



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       __lowercase  (					a__  )							->					List[Any]:
							__SCREAMING_SNAKE_CASE										=   checkpoints.load_tax_checkpoint(UpperCamelCase__  )
							__SCREAMING_SNAKE_CASE										=   flatten_dict(UpperCamelCase__  )
							return flax_params






def       __lowercase  (					a__  )							->					int:
							__SCREAMING_SNAKE_CASE										=   {}

							__SCREAMING_SNAKE_CASE										=   {
							    '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',
							}

							__SCREAMING_SNAKE_CASE										=   {
							    '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
																					__SCREAMING_SNAKE_CASE										=   '.'.join(key[1:]  )

																					# rename the key
																					for old, new in CONVERSION_MAPPING.items():
																												__SCREAMING_SNAKE_CASE										=   new_key.replace(UpperCamelCase__				,  UpperCamelCase__  )

																					if "decoder" in new_key:
																												for old, new in DECODER_CONVERSION_MAPPING.items():
																																			__SCREAMING_SNAKE_CASE										=   new_key.replace(UpperCamelCase__				,  UpperCamelCase__  )

																					if "layers" in new_key and "decoder" not in new_key:
																												# use regex to replace the layer number
																												__SCREAMING_SNAKE_CASE										=   re.sub(R'layers_(\d+)'				,  R'layer.\1'				,  UpperCamelCase__  )
																												__SCREAMING_SNAKE_CASE										=   new_key.replace('encoder'				,  'encoder.encoder'  )

																					elif "layers" in new_key and "decoder" in new_key:
																												# use regex to replace the layer number
																												__SCREAMING_SNAKE_CASE										=   re.sub(R'layers_(\d+)'				,  R'layer.\1'				,  UpperCamelCase__  )

																					__SCREAMING_SNAKE_CASE										=   flax_dict[key]

							__SCREAMING_SNAKE_CASE										=   {}
							# convert converted_dict into torch format
							for key in converted_dict.keys():
														if ("embed_tokens" not in key) and ("embedder" not in key):
																					__SCREAMING_SNAKE_CASE										=   torch.from_numpy(converted_dict[key].T  )
														else:
																					__SCREAMING_SNAKE_CASE										=   torch.from_numpy(converted_dict[key]  )

							return converted_torch_dict






def       __lowercase  (					a__				,  a__				,  a__=False				,  a__=False  )							->					str:
							__SCREAMING_SNAKE_CASE										=   get_flax_param(UpperCamelCase__  )

							if not use_large:
														__SCREAMING_SNAKE_CASE										=   PixaStructVisionConfig()
														__SCREAMING_SNAKE_CASE										=   PixaStructTextConfig()
							else:
														__SCREAMING_SNAKE_CASE										=   PixaStructVisionConfig(
														    hidden_size=15_36				,  d_ff=39_68				,  num_attention_heads=24				,  num_hidden_layers=18  )
														__SCREAMING_SNAKE_CASE										=   PixaStructTextConfig(hidden_size=15_36				,  d_ff=39_68				,  num_heads=24				,  num_layers=18  )
							__SCREAMING_SNAKE_CASE										=   PixaStructConfig(
							    vision_config=encoder_config.to_dict()				,  text_config=decoder_config.to_dict()				,  is_vqa=UpperCamelCase__  )

							__SCREAMING_SNAKE_CASE										=   PixaStructForConditionalGeneration(UpperCamelCase__  )

							__SCREAMING_SNAKE_CASE										=   rename_and_convert_flax_params(UpperCamelCase__  )
							model.load_state_dict(UpperCamelCase__  )

							__SCREAMING_SNAKE_CASE										=   AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer'  )
							__SCREAMING_SNAKE_CASE										=   PixaStructImageProcessor()
							__SCREAMING_SNAKE_CASE										=   PixaStructProcessor(image_processor=UpperCamelCase__				,  tokenizer=UpperCamelCase__  )

							if use_large:
														__SCREAMING_SNAKE_CASE										=   40_96

							__SCREAMING_SNAKE_CASE										=   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__":
			lowerCAmelCase__						:	Dict					=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.''')
			lowerCAmelCase__						:	Any					=parser.parse_args()

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












from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A								=       logging.get_logger(__name__)

__A								=       {
    "facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			'''vit_mae'''


			def __init__( self     ,		lowerCamelCase__=768     ,		lowerCamelCase__=12     ,		lowerCamelCase__=12     ,		lowerCamelCase__=3_072     ,		lowerCamelCase__="gelu"     ,		lowerCamelCase__=0.0     ,		lowerCamelCase__=0.0     ,		lowerCamelCase__=0.02     ,		lowerCamelCase__=1e-12     ,		lowerCamelCase__=224     ,		lowerCamelCase__=16     ,		lowerCamelCase__=3     ,		lowerCamelCase__=True     ,		lowerCamelCase__=16     ,		lowerCamelCase__=512     ,		lowerCamelCase__=8     ,		lowerCamelCase__=2_048     ,		lowerCamelCase__=0.75     ,		lowerCamelCase__=False     ,		**lowerCamelCase__     ,		)       -> Optional[int]:



							'''simple docstring'''
							super().__init__(**lowerCamelCase__				)

							__lowerCamelCase									=       hidden_size
							__lowerCamelCase									=       num_hidden_layers
							__lowerCamelCase									=       num_attention_heads
							__lowerCamelCase									=       intermediate_size
							__lowerCamelCase									=       hidden_act
							__lowerCamelCase									=       hidden_dropout_prob
							__lowerCamelCase									=       attention_probs_dropout_prob
							__lowerCamelCase									=       initializer_range
							__lowerCamelCase									=       layer_norm_eps
							__lowerCamelCase									=       image_size
							__lowerCamelCase									=       patch_size
							__lowerCamelCase									=       num_channels
							__lowerCamelCase									=       qkv_bias
							__lowerCamelCase									=       decoder_num_attention_heads
							__lowerCamelCase									=       decoder_hidden_size
							__lowerCamelCase									=       decoder_num_hidden_layers
							__lowerCamelCase									=       decoder_intermediate_size
							__lowerCamelCase									=       mask_ratio
							__lowerCamelCase									=       norm_pix_loss


