

import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
     from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


_snake_case						: Union[str, Any]											=							get_tests_dir('fixtures/test_sentencepiece.model')

_snake_case						: List[str]											=							{'target_lang': 'fi', 'source_lang': 'en'}
_snake_case						: List[str]											=							'>>zh<<'
_snake_case						: List[str]											=							'Helsinki-NLP/'

if is_torch_available():
     _snake_case						: Dict											=							'pt'
elif is_tf_available():
     _snake_case						: Any											=							'tf'
else:
     _snake_case						: Union[str, Any]											=							'jax'





@require_sentencepiece
class __SCREAMING_SNAKE_CASE     (	a__			,						unittest.TestCase		):
    SCREAMING_SNAKE_CASE__      =MarianTokenizer
    SCREAMING_SNAKE_CASE__      =False
    SCREAMING_SNAKE_CASE__      =True






    def 						__lowerCAmelCase    (       self )   ->					List[str]:
       super().setUp()
       __SCREAMING_SNAKE_CASE       =				["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
       __SCREAMING_SNAKE_CASE       =				dict(zip(lowerCamelCase_,		range(len(lowerCamelCase_ ) ) ) )
       __SCREAMING_SNAKE_CASE       =				Path(self.tmpdirname )
       save_json(lowerCamelCase_,		save_dir / VOCAB_FILES_NAMES["vocab"] )
       save_json(lowerCamelCase_,		save_dir / VOCAB_FILES_NAMES["tokenizer_config_file"] )
       if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
          copyfile(lowerCamelCase_,		save_dir / VOCAB_FILES_NAMES["source_spm"] )
          copyfile(lowerCamelCase_,		save_dir / VOCAB_FILES_NAMES["target_spm"] )

       __SCREAMING_SNAKE_CASE       =				MarianTokenizer.from_pretrained(self.tmpdirname )
       tokenizer.save_pretrained(self.tmpdirname )






    def 						__lowerCAmelCase    (       self,		**_a )   ->					MarianTokenizer:
       return MarianTokenizer.from_pretrained(self.tmpdirname,		**lowerCamelCase_ )






    def 						__lowerCAmelCase    (       self,		_a )   ->					Tuple:
       return (
           "This is a test",
           "This is a test",
       )






    def 						__lowerCAmelCase    (       self )   ->					Optional[int]:
       __SCREAMING_SNAKE_CASE       =				"</s>"
       __SCREAMING_SNAKE_CASE       =				0

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase_ ),		lowerCamelCase_ )
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase_ ),		lowerCamelCase_ )






    def 						__lowerCAmelCase    (       self )   ->					int:
       __SCREAMING_SNAKE_CASE       =				list(self.get_tokenizer().get_vocab().keys() )

       self.assertEqual(vocab_keys[0],		"</s>" )
       self.assertEqual(vocab_keys[1],		"<unk>" )
       self.assertEqual(vocab_keys[-1],		"<pad>" )
       self.assertEqual(len(lowerCamelCase_ ),		9 )






    def 						__lowerCAmelCase    (       self )   ->					List[Any]:
       self.assertEqual(self.get_tokenizer().vocab_size,		9 )






    def 						__lowerCAmelCase    (       self )   ->					List[str]:
       __SCREAMING_SNAKE_CASE       =				MarianTokenizer.from_pretrained(f'''{ORG_NAME}opus-mt-en-de''' )
       __SCREAMING_SNAKE_CASE       =				en_de_tokenizer(["I am a small frog"],		return_tensors=lowerCamelCase_ )
       self.assertIsInstance(lowerCamelCase_,		lowerCamelCase_ )
       __SCREAMING_SNAKE_CASE       =				[38, 1_21, 14, 6_97, 3_88_48, 0]
       self.assertListEqual(lowerCamelCase_,		batch.input_ids[0] )

       __SCREAMING_SNAKE_CASE       =				tempfile.mkdtemp()
       en_de_tokenizer.save_pretrained(lowerCamelCase_ )
       __SCREAMING_SNAKE_CASE       =				[x.name for x in Path(lowerCamelCase_ ).glob("*" )]
       self.assertIn("source.spm",		lowerCamelCase_ )
       MarianTokenizer.from_pretrained(lowerCamelCase_ )






    def 						__lowerCAmelCase    (       self )   ->					str:
       __SCREAMING_SNAKE_CASE       =				self.get_tokenizer()

