import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()







def 		_A(       UpperCamelCase__							:			str				,       UpperCamelCase__							:			str				,       UpperCamelCase__							:			List[str]				,       UpperCamelCase__							:			str="attention"				)    -> Tuple:
							'''simple docstring'''


							__lowercase      =    params[F'{prefix}/layers_{i}/{layer_name}/key/kernel']
							__lowercase      =    params[F'{prefix}/layers_{i}/{layer_name}/out/kernel']
							__lowercase      =    params[F'{prefix}/layers_{i}/{layer_name}/query/kernel']
							__lowercase      =    params[F'{prefix}/layers_{i}/{layer_name}/value/kernel']
							return k, o, q, v







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


							if split_mlp_wi:
														__lowercase      =    params[F'{prefix}/layers_{i}/mlp/wi_0/kernel']
														__lowercase      =    params[F'{prefix}/layers_{i}/mlp/wi_1/kernel']
														__lowercase      =    (wi_a, wi_a)
							else:
														__lowercase      =    params[F'{prefix}/layers_{i}/mlp/wi/kernel']

							__lowercase      =    params[F'{prefix}/layers_{i}/mlp/wo/kernel']
							return wi, wo







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


							return params[F'{prefix}/layers_{i}/{layer_name}/scale']







def 		_A(       UpperCamelCase__							:			dict				,       *, UpperCamelCase__							:			int				,       UpperCamelCase__							:			bool				)    -> int:
							'''simple docstring'''


							__lowercase      =    traverse_util.flatten_dict(variables['''target''']				)
							__lowercase      =    {"""/""".join(UpperCAmelCase__				): v for k, v in old.items()}

							# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
							__lowercase      =    """encoder/layers_0/mlp/wi_0/kernel""" in old
							print('''Split MLP:'''				,       UpperCAmelCase__				)

							__lowercase      =    collections.OrderedDict()

							# Shared embeddings.
							__lowercase      =    old["""token_embedder/embedding"""]

							# Encoder.
							for i in range(UpperCAmelCase__				):
														# Block i, layer 0 (Self Attention).
														__lowercase      =    tax_layer_norm_lookup(UpperCAmelCase__				,       UpperCAmelCase__				,       '''encoder'''				,       '''pre_attention_layer_norm'''				)
														__lowercase      =    tax_attention_lookup(UpperCAmelCase__				,       UpperCAmelCase__				,       '''encoder'''				,       '''attention'''				)
														__lowercase      =    layer_norm
														__lowercase      =    k.T
														__lowercase      =    o.T
														__lowercase      =    q.T
														__lowercase      =    v.T

														# Block i, layer 1 (MLP).
														__lowercase      =    tax_layer_norm_lookup(UpperCAmelCase__				,       UpperCAmelCase__				,       '''encoder'''				,       '''pre_mlp_layer_norm'''				)
														__lowercase      =    tax_mlp_lookup(UpperCAmelCase__				,       UpperCAmelCase__				,       '''encoder'''				,       UpperCAmelCase__				)
														__lowercase      =    layer_norm
														if split_mlp_wi:
																					__lowercase      =    wi[0].T
																					__lowercase      =    wi[1].T
														else:
																					__lowercase      =    wi.T
														__lowercase      =    wo.T

							__lowercase      =    old[
							    """encoder/relpos_bias/rel_embedding"""
							].T
							__lowercase      =    old["""encoder/encoder_norm/scale"""]

							if not is_encoder_only:
														# Decoder.
														for i in range(UpperCAmelCase__				):
																					# Block i, layer 0 (Self Attention).
																					__lowercase      =    tax_layer_norm_lookup(UpperCAmelCase__				,       UpperCAmelCase__				,       '''decoder'''				,       '''pre_self_attention_layer_norm'''				)
																					__lowercase      =    tax_attention_lookup(UpperCAmelCase__				,       UpperCAmelCase__				,       '''decoder'''				,       '''self_attention'''				)
																					__lowercase      =    layer_norm
																					__lowercase      =    k.T
																					__lowercase      =    o.T
																					__lowercase      =    q.T
																					__lowercase      =    v.T

