from typing import Optional, Union

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig


lowercase_     :   Optional[Any] 				=				logging.get_logger(__name__)


# General docstring
lowercase_     :   int 				=				'MobileNetV1Config'

# Base docstring
lowercase_     :   Tuple 				=				'google/mobilenet_v1_1.0_224'
lowercase_     :   Tuple 				=				[1, 1_0_2_4, 7, 7]

# Image classification docstring
lowercase_     :   List[Any] 				=				'google/mobilenet_v1_1.0_224'
lowercase_     :   List[str] 				=				'tabby, tabby cat'


lowercase_     :   int 				=				[
    'google/mobilenet_v1_1.0_224',
    'google/mobilenet_v1_0.75_192',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]







def 			A__      (	snake_case_ :		Dict       ,				snake_case_ :		List[str]       ,				snake_case_ :		str=None      ):
	SCREAMING_SNAKE_CASE__:   Dict=      {}

	if isinstance(UpperCAmelCase__       ,				UpperCAmelCase__      ):
		SCREAMING_SNAKE_CASE__:   int=      model.mobilenet_va
	else:
		SCREAMING_SNAKE_CASE__:   Optional[int]=      model

	SCREAMING_SNAKE_CASE__:   str=      '''MobilenetV1/Conv2d_0/'''
	SCREAMING_SNAKE_CASE__:   str=      backbone.conv_stem.convolution.weight
	SCREAMING_SNAKE_CASE__:   str=      backbone.conv_stem.normalization.bias
	SCREAMING_SNAKE_CASE__:   Tuple=      backbone.conv_stem.normalization.weight
	SCREAMING_SNAKE_CASE__:   str=      backbone.conv_stem.normalization.running_mean
	SCREAMING_SNAKE_CASE__:   Union[str, Any]=      backbone.conv_stem.normalization.running_var

	for i in range(13      ):
		SCREAMING_SNAKE_CASE__:   Union[str, Any]=      i + 1
		SCREAMING_SNAKE_CASE__:   List[str]=      i * 2

		SCREAMING_SNAKE_CASE__:   List[str]=      backbone.layer[pt_index]
		SCREAMING_SNAKE_CASE__:   List[str]=      F'MobilenetV1/Conv2d_{tf_index}_depthwise/'
		SCREAMING_SNAKE_CASE__:   Tuple=      pointer.convolution.weight
		SCREAMING_SNAKE_CASE__:   List[str]=      pointer.normalization.bias
		SCREAMING_SNAKE_CASE__:   Tuple=      pointer.normalization.weight
		SCREAMING_SNAKE_CASE__:   Dict=      pointer.normalization.running_mean
		SCREAMING_SNAKE_CASE__:   str=      pointer.normalization.running_var

		SCREAMING_SNAKE_CASE__:   int=      backbone.layer[pt_index + 1]
		SCREAMING_SNAKE_CASE__:   List[str]=      F'MobilenetV1/Conv2d_{tf_index}_pointwise/'
		SCREAMING_SNAKE_CASE__:   int=      pointer.convolution.weight
		SCREAMING_SNAKE_CASE__:   Optional[int]=      pointer.normalization.bias
		SCREAMING_SNAKE_CASE__:   int=      pointer.normalization.weight
		SCREAMING_SNAKE_CASE__:   Any=      pointer.normalization.running_mean
		SCREAMING_SNAKE_CASE__:   Union[str, Any]=      pointer.normalization.running_var

	if isinstance(UpperCAmelCase__       ,				UpperCAmelCase__      ):
		SCREAMING_SNAKE_CASE__:   Tuple=      '''MobilenetV1/Logits/Conv2d_1c_1x1/'''
		SCREAMING_SNAKE_CASE__:   List[str]=      model.classifier.weight
		SCREAMING_SNAKE_CASE__:   List[Any]=      model.classifier.bias

	return tf_to_pt_map







def 			A__      (	snake_case_ :		Dict       ,				snake_case_ :		Union[str, Any]       ,				snake_case_ :		Any      ):
	try:
		import numpy as np
		import tensorflow as tf
	except ImportError:
		logger.error(
		    '''Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '''
		    '''https://www.tensorflow.org/install/ for installation instructions.'''      )
		raise

