


'''simple docstring'''






import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class 						a__							(				a__							,							unittest.TestCase    ):





							'''simple docstring'''






							lowercase__     :  List[str]      		= XLMTokenizer
							lowercase__     :  Optional[int]      		= False







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														super().setUp()

														# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
														lowerCAmelCase__			  =      [
														    '''l''',
														    '''o''',
														    '''w''',
														    '''e''',
														    '''r''',
														    '''s''',
														    '''t''',
														    '''i''',
														    '''d''',
														    '''n''',
														    '''w</w>''',
														    '''r</w>''',
														    '''t</w>''',
														    '''lo''',
														    '''low''',
														    '''er</w>''',
														    '''low</w>''',
														    '''lowest</w>''',
														    '''newer</w>''',
														    '''wider</w>''',
														    '''<unk>''',
														]
														lowerCAmelCase__			  =      dict(zip(lowerCamelCase_   ,       range(len(lowerCamelCase_  )  )  )  )
														lowerCAmelCase__			  =      ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

														lowerCAmelCase__			  =      os.path.join(self.tmpdirname   ,       VOCAB_FILES_NAMES['''vocab_file''']  )
														lowerCAmelCase__			  =      os.path.join(self.tmpdirname   ,       VOCAB_FILES_NAMES['''merges_file''']  )
														with open(self.vocab_file   ,       '''w'''  ) as fp:
																					fp.write(json.dumps(lowerCamelCase_  )  )
														with open(self.merges_file   ,       '''w'''  ) as fp:
																					fp.write('''\n'''.join(lowerCamelCase_  )  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				Optional[Any]:
														lowerCAmelCase__			  =      '''lower newer'''
														lowerCAmelCase__			  =      '''lower newer'''
														return input_text, output_text







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														lowerCAmelCase__			  =      XLMTokenizer(self.vocab_file   ,       self.merges_file  )

														lowerCAmelCase__			  =      '''lower'''
														lowerCAmelCase__			  =      ['''low''', '''er</w>''']
														lowerCAmelCase__			  =      tokenizer.tokenize(lowerCamelCase_  )
														self.assertListEqual(lowerCamelCase_   ,       lowerCamelCase_  )

														lowerCAmelCase__			  =      tokens + ['''<unk>''']
														lowerCAmelCase__			  =      [14, 15, 20]
														self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_  )   ,       lowerCamelCase_  )







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														lowerCAmelCase__			  =      XLMTokenizer.from_pretrained('''xlm-mlm-en-2048'''  )

														lowerCAmelCase__			  =      tokenizer.encode('''sequence builders'''   ,       add_special_tokens=lowerCamelCase_  )
														lowerCAmelCase__			  =      tokenizer.encode('''multi-sequence build'''   ,       add_special_tokens=lowerCamelCase_  )

														lowerCAmelCase__			  =      tokenizer.build_inputs_with_special_tokens(lowerCamelCase_  )
														lowerCAmelCase__			  =      tokenizer.build_inputs_with_special_tokens(lowerCamelCase_   ,       lowerCamelCase_  )

														assert encoded_sentence == [0] + text + [1]
														assert encoded_pair == [0] + text + [1] + text_a + [1]



'''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