





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 		__magic_name__				( lowercase_				)		->					Any:


						'''simple docstring'''


						UpperCamelCase  							= checkpoints.load_tax_checkpoint(_lowerCAmelCase				)
						UpperCamelCase  							= flatten_dict(_lowerCAmelCase				)
						return flax_params
def 		__magic_name__				( lowercase_				)		->					Union[str, Any]:


						'''simple docstring'''


						UpperCamelCase  							= {}

						UpperCamelCase  							= {
						    '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',
						}

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

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

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

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

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

																		UpperCamelCase  							= flax_dict[key]

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

						return converted_torch_dict
def 		__magic_name__				( lowercase_						,		lowercase_						,		lowercase_=False						,		lowercase_=False				)		->					Any:


						'''simple docstring'''


						UpperCamelCase  							= get_flax_param(_lowerCAmelCase				)

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

						UpperCamelCase  							= PixaStructForConditionalGeneration(_lowerCAmelCase				)

						UpperCamelCase  							= rename_and_convert_flax_params(_lowerCAmelCase				)
						model.load_state_dict(_lowerCAmelCase				)

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

						if use_large:
												UpperCamelCase  							= 4096

						UpperCamelCase  							= 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__":
		__a      : str 				=				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      : Dict 				=				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 json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


_UpperCamelCase											=		logging.get_logger(__name__)

_UpperCamelCase											=		{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

_UpperCamelCase											=		{
    'vocab_file': {
        'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_char_small': (
            'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_chinese_char_base': (
            'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_discriminator': (
            'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_generator': (
            'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'
        ),
    }
}

_UpperCamelCase											=		{
    'junnyu/roformer_chinese_small': 1536,
    'junnyu/roformer_chinese_base': 1536,
    'junnyu/roformer_chinese_char_small': 512,
    'junnyu/roformer_chinese_char_base': 512,
    'junnyu/roformer_small_discriminator': 128,
    'junnyu/roformer_small_generator': 128,
}


_UpperCamelCase											=		{
    'junnyu/roformer_chinese_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_base': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_base': {'do_lower_case': True},
    'junnyu/roformer_small_discriminator': {'do_lower_case': True},
    'junnyu/roformer_small_generator': {'do_lower_case': True},
}






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =VOCAB_FILES_NAMES
  a_				   =PRETRAINED_VOCAB_FILES_MAP
  a_				   =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_				   =PRETRAINED_INIT_CONFIGURATION
  a_				   =RoFormerTokenizer




  def __init__(					self :      List[str]     ,      _a :      Optional[Any]=None     ,      _a :      Dict=None     ,      _a :      Dict=True     ,      _a :      Union[str, Any]="[UNK]"     ,      _a :      List[Any]="[SEP]"     ,      _a :      List[Any]="[PAD]"     ,      _a :      Any="[CLS]"     ,      _a :      Any="[MASK]"     ,      _a :      Dict=True     ,      _a :      Any=None     ,      **_a :      Tuple     ,      )   ->   Optional[int]:
    super().__init__(
        _a     ,      tokenizer_file=_a     ,      do_lower_case=_a     ,      unk_token=_a     ,      sep_token=_a     ,      pad_token=_a     ,      cls_token=_a     ,      mask_token=_a     ,      tokenize_chinese_chars=_a     ,      strip_accents=_a     ,      **_a     ,      )

    __lowerCamelCase			:			List[str]       			=					json.loads(self.backend_tokenizer.normalizer.__getstate__()  )
    if (
        pre_tok_state.get('lowercase'     ,      _a  ) != do_lower_case
        or pre_tok_state.get('strip_accents'     ,      _a  ) != strip_accents
    ):
      __lowerCamelCase			:			Dict       			=					getattr(_a     ,      pre_tok_state.pop('type'  )  )
      __lowerCamelCase			:			Tuple       			=					do_lower_case
      __lowerCamelCase			:			int       			=					strip_accents
      __lowerCamelCase			:			Dict       			=					pre_tok_class(**_a  )

    __lowerCamelCase			:			Any       			=					do_lower_case




  def __getstate__(					self :      Union[str, Any]  )   ->   List[Any]:
    __lowerCamelCase			:			Optional[Any]       			=					self.__dict__.copy()
    __lowerCamelCase			:			Any       			=					BertPreTokenizer()
    return state




  def __setstate__(					self :      Optional[Any]     ,      _a :      Optional[int]  )   ->   Tuple:
    __lowerCamelCase			:			Tuple       			=					d
    __lowerCamelCase			:			Optional[Any]       			=					self.__dict__['_tokenizer'].get_vocab()
    __lowerCamelCase			:			Any       			=					PreTokenizer.custom(JiebaPreTokenizer(_a  )  )




  def 		_lowercase	(					self :      Any     ,      _a :      Union[str, Any]     ,      _a :      str=None  )   ->   Any:
    __lowerCamelCase			:			Optional[Any]       			=					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

    if token_ids_a:
      output += token_ids_a + [self.sep_token_id]

    return output




  def 		_lowercase	(					self :      int     ,      _a :      List[int]     ,      _a :      Optional[List[int]] = None  )   ->   List[int]:
    __lowerCamelCase			:			int       			=					[self.sep_token_id]
    __lowerCamelCase			:			int       			=					[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  ) * [0] + len(token_ids_a + sep  ) * [1]




  def 		_lowercase	(					self :      List[str]     ,      _a :      str     ,      _a :      Optional[str] = None  )   ->   Tuple[str]:
    __lowerCamelCase			:			Optional[int]       			=					self._tokenizer.model.save(_a     ,      name=_a  )
    return tuple(_a  )




  def 		_lowercase	(					self :      int     ,      _a :      Optional[Any]     ,      _a :      Tuple=None     ,      _a :      List[str]=None     ,      _a :      Optional[int]=False     ,      **_a :      Dict     ,      )   ->   Dict:
    __lowerCamelCase			:			Dict       			=					BertPreTokenizer()
    return super().save_pretrained(_a     ,      _a     ,      _a     ,      _a     ,      **_a  )






