
import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin







class 							lowercase						(   lowerCamelCase_			,    unittest.TestCase						):
							lowercase_			:			Tuple			   =SpeechaTextaTokenizer
							lowercase_			:			str			   =False




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

												lowercase		      =		'''<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'''.split(''' ''')
												lowercase		      =		['''he re</w> 123''', '''here a 1456''']
												lowercase		      =		dict(zip(_lowercase  ,range(len(_lowercase))))

												lowercase		      =		{'''pad_token''': '''<pad>''', '''unk_token''': '''<unk>''', '''bos_token''': '''<s>''', '''eos_token''': '''</s>'''}

												lowercase		      =		tempfile.mkdtemp()
												lowercase		      =		os.path.join(self.tmpdirname  ,VOCAB_FILES_NAMES['''vocab_file'''])
												lowercase		      =		os.path.join(self.tmpdirname  ,VOCAB_FILES_NAMES['''merges_file'''])

												with open(self.vocab_file  ,'''w'''  ,encoding='''utf-8''') as fp:
																	fp.write(json.dumps(_lowercase) + '''\n''')

												with open(self.merges_file  ,'''w''') as fp:
																	fp.write('''\n'''.join(_lowercase))




							def 					A__ (							self):
												lowercase		      =		list(self.get_tokenizer().get_vocab().keys())

												self.assertEqual(vocab_keys[0]  ,'''<s>''')
												self.assertEqual(vocab_keys[1]  ,'''<pad>''')
												self.assertEqual(vocab_keys[-1]  ,'''vocab''')
												self.assertEqual(len(_lowercase)  ,1_4)




							def 					A__ (							self):
												self.assertEqual(self.get_tokenizer().vocab_size  ,1_4)




							def 					A__ (							self):
												lowercase		      =		SpeechaTextaTokenizer.from_pretrained(self.tmpdirname)

												# make sure @@ is correctly concatenated
												lowercase		      =		[4, 6, 8, 7, 1_0]  # ["here@@", "couple", "words", "of@@", "the"]
												lowercase		      =		tokenizer.decode(_lowercase)

												self.assertTrue(output_string == '''herecouple words ofthe''')




							def 					A__ (							self):
												lowercase		      =		SpeechaTextaTokenizer.from_pretrained(self.tmpdirname)

												with tempfile.TemporaryDirectory() as tmp_dirname:
																	tokenizer.save_pretrained(_lowercase)
																	os.remove(os.path.join(_lowercase  ,'''merges.txt'''))

																	# load tokenizer without merges file should not throw an error
																	lowercase		      =		SpeechaTextaTokenizer.from_pretrained(_lowercase)

												with tempfile.TemporaryDirectory() as tmp_dirname:
																	# save tokenizer and load again
																	tokenizer.save_pretrained(_lowercase)
																	lowercase		      =		SpeechaTextaTokenizer.from_pretrained(_lowercase)

												self.assertIsNotNone(_lowercase)





							def 					A__ (							self):






												if not self.test_slow_tokenizer:
																	return

												lowercase		      =		inspect.signature(self.tokenizer_class.__init__)
												lowercase		      =		self.get_tokenizer()

												for parameter_name, parameter in signature.parameters.items():
																	if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
																						self.assertIn(_lowercase  ,tokenizer.init_kwargs)





# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


__lowerCamelCase     :					List[str]				  =						logging.get_logger(__name__)


if is_torch_available():
   import torch
   from torch import nn
   from torch.utils.data import Dataset

   from transformers import Trainer







   class __snake_case							(		lowerCamelCase_						):




      def __init__(       self     :							Tuple			,      _lowercase     :							int = 1_01 ):



        """simple docstring"""






        SCREAMING_SNAKE_CASE__     				= length




      def __len__(       self     :							str ):



        """simple docstring"""






        return self.length




      def __getitem__(       self     :							Optional[Any]			,      _lowercase     :							Union[str, Any] ):



        """simple docstring"""






        return i







   class __snake_case							:




      def __call__(       self     :							Dict			,      _lowercase     :							Dict ):



        """simple docstring"""






        return {"input_ids": torch.tensor(_lowercase ), "labels": torch.tensor(_lowercase )}







   class __snake_case							(		nn.Module						):




      def __init__(       self     :							Tuple ):



        """simple docstring"""






        super().__init__()
        # Add some (unused) params otherwise DDP will complain.
        SCREAMING_SNAKE_CASE__     				= nn.Linear(1_20			,      80 )




      def       __a		(       self     :							Any			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							Dict=None ):



        """simple docstring"""






        if labels is not None:
          return torch.tensor(0.0			,      device=input_ids.device ), input_ids
        else:
          return input_ids



def       __SCREAMING_SNAKE_CASE    (   )				->   Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= HfArgumentParser((TrainingArguments,)      )
  sys.argv += ["--output_dir", "./examples"]
  SCREAMING_SNAKE_CASE__     				= parser.parse_args_into_dataclasses()[0]

  logger.warning(
      f"""Process rank: {training_args.local_rank}, device: {training_args.device}, """
      f"""tpu_num_cores: {training_args.tpu_num_cores}"""					,	)

  # Essentially, what we want to verify in the distributed case is
  # that we get all samples back, in the right order.
  # (this is crucial for prediction for instance)
  for dataset_length in [10_01, 2_56, 15]:
    SCREAMING_SNAKE_CASE__     				= DummyDataset(__UpperCamelCase      )

    def compute_metrics(__UpperCamelCase       :			EvalPrediction      ) -> Dict:
      SCREAMING_SNAKE_CASE__     				= list(range(len(__UpperCamelCase      )      )      )
      SCREAMING_SNAKE_CASE__     				= p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
      return {"success": success}

    SCREAMING_SNAKE_CASE__     				= Trainer(
        model=DummyModel()					,	args=__UpperCamelCase					,	data_collator=DummyDataCollator()					,	eval_dataset=__UpperCamelCase					,	compute_metrics=__UpperCamelCase					,	)
    SCREAMING_SNAKE_CASE__     				= trainer.evaluate()
    logger.info(__UpperCamelCase      )
    if metrics["eval_success"] is not True:
      logger.error(__UpperCamelCase      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= trainer.predict(__UpperCamelCase      )
    logger.info(p.metrics      )
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= 2

    SCREAMING_SNAKE_CASE__     				= trainer.evaluate()
    logger.info(__UpperCamelCase      )
    if metrics["eval_success"] is not True:
      logger.error(__UpperCamelCase      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= trainer.predict(__UpperCamelCase      )
    logger.info(p.metrics      )
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= None

  logger.info("""🔥 All distributed tests successful"""      )



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Union[str, Any]      )				->   Dict:







  """simple docstring"""





  main()


if __name__ == "__main__":
   main()