	# Load weights from TF model
	SCREAMING_SNAKE_CASE__:   List[Any]=      tf.train.list_variables(UpperCAmelCase__      )
	SCREAMING_SNAKE_CASE__:   List[Any]=      {}
	for name, shape in init_vars:
		logger.info(F'Loading TF weight {name} with shape {shape}'      )
		SCREAMING_SNAKE_CASE__:   str=      tf.train.load_variable(UpperCAmelCase__       ,				UpperCAmelCase__      )
		SCREAMING_SNAKE_CASE__:   Any=      array

	# Build TF to PyTorch weights loading map
	SCREAMING_SNAKE_CASE__:   Dict=      _build_tf_to_pytorch_map(UpperCAmelCase__       ,				UpperCAmelCase__       ,				UpperCAmelCase__      )

	for name, pointer in tf_to_pt_map.items():
		logger.info(F'Importing {name}'      )
		if name not in tf_weights:
			logger.info(F'{name} not in tf pre-trained weights, skipping'      )
			continue

		SCREAMING_SNAKE_CASE__:   Tuple=      tf_weights[name]

		if "depthwise_weights" in name:
			logger.info('''Transposing depthwise'''      )
			SCREAMING_SNAKE_CASE__:   Optional[int]=      np.transpose(UpperCAmelCase__       ,				(2, 3, 0, 1)      )
		elif "weights" in name:
			logger.info('''Transposing'''      )
			if len(pointer.shape      ) == 2:  # copying into linear layer
				SCREAMING_SNAKE_CASE__:   Optional[int]=      array.squeeze().transpose()
			else:
				SCREAMING_SNAKE_CASE__:   Any=      np.transpose(UpperCAmelCase__       ,				(3, 2, 0, 1)      )

		if pointer.shape != array.shape:
			raise ValueError(F'Pointer shape {pointer.shape} and array shape {array.shape} mismatched'      )

		logger.info(F'Initialize PyTorch weight {name} {array.shape}'      )
		SCREAMING_SNAKE_CASE__:   Dict=      torch.from_numpy(UpperCAmelCase__      )

		tf_weights.pop(UpperCAmelCase__       ,				UpperCAmelCase__      )
		tf_weights.pop(name + '''/RMSProp'''       ,				UpperCAmelCase__      )
		tf_weights.pop(name + '''/RMSProp_1'''       ,				UpperCAmelCase__      )
		tf_weights.pop(name + '''/ExponentialMovingAverage'''       ,				UpperCAmelCase__      )

	logger.info(F'Weights not copied to PyTorch model: {", ".join(tf_weights.keys()      )}'      )
	return model







def 			A__      (	snake_case_ :		torch.Tensor       ,				snake_case_ :		nn.Convad      ):
	SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Tuple=      features.shape[-2:]
	SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Union[str, Any]=      conv_layer.stride
	SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Dict=      conv_layer.kernel_size

	if in_height % stride_height == 0:
		SCREAMING_SNAKE_CASE__:   int=      max(kernel_height - stride_height       ,				0      )
	else:
		SCREAMING_SNAKE_CASE__:   Dict=      max(kernel_height - (in_height % stride_height)       ,				0      )

	if in_width % stride_width == 0:
		SCREAMING_SNAKE_CASE__:   List[str]=      max(kernel_width - stride_width       ,				0      )
	else:
		SCREAMING_SNAKE_CASE__:   Union[str, Any]=      max(kernel_width - (in_width % stride_width)       ,				0      )

	SCREAMING_SNAKE_CASE__:   Optional[Any]=      pad_along_width // 2
	SCREAMING_SNAKE_CASE__:   Optional[int]=      pad_along_width - pad_left
	SCREAMING_SNAKE_CASE__:   Dict=      pad_along_height // 2
	SCREAMING_SNAKE_CASE__:   Union[str, Any]=      pad_along_height - pad_top

	SCREAMING_SNAKE_CASE__:   Tuple=      (pad_left, pad_right, pad_top, pad_bottom)
	return nn.functional.pad(UpperCAmelCase__       ,				UpperCAmelCase__       ,				'''constant'''       ,				0.0      )






class 		_lowerCamelCase		(   nn.Module    ):
				def __init__(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = 1 , lowerCAmelCase = 1 , lowerCAmelCase = False , lowerCAmelCase = True , lowerCAmelCase = True , )       ->					Tuple:
					super().__init__()
					SCREAMING_SNAKE_CASE__:   str=      config

					if in_channels % groups != 0:
						raise ValueError(f'Input channels ({in_channels}) are not divisible by {groups} groups.'   )
					if out_channels % groups != 0:
						raise ValueError(f'Output channels ({out_channels}) are not divisible by {groups} groups.'   )

