

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 							__SCREAMING_SNAKE_CASE     (   a__    :   Tuple       )					->				Any:
	__A :			Dict		    =			checkpoints.load_tax_checkpoint(a__       )
	__A :			List[str]		    =			flatten_dict(a__       )
	return flax_params
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Union[str, Any]       )					->				List[Any]:
	__A :			Union[str, Any]		    =			{}

	__A :			Union[str, 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""",
	}

	__A :			Dict		    =			{
	    """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 :			List[Any]		    =			""".""".join(key[1:]       )

			# rename the key
			for old, new in CONVERSION_MAPPING.items():
				__A :			List[str]		    =			new_key.replace(a__			,a__       )

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

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

			elif "layers" in new_key and "decoder" in new_key:
				# use regex to replace the layer number
				__A :			int		    =			re.sub(r"""layers_(\d+)"""			,r"""layer.\1"""			,a__       )

			__A :			Optional[int]		    =			flax_dict[key]

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

	return converted_torch_dict
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Any			,a__    :   Union[str, Any]			,a__    :   Optional[Any]=False			,a__    :   Union[str, Any]=False       )					->				Union[str, Any]:
	__A :			Any		    =			get_flax_param(a__       )

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

	__A :			int		    =			PixaStructForConditionalGeneration(a__       )

	__A :			str		    =			rename_and_convert_flax_params(a__       )
	model.load_state_dict(a__       )

	__A :			Optional[int]		    =			AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer"""       )
	__A :			str		    =			PixaStructImageProcessor()
	__A :			Union[str, Any]		    =			PixaStructProcessor(image_processor=a__			,tokenizer=a__       )

	if use_large:
		__A :			int		    =			4096

	__A :			Dict		    =			True

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

	model.save_pretrained(a__       )
	processor.save_pretrained(a__       )

	print("""Model saved in {}""".format(a__       )       )


if __name__ == "__main__":
			UpperCAmelCase_							:					Tuple		 =   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.''')
			UpperCAmelCase_							:					Union[str, Any]		 =   parser.parse_args()

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








def 							__SCREAMING_SNAKE_CASE     (   a__    :   int       )					->				int:
	if not isinstance(a__			,a__       ):
		raise TypeError("""Input value must be an 'int' type"""       )

	__A :			Union[str, Any]		    =			0
	while number:
		position += 1
		number >>= 1

	return position


if __name__ == "__main__":
			import doctest

			doctest.testmod()





