import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig



def 							_lowerCamelCase(				__snake_case						,						__snake_case						,						__snake_case	)  ->   List[str]:
		__snake_case		       =     TaConfig.from_pretrained(__snake_case	)
		__snake_case		       =     FlaxTaForConditionalGeneration(config=__snake_case	)
		__snake_case		       =     checkpoints.load_tax_checkpoint(__snake_case	)

		__snake_case		       =     "wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

		# Encoder
		for layer_index in range(config.num_layers	):
				__snake_case		       =     f"""layers_{str(__snake_case	)}"""

				# Self-Attention
				__snake_case		       =     tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
				__snake_case		       =     tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
				__snake_case		       =     tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
				__snake_case		       =     tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

				# Layer Normalization
				__snake_case		       =     tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

				if split_mlp_wi:
						__snake_case		       =     tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
						__snake_case		       =     tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
				else:
						__snake_case		       =     tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

				__snake_case		       =     tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

				# Layer Normalization
				__snake_case		       =     tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

				# Assigning
				__snake_case		       =     tax_attention_key
				__snake_case		       =     tax_attention_out
				__snake_case		       =     tax_attention_query
				__snake_case		       =     tax_attention_value

				__snake_case		       =     tax_attention_layer_norm

				if split_mlp_wi:
						__snake_case		       =     tax_mlp_wi_a
						__snake_case		       =     tax_mlp_wi_a
				else:
						__snake_case		       =     tax_mlp_wi

				__snake_case		       =     tax_mlp_wo
				__snake_case		       =     tax_mlp_layer_norm

		# Only for layer 0:
		__snake_case		       =     tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
		__snake_case		       =     tax_encoder_rel_embedding

		# Assigning
		__snake_case		       =     tax_model["target"]["encoder"]["encoder_norm"]["scale"]
		__snake_case		       =     tax_encoder_norm

		# Decoder
		for layer_index in range(config.num_decoder_layers	):
				__snake_case		       =     f"""layers_{str(__snake_case	)}"""

				# Self-Attention
				__snake_case		       =     tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
				__snake_case		       =     tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
				__snake_case		       =     tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
				__snake_case		       =     tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

				# Layer Normalization
				__snake_case		       =     tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
				    "scale"
				]

				# Encoder-Decoder-Attention
				__snake_case		       =     tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["key"][
				    "kernel"
				]
				__snake_case		       =     tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["out"][
				    "kernel"
				]
				__snake_case		       =     tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["query"][
				    "kernel"
				]
				__snake_case		       =     tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["value"][
				    "kernel"
				]

				# Layer Normalization
				__snake_case		       =     tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

				# MLP
				if split_mlp_wi:
						__snake_case		       =     tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
						__snake_case		       =     tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
				else:
						__snake_case		       =     tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

				__snake_case		       =     tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

				# Layer Normalization
				__snake_case		       =     tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

				# Assigning
				__snake_case		       =     tax_attention_key
				__snake_case		       =     tax_attention_out
				__snake_case		       =     tax_attention_query
				__snake_case		       =     tax_attention_value

				__snake_case		       =     tax_pre_attention_layer_norm

				__snake_case		       =     tax_enc_dec_attention_key
				__snake_case		       =     tax_enc_dec_attention_out
				__snake_case		       =     tax_enc_dec_attention_query
				__snake_case		       =     tax_enc_dec_attention_value

				__snake_case		       =     tax_cross_layer_norm

				if split_mlp_wi:
						__snake_case		       =     tax_mlp_wi_a
						__snake_case		       =     tax_mlp_wi_a
				else:
						__snake_case		       =     tax_mlp_wi

				__snake_case		       =     tax_mlp_wo

				__snake_case		       =     txa_mlp_layer_norm

		# Decoder Normalization
		__snake_case		       =     tax_model["target"]["decoder"]["decoder_norm"]["scale"]
		__snake_case		       =     txa_decoder_norm

		# Only for layer 0:
		__snake_case		       =     tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
		__snake_case		       =     tax_decoder_rel_embedding

		# Token Embeddings
		__snake_case		       =     tax_model["target"]["token_embedder"]["embedding"]
		__snake_case		       =     txa_token_embeddings

		# LM Head (only in v1.1 checkpoints)
		if "logits_dense" in tax_model["target"]["decoder"]:
				__snake_case		       =     tax_model["target"]["decoder"]["logits_dense"]["kernel"]

		flax_model.save_pretrained(__snake_case	)
		print("T5X Model was sucessfully converted!"	)


if __name__ == "__main__":
		lowerCamelCase__           =				argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the TX5 checkpoint.'
		)
		parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of T5 model.')
		parser.add_argument(
		    '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
		)
		lowerCamelCase__           =				parser.parse_args()
		convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)

import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


lowerCamelCase__           =				logging.get_logger(__name__)

lowerCamelCase__           =				{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

lowerCamelCase__           =				{
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
}

lowerCamelCase__           =				{
    'roberta-base': 5_12,
    'roberta-large': 5_12,
    'roberta-large-mnli': 5_12,
    'distilroberta-base': 5_12,
    'roberta-base-openai-detector': 5_12,
    'roberta-large-openai-detector': 5_12,
}



