
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_		)						->      str:







				"""simple docstring"""



				__A       =					checkpoints.load_tax_checkpoint(__lowercase		)
				__A       =					flatten_dict(__lowercase		)
				return flax_params


def 		UpperCAmelCase     (      a_		)						->      str:







				"""simple docstring"""



				__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(__lowercase						,  __lowercase		)

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

												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"						,  __lowercase		)
																__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"						,  __lowercase		)

												__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 		UpperCAmelCase     (      a_						,  a_						,  a_=False						,  a_=False		)						->      Tuple:







				"""simple docstring"""



				__A       =					get_flax_param(__lowercase		)

				if not use_large:
								__A       =					PixaStructVisionConfig()
								__A       =					PixaStructTextConfig()
				else:
								__A       =					PixaStructVisionConfig(
								    hidden_size=1_5_3_6						,  d_ff=3_9_6_8						,  num_attention_heads=2_4						,  num_hidden_layers=1_8		)
								__A       =					PixaStructTextConfig(hidden_size=1_5_3_6						,  d_ff=3_9_6_8						,  num_heads=2_4						,  num_layers=1_8		)
				__A       =					PixaStructConfig(
				    vision_config=encoder_config.to_dict()						,  text_config=decoder_config.to_dict()						,  is_vqa=__lowercase		)

				__A       =					PixaStructForConditionalGeneration(__lowercase		)

				__A       =					rename_and_convert_flax_params(__lowercase		)
				model.load_state_dict(__lowercase		)

				__A       =					AutoTokenizer.from_pretrained("ybelkada/test-pix2struct-tokenizer"		)
				__A       =					PixaStructImageProcessor()
				__A       =					PixaStructProcessor(image_processor=__lowercase						,  tokenizer=__lowercase		)

				if use_large:
								__A       =					4_0_9_6

				__A       =					True

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

				model.save_pretrained(__lowercase		)
				processor.save_pretrained(__lowercase		)

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE				:Optional[Any]									=							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.')
					SCREAMING_SNAKE_CASE				:Optional[int]									=							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 os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__SCREAMING_SNAKE_CASE							=     logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							=     {'vocab_file': 'sentencepiece.bpe.model'}

__SCREAMING_SNAKE_CASE							=     {
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

__SCREAMING_SNAKE_CASE							=     {
    'camembert-base': 512,
}

__SCREAMING_SNAKE_CASE							=     '▁'





class 				a__					(						A__       ):
     UpperCAmelCase__         =      VOCAB_FILES_NAMES
     UpperCAmelCase__         =      PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__         =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__         =      ['''input_ids''', '''attention_mask''']



     def __init__(							self :Optional[Any]      ,  _lowerCamelCase :int      ,  _lowerCamelCase :List[Any]="<s>"      ,  _lowerCamelCase :List[Any]="</s>"      ,  _lowerCamelCase :Optional[int]="</s>"      ,  _lowerCamelCase :List[Any]="<s>"      ,  _lowerCamelCase :List[Any]="<unk>"      ,  _lowerCamelCase :Union[str, Any]="<pad>"      ,  _lowerCamelCase :int="<mask>"      ,  _lowerCamelCase :Union[str, Any]=["<s>NOTUSED", "</s>NOTUSED"]      ,  _lowerCamelCase :Optional[Dict[str, Any]] = None      ,  **_lowerCamelCase :Optional[int]      ,  ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[Any]   	=AddedToken(_lowerCamelCase      ,  lstrip=_lowerCamelCase      ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ) else mask_token

      UpperCamelCase_     :					List[str]   	={} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          bos_token=_lowerCamelCase      ,  eos_token=_lowerCamelCase      ,  unk_token=_lowerCamelCase      ,  sep_token=_lowerCamelCase      ,  cls_token=_lowerCamelCase      ,  pad_token=_lowerCamelCase      ,  mask_token=_lowerCamelCase      ,  additional_special_tokens=_lowerCamelCase      ,  sp_model_kwargs=self.sp_model_kwargs      ,  **_lowerCamelCase      ,  )
      UpperCamelCase_     :					Optional[int]   	=spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(str(_lowerCamelCase   )   )
      UpperCamelCase_     :					str   	=vocab_file
      # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
      # sentencepiece vocabulary (this is the case for <s> and </s>
      UpperCamelCase_     :					Tuple   	={'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
      UpperCamelCase_     :					List[str]   	=len(self.fairseq_tokens_to_ids   )
      UpperCamelCase_     :					List[str]   	=len(self.sp_model   ) + len(self.fairseq_tokens_to_ids   )
      UpperCamelCase_     :					Any   	={v: k for k, v in self.fairseq_tokens_to_ids.items()}



