






'''simple docstring'''







import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
_A     :      List[str]   					={
    '''/attention/''': '''/0/SelfAttention/''',
    '''/self_attention/''': '''/0/SelfAttention/''',
    '''/encoder_decoder_attention/''': '''/1/EncDecAttention/''',
    '''value''': '''v''',
    '''query''': '''q''',
    '''key''': '''k''',
    '''out''': '''o''',
    '''pre_self_attention_layer_norm''': '''0/layer_norm''',
    '''pre_cross_attention_layer_norm''': '''1/layer_norm''',
    '''pre_attention_layer_norm''': '''0/layer_norm''',  # previously 1, but seems wrong
    '''token_embedder''': '''shared''',
    '''encoder_norm''': '''final_layer_norm''',
    '''decoder_norm''': '''final_layer_norm''',
    '''relpos_bias/rel_embedding''': '''block/0/layer/0/SelfAttention/relative_attention_bias/weight''',
    '''router/router_weights/w/''': '''router/classifier/''',
    '''roer/roer_weights/w/''': '''router/classifier/''',
    '''logits_dense''': '''lm_head''',
}





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Optional[Any]:
							# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
							# the original model
							lowerCamelCase__ :     int	  =	list(s_dict.keys()				)
							for key in keys:
														lowerCamelCase__ :     str	  =	r""".*/layers_(\d+)"""
														lowerCamelCase__ :     int	  =	key
														if re.match(UpperCamelCase    ,      UpperCamelCase				):
																					lowerCamelCase__ :     List[str]	  =	re.sub(r"""layers_(\d+)"""    ,      r"""block/\1/layer"""    ,      UpperCamelCase				)

														lowerCamelCase__ :     Tuple	  =	r"""(encoder|decoder)\/"""

														if re.match(UpperCamelCase    ,      UpperCamelCase				):
																					lowerCamelCase__ :     List[Any]	  =	re.match(UpperCamelCase    ,      UpperCamelCase				).groups()
																					if groups[0] == "encoder":
																												lowerCamelCase__ :     Optional[Any]	  =	re.sub(r"""/mlp/"""    ,      r"""/1/mlp/"""    ,      UpperCamelCase				)
																												lowerCamelCase__ :     int	  =	re.sub(r"""/pre_mlp_layer_norm/"""    ,      r"""/1/layer_norm/"""    ,      UpperCamelCase				)

																					elif groups[0] == "decoder":
																												lowerCamelCase__ :     List[str]	  =	re.sub(r"""/mlp/"""    ,      r"""/2/mlp/"""    ,      UpperCamelCase				)
																												lowerCamelCase__ :     Optional[Any]	  =	re.sub(r"""/pre_mlp_layer_norm/"""    ,      r"""/2/layer_norm/"""    ,      UpperCamelCase				)

        # 2. Convert other classic mappings
														for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
																					if old_key in new_key:
																												lowerCamelCase__ :     Union[str, Any]	  =	new_key.replace(UpperCamelCase    ,      UpperCamelCase				)

														print(f'''{key} -> {new_key}'''				)
														lowerCamelCase__ :     str	  =	s_dict.pop(UpperCamelCase				)

							if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
														lowerCamelCase__ :     List[Any]	  =	s_dict[
														    """encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
														].T
							if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
														lowerCamelCase__ :     str	  =	s_dict[
														    """decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
														].T

							# 3. Take extra care of the EXPERTS layer
							for key in list(s_dict.keys()				):
														if "expert" in key:
																					lowerCamelCase__ :     Union[str, Any]	  =	s_dict[key].shape[0]
																					lowerCamelCase__ :     Tuple	  =	s_dict[key]
																					for idx in range(UpperCamelCase				):
																												lowerCamelCase__ :     Union[str, Any]	  =	expert_weihts[idx]
																												print(f'''{key} -> {key.replace('expert/'    ,      'nested fstring'				)}'''				)

																					s_dict.pop(UpperCamelCase				)

							return s_dict


_A     :      str   					={
    '''NUM_ENCODER_LAYERS''': '''num_layers''',
    '''NUM_DECODER_LAYERS''': '''num_decoder_layers''',
    '''NUM_HEADS''': '''num_heads''',
    '''HEAD_DIM''': '''d_kv''',
    '''EMBED_DIM''': '''d_model''',
    '''MLP_DIM''': '''d_ff''',
    '''NUM_SELECTED_EXPERTS''': '''num_selected_experts''',
    '''NUM_ENCODER_SPARSE_LAYERS''': '''num_sparse_encoder_layers''',
    '''NUM_DECODER_SPARSE_LAYERS''': '''num_sparse_decoder_layers''',
    '''dense.MlpBlock.activations''': '''feed_forward_proj''',
}





