
from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__   :			Optional[Any]												=    {
    "configuration_rembert": ["REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RemBertConfig", "RemBertOnnxConfig"]
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			Any												=    ["RemBertTokenizer"]

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

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			int												=    [
					    "REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "RemBertForCausalLM",
					    "RemBertForMaskedLM",
					    "RemBertForMultipleChoice",
					    "RemBertForQuestionAnswering",
					    "RemBertForSequenceClassification",
					    "RemBertForTokenClassification",
					    "RemBertLayer",
					    "RemBertModel",
					    "RemBertPreTrainedModel",
					    "load_tf_weights_in_rembert",
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			List[Any]												=    [
					    "TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFRemBertForCausalLM",
					    "TFRemBertForMaskedLM",
					    "TFRemBertForMultipleChoice",
					    "TFRemBertForQuestionAnswering",
					    "TFRemBertForSequenceClassification",
					    "TFRemBertForTokenClassification",
					    "TFRemBertLayer",
					    "TFRemBertModel",
					    "TFRemBertPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_rembert import RemBertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_rembert_fast import RemBertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_rembert import (
										    REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RemBertForCausalLM,
										    RemBertForMaskedLM,
										    RemBertForMultipleChoice,
										    RemBertForQuestionAnswering,
										    RemBertForSequenceClassification,
										    RemBertForTokenClassification,
										    RemBertLayer,
										    RemBertModel,
										    RemBertPreTrainedModel,
										    load_tf_weights_in_rembert,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_rembert import (
										    TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRemBertForCausalLM,
										    TFRemBertForMaskedLM,
										    TFRemBertForMultipleChoice,
										    TFRemBertForQuestionAnswering,
										    TFRemBertForSequenceClassification,
										    TFRemBertForTokenClassification,
										    TFRemBertLayer,
										    TFRemBertModel,
										    TFRemBertPreTrainedModel,
										)


else:
					import sys

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





import pprint

import requests

SCREAMING_SNAKE_CASE__   :			str												=    "https://zenquotes.io/api"




def  __magic_name__ (   )			->   list:
				return requests.get(API_ENDPOINT_URL + '''/today'''							).json()




def  __magic_name__ (   )			->   list:
				return requests.get(API_ENDPOINT_URL + '''/random'''							).json()


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :			int												=    random_quotes()
					pprint.pprint(response)



