






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

from accelerate.utils.dataclasses import DistributedType



class 				__UpperCAmelCase		:







			def __init__(     self:  Optional[Any]				,     UpperCAmelCase_:  List[str]=2				,     UpperCAmelCase_:  Dict=3				,     UpperCAmelCase_:  Dict=64				,     UpperCAmelCase_:  List[Any]=None					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  np.random.default_rng(UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  length
									_SCREAMING_SNAKE_CASE     				=  rng.normal(size=(length,)					).astype(np.floataa					)
									_SCREAMING_SNAKE_CASE     				=  a * self.x + b + rng.normal(scale=0.1				,     size=(length,)					).astype(np.floataa					)







			def __len__(     self:  Dict					):







									'''simple docstring'''




									return self.length







			def __getitem__(     self:  Union[str, Any]				,     UpperCAmelCase_:  int					):







									'''simple docstring'''




									return {"x": self.x[i], "y": self.y[i]}



class 				__UpperCAmelCase		(torch.nn.Module   ):







			def __init__(     self:  Any				,     UpperCAmelCase_:  int=0				,     UpperCAmelCase_:  Optional[Any]=0				,     UpperCAmelCase_:  Any=False					):







									'''simple docstring'''




									super().__init__()
									_SCREAMING_SNAKE_CASE     				=  torch.nn.Parameter(torch.tensor([2, 3]					).float()					)
									_SCREAMING_SNAKE_CASE     				=  torch.nn.Parameter(torch.tensor([2, 3]					).float()					)
									_SCREAMING_SNAKE_CASE     				=  True







			def 							UpperCamelCase				(     self:  Optional[Any]				,     UpperCAmelCase_:  Optional[Any]=None					):







									'''simple docstring'''




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



class 				__UpperCAmelCase		(torch.nn.Module   ):







			def __init__(     self:  Optional[int]				,     UpperCAmelCase_:  List[Any]=0				,     UpperCAmelCase_:  Union[str, Any]=0				,     UpperCAmelCase_:  int=False					):







									'''simple docstring'''




									super().__init__()
									_SCREAMING_SNAKE_CASE     				=  torch.nn.Parameter(torch.tensor(UpperCAmelCase_					).float()					)
									_SCREAMING_SNAKE_CASE     				=  torch.nn.Parameter(torch.tensor(UpperCAmelCase_					).float()					)
									_SCREAMING_SNAKE_CASE     				=  True







			def 							UpperCamelCase				(     self:  int				,     UpperCAmelCase_:  Optional[Any]=None					):







									'''simple docstring'''




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

def 	__lowerCamelCase      (		snake_case__	,snake_case__ = 16		)							->  List[str]:






						"""simple docstring"""





						from datasets import load_dataset
						from transformers import AutoTokenizer

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

						_SCREAMING_SNAKE_CASE     				=  {v: i for i, v in enumerate(snake_case__		)}

						def tokenize_function(snake_case__		):
												# max_length=None => use the model max length (it's actually the default)
												_SCREAMING_SNAKE_CASE     				=  tokenizer(
												    examples["""sentence1"""]	,examples["""sentence2"""]	,truncation=snake_case__	,max_length=snake_case__	,padding="""max_length"""		)
												if "label" in examples:
																		_SCREAMING_SNAKE_CASE     				=  [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
						_SCREAMING_SNAKE_CASE     				=  datasets.map(
						    snake_case__	,batched=snake_case__	,remove_columns=["""sentence1""", """sentence2""", """label"""]	,)

						def collate_fn(snake_case__		):
												# 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(snake_case__	,padding="""max_length"""	,max_length=1_28	,return_tensors="""pt"""		)
												return tokenizer.pad(snake_case__	,padding="""longest"""	,return_tensors="""pt"""		)

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

						return train_dataloader, eval_dataloader









import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class 				__UpperCAmelCase		(_UpperCAmelCase	,unittest.TestCase   ):
			__snake_case    :				int									=							TransfoXLTokenizer
			__snake_case    :				Tuple									=							False
			__snake_case    :				List[Any]									=							False







			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									super().setUp()

									_SCREAMING_SNAKE_CASE     				=  [
									    """<unk>""",
									    """[CLS]""",
									    """[SEP]""",
									    """want""",
									    """unwanted""",
									    """wa""",
									    """un""",
									    """running""",
									    """,""",
									    """low""",
									    """l""",
									]
									_SCREAMING_SNAKE_CASE     				=  os.path.join(self.tmpdirname				,     VOCAB_FILES_NAMES["""vocab_file"""]					)
									with open(self.vocab_file				,     """w"""				,     encoding="""utf-8"""					) as vocab_writer:
															vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]					)					)







			def 							UpperCamelCase				(     self:  Any				,     **UpperCAmelCase_:  Optional[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  True
									return TransfoXLTokenizer.from_pretrained(self.tmpdirname				,     **UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[int]				,     UpperCAmelCase_:  List[str]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  """<unk> UNwanted , running"""
									_SCREAMING_SNAKE_CASE     				=  """<unk> unwanted, running"""
									return input_text, output_text







			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  TransfoXLTokenizer(vocab_file=self.vocab_file				,     lower_case=UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  tokenizer.tokenize("""<unk> UNwanted , running"""					)
									self.assertListEqual(UpperCAmelCase_				,     ["""<unk>""", """unwanted""", """,""", """running"""]					)

									self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_					)				,     [0, 4, 8, 7]					)







			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  TransfoXLTokenizer(lower_case=UpperCAmelCase_					)

									self.assertListEqual(
									    tokenizer.tokenize(""" \tHeLLo ! how  \n Are yoU ?  """					)				,     ["""hello""", """!""", """how""", """are""", """you""", """?"""]					)







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  TransfoXLTokenizer(lower_case=UpperCAmelCase_					)

									self.assertListEqual(
									    tokenizer.tokenize(""" \tHeLLo ! how  \n Are yoU ?  """					)				,     ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]					)







			def 							UpperCamelCase				(     self:  List[str]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  TransfoXLTokenizer(lower_case=UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  """Hello (bracket) and side-scrolled [and] Henry's $5,000 with 3.34 m. What's up!?"""
									_SCREAMING_SNAKE_CASE     				=  [
									    """Hello""",
									    """(""",
									    """bracket""",
									    """)""",
									    """and""",
									    """side""",
									    """@-@""",
									    """scrolled""",
									    """[""",
									    """and""",
									    """]""",
									    """Henry""",
									    """'s""",
									    """$""",
									    """5""",
									    """@,@""",
									    """000""",
									    """with""",
									    """3""",
									    """@.@""",
									    """34""",
									    """m""",
									    """.""",
									    """What""",
									    """'s""",
									    """up""",
									    """!""",
									    """?""",
									]

									self.assertListEqual(tokenizer.tokenize(UpperCAmelCase_					)				,     UpperCAmelCase_					)

									self.assertEqual(tokenizer.convert_tokens_to_string(UpperCAmelCase_					)				,     UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  List[str]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.get_tokenizer()
									_SCREAMING_SNAKE_CASE     				=  len(UpperCAmelCase_					)

									tokenizer.add_tokens(["""new1""", """new2"""]					)
									tokenizer.move_added_token("""new1"""				,     1					)

									# Check that moved token is not copied (duplicate)
									self.assertEqual(len(UpperCAmelCase_					)				,     original_len + 2					)
									# Check that token is moved to specified id
									self.assertEqual(tokenizer.encode("""new1"""					)				,     [1]					)
									self.assertEqual(tokenizer.decode([1]					)				,     """new1"""					)

