






"""simple docstring"""




from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
 import torch

_snake_case					   =     logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
 from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):

 def     lowerCAmelCase__  (  cls       ):



   '''simple docstring'''
   return cls

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__ = None  ,     UpperCamelCase__ = None  ,     UpperCamelCase__ = None  ,     UpperCamelCase__ = None  ,     ):



  '''simple docstring'''
  _a						: List[Any]    =			device
  _a						: Dict    =			shape[0]

  _a						: List[str]    =			layout or torch.strided
  _a						: Tuple    =			device or torch.device("""cpu"""       )

  if generator is not None:
    _a						: Optional[Any]    =			generator.device.type if not isinstance(UpperCamelCase__  ,     UpperCamelCase__       ) else generator[0].device.type
    if gen_device_type != device.type and gen_device_type == "cpu":
      _a						: Optional[Any]    =			"""cpu"""
      if device != "mps":
        logger.info(
            F"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
            F""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
            F""" slighly speed up this function by passing a generator that was created on the {device} device."""       )
    elif gen_device_type != device.type and gen_device_type == "cuda":
      raise ValueError(F"""Cannot generate a {device} tensor from a generator of type {gen_device_type}."""       )

  if isinstance(UpperCamelCase__  ,     UpperCamelCase__       ):
    _a						: Optional[int]    =			(1,) + shape[1:]
    _a						: Tuple    =			[
        torch.randn(UpperCamelCase__  ,     generator=generator[i]  ,     device=UpperCamelCase__  ,     dtype=UpperCamelCase__  ,     layout=UpperCamelCase__       )
        for i in range(UpperCamelCase__       )
    ]
    _a						: Dict    =			torch.cat(UpperCamelCase__  ,     dim=0       ).to(UpperCamelCase__       )
  else:
    _a						: Optional[int]    =			torch.randn(UpperCamelCase__  ,     generator=UpperCamelCase__  ,     device=UpperCamelCase__  ,     dtype=UpperCamelCase__  ,     layout=UpperCamelCase__       ).to(UpperCamelCase__       )

  return latents



def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  if is_torch_version("""<"""  ,     """2.0.0"""       ) or not hasattr(UpperCamelCase__  ,     """_dynamo"""       ):
    return False
  return isinstance(UpperCamelCase__  ,     torch._dynamo.eval_frame.OptimizedModule       )









"""simple docstring"""




import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin






class  UpperCamelCase    (			snake_case_	,  unittest.TestCase   ):
 UpperCamelCase      : Union[str, Any]    =      BertweetTokenizer
 UpperCamelCase      : Optional[Any]    =      False


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

   # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
   _a						: Tuple    =			["""I""", """m""", """V@@""", """R@@""", """r""", """e@@"""]
   _a						: str    =			dict(zip(UpperCAmelCase__							,		range(len(UpperCAmelCase__							)							)							)							)
   _a						: Dict    =			["""#version: 0.2""", """a m</w>"""]
   _a						: int    =			{"""unk_token""": """<unk>"""}

   _a						: Union[str, Any]    =			os.path.join(self.tmpdirname							,		VOCAB_FILES_NAMES["""vocab_file"""]							)
   _a						: List[Any]    =			os.path.join(self.tmpdirname							,		VOCAB_FILES_NAMES["""merges_file"""]							)
   with open(self.vocab_file							,		"""w"""							,		encoding="""utf-8"""							) as fp:
     for token in vocab_tokens:
       fp.write(f"""{token} {vocab_tokens[token]}\n"""							)
   with open(self.merges_file							,		"""w"""							,		encoding="""utf-8"""							) as fp:
     fp.write("""\n""".join(UpperCAmelCase__							)							)


 def _lowercase			(  self	:		Any							,		**UpperCAmelCase__	:		Optional[Any]							)      -> Dict:
   kwargs.update(self.special_tokens_map							)
   return BertweetTokenizer.from_pretrained(self.tmpdirname							,		**UpperCAmelCase__							)


 def _lowercase			(  self	:		Dict							,		UpperCAmelCase__	:		Optional[int]							)      -> Dict:
   _a						: List[str]    =			"""I am VinAI Research"""
   _a						: Optional[int]    =			"""I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"""
   return input_text, output_text







 def _lowercase			(  self	:		Tuple							)      -> Optional[int]:
   _a						: Optional[int]    =			BertweetTokenizer(self.vocab_file							,		self.merges_file							,		**self.special_tokens_map							)
   _a						: Tuple    =			"""I am VinAI Research"""
   _a						: Dict    =			"""I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split()
   _a						: str    =			tokenizer.tokenize(UpperCAmelCase__							)
   self.assertListEqual(UpperCAmelCase__							,		UpperCAmelCase__							)

   _a						: Tuple    =			tokens + [tokenizer.unk_token]

   _a						: Tuple    =			[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__							)							,		UpperCAmelCase__							)

