




'''simple docstring'''





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							:       Optional[int]						=			logging.get_logger(__name__)


def 					UpperCamelCase_(     snake_case							:	nn.ModuleList							,    snake_case							:	nn.ModuleList							,    snake_case							:	List[int]					):

							'''simple docstring'''






							snake_case_      =				nn.ModuleList([src_layers[i] for i in layers_to_copy]					)
							assert len(snake_case					) == len(snake_case					), f'{len(snake_case					)} != {len(snake_case					)}'
							dest_layers.load_state_dict(layers_to_copy.state_dict()					)


_SCREAMING_SNAKE_CASE							:       str						=			{
    # 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							:       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 					UpperCamelCase_(     snake_case							:	List[str]							,    snake_case							:	Optional[Any]					):

							'''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(snake_case					)					)


def 					UpperCamelCase_(     snake_case							:	Tuple							,    snake_case							:	List[str]					):

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


def 					UpperCamelCase_(     snake_case							:	Union[str, PreTrainedModel]							,    snake_case							:	Union[str, Path] = "student"							,    snake_case							:	Union[int, None] = None							,    snake_case							:	Union[int, None] = None							,    snake_case							:	Tuple=False							,    snake_case							:	Union[str, Any]=None							,    snake_case							:	Dict=None							,    **snake_case							:	Any							,    ):

							'''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(snake_case							,    snake_case					):
														AutoTokenizer.from_pretrained(snake_case					).save_pretrained(snake_case					)  # purely for convenience
														snake_case_      =				AutoModelForSeqaSeqLM.from_pretrained(snake_case					).eval()
							else:
														assert isinstance(snake_case							,    snake_case					), f'teacher must be a model or string got type {type(snake_case					)}'
							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(snake_case					)

							# Copy weights
							snake_case_      =				teacher.config_class(**snake_case					)
							snake_case_      =				AutoModelForSeqaSeqLM.from_config(snake_case					)
							# 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=snake_case					)
							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(snake_case					)					), list(range(snake_case					)					)
														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(snake_case					)
														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(snake_case							,    snake_case					)
							if d_layers_to_copy is None:
														snake_case_      =				pick_layers_to_copy(snake_case							,    snake_case					)

							try:
														if hasattr(
														    snake_case							,    "prophetnet"					):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
																					copy_layers(teacher.prophetnet.encoder.layers							,    student.prophetnet.encoder.layers							,    snake_case					)
																					copy_layers(teacher.prophetnet.decoder.layers							,    student.prophetnet.decoder.layers							,    snake_case					)
														else:
																					copy_layers(teacher.model.encoder.layers							,    student.model.encoder.layers							,    snake_case					)
																					copy_layers(teacher.model.decoder.layers							,    student.model.decoder.layers							,    snake_case					)
							except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
														copy_layers(teacher.encoder.block							,    student.encoder.block							,    snake_case					)
														copy_layers(teacher.decoder.block							,    student.decoder.block							,    snake_case					)
							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(snake_case					)
							# 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)










'''simple docstring'''





import argparse
from collections import defaultdict

import yaml


_SCREAMING_SNAKE_CASE							:       Optional[Any]						=			"docs/source/en/_toctree.yml"


def 					UpperCamelCase_(     snake_case							:	Optional[Any]					):

							'''simple docstring'''






							snake_case_      =				defaultdict(snake_case					)
							for doc in model_doc:
														counts[doc["local"]] += 1
							snake_case_      =				[key for key, value in counts.items() if value > 1]

							snake_case_      =				[]
							for duplicate_key in duplicates:
														snake_case_      =				list({doc["title"] for doc in model_doc if doc["local"] == duplicate_key}					)
														if len(snake_case					) > 1:
																					raise ValueError(
																					    f'{duplicate_key} is present several times in the documentation table of content at '
																					    "`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the "
																					    "others."					)
														# Only add this once
														new_doc.append({"local": duplicate_key, "title": titles[0]}					)

							# Add none duplicate-keys
							new_doc.extend([doc for doc in model_doc if counts[doc["local"]] == 1]					)

							# Sort
							return sorted(snake_case							,    key=lambda snake_case					: s["title"].lower()					)


def 					UpperCamelCase_(     snake_case							:	Optional[int]=False					):

							'''simple docstring'''






							with open(snake_case							,    encoding="utf-8"					) as f:
														snake_case_      =				yaml.safe_load(f.read()					)

							# Get to the API doc
							snake_case_      =				0
							while content[api_idx]["title"] != "API":
														api_idx += 1
							snake_case_      =				content[api_idx]["sections"]

							# Then to the model doc
							snake_case_      =				0
							while api_doc[model_idx]["title"] != "Models":
														model_idx += 1

							snake_case_      =				api_doc[model_idx]["sections"]

							snake_case_      =				[(idx, section) for idx, section in enumerate(snake_case					) if "sections" in section]
							snake_case_      =				False
							for idx, modality_doc in modalities_docs:
														snake_case_      =				modality_doc["sections"]
														snake_case_      =				clean_model_doc_toc(snake_case					)

														if old_modality_doc != new_modality_doc:
																					snake_case_      =				True
																					if overwrite:
																												snake_case_      =				new_modality_doc

							if diff:
														if overwrite:
																					snake_case_      =				model_doc
																					snake_case_      =				api_doc
																					with open(snake_case							,    "w"							,    encoding="utf-8"					) as f:
																												f.write(yaml.dump(snake_case							,    allow_unicode=snake_case					)					)
														else:
																					raise ValueError(
																					    "The model doc part of the table of content is not properly sorted, run `make style` to fix this."					)


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:       List[str]						=			argparse.ArgumentParser()
		parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
		_SCREAMING_SNAKE_CASE							:       Any						=			parser.parse_args()

		check_model_doc(args.fix_and_overwrite)




