



'''simple docstring'''

import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor







def a_     (    __UpperCAmelCase					)				->					Tuple:





			"""simple docstring"""







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

			return name







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					Tuple:





			"""simple docstring"""







			for key in orig_state_dict.copy().keys():
						snake_case:    Tuple      							=orig_state_dict.pop(__UpperCAmelCase					)

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

						else:
									snake_case:    Optional[Any]      							=val

			return orig_state_dict







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			snake_case:    Tuple      							=ViTMAEConfig()
			if "large" in checkpoint_url:
						snake_case:    Any      							=10_24
						snake_case:    Tuple      							=40_96
						snake_case:    Any      							=24
						snake_case:    Optional[int]      							=16
			elif "huge" in checkpoint_url:
						snake_case:    str      							=14
						snake_case:    List[Any]      							=12_80
						snake_case:    int      							=51_20
						snake_case:    List[Any]      							=32
						snake_case:    int      							=16

			snake_case:    Tuple      							=ViTMAEForPreTraining(__UpperCAmelCase					)

			snake_case:    List[Any]      							=torch.hub.load_state_dict_from_url(__UpperCAmelCase				,							map_location='cpu'					)['model']

			snake_case:    Any      							=ViTMAEImageProcessor(size=config.image_size					)

			snake_case:    int      							=convert_state_dict(__UpperCAmelCase				,							__UpperCAmelCase					)

			model.load_state_dict(__UpperCAmelCase					)
			model.eval()

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

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

			# forward pass
			torch.manual_seed(2					)
			snake_case:    int      							=model(**__UpperCAmelCase					)
			snake_case:    Dict      							=outputs.logits

			if "large" in checkpoint_url:
						snake_case:    Any      							=torch.tensor(
						    [[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]]					)
			elif "huge" in checkpoint_url:
						snake_case:    Union[str, Any]      							=torch.tensor(
						    [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]					)
			else:
						snake_case:    Dict      							=torch.tensor(
						    [[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]]					)

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

			print(f'''Saving model to {pytorch_dump_folder_path}'''					)
			model.save_pretrained(__UpperCAmelCase					)

			print(f'''Saving image processor to {pytorch_dump_folder_path}'''					)
			image_processor.save_pretrained(__UpperCAmelCase					)


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)









'''simple docstring'''

import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)







def a_     (    __UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    List[str]      							=checkpoints.load_tax_checkpoint(__UpperCAmelCase					)
			snake_case:    str      							=flatten_dict(__UpperCAmelCase					)
			return flax_params







def a_     (    __UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    int      							={}

			snake_case:    Any      							={
			    'token_embedder': 'embeddings',
			    'encoder_norm': 'layernorm',
			    'kernel': 'weight',
			    '.out': '.output',
			    'scale': 'weight',
			    'embedders_0.pos_embedding': 'row_embedder.weight',
			    'embedders_1.pos_embedding': 'column_embedder.weight',
			}

			snake_case:    Union[str, Any]      							={
			    'query': 'attention.query',
			    'key': 'attention.key',
			    'value': 'attention.value',
			    'output.dense': 'output',
			    'encoder_decoder_attention.o': 'encoder_decoder_attention.attention.o',
			    'pre_self_attention_layer_norm': 'self_attention.layer_norm',
			    'pre_cross_attention_layer_norm': 'encoder_decoder_attention.layer_norm',
			    'mlp.': 'mlp.DenseReluDense.',
			    'pre_mlp_layer_norm': 'mlp.layer_norm',
			    'self_attention.o': 'self_attention.attention.o',
			    'decoder.embeddings.embedding': 'decoder.embed_tokens.weight',
			    'decoder.relpos_bias.rel_embedding': 'decoder.layer.0.self_attention.attention.relative_attention_bias.weight',
			    'decoder.decoder_norm.weight': 'decoder.final_layer_norm.weight',
			    'decoder.logits_dense.weight': 'decoder.lm_head.weight',
			}

			for key in flax_dict.keys():
						if "target" in key:
									# remove the first prefix from the key
									snake_case:    Dict      							='.'.join(key[1:]					)

