






import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__lowerCamelCase   :     str 		= """
@inproceedings{xu-etal-2016-optimizing,
    title = {Optimizing Statistical Machine Translation for Text Simplification},
    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
    journal = {Transactions of the Association for Computational Linguistics},
    volume = {4},
    year={2016},
    url = {https://www.aclweb.org/anthology/Q16-1029},
    pages = {401--415
},
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

__lowerCamelCase   :     str 		= """\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
"""


__lowerCamelCase   :     Optional[int] 		= """
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
Args:
    sources: list of source sentences where each sentence should be a string.
    predictions: list of predicted sentences where each sentence should be a string.
    references: list of lists of reference sentences where each sentence should be a string.
Returns:
    sari: sari score
    sacrebleu: sacrebleu score
    exact: exact score

Examples:
    >>> sources=[\"About 95 species are currently accepted .\"]
    >>> predictions=[\"About 95 you now get in .\"]
    >>> references=[[\"About 95 species are currently known .\"]]
    >>> wiki_split = datasets.load_metric(\"wiki_split\")
    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}
"""







def 	SCREAMING_SNAKE_CASE							(					snake_case_   :				List[Any]  ):

	def remove_articles(snake_case_   :				Optional[Any]  ):
		snake_case__					: str  =    re.compile(R"\b(a|an|the)\b"	,    re.UNICODE  )
		return re.sub(__UpperCamelCase	,    " "	,    __UpperCamelCase  )

	def white_space_fix(snake_case_   :				Optional[Any]  ):
		return " ".join(text.split()  )

	def remove_punc(snake_case_   :				Dict  ):
		snake_case__					: Union[str, Any]  =    set(string.punctuation  )
		return "".join(ch for ch in text if ch not in exclude  )

	def lower(snake_case_   :				List[Any]  ):
		return text.lower()

	return white_space_fix(remove_articles(remove_punc(lower(__UpperCamelCase  )  )  )  )







def 	SCREAMING_SNAKE_CASE							(					snake_case_   :				str	,    snake_case_   :				int  ):
	return int(normalize_answer(__UpperCamelCase  ) == normalize_answer(__UpperCamelCase  )  )







def 	SCREAMING_SNAKE_CASE							(					snake_case_   :				Dict	,    snake_case_   :				int  ):
	snake_case__					: List[Any]  =    [any(compute_exact(__UpperCamelCase	,    __UpperCamelCase  ) for ref in refs  ) for pred, refs in zip(__UpperCamelCase	,    __UpperCamelCase  )]
	return (sum(__UpperCamelCase  ) / len(__UpperCamelCase  )) * 100







def 	SCREAMING_SNAKE_CASE							(					snake_case_   :				Any	,    snake_case_   :				int	,    snake_case_   :				Dict	,    snake_case_   :				Union[str, Any]  ):
	snake_case__					: Any  =    [rgram for rgrams in rgramslist for rgram in rgrams]
	snake_case__					: Union[str, Any]  =    Counter(__UpperCamelCase  )

	snake_case__					: Tuple  =    Counter(__UpperCamelCase  )
	snake_case__					: List[Any]  =    Counter()
	for sgram, scount in sgramcounter.items():
		snake_case__					: Dict  =    scount * numref

	snake_case__					: str  =    Counter(__UpperCamelCase  )
	snake_case__					: Any  =    Counter()
	for cgram, ccount in cgramcounter.items():
		snake_case__					: Union[str, Any]  =    ccount * numref