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	Dict:
							# Convert a google style config to the hugging face fromat
							import regex as re

							with open(UpperCamelCase    ,      """r"""				) as f:
														lowerCamelCase__ :     Dict	  =	f.read()

							lowerCamelCase__ :     int	  =	re.findall(r"""(.*) = ([0-9.]*)"""    ,      UpperCamelCase				)
							lowerCamelCase__ :     int	  =	{}
							for param, value in regex_match:
														if param in GIN_TO_CONFIG_MAPPING and value != "":
																					lowerCamelCase__ :     Optional[Any]	  =	float(UpperCamelCase				) if """.""" in value else int(UpperCamelCase				)

							lowerCamelCase__ :     Optional[int]	  =	re.findall(r"""(.*activations) = \(\'(.*)\',\)"""    ,      UpperCamelCase				)[0]
							lowerCamelCase__ :     Optional[Any]	  =	str(activation[1]				)

							lowerCamelCase__ :     str	  =	num_experts
							lowerCamelCase__ :     str	  =	SwitchTransformersConfig(**UpperCamelCase				)
							return config





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase=None    ,      UpperCamelCase="./"    ,      UpperCamelCase=8				)				->	Tuple:
							# Initialise PyTorch model

							print(f'''Loading flax weights from : {flax_checkpoint_path}'''				)
							lowerCamelCase__ :     Union[str, Any]	  =	checkpoints.load_tax_checkpoint(UpperCamelCase				)

							if gin_file is not None:
														lowerCamelCase__ :     Dict	  =	convert_gin_to_config(UpperCamelCase    ,      UpperCamelCase				)
							else:
														lowerCamelCase__ :     Dict	  =	SwitchTransformersConfig.from_pretrained(UpperCamelCase				)

							lowerCamelCase__ :     List[Any]	  =	SwitchTransformersForConditionalGeneration(UpperCamelCase				)

							lowerCamelCase__ :     List[str]	  =	flax_params["""target"""]
							lowerCamelCase__ :     Union[str, Any]	  =	flatten_dict(UpperCamelCase    ,      sep="""/"""				)
							lowerCamelCase__ :     str	  =	rename_keys(UpperCamelCase				)
							lowerCamelCase__ :     Optional[Any]	  =	unflatten_dict(UpperCamelCase    ,      sep="""/"""				)

							# Load the flax params in the PT model
							load_flax_weights_in_pytorch_model(UpperCamelCase    ,      UpperCamelCase				)

							print(f'''Save PyTorch model to {pytorch_dump_path}'''				)
							pt_model.save_pretrained(UpperCamelCase				)


if __name__ == "__main__":
			_A     :      List[str]   					=argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--switch_t5x_checkpoint_path''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'''
			        ''' model architecture. If not provided, a `gin_file` has to be provided.'''
			    ),
			)
			parser.add_argument(
			    '''--gin_file''',
			    default=None,
			    type=str,
			    required=False,
			    help='''Path to the gin config file. If not provided, a `config_file` has to be passed   ''',
			)
			parser.add_argument(
			    '''--config_name''', default=None, type=str, required=False, help='''Config name of SwitchTransformers model.'''
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output pytorch model.'''
			)
			parser.add_argument('''--num_experts''', default=8, type=int, required=False, help='''Number of experts''')
			_A     :      int   					=parser.parse_args()
			convert_flax_checkpoint_to_pytorch(
			    args.switch_tax_checkpoint_path,
			    args.config_name,
			    args.gin_file,
			    args.pytorch_dump_folder_path,
			    args.num_experts,
			)









'''simple docstring'''







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


_A     :      Union[str, Any]   					=logging.get_logger(__name__)

_A     :      Optional[Any]   					={'''vocab_file''': '''spiece.model'''}

_A     :      Optional[Any]   					={
    '''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
_A     :      Union[str, Any]   					={
    '''t5-small''': 512,
    '''t5-base''': 512,
    '''t5-large''': 512,
    '''t5-3b''': 512,
    '''t5-11b''': 512,
}

