

"""simple docstring"""





import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_ ):
	if isinstance(A_						,     A_ ):
		lowerCAmelCase__	:						List[Any]  				=	np.full((len(A_ ), sequence_length, 2)						,     A_ )
	else:
		lowerCAmelCase__	:						List[str]  				=	np.full((len(A_ ), sequence_length)						,     A_ )

	for i, tensor in enumerate(A_ ):
		if padding_side == "right":
			if isinstance(A_						,     A_ ):
				lowerCAmelCase__	:						Any  				=	tensor[:sequence_length]
			else:
				lowerCAmelCase__	:						Optional[int]  				=	tensor[:sequence_length]
		else:
			if isinstance(A_						,     A_ ):
				lowerCAmelCase__	:						str  				=	tensor[:sequence_length]
			else:
				lowerCAmelCase__	:						Optional[Any]  				=	tensor[:sequence_length]

	return out_tensor.tolist()

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	lowerCAmelCase__	:						Tuple  				=	ord(A_ )
	if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
		return True
	lowerCAmelCase__	:						int  				=	unicodedata.category(A_ )
	if cat.startswith('''P''' ):
		return True
	return False







@dataclass
class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       42
					lowercase__				 =       True
					lowercase__				 =       None
					lowercase__				 =       None
					lowercase__				 =       -100
					lowercase__				 =       "pt"







					def        __lowerCAmelCase (       self			:				Optional[Any]   ,lowercase_			:				Union[str, Any]		):
						import torch

						lowerCAmelCase__	:						int  				=	'''label''' if '''label''' in features[0].keys() else '''labels'''
						lowerCAmelCase__	:						str  				=	[feature[label_name] for feature in features] if label_name in features[0].keys() else None
						lowerCAmelCase__	:						Tuple  				=	self.tokenizer.pad(
						    lowercase_   ,padding=self.padding   ,max_length=self.max_length   ,pad_to_multiple_of=self.pad_to_multiple_of   ,return_tensors='''pt''' if labels is None else None   ,)

						if labels is None:
							return batch

						lowerCAmelCase__	:						List[str]  				=	torch.tensor(batch['''entity_ids''']		).shape[1]
						lowerCAmelCase__	:						Optional[Any]  				=	self.tokenizer.padding_side
						if padding_side == "right":
							lowerCAmelCase__	:						List[str]  				=	[
							    list(lowercase_		) + [self.label_pad_token_id] * (sequence_length - len(lowercase_		)) for label in labels
							]
						else:
							lowerCAmelCase__	:						int  				=	[
							    [self.label_pad_token_id] * (sequence_length - len(lowercase_		)) + list(lowercase_		) for label in labels
							]

						lowerCAmelCase__	:						Tuple  				=	[feature['''ner_tags'''] for feature in features]
						lowerCAmelCase__	:						str  				=	padding_tensor(lowercase_   ,-1   ,lowercase_   ,lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	[feature['''original_entity_spans'''] for feature in features]
						lowerCAmelCase__	:						Optional[Any]  				=	padding_tensor(lowercase_   ,(-1, -1)   ,lowercase_   ,lowercase_		)
						lowerCAmelCase__	:						List[Any]  				=	{k: torch.tensor(lowercase_   ,dtype=torch.intaa		) for k, v in batch.items()}

						return batch










import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property




@unittest.skipUnless(os.path.exists(lowerCAmelCase__     )    ,				"""Tatoeba directory does not exist."""     )
class 		UpperCamelCase__	(unittest.TestCase     ):

    '''simple docstring'''



    @cached_property
    def     _lowercase     (    self     )     ->      int:
           lowerCamelCase   :      str			 =					tempfile.mkdtemp()
           return TatoebaConverter(save_dir=UpperCamelCase__     )







    @slow
    def     _lowercase     (    self     )     ->      List[Any]:
           self.resolver.convert_models(["heb-eng"]     )







    @slow
    def     _lowercase     (    self     )     ->      Tuple:
           lowerCamelCase ,      lowerCamelCase   :      Dict			 =					self.resolver.write_model_card("opus-mt-he-en"     ,  dry_run=UpperCamelCase__     )
           assert mmeta["long_pair"] == "heb-eng"






