

import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints

import yaml


SCREAMING_SNAKE_CASE_        =      NewType('DataClass', Any)
SCREAMING_SNAKE_CASE_        =      NewType('DataClassType', Any)

def 							__SCREAMING_SNAKE_CASE		(				lowerCAmelCase:  Union[str, Any] )   ->       int:
				if isinstance(lowerCAmelCase  ,      lowerCAmelCase ):
								return v
				if v.lower() in ("yes", "true", "t", "y", "1"):
								return True
				elif v.lower() in ("no", "false", "f", "n", "0"):
								return False
				else:
								raise ArgumentTypeError(
								    F'Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).' )

def 							__SCREAMING_SNAKE_CASE		(				lowerCAmelCase:  list )   ->       Callable[[str], Any]:
				_UpperCAmelCase       :		List[Any] 		=    {str(lowerCAmelCase ): choice for choice in choices}
				return lambda lowerCAmelCase : str_to_choice.get(lowerCAmelCase  ,      lowerCAmelCase )

def 							__SCREAMING_SNAKE_CASE		(				*,
    lowerCAmelCase:  Union[str, List[str]] = None  ,      lowerCAmelCase:  str = None  ,      lowerCAmelCase:  Any = dataclasses.MISSING  ,      lowerCAmelCase:  Callable[[], Any] = dataclasses.MISSING  ,      lowerCAmelCase:  dict = None  ,      **lowerCAmelCase:  Optional[Any]  ,      )   ->       dataclasses.Field:
				if metadata is None:
								# Important, don't use as default param in function signature because dict is mutable and shared across function calls
								_UpperCAmelCase       :		Optional[Any] 		=    {}
				if aliases is not None:
								_UpperCAmelCase       :		Any 		=    aliases
				if help is not None:
								_UpperCAmelCase       :		Union[str, Any] 		=    help

				return dataclasses.field(metadata=lowerCAmelCase  ,      default=lowerCAmelCase  ,      default_factory=lowerCAmelCase  ,      **lowerCAmelCase )





class 				a  (	UpperCAmelCase				):
						_lowercase    						=			4_2


						def __init__(       self						,					A_						,					**A_	):







										'''simple docstring'''


										if "formatter_class" not in kwargs:
														_UpperCAmelCase       :		Optional[Any] 		=    ArgumentDefaultsHelpFormatter
										super().__init__(**A_	)
										if dataclasses.is_dataclass(A_	):
														_UpperCAmelCase       :		Optional[int] 		=    [dataclass_types]
										_UpperCAmelCase       :		Optional[int] 		=    list(A_	)
										for dtype in self.dataclass_types:
														self._add_dataclass_arguments(A_	)


						@staticmethod
						def        _UpperCAmelCase				(       A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    f'--{field.name}'
										_UpperCAmelCase       :		List[str] 		=    field.metadata.copy()
										# field.metadata is not used at all by Data Classes,
										# it is provided as a third-party extension mechanism.
										if isinstance(field.type						,					A_	):
														raise RuntimeError(
														    "Unresolved type detected, which should have been done with the help of "
														    "`typing.get_type_hints` method by default"	)

										_UpperCAmelCase       :		List[Any] 		=    kwargs.pop("aliases"						,					[]	)
										if isinstance(A_						,					A_	):
														_UpperCAmelCase       :		Dict 		=    [aliases]

										_UpperCAmelCase       :		List[Any] 		=    getattr(field.type						,					"__origin__"						,					field.type	)
										if origin_type is Union or (hasattr(A_						,					"UnionType"	) and isinstance(A_						,					types.UnionType	)):
														if str not in field.type.__args__ and (
														    len(field.type.__args__	) != 2 or type(A_	) not in field.type.__args__
														):
																		raise ValueError(
																		    "Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because"
																		    " the argument parser only supports one type per argument."
																		    f' Problem encountered in field \'{field.name}\'.'	)
														if type(A_	) not in field.type.__args__:
																		# filter `str` in Union
																		_UpperCAmelCase       :		int 		=    field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
																		_UpperCAmelCase       :		int 		=    getattr(field.type						,					"__origin__"						,					field.type	)
														elif bool not in field.type.__args__:
																		# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
																		_UpperCAmelCase       :		Dict 		=    (
																		    field.type.__args__[0] if isinstance(A_						,					field.type.__args__[1]	) else field.type.__args__[1]
																		)
																		_UpperCAmelCase       :		Optional[int] 		=    getattr(field.type						,					"__origin__"						,					field.type	)

