import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


a		=logging.get_logger(__name__)






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__=None     ,			lowerCamelCase__=None      )  ->					Optional[int]:
	return field(default_factory=lambda: default     ,			metadata=_SCREAMING_SNAKE_CASE      )






@dataclass
class 	A_		:
							_UpperCAmelCase   :  List[str]										=				list_field(
							    default=[]      ,							metadata={
							        '''help''': (
							            '''Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'''
							            ''' of all available models'''
							        )
							    }      ,							)

							_UpperCAmelCase   :  List[int]										=				list_field(
							    default=[8]      ,							metadata={'''help''': '''List of batch sizes for which memory and time performance will be evaluated'''}    )

							_UpperCAmelCase   :  List[int]										=				list_field(
							    default=[8, 32, 128, 512]      ,							metadata={'''help''': '''List of sequence lengths for which memory and time performance will be evaluated'''}      ,							)

							_UpperCAmelCase   :  bool										=				field(
							    default=__SCREAMING_SNAKE_CASE      ,							metadata={'''help''': '''Whether to benchmark inference of model. Inference can be disabled via --no-inference.'''}      ,							)
							_UpperCAmelCase   :  bool										=				field(
							    default=__SCREAMING_SNAKE_CASE      ,							metadata={'''help''': '''Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'''}      ,							)
							_UpperCAmelCase   :  bool										=				field(
							    default=__SCREAMING_SNAKE_CASE      ,							metadata={'''help''': '''Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'''}    )
							_UpperCAmelCase   :  bool										=				field(default=__SCREAMING_SNAKE_CASE      ,							metadata={'''help''': '''Use FP16 to accelerate inference.'''}    )
							_UpperCAmelCase   :  bool										=				field(default=__SCREAMING_SNAKE_CASE      ,							metadata={'''help''': '''Benchmark training of model'''}    )
							_UpperCAmelCase   :  bool										=				field(default=__SCREAMING_SNAKE_CASE      ,							metadata={'''help''': '''Verbose memory tracing'''}    )
							_UpperCAmelCase   :  bool										=				field(
							    default=__SCREAMING_SNAKE_CASE      ,							metadata={'''help''': '''Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'''}      ,							)
							_UpperCAmelCase   :  bool										=				field(
							    default=__SCREAMING_SNAKE_CASE      ,							metadata={
							        '''help''': '''Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'''
							    }      ,							)
							_UpperCAmelCase   :  bool										=				field(default=__SCREAMING_SNAKE_CASE      ,							metadata={'''help''': '''Trace memory line by line'''}    )
							_UpperCAmelCase   :  bool										=				field(default=__SCREAMING_SNAKE_CASE      ,							metadata={'''help''': '''Save result to a CSV file'''}    )
							_UpperCAmelCase   :  bool										=				field(default=__SCREAMING_SNAKE_CASE      ,							metadata={'''help''': '''Save all print statements in a log file'''}    )
							_UpperCAmelCase   :  bool										=				field(default=__SCREAMING_SNAKE_CASE      ,							metadata={'''help''': '''Whether to print environment information'''}    )
							_UpperCAmelCase   :  bool										=				field(
							    default=__SCREAMING_SNAKE_CASE      ,							metadata={
							        '''help''': (
							            '''Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'''
							            ''' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'''
							            ''' for debugging / testing and on TPU.'''
							        )
							    }      ,							)
							_UpperCAmelCase   :  str										=				field(
							    default=f'''inference_time_{round(time()    )}.csv'''      ,							metadata={'''help''': '''CSV filename used if saving time results to csv.'''}      ,							)
							_UpperCAmelCase   :  str										=				field(
							    default=f'''inference_memory_{round(time()    )}.csv'''      ,							metadata={'''help''': '''CSV filename used if saving memory results to csv.'''}      ,							)
							_UpperCAmelCase   :  str										=				field(
							    default=f'''train_time_{round(time()    )}.csv'''      ,							metadata={'''help''': '''CSV filename used if saving time results to csv for training.'''}      ,							)
							_UpperCAmelCase   :  str										=				field(
							    default=f'''train_memory_{round(time()    )}.csv'''      ,							metadata={'''help''': '''CSV filename used if saving memory results to csv for training.'''}      ,							)
							_UpperCAmelCase   :  str										=				field(
							    default=f'''env_info_{round(time()    )}.csv'''      ,							metadata={'''help''': '''CSV filename used if saving environment information.'''}      ,							)
							_UpperCAmelCase   :  str										=				field(
							    default=f'''log_{round(time()    )}.csv'''      ,							metadata={'''help''': '''Log filename used if print statements are saved in log.'''}      ,							)
							_UpperCAmelCase   :  int										=				field(default=3      ,							metadata={'''help''': '''Times an experiment will be run.'''}    )
							_UpperCAmelCase   :  bool										=				field(
							    default=__SCREAMING_SNAKE_CASE      ,							metadata={
							        '''help''': (
							            '''Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'''
							            ''' model weights.'''
							        )
							    }      ,							)
							def    lowerCAmelCase					(		self		:      Optional[int]):
								warnings.warn(
								    F"The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"
								    ' are deprecated in general and it is advised to use external Benchmarking libraries '
								    ' to benchmark Transformer models.'       ,_snake_case       ,)
							def    lowerCAmelCase					(		self		:      Tuple):
								return json.dumps(dataclasses.asdict(self)       ,indent=2)
							@property
							def    lowerCAmelCase					(		self		:      Optional[int]):
								if len(self.models) <= 0:
									raise ValueError(
									    'Please make sure you provide at least one model name / model identifier, *e.g.* `--models'
									    ' bert-base-cased` or `args.models = [\'bert-base-cased\'].')
								return self.models


							@property
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								if not self.multi_process:
									return False
								elif self.is_tpu:
									logger.info('Multiprocessing is currently not possible on TPU.')
									return False
								else:
									return True







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)