_A     :      int   					='''▁'''

class _lowercase     (    _lowercase		):
							a    				=	VOCAB_FILES_NAMES
							a    				=	PRETRAINED_VOCAB_FILES_MAP
							a    				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a    				=	["""input_ids""", """attention_mask"""]




							def __init__(							self:    int      ,							UpperCamelCase__:    int      ,							UpperCamelCase__:    List[str]="</s>"      ,							UpperCamelCase__:    Optional[Any]="<unk>"      ,							UpperCamelCase__:    Dict="<pad>"      ,							UpperCamelCase__:    List[Any]=100      ,							UpperCamelCase__:    Dict=None      ,							UpperCamelCase__:    Optional[Dict[str, Any]] = None      ,							UpperCamelCase__:    Union[str, Any]=True      ,							**UpperCamelCase__:    Dict      ,							):
														# Add extra_ids to the special token list
														if extra_ids > 0 and additional_special_tokens is None:
																					lowerCamelCase__ :     Union[str, Any]	  =	[F'''<extra_id_{i}>''' for i in range(UpperCamelCase__					)]
														elif extra_ids > 0 and additional_special_tokens is not None:
																					# Check that we have the right number of extra_id special tokens
																					lowerCamelCase__ :     Optional[Any]	  =	len(set(filter(lambda UpperCamelCase__					: bool("""extra_id""" in str(UpperCamelCase__					)					)      ,							UpperCamelCase__					)					)					)
																					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"""					)

														lowerCamelCase__ :     Optional[int]	  =	legacy
														lowerCamelCase__ :     Any	  =	{} if sp_model_kwargs is None else sp_model_kwargs

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

														lowerCamelCase__ :     Tuple	  =	vocab_file
														lowerCamelCase__ :     Dict	  =	extra_ids

														lowerCamelCase__ :     Optional[Any]	  =	spm.SentencePieceProcessor(**self.sp_model_kwargs					)
														self.sp_model.Load(UpperCamelCase__					)




							@staticmethod
							def 		lowerCamelCase_    (							UpperCamelCase__:    Union[str, Any]      ,							UpperCamelCase__:    Optional[Any]      ,							UpperCamelCase__:    int					):
														if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
																					lowerCamelCase__ :     Any	  =	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."""      ,							UpperCamelCase__      ,							)

														return max_model_length




							@property
							def 		lowerCamelCase_    (							self:    Any					):
														return self.sp_model.get_piece_size() + self._extra_ids




							def 		lowerCamelCase_    (							self:    Dict					):
														lowerCamelCase__ :     str	  =	{self.convert_ids_to_tokens(UpperCamelCase__					): i for i in range(self.vocab_size					)}
														vocab.update(self.added_tokens_encoder					)
														return vocab




							def 		lowerCamelCase_    (							self:    Optional[Any]      ,							UpperCamelCase__:    List[int]      ,							UpperCamelCase__:    Optional[List[int]] = None      ,							UpperCamelCase__:    bool = False					):
														if already_has_special_tokens:
																					return super().get_special_tokens_mask(
																					    token_ids_a=UpperCamelCase__      ,							token_ids_a=UpperCamelCase__      ,							already_has_special_tokens=UpperCamelCase__					)

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




							def 		lowerCamelCase_    (							self:    Dict					):
														return list(
														    set(filter(lambda UpperCamelCase__					: bool(re.search(R"""<extra_id_\d+>"""      ,							UpperCamelCase__					)					) is not None      ,							self.additional_special_tokens					)					)					)




							def 		lowerCamelCase_    (							self:    str					):
														return [self._convert_token_to_id(UpperCamelCase__					) for token in self.get_sentinel_tokens()]




							def 		lowerCamelCase_    (							self:    Optional[Any]      ,							UpperCamelCase__:    List[int]					):
														if len(UpperCamelCase__					) > 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 		lowerCamelCase_    (							self:    str      ,							UpperCamelCase__:    List[int]      ,							UpperCamelCase__:    Optional[List[int]] = None					):
														lowerCamelCase__ :     Optional[Any]	  =	[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 		lowerCamelCase_    (							self:    int      ,							UpperCamelCase__:    List[int]      ,							UpperCamelCase__:    Optional[List[int]] = None					):
														lowerCamelCase__ :     List[str]	  =	self._add_eos_if_not_present(UpperCamelCase__					)
														if token_ids_a is None:
																					return token_ids_a
														else:
																					lowerCamelCase__ :     int	  =	self._add_eos_if_not_present(UpperCamelCase__					)
																					return token_ids_a + token_ids_a




							def __getstate__(							self:    List[str]					):
														lowerCamelCase__ :     Optional[int]	  =	self.__dict__.copy()
														lowerCamelCase__ :     Optional[Any]	  =	None
														return state




							def __setstate__(							self:    List[Any]      ,							UpperCamelCase__:    Any					):
														lowerCamelCase__ :     Tuple	  =	d

