




import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
			import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase__				     = logging.get_logger(__name__)

UpperCAmelCase__				     = {"vocab_file": "vocab.txt"}

UpperCAmelCase__				     = {
    "vocab_file": {
        "openbmb/cpm-ant-10b": "https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt",
    },
}

UpperCAmelCase__				     = {
    "openbmb/cpm-ant-10b": 1024,
}







def 		_A(       UpperCamelCase__							:			Tuple				)    -> Optional[int]:
							'''simple docstring'''


							__lowercase      =    collections.OrderedDict()
							with open(UpperCamelCase__				,       '''r'''				,       encoding='''utf-8'''				) as reader:
														__lowercase      =    reader.readlines()
							for index, token in enumerate(UpperCamelCase__				):
														__lowercase      =    token.rstrip('''\n'''				)
														__lowercase      =    index
							return vocab







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						def __init__( self :     Optional[int]				,							lowerCamelCase__ :     int				,							lowerCamelCase__ :     int="<unk>"				,							lowerCamelCase__ :     List[str]=200				)    ->						int:
													"""simple docstring"""


													__lowercase      =    vocab
													__lowercase      =    unk_token
													__lowercase      =    max_input_chars_per_word






						def     UpperCAmelCase_      ( self :     Tuple				,							lowerCamelCase__ :     Union[str, Any]				)    ->						List[Any]:
													"""simple docstring"""


													__lowercase      =    list(lowerCamelCase__				)
													if len(lowerCamelCase__				) > self.max_input_chars_per_word:
																				return [self.unk_token]

													__lowercase      =    0
													__lowercase      =    []
													while start < len(lowerCamelCase__				):
																				__lowercase      =    len(lowerCamelCase__				)
																				__lowercase      =    None
																				while start < end:
																											__lowercase      =    ''''''.join(chars[start:end]				)
																											if substr in self.vocab:
																																		__lowercase      =    substr
																																		break
																											end -= 1
																				if cur_substr is None:
																											sub_tokens.append(self.unk_token				)
																											start += 1
																				else:
																											sub_tokens.append(lowerCamelCase__				)
																											__lowercase      =    end

													return sub_tokens







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       List[str]       	=		VOCAB_FILES_NAMES
						UpperCamelCase_	:       List[Any]       	=		PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_	:       Optional[Any]       	=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_	:       int       	=		['input_ids', 'attention_mask']
						UpperCamelCase_	:       List[Any]       	=		False






						def __init__( self :     Optional[Any]				,							lowerCamelCase__ :     Optional[int]				,							lowerCamelCase__ :     int="<d>"				,							lowerCamelCase__ :     List[Any]="</d>"				,							lowerCamelCase__ :     Optional[Any]="<s>"				,							lowerCamelCase__ :     Optional[int]="</s>"				,							lowerCamelCase__ :     Optional[Any]="<pad>"				,							lowerCamelCase__ :     Dict="<unk>"				,							lowerCamelCase__ :     List[str]="</n>"				,							lowerCamelCase__ :     List[str]="</_>"				,							lowerCamelCase__ :     Any="left"				,							**lowerCamelCase__ :     Optional[Any]				,							)    ->						List[str]:
													"""simple docstring"""


													requires_backends(self				,							['''jieba''']				)
													super().__init__(
													    bod_token=lowerCamelCase__				,							eod_token=lowerCamelCase__				,							bos_token=lowerCamelCase__				,							eos_token=lowerCamelCase__				,							pad_token=lowerCamelCase__				,							unk_token=lowerCamelCase__				,							line_token=lowerCamelCase__				,							space_token=lowerCamelCase__				,							padding_side=lowerCamelCase__				,							**lowerCamelCase__				,							)
													__lowercase      =    bod_token
													__lowercase      =    eod_token
													__lowercase      =    load_vocab(lowerCamelCase__				)
													__lowercase      =    self.encoder[space_token]
													__lowercase      =    self.encoder[line_token]

													del self.encoder[space_token]
													del self.encoder[line_token]

													__lowercase      =    collections.OrderedDict(sorted(self.encoder.items()				,							key=lambda lowerCamelCase__				: x[1]				)				)
													__lowercase      =    {v: k for k, v in self.encoder.items()}

													__lowercase      =    WordpieceTokenizer(vocab=self.encoder				,							unk_token=self.unk_token				)






						@property
						def     UpperCAmelCase_      ( self :     List[str]				)    ->						List[str]:
													"""simple docstring"""


													return self.encoder[self.bod_token]






						@property
						def     UpperCAmelCase_      ( self :     Any				)    ->						Union[str, Any]:
													"""simple docstring"""


													return self.encoder[self.eod_token]






						@property
						def     UpperCAmelCase_      ( self :     Optional[int]				)    ->						Tuple:
													"""simple docstring"""


													return self.encoder["\n"]






