



"""simple docstring"""


import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class       A_			(     _lowerCamelCase	,   unittest.TestCase				):



						"""simple docstring"""
						__UpperCamelCase						=							LayoutLMTokenizer
						__UpperCamelCase						=							LayoutLMTokenizerFast
						__UpperCamelCase						=							True
						__UpperCamelCase						=							True






						def 	UpperCAmelCase__     (					self :Optional[Any]					)				->			List[str]:
												super().setUp()

												UpperCAmelCase             = [
												    '[UNK]',
												    '[CLS]',
												    '[SEP]',
												    'want',
												    '##want',
												    '##ed',
												    'wa',
												    'un',
												    'runn',
												    '##ing',
												    ',',
												    'low',
												    'lowest',
												]
												UpperCAmelCase             = os.path.join(self.tmpdirname   ,  VOCAB_FILES_NAMES['vocab_file']					)
												with open(self.vocab_file   ,  'w'   ,  encoding='utf-8'					) as vocab_writer:
																		vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]					)					)






						def 	UpperCAmelCase__     (					self :Any   ,  **lowercase_ :Optional[Any]					)				->			List[Any]:
												return LayoutLMTokenizer.from_pretrained(self.tmpdirname   ,  **lowercase_					)






						def 	UpperCAmelCase__     (					self :Union[str, Any]   ,  lowercase_ :Optional[int]					)				->			Tuple:
												UpperCAmelCase             = 'UNwant\u00E9d,running'
												UpperCAmelCase             = 'unwanted, running'
												return input_text, output_text






						def 	UpperCAmelCase__     (					self :Dict					)				->			Tuple:
												UpperCAmelCase             = self.tokenizer_class(self.vocab_file					)

												UpperCAmelCase             = tokenizer.tokenize('UNwant\u00E9d,running'					)
												self.assertListEqual(lowercase_   ,  ['un', '##want', '##ed', ',', 'runn', '##ing']					)
												self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_					)   ,  [7, 4, 5, 10, 8, 9]					)






						def 	UpperCAmelCase__     (					self :Any					)				->			Any:
												pass











'''simple docstring'''

from typing import TYPE_CHECKING

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


a  :				Tuple									=  {
    'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LlamaConfig'],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a  :				Optional[Any]									=  ['LlamaTokenizer']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a  :				str									=  ['LlamaTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a  :				Optional[Any]									=  [
							    'LlamaForCausalLM',
							    'LlamaModel',
							    'LlamaPreTrainedModel',
							    'LlamaForSequenceClassification',
							]


if TYPE_CHECKING:
							from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_llama import LlamaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_llama_fast import LlamaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
							import sys

							a  :				str									=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

