



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

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[int]				)  ->     Any:
   _A      =       ['''head.weight''', '''head.bias''']
   for k in ignore_keys:
      state_dict.pop(_snake_case	,   _snake_case				)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int				)  ->     Union[str, Any]:
   # projection head is used in the self-supervised pre-training in MSN,
   # for downstream task it's not needed.
   _A      =       [
       '''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	:List[str]	,   _snake_case	:Union[str, Any]	,   _snake_case	:Union[str, Any]				)  ->     List[Any]:
   _A      =       dct.pop(_snake_case				)
   _A      =       val

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Tuple	,   _snake_case	:str				)  ->     Optional[Any]:
   _A      =       ViTMSNConfig()
   _A      =       1_000

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

   if "s16" in checkpoint_url:
      _A      =       384
      _A      =       1_536
      _A      =       6
   elif "l16" in checkpoint_url:
      _A      =       1_024
      _A      =       4_096
      _A      =       24
      _A      =       16
      _A      =       0.1
   elif "b4" in checkpoint_url:
      _A      =       4
   elif "l7" in checkpoint_url:
      _A      =       7
      _A      =       1_024
      _A      =       4_096
      _A      =       24
      _A      =       16
      _A      =       0.1

   _A      =       ViTMSNModel(_snake_case				)

   _A      =       torch.hub.load_state_dict_from_url(_snake_case	,   map_location='''cpu'''				)['''target_encoder''']

   _A      =       ViTImageProcessor(size=config.image_size				)

   remove_projection_head(_snake_case				)
   _A      =       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()

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

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

   # forward pass
   torch.manual_seed(2				)
   _A      =       model(**_snake_case				)
   _A      =       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:
      _A      =       torch.tensor([[-1.0915, -1.4876, -1.1809]]				)
   elif "b16" in checkpoint_url:
      _A      =       torch.tensor([[14.2889, -18.9045, 11.7281]]				)
   elif "l16" in checkpoint_url:
      _A      =       torch.tensor([[41.5028, -22.8681, 45.6475]]				)
   elif "b4" in checkpoint_url:
      _A      =       torch.tensor([[-4.3868, 5.2932, -0.4137]]				)
   else:
      _A      =       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__":
   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)




'''simple docstring'''




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
						import torch

						from transformers.generation import DisjunctiveConstraint






@require_torch
class 							a__(   unittest.TestCase      ):


			'''simple docstring'''






			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    [[1, 2, 4], [1, 2, 3, 4]]
					lowerCAmelCase     =    DisjunctiveConstraint(__lowerCAmelCase)
					self.assertTrue(isinstance(dc.token_ids			,       __lowerCAmelCase))

					with self.assertRaises(__lowerCAmelCase):
							DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]))

					with self.assertRaises(__lowerCAmelCase):
							DisjunctiveConstraint([torch.LongTensor([1, 2, 4]), torch.LongTensor([1, 2, 3, 4, 5])])



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    [[1, 2], [1, 2, 3, 4]]

					with self.assertRaises(__lowerCAmelCase):
							DisjunctiveConstraint(__lowerCAmelCase)  # fails here



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    [[1, 2, 3], [1, 2, 4]]

					lowerCAmelCase     =    DisjunctiveConstraint(__lowerCAmelCase)

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(1)
					lowerCAmelCase     =    stepped is True and completed is False and reset is False
					self.assertTrue(__lowerCAmelCase)
					self.assertTrue(not dc.completed)
					self.assertTrue(dc.current_seq == [1])

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(2)
					lowerCAmelCase     =    stepped is True and completed is False and reset is False
					self.assertTrue(__lowerCAmelCase)
					self.assertTrue(not dc.completed)
					self.assertTrue(dc.current_seq == [1, 2])

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(3)
					lowerCAmelCase     =    stepped is True and completed is True and reset is False
					self.assertTrue(__lowerCAmelCase)
					self.assertTrue(dc.completed)  # Completed!
					self.assertTrue(dc.current_seq == [1, 2, 3])







			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

					lowerCAmelCase     =    DisjunctiveConstraint(__lowerCAmelCase)

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(1)
					self.assertTrue(not dc.completed)
					self.assertTrue(dc.current_seq == [1])

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(2)
					self.assertTrue(not dc.completed)
					self.assertTrue(dc.current_seq == [1, 2])

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(4)
					self.assertTrue(not dc.completed)
					self.assertTrue(dc.current_seq == [1, 2, 4])

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(5)
					self.assertTrue(dc.completed)  # Completed!
					self.assertTrue(dc.current_seq == [1, 2, 4, 5])

					dc.reset()

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(1)
					self.assertTrue(not dc.completed)
					self.assertTrue(dc.remaining() == 3)
					self.assertTrue(dc.current_seq == [1])

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(2)
					self.assertTrue(not dc.completed)
					self.assertTrue(dc.remaining() == 2)
					self.assertTrue(dc.current_seq == [1, 2])

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(5)
					self.assertTrue(dc.completed)  # Completed!
					self.assertTrue(dc.remaining() == 0)
					self.assertTrue(dc.current_seq == [1, 2, 5])




