






import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
A        =			logging.get_logger(__name__)



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[Any]    ,    lowerCAmelCase__				:		str    ,    lowerCAmelCase__				:		List[Any]      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	List[str]													=   os.path.abspath(_UpperCamelCase      )
				logger.info(F'''Converting TensorFlow checkpoint from {tf_path}'''      )
				# Load weights from TF model
				SCREAMING_SNAKE_CASE_   :	int													=   tf.train.list_variables(_UpperCamelCase      )
				SCREAMING_SNAKE_CASE_   :	List[str]													=   []
				SCREAMING_SNAKE_CASE_   :	List[Any]													=   []
				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   []
				for full_name, shape in init_vars:
								# logger.info(f"Loading TF weight {name} with shape {shape}")
								SCREAMING_SNAKE_CASE_   :	List[str]													=   full_name.split('/'      )
								if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
												logger.info(F'''Skipping non-model layer {full_name}'''      )
												continue
								if "optimizer" in full_name:
												logger.info(F'''Skipping optimization layer {full_name}'''      )
												continue
								if name[0] == "model":
												# ignore initial 'model'
												SCREAMING_SNAKE_CASE_   :	Optional[int]													=   name[1:]
								# figure out how many levels deep the name is
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   0
								for _name in name:
												if _name.startswith('layer_with_weights'      ):
																depth += 1
												else:
																break
								layer_depth.append(_UpperCamelCase      )
								# read data
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   tf.train.load_variable(_UpperCamelCase    ,    _UpperCamelCase      )
								names.append('/'.join(_UpperCamelCase      )      )
								arrays.append(_UpperCamelCase      )
				logger.info(F'''Read a total of {len(_UpperCamelCase      ):,} layers'''      )

				# Sanity check
				if len(set(_UpperCamelCase      )      ) != 1:
								raise ValueError(F'''Found layer names with different depths (layer depth {list(set(_UpperCamelCase      )      )})'''      )
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   list(set(_UpperCamelCase      )      )[0]
				if layer_depth != 1:
								raise ValueError(
								    'The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP'
								    ' heads.'      )

