
'''simple docstring'''





from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__     :    Any      							=					{
    "configuration_gpt2": ["GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPT2Config", "GPT2OnnxConfig"],
    "tokenization_gpt2": ["GPT2Tokenizer"],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__     :    Optional[Any]      							=					["GPT2TokenizerFast"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__     :    List[Any]      							=					[
					    "GPT2_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "GPT2DoubleHeadsModel",
					    "GPT2ForQuestionAnswering",
					    "GPT2ForSequenceClassification",
					    "GPT2ForTokenClassification",
					    "GPT2LMHeadModel",
					    "GPT2Model",
					    "GPT2PreTrainedModel",
					    "load_tf_weights_in_gpt2",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__     :    Any      							=					[
					    "TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFGPT2DoubleHeadsModel",
					    "TFGPT2ForSequenceClassification",
					    "TFGPT2LMHeadModel",
					    "TFGPT2MainLayer",
					    "TFGPT2Model",
					    "TFGPT2PreTrainedModel",
					]

try:
					if not is_keras_nlp_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__     :    str      							=					["TFGPT2Tokenizer"]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__     :    Optional[int]      							=					["FlaxGPT2LMHeadModel", "FlaxGPT2Model", "FlaxGPT2PreTrainedModel"]

if TYPE_CHECKING:
					from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
					from .tokenization_gpta import GPTaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_gpta_fast import GPTaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_gpta import (
										    GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GPTaDoubleHeadsModel,
										    GPTaForQuestionAnswering,
										    GPTaForSequenceClassification,
										    GPTaForTokenClassification,
										    GPTaLMHeadModel,
										    GPTaModel,
										    GPTaPreTrainedModel,
										    load_tf_weights_in_gpta,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_gpta import (
										    TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFGPTaDoubleHeadsModel,
										    TFGPTaForSequenceClassification,
										    TFGPTaLMHeadModel,
										    TFGPTaMainLayer,
										    TFGPTaModel,
										    TFGPTaPreTrainedModel,
										)

					try:
										if not is_keras_nlp_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_gpta_tf import TFGPTaTokenizer

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
					import sys

					SCREAMING_SNAKE_CASE__     :    Tuple      							=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





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

from ..bert.test_tokenization_bert import BertTokenizationTest

@require_tokenizers
class      lowerCAmelCase__  (			__lowercase    ):
		a__ :		Any   						=	SqueezeBertTokenizer
		a__ :		Union[str, Any]   						=	SqueezeBertTokenizerFast
		a__ :		int   						=	True






		def 				__A (	self	:							Optional[int]							,    **SCREAMING_SNAKE_CASE__	:							Union[str, Any]						)  ->							int:
						return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname							,    **SCREAMING_SNAKE_CASE__						)







		@slow
		def 				__A (	self	:							int						)  ->							Optional[int]:
						__lowerCamelCase					      =			SqueezeBertTokenizer.from_pretrained('''squeezebert/squeezebert-mnli-headless'''						)

						__lowerCamelCase					      =			tokenizer.encode('''sequence builders'''							,    add_special_tokens=SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			tokenizer.encode('''multi-sequence build'''							,    add_special_tokens=SCREAMING_SNAKE_CASE__						)

						__lowerCamelCase					      =			tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)

						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
						]