									# rename the key
									for old, new in CONVERSION_MAPPING.items():
												snake_case:    Union[str, Any]      							=new_key.replace(__UpperCAmelCase				,							__UpperCAmelCase					)

									if "decoder" in new_key:
												for old, new in DECODER_CONVERSION_MAPPING.items():
															snake_case:    Optional[Any]      							=new_key.replace(__UpperCAmelCase				,							__UpperCAmelCase					)

									if "layers" in new_key and "decoder" not in new_key:
												# use regex to replace the layer number
												snake_case:    Any      							=re.sub(R'layers_(\d+)'				,							R'layer.\1'				,							__UpperCAmelCase					)
												snake_case:    Optional[int]      							=new_key.replace('encoder'				,							'encoder.encoder'					)

									elif "layers" in new_key and "decoder" in new_key:
												# use regex to replace the layer number
												snake_case:    Optional[Any]      							=re.sub(R'layers_(\d+)'				,							R'layer.\1'				,							__UpperCAmelCase					)

									snake_case:    Optional[int]      							=flax_dict[key]

			snake_case:    Optional[int]      							={}
			# convert converted_dict into torch format
			for key in converted_dict.keys():
						if ("embed_tokens" not in key) and ("embedder" not in key):
									snake_case:    Union[str, Any]      							=torch.from_numpy(converted_dict[key].T					)
						else:
									snake_case:    Union[str, Any]      							=torch.from_numpy(converted_dict[key]					)

			return converted_torch_dict







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase=False				,							__UpperCAmelCase=False					)				->					Tuple:





			"""simple docstring"""







			snake_case:    Any      							=get_flax_param(__UpperCAmelCase					)

			if not use_large:
						snake_case:    Optional[int]      							=PixaStructVisionConfig()
						snake_case:    int      							=PixaStructTextConfig()
			else:
						snake_case:    Tuple      							=PixaStructVisionConfig(
						    hidden_size=15_36				,							d_ff=39_68				,							num_attention_heads=24				,							num_hidden_layers=18					)
						snake_case:    List[Any]      							=PixaStructTextConfig(hidden_size=15_36				,							d_ff=39_68				,							num_heads=24				,							num_layers=18					)
			snake_case:    Any      							=PixaStructConfig(
			    vision_config=encoder_config.to_dict()				,							text_config=decoder_config.to_dict()				,							is_vqa=__UpperCAmelCase					)

			snake_case:    List[Any]      							=PixaStructForConditionalGeneration(__UpperCAmelCase					)

			snake_case:    str      							=rename_and_convert_flax_params(__UpperCAmelCase					)
			model.load_state_dict(__UpperCAmelCase					)

			snake_case:    List[Any]      							=AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer'					)
			snake_case:    Optional[Any]      							=PixaStructImageProcessor()
			snake_case:    Optional[int]      							=PixaStructProcessor(image_processor=__UpperCAmelCase				,							tokenizer=__UpperCAmelCase					)

			if use_large:
						snake_case:    Optional[Any]      							=40_96

			snake_case:    str      							=True

			# mkdir if needed
			os.makedirs(__UpperCAmelCase				,							exist_ok=__UpperCAmelCase					)

			model.save_pretrained(__UpperCAmelCase					)
			processor.save_pretrained(__UpperCAmelCase					)

			print('Model saved in {}'.format(__UpperCAmelCase					)					)


if __name__ == "__main__":
	a							   =				argparse.ArgumentParser()
	parser.add_argument('--t5x_checkpoint_path', default=None, type=str, help='Path to the original T5x checkpoint.')
	parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	parser.add_argument('--use_large', action='store_true', help='Use large model.')
	parser.add_argument('--is_vqa', action='store_true', help='Use large model.')
	a							   =				parser.parse_args()

	convert_pixastruct_original_pytorch_checkpoint_to_hf(
	    args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
	)