				# convert layers
				logger.info('Converting weights...'      )
				for full_name, array in zip(_UpperCamelCase    ,    _UpperCamelCase      ):
								SCREAMING_SNAKE_CASE_   :	int													=   full_name.split('/'      )
								SCREAMING_SNAKE_CASE_   :	Any													=   model
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   []
								for i, m_name in enumerate(_UpperCamelCase      ):
												if m_name == ".ATTRIBUTES":
																# variable names end with .ATTRIBUTES/VARIABLE_VALUE
																break
												if m_name.startswith('layer_with_weights'      ):
																SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   int(m_name.split('-'      )[-1]      )
																if layer_num <= 2:
																				# embedding layers
																				# layer_num 0: word_embeddings
																				# layer_num 1: position_embeddings
																				# layer_num 2: token_type_embeddings
																				continue
																elif layer_num == 3:
																				# embedding LayerNorm
																				trace.extend(['embeddings', 'LayerNorm']      )
																				SCREAMING_SNAKE_CASE_   :	Tuple													=   getattr(_UpperCamelCase    ,    'embeddings'      )
																				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   getattr(_UpperCamelCase    ,    'LayerNorm'      )
																elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
																				# encoder layers
																				trace.extend(['encoder', 'layer', str(layer_num - 4      )]      )
																				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   getattr(_UpperCamelCase    ,    'encoder'      )
																				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   getattr(_UpperCamelCase    ,    'layer'      )
																				SCREAMING_SNAKE_CASE_   :	List[Any]													=   pointer[layer_num - 4]
																elif layer_num == config.num_hidden_layers + 4:
																				# pooler layer
																				trace.extend(['pooler', 'dense']      )
																				SCREAMING_SNAKE_CASE_   :	Tuple													=   getattr(_UpperCamelCase    ,    'pooler'      )
																				SCREAMING_SNAKE_CASE_   :	List[Any]													=   getattr(_UpperCamelCase    ,    'dense'      )
												elif m_name == "embeddings":
																trace.append('embeddings'      )
																SCREAMING_SNAKE_CASE_   :	List[str]													=   getattr(_UpperCamelCase    ,    'embeddings'      )
																if layer_num == 0:
																				trace.append('word_embeddings'      )
																				SCREAMING_SNAKE_CASE_   :	int													=   getattr(_UpperCamelCase    ,    'word_embeddings'      )
																elif layer_num == 1:
																				trace.append('position_embeddings'      )
																				SCREAMING_SNAKE_CASE_   :	List[str]													=   getattr(_UpperCamelCase    ,    'position_embeddings'      )
																elif layer_num == 2:
																				trace.append('token_type_embeddings'      )
																				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   getattr(_UpperCamelCase    ,    'token_type_embeddings'      )
																else:
																				raise ValueError(F'''Unknown embedding layer with name {full_name}'''      )
																trace.append('weight'      )
																SCREAMING_SNAKE_CASE_   :	Dict													=   getattr(_UpperCamelCase    ,    'weight'      )
												elif m_name == "_attention_layer":
																# self-attention layer
																trace.extend(['attention', 'self']      )
																SCREAMING_SNAKE_CASE_   :	Any													=   getattr(_UpperCamelCase    ,    'attention'      )
																SCREAMING_SNAKE_CASE_   :	Optional[int]													=   getattr(_UpperCamelCase    ,    'self'      )
												elif m_name == "_attention_layer_norm":
																# output attention norm
																trace.extend(['attention', 'output', 'LayerNorm']      )
																SCREAMING_SNAKE_CASE_   :	str													=   getattr(_UpperCamelCase    ,    'attention'      )
																SCREAMING_SNAKE_CASE_   :	Optional[int]													=   getattr(_UpperCamelCase    ,    'output'      )
																SCREAMING_SNAKE_CASE_   :	int													=   getattr(_UpperCamelCase    ,    'LayerNorm'      )
												elif m_name == "_attention_output_dense":
																# output attention dense
																trace.extend(['attention', 'output', 'dense']      )
																SCREAMING_SNAKE_CASE_   :	List[Any]													=   getattr(_UpperCamelCase    ,    'attention'      )
																SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   getattr(_UpperCamelCase    ,    'output'      )
																SCREAMING_SNAKE_CASE_   :	str													=   getattr(_UpperCamelCase    ,    'dense'      )
												elif m_name == "_output_dense":
																# output dense
																trace.extend(['output', 'dense']      )
																SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   getattr(_UpperCamelCase    ,    'output'      )
																SCREAMING_SNAKE_CASE_   :	Tuple													=   getattr(_UpperCamelCase    ,    'dense'      )
												elif m_name == "_output_layer_norm":
																# output dense
																trace.extend(['output', 'LayerNorm']      )
																SCREAMING_SNAKE_CASE_   :	List[str]													=   getattr(_UpperCamelCase    ,    'output'      )
																SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   getattr(_UpperCamelCase    ,    'LayerNorm'      )
												elif m_name == "_key_dense":
																# attention key
																trace.append('key'      )
																SCREAMING_SNAKE_CASE_   :	Any													=   getattr(_UpperCamelCase    ,    'key'      )
												elif m_name == "_query_dense":
																# attention query
																trace.append('query'      )
																SCREAMING_SNAKE_CASE_   :	List[str]													=   getattr(_UpperCamelCase    ,    'query'      )
												elif m_name == "_value_dense":
																# attention value
																trace.append('value'      )
																SCREAMING_SNAKE_CASE_   :	Tuple													=   getattr(_UpperCamelCase    ,    'value'      )
												elif m_name == "_intermediate_dense":
																# attention intermediate dense
																trace.extend(['intermediate', 'dense']      )
																SCREAMING_SNAKE_CASE_   :	Optional[int]													=   getattr(_UpperCamelCase    ,    'intermediate'      )
																SCREAMING_SNAKE_CASE_   :	List[Any]													=   getattr(_UpperCamelCase    ,    'dense'      )
												elif m_name == "_output_layer_norm":
																# output layer norm
																trace.append('output'      )
																SCREAMING_SNAKE_CASE_   :	List[Any]													=   getattr(_UpperCamelCase    ,    'output'      )
												# weights & biases
												elif m_name in ["bias", "beta"]:
																trace.append('bias'      )
																SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   getattr(_UpperCamelCase    ,    'bias'      )
												elif m_name in ["kernel", "gamma"]:
																trace.append('weight'      )
																SCREAMING_SNAKE_CASE_   :	Dict													=   getattr(_UpperCamelCase    ,    'weight'      )
												else:
																logger.warning(F'''Ignored {m_name}'''      )
        # for certain layers reshape is necessary
								SCREAMING_SNAKE_CASE_   :	str													=   '.'.join(_UpperCamelCase      )
								if re.match(R'(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)'    ,    _UpperCamelCase      ) or re.match(
								    R'(\S+)\.attention\.output\.dense\.weight'    ,    _UpperCamelCase      ):
												SCREAMING_SNAKE_CASE_   :	Dict													=   array.reshape(pointer.data.shape      )
								if "kernel" in full_name:
												SCREAMING_SNAKE_CASE_   :	Dict													=   array.transpose()
								if pointer.shape == array.shape:
												SCREAMING_SNAKE_CASE_   :	int													=   torch.from_numpy(_UpperCamelCase      )
								else:
												raise ValueError(
												    F'''Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:'''
												    F''' {array.shape}'''      )
								logger.info(F'''Successfully set variable {full_name} to PyTorch layer {trace}'''      )
				return model



def 					_lowerCamelCase(			lowerCAmelCase__				:		Any    ,    lowerCAmelCase__				:		Tuple    ,    lowerCAmelCase__				:		List[Any]      ):





