


'''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     a   (    __a				,      __a=False     )						->			Optional[int]:



					'''simple docstring'''






					UpperCamelCase__     :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"
										UpperCamelCase__     :Dict							      =				[(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     a   (    __a				,      __a				,      __a=False     )						->			int:



					'''simple docstring'''






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


def     a   (    __a     )						->			Tuple:



					'''simple docstring'''






					UpperCamelCase__     :Any							      =				['''head.weight''', '''head.bias''']
					for k in ignore_keys:
										state_dict.pop(_lowerCAmelCase				,      _lowerCAmelCase     )


def     a   (    __a     )						->			Optional[Any]:



					'''simple docstring'''






					UpperCamelCase__     :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(_lowerCAmelCase				,      _lowerCAmelCase     )


def     a   (    __a				,      __a				,      __a     )						->			Any:



					'''simple docstring'''






					UpperCamelCase__     :Any							      =				dct.pop(_lowerCAmelCase     )
					UpperCamelCase__     :Optional[int]							      =				val


def     a   (    __a				,      __a     )						->			Dict:



					'''simple docstring'''






					UpperCamelCase__     :str							      =				ViTMSNConfig()
					UpperCamelCase__     :Dict							      =				1000

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

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

					UpperCamelCase__     :List[str]							      =				ViTMSNModel(_lowerCAmelCase     )

					UpperCamelCase__     :Any							      =				torch.hub.load_state_dict_from_url(_lowerCAmelCase				,      map_location='''cpu'''     )['''target_encoder''']

					UpperCamelCase__     :List[Any]							      =				ViTImageProcessor(size=config.image_size     )

					remove_projection_head(_lowerCAmelCase     )
					UpperCamelCase__     :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()

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

					UpperCamelCase__     :Union[str, Any]							      =				Image.open(requests.get(_lowerCAmelCase				,      stream=_lowerCAmelCase     ).raw     )
					UpperCamelCase__     :int							      =				ViTImageProcessor(
					    size=config.image_size				,      image_mean=_lowerCAmelCase				,      image_std=_lowerCAmelCase     )
					UpperCamelCase__     :Optional[Any]							      =				image_processor(images=_lowerCAmelCase				,      return_tensors='''pt'''     )

					# forward pass
					torch.manual_seed(2     )
					UpperCamelCase__     :Dict							      =				model(**_lowerCAmelCase     )
					UpperCamelCase__     :Tuple							      =				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:
										UpperCamelCase__     :Tuple							      =				torch.tensor([[-1.0_9_1_5, -1.4_8_7_6, -1.1_8_0_9]]     )
					elif "b16" in checkpoint_url:
										UpperCamelCase__     :List[str]							      =				torch.tensor([[1_4.2_8_8_9, -1_8.9_0_4_5, 1_1.7_2_8_1]]     )
					elif "l16" in checkpoint_url:
										UpperCamelCase__     :Dict							      =				torch.tensor([[4_1.5_0_2_8, -2_2.8_6_8_1, 4_5.6_4_7_5]]     )
					elif "b4" in checkpoint_url:
										UpperCamelCase__     :int							      =				torch.tensor([[-4.3_8_6_8, 5.2_9_3_2, -0.4_1_3_7]]     )
					else:
										UpperCamelCase__     :Optional[int]							      =				torch.tensor([[-0.1_7_9_2, -0.6_4_6_5, 2.4_2_6_3]]     )

					# 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__":
		__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)



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case							   =  {'''configuration_reformer''': ['''REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ReformerConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							   =  ['''ReformerTokenizer''']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							   =  ['''ReformerTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							   =  [
		    '''REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ReformerAttention''',
		    '''ReformerForMaskedLM''',
		    '''ReformerForQuestionAnswering''',
		    '''ReformerForSequenceClassification''',
		    '''ReformerLayer''',
		    '''ReformerModel''',
		    '''ReformerModelWithLMHead''',
		    '''ReformerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_reformer import ReformerTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_reformer_fast import ReformerTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_reformer import (
				    REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ReformerAttention,
				    ReformerForMaskedLM,
				    ReformerForQuestionAnswering,
				    ReformerForSequenceClassification,
				    ReformerLayer,
				    ReformerModel,
				    ReformerModelWithLMHead,
				    ReformerPreTrainedModel,
				)

else:
		import sys

		__snake_case							   =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)