


'''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        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				Any	    =					checkpoints.load_tax_checkpoint(__lowerCAmelCase      )
		_UpperCAmelCase						:				List[str]	    =					flatten_dict(__lowerCAmelCase      )
		return flax_params






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				int	    =					{}

		_UpperCAmelCase						:				Tuple	    =					{
		    "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						:				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
						_UpperCAmelCase						:				Any	    =					".".join(key[1:]      )

						# rename the key
						for old, new in CONVERSION_MAPPING.items():
								_UpperCAmelCase						:				int	    =					new_key.replace(__lowerCAmelCase					,       __lowerCAmelCase      )

						if "decoder" in new_key:
								for old, new in DECODER_CONVERSION_MAPPING.items():
										_UpperCAmelCase						:				Dict	    =					new_key.replace(__lowerCAmelCase					,       __lowerCAmelCase      )

						if "layers" in new_key and "decoder" not in new_key:
								# use regex to replace the layer number
								_UpperCAmelCase						:				Union[str, Any]	    =					re.sub(R"layers_(\d+)"					,       R"layer.\1"					,       __lowerCAmelCase      )
								_UpperCAmelCase						:				int	    =					new_key.replace("encoder"					,       "encoder.encoder"      )

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

						_UpperCAmelCase						:				Union[str, Any]	    =					flax_dict[key]

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

		return converted_torch_dict






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase=False					,       __lowerCAmelCase=False      ):
		_UpperCAmelCase						:				List[Any]	    =					get_flax_param(__lowerCAmelCase      )

		if not use_large:
				_UpperCAmelCase						:				Optional[Any]	    =					PixaStructVisionConfig()
				_UpperCAmelCase						:				str	    =					PixaStructTextConfig()
		else:
				_UpperCAmelCase						:				Dict	    =					PixaStructVisionConfig(
				    hidden_size=1_536					,       d_ff=3_968					,       num_attention_heads=24					,       num_hidden_layers=18      )
				_UpperCAmelCase						:				Tuple	    =					PixaStructTextConfig(hidden_size=1_536					,       d_ff=3_968					,       num_heads=24					,       num_layers=18      )
		_UpperCAmelCase						:				Dict	    =					PixaStructConfig(
		    vision_config=encoder_config.to_dict()					,       text_config=decoder_config.to_dict()					,       is_vqa=__lowerCAmelCase      )

		_UpperCAmelCase						:				int	    =					PixaStructForConditionalGeneration(__lowerCAmelCase      )

		_UpperCAmelCase						:				Optional[Any]	    =					rename_and_convert_flax_params(__lowerCAmelCase      )
		model.load_state_dict(__lowerCAmelCase      )

		_UpperCAmelCase						:				Tuple	    =					AutoTokenizer.from_pretrained("ybelkada/test-pix2struct-tokenizer"      )
		_UpperCAmelCase						:				Any	    =					PixaStructImageProcessor()
		_UpperCAmelCase						:				Optional[Any]	    =					PixaStructProcessor(image_processor=__lowerCAmelCase					,       tokenizer=__lowerCAmelCase      )

		if use_large:
				_UpperCAmelCase						:				Tuple	    =					4_096

		_UpperCAmelCase						:				Optional[Any]	    =					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__					=   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__					=   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 List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_fnet import FNetTokenizer
else:
						lowerCamelCase__					=   None

lowerCamelCase__					=   logging.get_logger(__name__)
lowerCamelCase__					=   {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase__					=   {
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
    },
}

lowerCamelCase__					=   {
    'google/fnet-base': 512,
    'google/fnet-large': 512,
}

lowerCamelCase__					=   '▁'






class 		lowerCAmelCase__	(	UpperCAmelCase__		):
			lowerCAmelCase       :		Dict				      =   VOCAB_FILES_NAMES
			lowerCAmelCase       :		Optional[Any]				      =   PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase       :		List[str]				      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCAmelCase       :		Optional[int]				      =   ["input_ids", "token_type_ids"]
			lowerCAmelCase       :		Optional[Any]				      =   FNetTokenizer




			def __init__(						self     :   Dict		,     lowerCamelCase__     :   int=None		,     lowerCamelCase__     :   List[Any]=None		,     lowerCamelCase__     :   Any=False		,     lowerCamelCase__     :   List[str]=True		,     lowerCamelCase__     :   Dict=True		,     lowerCamelCase__     :   str="<unk>"		,     lowerCamelCase__     :   List[str]="[SEP]"		,     lowerCamelCase__     :   Union[str, Any]="<pad>"		,     lowerCamelCase__     :   Optional[Any]="[CLS]"		,     lowerCamelCase__     :   Any="[MASK]"		,     **lowerCamelCase__     :   Any		,     )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				Dict	    =					(
					    AddedToken(lowerCamelCase__		,     lstrip=lowerCamelCase__		,     rstrip=lowerCamelCase__		,     normalized=lowerCamelCase__       )
					    if isinstance(lowerCamelCase__		,     lowerCamelCase__       )
					    else mask_token
					)

					super().__init__(
					    lowerCamelCase__		,     tokenizer_file=lowerCamelCase__		,     do_lower_case=lowerCamelCase__		,     remove_space=lowerCamelCase__		,     keep_accents=lowerCamelCase__		,     unk_token=lowerCamelCase__		,     sep_token=lowerCamelCase__		,     pad_token=lowerCamelCase__		,     cls_token=lowerCamelCase__		,     mask_token=lowerCamelCase__		,     **lowerCamelCase__		,     )

					_UpperCAmelCase						:				Optional[Any]	    =					do_lower_case
					_UpperCAmelCase						:				Tuple	    =					remove_space
					_UpperCAmelCase						:				List[Any]	    =					keep_accents
					_UpperCAmelCase						:				Tuple	    =					vocab_file
					_UpperCAmelCase						:				str	    =					False if not self.vocab_file else True




			def 							lowerCAmelCase__			(						self     :   str		,     lowerCamelCase__     :   List[int]		,     lowerCamelCase__     :   Optional[List[int]] = None       )      ->List[int]:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[Any]	    =					[self.sep_token_id]
					_UpperCAmelCase						:				Tuple	    =					[self.cls_token_id]
					if token_ids_a is None:
							return cls + token_ids_a + sep
					return cls + token_ids_a + sep + token_ids_a + sep




			def 							lowerCAmelCase__			(						self     :   int		,     lowerCamelCase__     :   List[int]		,     lowerCamelCase__     :   Optional[List[int]] = None       )      ->List[int]:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[int]	    =					[self.sep_token_id]
					_UpperCAmelCase						:				Optional[Any]	    =					[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 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   str		,     lowerCamelCase__     :   Optional[str] = None       )      ->Tuple[str]:



					'''simple docstring'''







					if not os.path.isdir(lowerCamelCase__       ):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
							return
					_UpperCAmelCase						:				Union[str, 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__       ):
							copyfile(self.vocab_file		,     lowerCamelCase__       )

					return (out_vocab_file,)



