
'''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

lowerCAmelCase_      =   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    :  Dict      ):







           '''simple docstring'''






           return cls







def   lowerCAmelCase(  a__    :  Union[Tuple, List]      , a__    :  Optional[Union[List["torch.Generator"], "torch.Generator"]] = None      , a__    :  Optional["torch.device"] = None      , a__    :  Optional["torch.dtype"] = None      , a__    :  Optional["torch.layout"] = None      , ):







       '''simple docstring'''






       lowerCamelCase__				   =      device
       lowerCamelCase__				   =      shape[0]

       lowerCamelCase__				   =      layout or torch.strided
       lowerCamelCase__				   =      device or torch.device("cpu"      )

       if generator is not None:
              lowerCamelCase__				   =      generator.device.type if not isinstance(a__      , a__      ) else generator[0].device.type
              if gen_device_type != device.type and gen_device_type == "cpu":
                     lowerCamelCase__				   =      "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(a__      , a__      ):
              lowerCamelCase__				   =      (1,) + shape[1:]
              lowerCamelCase__				   =      [
                  torch.randn(a__      , generator=generator[i]      , device=a__      , dtype=a__      , layout=a__      )
                  for i in range(a__      )
              ]
              lowerCamelCase__				   =      torch.cat(a__      , dim=0      ).to(a__      )
       else:
              lowerCamelCase__				   =      torch.randn(a__      , generator=a__      , device=a__      , dtype=a__      , layout=a__      ).to(a__      )

       return latents







def   lowerCAmelCase(  a__    :  Union[str, Any]      ):







       '''simple docstring'''






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



'''simple docstring'''







import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 		snake_case_       (						A__		,	unittest.TestCase						):



   """simple docstring"""





   __lowerCAmelCase	:							Optional[Any]								=HerbertTokenizer
   __lowerCAmelCase	:							Optional[Any]								=HerbertTokenizerFast
   __lowerCAmelCase	:							int								=True







   def     __UpperCAmelCase      ( self):
          super().setUp()

          # Use a simpler test file without japanese/chinese characters
          with open(f"""{get_tests_dir()}/fixtures/sample_text_no_unicode.txt"""			,   encoding="utf-8") as f_data:
                 lowerCamelCase__				   =      f_data.read().replace("\n\n"			,   "\n").strip()

          lowerCamelCase__				   =      [
              "<s>",
              "</s>",
              "l",
              "o",
              "w",
              "e",
              "r",
              "s",
              "t",
              "i",
              "d",
              "n",
              "w</w>",
              "r</w>",
              "t</w>",
              "lo",
              "low",
              "er</w>",
              "low</w>",
              "lowest</w>",
              "newer</w>",
              "wider</w>",
              ",</w>",
              "<unk>",
          ]
          lowerCamelCase__				   =      dict(zip(UpperCamelCase			,   range(len(UpperCamelCase))))
          lowerCamelCase__				   =      ["l o 123", "lo w 1456", "e r</w> 1789", ""]

          lowerCamelCase__				   =      os.path.join(self.tmpdirname			,   VOCAB_FILES_NAMES["vocab_file"])
          lowerCamelCase__				   =      os.path.join(self.tmpdirname			,   VOCAB_FILES_NAMES["merges_file"])
          with open(self.vocab_file			,   "w") as fp:
                 fp.write(json.dumps(UpperCamelCase))
          with open(self.merges_file			,   "w") as fp:
                 fp.write("\n".join(UpperCamelCase))







   def     __UpperCAmelCase      ( self			,   UpperCamelCase):
          lowerCamelCase__				   =      "lower newer"
          lowerCamelCase__				   =      "lower newer"
          return input_text, output_text







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      self.tokenizer_class(vocab_file=self.vocab_file			,   merges_file=self.merges_file)

          lowerCamelCase__				   =      "lower"
          lowerCamelCase__				   =      ["low", "er</w>"]
          lowerCamelCase__				   =      tokenizer.tokenize(UpperCamelCase)
          self.assertListEqual(UpperCamelCase			,   UpperCamelCase)

          lowerCamelCase__				   =      tokens + ["<unk>"]
          lowerCamelCase__				   =      [16, 17, 23]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase)			,   UpperCamelCase)







   def     __UpperCAmelCase      ( self):
          if not self.test_rust_tokenizer:
                 return

          lowerCamelCase__				   =      self.get_tokenizer()
          lowerCamelCase__				   =      self.get_rust_tokenizer()

          lowerCamelCase__				   =      "lower,newer"

          lowerCamelCase__				   =      tokenizer.tokenize(UpperCamelCase)
          lowerCamelCase__				   =      rust_tokenizer.tokenize(UpperCamelCase)
          self.assertListEqual(UpperCamelCase			,   UpperCamelCase)

          lowerCamelCase__				   =      tokenizer.encode(UpperCamelCase			,   add_special_tokens=UpperCamelCase)
          lowerCamelCase__				   =      rust_tokenizer.encode(UpperCamelCase			,   add_special_tokens=UpperCamelCase)
          self.assertListEqual(UpperCamelCase			,   UpperCamelCase)

          lowerCamelCase__				   =      self.get_rust_tokenizer()
          lowerCamelCase__				   =      tokenizer.encode(UpperCamelCase)
          lowerCamelCase__				   =      rust_tokenizer.encode(UpperCamelCase)
          self.assertListEqual(UpperCamelCase			,   UpperCamelCase)







   @slow
   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      self.tokenizer_class.from_pretrained("allegro/herbert-base-cased")

          lowerCamelCase__				   =      tokenizer.encode("konstruowanie sekwencji"			,   add_special_tokens=UpperCamelCase)
          lowerCamelCase__				   =      tokenizer.encode("konstruowanie wielu sekwencji"			,   add_special_tokens=UpperCamelCase)

          lowerCamelCase__				   =      tokenizer.build_inputs_with_special_tokens(UpperCamelCase)
          lowerCamelCase__				   =      tokenizer.build_inputs_with_special_tokens(UpperCamelCase			,   UpperCamelCase)

          assert encoded_sentence == [0] + text + [2]
          assert encoded_pair == [0] + text + [2] + text_a + [2]

