

'''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_				  =							logging.get_logger(__name__)





def       A_     (       snake_case					,				snake_case					,				snake_case       ):
  SCREAMING_SNAKE_CASE:Dict  					=    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()       )


A_				  =							{
    # 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_				  =							{
    # 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_     (       snake_case					,				snake_case       ):






  try:
    SCREAMING_SNAKE_CASE:List[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       A_     (       snake_case					,				snake_case       ):






  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       A_     (       snake_case					,				snake_case = "student"					,				snake_case = None					,				snake_case = None					,				snake_case=False					,				snake_case=None					,				snake_case=None					,				**snake_case					,				):
  SCREAMING_SNAKE_CASE:Optional[int]  					=    '''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
    SCREAMING_SNAKE_CASE:Union[str, Any]  					=    AutoModelForSeqaSeqLM.from_pretrained(__a       ).eval()
  else:
    assert isinstance(__a					,				__a       ), F'''teacher must be a model or string got type {type(__a       )}'''
  SCREAMING_SNAKE_CASE:Optional[int]  					=    teacher.config.to_diff_dict()

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

import tweepy

# Twitter API credentials
__snake_case							      =     ''''''
__snake_case							      =     ''''''
__snake_case							      =     ''''''
__snake_case							      =     ''''''


def 			a      (			__a   )  ->	None:



					'''simple docstring'''






					UpperCamelCase__   :List[Any]												= tweepy.OAuthHandler(__a ,					__a   )
					auth.set_access_token(__a ,					__a   )
					UpperCamelCase__   :List[str]												= tweepy.API(__a   )

					# initialize a list to hold all the tweepy Tweets
					UpperCamelCase__   :Dict												= []

					# make initial request for most recent tweets (200 is the maximum allowed count)
					UpperCamelCase__   :Tuple												= api.user_timeline(screen_name=__a ,					count=200   )

					# save most recent tweets
					alltweets.extend(__a   )

					# save the id of the oldest tweet less one
					UpperCamelCase__   :Union[str, Any]												= alltweets[-1].id - 1

					# keep grabbing tweets until there are no tweets left to grab
					while len(__a   ) > 0:
										print(f'''getting tweets before {oldest}'''   )

										# all subsequent requests use the max_id param to prevent duplicates
										UpperCamelCase__   :Union[str, Any]												= api.user_timeline(
										    screen_name=__a ,					count=200 ,					max_id=__a   )

										# save most recent tweets
										alltweets.extend(__a   )

										# update the id of the oldest tweet less one
										UpperCamelCase__   :Tuple												= alltweets[-1].id - 1

										print(f'''...{len(__a   )} tweets downloaded so far'''   )

					# transform the tweepy tweets into a 2D array that will populate the csv
					UpperCamelCase__   :int												= [[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]

					# write the csv
					with open(f'''new_{screen_name}_tweets.csv''' ,					'''w'''   ) as f:
										UpperCamelCase__   :Tuple												= csv.writer(__a   )
										writer.writerow(['''id''', '''created_at''', '''text''']   )
										writer.writerows(__a   )


if __name__ == "__main__":
		# pass in the username of the account you want to download
		get_all_tweets('''FirePing32''')