				'''simple docstring'''







				logger.info(F'''Loading model based on config from {config_path}...'''      )
				SCREAMING_SNAKE_CASE_   :	Dict													=   BertConfig.from_json_file(_UpperCamelCase      )
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   BertModel(_UpperCamelCase      )

				# Load weights from checkpoint
				logger.info(F'''Loading weights from checkpoint {tf_checkpoint_path}...'''      )
				load_tfa_weights_in_bert(_UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase      )

				# Save pytorch-model
				logger.info(F'''Saving PyTorch model to {pytorch_dump_path}...'''      )
				torch.save(model.state_dict()    ,    _UpperCamelCase      )


if __name__ == "__main__":
				A        =			argparse.ArgumentParser()
				parser.add_argument(
				    '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow 2.x checkpoint path.'
				)
				parser.add_argument(
				    '--bert_config_file',
				    type=str,
				    required=True,
				    help='The config json file corresponding to the BERT model. This specifies the model architecture.',
				)
				parser.add_argument(
				    '--pytorch_dump_path',
				    type=str,
				    required=True,
				    help='Path to the output PyTorch model (must include filename).',
				)
				A        =			parser.parse_args()
				convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)







import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor



def 					_lowerCamelCase(			lowerCAmelCase__				:		Optional[Any]      ):





				'''simple docstring'''







				if "cls_token" in name:
								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   name.replace('cls_token'    ,    'vit.embeddings.cls_token'      )
				if "mask_token" in name:
								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   name.replace('mask_token'    ,    'decoder.mask_token'      )
				if "decoder_pos_embed" in name:
								SCREAMING_SNAKE_CASE_   :	Any													=   name.replace('decoder_pos_embed'    ,    'decoder.decoder_pos_embed'      )
				if "pos_embed" in name and "decoder" not in name:
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   name.replace('pos_embed'    ,    'vit.embeddings.position_embeddings'      )
				if "patch_embed.proj" in name:
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   name.replace('patch_embed.proj'    ,    'vit.embeddings.patch_embeddings.projection'      )
				if "patch_embed.norm" in name:
								SCREAMING_SNAKE_CASE_   :	Tuple													=   name.replace('patch_embed.norm'    ,    'vit.embeddings.norm'      )
				if "decoder_blocks" in name:
								SCREAMING_SNAKE_CASE_   :	Tuple													=   name.replace('decoder_blocks'    ,    'decoder.decoder_layers'      )
				if "blocks" in name:
								SCREAMING_SNAKE_CASE_   :	Any													=   name.replace('blocks'    ,    'vit.encoder.layer'      )
				if "attn.proj" in name:
								SCREAMING_SNAKE_CASE_   :	Tuple													=   name.replace('attn.proj'    ,    'attention.output.dense'      )
				if "attn" in name:
								SCREAMING_SNAKE_CASE_   :	Any													=   name.replace('attn'    ,    'attention.self'      )
				if "norm1" in name:
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   name.replace('norm1'    ,    'layernorm_before'      )
				if "norm2" in name:
								SCREAMING_SNAKE_CASE_   :	Any													=   name.replace('norm2'    ,    'layernorm_after'      )
				if "mlp.fc1" in name:
								SCREAMING_SNAKE_CASE_   :	Dict													=   name.replace('mlp.fc1'    ,    'intermediate.dense'      )
				if "mlp.fc2" in name:
								SCREAMING_SNAKE_CASE_   :	Tuple													=   name.replace('mlp.fc2'    ,    'output.dense'      )
				if "decoder_embed" in name:
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   name.replace('decoder_embed'    ,    'decoder.decoder_embed'      )
				if "decoder_norm" in name:
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   name.replace('decoder_norm'    ,    'decoder.decoder_norm'      )
				if "decoder_pred" in name:
								SCREAMING_SNAKE_CASE_   :	str													=   name.replace('decoder_pred'    ,    'decoder.decoder_pred'      )
				if "norm.weight" in name and "decoder" not in name:
								SCREAMING_SNAKE_CASE_   :	Tuple													=   name.replace('norm.weight'    ,    'vit.layernorm.weight'      )
				if "norm.bias" in name and "decoder" not in name:
								SCREAMING_SNAKE_CASE_   :	List[str]													=   name.replace('norm.bias'    ,    'vit.layernorm.bias'      )

				return name



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[str]    ,    lowerCAmelCase__				:		Optional[int]      ):





