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

def 			lowercase_				(			_lowerCamelCase							:       Dict):
    lowercase__ :   List[Any]    					=				["head.weight", "head.bias"]
    for k in ignore_keys:
        state_dict.pop(_lowerCamelCase		,	_lowerCamelCase)

def 			lowercase_				(			_lowerCamelCase							:       str):
    # projection head is used in the self-supervised pre-training in MSN,
    # for downstream task it's not needed.
    lowercase__ :   str    					=				[
        "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(_lowerCamelCase		,	_lowerCamelCase)

def 			lowercase_				(			_lowerCamelCase							:       Any		,	_lowerCamelCase							:       Union[str, Any]		,	_lowerCamelCase							:       List[Any]):
    lowercase__ :   Union[str, Any]    					=				dct.pop(_lowerCamelCase)
    lowercase__ :   Union[str, Any]    					=				val

def 			lowercase_				(			_lowerCamelCase							:       Optional[int]		,	_lowerCamelCase							:       Dict):
    lowercase__ :   Union[str, Any]    					=				ViTMSNConfig()
    lowercase__ :   Dict    					=				1000

    lowercase__ :   str    					=				"datasets/huggingface/label-files"
    lowercase__ :   List[str]    					=				"imagenet-1k-id2label.json"
    lowercase__ :   Union[str, Any]    					=				json.load(open(hf_hub_download(_lowerCamelCase		,	_lowerCamelCase)		,	"r"))
    lowercase__ :   str    					=				{int(_lowerCamelCase): v for k, v in idalabel.items()}
    lowercase__ :   str    					=				idalabel
    lowercase__ :   Optional[Any]    					=				{v: k for k, v in idalabel.items()}

    if "s16" in checkpoint_url:
        lowercase__ :   Any    					=				384
        lowercase__ :   Optional[Any]    					=				1536
        lowercase__ :   Any    					=				6
    elif "l16" in checkpoint_url:
        lowercase__ :   str    					=				1024
        lowercase__ :   Any    					=				4096
        lowercase__ :   Any    					=				24
        lowercase__ :   List[str]    					=				16
        lowercase__ :   Any    					=				0.1
    elif "b4" in checkpoint_url:
        lowercase__ :   int    					=				4
    elif "l7" in checkpoint_url:
        lowercase__ :   Optional[int]    					=				7
        lowercase__ :   Dict    					=				1024
        lowercase__ :   Union[str, Any]    					=				4096
        lowercase__ :   str    					=				24
        lowercase__ :   Dict    					=				16
        lowercase__ :   Any    					=				0.1

    lowercase__ :   Union[str, Any]    					=				ViTMSNModel(_lowerCamelCase)

    lowercase__ :   Dict    					=				torch.hub.load_state_dict_from_url(_lowerCamelCase		,	map_location="cpu")["target_encoder"]

    lowercase__ :   Dict    					=				ViTImageProcessor(size=config.image_size)

    remove_projection_head(_lowerCamelCase)
    lowercase__ :   Optional[Any]    					=				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		,	base_model=_lowerCamelCase)

    model.load_state_dict(_lowerCamelCase)
    model.eval()

    lowercase__ :   List[str]    					=				"http://images.cocodataset.org/val2017/000000039769.jpg"

    lowercase__ :   Optional[int]    					=				Image.open(requests.get(_lowerCamelCase		,	stream=_lowerCamelCase).raw)
    lowercase__ :   Any    					=				ViTImageProcessor(
        size=config.image_size		,	image_mean=_lowerCamelCase		,	image_std=_lowerCamelCase)
    lowercase__ :   Tuple    					=				image_processor(images=_lowerCamelCase		,	return_tensors="pt")

    # forward pass
    torch.manual_seed(2)
    lowercase__ :   int    					=				model(**_lowerCamelCase)
    lowercase__ :   List[str]    					=				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__ :   Optional[Any]    					=				torch.tensor([[-1.0915, -1.4876, -1.1809]])
    elif "b16" in checkpoint_url:
        lowercase__ :   Tuple    					=				torch.tensor([[14.2889, -18.9045, 11.7281]])
    elif "l16" in checkpoint_url:
        lowercase__ :   Tuple    					=				torch.tensor([[41.5028, -22.8681, 45.6475]])
    elif "b4" in checkpoint_url:
        lowercase__ :   Optional[int]    					=				torch.tensor([[-4.3868, 5.2932, -0.4137]])
    else:
        lowercase__ :   Optional[int]    					=				torch.tensor([[-0.1792, -0.6465, 2.4263]])

