attention_heads
          UpperCAmelCase__							: Any 							=						hidden_act
          UpperCAmelCase__							: Optional[int] 							=						hidden_dropout_prob
          UpperCAmelCase__							: Union[str, Any] 							=						attention_probs_dropout_prob
          UpperCAmelCase__							: Any 							=						classifier_dropout_prob
          UpperCAmelCase__							: Dict 							=						initializer_range
          UpperCAmelCase__							: Tuple 							=						drop_path_rate
          UpperCAmelCase__							: Optional[int] 							=						layer_norm_eps
          UpperCAmelCase__							: List[Any] 							=						decoder_hidden_size
          UpperCAmelCase__							: int 							=						kwargs.get("reshape_last_stage" ,			__UpperCamelCase  )
          UpperCAmelCase__							: List[Any] 							=						semantic_loss_ignore_index







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							version.parse('1.11'				)



     @property
     def  lowerCAmelCase__       (      self  )->					Mapping[str, Mapping[int, str]]:
          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ]  )



     @property
     def  lowerCAmelCase__       (      self  )->					float:
          return 1E-4



     @property
     def  lowerCAmelCase__       (      self  )->					int:
          return 12





"""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__      :	Tuple          =    logging.get_logger(__name__)



def 							a__ (				lowerCAmelCase	:       nn.ModuleList       ,    lowerCAmelCase	:       nn.ModuleList       ,    lowerCAmelCase	:       List[int]		):






     '''simple docstring'''
     UpperCAmelCase__							: Optional[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()		)


A__      :	List[Any]          =    {
    # 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))},
}
A__      :	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 							a__ (				lowerCAmelCase	:       Dict       ,    lowerCAmelCase	:       Dict		):






     '''simple docstring'''
     try:
          UpperCAmelCase__							: Tuple 							=						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 							a__ (				lowerCAmelCase	:       int       ,    lowerCAmelCase	:       Tuple		):






     '''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 							a__ (				lowerCAmelCase	:       Union[str, PreTrainedModel]       ,    lowerCAmelCase	:       Union[str, Path] = "student"       ,    lowerCAmelCase	:       Union[int, None] = None       ,    lowerCAmelCase	:       Union[int, None] = None       ,    lowerCAmelCase	:       List[str]=False       ,    lowerCAmelCase	:       List[str]=None       ,    lowerCAmelCase	:       List[str]=None       ,    **lowerCAmelCase	:       List[str]       ,    ):






     '''simple docstring'''
     UpperCAmelCase__							: List[str] 							=						"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__							: List[str] 							=						AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase		).eval()
     else:
          assert isinstance(lowerCAmelCase       ,    lowerCAmelCase		), F"teacher must be a model or string got type {type(lowerCAmelCase		)}"
     UpperCAmelCase__							: int 							=						teacher.config.to_diff_dict()

     try:
          UpperCAmelCase__							,	UpperCAmelCase__							: Optional[Any] 							=						teacher.config.encoder_layers, teacher.config.decoder_layers
          if e is None:
               UpperCAmelCase__							: Tuple 							=						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__							: Union[str, Any] 							=						teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
          else:
               UpperCAmelCase__							,	UpperCAmelCase__							: Union[str, Any] 							=						teacher.config.num_layers, teacher.config.num_decoder_layers
          if e is None:
               UpperCAmelCase__							: Optional[Any] 							=						teacher_e
          if d is None:
               UpperCAmelCase__							: Optional[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__							: Optional[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__							: int 							=						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__							: int 							=						{
         "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)



