



import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin
class 					a	(  lowercase_	,      unittest.TestCase       ):
  UpperCamelCase	:				List[Any]     						=					TransfoXLTokenizer
  UpperCamelCase	:				List[Any]     						=					False
  UpperCamelCase	:				Union[str, Any]     						=					False



  def 	lowerCamelCase__       (						self :					int		)     ->					Any:


     '''simple docstring'''




     super().setUp()

     SCREAMING_SNAKE_CASE_: Optional[int]     			=[
         """<unk>""",
         """[CLS]""",
         """[SEP]""",
         """want""",
         """unwanted""",
         """wa""",
         """un""",
         """running""",
         """,""",
         """low""",
         """l""",
     ]
     SCREAMING_SNAKE_CASE_: int     			=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 	lowerCamelCase__       (						self :					Any       ,		**lowerCAmelCase :					Optional[int]		)     ->					List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Any     			=True
     return TransfoXLTokenizer.from_pretrained(self.tmpdirname       ,		**lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					List[str]       ,		lowerCAmelCase :					str		)     ->					str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Dict     			="""<unk> UNwanted , running"""
     SCREAMING_SNAKE_CASE_: str     			="""<unk> unwanted, running"""
     return input_text, output_text



  def 	lowerCamelCase__       (						self :					Optional[int]		)     ->					List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Dict     			=TransfoXLTokenizer(vocab_file=self.vocab_file       ,		lower_case=lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: Tuple     			=tokenizer.tokenize("""<unk> UNwanted , running"""		)
     self.assertListEqual(lowerCAmelCase       ,		["""<unk>""", """unwanted""", """,""", """running"""]		)

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



  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: str     			=TransfoXLTokenizer(lower_case=lowerCAmelCase		)

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



  def 	lowerCamelCase__       (						self :					Dict		)     ->					int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[int]     			=TransfoXLTokenizer(lower_case=lowerCAmelCase		)

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



  def 	lowerCamelCase__       (						self :					Any		)     ->					str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: int     			=TransfoXLTokenizer(lower_case=lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[Any]     			="""Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?"""
     SCREAMING_SNAKE_CASE_: int     			=[
         """Hello""",
         """(""",
         """bracket""",
         """)""",
         """and""",
         """side""",
         """@-@""",
         """scrolled""",
         """[""",
         """and""",
         """]""",
         """Henry""",
         """\'s""",
         """$""",
         """5""",
         """@,@""",
         """000""",
         """with""",
         """3""",
         """@.@""",
         """34""",
         """m""",
         """.""",
         """What""",
         """\'s""",
         """up""",
         """!""",
         """?""",
     ]

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

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





  def 	lowerCamelCase__       (						self :					str		)     ->					Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[int]     			=self.get_tokenizer()
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=len(lowerCAmelCase		)

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

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








"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType
class 					a	:



  def __init__(						self :					Union[str, Any]       ,		lowerCAmelCase :					List[str]=2       ,		lowerCAmelCase :					int=3       ,		lowerCAmelCase :					Optional[Any]=64       ,		lowerCAmelCase :					Union[str, Any]=None		)     ->					Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=np.random.default_rng(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=length
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=rng.normal(size=(length,)		).astype(np.floataa		)
     SCREAMING_SNAKE_CASE_: Tuple     			=a * self.x + b + rng.normal(scale=0.1       ,		size=(length,)		).astype(np.floataa		)



  def __len__(						self :					List[Any]		)     ->					str:


     '''simple docstring'''




     return self.length





  def __getitem__(						self :					Union[str, Any]       ,		lowerCAmelCase :					Any		)     ->					List[str]:


     '''simple docstring'''




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







class 					a	(  torch.nn.Module       ):



  def __init__(						self :					Optional[int]       ,		lowerCAmelCase :					str=0       ,		lowerCAmelCase :					List[Any]=0       ,		lowerCAmelCase :					Optional[int]=False		)     ->					Tuple:


     '''simple docstring'''




     super().__init__()
     SCREAMING_SNAKE_CASE_: int     			=torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
     SCREAMING_SNAKE_CASE_: int     			=torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
     SCREAMING_SNAKE_CASE_: Dict     			=True





  def 	lowerCamelCase__       (						self :					str       ,		lowerCAmelCase :					Tuple=None		)     ->					int:


     '''simple docstring'''




     if self.first_batch:
        print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''		)
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=False
     return x * self.a[0] + self.b[0]







class 					a	(  torch.nn.Module       ):



  def __init__(						self :					Union[str, Any]       ,		lowerCAmelCase :					Any=0       ,		lowerCAmelCase :					List[Any]=0       ,		lowerCAmelCase :					List[Any]=False		)     ->					str:


     '''simple docstring'''




     super().__init__()
     SCREAMING_SNAKE_CASE_: List[str]     			=torch.nn.Parameter(torch.tensor(lowerCAmelCase		).float()		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=torch.nn.Parameter(torch.tensor(lowerCAmelCase		).float()		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=True





  def 	lowerCamelCase__       (						self :					List[str]       ,		lowerCAmelCase :					int=None		)     ->					Any:


     '''simple docstring'''




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







def      __magic_name__				(							lowercase							,			lowercase = 16 ):
   from datasets import load_dataset
   from transformers import AutoTokenizer

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

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

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

   def collate_fn(lowercase ):
      # 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(lowercase							,			padding="""max_length"""							,			max_length=128							,			return_tensors="""pt""" )
      return tokenizer.pad(lowercase							,			padding="""longest"""							,			return_tensors="""pt""" )

   # Instantiate dataloaders.
   SCREAMING_SNAKE_CASE_: Optional[int]     			=DataLoader(tokenized_datasets["""train"""]							,			shuffle=lowercase							,			collate_fn=lowercase							,			batch_size=2 )
   SCREAMING_SNAKE_CASE_: Dict     			=DataLoader(tokenized_datasets["""validation"""]							,			shuffle=lowercase							,			collate_fn=lowercase							,			batch_size=1 )

   return train_dataloader, eval_dataloader



