

from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig


A									=  logging.get_logger(__name__)

# General docstring
A									=  'RegNetConfig'

# Base docstring
A									=  'facebook/regnet-y-040'
A									=  [1, 1088, 7, 7]

# Image classification docstring
A									=  'facebook/regnet-y-040'
A									=  'tabby, tabby cat'

A									=  [
    'facebook/regnet-y-040',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]


class SCREAMING_SNAKE_CASE	(		nn.Module				):






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase = 3			,					__UpperCamelCase = 1			,					__UpperCamelCase = 1			,					__UpperCamelCase = "relu"			,					):

				"""simple docstring"""


				super().__init__()
				snake_case_   		=			nn.Convad(
				    __UpperCamelCase			,					__UpperCamelCase			,					kernel_size=__UpperCamelCase			,					stride=__UpperCamelCase			,					padding=kernel_size // 2			,					groups=__UpperCamelCase			,					bias=__UpperCamelCase			,					)
				snake_case_   		=			nn.BatchNormad(__UpperCamelCase				)
				snake_case_   		=			ACTaFN[activation] if activation is not None else nn.Identity()







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			self.convolution(__UpperCamelCase				)
				snake_case_   		=			self.normalization(__UpperCamelCase				)
				snake_case_   		=			self.activation(__UpperCamelCase				)
				return hidden_state







class SCREAMING_SNAKE_CASE	(		nn.Module				):






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				super().__init__()
				snake_case_   		=			RegNetConvLayer(
				    config.num_channels			,					config.embedding_size			,					kernel_size=3			,					stride=2			,					activation=config.hidden_act				)
				snake_case_   		=			config.num_channels







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			pixel_values.shape[1]
				if num_channels != self.num_channels:
					raise ValueError(
					    'Make sure that the channel dimension of the pixel values match with the one set in the configuration.'				)
				snake_case_   		=			self.embedder(__UpperCamelCase				)
				return hidden_state







class SCREAMING_SNAKE_CASE	(		nn.Module				):






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase = 2				):

				"""simple docstring"""


				super().__init__()
				snake_case_   		=			nn.Convad(__UpperCamelCase			,					__UpperCamelCase			,					kernel_size=1			,					stride=__UpperCamelCase			,					bias=__UpperCamelCase				)
				snake_case_   		=			nn.BatchNormad(__UpperCamelCase				)







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			self.convolution(__UpperCamelCase				)
				snake_case_   		=			self.normalization(__UpperCamelCase				)
				return hidden_state







class SCREAMING_SNAKE_CASE	(		nn.Module				):






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


				super().__init__()

				snake_case_   		=			nn.AdaptiveAvgPoolad((1, 1)				)
				snake_case_   		=			nn.Sequential(
				    nn.Convad(__UpperCamelCase			,					__UpperCamelCase			,					kernel_size=1				)			,					nn.ReLU()			,					nn.Convad(__UpperCamelCase			,					__UpperCamelCase			,					kernel_size=1				)			,					nn.Sigmoid()			,					)







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			self.pooler(__UpperCamelCase				)
				snake_case_   		=			self.attention(__UpperCamelCase				)
				snake_case_   		=			hidden_state * attention
				return hidden_state







class SCREAMING_SNAKE_CASE	(		nn.Module				):






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase = 1				):

				"""simple docstring"""


				super().__init__()
				snake_case_   		=			in_channels != out_channels or stride != 1
				snake_case_   		=			max(1			,					out_channels // config.groups_width				)
				snake_case_   		=			(
				    RegNetShortCut(__UpperCamelCase			,					__UpperCamelCase			,					stride=__UpperCamelCase				) if should_apply_shortcut else nn.Identity()
				)
				snake_case_   		=			nn.Sequential(
				    RegNetConvLayer(__UpperCamelCase			,					__UpperCamelCase			,					kernel_size=1			,					activation=config.hidden_act				)			,					RegNetConvLayer(__UpperCamelCase			,					__UpperCamelCase			,					stride=__UpperCamelCase			,					groups=__UpperCamelCase			,					activation=config.hidden_act				)			,					RegNetConvLayer(__UpperCamelCase			,					__UpperCamelCase			,					kernel_size=1			,					activation=__UpperCamelCase				)			,					)
				snake_case_   		=			ACTaFN[config.hidden_act]







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			hidden_state
				snake_case_   		=			self.layer(__UpperCamelCase				)
				snake_case_   		=			self.shortcut(__UpperCamelCase				)
				hidden_state += residual
				snake_case_   		=			self.activation(__UpperCamelCase				)
				return hidden_state







class SCREAMING_SNAKE_CASE	(		nn.Module				):






