'''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,
)


lowercase_     		= {
    '''configuration_xlm_roberta''': [
        '''XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XLMRobertaConfig''',
        '''XLMRobertaOnnxConfig''',
    ],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_     		= ['''XLMRobertaTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_     		= ['''XLMRobertaTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_     		= [
							    '''XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''XLMRobertaForCausalLM''',
							    '''XLMRobertaForMaskedLM''',
							    '''XLMRobertaForMultipleChoice''',
							    '''XLMRobertaForQuestionAnswering''',
							    '''XLMRobertaForSequenceClassification''',
							    '''XLMRobertaForTokenClassification''',
							    '''XLMRobertaModel''',
							    '''XLMRobertaPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_     		= [
							    '''TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFXLMRobertaForCausalLM''',
							    '''TFXLMRobertaForMaskedLM''',
							    '''TFXLMRobertaForMultipleChoice''',
							    '''TFXLMRobertaForQuestionAnswering''',
							    '''TFXLMRobertaForSequenceClassification''',
							    '''TFXLMRobertaForTokenClassification''',
							    '''TFXLMRobertaModel''',
							    '''TFXLMRobertaPreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_     		= [
							    '''FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''FlaxXLMRobertaForMaskedLM''',
							    '''FlaxXLMRobertaForCausalLM''',
							    '''FlaxXLMRobertaForMultipleChoice''',
							    '''FlaxXLMRobertaForQuestionAnswering''',
							    '''FlaxXLMRobertaForSequenceClassification''',
							    '''FlaxXLMRobertaForTokenClassification''',
							    '''FlaxXLMRobertaModel''',
							    '''FlaxXLMRobertaPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_xlm_roberta import (
							    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    XLMRobertaConfig,
							    XLMRobertaOnnxConfig,
							)

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xlm_roberta import XLMRobertaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xlm_roberta import (
														    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    XLMRobertaForCausalLM,
														    XLMRobertaForMaskedLM,
														    XLMRobertaForMultipleChoice,
														    XLMRobertaForQuestionAnswering,
														    XLMRobertaForSequenceClassification,
														    XLMRobertaForTokenClassification,
														    XLMRobertaModel,
														    XLMRobertaPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xlm_roberta import (
														    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXLMRobertaForCausalLM,
														    TFXLMRobertaForMaskedLM,
														    TFXLMRobertaForMultipleChoice,
														    TFXLMRobertaForQuestionAnswering,
														    TFXLMRobertaForSequenceClassification,
														    TFXLMRobertaForTokenClassification,
														    TFXLMRobertaModel,
														    TFXLMRobertaPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xlm_roberta import (
														    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FlaxXLMRobertaForCausalLM,
														    FlaxXLMRobertaForMaskedLM,
														    FlaxXLMRobertaForMultipleChoice,
														    FlaxXLMRobertaForQuestionAnswering,
														    FlaxXLMRobertaForSequenceClassification,
														    FlaxXLMRobertaForTokenClassification,
														    FlaxXLMRobertaModel,
														    FlaxXLMRobertaPreTrainedModel,
														)

else:
							import sys

							lowercase_     		= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






'''simple docstring'''

from __future__ import annotations



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=len(a__      ) // 2

						# choose the middle 3 elements
						_lowerCAmelCase										=lst[m - 1 : m + 2]

						# if middle element is peak
						if three[1] > three[0] and three[1] > three[2]:
												return three[1]

						# if increasing, recurse on right
						elif three[0] < three[2]:
												if len(lst[:m]      ) == 2:
																		m -= 1
												return peak(lst[m:]      )

						# decreasing
						else:
												if len(lst[:m]      ) == 2:
																		m += 1
												return peak(lst[:m]      )


if __name__ == "__main__":
							import doctest

							doctest.testmod()





