'''simple docstring'''




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




def 							lowerCamelCase			(   UpperCAmelCase__ :      Optional[Any]				)    ->   Dict:
							lowercase_      :    str       		= ["""head.weight""", """head.bias"""]
							for k in ignore_keys:
														state_dict.pop(_A   ,   _A				)




def 							lowerCamelCase			(   UpperCAmelCase__ :      str				)    ->   int:
							lowercase_      :    Any       		= [
							    """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(_A   ,   _A				)




def 							lowerCamelCase			(   UpperCAmelCase__ :      Optional[int]   ,   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      Union[str, Any]				)    ->   int:
							lowercase_      :    Dict       		= dct.pop(_A				)
							lowercase_      :    Optional[Any]       		= val




def 							lowerCamelCase			(   UpperCAmelCase__ :      Any   ,   UpperCAmelCase__ :      Optional[int]				)    ->   Any:
							lowercase_      :    str       		= ViTMSNConfig()
							lowercase_      :    Tuple       		= 1000

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

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

							lowercase_      :    Tuple       		= ViTMSNModel(_A				)

							lowercase_      :    List[Any]       		= torch.hub.load_state_dict_from_url(_A   ,   map_location="""cpu"""				)["""target_encoder"""]

							lowercase_      :    Tuple       		= ViTImageProcessor(size=config.image_size				)

							remove_projection_head(_A				)
							lowercase_      :    str       		= create_rename_keys(_A   ,   base_model=_A				)

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

							model.load_state_dict(_A				)
							model.eval()

							lowercase_      :    List[Any]       		= """http://images.cocodataset.org/val2017/000000039769.jpg"""

							lowercase_      :    Any       		= Image.open(requests.get(_A   ,   stream=_A				).raw				)
							lowercase_      :    List[str]       		= ViTImageProcessor(
							    size=config.image_size   ,   image_mean=_A   ,   image_std=_A				)
							lowercase_      :    Tuple       		= image_processor(images=_A   ,   return_tensors="""pt"""				)

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

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

							print(F'''Saving model to {pytorch_dump_folder_path}'''				)
							model.save_pretrained(_A				)

							print(F'''Saving image processor to {pytorch_dump_folder_path}'''				)
							image_processor.save_pretrained(_A				)


if __name__ == "__main__":
			_lowercase		:				int	       =		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."
			)

			_lowercase		:				int	       =		parser.parse_args()
			convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






'''simple docstring'''




import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset




@pytest.mark.parametrize("""dataset_size"""   ,   [None, 400 * 2**20, 600 * 2**20]				)
@pytest.mark.parametrize("""input_in_memory_max_size"""   ,   ["""default""", 0, 100 * 2**20, 900 * 2**20]				)
def 							lowerCamelCase			(   UpperCAmelCase__ :      Union[str, Any]   ,   UpperCAmelCase__ :      List[Any]   ,   UpperCAmelCase__ :      str				)    ->   Any:
							if input_in_memory_max_size != "default":
														monkeypatch.setattr(datasets.config   ,   """IN_MEMORY_MAX_SIZE"""   ,   UpperCAmelCase__				)
							lowercase_      :    List[Any]       		= datasets.config.IN_MEMORY_MAX_SIZE
							if input_in_memory_max_size == "default":
														assert in_memory_max_size == 0
							else:
														assert in_memory_max_size == input_in_memory_max_size
							if dataset_size and in_memory_max_size:
														lowercase_      :    str       		= dataset_size < in_memory_max_size
							else:
														lowercase_      :    List[Any]       		= False
							lowercase_      :    Any       		= is_small_dataset(UpperCAmelCase__				)
							assert result == expected





