

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A									=  logging.get_logger(__name__)

A									=  {
    'facebook/xlm-roberta-xl': 'https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json',
    'facebook/xlm-roberta-xxl': 'https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}


class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			__A    	=	"""xlm-roberta-xl"""


			def __init__(     self			,					__UpperCamelCase=25_08_80			,					__UpperCamelCase=25_60			,					__UpperCamelCase=36			,					__UpperCamelCase=32			,					__UpperCamelCase=1_02_40			,					__UpperCamelCase="gelu"			,					__UpperCamelCase=0.1			,					__UpperCamelCase=0.1			,					__UpperCamelCase=5_14			,					__UpperCamelCase=1			,					__UpperCamelCase=0.02			,					__UpperCamelCase=1E-05			,					__UpperCamelCase=1			,					__UpperCamelCase=0			,					__UpperCamelCase=2			,					__UpperCamelCase="absolute"			,					__UpperCamelCase=True			,					__UpperCamelCase=None			,					**__UpperCamelCase			,					):

				"""simple docstring"""


				super().__init__(pad_token_id=__UpperCamelCase			,					bos_token_id=__UpperCamelCase			,					eos_token_id=__UpperCamelCase			,					**__UpperCamelCase				)
				snake_case_   		=			vocab_size
				snake_case_   		=			hidden_size
				snake_case_   		=			num_hidden_layers
				snake_case_   		=			num_attention_heads
				snake_case_   		=			hidden_act
				snake_case_   		=			intermediate_size
				snake_case_   		=			hidden_dropout_prob
				snake_case_   		=			attention_probs_dropout_prob
				snake_case_   		=			max_position_embeddings
				snake_case_   		=			type_vocab_size
				snake_case_   		=			initializer_range
				snake_case_   		=			layer_norm_eps
				snake_case_   		=			position_embedding_type
				snake_case_   		=			use_cache
				snake_case_   		=			classifier_dropout







class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				if self.task == "multiple-choice":
					snake_case_   		=			{0: 'batch', 1: 'choice', 2: 'sequence'}
				else:
					snake_case_   		=			{0: 'batch', 1: 'sequence'}
				return OrderedDict(
				    [
				        ('input_ids', dynamic_axis),
				        ('attention_mask', dynamic_axis),
				    ]				)



from typing import TYPE_CHECKING

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


A									=  {
    'configuration_speech_to_text': ['SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2TextConfig'],
    'processing_speech_to_text': ['Speech2TextProcessor'],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A									=  ['Speech2TextTokenizer']

try:
		if not is_speech_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A									=  ['Speech2TextFeatureExtractor']

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A									=  [
		    'TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFSpeech2TextForConditionalGeneration',
		    'TFSpeech2TextModel',
		    'TFSpeech2TextPreTrainedModel',
		]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A									=  [
		    'SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'Speech2TextForConditionalGeneration',
		    'Speech2TextModel',
		    'Speech2TextPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
		from .processing_speech_to_text import SpeechaTextProcessor

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_speech_to_text import SpeechaTextTokenizer

		try:
				if not is_speech_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_speech_to_text import (
				    TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFSpeechaTextForConditionalGeneration,
				    TFSpeechaTextModel,
				    TFSpeechaTextPreTrainedModel,
				)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_speech_to_text import (
				    SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SpeechaTextForConditionalGeneration,
				    SpeechaTextModel,
				    SpeechaTextPreTrainedModel,
				)

else:
		import sys

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