					SCREAMING_SNAKE_CASE__:   List[str]=      0 if config.tf_padding else int((kernel_size - 1) / 2   )

					SCREAMING_SNAKE_CASE__:   Union[str, Any]=      nn.Convad(
					    in_channels=__UpperCamelCase , out_channels=__UpperCamelCase , kernel_size=__UpperCamelCase , stride=__UpperCamelCase , padding=__UpperCamelCase , groups=__UpperCamelCase , bias=__UpperCamelCase , padding_mode='''zeros''' , )

					if use_normalization:
						SCREAMING_SNAKE_CASE__:   Any=      nn.BatchNormad(
						    num_features=__UpperCamelCase , eps=config.layer_norm_eps , momentum=0.9997 , affine=__UpperCamelCase , track_running_stats=__UpperCamelCase , )
					else:
						SCREAMING_SNAKE_CASE__:   Any=      None

					if use_activation:
						if isinstance(__UpperCamelCase , __UpperCamelCase   ):
							SCREAMING_SNAKE_CASE__:   int=      ACTaFN[use_activation]
						elif isinstance(config.hidden_act , __UpperCamelCase   ):
							SCREAMING_SNAKE_CASE__:   Optional[Any]=      ACTaFN[config.hidden_act]
						else:
							SCREAMING_SNAKE_CASE__:   str=      config.hidden_act
					else:
						SCREAMING_SNAKE_CASE__:   Tuple=      None



				def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					List[Any]:
					if self.config.tf_padding:
						SCREAMING_SNAKE_CASE__:   Any=      apply_tf_padding(__UpperCamelCase , self.convolution   )
					SCREAMING_SNAKE_CASE__:   Dict=      self.convolution(__UpperCamelCase   )
					if self.normalization is not None:
						SCREAMING_SNAKE_CASE__:   List[Any]=      self.normalization(__UpperCamelCase   )
					if self.activation is not None:
						SCREAMING_SNAKE_CASE__:   Any=      self.activation(__UpperCamelCase   )
					return features






class 		_lowerCamelCase		(   UpperCamelCase_    ):
				__a  =      MobileNetVaConfig
				__a  =      load_tf_weights_in_mobilenet_va
				__a  =      "mobilenet_v1"
				__a  =      "pixel_values"
				__a  =      False
				def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					Any:
					if isinstance(__UpperCamelCase , (nn.Linear, nn.Convad)   ):
						module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range   )
						if module.bias is not None:
							module.bias.data.zero_()
					elif isinstance(__UpperCamelCase , nn.BatchNormad   ):
						module.bias.data.zero_()
						module.weight.data.fill_(1.0   )
lowercase_     :   List[Any] 				=				R'\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'

lowercase_     :   Any 				=				R'\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`MobileNetV1ImageProcessor.__call__`] for details.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'






@add_start_docstrings(
    "The bare MobileNetV1 model outputting raw hidden-states without any specific head on top."    ,     UpperCamelCase_    ,     )
class 		_lowerCamelCase		(   UpperCamelCase_    ):
				def __init__(  self , lowerCAmelCase , lowerCAmelCase = True   )       ->					str:
					super().__init__(__UpperCamelCase   )
					SCREAMING_SNAKE_CASE__:   Union[str, Any]=      config

					SCREAMING_SNAKE_CASE__:   str=      32
					SCREAMING_SNAKE_CASE__:   Optional[int]=      max(int(depth * config.depth_multiplier   ) , config.min_depth   )

					SCREAMING_SNAKE_CASE__:   Optional[Any]=      MobileNetVaConvLayer(
					    __UpperCamelCase , in_channels=config.num_channels , out_channels=__UpperCamelCase , kernel_size=3 , stride=2 , )

					SCREAMING_SNAKE_CASE__:   Union[str, Any]=      [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

