
"""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			:       Union[str, Any]									=				logging.get_logger(__name__)






def 							A		(       snake_case   :Any       ,      snake_case   :int       ,      snake_case   :Dict	)   ->			None:
 __UpperCamelCase       						= nn.ModuleList([src_layers[i] for i in layers_to_copy]	)
 assert len(__SCREAMING_SNAKE_CASE	) == len(__SCREAMING_SNAKE_CASE	), f'{len(__SCREAMING_SNAKE_CASE	)} != {len(__SCREAMING_SNAKE_CASE	)}'
 dest_layers.load_state_dict(layers_to_copy.state_dict()	)


UpperCamelCase			:       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))},
}
UpperCamelCase			:       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]},
    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 							A		(       snake_case   :Tuple       ,      snake_case   :Union[str, Any]	)   ->			str:
 try:
  __UpperCamelCase       						= 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(__SCREAMING_SNAKE_CASE	)	)






def 							A		(       snake_case   :Tuple       ,      snake_case   :Optional[Any]	)   ->			List[int]:
 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(__SCREAMING_SNAKE_CASE	)	)
 elif n_student == 1:
  return [n_teacher - 1]
 else:
  return LAYERS_TO_SUPERVISE[n_teacher][n_student]






def 							A		(       snake_case   :List[Any]       ,      snake_case   :List[str] = "student"       ,      snake_case   :str = None       ,      snake_case   :Optional[int] = None       ,      snake_case   :Union[str, Any]=False       ,      snake_case   :Dict=None       ,      snake_case   :str=None       ,      **snake_case   :Dict       ,      )   ->			Tuple[PreTrainedModel, List[int], List[int]]:
 __UpperCamelCase       						= '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(__SCREAMING_SNAKE_CASE       ,      __SCREAMING_SNAKE_CASE	):
  AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE	).save_pretrained(__SCREAMING_SNAKE_CASE	)  # purely for convenience
  __UpperCamelCase       						= AutoModelForSeqaSeqLM.from_pretrained(__SCREAMING_SNAKE_CASE	).eval()
 else:
  assert isinstance(__SCREAMING_SNAKE_CASE       ,      __SCREAMING_SNAKE_CASE	), f'teacher must be a model or string got type {type(__SCREAMING_SNAKE_CASE	)}'
 __UpperCamelCase       						= teacher.config.to_diff_dict()

 try:
  __UpperCamelCase						,			__UpperCamelCase       						= teacher.config.encoder_layers, teacher.config.decoder_layers
  if e is None:
   __UpperCamelCase       						= teacher_e
  if d is None:
   __UpperCamelCase       						= teacher_d
  init_kwargs.update({'encoder_layers': e, 'decoder_layers': d}	)
 except AttributeError:  # T5
  if hasattr(teacher.config       ,      'num_encoder_layers'	):
   __UpperCamelCase						,			__UpperCamelCase       						= teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
  else:
   __UpperCamelCase						,			__UpperCamelCase       						= teacher.config.num_layers, teacher.config.num_decoder_layers
  if e is None:
   __UpperCamelCase       						= teacher_e
  if d is None:
   __UpperCamelCase       						= 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(__SCREAMING_SNAKE_CASE	)

 # Copy weights
 __UpperCamelCase       						= teacher.config_class(**__SCREAMING_SNAKE_CASE	)
 __UpperCamelCase       						= AutoModelForSeqaSeqLM.from_config(__SCREAMING_SNAKE_CASE	)
 # Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
 __UpperCamelCase       						= student.load_state_dict(teacher.state_dict()       ,      strict=__SCREAMING_SNAKE_CASE	)
 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(range(__SCREAMING_SNAKE_CASE	)	), list(range(__SCREAMING_SNAKE_CASE	)	)
  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(__SCREAMING_SNAKE_CASE	)
  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       						= pick_layers_to_copy(__SCREAMING_SNAKE_CASE       ,      __SCREAMING_SNAKE_CASE	)
 if d_layers_to_copy is None:
  __UpperCamelCase       						= pick_layers_to_copy(__SCREAMING_SNAKE_CASE       ,      __SCREAMING_SNAKE_CASE	)

 try:
  if hasattr(
      __SCREAMING_SNAKE_CASE       ,      'prophetnet'	):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
   copy_layers(teacher.prophetnet.encoder.layers       ,      student.prophetnet.encoder.layers       ,      __SCREAMING_SNAKE_CASE	)
   copy_layers(teacher.prophetnet.decoder.layers       ,      student.prophetnet.decoder.layers       ,      __SCREAMING_SNAKE_CASE	)
  else:
   copy_layers(teacher.model.encoder.layers       ,      student.model.encoder.layers       ,      __SCREAMING_SNAKE_CASE	)
   copy_layers(teacher.model.decoder.layers       ,      student.model.decoder.layers       ,      __SCREAMING_SNAKE_CASE	)
 except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
  copy_layers(teacher.encoder.block       ,      student.encoder.block       ,      __SCREAMING_SNAKE_CASE	)
  copy_layers(teacher.decoder.block       ,      student.decoder.block       ,      __SCREAMING_SNAKE_CASE	)
 logger.info(
     f'Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}'	)
 __UpperCamelCase       						= {
     'teacher_type': teacher.config.model_type,
     'copied_encoder_layers': e_layers_to_copy,
     'copied_decoder_layers': d_layers_to_copy,
 }
 student.save_pretrained(__SCREAMING_SNAKE_CASE	)
 # 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 time
from contextlib import contextmanager
from pathlib import Path

import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder


UpperCamelCase			:       Union[str, Any]									=				"__DUMMY_TRANSFORMERS_USER__"
UpperCamelCase			:       List[Any]									=				"Dummy User"
UpperCamelCase			:       List[Any]									=				"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"