				'''simple docstring'''







				for key in orig_state_dict.copy().keys():
								SCREAMING_SNAKE_CASE_   :	Tuple													=   orig_state_dict.pop(lowerCAmelCase__      )

								if "qkv" in key:
												SCREAMING_SNAKE_CASE_   :	Optional[int]													=   key.split('.'      )
												SCREAMING_SNAKE_CASE_   :	List[Any]													=   int(key_split[1]      )
												if "decoder_blocks" in key:
																SCREAMING_SNAKE_CASE_   :	Any													=   config.decoder_hidden_size
																SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   'decoder.decoder_layers.'
																if "weight" in key:
																				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   val[:dim, :]
																				SCREAMING_SNAKE_CASE_   :	List[str]													=   val[dim : dim * 2, :]
																				SCREAMING_SNAKE_CASE_   :	Any													=   val[-dim:, :]
																elif "bias" in key:
																				SCREAMING_SNAKE_CASE_   :	str													=   val[:dim]
																				SCREAMING_SNAKE_CASE_   :	List[Any]													=   val[dim : dim * 2]
																				SCREAMING_SNAKE_CASE_   :	List[Any]													=   val[-dim:]
												else:
																SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   config.hidden_size
																SCREAMING_SNAKE_CASE_   :	str													=   'vit.encoder.layer.'
																if "weight" in key:
																				SCREAMING_SNAKE_CASE_   :	Any													=   val[:dim, :]
																				SCREAMING_SNAKE_CASE_   :	Tuple													=   val[dim : dim * 2, :]
																				SCREAMING_SNAKE_CASE_   :	int													=   val[-dim:, :]
																elif "bias" in key:
																				SCREAMING_SNAKE_CASE_   :	List[Any]													=   val[:dim]
																				SCREAMING_SNAKE_CASE_   :	Dict													=   val[dim : dim * 2]
																				SCREAMING_SNAKE_CASE_   :	str													=   val[-dim:]

								else:
												SCREAMING_SNAKE_CASE_   :	Optional[int]													=   val

				return orig_state_dict



def 					_lowerCamelCase(			lowerCAmelCase__				:		Optional[Any]    ,    lowerCAmelCase__				:		List[str]      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Dict													=   ViTMAEConfig()
				if "large" in checkpoint_url:
								SCREAMING_SNAKE_CASE_   :	Any													=   1024
								SCREAMING_SNAKE_CASE_   :	Any													=   4096
								SCREAMING_SNAKE_CASE_   :	List[str]													=   24
								SCREAMING_SNAKE_CASE_   :	List[str]													=   16
				elif "huge" in checkpoint_url:
								SCREAMING_SNAKE_CASE_   :	str													=   14
								SCREAMING_SNAKE_CASE_   :	List[str]													=   1280
								SCREAMING_SNAKE_CASE_   :	int													=   5120
								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   32
								SCREAMING_SNAKE_CASE_   :	List[str]													=   16

				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   ViTMAEForPreTraining(lowerCAmelCase__      )

				SCREAMING_SNAKE_CASE_   :	List[str]													=   torch.hub.load_state_dict_from_url(lowerCAmelCase__    ,    map_location='cpu'      )['model']

				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   ViTMAEImageProcessor(size=config.image_size      )

				SCREAMING_SNAKE_CASE_   :	int													=   convert_state_dict(lowerCAmelCase__    ,    lowerCAmelCase__      )

				model.load_state_dict(lowerCAmelCase__      )
				model.eval()

				SCREAMING_SNAKE_CASE_   :	Any													=   'https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'

				SCREAMING_SNAKE_CASE_   :	Dict													=   Image.open(requests.get(lowerCAmelCase__    ,    stream=lowerCAmelCase__      ).raw      )
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   ViTMAEImageProcessor(size=config.image_size      )
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   image_processor(images=lowerCAmelCase__    ,    return_tensors='pt'      )

				# forward pass
				torch.manual_seed(2      )
				SCREAMING_SNAKE_CASE_   :	Any													=   model(**lowerCAmelCase__      )
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   outputs.logits

				if "large" in checkpoint_url:
								SCREAMING_SNAKE_CASE_   :	Any													=   torch.tensor(
								    [[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]]      )
				elif "huge" in checkpoint_url:
								SCREAMING_SNAKE_CASE_   :	Dict													=   torch.tensor(
								    [[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]]      )
				else:
								SCREAMING_SNAKE_CASE_   :	int													=   torch.tensor(
								    [[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]]      )

				# verify logits
				assert torch.allclose(logits[0, :3, :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__":
				A        =			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--checkpoint_url',
				    default='https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth',
				    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.'
				)

				A        =			parser.parse_args()
				convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)