






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 __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  None:





						"""simple docstring"""



						_UpperCAmelCase									=			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 __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  Dict:





						"""simple docstring"""



						try:
												_UpperCAmelCase									=			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 __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  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(__lowerCAmelCase	)	)
						elif n_student == 1:
												return [n_teacher - 1]
						else:
												return LAYERS_TO_SUPERVISE[n_teacher][n_student]


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





						"""simple docstring"""



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

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








import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
_a      	=  logging.get_logger(__name__)


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase=False	)->  Union[str, Any]:





						"""simple docstring"""



						_UpperCAmelCase									=			[]
						for i in range(config.num_hidden_layers	):
												# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
												rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""vit.encoder.layer.{i}.layernorm_before.weight""")	)
												rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""vit.encoder.layer.{i}.layernorm_before.bias""")	)
												rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""vit.encoder.layer.{i}.attention.output.dense.weight""")	)
												rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""vit.encoder.layer.{i}.attention.output.dense.bias""")	)
												rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""vit.encoder.layer.{i}.layernorm_after.weight""")	)
												rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""vit.encoder.layer.{i}.layernorm_after.bias""")	)
												rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""vit.encoder.layer.{i}.intermediate.dense.weight""")	)
												rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""vit.encoder.layer.{i}.intermediate.dense.bias""")	)
												rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""vit.encoder.layer.{i}.output.dense.weight""")	)
												rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""vit.encoder.layer.{i}.output.dense.bias""")	)

						# projection layer + position embeddings
						rename_keys.extend(
						    [
						        ('cls_token', 'vit.embeddings.cls_token'),
						        ('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
						        ('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
						        ('pos_embed', 'vit.embeddings.position_embeddings'),
						    ]	)

						if base_model:
												# layernorm + pooler
												rename_keys.extend(
												    [
												        ('norm.weight', 'layernorm.weight'),
												        ('norm.bias', 'layernorm.bias'),
												    ]	)

												# if just the base model, we should remove "vit" from all keys that start with "vit"
												_UpperCAmelCase									=			[(pair[0], pair[1][4:]) if pair[1].startswith('vit'	) else pair for pair in rename_keys]
						else:
												# layernorm + classification head
												rename_keys.extend(
												    [
												        ('norm.weight', 'vit.layernorm.weight'),
												        ('norm.bias', 'vit.layernorm.bias'),
												        ('head.weight', 'classifier.weight'),
												        ('head.bias', 'classifier.bias'),
												    ]	)

						return rename_keys


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase=False	)->  List[str]:





						"""simple docstring"""



						for i in range(config.num_hidden_layers	):
												if base_model:
																		_UpperCAmelCase									=			''
												else:
																		_UpperCAmelCase									=			'vit.'
												# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
												_UpperCAmelCase									=			state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""	)
												_UpperCAmelCase									=			state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""	)
												# next, add query, keys and values (in that order) to the state dict
												_UpperCAmelCase									=			in_proj_weight[
												    : config.hidden_size, :
												]
												_UpperCAmelCase									=			in_proj_bias[: config.hidden_size]
												_UpperCAmelCase									=			in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												_UpperCAmelCase									=			in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												_UpperCAmelCase									=			in_proj_weight[
												    -config.hidden_size :, :
												]
												_UpperCAmelCase									=			in_proj_bias[-config.hidden_size :]


def __A    (      __lowerCAmelCase	)->  Optional[Any]:





						"""simple docstring"""



						_UpperCAmelCase									=			['head.weight', 'head.bias']
						for k in ignore_keys:
												state_dict.pop(__lowerCAmelCase ,		__lowerCAmelCase	)


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  int:





						"""simple docstring"""



						_UpperCAmelCase									=			dct.pop(__lowerCAmelCase	)
						_UpperCAmelCase									=			val


def __A    (      )->  str:





						"""simple docstring"""



