





import argparse
import json

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

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)

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

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

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

    # if just the base model, we should remove "vit" from all keys that start with "vit"
    _lowercase							= [(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 							SCREAMING_SNAKE_CASE__       (  snake_case__  :int			,							snake_case__  :str			,							snake_case__  :int=False     )      ->						Any:
  for i in range(config.num_hidden_layers     ):
    if base_model:
      _lowercase							= ''
    else:
      _lowercase							= 'vit.'
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    _lowercase							= state_dict.pop(F"""module.blocks.{i}.attn.qkv.weight"""     )
    _lowercase							= state_dict.pop(F"""module.blocks.{i}.attn.qkv.bias"""     )
    # next, add query, keys and values (in that order) to the state dict
    _lowercase							= in_proj_weight[
        : config.hidden_size, :
    ]
    _lowercase							= in_proj_bias[: config.hidden_size]
    _lowercase							= in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    _lowercase							= in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    _lowercase							= in_proj_weight[
        -config.hidden_size :, :
    ]
    _lowercase							= in_proj_bias[-config.hidden_size :]

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :List[str]     )      ->						List[Any]:
  _lowercase							= ['head.weight', 'head.bias']
  for k in ignore_keys:
    state_dict.pop(snake_case__			,							snake_case__     )

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Union[str, Any]     )      ->						List[str]:
  # projection head is used in the self-supervised pre-training in MSN,
  # for downstream task it's not needed.
  _lowercase							= [
      'module.fc.fc1.weight',
      'module.fc.fc1.bias',
      'module.fc.bn1.weight',
      'module.fc.bn1.bias',
      'module.fc.bn1.running_mean',
      'module.fc.bn1.running_var',
      'module.fc.bn1.num_batches_tracked',
      'module.fc.fc2.weight',
      'module.fc.fc2.bias',
      'module.fc.bn2.weight',
      'module.fc.bn2.bias',
      'module.fc.bn2.running_mean',
      'module.fc.bn2.running_var',
      'module.fc.bn2.num_batches_tracked',
      'module.fc.fc3.weight',
      'module.fc.fc3.bias',
  ]
  for k in ignore_keys:
    state_dict.pop(snake_case__			,							snake_case__     )

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Any			,							snake_case__  :Optional[int]			,							snake_case__  :List[str]     )      ->						Tuple:
  _lowercase							= dct.pop(snake_case__     )
  _lowercase							= val

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :str			,							snake_case__  :Dict     )      ->						Any:
  _lowercase							= ViTMSNConfig()
  _lowercase							= 1000

  _lowercase							= 'datasets/huggingface/label-files'
  _lowercase							= 'imagenet-1k-id2label.json'
  _lowercase							= json.load(open(hf_hub_download(snake_case__			,							snake_case__     )			,							'r'     )     )
  _lowercase							= {int(snake_case__     ): v for k, v in idalabel.items()}
  _lowercase							= idalabel
  _lowercase							= {v: k for k, v in idalabel.items()}

  if "s16" in checkpoint_url:
    _lowercase							= 384
    _lowercase							= 1536
    _lowercase							= 6
  elif "l16" in checkpoint_url:
    _lowercase							= 1024
    _lowercase							= 4096
    _lowercase							= 24
    _lowercase							= 16
    _lowercase							= 0.1
  elif "b4" in checkpoint_url:
    _lowercase							= 4
  elif "l7" in checkpoint_url:
    _lowercase							= 7
    _lowercase							= 1024
    _lowercase							= 4096
    _lowercase							= 24
    _lowercase							= 16
    _lowercase							= 0.1

  _lowercase							= ViTMSNModel(snake_case__     )

  _lowercase							= torch.hub.load_state_dict_from_url(snake_case__			,							map_location='cpu'     )['target_encoder']

  _lowercase							= ViTImageProcessor(size=config.image_size     )

  remove_projection_head(snake_case__     )
  _lowercase							= create_rename_keys(snake_case__			,							base_model=snake_case__     )

  for src, dest in rename_keys:
    rename_key(snake_case__			,							snake_case__			,							snake_case__     )
  read_in_q_k_v(snake_case__			,							snake_case__			,							base_model=snake_case__     )

  model.load_state_dict(snake_case__     )
  model.eval()

