

'''simple docstring'''





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

from accelerate.utils.dataclasses import DistributedType





class 		__SCREAMING_SNAKE_CASE       :







				def __init__(					self    :		List[str]		,				UpperCAmelCase__    :		Optional[int]=2		,				UpperCAmelCase__    :		Optional[Any]=3		,				UpperCAmelCase__    :		List[Any]=64		,				UpperCAmelCase__    :		Any=None      ):
										'''simple docstring'''
										lowercase   :       Dict								=np.random.default_rng(UpperCAmelCase__      )
										lowercase   :       Tuple								=length
										lowercase   :       Tuple								=rng.normal(size=(length,)      ).astype(np.floataa      )
										lowercase   :       Optional[Any]								=a * self.x + b + rng.normal(scale=0.1		,				size=(length,)      ).astype(np.floataa      )







				def __len__(					self    :		List[str]      ):
										'''simple docstring'''
										return self.length







				def __getitem__(					self    :		Union[str, Any]		,				UpperCAmelCase__    :		Dict      ):
										'''simple docstring'''
										return {"x": self.x[i], "y": self.y[i]}





class 		__SCREAMING_SNAKE_CASE       ( torch.nn.Module			):







				def __init__(					self    :		Dict		,				UpperCAmelCase__    :		Tuple=0		,				UpperCAmelCase__    :		Optional[Any]=0		,				UpperCAmelCase__    :		Optional[int]=False      ):
										'''simple docstring'''
										super().__init__()
										lowercase   :       Union[str, Any]								=torch.nn.Parameter(torch.tensor([2, 3]      ).float()      )
										lowercase   :       List[Any]								=torch.nn.Parameter(torch.tensor([2, 3]      ).float()      )
										lowercase   :       Any								=True







				def    lowerCamelCase_  (					self    :		Union[str, Any]		,				UpperCAmelCase__    :		Optional[int]=None      ):
										'''simple docstring'''







										if self.first_batch:
																print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''      )
																lowercase   :       Optional[Any]								=False
										return x * self.a[0] + self.b[0]





class 		__SCREAMING_SNAKE_CASE       ( torch.nn.Module			):







				def __init__(					self    :		Any		,				UpperCAmelCase__    :		List[Any]=0		,				UpperCAmelCase__    :		Optional[Any]=0		,				UpperCAmelCase__    :		Tuple=False      ):
										'''simple docstring'''
										super().__init__()
										lowercase   :       int								=torch.nn.Parameter(torch.tensor(UpperCAmelCase__      ).float()      )
										lowercase   :       str								=torch.nn.Parameter(torch.tensor(UpperCAmelCase__      ).float()      )
										lowercase   :       Tuple								=True







				def    lowerCamelCase_  (					self    :		str		,				UpperCAmelCase__    :		int=None      ):
										'''simple docstring'''







										if self.first_batch:
																print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''      )
																lowercase   :       Optional[int]								=False
										return x * self.a + self.b






def 				_lowerCAmelCase (      __magic_name__	:    int      ,   __magic_name__	:    Union[str, Any] = 16	)							->     Dict:
						from datasets import load_dataset
						from transformers import AutoTokenizer

						lowercase   :       List[str]								=AutoTokenizer.from_pretrained('''bert-base-cased'''	)
						lowercase   :       int								={'train': 'tests/test_samples/MRPC/train.csv', 'validation': 'tests/test_samples/MRPC/dev.csv'}
						lowercase   :       str								=load_dataset('''csv'''      ,   data_files=SCREAMING_SNAKE_CASE_	)
						lowercase   :       Tuple								=datasets['train'].unique('''label'''	)

						lowercase   :       Union[str, Any]								={v: i for i, v in enumerate(SCREAMING_SNAKE_CASE_	)}

						def tokenize_function(__magic_name__	:    List[str]	):
												# max_length=None => use the model max length (it's actually the default)
												lowercase   :       Union[str, Any]								=tokenizer(
												    examples['''sentence1''']      ,   examples['''sentence2''']      ,   truncation=SCREAMING_SNAKE_CASE_      ,   max_length=SCREAMING_SNAKE_CASE_      ,   padding='''max_length'''	)
												if "label" in examples:
																		lowercase   :       Optional[Any]								=[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
						lowercase   :       Union[str, Any]								=datasets.map(
						    SCREAMING_SNAKE_CASE_      ,   batched=SCREAMING_SNAKE_CASE_      ,   remove_columns=['''sentence1''', '''sentence2''', '''label''']      ,   )

						def collate_fn(__magic_name__	:    str	):
												# 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(SCREAMING_SNAKE_CASE_      ,   padding='''max_length'''      ,   max_length=128      ,   return_tensors='''pt'''	)
												return tokenizer.pad(SCREAMING_SNAKE_CASE_      ,   padding='''longest'''      ,   return_tensors='''pt'''	)