     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :List[int]      ,  _lowerCamelCase :Optional[List[int]] = None   ):







      '''simple docstring'''



      if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      UpperCamelCase_     :					Any   	=[self.cls_token_id]
      UpperCamelCase_     :					str   	=[self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep



     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :List[int]      ,  _lowerCamelCase :Optional[List[int]] = None      ,  _lowerCamelCase :bool = False   ):







      '''simple docstring'''



      if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=_lowerCamelCase      ,  token_ids_a=_lowerCamelCase      ,  already_has_special_tokens=_lowerCamelCase   )

      if token_ids_a is None:
       return [1] + ([0] * len(_lowerCamelCase   )) + [1]
      return [1] + ([0] * len(_lowerCamelCase   )) + [1, 1] + ([0] * len(_lowerCamelCase   )) + [1]



     def  lowerCamelCase_						(							self :Union[str, Any]      ,  _lowerCamelCase :List[int]      ,  _lowerCamelCase :Optional[List[int]] = None   ):







      '''simple docstring'''



      UpperCamelCase_     :					Union[str, Any]   	=[self.sep_token_id]
      UpperCamelCase_     :					Dict   	=[self.cls_token_id]

      if token_ids_a is None:
       return len(cls + token_ids_a + sep   ) * [0]
      return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]



     @property
     def  lowerCamelCase_						(							self :str   ):







      '''simple docstring'''



      return len(self.fairseq_tokens_to_ids   ) + len(self.sp_model   )



     def  lowerCamelCase_						(							self :Tuple   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[Any]   	={self.convert_ids_to_tokens(_lowerCamelCase   ): i for i in range(self.vocab_size   )}
      vocab.update(self.added_tokens_encoder   )
      return vocab



     def  lowerCamelCase_						(							self :List[str]      ,  _lowerCamelCase :str   ):







      '''simple docstring'''



      return self.sp_model.encode(_lowerCamelCase      ,  out_type=_lowerCamelCase   )



     def  lowerCamelCase_						(							self :str      ,  _lowerCamelCase :Optional[Any]   ):







      '''simple docstring'''



      if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
      elif self.sp_model.PieceToId(_lowerCamelCase   ) == 0:
       # Convert sentence piece unk token to fairseq unk token index
       return self.unk_token_id
      return self.fairseq_offset + self.sp_model.PieceToId(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :List[Any]   ):







      '''simple docstring'''



      if index in self.fairseq_ids_to_tokens:
       return self.fairseq_ids_to_tokens[index]
      return self.sp_model.IdToPiece(index - self.fairseq_offset   )



     def  lowerCamelCase_						(							self :int      ,  _lowerCamelCase :List[str]   ):







      '''simple docstring'''



      UpperCamelCase_     :					int   	=[]
      UpperCamelCase_     :					Optional[Any]   	=''
      UpperCamelCase_     :					Optional[Any]   	=False
      for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
        if not prev_is_special:
         out_string += " "
        out_string += self.sp_model.decode(_lowerCamelCase   ) + token
        UpperCamelCase_     :					Union[str, Any]   	=True
        UpperCamelCase_     :					Optional[Any]   	=[]
       else:
        current_sub_tokens.append(_lowerCamelCase   )
        UpperCamelCase_     :					Any   	=False
      out_string += self.sp_model.decode(_lowerCamelCase   )
      return out_string.strip()



     def __getstate__(							self :Optional[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[Any]   	=self.__dict__.copy()
      UpperCamelCase_     :					str   	=None
      return state



     def __setstate__(							self :Optional[Any]      ,  _lowerCamelCase :List[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					str   	=d

      # for backward compatibility
      if not hasattr(self      ,  'sp_model_kwargs'   ):
       UpperCamelCase_     :					Any   	={}

      UpperCamelCase_     :					List[Any]   	=spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(self.vocab_file   )




     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :str      ,  _lowerCamelCase :Optional[str] = None   ):







      '''simple docstring'''



      if not os.path.isdir(_lowerCamelCase   ):
       logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''   )
       return
      UpperCamelCase_     :					Any   	=os.path.join(
          _lowerCamelCase      ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

      if os.path.abspath(self.vocab_file   ) != os.path.abspath(_lowerCamelCase   ) and os.path.isfile(self.vocab_file   ):
       copyfile(self.vocab_file      ,  _lowerCamelCase   )
      elif not os.path.isfile(self.vocab_file   ):
       with open(_lowerCamelCase      ,  'wb'   ) as fi:
        UpperCamelCase_     :					int   	=self.sp_model.serialized_model_proto()
        fi.write(_lowerCamelCase   )

      return (out_vocab_file,)
