





'''simple docstring'''


import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class    lowerCAmelCase_		(		__magic_name__						,unittest.TestCase		):
 __lowerCamelCase						:							Optional[int]         =					MgpstrTokenizer
 __lowerCamelCase						:							Dict         =					False
 __lowerCamelCase						:							str         =					{}
 __lowerCamelCase						:							List[Any]         =					False




 def 							_snake_case   (	self		)    ->							Tuple:
     super().setUp()

     # fmt: off
     _lowerCAmelCase				=      ["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
     # fmt: on
     _lowerCAmelCase				=      dict(zip(_lowerCAmelCase			,     range(len(_lowerCAmelCase		)		)		)		)

     _lowerCAmelCase				=      os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES["vocab_file"]		)
     with open(self.vocab_file			,     "w"			,     encoding="utf-8"		) as fp:
         fp.write(json.dumps(_lowerCAmelCase		) + "\n"		)




 def 							_snake_case   (	self			,     **_lowerCAmelCase		)    ->							Union[str, Any]:
     return MgpstrTokenizer.from_pretrained(self.tmpdirname			,     **_lowerCAmelCase		)




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Dict:
     _lowerCAmelCase				=      "tester"
     _lowerCAmelCase				=      "tester"
     return input_text, output_text




 @unittest.skip("MGP-STR always lower cases letters."		)
 def 							_snake_case   (	self		)    ->							List[Any]:
     pass




 def 							_snake_case   (	self		)    ->							List[Any]:
     _lowerCAmelCase				=      self.get_tokenizers(do_lower_case=_lowerCAmelCase		)
     for tokenizer in tokenizers:
         with self.subTest(f'''{tokenizer.__class__.__name__}'''		):
             _lowerCAmelCase				=      "[SPECIAL_TOKEN]"

             tokenizer.add_special_tokens({"cls_token": special_token}		)
             _lowerCAmelCase				=      tokenizer.encode([special_token]			,     add_special_tokens=_lowerCAmelCase		)
             self.assertEqual(len(_lowerCAmelCase		)			,     1		)

             _lowerCAmelCase				=      tokenizer.decode(_lowerCAmelCase			,     skip_special_tokens=_lowerCAmelCase		)
             self.assertTrue(special_token not in decoded		)




 def 							_snake_case   (	self		)    ->							int:
     _lowerCAmelCase				=      self.get_tokenizers()
     for tokenizer in tokenizers:
         with self.subTest(f'''{tokenizer.__class__.__name__}'''		):
             _lowerCAmelCase			,			_lowerCAmelCase				=      self.get_input_output_texts(_lowerCAmelCase		)

             _lowerCAmelCase				=      tokenizer.tokenize(_lowerCAmelCase		)
             _lowerCAmelCase				=      tokenizer.convert_tokens_to_ids(_lowerCAmelCase		)
             _lowerCAmelCase				=      tokenizer.encode(_lowerCAmelCase			,     add_special_tokens=_lowerCAmelCase		)
             self.assertListEqual(_lowerCAmelCase			,     _lowerCAmelCase		)

             _lowerCAmelCase				=      tokenizer.convert_ids_to_tokens(_lowerCAmelCase		)
             self.assertNotEqual(len(_lowerCAmelCase		)			,     0		)
             _lowerCAmelCase				=      tokenizer.decode(_lowerCAmelCase		)
             self.assertIsInstance(_lowerCAmelCase			,     _lowerCAmelCase		)

             self.assertEqual(text_a.replace(" "			,     ""		)			,     _lowerCAmelCase		)




 @unittest.skip("MGP-STR tokenizer only handles one sequence."		)
 def 							_snake_case   (	self		)    ->							List[Any]:
     pass





 @unittest.skip("inputs cannot be pretokenized in MgpstrTokenizer"		)
 def 							_snake_case   (	self		)    ->							Dict:
     pass









'''simple docstring'''


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,
)


_SCREAMING_SNAKE_CASE	 =							{"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}

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

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							[
           "XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "XGLMForCausalLM",
           "XGLMModel",
           "XGLMPreTrainedModel",
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							[
           "FlaxXGLMForCausalLM",
           "FlaxXGLMModel",
           "FlaxXGLMPreTrainedModel",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							[
           "TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFXGLMForCausalLM",
           "TFXGLMModel",
           "TFXGLMPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xglm import XGLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xglm_fast import XGLMTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_xglm import (
                  TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFXGLMForCausalLM,
                  TFXGLMModel,
                  TFXGLMPreTrainedModel,
              )


else:
       import sys

       _SCREAMING_SNAKE_CASE	 =							_LazyModule(__name__, globals()["__file__"], _import_structure)


