
'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase_      =   logging.get_logger(__name__)

lowerCAmelCase_      =   {
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}







class 		snake_case_       (						A__						):



   """simple docstring"""





   __lowerCAmelCase	:							List[Any]								=MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
   __lowerCAmelCase	:							Union[str, Any]								="mobilebert"







   def __init__( self			,   UpperCamelCase=3_05_22			,   UpperCamelCase=5_12			,   UpperCamelCase=24			,   UpperCamelCase=4			,   UpperCamelCase=5_12			,   UpperCamelCase="relu"			,   UpperCamelCase=0.0			,   UpperCamelCase=0.1			,   UpperCamelCase=5_12			,   UpperCamelCase=2			,   UpperCamelCase=0.0_2			,   UpperCamelCase=1E-1_2			,   UpperCamelCase=0			,   UpperCamelCase=1_28			,   UpperCamelCase=True			,   UpperCamelCase=True			,   UpperCamelCase=1_28			,   UpperCamelCase=False			,   UpperCamelCase=True			,   UpperCamelCase=4			,   UpperCamelCase="no_norm"			,   UpperCamelCase=True			,   UpperCamelCase=None			,   **UpperCamelCase			,   ):
          super().__init__(pad_token_id=UpperCAmelCase__			,   **UpperCAmelCase__)

          lowerCamelCase__				   =      vocab_size
          lowerCamelCase__				   =      hidden_size
          lowerCamelCase__				   =      num_hidden_layers
          lowerCamelCase__				   =      num_attention_heads
          lowerCamelCase__				   =      hidden_act
          lowerCamelCase__				   =      intermediate_size
          lowerCamelCase__				   =      hidden_dropout_prob
          lowerCamelCase__				   =      attention_probs_dropout_prob
          lowerCamelCase__				   =      max_position_embeddings
          lowerCamelCase__				   =      type_vocab_size
          lowerCamelCase__				   =      initializer_range
          lowerCamelCase__				   =      layer_norm_eps
          lowerCamelCase__				   =      embedding_size
          lowerCamelCase__				   =      trigram_input
          lowerCamelCase__				   =      use_bottleneck
          lowerCamelCase__				   =      intra_bottleneck_size
          lowerCamelCase__				   =      use_bottleneck_attention
          lowerCamelCase__				   =      key_query_shared_bottleneck
          lowerCamelCase__				   =      num_feedforward_networks
          lowerCamelCase__				   =      normalization_type
          lowerCamelCase__				   =      classifier_activation

          if self.use_bottleneck:
                 lowerCamelCase__				   =      intra_bottleneck_size
          else:
                 lowerCamelCase__				   =      hidden_size

          lowerCamelCase__				   =      classifier_dropout







class 		snake_case_       (						A__						):



   """simple docstring"""





   @property
   def     __UpperCAmelCase      ( self):
          if self.task == "multiple-choice":
                 lowerCamelCase__				   =      {0: "batch", 1: "choice", 2: "sequence"}
          else:
                 lowerCamelCase__				   =      {0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
                  ("token_type_ids", dynamic_axis),
              ])





"""simple docstring"""






from typing import TYPE_CHECKING

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


_lowerCamelCase  =	{
    'configuration_open_llama': ['OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenLlamaConfig'],
}

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

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase  =	[
	    'OpenLlamaForCausalLM',
	    'OpenLlamaModel',
	    'OpenLlamaPreTrainedModel',
	    'OpenLlamaForSequenceClassification',
	]


if TYPE_CHECKING:
	from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from transformers import LlamaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from transformers import LlamaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_open_llama import (
		    OpenLlamaForCausalLM,
		    OpenLlamaForSequenceClassification,
		    OpenLlamaModel,
		    OpenLlamaPreTrainedModel,
		)


else:
	import sys

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