



"""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


A    =			logging.get_logger(__name__)

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			) ->							None:
							A         =					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
    1_2: {
        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, 1_1],
        4: [0, 4, 8, 1_1],
        6: [0, 2, 4, 7, 9, 1_1],
        9: [0, 1, 2, 4, 5, 7, 9, 1_0, 1_1],
        1_2: list(range(1_2)),
    },
    1_6: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 1_5],
        3: [0, 8, 1_5],
        4: [0, 5, 1_0, 1_5],
        6: [0, 3, 6, 9, 1_2, 1_5],
        8: [0, 2, 4, 6, 8, 1_0, 1_2, 1_5],
        9: [0, 1, 3, 5, 7, 9, 1_1, 1_3, 1_5],
        1_2: [0, 1, 2, 3, 4, 5, 6, 7, 9, 1_1, 1_3, 1_5],
        1_6: list(range(1_6)),
    },
    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]},
    1_2: {1: [1_1], 2: [5, 1_1], 3: [3, 7, 1_1], 6: [1, 3, 5, 8, 1_0, 1_1]},
    1_6: {1: [1_5], 4: [4, 9, 1_2, 1_5], 8: [1, 3, 5, 7, 9, 1_1, 1_3, 1_5]},
}

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							Union[str, Any]:
							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(lowerCamelCase__			)			)

def   lowerCAmelCase__			(     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   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__ = "student"				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__=False				,      lowerCamelCase__=None				,      lowerCamelCase__=None				,      **lowerCamelCase__				,      ) ->							Tuple[PreTrainedModel, List[int], List[int]]:
							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(lowerCamelCase__				,      lowerCamelCase__			):
														AutoTokenizer.from_pretrained(lowerCamelCase__			).save_pretrained(lowerCamelCase__			)  # purely for convenience
														A         =					AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__			).eval()
							else:
														assert isinstance(lowerCamelCase__				,      lowerCamelCase__			), f"""teacher must be a model or string got type {type(lowerCamelCase__			)}"""
							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(lowerCamelCase__			)

							# Copy weights
							A         =					teacher.config_class(**lowerCamelCase__			)
							A         =					AutoModelForSeqaSeqLM.from_config(lowerCamelCase__			)
							# 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=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
														A      ,  A         =					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:
														A         =					pick_layers_to_copy(lowerCamelCase__				,      lowerCamelCase__			)
							if d_layers_to_copy is None:
														A         =					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}"""			)
							A         =					{
							    '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)








"""simple docstring"""







import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import (
							    AutoProcessor,
							    BertTokenizerFast,
							    BlipImageProcessor,
							    GPTaTokenizer,
							    InstructBlipProcessor,
							    PreTrainedTokenizerFast,
							)






@require_vision
class      UpperCAmelCase__		(      unittest.TestCase     ):



		def 					A_		( self  :    Dict				)			->						Dict:






									'''simple docstring'''

									A         =					tempfile.mkdtemp()

									A         =					BlipImageProcessor()
									A         =					GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model'				)
									A         =					BertTokenizerFast.from_pretrained('hf-internal-testing/tiny-random-bert'				)

									A         =					InstructBlipProcessor(snake_case      ,							snake_case      ,							snake_case				)

									processor.save_pretrained(self.tmpdirname				)



		def 					A_		( self  :    List[str]      ,							**snake_case  :    str				)			->						Dict:






									'''simple docstring'''

									return AutoProcessor.from_pretrained(self.tmpdirname      ,							**snake_case				).tokenizer



		def 					A_		( self  :    int      ,							**snake_case  :    Optional[Any]				)			->						Any:






									'''simple docstring'''

									return AutoProcessor.from_pretrained(self.tmpdirname      ,							**snake_case				).image_processor



		def 					A_		( self  :    Any      ,							**snake_case  :    Union[str, Any]				)			->						Any:






									'''simple docstring'''

									return AutoProcessor.from_pretrained(self.tmpdirname      ,							**snake_case				).qformer_tokenizer



		def 					A_		( self  :    int				)			->						Union[str, Any]:






									'''simple docstring'''

									shutil.rmtree(self.tmpdirname				)



		def 					A_		( self  :    List[Any]				)			->						Tuple:






									'''simple docstring'''

									A         =					[np.random.randint(255      ,							size=(3, 30, 400)      ,							dtype=np.uinta				)]

									A         =					[Image.fromarray(np.moveaxis(snake_case      ,							0      ,							-1				)				) for x in image_inputs]

									return image_inputs



		def 					A_		( self  :    List[Any]				)			->						Optional[int]:






									'''simple docstring'''

									A         =					InstructBlipProcessor(
									    tokenizer=self.get_tokenizer()      ,							image_processor=self.get_image_processor()      ,							qformer_tokenizer=self.get_qformer_tokenizer()      ,							)
									processor.save_pretrained(self.tmpdirname				)