																					# Block i, layer 1 (Cross Attention).
																					__lowercase      =    tax_layer_norm_lookup(UpperCAmelCase__				,       UpperCAmelCase__				,       '''decoder'''				,       '''pre_cross_attention_layer_norm'''				)
																					__lowercase      =    tax_attention_lookup(UpperCAmelCase__				,       UpperCAmelCase__				,       '''decoder'''				,       '''encoder_decoder_attention'''				)
																					__lowercase      =    layer_norm
																					__lowercase      =    k.T
																					__lowercase      =    o.T
																					__lowercase      =    q.T
																					__lowercase      =    v.T

																					# Block i, layer 2 (MLP).
																					__lowercase      =    tax_layer_norm_lookup(UpperCAmelCase__				,       UpperCAmelCase__				,       '''decoder'''				,       '''pre_mlp_layer_norm'''				)
																					__lowercase      =    tax_mlp_lookup(UpperCAmelCase__				,       UpperCAmelCase__				,       '''decoder'''				,       UpperCAmelCase__				)
																					__lowercase      =    layer_norm
																					if split_mlp_wi:
																												__lowercase      =    wi[0].T
																												__lowercase      =    wi[1].T
																					else:
																												__lowercase      =    wi.T
																					__lowercase      =    wo.T

														__lowercase      =    old["""decoder/decoder_norm/scale"""]
														__lowercase      =    old[
														    """decoder/relpos_bias/rel_embedding"""
														].T

														# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
														if "decoder/logits_dense/kernel" in old:
																					__lowercase      =    old["""decoder/logits_dense/kernel"""].T

							return new







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


							__lowercase      =    collections.OrderedDict([(k, torch.from_numpy(v.copy()				)) for (k, v) in converted_params.items()]				)

							# Add what is missing.
							if "encoder.embed_tokens.weight" not in state_dict:
														__lowercase      =    state_dict["""shared.weight"""]

							if not is_encoder_only:
														if "decoder.embed_tokens.weight" not in state_dict:
																					__lowercase      =    state_dict["""shared.weight"""]

														if "lm_head.weight" not in state_dict:  # For old 1.0 models.
																					print('''Using shared word embeddings as lm_head.'''				)
																					__lowercase      =    state_dict["""shared.weight"""]

							return state_dict







def 		_A(       UpperCamelCase__							:			int				,       UpperCamelCase__							:			str				,       UpperCamelCase__							:			Dict				,       UpperCamelCase__							:			Optional[int]				)    -> Any:
							'''simple docstring'''


							__lowercase      =    checkpoints.load_tax_checkpoint(UpperCAmelCase__				)
							__lowercase      =    convert_tax_to_pytorch(UpperCAmelCase__				,       num_layers=config.num_layers				,       is_encoder_only=UpperCAmelCase__				)
							__lowercase      =    make_state_dict(UpperCAmelCase__				,       UpperCAmelCase__				)
							model.load_state_dict(UpperCAmelCase__				,       strict=UpperCAmelCase__				)







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


							__lowercase      =    TaConfig.from_json_file(UpperCAmelCase__				)
							print(F'Building PyTorch model from configuration: {config}'				)
							# Non-v1.1 checkpoints could also use T5Model, but this works for all.
							# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
							if is_encoder_only:
														__lowercase      =    TaEncoderModel(UpperCAmelCase__				)
							else:
														__lowercase      =    TaForConditionalGeneration(UpperCAmelCase__				)

							# Load weights from tf checkpoint
							load_tax_weights_in_ta(UpperCAmelCase__				,       UpperCAmelCase__				,       UpperCAmelCase__				,       UpperCAmelCase__				)

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}'				)
							model.save_pretrained(UpperCAmelCase__				)