	# KEEP
	snake_case__					: List[str]  =    sgramcounter_rep & cgramcounter_rep
	snake_case__					: int  =    keepgramcounter_rep & rgramcounter
	snake_case__					: List[Any]  =    sgramcounter_rep & rgramcounter

	snake_case__					: Tuple  =    0
	snake_case__					: Optional[int]  =    0
	for keepgram in keepgramcountergood_rep:
		keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
		# Fix an alleged bug [2] in the keep score computation.
		# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
		keeptmpscorea += keepgramcountergood_rep[keepgram]
	# Define 0/0=1 instead of 0 to give higher scores for predictions that match
	#      a target exactly.
	snake_case__					: Union[str, Any]  =    1
	snake_case__					: Tuple  =    1
	if len(__UpperCamelCase  ) > 0:
		snake_case__					: Any  =    keeptmpscorea / len(__UpperCamelCase  )
	if len(__UpperCamelCase  ) > 0:
		# Fix an alleged bug [2] in the keep score computation.
		# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
		snake_case__					: str  =    keeptmpscorea / sum(keepgramcounterall_rep.values()  )
	snake_case__					: Union[str, Any]  =    0
	if keepscore_precision > 0 or keepscore_recall > 0:
		snake_case__					: Optional[Any]  =    2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

	# DELETION
	snake_case__					: int  =    sgramcounter_rep - cgramcounter_rep
	snake_case__					: List[Any]  =    delgramcounter_rep - rgramcounter
	snake_case__					: Tuple  =    sgramcounter_rep - rgramcounter
	snake_case__					: int  =    0
	snake_case__					: Optional[int]  =    0
	for delgram in delgramcountergood_rep:
		deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
		deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
	# Define 0/0=1 instead of 0 to give higher scores for predictions that match
	# a target exactly.
	snake_case__					: Any  =    1
	if len(__UpperCamelCase  ) > 0:
		snake_case__					: Tuple  =    deltmpscorea / len(__UpperCamelCase  )

	# ADDITION
	snake_case__					: int  =    set(__UpperCamelCase  ) - set(__UpperCamelCase  )
	snake_case__					: List[str]  =    set(__UpperCamelCase  ) & set(__UpperCamelCase  )
	snake_case__					: Tuple  =    set(__UpperCamelCase  ) - set(__UpperCamelCase  )

	snake_case__					: Tuple  =    0
	for addgram in addgramcountergood:
		addtmpscore += 1

	# Define 0/0=1 instead of 0 to give higher scores for predictions that match
	# a target exactly.
	snake_case__					: str  =    1
	snake_case__					: Any  =    1
	if len(__UpperCamelCase  ) > 0:
		snake_case__					: Optional[int]  =    addtmpscore / len(__UpperCamelCase  )
	if len(__UpperCamelCase  ) > 0:
		snake_case__					: Any  =    addtmpscore / len(__UpperCamelCase  )
	snake_case__					: Dict  =    0
	if addscore_precision > 0 or addscore_recall > 0:
		snake_case__					: List[Any]  =    2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

	return (keepscore, delscore_precision, addscore)







def 	SCREAMING_SNAKE_CASE							(					snake_case_   :				Optional[Any]	,    snake_case_   :				List[str]	,    snake_case_   :				Any  ):
	snake_case__					: Optional[int]  =    len(__UpperCamelCase  )

	snake_case__					: str  =    ssent.split(" "  )
	snake_case__					: Tuple  =    csent.split(" "  )
	snake_case__					: List[Any]  =    []
	snake_case__					: Dict  =    []
	snake_case__					: int  =    []
	snake_case__					: Union[str, Any]  =    []
	snake_case__					: str  =    []
	snake_case__					: Dict  =    []

	snake_case__					: Any  =    []
	snake_case__					: List[str]  =    []
	snake_case__					: str  =    []
	snake_case__					: Tuple  =    []
	for rsent in rsents:
		snake_case__					: List[Any]  =    rsent.split(" "  )
		snake_case__					: str  =    []
		snake_case__					: Dict  =    []
		snake_case__					: str  =    []
		ragramslist.append(__UpperCamelCase  )
		for i in range(0	,    len(__UpperCamelCase  ) - 1  ):
			if i < len(__UpperCamelCase  ) - 1:
				snake_case__					: Optional[Any]  =    ragrams[i] + " " + ragrams[i + 1]
				ragrams.append(__UpperCamelCase  )
			if i < len(__UpperCamelCase  ) - 2:
				snake_case__					: List[str]  =    ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
				ragrams.append(__UpperCamelCase  )
			if i < len(__UpperCamelCase  ) - 3:
				snake_case__					: Tuple  =    ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2] + " " + ragrams[i + 3]
				ragrams.append(__UpperCamelCase  )
		ragramslist.append(__UpperCamelCase  )
		ragramslist.append(__UpperCamelCase  )
		ragramslist.append(__UpperCamelCase  )

