

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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					None:
	__lowerCamelCase    :    Tuple 	=			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()      )


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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					str:
	try:
		__lowerCamelCase    :    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(lowerCamelCase__      )      )






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

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







import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar






class 	A_		(	unittest.TestCase    ):
							def    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase    :    List[Any] 	=			logging.get_logger()

								# the current default level is logging.WARNING
								__lowerCamelCase    :    Dict 	=			logging.get_verbosity()

								logging.set_verbosity_error()
								self.assertEqual(logger.getEffectiveLevel()       ,logging.get_verbosity())

								logging.set_verbosity_warning()
								self.assertEqual(logger.getEffectiveLevel()       ,logging.get_verbosity())

								logging.set_verbosity_info()
								self.assertEqual(logger.getEffectiveLevel()       ,logging.get_verbosity())

								logging.set_verbosity_debug()
								self.assertEqual(logger.getEffectiveLevel()       ,logging.get_verbosity())

								# restore to the original level
								logging.set_verbosity(SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    Optional[int] 	=			logging.get_verbosity()

								__lowerCamelCase    :    str 	=			logging.get_logger('transformers.models.bart.tokenization_bart')
								__lowerCamelCase    :    Tuple 	=			'Testing 1, 2, 3'

								# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
								if level_origin <= logging.WARNING:
									with CaptureLogger(SCREAMING_SNAKE_CASE__) as cl:
										logger.warning(SCREAMING_SNAKE_CASE__)
									self.assertEqual(cl.out       ,msg + '\n')

								# this is setting the level for all of `transformers.*` loggers
								logging.set_verbosity_error()

								# should not be able to log warnings
								with CaptureLogger(SCREAMING_SNAKE_CASE__) as cl:
									logger.warning(SCREAMING_SNAKE_CASE__)
								self.assertEqual(cl.out       ,'')

								# should be able to log warnings again
								logging.set_verbosity_warning()
								with CaptureLogger(SCREAMING_SNAKE_CASE__) as cl:
									logger.warning(SCREAMING_SNAKE_CASE__)
								self.assertEqual(cl.out       ,msg + '\n')

								# restore to the original level
								logging.set_verbosity(SCREAMING_SNAKE_CASE__)
							@mockenv(TRANSFORMERS_VERBOSITY='error')
							def    lowerCAmelCase					(		self		:      Tuple):
								# reset for the env var to take effect, next time some logger call is made
								transformers.utils.logging._reset_library_root_logger()
								# this action activates the env var
								__lowerCamelCase    :    int 	=			logging.get_logger('transformers.models.bart.tokenization_bart')

								__lowerCamelCase    :    int 	=			os.getenv('TRANSFORMERS_VERBOSITY'       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[Any] 	=			logging.log_levels[env_level_str]

								__lowerCamelCase    :    Tuple 	=			logging.get_verbosity()
								self.assertEqual(
								    SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,F"TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}"       ,)

								# restore to the original level
								__lowerCamelCase    :    List[str] 	=			''
								transformers.utils.logging._reset_library_root_logger()
							@mockenv(TRANSFORMERS_VERBOSITY='super-error')
							def    lowerCAmelCase					(		self		:      List[Any]):
								# reset for the env var to take effect, next time some logger call is made
								transformers.utils.logging._reset_library_root_logger()
								__lowerCamelCase    :    List[str] 	=			logging.logging.getLogger()
								with CaptureLogger(SCREAMING_SNAKE_CASE__) as cl:
									# this action activates the env var
									logging.get_logger('transformers.models.bart.tokenization_bart')
								self.assertIn('Unknown option TRANSFORMERS_VERBOSITY=super-error'       ,cl.out)

								# no need to restore as nothing was changed


							def    lowerCAmelCase					(		self		:      Any):
								# testing `logger.warning_advice()`
								transformers.utils.logging._reset_library_root_logger()

								__lowerCamelCase    :    Tuple 	=			logging.get_logger('transformers.models.bart.tokenization_bart')
								__lowerCamelCase    :    Optional[int] 	=			'Testing 1, 2, 3'

								with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='1'):
									# nothing should be logged as env var disables this method
									with CaptureLogger(SCREAMING_SNAKE_CASE__) as cl:
										logger.warning_advice(SCREAMING_SNAKE_CASE__)
									self.assertEqual(cl.out       ,'')

								with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=''):
									# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
									with CaptureLogger(SCREAMING_SNAKE_CASE__) as cl:
										logger.warning_advice(SCREAMING_SNAKE_CASE__)
									self.assertEqual(cl.out       ,msg + '\n')






def        SCREAMING_SNAKE_CASE__	(  )  ->					Any:
	disable_progress_bar()
	assert are_progress_bars_disabled()

	enable_progress_bar()
	assert not are_progress_bars_disabled()




