
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							:Tuple										=    logging.get_logger(__name__)


def 						UpperCAmelCase      (     a_		,       a_		,       a_						)		->     None:







				"""simple docstring"""



				__A             =			nn.ModuleList([src_layers[i] for i in layers_to_copy]						)
				assert len(a_						) == len(a_						), F'''{len(a_						)} != {len(a_						)}'''
				dest_layers.load_state_dict(layers_to_copy.state_dict()						)


SCREAMING_SNAKE_CASE							:List[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							:str										=    {
    # 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      (     a_		,       a_						)		->     Optional[int]:







				"""simple docstring"""



				try:
								__A             =			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(a_						)						)


def 						UpperCAmelCase      (     a_		,       a_						)		->     List[int]:







				"""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(a_						)						)
				elif n_student == 1:
								return [n_teacher - 1]
				else:
								return LAYERS_TO_SUPERVISE[n_teacher][n_student]


def 						UpperCAmelCase      (     a_		,       a_ = "student"		,       a_ = None		,       a_ = None		,       a_=False		,       a_=None		,       a_=None		,       **a_		,       )		->     Tuple[PreTrainedModel, List[int], List[int]]:







				"""simple docstring"""



				__A             =			"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(a_		,       a_						):
								AutoTokenizer.from_pretrained(a_						).save_pretrained(a_						)  # purely for convenience
								__A             =			AutoModelForSeqaSeqLM.from_pretrained(a_						).eval()
				else:
								assert isinstance(a_		,       a_						), F'''teacher must be a model or string got type {type(a_						)}'''
				__A             =			teacher.config.to_diff_dict()

				try:
								__A      ,							__A             =			teacher.config.encoder_layers, teacher.config.decoder_layers
								if e is None:
												__A             =			teacher_e
								if d is None:
												__A             =			teacher_d
								init_kwargs.update({"encoder_layers": e, "decoder_layers": d}						)
				except AttributeError:  # T5
								if hasattr(teacher.config		,       "num_encoder_layers"						):
												__A      ,							__A             =			teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
								else:
												__A      ,							__A             =			teacher.config.num_layers, teacher.config.num_decoder_layers
								if e is None:
												__A             =			teacher_e
								if d is None:
												__A             =			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(a_						)

				# Copy weights
				__A             =			teacher.config_class(**a_						)
				__A             =			AutoModelForSeqaSeqLM.from_config(a_						)
				# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
				__A             =			student.load_state_dict(teacher.state_dict()		,       strict=a_						)
				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
								__A      ,							__A             =			list(range(a_						)						), list(range(a_						)						)
								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(a_						)
								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:
								__A             =			pick_layers_to_copy(a_		,       a_						)
				if d_layers_to_copy is None:
								__A             =			pick_layers_to_copy(a_		,       a_						)

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


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					"facebook/bart-large-mnli"
	snake_case_    =					(
	    "This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which "
	    "should be the text to classify, and `labels`, which should be the list of labels to use for classification. "
	    "It returns the most likely label in the list of provided `labels` for the input text."
	)
	snake_case_    =					"text_classifier"
	snake_case_    =					AutoTokenizer
	snake_case_    =					AutoModelForSequenceClassification

	snake_case_    =					["text", ["text"]]
	snake_case_    =					["text"]






	def     UpperCamelCase_  (	self :			str			):
					super().setup()
					__A             =			self.model.config
					__A             =			-1
					for idx, label in config.idalabel.items():
									if label.lower().startswith("entail"			):
													__A             =			int(A			)
					if self.entailment_id == -1:
									raise ValueError("Could not determine the entailment ID from the model config, please pass it at init."			)






	def     UpperCamelCase_  (	self :			int							,A :			Optional[Any]							,A :			Dict			):
					__A             =			labels
					return self.pre_processor(
					    [text] * len(A			)							,[f'''This example is {label}''' for label in labels]							,return_tensors="pt"							,padding="max_length"							,)







	def     UpperCamelCase_  (	self :			Union[str, Any]							,A :			Tuple			):
					__A             =			outputs.logits
					__A             =			torch.argmax(logits[:, 2]			).item()
					return self._labels[label_id]