	for i in range(0	,    len(__UpperCamelCase  ) - 1  ):
		if i < len(__UpperCamelCase  ) - 1:
			snake_case__					: List[Any]  =    sagrams[i] + " " + sagrams[i + 1]
			sagrams.append(__UpperCamelCase  )
		if i < len(__UpperCamelCase  ) - 2:
			snake_case__					: Union[str, Any]  =    sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
			sagrams.append(__UpperCamelCase  )
		if i < len(__UpperCamelCase  ) - 3:
			snake_case__					: int  =    sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
			sagrams.append(__UpperCamelCase  )

	for i in range(0	,    len(__UpperCamelCase  ) - 1  ):
		if i < len(__UpperCamelCase  ) - 1:
			snake_case__					: Optional[Any]  =    cagrams[i] + " " + cagrams[i + 1]
			cagrams.append(__UpperCamelCase  )
		if i < len(__UpperCamelCase  ) - 2:
			snake_case__					: Optional[Any]  =    cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
			cagrams.append(__UpperCamelCase  )
		if i < len(__UpperCamelCase  ) - 3:
			snake_case__					: List[Any]  =    cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
			cagrams.append(__UpperCamelCase  )

	(snake_case__)					: Optional[int]  =    SARIngram(__UpperCamelCase	,    __UpperCamelCase	,    __UpperCamelCase	,    __UpperCamelCase  )
	(snake_case__)					: Optional[int]  =    SARIngram(__UpperCamelCase	,    __UpperCamelCase	,    __UpperCamelCase	,    __UpperCamelCase  )
	(snake_case__)					: Union[str, Any]  =    SARIngram(__UpperCamelCase	,    __UpperCamelCase	,    __UpperCamelCase	,    __UpperCamelCase  )
	(snake_case__)					: Dict  =    SARIngram(__UpperCamelCase	,    __UpperCamelCase	,    __UpperCamelCase	,    __UpperCamelCase  )
	snake_case__					: Any  =    sum([keepascore, keepascore, keepascore, keepascore]  ) / 4
	snake_case__					: Union[str, Any]  =    sum([delascore, delascore, delascore, delascore]  ) / 4
	snake_case__					: Union[str, Any]  =    sum([addascore, addascore, addascore, addascore]  ) / 4
	snake_case__					: Any  =    (avgkeepscore + avgdelscore + avgaddscore) / 3
	return finalscore







def 	SCREAMING_SNAKE_CASE							(					snake_case_   :				Optional[int]	,    snake_case_   :				Optional[int] = True	,    snake_case_   :				Dict = "13a"	,    snake_case_   :				Union[str, Any] = True  ):
	# Normalization is requried for the ASSET dataset (one of the primary
	# datasets in sentence simplification) to allow using space
	# to split the sentence. Even though Wiki-Auto and TURK datasets,
	# do not require normalization, we do it for consistency.
	# Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
	# [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

	if lowercase:
		snake_case__					: Union[str, Any]  =    sentence.lower()

	if tokenizer in ["13a", "intl"]:
		if version.parse(sacrebleu.__version__  ).major >= 2:
			snake_case__					: List[Any]  =    sacrebleu.metrics.bleu._get_tokenizer(__UpperCamelCase  )()(__UpperCamelCase  )
		else:
			snake_case__					: Optional[int]  =    sacrebleu.TOKENIZERS[tokenizer]()(__UpperCamelCase  )
	elif tokenizer == "moses":
		snake_case__					: List[Any]  =    sacremoses.MosesTokenizer().tokenize(__UpperCamelCase	,    return_str=__UpperCamelCase	,    escape=__UpperCamelCase  )
	elif tokenizer == "penn":
		snake_case__					: Any  =    sacremoses.MosesTokenizer().penn_tokenize(__UpperCamelCase	,    return_str=__UpperCamelCase  )
	else:
		snake_case__					: str  =    sentence