UpperCamelCase			:       Any									=				"https://hub-ci.huggingface.co"
UpperCamelCase			:       str									=				CI_HUB_ENDPOINT + "/datasets/{repo_id}/resolve/{revision}/{path}"
UpperCamelCase			:       Optional[Any]									=				CI_HUB_ENDPOINT + "/{repo_id}/resolve/{revision}/{filename}"
UpperCamelCase			:       Optional[int]									=				Path("~/.huggingface/hub_ci_token").expanduser()






@pytest.fixture
def 							A		(       snake_case   :Optional[Any]	)   ->			Union[str, Any]:
 monkeypatch.setattr(
     'huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE'       ,      snake_case	)






@pytest.fixture
def 							A		(       snake_case   :str	)   ->			List[Any]:
 monkeypatch.setattr('datasets.config.HF_ENDPOINT'       ,      snake_case	)
 monkeypatch.setattr('datasets.config.HUB_DATASETS_URL'       ,      snake_case	)






@pytest.fixture
def 							A		(       snake_case   :Optional[int]	)   ->			Dict:
 monkeypatch.setattr('huggingface_hub.hf_api.HfFolder.path_token'       ,      snake_case	)






@pytest.fixture
def 							A		(       snake_case   :List[Any]       ,      snake_case   :Tuple	)   ->			Tuple:
 HfFolder.save_token(snake_case	)
 yield
 HfFolder.delete_token()






@pytest.fixture(scope='session'	)
def 							A		(       )   ->			List[Any]:
 return HfApi(endpoint=snake_case	)






@pytest.fixture(scope='session'	)
def 							A		(       snake_case   :HfApi	)   ->			List[Any]:
 __UpperCamelCase       						= HfFolder.get_token()
 HfFolder.save_token(snake_case	)
 yield CI_HUB_USER_TOKEN
 if previous_token is not None:
  HfFolder.save_token(snake_case	)






@pytest.fixture
def 							A		(       snake_case   :str	)   ->			str:
 def _cleanup_repo(snake_case   :Union[str, Any]	):
  hf_api.delete_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'	)

 return _cleanup_repo






@pytest.fixture
def 							A		(       snake_case   :List[str]	)   ->			Any:
 @contextmanager
 def _temporary_repo(snake_case   :Tuple	):
  try:
   yield repo_id
  finally:
   cleanup_repo(snake_case	)

 return _temporary_repo






@pytest.fixture(scope='session'	)
def 							A		(       snake_case   :HfApi       ,      snake_case   :Dict       ,      snake_case   :Dict	)   ->			List[str]:
 __UpperCamelCase       						= f'repo_txt_data-{int(time.time() * 10e3	)}'
 __UpperCamelCase       						= f'{CI_HUB_USER}/{repo_name}'
 hf_api.create_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'       ,      private=snake_case	)
 hf_api.upload_file(
     token=snake_case       ,      path_or_fileobj=str(snake_case	)       ,      path_in_repo='data/text_data.txt'       ,      repo_id=snake_case       ,      repo_type='dataset'       ,      )
 yield repo_id
 try:
  hf_api.delete_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'	)
 except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
  pass






@pytest.fixture()
def 							A		(       snake_case   :Optional[int]       ,      snake_case   :str       ,      snake_case   :Tuple	)   ->			Any:
 return hf_private_dataset_repo_txt_data_






@pytest.fixture(scope='session'	)
def 							A		(       snake_case   :HfApi       ,      snake_case   :List[Any]       ,      snake_case   :str	)   ->			Optional[int]:
 __UpperCamelCase       						= f'repo_zipped_txt_data-{int(time.time() * 10e3	)}'
 __UpperCamelCase       						= f'{CI_HUB_USER}/{repo_name}'
 hf_api.create_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'       ,      private=snake_case	)
 hf_api.upload_file(
     token=snake_case       ,      path_or_fileobj=str(snake_case	)       ,      path_in_repo='data.zip'       ,      repo_id=snake_case       ,      repo_type='dataset'       ,      )
 yield repo_id
 try:
  hf_api.delete_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'	)
 except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
  pass






@pytest.fixture()
def 							A		(       snake_case   :int       ,      snake_case   :Optional[int]       ,      snake_case   :Optional[int]	)   ->			str:
 return hf_private_dataset_repo_zipped_txt_data_






@pytest.fixture(scope='session'	)
def 							A		(       snake_case   :HfApi       ,      snake_case   :List[str]       ,      snake_case   :Any	)   ->			List[Any]:
 __UpperCamelCase       						= f'repo_zipped_img_data-{int(time.time() * 10e3	)}'
 __UpperCamelCase       						= f'{CI_HUB_USER}/{repo_name}'
 hf_api.create_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'       ,      private=snake_case	)
 hf_api.upload_file(
     token=snake_case       ,      path_or_fileobj=str(snake_case	)       ,      path_in_repo='data.zip'       ,      repo_id=snake_case       ,      repo_type='dataset'       ,      )
 yield repo_id
 try:
  hf_api.delete_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'	)
 except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
  pass






@pytest.fixture()
def 							A		(       snake_case   :List[str]       ,      snake_case   :Optional[Any]       ,      snake_case   :Optional[int]	)   ->			Optional[int]:
 return hf_private_dataset_repo_zipped_img_data_