					SCREAMING_SNAKE_CASE__:   Dict=      nn.ModuleList()
					for i in range(13   ):
						SCREAMING_SNAKE_CASE__:   List[Any]=      out_channels

						if strides[i] == 2 or i == 0:
							depth *= 2
							SCREAMING_SNAKE_CASE__:   Dict=      max(int(depth * config.depth_multiplier   ) , config.min_depth   )

						self.layer.append(
						    MobileNetVaConvLayer(
						        __UpperCamelCase , in_channels=__UpperCamelCase , out_channels=__UpperCamelCase , kernel_size=3 , stride=strides[i] , groups=__UpperCamelCase , )   )

						self.layer.append(
						    MobileNetVaConvLayer(
						        __UpperCamelCase , in_channels=__UpperCamelCase , out_channels=__UpperCamelCase , kernel_size=1 , )   )

					SCREAMING_SNAKE_CASE__:   int=      nn.AdaptiveAvgPoolad((1, 1)   ) if add_pooling_layer else None

					# Initialize weights and apply final processing
					self.post_init()
				def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					int:
					raise NotImplementedError



				@add_start_docstrings_to_model_forward(__UpperCamelCase   )
				@add_code_sample_docstrings(
				    checkpoint=_CHECKPOINT_FOR_DOC , output_type=__UpperCamelCase , config_class=_CONFIG_FOR_DOC , modality='''vision''' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
				def 						UpperCamelCase_				(  self , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , )       ->					Optional[Any]:
					SCREAMING_SNAKE_CASE__:   Optional[int]=      (
					    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
					)
					SCREAMING_SNAKE_CASE__:   int=      return_dict if return_dict is not None else self.config.use_return_dict

					if pixel_values is None:
						raise ValueError('''You have to specify pixel_values'''   )

					SCREAMING_SNAKE_CASE__:   Any=      self.conv_stem(__UpperCamelCase   )

					SCREAMING_SNAKE_CASE__:   Dict=      () if output_hidden_states else None

					for i, layer_module in enumerate(self.layer   ):
						SCREAMING_SNAKE_CASE__:   str=      layer_module(__UpperCamelCase   )

						if output_hidden_states:
							SCREAMING_SNAKE_CASE__:   str=      all_hidden_states + (hidden_states,)

					SCREAMING_SNAKE_CASE__:   str=      hidden_states

					if self.pooler is not None:
						SCREAMING_SNAKE_CASE__:   Dict=      torch.flatten(self.pooler(__UpperCamelCase   ) , start_dim=1   )
					else:
						SCREAMING_SNAKE_CASE__:   Union[str, Any]=      None

					if not return_dict:
						return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None   )

					return BaseModelOutputWithPoolingAndNoAttention(
					    last_hidden_state=__UpperCamelCase , pooler_output=__UpperCamelCase , hidden_states=__UpperCamelCase , )






@add_start_docstrings(
    "\n    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "    ,     UpperCamelCase_    ,     )
class 		_lowerCamelCase		(   UpperCamelCase_    ):
				def __init__(  self , lowerCAmelCase   )       ->					str:
					super().__init__(__UpperCamelCase   )

					SCREAMING_SNAKE_CASE__:   str=      config.num_labels
					SCREAMING_SNAKE_CASE__:   Any=      MobileNetVaModel(__UpperCamelCase   )

					SCREAMING_SNAKE_CASE__:   int=      self.mobilenet_va.layer[-1].convolution.out_channels

					# Classifier head
					SCREAMING_SNAKE_CASE__:   int=      nn.Dropout(config.classifier_dropout_prob , inplace=__UpperCamelCase   )
					SCREAMING_SNAKE_CASE__:   List[Any]=      nn.Linear(__UpperCamelCase , config.num_labels   ) if config.num_labels > 0 else nn.Identity()

					# Initialize weights and apply final processing
					self.post_init()



				@add_start_docstrings_to_model_forward(__UpperCamelCase   )
				@add_code_sample_docstrings(
				    checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=__UpperCamelCase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
				def 						UpperCamelCase_				(  self , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , )       ->					int:
					SCREAMING_SNAKE_CASE__:   Optional[int]=      return_dict if return_dict is not None else self.config.use_return_dict

					SCREAMING_SNAKE_CASE__:   Any=      self.mobilenet_va(__UpperCamelCase , output_hidden_states=__UpperCamelCase , return_dict=__UpperCamelCase   )

					SCREAMING_SNAKE_CASE__:   int=      outputs.pooler_output if return_dict else outputs[1]

					SCREAMING_SNAKE_CASE__:   Optional[Any]=      self.classifier(self.dropout(__UpperCamelCase   )   )