	if not return_str:
		snake_case__					: Union[str, Any]  =    normalized_sent.split()

	return normalized_sent







def 	SCREAMING_SNAKE_CASE							(					snake_case_   :				List[str]	,    snake_case_   :				Optional[int]	,    snake_case_   :				Dict  ):
	if not (len(__UpperCamelCase  ) == len(__UpperCamelCase  ) == len(__UpperCamelCase  )):
		raise ValueError("Sources length must match predictions and references lengths."  )
	snake_case__					: Tuple  =    0
	for src, pred, refs in zip(__UpperCamelCase	,    __UpperCamelCase	,    __UpperCamelCase  ):
		sari_score += SARIsent(normalize(__UpperCamelCase  )	,    normalize(__UpperCamelCase  )	,    [normalize(__UpperCamelCase  ) for sent in refs]  )
	snake_case__					: str  =    sari_score / len(__UpperCamelCase  )
	return 100 * sari_score







def 	SCREAMING_SNAKE_CASE							(					snake_case_   :				Optional[int]	,    snake_case_   :				Optional[int]	,    snake_case_   :				Union[str, Any]="exp"	,    snake_case_   :				Optional[int]=None	,    snake_case_   :				Optional[int]=False	,    snake_case_   :				str=False	,    snake_case_   :				Dict=False	,    ):
	snake_case__					: Union[str, Any]  =    len(references[0]  )
	if any(len(__UpperCamelCase  ) != references_per_prediction for refs in references  ):
		raise ValueError("Sacrebleu requires the same number of references for each prediction"  )
	snake_case__					: Union[str, Any]  =    [[refs[i] for refs in references] for i in range(__UpperCamelCase  )]
	snake_case__					: Optional[Any]  =    sacrebleu.corpus_bleu(
	    __UpperCamelCase	,    __UpperCamelCase	,    smooth_method=__UpperCamelCase	,    smooth_value=__UpperCamelCase	,    force=__UpperCamelCase	,    lowercase=__UpperCamelCase	,    use_effective_order=__UpperCamelCase	,    )
	return output.score





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,  _KWARGS_DESCRIPTION		)
class 				SCREAMING_SNAKE_CASE__		(							datasets.Metric		):





					"""simple docstring"""






					def 			_lowercase	(   self	:    int			):
						return datasets.MetricInfo(
						    description=_DESCRIPTION				,						citation=_CITATION				,						inputs_description=_KWARGS_DESCRIPTION				,						features=datasets.Features(
						        {
						            "predictions": datasets.Value("string"				,						id="sequence"			),
						            "references": datasets.Sequence(datasets.Value("string"				,						id="sequence"			)				,						id="references"			),
						        }			)				,						codebase_urls=[
						        "https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py",
						        "https://github.com/cocoxu/simplification/blob/master/SARI.py",
						        "https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py",
						        "https://github.com/mjpost/sacreBLEU",
						    ]				,						reference_urls=[
						        "https://www.aclweb.org/anthology/Q16-1029.pdf",
						        "https://github.com/mjpost/sacreBLEU",
						        "https://en.wikipedia.org/wiki/BLEU",
						        "https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
						    ]				,						)






					def 			_lowercase	(   self	:    Union[str, Any]				,						__A	:    Optional[int]				,						__A	:    Union[str, Any]				,						__A	:    Any			):
						snake_case__					: Optional[int]  =    {}
						result.update({"sari": compute_sari(sources=__UpperCAmelCase				,						predictions=__UpperCAmelCase				,						references=__UpperCAmelCase			)}			)
						result.update({"sacrebleu": compute_sacrebleu(predictions=__UpperCAmelCase				,						references=__UpperCAmelCase			)}			)
						result.update({"exact": compute_em(predictions=__UpperCAmelCase				,						references=__UpperCAmelCase			)}			)
						return result








