






"""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


UpperCamelCase_	:				List[str]										= logging.get_logger(__name__)



def     __lowercase					(    a :				Optional[Any]       ,		a :				Optional[Any]       ,		a :				Union[str, Any] ):
      __snake_case      :    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() )


UpperCamelCase_	:				int										= {
    # 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))},
}
UpperCamelCase_	:				List[str]										= {
    # 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     __lowercase					(    a :				List[str]       ,		a :				Any ):





      try:
            __snake_case      :    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(A_ ) )



def     __lowercase					(    a :				Union[str, Any]       ,		a :				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(A_ ) )
      elif n_student == 1:
            return [n_teacher - 1]
      else:
            return LAYERS_TO_SUPERVISE[n_teacher][n_student]



def     __lowercase					(    a :				Any       ,		a :				Optional[int] = "student"       ,		a :				Any = None       ,		a :				Optional[int] = None       ,		a :				Optional[Any]=False       ,		a :				Any=None       ,		a :				str=None       ,		**a :				Optional[int]       ,		):
      __snake_case      :    Tuple  			='''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
            __snake_case      :    List[str]  			=AutoModelForSeqaSeqLM.from_pretrained(A_ ).eval()
      else:
            assert isinstance(A_       ,		A_ ), f'''teacher must be a model or string got type {type(A_ )}'''
      __snake_case      :    int  			=teacher.config.to_diff_dict()

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

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

UpperCamelCase_	:				Tuple										= {"""UserAgent""": UserAgent().random}



def     __lowercase					(    a :				str )					->					dict:
      __snake_case      :    Union[str, Any]  			=script.contents[0]
      __snake_case      :    List[str]  			=json.loads(data[data.find('''{"config"''' ) : -1] )
      return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]


class      _lowercase :


      def __init__(    self				:	int							,   a				:	Dict						):





            """simple docstring"""







            __snake_case      :    Dict  			=f'''https://www.instagram.com/{username}/'''
            __snake_case      :    str  			=self.get_json()


      def _UpperCamelCase						(    self				:	Union[str, Any]						):





            """simple docstring"""







            __snake_case      :    List[str]  			=requests.get(self.url							,   headers=a						).text
            __snake_case      :    int  			=BeautifulSoup(a							,   '''html.parser'''						).find_all('''script'''						)
            try:
                  return extract_user_profile(scripts[4]						)
            except (json.decoder.JSONDecodeError, KeyError):
                  return extract_user_profile(scripts[3]						)


      def __repr__(    self				:	int						):





            """simple docstring"""







            return f'''{self.__class__.__name__}(\'{self.username}\')'''


      def __str__(    self				:	Any						):





            """simple docstring"""







            return f'''{self.fullname} ({self.username}) is {self.biography}'''


      @property
      def _UpperCamelCase						(    self				:	Union[str, Any]						):





            """simple docstring"""







            return self.user_data["username"]


      @property
      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            return self.user_data["full_name"]


      @property
      def _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







            return self.user_data["biography"]


      @property
      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            return self.user_data["business_email"]


      @property
      def _UpperCamelCase						(    self				:	Optional[int]						):





            """simple docstring"""







            return self.user_data["external_url"]


      @property
      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            return self.user_data["edge_followed_by"]["count"]


      @property
      def _UpperCamelCase						(    self				:	int						):





            """simple docstring"""







            return self.user_data["edge_follow"]["count"]


      @property
      def _UpperCamelCase						(    self				:	Tuple						):





            """simple docstring"""







            return self.user_data["edge_owner_to_timeline_media"]["count"]


      @property
      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            return self.user_data["profile_pic_url_hd"]


      @property
      def _UpperCamelCase						(    self				:	str						):





            """simple docstring"""







            return self.user_data["is_verified"]







      @property
      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            return self.user_data["is_private"]



def     __lowercase					(    a :				str = "github" )					->					None:
      import os

      if os.environ.get('''CI''' ):
            return  # test failing on GitHub Actions
      __snake_case      :    Union[str, Any]  			=InstagramUser(a )
      assert instagram_user.user_data
      assert isinstance(instagram_user.user_data       ,		a )
      assert instagram_user.username == username
      if username != "github":
            return
      assert instagram_user.fullname == "GitHub"
      assert instagram_user.biography == "Built for developers."
      assert instagram_user.number_of_posts > 150
      assert instagram_user.number_of_followers > 120_000
      assert instagram_user.number_of_followings > 15
      assert instagram_user.email == "support@github.com"
      assert instagram_user.website == "https://github.com/readme"
      assert instagram_user.profile_picture_url.startswith('''https://instagram.''' )
      assert instagram_user.is_verified is True
      assert instagram_user.is_private is False


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    UpperCamelCase_	:				Optional[int]										= InstagramUser("""github""")
    print(instagram_user)
    print(F'''{instagram_user.number_of_posts = }''')
    print(F'''{instagram_user.number_of_followers = }''')
    print(F'''{instagram_user.number_of_followings = }''')
    print(F'''{instagram_user.email = }''')
    print(F'''{instagram_user.website = }''')
    print(F'''{instagram_user.profile_picture_url = }''')
    print(F'''{instagram_user.is_verified = }''')
    print(F'''{instagram_user.is_private = }''')


