
import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 	_lowerCAmelCase					(	__a					,      unittest.TestCase       ):
				_lowercase					=MgpstrTokenizer
				_lowercase					=False
				_lowercase					={}
				_lowercase					=False




				def      __a						(					self     )				->      List[str]:
							super().setUp()

							# fmt: off
							lowerCAmelCase_						=	["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
							# fmt: on
							lowerCAmelCase_						=	dict(zip(_UpperCamelCase				,				range(len(_UpperCamelCase     )     )     )     )

							lowerCAmelCase_						=	os.path.join(self.tmpdirname				,				VOCAB_FILES_NAMES["vocab_file"]     )
							with open(self.vocab_file				,				"w"				,				encoding="utf-8"     ) as fp:
										fp.write(json.dumps(_UpperCamelCase     ) + "\n"     )




				def      __a						(					self				,				**_UpperCamelCase     )				->      List[str]:
							return MgpstrTokenizer.from_pretrained(self.tmpdirname				,				**_UpperCamelCase     )




				def      __a						(					self				,				_UpperCamelCase     )				->      Optional[Any]:
							lowerCAmelCase_						=	"tester"
							lowerCAmelCase_						=	"tester"
							return input_text, output_text




				@unittest.skip("MGP-STR always lower cases letters."     )
				def      __a						(					self     )				->      Dict:
							pass




				def      __a						(					self     )				->      Any:
							lowerCAmelCase_						=	self.get_tokenizers(do_lower_case=_UpperCamelCase     )
							for tokenizer in tokenizers:
										with self.subTest(f"""{tokenizer.__class__.__name__}"""     ):
													lowerCAmelCase_						=	"[SPECIAL_TOKEN]"

													tokenizer.add_special_tokens({"cls_token": special_token}     )
													lowerCAmelCase_						=	tokenizer.encode([special_token]				,				add_special_tokens=_UpperCamelCase     )
													self.assertEqual(len(_UpperCamelCase     )				,				1     )

													lowerCAmelCase_						=	tokenizer.decode(_UpperCamelCase				,				skip_special_tokens=_UpperCamelCase     )
													self.assertTrue(special_token not in decoded     )




				def      __a						(					self     )				->      Optional[int]:
							lowerCAmelCase_						=	self.get_tokenizers()
							for tokenizer in tokenizers:
										with self.subTest(f"""{tokenizer.__class__.__name__}"""     ):
													lowerCAmelCase_	,  lowerCAmelCase_						=	self.get_input_output_texts(_UpperCamelCase     )

													lowerCAmelCase_						=	tokenizer.tokenize(_UpperCamelCase     )
													lowerCAmelCase_						=	tokenizer.convert_tokens_to_ids(_UpperCamelCase     )
													lowerCAmelCase_						=	tokenizer.encode(_UpperCamelCase				,				add_special_tokens=_UpperCamelCase     )
													self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )

													lowerCAmelCase_						=	tokenizer.convert_ids_to_tokens(_UpperCamelCase     )
													self.assertNotEqual(len(_UpperCamelCase     )				,				0     )
													lowerCAmelCase_						=	tokenizer.decode(_UpperCamelCase     )
													self.assertIsInstance(_UpperCamelCase				,				_UpperCamelCase     )

													self.assertEqual(text_a.replace(" "				,				""     )				,				_UpperCamelCase     )




				@unittest.skip("MGP-STR tokenizer only handles one sequence."     )
				def      __a						(					self     )				->      List[Any]:
							pass




				@unittest.skip("inputs cannot be pretokenized in MgpstrTokenizer"     )
				def      __a						(					self     )				->      List[str]:
							pass





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

from accelerate.utils.dataclasses import DistributedType





class 	_lowerCAmelCase					:




				def __init__(					self				,				_UpperCamelCase=2				,				_UpperCamelCase=3				,				_UpperCamelCase=64				,				_UpperCamelCase=None     )				->      Optional[Any]:
							lowerCAmelCase_						=	np.random.default_rng(_UpperCamelCase     )
							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     )				->      int:
							return self.length




				def __getitem__(					self				,				_UpperCamelCase     )				->      Dict:
							return {"x": self.x[i], "y": self.y[i]}





class 	_lowerCAmelCase					(	torch.nn.Module       ):




				def __init__(					self				,				_UpperCamelCase=0				,				_UpperCamelCase=0				,				_UpperCamelCase=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      __a						(					self				,				_UpperCamelCase=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[0] + self.b[0]





class 	_lowerCAmelCase					(	torch.nn.Module       ):




				def __init__(					self				,				_UpperCamelCase=0				,				_UpperCamelCase=0				,				_UpperCamelCase=False     )				->      Optional[int]:
							super().__init__()
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor(_UpperCamelCase     ).float()     )
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor(_UpperCamelCase     ).float()     )
							lowerCAmelCase_						=	True




				def      __a						(					self				,				_UpperCamelCase=None     )				->      int:
							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    lowerCamelCase__				(				__lowerCAmelCase  :  List[Any]    ,							__lowerCAmelCase  :  int = 16				):




			"""simple docstring"""


			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=__lowerCAmelCase				)
			lowerCAmelCase_						=	datasets["train"].unique("label"				)

			lowerCAmelCase_						=	{v: i for i, v in enumerate(__lowerCAmelCase				)}

			def tokenize_function(__lowerCAmelCase  :  List[Any]				):
						# max_length=None => use the model max length (it's actually the default)
						lowerCAmelCase_						=	tokenizer(
						    examples["sentence1"]    ,							examples["sentence2"]    ,							truncation=__lowerCAmelCase    ,							max_length=__lowerCAmelCase    ,							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(
			    __lowerCAmelCase    ,							batched=__lowerCAmelCase    ,							remove_columns=["sentence1", "sentence2", "label"]    ,							)

			def collate_fn(__lowerCAmelCase  :  Optional[int]				):
						# 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(__lowerCAmelCase    ,							padding="max_length"    ,							max_length=128    ,							return_tensors="pt"				)
						return tokenizer.pad(__lowerCAmelCase    ,							padding="longest"    ,							return_tensors="pt"				)

			# Instantiate dataloaders.
			lowerCAmelCase_						=	DataLoader(tokenized_datasets["train"]    ,							shuffle=__lowerCAmelCase    ,							collate_fn=__lowerCAmelCase    ,							batch_size=2				)
			lowerCAmelCase_						=	DataLoader(tokenized_datasets["validation"]    ,							shuffle=__lowerCAmelCase    ,							collate_fn=__lowerCAmelCase    ,							batch_size=1				)

			return train_dataloader, eval_dataloader



