



from typing import TYPE_CHECKING

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


snake_case    :						Dict  =						{
    'configuration_electra': ['ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ElectraConfig', 'ElectraOnnxConfig'],
    'tokenization_electra': ['ElectraTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case    :						Dict  =						['ElectraTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case    :						Optional[Any]  =						[
		    'ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ElectraForCausalLM',
		    'ElectraForMaskedLM',
		    'ElectraForMultipleChoice',
		    'ElectraForPreTraining',
		    'ElectraForQuestionAnswering',
		    'ElectraForSequenceClassification',
		    'ElectraForTokenClassification',
		    'ElectraModel',
		    'ElectraPreTrainedModel',
		    'load_tf_weights_in_electra',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case    :						Union[str, Any]  =						[
		    'TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFElectraForMaskedLM',
		    'TFElectraForMultipleChoice',
		    'TFElectraForPreTraining',
		    'TFElectraForQuestionAnswering',
		    'TFElectraForSequenceClassification',
		    'TFElectraForTokenClassification',
		    'TFElectraModel',
		    'TFElectraPreTrainedModel',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case    :						Tuple  =						[
		    'FlaxElectraForCausalLM',
		    'FlaxElectraForMaskedLM',
		    'FlaxElectraForMultipleChoice',
		    'FlaxElectraForPreTraining',
		    'FlaxElectraForQuestionAnswering',
		    'FlaxElectraForSequenceClassification',
		    'FlaxElectraForTokenClassification',
		    'FlaxElectraModel',
		    'FlaxElectraPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
		from .tokenization_electra import ElectraTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_electra_fast import ElectraTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_electra import (
				    ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ElectraForCausalLM,
				    ElectraForMaskedLM,
				    ElectraForMultipleChoice,
				    ElectraForPreTraining,
				    ElectraForQuestionAnswering,
				    ElectraForSequenceClassification,
				    ElectraForTokenClassification,
				    ElectraModel,
				    ElectraPreTrainedModel,
				    load_tf_weights_in_electra,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_electra import (
				    TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFElectraForMaskedLM,
				    TFElectraForMultipleChoice,
				    TFElectraForPreTraining,
				    TFElectraForQuestionAnswering,
				    TFElectraForSequenceClassification,
				    TFElectraForTokenClassification,
				    TFElectraModel,
				    TFElectraPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_electra import (
				    FlaxElectraForCausalLM,
				    FlaxElectraForMaskedLM,
				    FlaxElectraForMultipleChoice,
				    FlaxElectraForPreTraining,
				    FlaxElectraForQuestionAnswering,
				    FlaxElectraForSequenceClassification,
				    FlaxElectraForTokenClassification,
				    FlaxElectraModel,
				    FlaxElectraPreTrainedModel,
				)

else:
		import sys

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







import requests

snake_case    :						int  =						''  # <-- Put your OpenWeatherMap appid here!
snake_case    :						int  =						'https://api.openweathermap.org/data/2.5/'
def 	snake_case__       (					__lowercase = "Chicago"							,     __lowercase = APPID       )		->		dict:







						"""simple docstring"""

						return requests.get(URL_BASE + "weather"							,     params=locals()       ).json()
def 	snake_case__       (					__lowercase = "Kolkata, India"							,     __lowercase = APPID       )		->		dict:







						"""simple docstring"""

						return requests.get(URL_BASE + "forecast"							,     params=locals()       ).json()
def 	snake_case__       (					__lowercase = 55.68							,     __lowercase = 12.57							,     __lowercase = APPID       )		->		dict:







						"""simple docstring"""

						return requests.get(URL_BASE + "onecall"							,     params=locals()       ).json()


if __name__ == "__main__":
		from pprint import pprint

		while True:
				snake_case    :						int  =						input('Enter a location:').strip()
				if location:
						pprint(current_weather(location))
				else:
						break