



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      UpperCamelCase(    __UpperCamelCase				:						Tuple					):
		lowerCAmelCase_     :	Dict							   =					checkpoints.load_tax_checkpoint(a__					)
		lowerCAmelCase_     :	List[str]							   =					flatten_dict(a__					)
		return flax_params


def      UpperCamelCase(    __UpperCamelCase				:						Union[str, Any]					):
		lowerCAmelCase_     :	Union[str, Any]							   =					{}

		lowerCAmelCase_     :	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""",
		}

		lowerCAmelCase_     :	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
						lowerCAmelCase_     :	List[Any]							   =					""".""".join(key[1:]					)

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

						if "decoder" in new_key:
								for old, new in DECODER_CONVERSION_MAPPING.items():
										lowerCAmelCase_     :	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
								lowerCAmelCase_     :	Optional[Any]							   =					re.sub(R'''layers_(\d+)'''	,R'''layer.\1'''	,a__					)
								lowerCAmelCase_     :	Any							   =					new_key.replace('''encoder'''	,'''encoder.encoder'''					)

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

						lowerCAmelCase_     :	Optional[int]							   =					flax_dict[key]

		lowerCAmelCase_     :	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):
						lowerCAmelCase_     :	Optional[int]							   =					torch.from_numpy(converted_dict[key].T					)
				else:
						lowerCAmelCase_     :	List[str]							   =					torch.from_numpy(converted_dict[key]					)

		return converted_torch_dict


def      UpperCamelCase(    __UpperCamelCase				:						Any	,__UpperCamelCase				:						Union[str, Any]	,__UpperCamelCase				:						Optional[Any]=False	,__UpperCamelCase				:						Union[str, Any]=False					):
		lowerCAmelCase_     :	Any							   =					get_flax_param(a__					)

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

		lowerCAmelCase_     :	int							   =					PixaStructForConditionalGeneration(a__					)

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

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

		if use_large:
				lowerCAmelCase_     :	int							   =					4096

		lowerCAmelCase_     :	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__":
						A__    :    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.''')
						A__    :    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()