							# Verify that we can load the checkpoint.
							model.from_pretrained(UpperCAmelCase__				)
							print('''Done'''				)


if __name__ == "__main__":
			UpperCAmelCase__				     = argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
			# Required parameters
			parser.add_argument(
			    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
			)
			parser.add_argument(
			    "--config_file",
			    default=None,
			    type=str,
			    required=True,
			    help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
			)
			parser.add_argument(
			    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			parser.add_argument(
			    "--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
			)
			UpperCAmelCase__				     = parser.parse_args()
			convert_tax_checkpoint_to_pytorch(
			    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
			)












import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
			from ...tokenization_utils_base import TextInput
from ...utils import logging


UpperCAmelCase__				     = logging.get_logger(__name__)

UpperCAmelCase__				     = {"vocab_file": "spiece.model"}

UpperCAmelCase__				     = {
    "vocab_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
    }
}


# TODO(PVP) - this should be removed in Transformers v5
UpperCAmelCase__				     = {
    "t5-small": 512,
    "t5-base": 512,
    "t5-large": 512,
    "t5-3b": 512,
    "t5-11b": 512,
}

UpperCAmelCase__				     = "▁"







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

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






						def __init__( self :     Optional[Any]				,							lowerCamelCase__ :     int				,							lowerCamelCase__ :     Optional[int]="</s>"				,							lowerCamelCase__ :     Optional[int]="<unk>"				,							lowerCamelCase__ :     Dict="<pad>"				,							lowerCamelCase__ :     Optional[int]=100				,							lowerCamelCase__ :     int=None				,							lowerCamelCase__ :     Optional[Dict[str, Any]] = None				,							lowerCamelCase__ :     int=True				,							**lowerCamelCase__ :     Optional[int]				,							)    ->						None:
													"""simple docstring"""


													if extra_ids > 0 and additional_special_tokens is None:
																				__lowercase      =    [f'<extra_id_{i}>' for i in range(lowerCamelCase__				)]
													elif extra_ids > 0 and additional_special_tokens is not None:
																				# Check that we have the right number of extra_id special tokens
																				__lowercase      =    len(set(filter(lambda lowerCamelCase__				: bool('''extra_id''' in str(lowerCamelCase__				)				)				,							lowerCamelCase__				)				)				)
																				if extra_tokens != extra_ids:
																											raise ValueError(
																											    f'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
																											    ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
																											    ''' tokens'''				)
													if legacy:
																				logger.warning_once(
																				    f'You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'
																				    ''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'''				)

													__lowercase      =    legacy
													__lowercase      =    {} if sp_model_kwargs is None else sp_model_kwargs

													super().__init__(
													    eos_token=lowerCamelCase__				,							unk_token=lowerCamelCase__				,							pad_token=lowerCamelCase__				,							extra_ids=lowerCamelCase__				,							additional_special_tokens=lowerCamelCase__				,							sp_model_kwargs=self.sp_model_kwargs				,							legacy=lowerCamelCase__				,							**lowerCamelCase__				,							)

													__lowercase      =    vocab_file
													__lowercase      =    extra_ids

													__lowercase      =    spm.SentencePieceProcessor(**self.sp_model_kwargs				)
													self.sp_model.Load(lowerCamelCase__				)






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


													if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
																				__lowercase      =    TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
																				if init_max_model_length is not None and init_max_model_length != max_model_length:
																											return init_max_model_length
																				elif init_max_model_length is None:
																											warnings.warn(
																											    '''This tokenizer was incorrectly instantiated with a model max length of'''
																											    f' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
																											    ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
																											    ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
																											    f' {pretrained_model_name_or_path} automatically truncating your input to'
																											    f' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
																											    f' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
																											    ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
																											    ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''				,							lowerCamelCase__				,							)

													return max_model_length






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


													return self.sp_model.get_piece_size() + self._extra_ids






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


													__lowercase      =    {self.convert_ids_to_tokens(lowerCamelCase__				): i for i in range(self.vocab_size				)}
													vocab.update(self.added_tokens_encoder				)
													return vocab






						def     UpperCAmelCase_      ( self :     int				,							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__				)