						_UpperCAmelCase									=			'http://images.cocodataset.org/val2017/000000039769.jpg'
						_UpperCAmelCase									=			Image.open(requests.get(__lowerCAmelCase ,		stream=__lowerCAmelCase	).raw	)
						return im


@torch.no_grad()
def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase=True	)->  List[str]:





						"""simple docstring"""



						_UpperCAmelCase									=			ViTConfig()
						# patch_size
						if model_name[-1] == "8":
												_UpperCAmelCase									=			8
						# set labels if required
						if not base_model:
												_UpperCAmelCase									=			1_000
												_UpperCAmelCase									=			'huggingface/label-files'
												_UpperCAmelCase									=			'imagenet-1k-id2label.json'
												_UpperCAmelCase									=			json.load(open(hf_hub_download(__lowerCAmelCase ,		__lowerCAmelCase ,		repo_type='dataset'	) ,		'r'	)	)
												_UpperCAmelCase									=			{int(__lowerCAmelCase	): v for k, v in idalabel.items()}
												_UpperCAmelCase									=			idalabel
												_UpperCAmelCase									=			{v: k for k, v in idalabel.items()}
						# size of the architecture
						if model_name in ["dino_vits8", "dino_vits16"]:
												_UpperCAmelCase									=			384
												_UpperCAmelCase									=			1_536
												_UpperCAmelCase									=			12
												_UpperCAmelCase									=			6

						# load original model from torch hub
						_UpperCAmelCase									=			torch.hub.load('facebookresearch/dino:main' ,		__lowerCAmelCase	)
						original_model.eval()

						# load state_dict of original model, remove and rename some keys
						_UpperCAmelCase									=			original_model.state_dict()
						if base_model:
												remove_classification_head_(__lowerCAmelCase	)
						_UpperCAmelCase									=			create_rename_keys(__lowerCAmelCase ,		base_model=__lowerCAmelCase	)
						for src, dest in rename_keys:
												rename_key(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)
						read_in_q_k_v(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)

						# load HuggingFace model
						if base_model:
												_UpperCAmelCase									=			ViTModel(__lowerCAmelCase ,		add_pooling_layer=__lowerCAmelCase	).eval()
						else:
												_UpperCAmelCase									=			ViTForImageClassification(__lowerCAmelCase	).eval()
						model.load_state_dict(__lowerCAmelCase	)

						# Check outputs on an image, prepared by ViTImageProcessor
						_UpperCAmelCase									=			ViTImageProcessor()
						_UpperCAmelCase									=			image_processor(images=prepare_img() ,		return_tensors='pt'	)
						_UpperCAmelCase									=			encoding['pixel_values']
						_UpperCAmelCase									=			model(__lowerCAmelCase	)

						if base_model:
												_UpperCAmelCase									=			original_model(__lowerCAmelCase	)
												assert torch.allclose(__lowerCAmelCase ,		outputs.last_hidden_state[:, 0, :] ,		atol=1E-1	)
						else:
												_UpperCAmelCase									=			original_model(__lowerCAmelCase	)
												assert logits.shape == outputs.logits.shape
												assert torch.allclose(__lowerCAmelCase ,		outputs.logits ,		atol=1E-3	)

						Path(__lowerCAmelCase	).mkdir(exist_ok=__lowerCAmelCase	)
						print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""	)
						model.save_pretrained(__lowerCAmelCase	)
						print(F"""Saving image processor to {pytorch_dump_folder_path}"""	)
						image_processor.save_pretrained(__lowerCAmelCase	)


if __name__ == "__main__":
		_a      	=  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--model_name''',
		    default='''dino_vitb16''',
		    type=str,
		    help='''Name of the model trained with DINO you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
		)
		parser.add_argument(
		    '''--base_model''',
		    action='''store_true''',
		    help='''Whether to only convert the base model (no projection head weights).''',
		)

		parser.set_defaults(base_model=True)
		_a      	=  parser.parse_args()
		convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)