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

    print(f'''Saving model 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__":
      UpperCamelCase  							=						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.'''
      )

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






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
 import torch


if is_vision_available():
 from PIL import Image

 from transformers import MgpstrProcessor, ViTImageProcessor







@require_torch
@require_vision
class   __lowercase      (unittest.TestCase ):
 _UpperCamelCase			  =  ViTImageProcessor if is_vision_available() else None
 @property
 def 	UpperCamelCase__  (							self					)					->str:



       '''simple docstring'''





       return self.image_processor_tester.prepare_image_processor_dict()
 def 	UpperCamelCase__  (							self					)					->str:



       '''simple docstring'''





       __lowerCAmelCase					:						Tuple					=					(3, 32, 128)
       __lowerCAmelCase					:						List[str]					=					tempfile.mkdtemp()

       # fmt: off
       __lowerCAmelCase					:						List[str]					=					['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
       # fmt: on
       __lowerCAmelCase					:						Optional[int]					=					dict(zip(A_      ,							range(len(A_					)					)					)					)

       __lowerCAmelCase					:						Tuple					=					os.path.join(self.tmpdirname      ,							VOCAB_FILES_NAMES['''vocab_file''']					)
       with open(self.vocab_file      ,							'''w'''      ,							encoding='''utf-8'''					) as fp:
             fp.write(json.dumps(A_					) + '''\n'''					)

       __lowerCAmelCase					:						Union[str, Any]					=					{
           '''do_normalize''': False,
           '''do_resize''': True,
           '''image_processor_type''': '''ViTImageProcessor''',
           '''resample''': 3,
           '''size''': {'''height''': 32, '''width''': 128},
       }
       __lowerCAmelCase					:						Optional[Any]					=					os.path.join(self.tmpdirname      ,							A_					)
       with open(self.image_processor_file      ,							'''w'''      ,							encoding='''utf-8'''					) as fp:
             json.dump(A_      ,							A_					)
 def 	UpperCamelCase__  (							self      ,							**A_					)					->Tuple:



       '''simple docstring'''





       return MgpstrTokenizer.from_pretrained(self.tmpdirname      ,							**A_					)
 def 	UpperCamelCase__  (							self      ,							**A_					)					->Tuple:



       '''simple docstring'''





       return ViTImageProcessor.from_pretrained(self.tmpdirname      ,							**A_					)
 def 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





       shutil.rmtree(self.tmpdirname					)
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Tuple					=					np.random.randint(255      ,							size=(3, 30, 400)      ,							dtype=np.uinta					)

       __lowerCAmelCase					:						str					=					Image.fromarray(np.moveaxis(A_      ,							0      ,							-1					)					)

       return image_input
 def 	UpperCamelCase__  (							self					)					->Any:



       '''simple docstring'''





       __lowerCAmelCase					:						Dict					=					self.get_tokenizer()
       __lowerCAmelCase					:						List[Any]					=					self.get_image_processor()

       __lowerCAmelCase					:						List[Any]					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)
       processor.save_pretrained(self.tmpdirname					)
       __lowerCAmelCase					:						Union[str, Any]					=					MgpstrProcessor.from_pretrained(self.tmpdirname      ,							use_fast=A_					)

       self.assertEqual(processor.char_tokenizer.get_vocab()      ,							tokenizer.get_vocab()					)
       self.assertIsInstance(processor.char_tokenizer      ,							A_					)

       self.assertEqual(processor.image_processor.to_json_string()      ,							image_processor.to_json_string()					)
       self.assertIsInstance(processor.image_processor      ,							A_					)
 def 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					self.get_tokenizer()
       __lowerCAmelCase					:						Union[str, Any]					=					self.get_image_processor()

       __lowerCAmelCase					:						List[Any]					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)
       processor.save_pretrained(self.tmpdirname					)

       __lowerCAmelCase					:						List[Any]					=					self.get_tokenizer(bos_token='''(BOS)'''      ,							eos_token='''(EOS)'''					)
       __lowerCAmelCase					:						int					=					self.get_image_processor(do_normalize=A_      ,							padding_value=1.0					)

