






'''simple docstring'''





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							:							Dict  							=		logging.get_logger(__name__)

# General docstring
__A							:							Optional[Any]  							=		"""RegNetConfig"""

# Base docstring
__A							:							Dict  							=		"""facebook/regnet-y-040"""
__A							:							int  							=		[1, 10_88, 7, 7]

# Image classification docstring
__A							:							str  							=		"""facebook/regnet-y-040"""
__A							:							Union[str, Any]  							=		"""tabby, tabby cat"""

__A							:							int  							=		[
    """facebook/regnet-y-040""",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]
class 				lowercase       (							nn.Module      ):


				'''simple docstring'''

				def __init__(					self     :     List[str]				,     __lowerCamelCase     :     int				,     __lowerCamelCase     :     int				,     __lowerCamelCase     :     int = 3				,     __lowerCamelCase     :     int = 1				,     __lowerCamelCase     :     int = 1				,     __lowerCamelCase     :     Optional[str] = "relu"				,     )							->							int:

					'''simple docstring'''




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




				def    a__				(					self     :     Optional[int]				,     __lowerCamelCase     :     str     )							->							str:

					'''simple docstring'''




					lowerCamelCase__  =     self.convolution(__lowerCamelCase     )
					lowerCamelCase__  =     self.normalization(__lowerCamelCase     )
					lowerCamelCase__  =     self.activation(__lowerCamelCase     )
					return hidden_state
class 				lowercase       (							nn.Module      ):


				'''simple docstring'''

				def __init__(					self     :     Dict				,     __lowerCamelCase     :     RegNetConfig     )							->							Dict:

					'''simple docstring'''




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




				def    a__				(					self     :     Tuple				,     __lowerCamelCase     :     Optional[int]     )							->							Optional[Any]:

					'''simple docstring'''




					lowerCamelCase__  =     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."     )
					lowerCamelCase__  =     self.embedder(__lowerCamelCase     )
					return hidden_state
class 				lowercase       (							nn.Module      ):


				'''simple docstring'''

				def __init__(					self     :     int				,     __lowerCamelCase     :     int				,     __lowerCamelCase     :     int				,     __lowerCamelCase     :     int = 2     )							->							Tuple:

					'''simple docstring'''




					super().__init__()
					lowerCamelCase__  =     nn.Convad(__lowerCamelCase				,     __lowerCamelCase				,     kernel_size=1				,     stride=__lowerCamelCase				,     bias=__lowerCamelCase     )
					lowerCamelCase__  =     nn.BatchNormad(__lowerCamelCase     )




				def    a__				(					self     :     List[Any]				,     __lowerCamelCase     :     Tensor     )							->							Tensor:

					'''simple docstring'''




					lowerCamelCase__  =     self.convolution(__lowerCamelCase     )
					lowerCamelCase__  =     self.normalization(__lowerCamelCase     )
					return hidden_state
class 				lowercase       (							nn.Module      ):


				'''simple docstring'''

				def __init__(					self     :     str				,     __lowerCamelCase     :     int				,     __lowerCamelCase     :     int     )							->							str:

					'''simple docstring'''




					super().__init__()

					lowerCamelCase__  =     nn.AdaptiveAvgPoolad((1, 1)     )
					lowerCamelCase__  =     nn.Sequential(
					    nn.Convad(__lowerCamelCase				,     __lowerCamelCase				,     kernel_size=1     )				,     nn.ReLU()				,     nn.Convad(__lowerCamelCase				,     __lowerCamelCase				,     kernel_size=1     )				,     nn.Sigmoid()				,     )




				def    a__				(					self     :     List[Any]				,     __lowerCamelCase     :     int     )							->							Any:

					'''simple docstring'''




					lowerCamelCase__  =     self.pooler(__lowerCamelCase     )
					lowerCamelCase__  =     self.attention(__lowerCamelCase     )
					lowerCamelCase__  =     hidden_state * attention
					return hidden_state
class 				lowercase       (							nn.Module      ):


				'''simple docstring'''

				def __init__(					self     :     Union[str, Any]				,     __lowerCamelCase     :     RegNetConfig				,     __lowerCamelCase     :     int				,     __lowerCamelCase     :     int				,     __lowerCamelCase     :     int = 1     )							->							int:

					'''simple docstring'''




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




				def    a__				(					self     :     List[str]				,     __lowerCamelCase     :     List[Any]     )							->							Dict:

					'''simple docstring'''




					lowerCamelCase__  =     hidden_state
					lowerCamelCase__  =     self.layer(__lowerCamelCase     )
					lowerCamelCase__  =     self.shortcut(__lowerCamelCase     )
					hidden_state += residual
					lowerCamelCase__  =     self.activation(__lowerCamelCase     )
					return hidden_state
class 				lowercase       (							nn.Module      ):


				'''simple docstring'''

				def __init__(					self     :     Optional[Any]				,     __lowerCamelCase     :     RegNetConfig				,     __lowerCamelCase     :     int				,     __lowerCamelCase     :     int				,     __lowerCamelCase     :     int = 1     )							->							Union[str, Any]:

					'''simple docstring'''




