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_	:			Tuple  	=      logging.get_logger(__name__)

def 						UpperCamelCase (lowercase_:			nn.ModuleList   ,				lowercase_:			nn.ModuleList   ,				lowercase_:			List[int] )					->				None:
	A__							:   str					       =		nn.ModuleList([src_layers[i] for i in layers_to_copy] )
	assert len(UpperCamelCase__ ) == len(UpperCamelCase__ ), f"""{len(UpperCamelCase__ )} != {len(UpperCamelCase__ )}"""
	dest_layers.load_state_dict(layers_to_copy.state_dict() )


A_	:			Optional[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))},
}
A_	:			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 						UpperCamelCase (lowercase_:			int   ,				lowercase_:			List[str] )					->				Tuple:
	try:
		A__							:   Optional[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(UpperCamelCase__ ) )

def 						UpperCamelCase (lowercase_:			Union[str, Any]   ,				lowercase_:			Union[str, 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(UpperCamelCase__ ) )
	elif n_student == 1:
		return [n_teacher - 1]
	else:
		return LAYERS_TO_SUPERVISE[n_teacher][n_student]

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

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

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

	try:
		if hasattr(
		    UpperCamelCase__   ,				"""prophetnet""" ):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
			copy_layers(teacher.prophetnet.encoder.layers   ,				student.prophetnet.encoder.layers   ,				UpperCamelCase__ )
			copy_layers(teacher.prophetnet.decoder.layers   ,				student.prophetnet.decoder.layers   ,				UpperCamelCase__ )
		else:
			copy_layers(teacher.model.encoder.layers   ,				student.model.encoder.layers   ,				UpperCamelCase__ )
			copy_layers(teacher.model.decoder.layers   ,				student.model.decoder.layers   ,				UpperCamelCase__ )
	except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
		copy_layers(teacher.encoder.block   ,				student.encoder.block   ,				UpperCamelCase__ )
		copy_layers(teacher.decoder.block   ,				student.decoder.block   ,				UpperCamelCase__ )
	logger.info(
	    f"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}""" )
	A__							:   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(UpperCamelCase__ )
	# 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)






import functools
import logging
import os
import sys
import threading
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib


A_	:			Any  	=      threading.Lock()
A_	:			Optional[logging.Handler]  	=      None

A_	:			Any  	=      {
    'debug': logging.DEBUG,
    'info': logging.INFO,
    'warning': logging.WARNING,
    'error': logging.ERROR,
    'critical': logging.CRITICAL,
}

A_	:			Optional[int]  	=      logging.WARNING

A_	:			Tuple  	=      True

def 						UpperCamelCase ()					->				List[Any]:
	A__							:   List[str]					       =		os.getenv("""TRANSFORMERS_VERBOSITY"""   ,				lowercase_ )
	if env_level_str:
		if env_level_str in log_levels:
			return log_levels[env_level_str]
		else:
			logging.getLogger().warning(
			    f"""Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, """
			    f"""has to be one of: { ', '.join(log_levels.keys() ) }""" )
	return _default_log_level

def 						UpperCamelCase ()					->				str:
	return __name__.split(""".""" )[0]

def 						UpperCamelCase ()					->				logging.Logger:
	return logging.getLogger(_get_library_name() )

def 						UpperCamelCase ()					->				None:
	global _default_handler

	with _lock:
		if _default_handler:
			# This library has already configured the library root logger.
			return
		A__							:   Tuple					       =		logging.StreamHandler()  # Set sys.stderr as stream.
		A__							:   Union[str, Any]					       =		sys.stderr.flush

		# Apply our default configuration to the library root logger.
		A__							:   Optional[int]					       =		_get_library_root_logger()
		library_root_logger.addHandler(_default_handler )
		library_root_logger.setLevel(_get_default_logging_level() )
		A__							:   str					       =		False

def 						UpperCamelCase ()					->				None:
	global _default_handler

	with _lock:
		if not _default_handler:
			return

		A__							:   Tuple					       =		_get_library_root_logger()
		library_root_logger.removeHandler(_default_handler )
		library_root_logger.setLevel(logging.NOTSET )
		A__							:   Dict					       =		None

def 						UpperCamelCase ()					->				Dict:
	return log_levels

def 						UpperCamelCase (lowercase_:			Optional[str] = None )					->				logging.Logger:

	if name is None:
		A__							:   List[Any]					       =		_get_library_name()