						@property
						def     UpperCAmelCase_      ( self :     str				)    ->						int:
													"""simple docstring"""


													return len(self.encoder				)






						def     UpperCAmelCase_      ( self :     Dict				)    ->						List[str]:
													"""simple docstring"""


													return dict(self.encoder				,							**self.added_tokens_encoder				)






						def     UpperCAmelCase_      ( self :     List[Any]				,							lowerCamelCase__ :     List[str]				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase      =    []
													for x in jieba.cut(lowerCamelCase__				,							cut_all=lowerCamelCase__				):
																				output_tokens.extend(self.wordpiece_tokenizer.tokenize(lowerCamelCase__				)				)
													return output_tokens






						def     UpperCAmelCase_      ( self :     List[str]				,							lowerCamelCase__ :     str				,							**lowerCamelCase__ :     int				)    ->						Union[str, Any]:
													"""simple docstring"""


													__lowercase      =    [i for i in token_ids if i >= 0]
													__lowercase      =    [
													    x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
													]
													return super()._decode(lowerCamelCase__				,							**lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     List[str]				,							lowerCamelCase__ :     List[Any]				)    ->						Optional[int]:
													"""simple docstring"""


													return token in self.encoder






						def     UpperCAmelCase_      ( self :     int				,							lowerCamelCase__ :     List[str]				)    ->						str:
													"""simple docstring"""


													return "".join(lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     Any				,							lowerCamelCase__ :     Dict				)    ->						int:
													"""simple docstring"""


													return self.encoder.get(lowerCamelCase__				,							self.encoder.get(self.unk_token				)				)






						def     UpperCAmelCase_      ( self :     Optional[int]				,							lowerCamelCase__ :     Tuple				)    ->						Any:
													"""simple docstring"""


													return self.decoder.get(lowerCamelCase__				,							self.unk_token				)






						def     UpperCAmelCase_      ( self :     Optional[Any]				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     Optional[str] = None				)    ->						Tuple[str]:
													"""simple docstring"""


													if os.path.isdir(lowerCamelCase__				):
																				__lowercase      =    os.path.join(
																				    lowerCamelCase__				,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)
													else:
																				__lowercase      =    (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
													__lowercase      =    0
													if " " in self.encoder:
																				__lowercase      =    self.encoder[''' ''']
																				del self.encoder[" "]
													if "\n" in self.encoder:
																				__lowercase      =    self.encoder['''\n''']
																				del self.encoder["\n"]
													__lowercase      =    collections.OrderedDict(sorted(self.encoder.items()				,							key=lambda lowerCamelCase__				: x[1]				)				)
													with open(lowerCamelCase__				,							'''w'''				,							encoding='''utf-8'''				) as writer:
																				for token, token_index in self.encoder.items():
																											if index != token_index:
																																		logger.warning(
																																		    f'Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'
																																		    ''' Please check that the vocabulary is not corrupted!'''				)
																																		__lowercase      =    token_index
																											writer.write(token + '''\n'''				)
																											index += 1
													return (vocab_file,)






						def     UpperCAmelCase_      ( self :     Any				,							lowerCamelCase__ :     List[int]				,							lowerCamelCase__ :     List[int] = None				)    ->						List[int]:
													"""simple docstring"""


													if token_ids_a is None:
																				return [self.bos_token_id] + token_ids_a
													return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a






						def     UpperCAmelCase_      ( self :     List[Any]				,							lowerCamelCase__ :     List[int]				,							lowerCamelCase__ :     Optional[List[int]] = None				,							lowerCamelCase__ :     bool = False				)    ->						List[int]:
													"""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 not None:
																				return [1] + ([0] * len(lowerCamelCase__				)) + [1] + ([0] * len(lowerCamelCase__				))
													return [1] + ([0] * len(lowerCamelCase__				))












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__							:			Tuple				)    -> Tuple:
							'''simple docstring'''


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







def 		_A(       UpperCamelCase__							:			str				)    -> Dict:
							'''simple docstring'''


							__lowercase      =    {}

							__lowercase      =    {
							    '''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''',
							}

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

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

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

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

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

																					__lowercase      =    flax_dict[key]

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

							return converted_torch_dict







def 		_A(       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			int				,       UpperCamelCase__							:			List[str]=False				,       UpperCamelCase__							:			List[str]=False				)    -> Any:
							'''simple docstring'''


							__lowercase      =    get_flax_param(UpperCamelCase__				)

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

							__lowercase      =    PixaStructForConditionalGeneration(UpperCamelCase__				)

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

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

							if use_large:
														__lowercase      =    4096

							__lowercase      =    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__":
			UpperCAmelCase__				     = 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.")
			UpperCAmelCase__				     = parser.parse_args()

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