  _lowercase							= 'http://images.cocodataset.org/val2017/000000039769.jpg'

  _lowercase							= Image.open(requests.get(snake_case__			,							stream=snake_case__     ).raw     )
  _lowercase							= ViTImageProcessor(
      size=config.image_size			,							image_mean=snake_case__			,							image_std=snake_case__     )
  _lowercase							= image_processor(images=snake_case__			,							return_tensors='pt'     )

  # forward pass
  torch.manual_seed(2     )
  _lowercase							= model(**snake_case__     )
  _lowercase							= outputs.last_hidden_state

  # The following Colab Notebook was used to generate these outputs:
  # https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
  if "s16" in checkpoint_url:
    _lowercase							= torch.tensor([[-1.0915, -1.4876, -1.1809]]     )
  elif "b16" in checkpoint_url:
    _lowercase							= torch.tensor([[14.2889, -18.9045, 11.7281]]     )
  elif "l16" in checkpoint_url:
    _lowercase							= torch.tensor([[41.5028, -22.8681, 45.6475]]     )
  elif "b4" in checkpoint_url:
    _lowercase							= torch.tensor([[-4.3868, 5.2932, -0.4137]]     )
  else:
    _lowercase							= torch.tensor([[-0.1792, -0.6465, 2.4263]]     )

  # verify logits
  assert torch.allclose(last_hidden_state[:, 0, :3]			,							snake_case__			,							atol=1E-4     )

  print(F"""Saving model to {pytorch_dump_folder_path}"""     )
  model.save_pretrained(snake_case__     )

  print(F"""Saving image processor to {pytorch_dump_folder_path}"""     )
  image_processor.save_pretrained(snake_case__     )


if __name__ == "__main__":
      snake_case 				=       argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--checkpoint_url""",
          default="""https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar""",
          type=str,
          help="""URL of the checkpoint you'd like to convert.""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
      )

      snake_case 				=       parser.parse_args()
      convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
      import jax.numpy as jnp

      from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
          FlaxRobertaPreLayerNormForCausalLM,
          FlaxRobertaPreLayerNormForMaskedLM,
          FlaxRobertaPreLayerNormForMultipleChoice,
          FlaxRobertaPreLayerNormForQuestionAnswering,
          FlaxRobertaPreLayerNormForSequenceClassification,
          FlaxRobertaPreLayerNormForTokenClassification,
          FlaxRobertaPreLayerNormModel,
      )





class 				A_				(      unittest.TestCase	):







       """simple docstring"""





       def __init__(    self	:	List[Any]						,__A	:	int						,__A	:	str=13						,__A	:	str=7						,__A	:	Optional[Any]=True						,__A	:	Optional[int]=True						,__A	:	int=True						,__A	:	List[str]=True						,__A	:	Dict=99						,__A	:	Dict=32						,__A	:	Dict=5						,__A	:	Dict=4						,__A	:	int=37						,__A	:	Any="gelu"						,__A	:	int=0.1						,__A	:	int=0.1						,__A	:	Dict=512						,__A	:	List[str]=16						,__A	:	Tuple=2						,__A	:	Dict=0.02						,__A	:	int=4						,)						->					Optional[int]:
         _lowercase							= parent
         _lowercase							= batch_size
         _lowercase							= seq_length
         _lowercase							= is_training
         _lowercase							= use_attention_mask
         _lowercase							= use_token_type_ids
         _lowercase							= use_labels
         _lowercase							= vocab_size
         _lowercase							= hidden_size
         _lowercase							= num_hidden_layers
         _lowercase							= num_attention_heads
         _lowercase							= intermediate_size
         _lowercase							= hidden_act
         _lowercase							= hidden_dropout_prob
         _lowercase							= attention_probs_dropout_prob
         _lowercase							= max_position_embeddings
         _lowercase							= type_vocab_size
         _lowercase							= type_sequence_label_size
         _lowercase							= initializer_range
         _lowercase							= num_choices







       def    __UpperCAmelCase (    self	:	int  )						->					str:
         _lowercase							= ids_tensor([self.batch_size, self.seq_length]						,self.vocab_size  )

