


from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest


@require_tokenizers
class        __lowerCamelCase      (				lowerCamelCase_ ):


				"""simple docstring"""







				a_:		List[Any]					      =	SqueezeBertTokenizer
				a_:		str					      =	SqueezeBertTokenizerFast
				a_:		Dict					      =	True






				def        lowerCAmelCase__			(				self     :    Any	,							**lowerCamelCase_     :    Optional[Any]					):
								return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname	,							**lowerCamelCase_					)






				@slow
				def        lowerCAmelCase__			(				self     :    Tuple					):
								_lowerCAmelCase  						=SqueezeBertTokenizer.from_pretrained("""squeezebert/squeezebert-mnli-headless"""					)

								_lowerCAmelCase  						=tokenizer.encode("""sequence builders"""	,							add_special_tokens=lowerCamelCase_					)
								_lowerCAmelCase  						=tokenizer.encode("""multi-sequence build"""	,							add_special_tokens=lowerCamelCase_					)

								_lowerCAmelCase  						=tokenizer.build_inputs_with_special_tokens(lowerCamelCase_					)
								_lowerCAmelCase  						=tokenizer.build_inputs_with_special_tokens(lowerCamelCase_	,							lowerCamelCase_					)

								assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
								assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
								    tokenizer.sep_token_id
								]





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	:							Any           =  {'''configuration_pegasus''': ['''PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							str           =  ['''PegasusTokenizer''']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							Tuple           =  ['''PegasusTokenizerFast''']

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

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							List[str]           =  [
		    '''TFPegasusForConditionalGeneration''',
		    '''TFPegasusModel''',
		    '''TFPegasusPreTrainedModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							List[str]           =  [
		    '''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

		__SCREAMING_SNAKE_CASE	:							Dict           =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