													# normal case: some special tokens
													if token_ids_a is None:
																				return ([0] * len(lowerCamelCase__				)) + [1]
													return ([0] * len(lowerCamelCase__				)) + [1] + ([0] * len(lowerCamelCase__				)) + [1]






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


													return list(
													    set(filter(lambda lowerCamelCase__				: bool(re.search(R'''<extra_id_\d+>'''				,							lowerCamelCase__				)				) is not None				,							self.additional_special_tokens				)				)				)






						def     UpperCAmelCase_      ( self :     Dict				)    ->						Optional[int]:
													"""simple docstring"""


													return [self._convert_token_to_id(lowerCamelCase__				) for token in self.get_sentinel_tokens()]






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


													if len(lowerCamelCase__				) > 0 and token_ids[-1] == self.eos_token_id:
																				warnings.warn(
																				    f'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
																				    ''' eos tokens being added.'''				)
																				return token_ids
													else:
																				return token_ids + [self.eos_token_id]






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


													__lowercase      =    [self.eos_token_id]

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






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


													__lowercase      =    self._add_eos_if_not_present(lowerCamelCase__				)
													if token_ids_a is None:
																				return token_ids_a
													else:
																				__lowercase      =    self._add_eos_if_not_present(lowerCamelCase__				)
																				return token_ids_a + token_ids_a






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


													__lowercase      =    self.__dict__.copy()
													__lowercase      =    None
													return state






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


													__lowercase      =    d

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

													__lowercase      =    spm.SentencePieceProcessor(**self.sp_model_kwargs				)
													self.sp_model.Load(self.vocab_file				)






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


													if not self.legacy:
																				__lowercase      =    SPIECE_UNDERLINE + text.replace(lowerCamelCase__				,							''' '''				)
													return super().tokenize(lowerCamelCase__				,							**lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     Dict				,							lowerCamelCase__ :     Tuple				,							**lowerCamelCase__ :     Tuple				)    ->						Dict:
													"""simple docstring"""


													if not self.legacy:
																				__lowercase      =    text.startswith(lowerCamelCase__				)
																				if is_first:
																											__lowercase      =    text[1:]

													__lowercase      =    self.sp_model.encode(lowerCamelCase__				,							out_type=lowerCamelCase__				)

													if not self.legacy and not is_first and not text.startswith(''' '''				) and tokens[0].startswith(lowerCamelCase__				):
																				__lowercase      =    ([tokens[0][1:]] if len(tokens[0]				) > 1 else []) + tokens[1:]
													return tokens






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


													if token.startswith('''<extra_id_'''				):
																				__lowercase      =    re.match(R'''<extra_id_(\d+)>'''				,							lowerCamelCase__				)
																				__lowercase      =    int(match.group(1				)				)
																				return self.vocab_size - num - 1
													return self.sp_model.piece_to_id(lowerCamelCase__				)






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


													if index < self.sp_model.get_piece_size():
																				__lowercase      =    self.sp_model.IdToPiece(lowerCamelCase__				)
													else:
																				__lowercase      =    f'<extra_id_{self.vocab_size - 1 - index}>'
													return token






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


													__lowercase      =    []
													__lowercase      =    ''''''
													__lowercase      =    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
																											__lowercase      =    True
																											__lowercase      =    []
																				else:
																											current_sub_tokens.append(lowerCamelCase__				)
																											__lowercase      =    False
													out_string += self.sp_model.decode(lowerCamelCase__				)
													return out_string.strip()






						def     UpperCAmelCase_      ( self :     Tuple				,							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
													__lowercase      =    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:
																											__lowercase      =    self.sp_model.serialized_model_proto()
																											fi.write(lowerCamelCase__				)

													return (out_vocab_file,)






