






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


SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		logging.get_logger(__name__)

def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






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


SCREAMING_SNAKE_CASE__   :      List[str]				=		{
    # 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))},
}
SCREAMING_SNAKE_CASE__   :      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   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				try:
								__magic_name__		:List[Any]   =   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(snake_case			)			)

def   __lowercase       (			snake_case,	snake_case			):







				"""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(snake_case			)			)
				elif n_student == 1:
								return [n_teacher - 1]
				else:
								return LAYERS_TO_SUPERVISE[n_teacher][n_student]

def   __lowercase       (			snake_case,	snake_case = "student",	snake_case = None,	snake_case = None,	snake_case=False,	snake_case=None,	snake_case=None,	**snake_case,	):







				"""simple docstring"""






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

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

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

				try:
								if hasattr(
								    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,	snake_case			)
												copy_layers(teacher.prophetnet.decoder.layers,	student.prophetnet.decoder.layers,	snake_case			)
								else:
												copy_layers(teacher.model.encoder.layers,	student.model.encoder.layers,	snake_case			)
												copy_layers(teacher.model.decoder.layers,	student.model.decoder.layers,	snake_case			)
				except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
								copy_layers(teacher.encoder.block,	student.encoder.block,	snake_case			)
								copy_layers(teacher.decoder.block,	student.decoder.block,	snake_case			)
				logger.info(
				    f'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}'''			)
				__magic_name__		:Optional[int]   =   {
				    '''teacher_type''': teacher.config.model_type,
				    '''copied_encoder_layers''': e_layers_to_copy,
				    '''copied_decoder_layers''': d_layers_to_copy,
				}
				student.save_pretrained(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 argparse
import os
import re


UpperCAmelCase_							:	List[str]	     =		'''src/transformers/models/auto'''


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
UpperCAmelCase_							:	Tuple	     =		re.compile(R'''[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict''')
# re pattern that matches identifiers in mappings
UpperCAmelCase_							:	Dict	     =		re.compile(R'''\s*\(\s*"(\S[^"]+)"''')







def       _UpperCamelCase			(_lowerCamelCase				:  List[Any] ,						_lowerCamelCase				:  bool = False						)-> str:




     '''simple docstring'''





     with open(_lowerCamelCase ,						'''r''' ,						encoding='''utf-8'''						) as f:
          __snake_case            =	f.read()

     __snake_case            =	content.split('''\n'''						)
     __snake_case            =	[]
     __snake_case            =	0
     while line_idx < len(_lowerCamelCase						):
          if _re_intro_mapping.search(lines[line_idx]						) is not None:
               __snake_case            =	len(re.search(R'''^(\s*)\S''' ,						lines[line_idx]						).groups()[0]						) + 8
               # Start of a new mapping!
               while not lines[line_idx].startswith(''' ''' * indent + '''('''						):
                    new_lines.append(lines[line_idx]						)
                    line_idx += 1

               __snake_case            =	[]
               while lines[line_idx].strip() != "]":
                    # Blocks either fit in one line or not
                    if lines[line_idx].strip() == "(":
                         __snake_case            =	line_idx
                         while not lines[line_idx].startswith(''' ''' * indent + ''')'''						):
                              line_idx += 1
                         blocks.append('''\n'''.join(lines[start_idx : line_idx + 1]						)						)
                    else:
                         blocks.append(lines[line_idx]						)
                    line_idx += 1

               # Sort blocks by their identifiers
               __snake_case            =	sorted(_lowerCamelCase ,						key=lambda _lowerCamelCase						: _re_identifier.search(_lowerCamelCase						).groups()[0]						)
               new_lines += blocks
          else:
               new_lines.append(lines[line_idx]						)
               line_idx += 1

     if overwrite:
          with open(_lowerCamelCase ,						'''w''' ,						encoding='''utf-8'''						) as f:
               f.write('''\n'''.join(_lowerCamelCase						)						)
     elif "\n".join(_lowerCamelCase						) != content:
          return True







def       _UpperCamelCase			(_lowerCamelCase				:  bool = False						)-> Tuple:




     '''simple docstring'''





     __snake_case            =	[os.path.join(_lowerCamelCase ,						_lowerCamelCase						) for f in os.listdir(_lowerCamelCase						) if f.endswith('''.py'''						)]
     __snake_case            =	[sort_auto_mapping(_lowerCamelCase ,						overwrite=_lowerCamelCase						) for fname in fnames]

     if not overwrite and any(_lowerCamelCase						):
          __snake_case            =	[f for f, d in zip(_lowerCamelCase ,						_lowerCamelCase						) if d]
          raise ValueError(
              f'''The following files have auto mappings that need sorting: {", ".join(_lowerCamelCase						)}. Run `make style` to fix'''
              ''' this.'''						)


if __name__ == "__main__":
     UpperCAmelCase_							:	str	     =		argparse.ArgumentParser()
     parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
     UpperCAmelCase_							:	List[Any]	     =		parser.parse_args()

     sort_all_auto_mappings(not args.check_only)






