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






def _lowerCamelCase  (		snake_case  ):
							_lowerCAmelCase							=							['head.weight', 'head.bias']
							for k in ignore_keys:
														state_dict.pop(snake_case     , snake_case  )






def _lowerCamelCase  (		snake_case  ):
							# projection head is used in the self-supervised pre-training in MSN,
							# for downstream task it's not needed.
							_lowerCAmelCase							=							[
							    '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 _lowerCamelCase  (		snake_case     , snake_case     , snake_case  ):
							_lowerCAmelCase							=							dct.pop(snake_case  )
							_lowerCAmelCase							=							val






def _lowerCamelCase  (		snake_case     , snake_case  ):
							_lowerCAmelCase							=							ViTMSNConfig()
							_lowerCAmelCase							=							1_000

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

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

							_lowerCAmelCase							=							ViTMSNModel(snake_case  )

							_lowerCAmelCase							=							torch.hub.load_state_dict_from_url(snake_case     , map_location='cpu'  )['target_encoder']

							_lowerCAmelCase							=							ViTImageProcessor(size=config.image_size  )

							remove_projection_head(snake_case  )
							_lowerCAmelCase							=							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()

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

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

							# forward pass
							torch.manual_seed(2  )
							_lowerCAmelCase							=							model(**snake_case  )
							_lowerCAmelCase							=							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:
														_lowerCAmelCase							=							torch.tensor([[-1.09_15, -1.48_76, -1.18_09]]  )
							elif "b16" in checkpoint_url:
														_lowerCAmelCase							=							torch.tensor([[14.28_89, -18.90_45, 11.72_81]]  )
							elif "l16" in checkpoint_url:
														_lowerCAmelCase							=							torch.tensor([[41.50_28, -22.86_81, 45.64_75]]  )
							elif "b4" in checkpoint_url:
														_lowerCAmelCase							=							torch.tensor([[-4.38_68, 5.29_32, -0.41_37]]  )
							else:
														_lowerCAmelCase							=							torch.tensor([[-0.17_92, -0.64_65, 2.42_63]]  )

							# 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__":
				_lowercase:	Union[str, Any]       			=		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:	List[Any]       			=		parser.parse_args()
				convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_lowercase:	Optional[Any]       			=		{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase:	Optional[Any]       			=		[
				    '''RagModel''',
				    '''RagPreTrainedModel''',
				    '''RagSequenceForGeneration''',
				    '''RagTokenForGeneration''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase:	Optional[Any]       			=		[
				    '''TFRagModel''',
				    '''TFRagPreTrainedModel''',
				    '''TFRagSequenceForGeneration''',
				    '''TFRagTokenForGeneration''',
				]


if TYPE_CHECKING:
				from .configuration_rag import RagConfig
				from .retrieval_rag import RagRetriever
				from .tokenization_rag import RagTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_rag import (
								    TFRagModel,
								    TFRagPreTrainedModel,
								    TFRagSequenceForGeneration,
								    TFRagTokenForGeneration,
								)

else:
				import sys

				_lowercase:	Optional[int]       			=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