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase = 1				):

				"""simple docstring"""


				super().__init__()
				snake_case_   		=			in_channels != out_channels or stride != 1
				snake_case_   		=			max(1			,					out_channels // config.groups_width				)
				snake_case_   		=			(
				    RegNetShortCut(__UpperCamelCase			,					__UpperCamelCase			,					stride=__UpperCamelCase				) if should_apply_shortcut else nn.Identity()
				)
				snake_case_   		=			nn.Sequential(
				    RegNetConvLayer(__UpperCamelCase			,					__UpperCamelCase			,					kernel_size=1			,					activation=config.hidden_act				)			,					RegNetConvLayer(__UpperCamelCase			,					__UpperCamelCase			,					stride=__UpperCamelCase			,					groups=__UpperCamelCase			,					activation=config.hidden_act				)			,					RegNetSELayer(__UpperCamelCase			,					reduced_channels=int(round(in_channels / 4				)				)				)			,					RegNetConvLayer(__UpperCamelCase			,					__UpperCamelCase			,					kernel_size=1			,					activation=__UpperCamelCase				)			,					)
				snake_case_   		=			ACTaFN[config.hidden_act]







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			hidden_state
				snake_case_   		=			self.layer(__UpperCamelCase				)
				snake_case_   		=			self.shortcut(__UpperCamelCase				)
				hidden_state += residual
				snake_case_   		=			self.activation(__UpperCamelCase				)
				return hidden_state







class SCREAMING_SNAKE_CASE	(		nn.Module				):






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase = 2			,					__UpperCamelCase = 2			,					):

				"""simple docstring"""


				super().__init__()

				snake_case_   		=			RegNetXLayer if config.layer_type == 'x' else RegNetYLayer

				snake_case_   		=			nn.Sequential(
				    # downsampling is done in the first layer with stride of 2
				    layer(
				        __UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					stride=__UpperCamelCase			,					)			,					*[layer(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				) for _ in range(depth - 1				)]			,					)







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			self.layers(__UpperCamelCase				)
				return hidden_state







class SCREAMING_SNAKE_CASE	(		nn.Module				):






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				super().__init__()
				snake_case_   		=			nn.ModuleList([]				)
				# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
				self.stages.append(
				    RegNetStage(
				        __UpperCamelCase			,					config.embedding_size			,					config.hidden_sizes[0]			,					stride=2 if config.downsample_in_first_stage else 1			,					depth=config.depths[0]			,					)				)
				snake_case_   		=			zip(config.hidden_sizes			,					config.hidden_sizes[1:]				)
				for (in_channels, out_channels), depth in zip(__UpperCamelCase			,					config.depths[1:]				):
					self.stages.append(RegNetStage(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					depth=__UpperCamelCase				)				)







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase = False			,					__UpperCamelCase = True				):

				"""simple docstring"""


				snake_case_   		=			() if output_hidden_states else None

				for stage_module in self.stages:
					if output_hidden_states:
						snake_case_   		=			hidden_states + (hidden_state,)

					snake_case_   		=			stage_module(__UpperCamelCase				)

				if output_hidden_states:
					snake_case_   		=			hidden_states + (hidden_state,)

				if not return_dict:
					return tuple(v for v in [hidden_state, hidden_states] if v is not None				)

				return BaseModelOutputWithNoAttention(last_hidden_state=__UpperCamelCase			,					hidden_states=__UpperCamelCase				)







class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			__A    	=	RegNetConfig
			__A    	=	"""regnet"""
			__A    	=	"""pixel_values"""
			__A    	=	True


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				if isinstance(__UpperCamelCase			,					nn.Convad				):
					nn.init.kaiming_normal_(module.weight			,					mode='fan_out'			,					nonlinearity='relu'				)
				elif isinstance(__UpperCamelCase			,					(nn.BatchNormad, nn.GroupNorm)				):
					nn.init.constant_(module.weight			,					1				)
					nn.init.constant_(module.bias			,					0				)







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase=False				):

				"""simple docstring"""


				if isinstance(__UpperCamelCase			,					__UpperCamelCase				):
					snake_case_   		=			value







A									=  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 ([`RegNetConfig`]): 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'

A									=  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            [`ConvNextImageProcessor.__call__`] for details.\n\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 [`~file_utils.ModelOutput`] instead of a plain tuple.\n'


