

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   ):
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	checkpoints.load_tax_checkpoint(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			List[str]  		=	flatten_dict(_UpperCAmelCase   )
						return flax_params







def       A_				(   _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	{}

						SCREAMING_SNAKE_CASE_:			List[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",
						}

						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	{
						    "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_:			Union[str, Any]  		=	".".join(key[1:]   )

																		# rename the key
																		for old, new in CONVERSION_MAPPING.items():
																								SCREAMING_SNAKE_CASE_:			Tuple  		=	new_key.replace(_UpperCAmelCase    ,     _UpperCAmelCase   )

																		if "decoder" in new_key:
																								for old, new in DECODER_CONVERSION_MAPPING.items():
																														SCREAMING_SNAKE_CASE_:			Optional[int]  		=	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_:			Tuple  		=	re.sub(R"layers_(\d+)"    ,     R"layer.\1"    ,     _UpperCAmelCase   )
																								SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	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_:			int  		=	re.sub(R"layers_(\d+)"    ,     R"layer.\1"    ,     _UpperCAmelCase   )

																		SCREAMING_SNAKE_CASE_:			List[str]  		=	flax_dict[key]

						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	{}
						# 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_:			List[str]  		=	torch.from_numpy(converted_dict[key].T   )
												else:
																		SCREAMING_SNAKE_CASE_:			str  		=	torch.from_numpy(converted_dict[key]   )

						return converted_torch_dict







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase=False    ,     _UpperCAmelCase=False   ):
						SCREAMING_SNAKE_CASE_:			int  		=	get_flax_param(_UpperCAmelCase   )

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

						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	PixaStructForConditionalGeneration(_UpperCAmelCase   )

						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	rename_and_convert_flax_params(_UpperCAmelCase   )
						model.load_state_dict(_UpperCAmelCase   )

						SCREAMING_SNAKE_CASE_:			List[Any]  		=	AutoTokenizer.from_pretrained("ybelkada/test-pix2struct-tokenizer"   )
						SCREAMING_SNAKE_CASE_:			str  		=	PixaStructImageProcessor()
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	PixaStructProcessor(image_processor=_UpperCAmelCase    ,     tokenizer=_UpperCAmelCase   )

						if use_large:
												SCREAMING_SNAKE_CASE_:			str  		=	40_96

						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	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							:  List[str]            =						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							:  Dict            =						parser.parse_args()

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









def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase   ):

						if density <= 0:
												raise ValueError("Impossible fluid density"   )
						if bulk_modulus <= 0:
												raise ValueError("Impossible bulk modulus"   )

						return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
						import doctest

						doctest.testmod()