import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase )	->						Optional[Any]:
				if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
								# expert layer
								a__	:							List[str]      						= flax_key_tuple[:-1] + ("weight",)
								a__	:							Optional[int]      						= torch.permute(__UpperCamelCase	,				(0, 2, 1) )
				elif flax_key_tuple[-1] == "kernel" and ".".join(__UpperCamelCase ):
								# linear layer
								a__	:							Tuple      						= flax_key_tuple[:-1] + ("weight",)
								a__	:							Tuple      						= flax_tensor.T
				elif flax_key_tuple[-1] in ["scale", "embedding"]:
								a__	:							str      						= flax_key_tuple[:-1] + ("weight",)

				return flax_key_tuple, flax_tensor






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						List[Any]:
				if "metadata" in layer:
								a__	:							Union[str, Any]      						= layer.split("metadata" )
								a__	:							Any      						= "".join(split_layer[0] )[:-1]
								a__	:							Optional[int]      						= [tuple(("metadata" + split_layer[1]).split("/" ) )]
				elif "kvstore" in layer:
								a__	:							Optional[int]      						= layer.split("kvstore" )
								a__	:							Union[str, Any]      						= "".join(split_layer[0] )[:-1]
								a__	:							Optional[Any]      						= [tuple(("kvstore" + split_layer[1]).split("/" ) )]

				else:
								a__	:							Any      						= layer.split("/" )
								a__	:							Union[str, Any]      						= "/".join(split_layer[:-1] )
								a__	:							Tuple      						= (split_layer[-1],)

				if "kvstore/path" in layer:
								a__	:							Tuple      						= F'{switch_checkpoint_path}/{checkpoint_info[layer]}'
				elif "kvstore/driver" in layer:
								a__	:							int      						= "file"
				else:
								a__	:							List[str]      						= checkpoint_info[layer]

				return curr_real_layer_name, split_layer, content






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase )	->						Optional[int]:
				a__	:							List[Any]      						= rename_keys(__UpperCamelCase )
				a__	:							Tuple      						= {}
				for k, v in current_block.items():
								a__	:							str      						= v
				a__	:							Optional[int]      						= new_current_block
				torch.save(__UpperCamelCase	,				__UpperCamelCase )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase = WEIGHTS_NAME )	->						Any:
				a__	:							Optional[Any]      						= convert_file_size_to_int(__UpperCamelCase )
				a__	:							Tuple      						= []
				a__	:							Dict      						= {}
				a__	:							str      						= 0
				a__	:							Tuple      						= 0

				os.makedirs(__UpperCamelCase	,				exist_ok=__UpperCamelCase )
				with gfile.GFile(switch_checkpoint_path + "/checkpoint"	,				"rb" ) as fp:
								a__	:							Any      						= serialization.msgpack_restore(fp.read() )["optimizer"]["target"]
								a__	:							Tuple      						= flatten_dict(__UpperCamelCase	,				sep="/" )

				a__	:							Any      						= {}
				for layer in checkpoint_info.keys():
								a__						,   a__						,   a__	:							Optional[Any]      						= get_key_and_tensorstore_dict(
								    __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )
								if curr_real_layer_name in all_layers:
												a__	:							int      						= content
								else:
												a__	:							List[Any]      						= {split_layer[-1]: content}

				for key in all_layers.keys():
								# open tensorstore file
								a__	:							Tuple      						= ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
								a__	:							Tuple      						= torch.tensor(__UpperCamelCase )
								a__	:							str      						= raw_weights.numel() * dtype_byte_size(raw_weights.dtype )

								# use the renaming pattern from the small conversion scripts
								a__						,   a__	:							int      						= rename_base_flax_keys(tuple(key.split("/" ) )	,				__UpperCamelCase )
								a__	:							Union[str, Any]      						= "/".join(__UpperCamelCase )