														# for backward compatibility
														if not hasattr(self      ,							"""sp_model_kwargs"""					):
																					lowerCamelCase__ :     str	  =	{}

														lowerCamelCase__ :     Optional[Any]	  =	spm.SentencePieceProcessor(**self.sp_model_kwargs					)
														self.sp_model.Load(self.vocab_file					)




							def 		lowerCamelCase_    (							self:    Optional[Any]      ,							UpperCamelCase__:    "TextInput"      ,							**UpperCamelCase__:    List[str]					):
														# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
														# the beginning of the text
														if not self.legacy:
																					lowerCamelCase__ :     List[Any]	  =	SPIECE_UNDERLINE + text.replace(UpperCamelCase__      ,							""" """					)
														return super().tokenize(UpperCamelCase__      ,							**UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    List[Any]      ,							UpperCamelCase__:    str      ,							**UpperCamelCase__:    str					):
														if not self.legacy:
																					lowerCamelCase__ :     List[Any]	  =	text.startswith(UpperCamelCase__					)
																					if is_first:
																												lowerCamelCase__ :     Optional[int]	  =	text[1:]

														lowerCamelCase__ :     int	  =	self.sp_model.encode(UpperCamelCase__      ,							out_type=UpperCamelCase__					)

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




							def 		lowerCamelCase_    (							self:    Optional[int]      ,							UpperCamelCase__:    Optional[Any]					):
														if token.startswith("""<extra_id_"""					):
																					lowerCamelCase__ :     List[Any]	  =	re.match(R"""<extra_id_(\d+)>"""      ,							UpperCamelCase__					)
																					lowerCamelCase__ :     List[Any]	  =	int(match.group(1					)					)
																					return self.vocab_size - num - 1
														return self.sp_model.piece_to_id(UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    List[str]      ,							UpperCamelCase__:    int					):
														if index < self.sp_model.get_piece_size():
																					lowerCamelCase__ :     str	  =	self.sp_model.IdToPiece(UpperCamelCase__					)
														else:
																					lowerCamelCase__ :     Tuple	  =	F'''<extra_id_{self.vocab_size - 1 - index}>'''
														return token




							def 		lowerCamelCase_    (							self:    str      ,							UpperCamelCase__:    Tuple					):
														lowerCamelCase__ :     str	  =	[]
														lowerCamelCase__ :     Any	  =	""""""
														lowerCamelCase__ :     Union[str, Any]	  =	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(UpperCamelCase__					) + token
																												lowerCamelCase__ :     Dict	  =	True
																												lowerCamelCase__ :     str	  =	[]
																					else:
																												current_sub_tokens.append(UpperCamelCase__					)
																												lowerCamelCase__ :     List[str]	  =	False
														out_string += self.sp_model.decode(UpperCamelCase__					)
														return out_string.strip()




							def 		lowerCamelCase_    (							self:    Optional[int]      ,							UpperCamelCase__:    str      ,							UpperCamelCase__:    Optional[str] = None					):
														if not os.path.isdir(UpperCamelCase__					):
																					logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
																					return
														lowerCamelCase__ :     List[Any]	  =	os.path.join(
														    UpperCamelCase__      ,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

														if os.path.abspath(self.vocab_file					) != os.path.abspath(UpperCamelCase__					) and os.path.isfile(self.vocab_file					):
																					copyfile(self.vocab_file      ,							UpperCamelCase__					)
														elif not os.path.isfile(self.vocab_file					):
																					with open(UpperCamelCase__      ,							"""wb"""					) as fi:
																												lowerCamelCase__ :     Union[str, Any]	  =	self.sp_model.serialized_model_proto()
																												fi.write(UpperCamelCase__					)

														return (out_vocab_file,)

