





import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 							__lowercase       (      lowerCAmelCase__ ,      unittest.TestCase						):







	'''simple docstring'''

	a    :				Union[str, Any]											=       FunnelTokenizer
	a    :				Union[str, Any]											=       FunnelTokenizerFast
	a    :				Optional[Any]											=       True
	a    :				Optional[int]											=       True


	def 						_UpperCAmelCase     (self )							->							int:







				'''simple docstring'''





				super().setUp()

				__lowercase										=							[
				    '''<unk>''',
				    '''<cls>''',
				    '''<sep>''',
				    '''want''',
				    '''##want''',
				    '''##ed''',
				    '''wa''',
				    '''un''',
				    '''runn''',
				    '''##ing''',
				    ''',''',
				    '''low''',
				    '''lowest''',
				]
				__lowercase										=							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     ,**_lowerCamelCase )							->							Any:







				'''simple docstring'''





				return FunnelTokenizer.from_pretrained(self.tmpdirname     ,**_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,**_lowerCamelCase )							->							Union[str, Any]:







				'''simple docstring'''





				return FunnelTokenizerFast.from_pretrained(self.tmpdirname     ,**_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							int:







				'''simple docstring'''





				__lowercase										=							'''UNwant\u00E9d,running'''
				__lowercase										=							'''unwanted, running'''
				return input_text, output_text


	def 						_UpperCAmelCase     (self )							->							Optional[int]:







				'''simple docstring'''





				__lowercase										=							self.tokenizer_class(self.vocab_file )

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


	def 						_UpperCAmelCase     (self )							->							Optional[Any]:







				'''simple docstring'''





				__lowercase										=							self.get_tokenizers(do_lower_case=_lowerCamelCase )
				for tokenizer in tokenizers:
							__lowercase										=							tokenizer('''UNwant\u00E9d,running''' )
							__lowercase										=							len(inputs['''input_ids'''] ) - 1
							self.assertListEqual(inputs['''token_type_ids''']     ,[2] + [0] * sentence_len )

							__lowercase										=							tokenizer('''UNwant\u00E9d,running'''     ,'''UNwant\u00E9d,running''' )
							self.assertListEqual(inputs['''token_type_ids''']     ,[2] + [0] * sentence_len + [1] * sentence_len )












'''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)