@lru_cache()
def 							_lowerCamelCase(				)  ->   List[str]:
		__snake_case		       =     (
		    list(range(ord("!"	)						,						ord("~"	) + 1	)	) + list(range(ord("¡"	)						,						ord("¬"	) + 1	)	) + list(range(ord("®"	)						,						ord("ÿ"	) + 1	)	)
		)
		__snake_case		       =     bs[:]
		__snake_case		       =     0
		for b in range(2**8	):
				if b not in bs:
						bs.append(__snake_case	)
						cs.append(2**8 + n	)
						n += 1
		__snake_case		       =     [chr(__snake_case	) for n in cs]
		return dict(zip(__snake_case						,						__snake_case	)	)



def 							_lowerCamelCase(				__snake_case	)  ->   Optional[Any]:
		__snake_case		       =     set()
		__snake_case		       =     word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)	)
				__snake_case		       =     char
		return pairs





class 					UpperCamelCase	(     snake_case__      ):
					__UpperCamelCase						=   VOCAB_FILES_NAMES
					__UpperCamelCase						=   PRETRAINED_VOCAB_FILES_MAP
					__UpperCamelCase						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__UpperCamelCase						=   ["""input_ids""", """attention_mask"""]




					def __init__( self :					str      ,_lowerCAmelCase :					Tuple      ,_lowerCAmelCase :					int      ,_lowerCAmelCase :					Union[str, Any]="replace"      ,_lowerCAmelCase :					List[Any]="<s>"      ,_lowerCAmelCase :					Union[str, Any]="</s>"      ,_lowerCAmelCase :					Any="</s>"      ,_lowerCAmelCase :					Optional[int]="<s>"      ,_lowerCAmelCase :					int="<unk>"      ,_lowerCAmelCase :					Tuple="<pad>"      ,_lowerCAmelCase :					Optional[Any]="<mask>"      ,_lowerCAmelCase :					List[str]=False      ,**_lowerCAmelCase :					Union[str, Any]      ,):


							"""simple docstring"""




							__snake_case		       =     AddedToken(_lowerCAmelCase      ,lstrip=_lowerCAmelCase      ,rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase      ,_lowerCAmelCase						) else bos_token
							__snake_case		       =     AddedToken(_lowerCAmelCase      ,lstrip=_lowerCAmelCase      ,rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase      ,_lowerCAmelCase						) else eos_token
							__snake_case		       =     AddedToken(_lowerCAmelCase      ,lstrip=_lowerCAmelCase      ,rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase      ,_lowerCAmelCase						) else sep_token
							__snake_case		       =     AddedToken(_lowerCAmelCase      ,lstrip=_lowerCAmelCase      ,rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase      ,_lowerCAmelCase						) else cls_token
							__snake_case		       =     AddedToken(_lowerCAmelCase      ,lstrip=_lowerCAmelCase      ,rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase      ,_lowerCAmelCase						) else unk_token
							__snake_case		       =     AddedToken(_lowerCAmelCase      ,lstrip=_lowerCAmelCase      ,rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase      ,_lowerCAmelCase						) else pad_token

							# Mask token behave like a normal word, i.e. include the space before it
							__snake_case		       =     AddedToken(_lowerCAmelCase      ,lstrip=_lowerCAmelCase      ,rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase      ,_lowerCAmelCase						) else mask_token

							super().__init__(
							    errors=_lowerCAmelCase      ,bos_token=_lowerCAmelCase      ,eos_token=_lowerCAmelCase      ,unk_token=_lowerCAmelCase      ,sep_token=_lowerCAmelCase      ,cls_token=_lowerCAmelCase      ,pad_token=_lowerCAmelCase      ,mask_token=_lowerCAmelCase      ,add_prefix_space=_lowerCAmelCase      ,**_lowerCAmelCase      ,)

							with open(_lowerCAmelCase      ,encoding="utf-8"						) as vocab_handle:
									__snake_case		       =     json.load(_lowerCAmelCase						)
							__snake_case		       =     {v: k for k, v in self.encoder.items()}
							__snake_case		       =     errors  # how to handle errors in decoding
							__snake_case		       =     bytes_to_unicode()
							__snake_case		       =     {v: k for k, v in self.byte_encoder.items()}
							with open(_lowerCAmelCase      ,encoding="utf-8"						) as merges_handle:
									__snake_case		       =     merges_handle.read().split("\n"						)[1:-1]
							__snake_case		       =     [tuple(merge.split()						) for merge in bpe_merges]
							__snake_case		       =     dict(zip(_lowerCAmelCase      ,range(len(_lowerCAmelCase						)						)						)						)
							__snake_case		       =     {}
							__snake_case		       =     add_prefix_space

