



'''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      __lowerCamelCase     (      lowerCAmelCase_  )			->      List[str]:
		_a     :      str        =							checkpoints.load_tax_checkpoint(lowerCAmelCase_  )
		_a     :      str        =							flatten_dict(lowerCAmelCase_  )
		return flax_params



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      str:
		_a     :      Optional[Any]        =							{}

		_a     :      str        =							{
		    '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',
		}

		_a     :      str        =							{
		    '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
						_a     :      Dict        =							'.'.join(key[1:]  )

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

						if "decoder" in new_key:
								for old, new in DECODER_CONVERSION_MAPPING.items():
										_a     :      int        =							new_key.replace(lowerCAmelCase_						,			lowerCAmelCase_  )

						if "layers" in new_key and "decoder" not in new_key:
								# use regex to replace the layer number
								_a     :      Union[str, Any]        =							re.sub(r'layers_(\d+)'						,			r'layer.\1'						,			lowerCAmelCase_  )
								_a     :      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
								_a     :      Optional[int]        =							re.sub(r'layers_(\d+)'						,			r'layer.\1'						,			lowerCAmelCase_  )

						_a     :      Optional[Any]        =							flax_dict[key]

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

		return converted_torch_dict



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=False						,			lowerCAmelCase_=False  )			->      Any:
		_a     :      Tuple        =							get_flax_param(lowerCAmelCase_  )

		if not use_large:
				_a     :      Optional[Any]        =							PixaStructVisionConfig()
				_a     :      Union[str, Any]        =							PixaStructTextConfig()
		else:
				_a     :      List[Any]        =							PixaStructVisionConfig(
				    hidden_size=1536						,			d_ff=3968						,			num_attention_heads=24						,			num_hidden_layers=18  )
				_a     :      List[Any]        =							PixaStructTextConfig(hidden_size=1536						,			d_ff=3968						,			num_heads=24						,			num_layers=18  )
		_a     :      Any        =							PixaStructConfig(
		    vision_config=encoder_config.to_dict()						,			text_config=decoder_config.to_dict()						,			is_vqa=lowerCAmelCase_  )

		_a     :      Optional[Any]        =							PixaStructForConditionalGeneration(lowerCAmelCase_  )

		_a     :      Union[str, Any]        =							rename_and_convert_flax_params(lowerCAmelCase_  )
		model.load_state_dict(lowerCAmelCase_  )

		_a     :      List[Any]        =							AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer'  )
		_a     :      Dict        =							PixaStructImageProcessor()
		_a     :      Dict        =							PixaStructProcessor(image_processor=lowerCAmelCase_						,			tokenizer=lowerCAmelCase_  )

		if use_large:
				_a     :      Optional[int]        =							4096

		_a     :      List[str]        =							True

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

		model.save_pretrained(lowerCAmelCase_  )
		processor.save_pretrained(lowerCAmelCase_  )

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


if __name__ == "__main__":
							__lowerCAmelCase									=   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									=   parser.parse_args()

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





"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase__  :  Tuple						     =	{'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  :  List[Any]						     =	[
						    'VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'ViTMAEForPreTraining',
						    'ViTMAELayer',
						    'ViTMAEModel',
						    'ViTMAEPreTrainedModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  :  int						     =	[
						    'TFViTMAEForPreTraining',
						    'TFViTMAEModel',
						    'TFViTMAEPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vit_mae import (
												    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ViTMAEForPreTraining,
												    ViTMAELayer,
												    ViTMAEModel,
												    ViTMAEPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
						import sys

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