@add_start_docstrings(
    """The bare RegNet model outputting raw features without any specific head on top."""				,    __snake_case				,    )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				super().__init__(__UpperCamelCase				)
				snake_case_   		=			config
				snake_case_   		=			RegNetEmbeddings(__UpperCamelCase				)
				snake_case_   		=			RegNetEncoder(__UpperCamelCase				)
				snake_case_   		=			nn.AdaptiveAvgPoolad((1, 1)				)
				# Initialize weights and apply final processing
				self.post_init()







			@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       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase = None			,					__UpperCamelCase = None				):

				"""simple docstring"""


				snake_case_   		=			(
				    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
				)
				snake_case_   		=			return_dict if return_dict is not None else self.config.use_return_dict

				snake_case_   		=			self.embedder(__UpperCamelCase				)

				snake_case_   		=			self.encoder(
				    __UpperCamelCase			,					output_hidden_states=__UpperCamelCase			,					return_dict=__UpperCamelCase				)

				snake_case_   		=			encoder_outputs[0]

				snake_case_   		=			self.pooler(__UpperCamelCase				)

				if not return_dict:
					return (last_hidden_state, pooled_output) + encoder_outputs[1:]

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







@add_start_docstrings(
    """
    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """				,    __snake_case				,    )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				super().__init__(__UpperCamelCase				)
				snake_case_   		=			config.num_labels
				snake_case_   		=			RegNetModel(__UpperCamelCase				)
				# classification head
				snake_case_   		=			nn.Sequential(
				    nn.Flatten()			,					nn.Linear(config.hidden_sizes[-1]			,					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       __lowerCAmelCase			(     self			,					__UpperCamelCase = None			,					__UpperCamelCase = None			,					__UpperCamelCase = None			,					__UpperCamelCase = None			,					):

				"""simple docstring"""


				snake_case_   		=			return_dict if return_dict is not None else self.config.use_return_dict

				snake_case_   		=			self.regnet(__UpperCamelCase			,					output_hidden_states=__UpperCamelCase			,					return_dict=__UpperCamelCase				)

				snake_case_   		=			outputs.pooler_output if return_dict else outputs[1]

				snake_case_   		=			self.classifier(__UpperCamelCase				)

				snake_case_   		=			None

				if labels is not None:
					if self.config.problem_type is None:
						if self.num_labels == 1:
							snake_case_   		=			'regression'
						elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
							snake_case_   		=			'single_label_classification'
						else:
							snake_case_   		=			'multi_label_classification'
					if self.config.problem_type == "regression":
						snake_case_   		=			MSELoss()
						if self.num_labels == 1:
							snake_case_   		=			loss_fct(logits.squeeze()			,					labels.squeeze()				)
						else:
							snake_case_   		=			loss_fct(__UpperCamelCase			,					__UpperCamelCase				)
					elif self.config.problem_type == "single_label_classification":
						snake_case_   		=			CrossEntropyLoss()
						snake_case_   		=			loss_fct(logits.view(-1			,					self.num_labels				)			,					labels.view(-1				)				)
					elif self.config.problem_type == "multi_label_classification":
						snake_case_   		=			BCEWithLogitsLoss()
						snake_case_   		=			loss_fct(__UpperCamelCase			,					__UpperCamelCase				)

				if not return_dict:
					snake_case_   		=			(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 Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


A									=  logging.get_logger(__name__)

A									=  {
    'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}


class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			__A    	=	"""gpt_neo"""
			__A    	=	["""past_key_values"""]
			__A    	=	{"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}


			def __init__(     self			,					__UpperCamelCase=5_02_57			,					__UpperCamelCase=20_48			,					__UpperCamelCase=20_48			,					__UpperCamelCase=24			,					__UpperCamelCase=[[["global", "local"], 12]]			,					__UpperCamelCase=16			,					__UpperCamelCase=None			,					__UpperCamelCase=2_56			,					__UpperCamelCase="gelu_new"			,					__UpperCamelCase=0.0			,					__UpperCamelCase=0.0			,					__UpperCamelCase=0.0			,					__UpperCamelCase=0.1			,					__UpperCamelCase=1E-5			,					__UpperCamelCase=0.02			,					__UpperCamelCase=True			,					__UpperCamelCase=5_02_56			,					__UpperCamelCase=5_02_56			,					**__UpperCamelCase			,					):