	_configure_library_root_logger()
	return logging.getLogger(lowercase_ )

def 						UpperCamelCase ()					->				int:
	_configure_library_root_logger()
	return _get_library_root_logger().getEffectiveLevel()

def 						UpperCamelCase (lowercase_:			int )					->				None:
	_configure_library_root_logger()
	_get_library_root_logger().setLevel(lowercase_ )

def 						UpperCamelCase ()					->				Union[str, Any]:
	return set_verbosity(lowercase_ )

def 						UpperCamelCase ()					->				List[str]:
	return set_verbosity(lowercase_ )

def 						UpperCamelCase ()					->				Any:
	return set_verbosity(lowercase_ )

def 						UpperCamelCase ()					->				List[str]:
	return set_verbosity(lowercase_ )

def 						UpperCamelCase ()					->				None:
	_configure_library_root_logger()

	assert _default_handler is not None
	_get_library_root_logger().removeHandler(_default_handler )

def 						UpperCamelCase ()					->				None:
	_configure_library_root_logger()

	assert _default_handler is not None
	_get_library_root_logger().addHandler(_default_handler )

def 						UpperCamelCase (lowercase_:			logging.Handler )					->				None:
	_configure_library_root_logger()

	assert handler is not None
	_get_library_root_logger().addHandler(lowercase_ )

def 						UpperCamelCase (lowercase_:			logging.Handler )					->				None:
	_configure_library_root_logger()

	assert handler is not None and handler not in _get_library_root_logger().handlers
	_get_library_root_logger().removeHandler(lowercase_ )

def 						UpperCamelCase ()					->				None:
	_configure_library_root_logger()
	A__							:   Dict					       =		False

def 						UpperCamelCase ()					->				None:
	_configure_library_root_logger()
	A__							:   List[str]					       =		True

def 						UpperCamelCase ()					->				None:
	A__							:   List[str]					       =		_get_library_root_logger().handlers

	for handler in handlers:
		A__							:   Union[str, Any]					       =		logging.Formatter("""[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s""" )
		handler.setFormatter(lowercase_ )

def 						UpperCamelCase ()					->				None:
	A__							:   Dict					       =		_get_library_root_logger().handlers

	for handler in handlers:
		handler.setFormatter(lowercase_ )

def 						UpperCamelCase (self:			Tuple   ,				*lowercase_:			int   ,				**lowercase_:			List[Any] )					->				Optional[Any]:
	A__							:   int					       =		os.getenv("""TRANSFORMERS_NO_ADVISORY_WARNINGS"""   ,				lowercase_ )
	if no_advisory_warnings:
		return
	self.warning(*lowercase_   ,				**lowercase_ )


A_	:			int  	=      warning_advice

@functools.lru_cache(lowercase_ )
def 						UpperCamelCase (self:			Any   ,				*lowercase_:			List[str]   ,				**lowercase_:			Dict )					->				Optional[int]:
	self.warning(*lowercase_   ,				**lowercase_ )


A_	:			Tuple  	=      warning_once



class    _a						:


					'''simple docstring'''






					def __init__(			self   ,	*A__   ,	**A__      ):  # pylint: disable=unused-argument
						A__							:   int					       =		args[0] if args else None






					def __iter__(			self      ):
						return iter(self._iterator      )






					def __getattr__(			self   ,	A__      ):

						def empty_fn(*A__   ,	**A__      ):  # pylint: disable=unused-argument
							return

						return empty_fn






					def __enter__(			self      ):
						return self






					def __exit__(			self   ,	A__   ,	A__   ,	A__      ):
						return



class    _a						:


					'''simple docstring'''






					def __call__(			self   ,	*A__   ,	**A__      ):
						if _tqdm_active:
							return tqdm_lib.tqdm(*A__   ,	**A__      )
						else:
							return EmptyTqdm(*A__   ,	**A__      )






					def   __A      (			self   ,	*A__   ,	**A__      ):
						A__							:   List[Any]					       =		None
						if _tqdm_active:
							return tqdm_lib.tqdm.set_lock(*A__   ,	**A__      )






					def   __A      (			self      ):
						if _tqdm_active:
							return tqdm_lib.tqdm.get_lock()



A_	:			List[Any]  	=      _tqdm_cls()

def 						UpperCamelCase ()					->				bool:
	global _tqdm_active
	return bool(_tqdm_active )

def 						UpperCamelCase ()					->				List[str]:
	global _tqdm_active
	A__							:   int					       =		True
	hf_hub_utils.enable_progress_bars()


def 						UpperCamelCase ()					->				Optional[Any]:
	global _tqdm_active
	A__							:   Tuple					       =		False
	hf_hub_utils.disable_progress_bars()