						# Instantiate dataloaders.
						lowercase   :       int								=DataLoader(tokenized_datasets['''train''']      ,   shuffle=SCREAMING_SNAKE_CASE_      ,   collate_fn=SCREAMING_SNAKE_CASE_      ,   batch_size=2	)
						lowercase   :       List[Any]								=DataLoader(tokenized_datasets['''validation''']      ,   shuffle=SCREAMING_SNAKE_CASE_      ,   collate_fn=SCREAMING_SNAKE_CASE_      ,   batch_size=1	)

						return train_dataloader, eval_dataloader








'''simple docstring'''





import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 		__SCREAMING_SNAKE_CASE       ( lowercase__	,       unittest.TestCase			):
				lowerCamelCase_							=						None
				lowerCamelCase_							=						BloomTokenizerFast
				lowerCamelCase_							=						BloomTokenizerFast
				lowerCamelCase_							=						True
				lowerCamelCase_							=						False
				lowerCamelCase_							=						'tokenizer_file'
				lowerCamelCase_							=						{'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '<unk>', 'pad_token': '<pad>'}







				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										super().setUp()
										lowercase   :       Union[str, Any]								=BloomTokenizerFast.from_pretrained('''bigscience/tokenizer'''      )
										tokenizer.save_pretrained(self.tmpdirname      )







				def    lowerCamelCase_  (					self    :		Union[str, Any]		,				**UpperCAmelCase__    :		Any      ):
										'''simple docstring'''
										kwargs.update(self.special_tokens_map      )
										return BloomTokenizerFast.from_pretrained(self.tmpdirname		,				**UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										lowercase   :       str								=self.get_rust_tokenizer()

										lowercase   :       List[str]								=['''The quick brown fox</s>''', '''jumps over the lazy dog</s>''']
										lowercase   :       Any								=[[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]

										lowercase   :       Any								=tokenizer.batch_encode_plus(UpperCAmelCase__      )['''input_ids''']
										self.assertListEqual(UpperCAmelCase__		,				UpperCAmelCase__      )

										lowercase   :       int								=tokenizer.batch_decode(UpperCAmelCase__      )
										self.assertListEqual(UpperCAmelCase__		,				UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Tuple		,				UpperCAmelCase__    :		Any=6      ):
										'''simple docstring'''
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
																						lowercase   :       Optional[int]								=self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__		,				**UpperCAmelCase__      )
																						# tokenizer_r.pad_token = None # Hotfixing padding = None
																						# Simple input
																						lowercase   :       Tuple								='''This is a simple input'''
																						lowercase   :       int								=['''This is a simple input 1''', '''This is a simple input 2''']
																						lowercase   :       Optional[Any]								=('''This is a simple input''', '''This is a pair''')
																						lowercase   :       int								=[
																						    ('''This is a simple input 1''', '''This is a simple input 2'''),
																						    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
																						]

																						# Simple input tests
																						try:
																												tokenizer_r.encode(UpperCAmelCase__		,				max_length=UpperCAmelCase__      )
																												tokenizer_r.encode_plus(UpperCAmelCase__		,				max_length=UpperCAmelCase__      )

																												tokenizer_r.batch_encode_plus(UpperCAmelCase__		,				max_length=UpperCAmelCase__      )
																												tokenizer_r.encode(UpperCAmelCase__		,				max_length=UpperCAmelCase__      )
																												tokenizer_r.batch_encode_plus(UpperCAmelCase__		,				max_length=UpperCAmelCase__      )
																						except ValueError:
																												self.fail('''Bloom Tokenizer should be able to deal with padding'''      )

																						lowercase   :       Optional[int]								=None  # Hotfixing padding = None
																						self.assertRaises(UpperCAmelCase__		,				tokenizer_r.encode		,				UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				padding='''max_length'''      )

																						# Simple input
																						self.assertRaises(UpperCAmelCase__		,				tokenizer_r.encode_plus		,				UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				padding='''max_length'''      )

																						# Simple input
																						self.assertRaises(
																						    UpperCAmelCase__		,				tokenizer_r.batch_encode_plus		,				UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				padding='''max_length'''		,				)

																						# Pair input
																						self.assertRaises(UpperCAmelCase__		,				tokenizer_r.encode		,				UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				padding='''max_length'''      )

																						# Pair input
																						self.assertRaises(UpperCAmelCase__		,				tokenizer_r.encode_plus		,				UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				padding='''max_length'''      )

																						# Pair input
																						self.assertRaises(
																						    UpperCAmelCase__		,				tokenizer_r.batch_encode_plus		,				UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				padding='''max_length'''		,				)







				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=self.get_rust_tokenizer()
										lowercase   :       Dict								=load_dataset('''xnli'''		,				'''all_languages'''		,				split='''test'''		,				streaming=UpperCAmelCase__      )

										lowercase   :       Union[str, Any]								=next(iter(UpperCAmelCase__      )      )['''premise''']  # pick up one data
										lowercase   :       int								=list(sample_data.values()      )

										lowercase   :       Any								=list(map(tokenizer.encode		,				UpperCAmelCase__      )      )
										lowercase   :       List[str]								=[tokenizer.decode(UpperCAmelCase__		,				clean_up_tokenization_spaces=UpperCAmelCase__      ) for x in output_tokens]
										self.assertListEqual(UpperCAmelCase__		,				UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
										# any sequence length constraints. This test of the parent class will fail since it relies on the
										# maximum sequence length of the positoonal embeddings.
										self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map      )		,				1      )
										self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()      )[0]      )		,				1      )





