






import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger



class 				_A			(     UpperCamelCase				):
    """simple docstring"""

    def __init__(				self      :     Optional[Any] ,     __SCREAMING_SNAKE_CASE      :     Dict ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]      )      ->							Optional[Any]:
           __UpperCAmelCase     				=params

           __UpperCAmelCase     				=np.array(__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=np.array([len(__SCREAMING_SNAKE_CASE      ) for t in data]      )

           self.check()
           self.remove_long_sequences()
           self.remove_empty_sequences()
           self.remove_unknown_sequences()
           self.check()
           self.print_statistics()



    def __getitem__(				self      :     int ,     __SCREAMING_SNAKE_CASE      :     int      )      ->							List[Any]:
           return (self.token_ids[index], self.lengths[index])



    def __len__(				self      :     int      )      ->							Optional[Any]:
           return len(self.lengths      )



    def _a							(				self      :     Dict      )      ->							List[str]:
           assert len(self.token_ids      ) == len(self.lengths      )
           assert all(self.lengths[i] == len(self.token_ids[i]      ) for i in range(len(self.lengths      )      )      )



    def _a							(				self      :     List[Any]      )      ->							List[Any]:
           __UpperCAmelCase     				=self.params.max_model_input_size
           __UpperCAmelCase     				=self.lengths > max_len
           logger.info(f'''Splitting {sum(__SCREAMING_SNAKE_CASE      )} too long sequences.'''      )

           def divide_chunks(__SCREAMING_SNAKE_CASE      :     Tuple ,     __SCREAMING_SNAKE_CASE      :     Tuple      ):
                  return [l[i : i + n] for i in range(0 ,     len(__SCREAMING_SNAKE_CASE      ) ,     __SCREAMING_SNAKE_CASE      )]

           __UpperCAmelCase     				=[]
           __UpperCAmelCase     				=[]
           if self.params.mlm:
                  __UpperCAmelCase				, __UpperCAmelCase     				=self.params.special_tok_ids["""cls_token"""], self.params.special_tok_ids["""sep_token"""]
           else:
                  __UpperCAmelCase				, __UpperCAmelCase     				=self.params.special_tok_ids["""bos_token"""], self.params.special_tok_ids["""eos_token"""]

           for seq_, len_ in zip(self.token_ids ,     self.lengths      ):
                  assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
                  if len_ <= max_len:
                         new_tok_ids.append(seq_      )
                         new_lengths.append(len_      )
                  else:
                         __UpperCAmelCase     				=[]
                         for sub_s in divide_chunks(seq_ ,     max_len - 2      ):
                                if sub_s[0] != cls_id:
                                       __UpperCAmelCase     				=np.insert(__SCREAMING_SNAKE_CASE ,     0 ,     __SCREAMING_SNAKE_CASE      )
                                if sub_s[-1] != sep_id:
                                       __UpperCAmelCase     				=np.insert(__SCREAMING_SNAKE_CASE ,     len(__SCREAMING_SNAKE_CASE      ) ,     __SCREAMING_SNAKE_CASE      )
                                assert len(__SCREAMING_SNAKE_CASE      ) <= max_len
                                assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
                                sub_seqs.append(__SCREAMING_SNAKE_CASE      )

                         new_tok_ids.extend(__SCREAMING_SNAKE_CASE      )
                         new_lengths.extend([len(__SCREAMING_SNAKE_CASE      ) for l in sub_seqs]      )

           __UpperCAmelCase     				=np.array(__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=np.array(__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Optional[Any]      )      ->							Optional[Any]:
           __UpperCAmelCase     				=len(self      )
           __UpperCAmelCase     				=self.lengths > 11
           __UpperCAmelCase     				=self.token_ids[indices]
           __UpperCAmelCase     				=self.lengths[indices]
           __UpperCAmelCase     				=len(self      )
           logger.info(f'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''      )



    def _a							(				self      :     int      )      ->							Any:
           if "unk_token" not in self.params.special_tok_ids:
                  return
           else:
                  __UpperCAmelCase     				=self.params.special_tok_ids["""unk_token"""]
           __UpperCAmelCase     				=len(self      )
           __UpperCAmelCase     				=np.array([np.count_nonzero(a == unk_token_id      ) for a in self.token_ids]      )
           __UpperCAmelCase     				=(unk_occs / self.lengths) < 0.5
           __UpperCAmelCase     				=self.token_ids[indices]
           __UpperCAmelCase     				=self.lengths[indices]
           __UpperCAmelCase     				=len(self      )
           logger.info(f'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).'''      )



    def _a							(				self      :     Any      )      ->							Tuple:
           if not self.params.is_master:
                  return
           logger.info(f'''{len(self      )} sequences'''      )
           # data_len = sum(self.lengths)
           # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
           # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