									A         =					self.get_tokenizer(bos_token='(BOS)'      ,							eos_token='(EOS)'				)
									A         =					self.get_image_processor(do_normalize=snake_case      ,							padding_value=1.0				)

									A         =					InstructBlipProcessor.from_pretrained(
									    self.tmpdirname      ,							bos_token='(BOS)'      ,							eos_token='(EOS)'      ,							do_normalize=snake_case      ,							padding_value=1.0				)

									self.assertEqual(processor.tokenizer.get_vocab()      ,							tokenizer_add_kwargs.get_vocab()				)
									self.assertIsInstance(processor.tokenizer      ,							snake_case				)

									self.assertEqual(processor.image_processor.to_json_string()      ,							image_processor_add_kwargs.to_json_string()				)
									self.assertIsInstance(processor.image_processor      ,							snake_case				)
									self.assertIsInstance(processor.qformer_tokenizer      ,							snake_case				)



		def 					A_		( self  :    Optional[Any]				)			->						Dict:






									'''simple docstring'''

									A         =					self.get_image_processor()
									A         =					self.get_tokenizer()
									A         =					self.get_qformer_tokenizer()

									A         =					InstructBlipProcessor(
									    tokenizer=snake_case      ,							image_processor=snake_case      ,							qformer_tokenizer=snake_case				)

									A         =					self.prepare_image_inputs()

									A         =					image_processor(snake_case      ,							return_tensors='np'				)
									A         =					processor(images=snake_case      ,							return_tensors='np'				)

									for key in input_feat_extract.keys():
																self.assertAlmostEqual(input_feat_extract[key].sum()      ,							input_processor[key].sum()      ,							delta=1E-2				)



		def 					A_		( self  :    Tuple				)			->						List[str]:






									'''simple docstring'''

									A         =					self.get_image_processor()
									A         =					self.get_tokenizer()
									A         =					self.get_qformer_tokenizer()

									A         =					InstructBlipProcessor(
									    tokenizer=snake_case      ,							image_processor=snake_case      ,							qformer_tokenizer=snake_case				)

									A         =					'lower newer'

									A         =					processor(text=snake_case				)

									A         =					tokenizer(snake_case      ,							return_token_type_ids=snake_case				)
									A         =					qformer_tokenizer(snake_case      ,							return_token_type_ids=snake_case				)

									for key in encoded_tokens.keys():
																self.assertListEqual(encoded_tokens[key]      ,							encoded_processor[key]				)

									for key in encoded_tokens_qformer.keys():
																self.assertListEqual(encoded_tokens_qformer[key]      ,							encoded_processor['qformer_' + key]				)



		def 					A_		( self  :    List[Any]				)			->						Optional[int]:






									'''simple docstring'''

									A         =					self.get_image_processor()
									A         =					self.get_tokenizer()
									A         =					self.get_qformer_tokenizer()

									A         =					InstructBlipProcessor(
									    tokenizer=snake_case      ,							image_processor=snake_case      ,							qformer_tokenizer=snake_case				)

									A         =					'lower newer'
									A         =					self.prepare_image_inputs()

									A         =					processor(text=snake_case      ,							images=snake_case				)

									self.assertListEqual(
									    list(inputs.keys()				)      ,							['input_ids', 'attention_mask', 'qformer_input_ids', 'qformer_attention_mask', 'pixel_values']      ,							)

									# test if it raises when no input is passed
									with pytest.raises(snake_case				):
																processor()



		def 					A_		( self  :    Optional[Any]				)			->						List[str]:






									'''simple docstring'''

									A         =					self.get_image_processor()
									A         =					self.get_tokenizer()
									A         =					self.get_qformer_tokenizer()

									A         =					InstructBlipProcessor(
									    tokenizer=snake_case      ,							image_processor=snake_case      ,							qformer_tokenizer=snake_case				)

									A         =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									A         =					processor.batch_decode(snake_case				)
									A         =					tokenizer.batch_decode(snake_case				)

									self.assertListEqual(snake_case      ,							snake_case				)



		def 					A_		( self  :    Tuple				)			->						List[Any]:






									'''simple docstring'''

									A         =					self.get_image_processor()
									A         =					self.get_tokenizer()
									A         =					self.get_qformer_tokenizer()

									A         =					InstructBlipProcessor(
									    tokenizer=snake_case      ,							image_processor=snake_case      ,							qformer_tokenizer=snake_case				)

									A         =					'lower newer'
									A         =					self.prepare_image_inputs()

									A         =					processor(text=snake_case      ,							images=snake_case				)

									self.assertListEqual(
									    list(inputs.keys()				)      ,							['input_ids', 'attention_mask', 'qformer_input_ids', 'qformer_attention_mask', 'pixel_values']      ,							)