        # A variable to store kwargs for a boolean field, if needed
        # so that we can init a `no_*` complement argument (see below)
										_UpperCAmelCase       :		Optional[int] 		=    {}
										if origin_type is Literal or (isinstance(field.type						,					A_	) and issubclass(field.type						,					A_	)):
														if origin_type is Literal:
																		_UpperCAmelCase       :		Dict 		=    field.type.__args__
														else:
																		_UpperCAmelCase       :		int 		=    [x.value for x in field.type]

														_UpperCAmelCase       :		List[Any] 		=    make_choice_type_function(kwargs["choices"]	)

														if field.default is not dataclasses.MISSING:
																		_UpperCAmelCase       :		int 		=    field.default
														else:
																		_UpperCAmelCase       :		Optional[int] 		=    True
										elif field.type is bool or field.type == Optional[bool]:
														# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
														# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
														_UpperCAmelCase       :		Optional[int] 		=    copy(A_	)

														# Hack because type=bool in argparse does not behave as we want.
														_UpperCAmelCase       :		int 		=    string_to_bool
														if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
																		# Default value is False if we have no default when of type bool.
																		_UpperCAmelCase       :		Optional[int] 		=    False if field.default is dataclasses.MISSING else field.default
																		# This is the value that will get picked if we don't include --field_name in any way
																		_UpperCAmelCase       :		Optional[Any] 		=    default
																		# This tells argparse we accept 0 or 1 value after --field_name
																		_UpperCAmelCase       :		Union[str, Any] 		=    "?"
																		# This is the value that will get picked if we do --field_name (without value)
																		_UpperCAmelCase       :		Dict 		=    True
										elif isclass(A_	) and issubclass(A_						,					A_	):
														_UpperCAmelCase       :		Union[str, Any] 		=    field.type.__args__[0]
														_UpperCAmelCase       :		Union[str, Any] 		=    "+"
														if field.default_factory is not dataclasses.MISSING:
																		_UpperCAmelCase       :		int 		=    field.default_factory()
														elif field.default is dataclasses.MISSING:
																		_UpperCAmelCase       :		Dict 		=    True
										else:
														_UpperCAmelCase       :		Optional[int] 		=    field.type
														if field.default is not dataclasses.MISSING:
																		_UpperCAmelCase       :		Union[str, Any] 		=    field.default
														elif field.default_factory is not dataclasses.MISSING:
																		_UpperCAmelCase       :		List[Any] 		=    field.default_factory()
														else:
																		_UpperCAmelCase       :		Optional[Any] 		=    True
										parser.add_argument(A_						,					*A_						,					**A_	)

										# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
										# Order is important for arguments with the same destination!
										# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
										# here and we do not need those changes/additional keys.
										if field.default is True and (field.type is bool or field.type == Optional[bool]):
														_UpperCAmelCase       :		Tuple 		=    False
														parser.add_argument(f'--no_{field.name}'						,					action="store_false"						,					dest=field.name						,					**A_	)


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										if hasattr(A_						,					"_argument_group_name"	):
														_UpperCAmelCase       :		Optional[Any] 		=    self.add_argument_group(dtype._argument_group_name	)
										else:
														_UpperCAmelCase       :		Optional[Any] 		=    self

										try:
														_UpperCAmelCase       :		Dict[str, type] 		=    get_type_hints(A_	)
										except NameError:
														raise RuntimeError(
														    f'Type resolution failed for {dtype}. Try declaring the class in global scope or '
														    "removing line of `from __future__ import annotations` which opts in Postponed "
														    "Evaluation of Annotations (PEP 563)"	)
										except TypeError as ex:
														# Remove this block when we drop Python 3.9 support
														if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(A_	):
																		_UpperCAmelCase       :		List[str] 		=    ".".join(map(A_						,					sys.version_info[:3]	)	)
																		raise RuntimeError(
																		    f'Type resolution failed for {dtype} on Python {python_version}. Try removing '
																		    "line of `from __future__ import annotations` which opts in union types as "
																		    "`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To "
																		    "support Python versions that lower than 3.10, you need to use "
																		    "`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of "
																		    "`X | None`."	) from ex
														raise

										for field in dataclasses.fields(A_	):
														if not field.init:
																		continue
														_UpperCAmelCase       :		Dict 		=    type_hints[field.name]
														self._parse_dataclass_field(A_						,					A_	)


						def        _UpperCAmelCase				(       self						,					A_=None						,					A_=False						,					A_=True						,					A_=None						,					A_=None						,					):







										'''simple docstring'''


										if args_file_flag or args_filename or (look_for_args_file and len(sys.argv	)):
														_UpperCAmelCase       :		Any 		=    []

														if args_filename:
																		args_files.append(Path(A_	)	)
														elif look_for_args_file and len(sys.argv	):
																		args_files.append(Path(sys.argv[0]	).with_suffix(".args"	)	)