       __SCREAMING_SNAKE_CASE       =				tok(
           ["I am a small frog" * 10_00, "I am a small frog"],		padding=lowerCamelCase_,		truncation=lowerCamelCase_,		return_tensors=lowerCamelCase_ )
       self.assertIsInstance(lowerCamelCase_,		lowerCamelCase_ )
       self.assertEqual(batch.input_ids.shape,		(2, 5_12) )






    def 						__lowerCAmelCase    (       self )   ->					int:
       __SCREAMING_SNAKE_CASE       =				self.get_tokenizer()
       __SCREAMING_SNAKE_CASE       =				tok(["I am a tiny frog", "I am a small frog"],		padding=lowerCamelCase_,		return_tensors=lowerCamelCase_ )
       self.assertIsInstance(lowerCamelCase_,		lowerCamelCase_ )
       self.assertEqual(batch_smaller.input_ids.shape,		(2, 10) )






    @slow
    def 						__lowerCAmelCase    (       self )   ->					Tuple:
       # fmt: off
       __SCREAMING_SNAKE_CASE       =				{"input_ids": [[4_34_95, 4_62, 20, 4_21_64, 13_69, 52, 4_64, 1_32, 17_03, 4_92, 13, 74_91, 3_89_99, 6, 8, 4_64, 1_32, 17_03, 4_92, 13, 46_69, 3_78_67, 13, 75_25, 27, 15_93, 9_88, 13, 3_39_72, 70_29, 6, 20, 82_51, 3_83, 2, 2_70, 58_66, 37_88, 2, 23_53, 82_51, 1_23_38, 2, 1_39_58, 3_87, 2, 36_29, 69_53, 1_88, 29_00, 2, 1_39_58, 80_11, 1_15_01, 23, 84_60, 40_73, 3_40_09, 20, 4_35, 1_14_39, 27, 8, 84_60, 40_73, 60_04, 20, 99_88, 3_75, 27, 33, 2_66, 19_45, 10_76, 13_50, 3_78_67, 32_88, 5, 5_77, 10_76, 43_74, 8, 50_82, 5, 2_64_53, 2_57, 5_56, 4_03, 2, 2_42, 1_32, 3_83, 3_16, 4_92, 8, 1_07_67, 6, 3_16, 3_04, 42_39, 3, 0], [1_48, 1_57_22, 19, 18_39, 12, 13_50, 13, 2_23_27, 50_82, 54_18, 4_75_67, 3_59_38, 59, 3_18, 1_95_52, 1_08, 21_83, 54, 1_49_76, 48_35, 32, 5_47, 11_14, 8, 3_15, 24_17, 5, 92, 1_90_88, 3, 0, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00], [36, 63_95, 1_25_70, 3_91_47, 1_15_97, 6, 2_66, 4, 4_54_05, 72_96, 3, 0, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=lowerCamelCase_,		model_name="Helsinki-NLP/opus-mt-en-de",		revision="1a8c2263da11e68e50938f97e10cd57820bd504c",		decode_kwargs={"use_source_tokenizer": True},		)






    def 						__lowerCAmelCase    (       self )   ->					List[str]:
       __SCREAMING_SNAKE_CASE       =				MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs" )

       __SCREAMING_SNAKE_CASE       =				"Tämä on testi"
       __SCREAMING_SNAKE_CASE       =				"This is a test"

       __SCREAMING_SNAKE_CASE       =				[76, 7, 20_47, 2]
       __SCREAMING_SNAKE_CASE       =				[69, 12, 11, 9_40, 2]

       __SCREAMING_SNAKE_CASE       =				tokenizer(lowerCamelCase_ ).input_ids
       self.assertListEqual(lowerCamelCase_,		lowerCamelCase_ )

       __SCREAMING_SNAKE_CASE       =				tokenizer(text_target=lowerCamelCase_ ).input_ids
       self.assertListEqual(lowerCamelCase_,		lowerCamelCase_ )

