


'''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


__snake_case							   =  logging.get_logger(__name__)


def     a   (    __a				,      __a				,      __a     )						->			None:



					'''simple docstring'''






					UpperCamelCase__     :List[Any]							      =				nn.ModuleList([src_layers[i] for i in layers_to_copy]     )
					assert len(_lowercase     ) == len(_lowercase     ), f'''{len(_lowercase     )} != {len(_lowercase     )}'''
					dest_layers.load_state_dict(layers_to_copy.state_dict()     )


__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))},
}
__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     a   (    __a				,      __a     )						->			Any:



					'''simple docstring'''






					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(_lowercase     )     )


def     a   (    __a				,      __a     )						->			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(_lowercase     )     )
					elif n_student == 1:
										return [n_teacher - 1]
					else:
										return LAYERS_TO_SUPERVISE[n_teacher][n_student]


def     a   (    __a				,      __a = "student"				,      __a = None				,      __a = None				,      __a=False				,      __a=None				,      __a=None				,      **__a				,      )						->			Tuple[PreTrainedModel, List[int], List[int]]:



					'''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(_lowercase				,      _lowercase     ):
										AutoTokenizer.from_pretrained(_lowercase     ).save_pretrained(_lowercase     )  # purely for convenience
										UpperCamelCase__     :Any							      =				AutoModelForSeqaSeqLM.from_pretrained(_lowercase     ).eval()
					else:
										assert isinstance(_lowercase				,      _lowercase     ), f'''teacher must be a model or string got type {type(_lowercase     )}'''
					UpperCamelCase__     :str							      =				teacher.config.to_diff_dict()

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

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

					try:
										if hasattr(
										    _lowercase				,      '''prophetnet'''     ):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
															copy_layers(teacher.prophetnet.encoder.layers				,      student.prophetnet.encoder.layers				,      _lowercase     )
															copy_layers(teacher.prophetnet.decoder.layers				,      student.prophetnet.decoder.layers				,      _lowercase     )
										else:
															copy_layers(teacher.model.encoder.layers				,      student.model.encoder.layers				,      _lowercase     )
															copy_layers(teacher.model.decoder.layers				,      student.model.decoder.layers				,      _lowercase     )
					except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
										copy_layers(teacher.encoder.block				,      student.encoder.block				,      _lowercase     )
										copy_layers(teacher.decoder.block				,      student.decoder.block				,      _lowercase     )
					logger.info(
					    f'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}'''     )
					UpperCamelCase__     :List[str]							      =				{
					    'teacher_type': teacher.config.model_type,
					    'copied_encoder_layers': e_layers_to_copy,
					    'copied_decoder_layers': d_layers_to_copy,
					}
					student.save_pretrained(_lowercase     )
					# 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 csv

import requests
from bsa import BeautifulSoup


def     a   (    __a = ""     )						->			dict[str, float]:



					'''simple docstring'''






					UpperCamelCase__     :Optional[Any]							      =				url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
					UpperCamelCase__     :Optional[int]							      =				BeautifulSoup(requests.get(__a     ).text				,      '''html.parser'''     )
					UpperCamelCase__     :Tuple							      =				soup.find_all('''td'''				,      attrs='''titleColumn'''     )
					UpperCamelCase__     :Union[str, Any]							      =				soup.find_all('''td'''				,      class_='''ratingColumn imdbRating'''     )
					return {
					    title.a.text: float(rating.strong.text     )
					    for title, rating in zip(__a				,      __a     )
					}


def     a   (    __a = "IMDb_Top_250_Movies.csv"     )						->			None:



					'''simple docstring'''






					UpperCamelCase__     :Optional[Any]							      =				get_imdb_top_aaa_movies()
					with open(__a				,      '''w'''				,      newline=''''''     ) as out_file:
										UpperCamelCase__     :int							      =				csv.writer(__a     )
										writer.writerow(['''Movie title''', '''IMDb rating''']     )
										for title, rating in movies.items():
															writer.writerow([title, rating]     )


if __name__ == "__main__":
		write_movies()