

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


_UpperCAmelCase		:       List[str]						    =	logging.get_logger(__name__)





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   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()				)


_UpperCAmelCase		:       Optional[Any]						    =	{
    # 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))},
}
_UpperCAmelCase		:       Dict						    =	{
    # 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  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							try:
														snake_case_          =   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  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__				):







							'''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(UpperCamelCase__				)				)
							elif n_student == 1:
														return [n_teacher - 1]
							else:
														return LAYERS_TO_SUPERVISE[n_teacher][n_student]





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__ = "student"       ,							UpperCamelCase__ = None       ,							UpperCamelCase__ = None       ,							UpperCamelCase__=False       ,							UpperCamelCase__=None       ,							UpperCamelCase__=None       ,							**UpperCamelCase__       ,							):







							'''simple docstring'''






							snake_case_          =   '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
														snake_case_          =   AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__				).eval()
							else:
														assert isinstance(UpperCamelCase__       ,							UpperCamelCase__				), F'''teacher must be a model or string got type {type(UpperCamelCase__				)}'''
							snake_case_          =   teacher.config.to_diff_dict()

							try:
														snake_case_    ,   snake_case_          =   teacher.config.encoder_layers, teacher.config.decoder_layers
														if e is None:
																					snake_case_          =   teacher_e
														if d is None:
																					snake_case_          =   teacher_d
														init_kwargs.update({'encoder_layers': e, 'decoder_layers': d}				)
							except AttributeError:  # T5
														if hasattr(teacher.config       ,							'num_encoder_layers'				):
																					snake_case_    ,   snake_case_          =   teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
														else:
																					snake_case_    ,   snake_case_          =   teacher.config.num_layers, teacher.config.num_decoder_layers
														if e is None:
																					snake_case_          =   teacher_e
														if d is None:
																					snake_case_          =   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
							snake_case_          =   teacher.config_class(**UpperCamelCase__				)
							snake_case_          =   AutoModelForSeqaSeqLM.from_config(UpperCamelCase__				)
							# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
							snake_case_          =   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
														snake_case_    ,   snake_case_          =   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:
														snake_case_          =   pick_layers_to_copy(UpperCamelCase__       ,							UpperCamelCase__				)
							if d_layers_to_copy is None:
														snake_case_          =   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}'''				)
							snake_case_          =   {
							    '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 argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   BigBirdConfig.from_json_file(UpperCamelCase__				)
							print(F'''Building PyTorch model from configuration: {config}'''				)

							if is_trivia_qa:
														snake_case_          =   BigBirdForQuestionAnswering(UpperCamelCase__				)
							else:
														snake_case_          =   BigBirdForPreTraining(UpperCamelCase__				)

							# Load weights from tf checkpoint
							load_tf_weights_in_big_bird(UpperCamelCase__       ,							UpperCamelCase__       ,							is_trivia_qa=UpperCamelCase__				)

							# Save pytorch-model
							print(F'''Save PyTorch model to {pytorch_dump_path}'''				)
							model.save_pretrained(UpperCamelCase__				)


if __name__ == "__main__":
					_UpperCAmelCase		:       Dict						    =	argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
					)
					parser.add_argument(
					    """--big_bird_config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """The config json file corresponding to the pre-trained BERT model. \n"""
					        """This specifies the model architecture."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					parser.add_argument(
					    """--is_trivia_qa""", action="""store_true""", help="""Whether to convert a model with a trivia_qa head."""
					)
					_UpperCAmelCase		:       str						    =	parser.parse_args()
					convert_tf_checkpoint_to_pytorch(
					    args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
					)




