


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				:						Union[str, Any]     ):
							a__      =checkpoints.load_tax_checkpoint(UpperCamelCase__     )
							a__      =flatten_dict(UpperCamelCase__     )
							return flax_params






def   _lowercase(					__a				:						Optional[int]     ):
							a__      ={}

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

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

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

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

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

																					a__      =flax_dict[key]

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

							return converted_torch_dict






def   _lowercase(					__a				:						str    ,     __a				:						Union[str, Any]    ,     __a				:						Optional[int]=False    ,     __a				:						List[str]=False     ):
							a__      =get_flax_param(UpperCamelCase__     )

							if not use_large:
														a__      =PixaStructVisionConfig()
														a__      =PixaStructTextConfig()
							else:
														a__      =PixaStructVisionConfig(
														    hidden_size=1536    ,     d_ff=3968    ,     num_attention_heads=24    ,     num_hidden_layers=18     )
														a__      =PixaStructTextConfig(hidden_size=1536    ,     d_ff=3968    ,     num_heads=24    ,     num_layers=18     )
							a__      =PixaStructConfig(
							    vision_config=encoder_config.to_dict()    ,     text_config=decoder_config.to_dict()    ,     is_vqa=UpperCamelCase__     )

							a__      =PixaStructForConditionalGeneration(UpperCamelCase__     )

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

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

							if use_large:
														a__      =4096

							a__      =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:						List[str]   				=       parser.parse_args()

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




_UpperCAmelCase						:  int									=			range(2, 20 + 1)
_UpperCAmelCase						:  Dict									=			[10**k for k in range(ks[-1] + 1)]
_UpperCAmelCase						:  dict[int, dict[int, list[list[int]]]]									=			{}





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       sum(a_i[j] for j in range(UpperCamelCase__  ,				len(UpperCamelCase__ ) ) )
							snake_case_     							=       sum(a_i[j] * base[j] for j in range(min(len(UpperCamelCase__ )  ,				UpperCamelCase__ ) ) )

							snake_case_							,       snake_case_     							=       0, 0
							snake_case_     							=       n - i

							snake_case_     							=       memo.get(UpperCamelCase__ )

							if sub_memo is not None:
														snake_case_     							=       sub_memo.get(UpperCamelCase__ )

														if jumps is not None and len(UpperCamelCase__ ) > 0:
																					# find and make the largest jump without going over
																					snake_case_     							=       -1
																					for _k in range(len(UpperCamelCase__ ) - 1  ,				-1  ,				-1 ):
																												if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
																																			snake_case_     							=       _k
																																			break

																					if max_jump >= 0:
																												snake_case_							,       snake_case_							,       snake_case_     							=       jumps[max_jump]
																												# since the difference between jumps is cached, add c
																												snake_case_     							=       diff + c
																												for j in range(min(UpperCamelCase__  ,				len(UpperCamelCase__ ) ) ):
																																			snake_case_							,       snake_case_     							=       divmod(UpperCamelCase__  ,				10 )
																												if new_c > 0:
																																			add(UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ )

														else:
																					snake_case_     							=       []
							else:
														snake_case_     							=       {c: []}
														snake_case_     							=       sub_memo

							if dn >= max_dn or c + diff >= base[k]:
														return diff, dn

							if k > ks[0]:
														while True:
																					# keep doing smaller jumps
																					snake_case_							,       snake_case_     							=       next_term(UpperCamelCase__  ,				k - 1  ,				i + dn  ,				UpperCamelCase__ )
																					diff += _diff
																					dn += terms_jumped

																					if dn >= max_dn or c + diff >= base[k]:
																												break
							else:
														# would be too small a jump, just compute sequential terms instead
														snake_case_							,       snake_case_     							=       compute(UpperCamelCase__  ,				UpperCamelCase__  ,				i + dn  ,				UpperCamelCase__ )
														diff += _diff
														dn += terms_jumped

							snake_case_     							=       sub_memo[c]

							# keep jumps sorted by # of terms skipped
							snake_case_     							=       0
							while j < len(UpperCamelCase__ ):
														if jumps[j][1] > dn:
																					break
														j += 1

							# cache the jump for this value digitsum(b) and c
							sub_memo[c].insert(UpperCamelCase__  ,				(diff, dn, k) )
							return (diff, dn)





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							if i >= n:
														return 0, i
							if k > len(UpperCamelCase__ ):
														a_i.extend([0 for _ in range(k - len(UpperCamelCase__ ) )] )

							# note: a_i -> b * 10^k + c
							# ds_b -> digitsum(b)
							# ds_c -> digitsum(c)
							snake_case_     							=       i
							snake_case_							,       snake_case_							,       snake_case_     							=       0, 0, 0
							for j in range(len(UpperCamelCase__ ) ):
														if j >= k:
																					ds_b += a_i[j]
														else:
																					ds_c += a_i[j]

							while i < n:
														i += 1
														snake_case_     							=       ds_c + ds_b
														diff += addend
														snake_case_     							=       0
														for j in range(UpperCamelCase__ ):
																					snake_case_     							=       a_i[j] + addend
																					snake_case_							,       snake_case_     							=       divmod(UpperCamelCase__  ,				10 )

																					ds_c += a_i[j]

														if addend > 0:
																					break

							if addend > 0:
														add(UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ )
							return diff, i - start_i





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							for j in range(UpperCamelCase__  ,				len(UpperCamelCase__ ) ):
														snake_case_     							=       digits[j] + addend
														if s >= 10:
																					snake_case_							,       snake_case_     							=       divmod(UpperCamelCase__  ,				10 )
																					snake_case_     							=       addend // 10 + quotient
														else:
																					snake_case_     							=       s
																					snake_case_     							=       addend // 10

														if addend == 0:
																					break

							while addend > 0:
														snake_case_							,       snake_case_     							=       divmod(UpperCamelCase__  ,				10 )
														digits.append(UpperCamelCase__ )





def    __lowerCamelCase      (					UpperCamelCase__ = 10**15 ):







							'''simple docstring'''






							snake_case_     							=       [1]
							snake_case_     							=       1
							snake_case_     							=       0
							while True:
														snake_case_							,       snake_case_     							=       next_term(UpperCamelCase__  ,				20  ,				i + dn  ,				UpperCamelCase__ )
														dn += terms_jumped
														if dn == n - i:
																					break

							snake_case_     							=       0
							for j in range(len(UpperCamelCase__ ) ):
														a_n += digits[j] * 10**j
							return a_n


if __name__ == "__main__":
					print(F'''{solution() = }''')