       __lowerCAmelCase					:						int					=					MgpstrProcessor.from_pretrained(
           self.tmpdirname      ,							bos_token='''(BOS)'''      ,							eos_token='''(EOS)'''      ,							do_normalize=A_      ,							padding_value=1.0					)

       self.assertEqual(processor.char_tokenizer.get_vocab()      ,							tokenizer_add_kwargs.get_vocab()					)
       self.assertIsInstance(processor.char_tokenizer      ,							A_					)

       self.assertEqual(processor.image_processor.to_json_string()      ,							image_processor_add_kwargs.to_json_string()					)
       self.assertIsInstance(processor.image_processor      ,							A_					)
 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					self.get_image_processor()
       __lowerCAmelCase					:						Optional[Any]					=					self.get_tokenizer()

       __lowerCAmelCase					:						int					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

       __lowerCAmelCase					:						Optional[int]					=					self.prepare_image_inputs()

       __lowerCAmelCase					:						Optional[Any]					=					image_processor(A_      ,							return_tensors='''np'''					)
       __lowerCAmelCase					:						Tuple					=					processor(images=A_      ,							return_tensors='''np'''					)

       for key in input_image_proc.keys():
             self.assertAlmostEqual(input_image_proc[key].sum()      ,							input_processor[key].sum()      ,							delta=1e-2					)
 def 	UpperCamelCase__  (							self					)					->str:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					self.get_image_processor()
       __lowerCAmelCase					:						Union[str, Any]					=					self.get_tokenizer()

       __lowerCAmelCase					:						Optional[Any]					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

       __lowerCAmelCase					:						Any					=					'''test'''

       __lowerCAmelCase					:						Dict					=					processor(text=A_					)

       __lowerCAmelCase					:						str					=					tokenizer(A_					)
       for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]      ,							encoded_processor[key]					)
 def 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase					:						Dict					=					self.get_image_processor()
       __lowerCAmelCase					:						Any					=					self.get_tokenizer()

       __lowerCAmelCase					:						str					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

       __lowerCAmelCase					:						List[Any]					=					'''test'''
       __lowerCAmelCase					:						int					=					self.prepare_image_inputs()

       __lowerCAmelCase					:						int					=					processor(text=A_      ,							images=A_					)

       self.assertListEqual(list(inputs.keys()					)      ,							['''pixel_values''', '''labels''']					)

       # test if it raises when no input is passed
       with pytest.raises(A_					):
             processor()
 def 	UpperCamelCase__  (							self					)					->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					self.get_image_processor()
       __lowerCAmelCase					:						int					=					self.get_tokenizer()

       __lowerCAmelCase					:						Any					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

       __lowerCAmelCase					:						List[str]					=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

       __lowerCAmelCase					:						Optional[int]					=					processor.char_decode(A_					)
       __lowerCAmelCase					:						Tuple					=					tokenizer.batch_decode(A_					)
       __lowerCAmelCase					:						Any					=					[seq.replace(''' '''      ,							''''''					) for seq in decoded_tok]

       self.assertListEqual(A_      ,							A_					)
 def 	UpperCamelCase__  (							self					)					->Any:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					self.get_image_processor()
       __lowerCAmelCase					:						Any					=					self.get_tokenizer()

       __lowerCAmelCase					:						int					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

       __lowerCAmelCase					:						Union[str, Any]					=					None
       __lowerCAmelCase					:						Optional[Any]					=					self.prepare_image_inputs()

       __lowerCAmelCase					:						List[Any]					=					processor(text=A_      ,							images=A_					)

       self.assertListEqual(list(inputs.keys()					)      ,							processor.model_input_names					)





 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						List[str]					=					self.get_image_processor()
       __lowerCAmelCase					:						List[str]					=					self.get_tokenizer()

       __lowerCAmelCase					:						Any					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

       __lowerCAmelCase					:						List[Any]					=					torch.randn(1      ,							27      ,							38					)
       __lowerCAmelCase					:						Optional[int]					=					torch.randn(1      ,							27      ,							5_0257					)
       __lowerCAmelCase					:						Optional[Any]					=					torch.randn(1      ,							27      ,							3_0522					)

       __lowerCAmelCase					:						List[str]					=					processor.batch_decode([char_input, bpe_input, wp_input]					)

       self.assertListEqual(list(results.keys()					)      ,							['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds''']					)