				"""simple docstring"""


				snake_case_   		=			vocab_size
				snake_case_   		=			max_position_embeddings
				snake_case_   		=			hidden_size
				snake_case_   		=			num_layers
				snake_case_   		=			num_heads
				snake_case_   		=			intermediate_size
				snake_case_   		=			window_size
				snake_case_   		=			activation_function
				snake_case_   		=			resid_dropout
				snake_case_   		=			embed_dropout
				snake_case_   		=			attention_dropout
				snake_case_   		=			classifier_dropout
				snake_case_   		=			layer_norm_epsilon
				snake_case_   		=			initializer_range
				snake_case_   		=			use_cache

				snake_case_   		=			bos_token_id
				snake_case_   		=			eos_token_id

				snake_case_   		=			attention_types
				snake_case_   		=			self.expand_attention_types_params(__UpperCamelCase				)

				if len(self.attention_layers				) != self.num_layers:
					raise ValueError(
					    'Configuration for convolutional module is incorrect. '
					    'It is required that `len(config.attention_layers)` == `config.num_layers` '
					    f"""but is `len(config.attention_layers) = {len(self.attention_layers				)}`, """
					    f"""`config.num_layers = {self.num_layers}`. """
					    '`config.attention_layers` is prepared using `config.attention_types`. '
					    'Please verify the value of `config.attention_types` argument.'				)

				super().__init__(bos_token_id=__UpperCamelCase			,					eos_token_id=__UpperCamelCase			,					**__UpperCamelCase				)







			@staticmethod
			def       __lowerCAmelCase			(     __UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			[]
				for item in attention_types:
					for _ in range(item[1]				):
						attentions.extend(item[0]				)
				return attentions







def a(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__						):





	'''simple docstring'''
	import torch

	snake_case_   		=			input.size()
	snake_case_   		=			len(lowercase__						)
	snake_case_   		=			shape[dimension]

	snake_case_   		=			torch.arange(0      ,			lowercase__      ,			lowercase__						)
	snake_case_   		=			torch.div(sizedim - size      ,			lowercase__      ,			rounding_mode='floor'						) + 1
	snake_case_   		=			torch.arange(lowercase__						) + low_indices[:min_length][:, None]

	snake_case_   		=			[slice(lowercase__						)] * rank
	snake_case_   		=			indices
	snake_case_   		=			input[s]

	snake_case_   		=			list(range(0      ,			rank + 1						)						)
	perm.append(perm.pop(dimension + 1						)						)

	return sliced.permute(lowercase__						)





def a(lowercase__      ,			lowercase__						):





	'''simple docstring'''
	import torch

	snake_case_   		=			torch.arange(1      ,			lowercase__						)
	snake_case_   		=			torch.remainder(lowercase__      ,			lowercase__						)
	snake_case_   		=			remainders == 0
	snake_case_   		=			candidates[divisor_indices]
	snake_case_   		=			torch.max(lowercase__						)
	return largest_divisor, torch.div(lowercase__      ,			lowercase__      ,			rounding_mode='floor'						)






class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}				)
				if self.use_past:
					self.fill_with_past_key_values_(__UpperCamelCase			,					direction='inputs'				)
					snake_case_   		=			{0: 'batch', 1: 'past_sequence + sequence'}
				else:
					snake_case_   		=			{0: 'batch', 1: 'sequence'}

				return common_inputs


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return self._config.num_heads


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase = -1			,					__UpperCamelCase = -1			,					__UpperCamelCase = False			,					__UpperCamelCase = None			,					):

				"""simple docstring"""


				snake_case_   		=			super(__UpperCamelCase			,					self				).generate_dummy_inputs(
				    __UpperCamelCase			,					batch_size=__UpperCamelCase			,					seq_length=__UpperCamelCase			,					is_pair=__UpperCamelCase			,					framework=__UpperCamelCase				)

				# We need to order the input in the way they appears in the forward()
				snake_case_   		=			OrderedDict({'input_ids': common_inputs['input_ids']}				)

				# Need to add the past_keys
				if self.use_past:
					if not is_torch_available():
						raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'				)
					else:
						import torch

						snake_case_		,							snake_case_   		=			common_inputs['input_ids'].shape
						# Not using the same length for past_key_values
						snake_case_   		=			seqlen + 2
						snake_case_   		=			(
						    batch,
						    self.num_attention_heads,
						    past_key_values_length,
						    self._config.hidden_size // self.num_attention_heads,
						)
						snake_case_   		=			[
						    (torch.zeros(__UpperCamelCase				), torch.zeros(__UpperCamelCase				)) for _ in range(self.num_layers				)
						]

				snake_case_   		=			common_inputs['attention_mask']
				if self.use_past:
					snake_case_   		=			ordered_inputs['attention_mask'].dtype
					snake_case_   		=			torch.cat(
					    [ordered_inputs['attention_mask'], torch.ones(__UpperCamelCase			,					__UpperCamelCase			,					dtype=__UpperCamelCase				)]			,					dim=1				)

				return ordered_inputs







			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return 13
