






"""simple docstring"""



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 	_UpperCamelCase ( __UpperCamelCase		,     unittest.TestCase							):



    '''simple docstring'''


    __lowercase    :   Optional[Any]											=						SpeechaTextaTokenizer
    __lowercase    :   str											=						False


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

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

     UpperCAmelCase__ 						=				{"""pad_token""": """<pad>""", """unk_token""": """<unk>""", """bos_token""": """<s>""", """eos_token""": """</s>"""}

     UpperCAmelCase__ 						=				tempfile.mkdtemp()
     UpperCAmelCase__ 						=				os.path.join(self.tmpdirname   ,			VOCAB_FILES_NAMES["""vocab_file"""]			)
     UpperCAmelCase__ 						=				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(__UpperCamelCase			) + """\n"""			)

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


    def 		A__      (      self			):
     UpperCAmelCase__ 						=				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(__UpperCamelCase			)   ,			14			)


    def 		A__      (      self			):
     self.assertEqual(self.get_tokenizer().vocab_size   ,			14			)


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

     # make sure @@ is correctly concatenated
     UpperCAmelCase__ 						=				[4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
     UpperCAmelCase__ 						=				tokenizer.decode(__UpperCamelCase			)

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


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

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

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

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

     self.assertIsNotNone(__UpperCamelCase			)






    def 		A__      (      self			):





     if not self.test_slow_tokenizer:
      return

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

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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A__							=				{"configuration_pegasus": ["PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP", "PegasusConfig"]}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__							=				["PegasusTokenizer"]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__							=				["PegasusTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__							=				[
						    "PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "PegasusForCausalLM",
						    "PegasusForConditionalGeneration",
						    "PegasusModel",
						    "PegasusPreTrainedModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__							=				[
						    "TFPegasusForConditionalGeneration",
						    "TFPegasusModel",
						    "TFPegasusPreTrainedModel",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__							=				[
						    "FlaxPegasusForConditionalGeneration",
						    "FlaxPegasusModel",
						    "FlaxPegasusPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_pegasus import PegasusTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_pegasus_fast import PegasusTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_pegasus import (
												    PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
												    PegasusForCausalLM,
												    PegasusForConditionalGeneration,
												    PegasusModel,
												    PegasusPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_pegasus import (
												    FlaxPegasusForConditionalGeneration,
												    FlaxPegasusModel,
												    FlaxPegasusPreTrainedModel,
												)

else:
						import sys

						A__							=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