					SCREAMING_SNAKE_CASE__:   List[Any]=      None
					if labels is not None:
						if self.config.problem_type is None:
							if self.num_labels == 1:
								SCREAMING_SNAKE_CASE__:   int=      '''regression'''
							elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
								SCREAMING_SNAKE_CASE__:   List[str]=      '''single_label_classification'''
							else:
								SCREAMING_SNAKE_CASE__:   Optional[int]=      '''multi_label_classification'''

						if self.config.problem_type == "regression":
							SCREAMING_SNAKE_CASE__:   int=      MSELoss()
							if self.num_labels == 1:
								SCREAMING_SNAKE_CASE__:   List[str]=      loss_fct(logits.squeeze() , labels.squeeze()   )
							else:
								SCREAMING_SNAKE_CASE__:   Tuple=      loss_fct(__UpperCamelCase , __UpperCamelCase   )
						elif self.config.problem_type == "single_label_classification":
							SCREAMING_SNAKE_CASE__:   Optional[int]=      CrossEntropyLoss()
							SCREAMING_SNAKE_CASE__:   Tuple=      loss_fct(logits.view(-1 , self.num_labels   ) , labels.view(-1   )   )
						elif self.config.problem_type == "multi_label_classification":
							SCREAMING_SNAKE_CASE__:   List[Any]=      BCEWithLogitsLoss()
							SCREAMING_SNAKE_CASE__:   Optional[int]=      loss_fct(__UpperCamelCase , __UpperCamelCase   )

					if not return_dict:
						SCREAMING_SNAKE_CASE__:   Optional[Any]=      (logits,) + outputs[2:]
						return ((loss,) + output) if loss is not None else output

					return ImageClassifierOutputWithNoAttention(
					    loss=__UpperCamelCase , logits=__UpperCamelCase , hidden_states=outputs.hidden_states , )


from collections import OrderedDict
from typing import Mapping

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


lowercase_     :   List[Any] 				=				logging.get_logger(__name__)

lowercase_     :   List[Any] 				=				{
    '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 		_lowerCamelCase		(   UpperCamelCase_    ):
    __a  =      "xlm-roberta-xl"
    def __init__(  self , lowerCAmelCase=250880 , lowerCAmelCase=2560 , lowerCAmelCase=36 , lowerCAmelCase=32 , lowerCAmelCase=10240 , lowerCAmelCase="gelu" , lowerCAmelCase=0.1 , lowerCAmelCase=0.1 , lowerCAmelCase=514 , lowerCAmelCase=1 , lowerCAmelCase=0.02 , lowerCAmelCase=1e-05 , lowerCAmelCase=1 , lowerCAmelCase=0 , lowerCAmelCase=2 , lowerCAmelCase="absolute" , lowerCAmelCase=True , lowerCAmelCase=None , **lowerCAmelCase , )       ->					Union[str, Any]:
     super().__init__(pad_token_id=lowerCAmelCase , bos_token_id=lowerCAmelCase , eos_token_id=lowerCAmelCase , **lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   List[Any]=      vocab_size
     SCREAMING_SNAKE_CASE__:   Dict=      hidden_size
     SCREAMING_SNAKE_CASE__:   Tuple=      num_hidden_layers
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      num_attention_heads
     SCREAMING_SNAKE_CASE__:   int=      hidden_act
     SCREAMING_SNAKE_CASE__:   Any=      intermediate_size
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      hidden_dropout_prob
     SCREAMING_SNAKE_CASE__:   str=      attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__:   List[Any]=      max_position_embeddings
     SCREAMING_SNAKE_CASE__:   Dict=      type_vocab_size
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      initializer_range
     SCREAMING_SNAKE_CASE__:   Dict=      layer_norm_eps
     SCREAMING_SNAKE_CASE__:   Dict=      position_embedding_type
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      use_cache
     SCREAMING_SNAKE_CASE__:   str=      classifier_dropout






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    @property
    def 						UpperCamelCase_				(  self   )       ->					Mapping[str, Mapping[int, str]]:
     if self.task == "multiple-choice":
      SCREAMING_SNAKE_CASE__:   Dict=      {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
     else:
      SCREAMING_SNAKE_CASE__:   List[str]=      {0: '''batch''', 1: '''sequence'''}
     return OrderedDict(
         [
             ('''input_ids''', dynamic_axis),
             ('''attention_mask''', dynamic_axis),
         ]   )