         _lowercase							= None
         if self.use_attention_mask:
           _lowercase							= random_attention_mask([self.batch_size, self.seq_length]  )

         _lowercase							= None
         if self.use_token_type_ids:
           _lowercase							= ids_tensor([self.batch_size, self.seq_length]						,self.type_vocab_size  )

         _lowercase							= RobertaPreLayerNormConfig(
             vocab_size=self.vocab_size						,hidden_size=self.hidden_size						,num_hidden_layers=self.num_hidden_layers						,num_attention_heads=self.num_attention_heads						,intermediate_size=self.intermediate_size						,hidden_act=self.hidden_act						,hidden_dropout_prob=self.hidden_dropout_prob						,attention_probs_dropout_prob=self.attention_probs_dropout_prob						,max_position_embeddings=self.max_position_embeddings						,type_vocab_size=self.type_vocab_size						,is_decoder=__A						,initializer_range=self.initializer_range						,)

         return config, input_ids, token_type_ids, attention_mask







       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					Tuple:
         _lowercase							= self.prepare_config_and_inputs()
         _lowercase						,		_lowercase						,		_lowercase						,		_lowercase							= config_and_inputs
         _lowercase							= {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
         return config, inputs_dict







       def    __UpperCAmelCase (    self	:	Tuple  )						->					List[Any]:
         _lowercase							= self.prepare_config_and_inputs()
         _lowercase						,		_lowercase						,		_lowercase						,		_lowercase							= config_and_inputs

         _lowercase							= True
         _lowercase							= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
         _lowercase							= ids_tensor([self.batch_size, self.seq_length]						,vocab_size=2  )

         return (
             config,
             input_ids,
             token_type_ids,
             encoder_hidden_states,
             encoder_attention_mask,
         )





@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 				A_				(      UpperCAmelCase					,	unittest.TestCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Optional[int]      =  True

       SCREAMING_SNAKE_CASE_      : Dict      =  (
           (
               FlaxRobertaPreLayerNormModel,
               FlaxRobertaPreLayerNormForCausalLM,
               FlaxRobertaPreLayerNormForMaskedLM,
               FlaxRobertaPreLayerNormForSequenceClassification,
               FlaxRobertaPreLayerNormForTokenClassification,
               FlaxRobertaPreLayerNormForMultipleChoice,
               FlaxRobertaPreLayerNormForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					Any:
         _lowercase							= FlaxRobertaPreLayerNormModelTester(self  )







       @slow
       def    __UpperCAmelCase (    self	:	Dict  )						->					Dict:
         for model_class_name in self.all_model_classes:
           _lowercase							= model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'						,from_pt=__A  )
           _lowercase							= model(np.ones((1, 1)  )  )
           self.assertIsNotNone(__A  )





@require_flax
class 				A_				(      unittest.TestCase	):







       """simple docstring"""





       @slow
       def    __UpperCAmelCase (    self	:	int  )						->					Optional[Any]:
         _lowercase							= FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'						,from_pt=__A  )

         _lowercase							= np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]						,dtype=jnp.intaa  )
         _lowercase							= model(__A  )[0]
         _lowercase							= [1, 11, 5_0265]
         self.assertEqual(list(output.shape  )						,__A  )
         # compare the actual values for a slice.
         _lowercase							= np.array(
             [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]]						,dtype=np.floataa  )
         self.assertTrue(np.allclose(output[:, :3, :3]						,__A						,atol=1e-4  )  )







       @slow
       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					List[Any]:
         _lowercase							= FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'						,from_pt=__A  )

         _lowercase							= np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]						,dtype=jnp.intaa  )
         _lowercase							= model(__A  )[0]
         # compare the actual values for a slice.
         _lowercase							= np.array(
             [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]]						,dtype=np.floataa  )
         self.assertTrue(np.allclose(output[:, :3, :3]						,__A						,atol=1e-4  )  )