





"""simple docstring"""






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()
lowerCAmelCase_			=							logging.get_logger(__name__)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE=False							)		->       str:






       """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       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE=False							)		->       int:






       """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       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       Tuple:






       """simple docstring"""




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

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       Optional[Any]:






       """simple docstring"""




       _UpperCAmelCase     =     dct.pop(SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     val

def       __lowerCamelCase    (       )		->       str:






       """simple docstring"""




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

@torch.no_grad()
def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE=True							)		->       Optional[Any]:






       """simple docstring"""




       _UpperCAmelCase     =     ViTConfig()
       # patch_size
       if model_name[-1] == "8":
              _UpperCAmelCase     =     8
       # set labels if required
       if not base_model:
              _UpperCAmelCase     =     1000
              _UpperCAmelCase     =     'huggingface/label-files'
              _UpperCAmelCase     =     'imagenet-1k-id2label.json'
              _UpperCAmelCase     =     json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,repo_type='dataset'							),'r'							)							)
              _UpperCAmelCase     =     {int(SCREAMING_SNAKE_CASE							): 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     =     1536
              _UpperCAmelCase     =     12
              _UpperCAmelCase     =     6

       # load original model from torch hub
       _UpperCAmelCase     =     torch.hub.load('facebookresearch/dino:main',SCREAMING_SNAKE_CASE							)
       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_(SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     create_rename_keys(SCREAMING_SNAKE_CASE,base_model=SCREAMING_SNAKE_CASE							)
       for src, dest in rename_keys:
              rename_key(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)
       read_in_q_k_v(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)

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

       # 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(SCREAMING_SNAKE_CASE							)

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

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


if __name__ == "__main__":
    lowerCAmelCase_			=							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)
    lowerCAmelCase_			=							parser.parse_args()
    convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)









"""simple docstring"""






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       str:






       """simple docstring"""




       _UpperCAmelCase     =     [
           'encoder.version',
           'decoder.version',
           'model.encoder.version',
           'model.decoder.version',
           'decoder.output_projection.weight',
           '_float_tensor',
           'encoder.embed_positions._float_tensor',
           'decoder.embed_positions._float_tensor',
       ]
       for k in ignore_keys:
              state_dict.pop(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       List[Any]:






       """simple docstring"""




       _UpperCAmelCase    ,							_UpperCAmelCase     =     emb.weight.shape
       _UpperCAmelCase     =     nn.Linear(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,bias=SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     emb.weight.data
       return lin_layer

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       int:






       """simple docstring"""




       _UpperCAmelCase     =     torch.load(SCREAMING_SNAKE_CASE,map_location='cpu'							)
       _UpperCAmelCase     =     mam_aaa['args'] or mam_aaa['cfg']['model']
       _UpperCAmelCase     =     mam_aaa['model']
       remove_ignore_keys_(SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     state_dict['encoder.embed_tokens.weight'].shape[0]

       _UpperCAmelCase     =     MaMaaaConfig(
           vocab_size=SCREAMING_SNAKE_CASE,max_position_embeddings=1024,encoder_layers=args.encoder_layers,decoder_layers=args.decoder_layers,encoder_attention_heads=args.encoder_attention_heads,decoder_attention_heads=args.decoder_attention_heads,encoder_ffn_dim=args.encoder_ffn_embed_dim,decoder_ffn_dim=args.decoder_ffn_embed_dim,d_model=args.encoder_embed_dim,encoder_layerdrop=args.encoder_layerdrop,decoder_layerdrop=args.decoder_layerdrop,dropout=args.dropout,attention_dropout=args.attention_dropout,activation_dropout=args.activation_dropout,activation_function='relu',)

       _UpperCAmelCase     =     state_dict['decoder.embed_tokens.weight']
       _UpperCAmelCase     =     MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE							)
       model.model.load_state_dict(SCREAMING_SNAKE_CASE,strict=SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     make_linear_from_emb(model.model.shared							)

       return model


if __name__ == "__main__":
    lowerCAmelCase_			=							argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
    parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    lowerCAmelCase_			=							parser.parse_args()
    lowerCAmelCase_			=							convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)