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							__snake_case		       =     re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"						)




					@property
					def 							UpperCamelCase_						( self :					int						):


							"""simple docstring"""




							return len(self.encoder						)




					def 							UpperCamelCase_						( self :					List[Any]						):


							"""simple docstring"""




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




					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					Tuple						):


							"""simple docstring"""




							if token in self.cache:
									return self.cache[token]
							__snake_case		       =     tuple(_lowerCAmelCase						)
							__snake_case		       =     get_pairs(_lowerCAmelCase						)

							if not pairs:
									return token

							while True:
									__snake_case		       =     min(_lowerCAmelCase      ,key=lambda _lowerCAmelCase						: self.bpe_ranks.get(_lowerCAmelCase      ,float("inf"						)						)						)
									if bigram not in self.bpe_ranks:
											break
									__snake_case       ,       __snake_case		       =     bigram
									__snake_case		       =     []
									__snake_case		       =     0
									while i < len(_lowerCAmelCase						):
											try:
													__snake_case		       =     word.index(_lowerCAmelCase      ,_lowerCAmelCase						)
											except ValueError:
													new_word.extend(word[i:]						)
													break
											else:
													new_word.extend(word[i:j]						)
													__snake_case		       =     j

											if word[i] == first and i < len(_lowerCAmelCase						) - 1 and word[i + 1] == second:
													new_word.append(first + second						)
													i += 2
											else:
													new_word.append(word[i]						)
													i += 1
									__snake_case		       =     tuple(_lowerCAmelCase						)
									__snake_case		       =     new_word
									if len(_lowerCAmelCase						) == 1:
											break
									else:
											__snake_case		       =     get_pairs(_lowerCAmelCase						)
							__snake_case		       =     " ".join(_lowerCAmelCase						)
							__snake_case		       =     word
							return word




					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					Dict						):


							"""simple docstring"""




							__snake_case		       =     []
							for token in re.findall(self.pat      ,_lowerCAmelCase						):
									__snake_case		       =     "".join(
									    self.byte_encoder[b] for b in token.encode("utf-8"						)						)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
									bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_lowerCAmelCase						).split(" "						)						)
							return bpe_tokens




					def 							UpperCamelCase_						( self :					List[str]      ,_lowerCAmelCase :					Dict						):


							"""simple docstring"""




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




					def 							UpperCamelCase_						( self :					Optional[Any]      ,_lowerCAmelCase :					Union[str, Any]						):


							"""simple docstring"""




							return self.decoder.get(_lowerCAmelCase						)




					def 							UpperCamelCase_						( self :					Dict      ,_lowerCAmelCase :					List[Any]						):


							"""simple docstring"""




							__snake_case		       =     "".join(_lowerCAmelCase						)
							__snake_case		       =     bytearray([self.byte_decoder[c] for c in text]						).decode("utf-8"      ,errors=self.errors						)
							return text




					def 							UpperCamelCase_						( self :					str      ,_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
							__snake_case		       =     os.path.join(
							    _lowerCAmelCase      ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)
							__snake_case		       =     os.path.join(
							    _lowerCAmelCase      ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]						)

							with open(_lowerCAmelCase      ,"w"      ,encoding="utf-8"						) as f:
									f.write(json.dumps(self.encoder      ,indent=2      ,sort_keys=_lowerCAmelCase      ,ensure_ascii=_lowerCAmelCase						) + "\n"						)

							__snake_case		       =     0
							with open(_lowerCAmelCase      ,"w"      ,encoding="utf-8"						) as writer:
									writer.write("#version: 0.2\n"						)
									for bpe_tokens, token_index in sorted(self.bpe_ranks.items()      ,key=lambda _lowerCAmelCase						: kv[1]						):
											if index != token_index:
													logger.warning(
													    F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
													    " Please check that the tokenizer is not corrupted!"						)
													__snake_case		       =     token_index
											writer.write(" ".join(_lowerCAmelCase						) + "\n"						)
											index += 1

							return vocab_file, merge_file




					def 							UpperCamelCase_						( self :					Dict      ,_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]
							__snake_case		       =     [self.cls_token_id]
							__snake_case		       =     [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep




					def 							UpperCamelCase_						( self :					int      ,_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 							UpperCamelCase_						( self :					Tuple      ,_lowerCAmelCase :					List[int]      ,_lowerCAmelCase :					Optional[List[int]] = None						):


							"""simple docstring"""




							__snake_case		       =     [self.sep_token_id]
							__snake_case		       =     [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]







					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					List[Any]      ,_lowerCAmelCase :					Optional[Any]=False      ,**_lowerCAmelCase :					List[str]						):


							"""simple docstring"""




							__snake_case		       =     kwargs.pop("add_prefix_space"      ,self.add_prefix_space						)
							if (is_split_into_words or add_prefix_space) and (len(_lowerCAmelCase						) > 0 and not text[0].isspace()):
									__snake_case		       =     " " + text
							return (text, kwargs)





