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__						(				A__    )   ->					List[str]:
				snake_case__					:	str												=      checkpoints.load_tax_checkpoint(lowerCAmelCase_    )
				snake_case__					:	str												=      flatten_dict(lowerCAmelCase_    )
				return flax_params
def  UpperCamelCase__						(				A__    )   ->					str:
				snake_case__					:	Optional[Any]												=      {}

				snake_case__					:	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',
				}

				snake_case__					:	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
												snake_case__					:	Dict												=      '.'.join(key[1:]    )

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

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

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

												snake_case__					:	Optional[Any]												=      flax_dict[key]

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

				return converted_torch_dict
def  UpperCamelCase__						(				A__			, A__			, A__=False			, A__=False    )   ->					Any:
				snake_case__					:	Tuple												=      get_flax_param(lowerCAmelCase_    )

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

				snake_case__					:	Optional[Any]												=      PixaStructForConditionalGeneration(lowerCAmelCase_    )

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

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

				if use_large:
								snake_case__					:	Optional[int]												=      4096

				snake_case__					:	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__     :   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.''')
				lowerCAmelCase__     :   str		=   parser.parse_args()

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







'''simple docstring'''

import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=1024						,			lowerCAmelCase_=1024						,			lowerCAmelCase_=False						,			**lowerCAmelCase_  )			->      List[Any]:
		_a     :      str        =							AutoTokenizer.from_pretrained(lowerCAmelCase_  )
		_a     :      List[Any]        =							SeqaSeqDataset(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			type_path='train'						,			**lowerCAmelCase_  )
		_a     :      List[str]        =							tok.pad_token_id

		def get_lens(lowerCAmelCase_  ):
				_a     :      Dict        =							tqdm(
				    DataLoader(lowerCAmelCase_						,			batch_size=512						,			num_workers=8						,			shuffle=lowerCAmelCase_						,			collate_fn=ds.collate_fn  )						,			desc=str(ds.len_file  )						,			)
				_a     :      Union[str, Any]        =							[]
				for batch in dl:
						_a     :      Optional[Any]        =							batch['input_ids'].ne(lowerCAmelCase_  ).sum(1  ).tolist()
						_a     :      Optional[Any]        =							batch['labels'].ne(lowerCAmelCase_  ).sum(1  ).tolist()
						if consider_target:
								for src, tgt in zip(lowerCAmelCase_						,			lowerCAmelCase_  ):
										max_lens.append(max(lowerCAmelCase_						,			lowerCAmelCase_  )  )
						else:
								max_lens.extend(lowerCAmelCase_  )
				return max_lens

		_a     :      str        =							get_lens(lowerCAmelCase_  )
		_a     :      Optional[int]        =							SeqaSeqDataset(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			type_path='val'						,			**lowerCAmelCase_  )
		_a     :      Dict        =							get_lens(lowerCAmelCase_  )
		pickle_save(lowerCAmelCase_						,			train_ds.len_file  )
		pickle_save(lowerCAmelCase_						,			val_ds.len_file  )


if __name__ == "__main__":
							fire.Fire(save_len_file)




