

from collections import OrderedDict
from typing import Mapping

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


_UpperCAmelCase	:     Tuple		       = logging.get_logger(__name__)

_UpperCAmelCase	:     Tuple		       = {
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				int     =    '''camembert'''




				def __init__(      self   ,     snake_case=3_0522   ,     snake_case=768   ,     snake_case=12   ,     snake_case=12   ,     snake_case=3072   ,     snake_case="gelu"   ,     snake_case=0.1   ,     snake_case=0.1   ,     snake_case=512   ,     snake_case=2   ,     snake_case=0.02   ,     snake_case=1e-1_2   ,     snake_case=1   ,     snake_case=0   ,     snake_case=2   ,     snake_case="absolute"   ,     snake_case=True   ,     snake_case=None   ,     **snake_case   ,     ):
											super().__init__(pad_token_id=snake_case   ,     bos_token_id=snake_case   ,     eos_token_id=snake_case   ,     **snake_case )

											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 				lowercase       ( lowercase_    ):




				@property
				def 					a							(      self ):
											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_flax_available, is_torch_available


_UpperCAmelCase	:     Any		       = {
    """configuration_longt5""": ["""LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LongT5Config""", """LongT5OnnxConfig"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase	:     Any		       = [
					    """LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """LongT5EncoderModel""",
					    """LongT5ForConditionalGeneration""",
					    """LongT5Model""",
					    """LongT5PreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase	:     Optional[int]		       = [
					    """FlaxLongT5ForConditionalGeneration""",
					    """FlaxLongT5Model""",
					    """FlaxLongT5PreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_longta import (
										    LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LongTaEncoderModel,
										    LongTaForConditionalGeneration,
										    LongTaModel,
										    LongTaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_longta import (
										    FlaxLongTaForConditionalGeneration,
										    FlaxLongTaModel,
										    FlaxLongTaPreTrainedModel,
										)


else:
					import sys

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