														# args files specified via command line flag should overwrite default args files so we add them last
														if args_file_flag:
																		# Create special parser just to extract the args_file_flag values
																		_UpperCAmelCase       :		Optional[int] 		=    ArgumentParser()
																		args_file_parser.add_argument(A_						,					type=A_						,					action="append"	)

																		# Use only remaining args for further parsing (remove the args_file_flag)
																		_UpperCAmelCase       :		int 		=    args_file_parser.parse_known_args(args=A_	)
																		_UpperCAmelCase       :		Optional[int] 		=    vars(A_	).get(args_file_flag.lstrip("-"	)						,					A_	)

																		if cmd_args_file_paths:
																						args_files.extend([Path(A_	) for p in cmd_args_file_paths]	)

														_UpperCAmelCase       :		Tuple 		=    []
														for args_file in args_files:
																		if args_file.exists():
																						file_args += args_file.read_text().split()

            # in case of duplicate arguments the last one has precedence
            # args specified via the command line should overwrite args from files, so we add them last
														_UpperCAmelCase       :		int 		=    file_args + args if args is not None else file_args + sys.argv[1:]
										_UpperCAmelCase       :		List[str] 		=    self.parse_known_args(args=A_	)
										_UpperCAmelCase       :		List[str] 		=    []
										for dtype in self.dataclass_types:
														_UpperCAmelCase       :		Union[str, Any] 		=    {f.name for f in dataclasses.fields(A_	) if f.init}
														_UpperCAmelCase       :		int 		=    {k: v for k, v in vars(A_	).items() if k in keys}
														for k in keys:
																		delattr(A_						,					A_	)
														_UpperCAmelCase       :		str 		=    dtype(**A_	)
														outputs.append(A_	)
										if len(namespace.__dict__	) > 0:
														# additional namespace.
														outputs.append(A_	)
										if return_remaining_strings:
														return (*outputs, remaining_args)
										else:
														if remaining_args:
																		raise ValueError(f'Some specified arguments are not used by the HfArgumentParser: {remaining_args}'	)

														return (*outputs,)


						def        _UpperCAmelCase				(       self						,					A_						,					A_ = False	):







										'''simple docstring'''


										_UpperCAmelCase       :		str 		=    set(args.keys()	)
										_UpperCAmelCase       :		List[Any] 		=    []
										for dtype in self.dataclass_types:
														_UpperCAmelCase       :		Any 		=    {f.name for f in dataclasses.fields(A_	) if f.init}
														_UpperCAmelCase       :		int 		=    {k: v for k, v in args.items() if k in keys}
														unused_keys.difference_update(inputs.keys()	)
														_UpperCAmelCase       :		List[str] 		=    dtype(**A_	)
														outputs.append(A_	)
										if not allow_extra_keys and unused_keys:
														raise ValueError(f'Some keys are not used by the HfArgumentParser: {sorted(A_	)}'	)
										return tuple(A_	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_ = False	):







										'''simple docstring'''


										with open(Path(A_	)						,					encoding="utf-8"	) as open_json_file:
														_UpperCAmelCase       :		int 		=    json.loads(open_json_file.read()	)
										_UpperCAmelCase       :		Dict 		=    self.parse_dict(A_						,					allow_extra_keys=A_	)
										return tuple(A_	)





						def        _UpperCAmelCase				(       self						,					A_						,					A_ = False	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    self.parse_dict(yaml.safe_load(Path(A_	).read_text()	)						,					allow_extra_keys=A_	)
										return tuple(A_	)





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_        =      logging.get_logger(__name__)

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  nn.ModuleList  ,      lowerCAmelCase:  nn.ModuleList  ,      lowerCAmelCase:  List[int] )   ->       None:
				_UpperCAmelCase       :		str 		=    nn.ModuleList([src_layers[i] for i in layers_to_copy] )
				assert len(lowerCAmelCase ) == len(lowerCAmelCase ), F'{len(lowerCAmelCase )} != {len(lowerCAmelCase )}'
				dest_layers.load_state_dict(layers_to_copy.state_dict() )


SCREAMING_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))},
}
SCREAMING_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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any  ,      lowerCAmelCase:  List[str] )   ->       Dict:
				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(lowerCAmelCase ) )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[Any]  ,      lowerCAmelCase:  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(lowerCAmelCase ) )
				elif n_student == 1:
								return [n_teacher - 1]
				else:
								return LAYERS_TO_SUPERVISE[n_teacher][n_student]

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

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

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

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