								# If this weight is going to tip up over the maximal size, we split.
								if current_block_size + weight_size > max_shard_size:
												a__	:							int      						= os.path.join(
												    __UpperCamelCase	,				weights_name.replace(".bin"	,				F'-{len(__UpperCamelCase )+1:05d}-of-???.bin' ) )
												rename_and_save_block(__UpperCamelCase	,				__UpperCamelCase )
												sharded_state_dicts.append(current_block.keys() )
												del current_block
												a__	:							int      						= {}
												a__	:							Optional[int]      						= 0

								a__	:							List[str]      						= raw_weights.to(getattr(__UpperCamelCase	,				__UpperCamelCase ) )
								current_block_size += weight_size
								total_size += weight_size

				# Add the last block
				a__	:							Optional[int]      						= os.path.join(__UpperCamelCase	,				weights_name.replace(".bin"	,				F'-{len(__UpperCamelCase )+1:05d}-of-???.bin' ) )
				rename_and_save_block(__UpperCamelCase	,				__UpperCamelCase )
				sharded_state_dicts.append(current_block.keys() )

				# If we only have one shard, we return it
				if len(__UpperCamelCase ) == 1:
								return {weights_name: sharded_state_dicts[0]}, None

				# Otherwise, let's build the index
				a__	:							int      						= {}
				a__	:							Tuple      						= {}
				for idx, shard in enumerate(__UpperCamelCase ):
								a__	:							Tuple      						= weights_name.replace(
								    ".bin"	,				F'-{idx+1:05d}-of-{len(__UpperCamelCase ):05d}.bin' )  # len(sharded_state_dicts):05d}
								a__	:							Optional[int]      						= os.path.join(__UpperCamelCase	,				weights_name.replace(".bin"	,				F'-{idx+1:05d}-of-???.bin' ) )
								os.rename(__UpperCamelCase	,				os.path.join(__UpperCamelCase	,				__UpperCamelCase ) )
								a__	:							List[str]      						= shard
								for key in shard:
												a__	:							Tuple      						= shard_file

    # Add the metadata
				a__	:							int      						= {"total_size": total_size}
				a__	:							List[str]      						= {"metadata": metadata, "weight_map": weight_map}

				with open(os.path.join(__UpperCamelCase	,				__UpperCamelCase )	,				"w"	,				encoding="utf-8" ) as f:
								a__	:							List[str]      						= json.dumps(__UpperCamelCase	,				indent=2	,				sort_keys=__UpperCamelCase ) + "\n"
								f.write(__UpperCamelCase )

				return metadata, index


if __name__ == "__main__":
						lowerCamelCase		       =      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--switch_t5x_checkpoint_path""",
						    default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600""",
						    type=str,
						    required=False,
						    help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
						)
						parser.add_argument("""--max_shard_size""", default="""10GB""", required=False, help="""Max shard size""")
						parser.add_argument("""--dtype""", default="""bfloat16""", type=str, required=False, help="""dtype of the saved model""")
						parser.add_argument(
						    """--pytorch_dump_folder_path""",
						    default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted""",
						    type=str,
						    required=False,
						    help="""Path to the output pytorch model.""",
						)
						lowerCamelCase		       =      parser.parse_args()
						shard_on_the_fly(
						    args.switch_tax_checkpoint_path,
						    args.pytorch_dump_folder_path,
						    args.max_shard_size,
						    args.dtype,
						)






def     SCREAMING_SNAKE_CASE(      )	->						int:
				from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

				a__	:							Any      						= SwitchTransformersConfig.from_pretrained("google/switch-base-8" )
				config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted" )
				a__	:							List[str]      						= SwitchTransformersForConditionalGeneration.from_pretrained(
				    "/home/arthur_huggingface_co/transformers/switch_converted"	,				device_map="auto" )

				a__	:							str      						= TaTokenizer.from_pretrained("t5-small" )
				a__	:							Tuple      						= "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."

				a__	:							Optional[int]      						= tokenizer(__UpperCamelCase	,				return_tensors="pt" ).input_ids
				a__	:							Optional[Any]      						= model.generate(__UpperCamelCase	,				decoder_start_token_id=0 )
				print(tokenizer.decode(out[0] ) )