       __SCREAMING_SNAKE_CASE       =				tokenizer.decode(lowerCamelCase_,		skip_special_tokens=lowerCamelCase_ )
       self.assertEqual(lowerCamelCase_,		lowerCamelCase_ )





'''simple docstring'''






import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType




class 						a__							:





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_=2   ,       lowerCamelCase_=3   ,       lowerCamelCase_=64   ,       lowerCamelCase_=None  )			->				Dict:
														lowerCAmelCase__			  =      np.random.default_rng(lowerCamelCase_  )
														lowerCAmelCase__			  =      length
														lowerCAmelCase__			  =      rng.normal(size=(length,)  ).astype(np.floataa  )
														lowerCAmelCase__			  =      a * self.x + b + rng.normal(scale=0.1   ,       size=(length,)  ).astype(np.floataa  )







							def __len__(						self  )			->				Any:
														return self.length







							def __getitem__(						self   ,       lowerCamelCase_  )			->				List[str]:
														return {"x": self.x[i], "y": self.y[i]}




class 						a__							(				torch.nn.Module    ):





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_=0   ,       lowerCamelCase_=0   ,       lowerCamelCase_=False  )			->				List[Any]:
														super().__init__()
														lowerCAmelCase__			  =      torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
														lowerCAmelCase__			  =      torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
														lowerCAmelCase__			  =      True







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_=None  )			->				Optional[Any]:
														if self.first_batch:
																					print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""  )
																					lowerCAmelCase__			  =      False
														return x * self.a[0] + self.b[0]




class 						a__							(				torch.nn.Module    ):





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_=0   ,       lowerCamelCase_=0   ,       lowerCamelCase_=False  )			->				Any:
														super().__init__()
														lowerCAmelCase__			  =      torch.nn.Parameter(torch.tensor(lowerCamelCase_  ).float()  )
														lowerCAmelCase__			  =      torch.nn.Parameter(torch.tensor(lowerCamelCase_  ).float()  )
														lowerCAmelCase__			  =      True







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_=None  )			->				Any:
														if self.first_batch:
																					print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""  )
																					lowerCAmelCase__			  =      False
														return x * self.a + self.b


def    _snake_case  (	A					,       A = 16			)		->       Any:
							from datasets import load_dataset
							from transformers import AutoTokenizer

							lowerCAmelCase__			  =      AutoTokenizer.from_pretrained('''bert-base-cased'''			)
							lowerCAmelCase__			  =      {'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
							lowerCAmelCase__			  =      load_dataset('''csv'''					,       data_files=A			)
							lowerCAmelCase__			  =      datasets['''train'''].unique('''label'''			)

							lowerCAmelCase__			  =      {v: i for i, v in enumerate(A			)}

							def tokenize_function(A			):
														# max_length=None => use the model max length (it's actually the default)
														lowerCAmelCase__			  =      tokenizer(
														    examples['''sentence1''']					,       examples['''sentence2''']					,       truncation=A					,       max_length=A					,       padding='''max_length'''			)
														if "label" in examples:
																					lowerCAmelCase__			  =      [label_to_id[l] for l in examples['''label''']]
														return outputs

							# Apply the method we just defined to all the examples in all the splits of the dataset
							lowerCAmelCase__			  =      datasets.map(
							    A					,       batched=A					,       remove_columns=['''sentence1''', '''sentence2''', '''label''']					,       )

							def collate_fn(A			):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														if accelerator.distributed_type == DistributedType.TPU:
																					return tokenizer.pad(A					,       padding='''max_length'''					,       max_length=128					,       return_tensors='''pt'''			)
														return tokenizer.pad(A					,       padding='''longest'''					,       return_tensors='''pt'''			)

							# Instantiate dataloaders.
							lowerCAmelCase__			  =      DataLoader(tokenized_datasets['''train''']					,       shuffle=A					,       collate_fn=A					,       batch_size=2			)
							lowerCAmelCase__			  =      DataLoader(tokenized_datasets['''validation''']					,       shuffle=A					,       collate_fn=A					,       batch_size=1			)

							return train_dataloader, eval_dataloader