           # unk_idx = self.params.special_tok_ids['unk_token']
           # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
           # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')



    def _a							(				self      :     Any ,     __SCREAMING_SNAKE_CASE      :     Any      )      ->							Tuple:
           __UpperCAmelCase     				=[t[0] for t in batch]
           __UpperCAmelCase     				=[t[1] for t in batch]
           assert len(__SCREAMING_SNAKE_CASE      ) == len(__SCREAMING_SNAKE_CASE      )

           # Max for paddings
           __UpperCAmelCase     				=max(__SCREAMING_SNAKE_CASE      )

           # Pad token ids
           if self.params.mlm:
                  __UpperCAmelCase     				=self.params.special_tok_ids["""pad_token"""]
           else:
                  __UpperCAmelCase     				=self.params.special_tok_ids["""unk_token"""]
           __UpperCAmelCase     				=[list(t.astype(__SCREAMING_SNAKE_CASE      )      ) + [pad_idx] * (max_seq_len_ - len(__SCREAMING_SNAKE_CASE      )) for t in token_ids]
           assert len(tk_      ) == len(__SCREAMING_SNAKE_CASE      )
           assert all(len(__SCREAMING_SNAKE_CASE      ) == max_seq_len_ for t in tk_      )

           __UpperCAmelCase     				=torch.tensor(tk_      )  # (bs, max_seq_len_)
           __UpperCAmelCase     				=torch.tensor(__SCREAMING_SNAKE_CASE      )  # (bs)
           return tk_t, lg_t









'''simple docstring'''

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					)				->					int:





			"""simple docstring"""







			snake_case:    List[str]      							=checkpoints.load_tax_checkpoint(__UpperCAmelCase					)
			snake_case:    str      							=flatten_dict(__UpperCAmelCase					)
			return flax_params







def a_     (    __UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    int      							={}

			snake_case:    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',
			}

			snake_case:    Union[str, Any]      							={
			    '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(__UpperCAmelCase				,							__UpperCAmelCase					)

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

									if "layers" in new_key and "decoder" not in new_key:
												# use regex to replace the layer number
												snake_case:    Any      							=re.sub(R'layers_(\d+)'				,							R'layer.\1'				,							__UpperCAmelCase					)
												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[Any]      							=re.sub(R'layers_(\d+)'				,							R'layer.\1'				,							__UpperCAmelCase					)

									snake_case:    Optional[int]      							=flax_dict[key]

			snake_case:    Optional[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:    Union[str, Any]      							=torch.from_numpy(converted_dict[key].T					)
						else:
									snake_case:    Union[str, Any]      							=torch.from_numpy(converted_dict[key]					)

			return converted_torch_dict







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase=False				,							__UpperCAmelCase=False					)				->					Tuple:





			"""simple docstring"""







			snake_case:    Any      							=get_flax_param(__UpperCAmelCase					)

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

			snake_case:    List[Any]      							=PixaStructForConditionalGeneration(__UpperCAmelCase					)

			snake_case:    str      							=rename_and_convert_flax_params(__UpperCAmelCase					)
			model.load_state_dict(__UpperCAmelCase					)

			snake_case:    List[Any]      							=AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer'					)
			snake_case:    Optional[Any]      							=PixaStructImageProcessor()
			snake_case:    Optional[int]      							=PixaStructProcessor(image_processor=__UpperCAmelCase				,							tokenizer=__UpperCAmelCase					)

			if use_large:
						snake_case:    Optional[Any]      							=40_96

			snake_case:    str      							=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__":
	a							   =				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							   =				parser.parse_args()

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