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




				def    a__				(					self     :     Tuple				,     __lowerCamelCase     :     int     )							->							str:

					'''simple docstring'''




					lowerCamelCase__  =     hidden_state
					lowerCamelCase__  =     self.layer(__lowerCamelCase     )
					lowerCamelCase__  =     self.shortcut(__lowerCamelCase     )
					hidden_state += residual
					lowerCamelCase__  =     self.activation(__lowerCamelCase     )
					return hidden_state
class 				lowercase       (							nn.Module      ):


				'''simple docstring'''

				def __init__(					self     :     Dict				,     __lowerCamelCase     :     RegNetConfig				,     __lowerCamelCase     :     int				,     __lowerCamelCase     :     int				,     __lowerCamelCase     :     int = 2				,     __lowerCamelCase     :     int = 2				,     )							->							int:

					'''simple docstring'''




					super().__init__()

					lowerCamelCase__  =     RegNetXLayer if config.layer_type == "x" else RegNetYLayer

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




				def    a__				(					self     :     str				,     __lowerCamelCase     :     int     )							->							str:

					'''simple docstring'''




					lowerCamelCase__  =     self.layers(__lowerCamelCase     )
					return hidden_state
class 				lowercase       (							nn.Module      ):


				'''simple docstring'''

				def __init__(					self     :     List[Any]				,     __lowerCamelCase     :     RegNetConfig     )							->							int:

					'''simple docstring'''




					super().__init__()
					lowerCamelCase__  =     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(
					        __lowerCamelCase				,     config.embedding_size				,     config.hidden_sizes[0]				,     stride=2 if config.downsample_in_first_stage else 1				,     depth=config.depths[0]				,     )     )
					lowerCamelCase__  =     zip(config.hidden_sizes				,     config.hidden_sizes[1:]     )
					for (in_channels, out_channels), depth in zip(__lowerCamelCase				,     config.depths[1:]     ):
						self.stages.append(RegNetStage(__lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     depth=__lowerCamelCase     )     )




				def    a__				(					self     :     int				,     __lowerCamelCase     :     Tensor				,     __lowerCamelCase     :     bool = False				,     __lowerCamelCase     :     bool = True     )							->							BaseModelOutputWithNoAttention:

					'''simple docstring'''




					lowerCamelCase__  =     () if output_hidden_states else None

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

						lowerCamelCase__  =     stage_module(__lowerCamelCase     )

					if output_hidden_states:
						lowerCamelCase__  =     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=__lowerCamelCase				,     hidden_states=__lowerCamelCase     )
class 				lowercase       (							_lowerCamelCase      ):


				'''simple docstring'''

				lowerCAmelCase__      				=							RegNetConfig
				lowerCAmelCase__      				=							"regnet"
				lowerCAmelCase__      				=							"pixel_values"
				lowerCAmelCase__      				=							True

				def    a__				(					self     :     Dict				,     __lowerCamelCase     :     Any     )							->							Optional[Any]:

					'''simple docstring'''




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




				def    a__				(					self     :     Union[str, Any]				,     __lowerCamelCase     :     int				,     __lowerCamelCase     :     List[str]=False     )							->							List[Any]:

					'''simple docstring'''




					if isinstance(__lowerCamelCase				,     __lowerCamelCase     ):
						lowerCamelCase__  =     value
__A							:							List[Any]  							=		R"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

__A							:							Dict  							=		R"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
    "The bare RegNet model outputting raw features without any specific head on top." ,       _lowerCamelCase ,       )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 				lowercase       (							_lowerCamelCase      ):


				'''simple docstring'''

				def __init__(					self     :     Union[str, Any]				,     __lowerCamelCase     :     int     )							->							int:

					'''simple docstring'''




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




				@add_start_docstrings_to_model_forward(__lowerCamelCase     )
				@add_code_sample_docstrings(
				    checkpoint=_CHECKPOINT_FOR_DOC				,     output_type=__lowerCamelCase				,     config_class=_CONFIG_FOR_DOC				,     modality="vision"				,     expected_output=_EXPECTED_OUTPUT_SHAPE				,     )
				def    a__				(					self     :     Any				,     __lowerCamelCase     :     Tensor				,     __lowerCamelCase     :     Optional[bool] = None				,     __lowerCamelCase     :     Optional[bool] = None     )							->							BaseModelOutputWithPoolingAndNoAttention:

					'''simple docstring'''




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

					lowerCamelCase__  =     self.embedder(__lowerCamelCase     )

					lowerCamelCase__  =     self.encoder(
					    __lowerCamelCase				,     output_hidden_states=__lowerCamelCase				,     return_dict=__lowerCamelCase     )

					lowerCamelCase__  =     encoder_outputs[0]

					lowerCamelCase__  =     self.pooler(__lowerCamelCase     )

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

					return BaseModelOutputWithPoolingAndNoAttention(
					    last_hidden_state=__lowerCamelCase				,     pooler_output=__lowerCamelCase				,     hidden_states=encoder_outputs.hidden_states				,     )
@add_start_docstrings(
    "\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    " ,       _lowerCamelCase ,       )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 				lowercase       (							_lowerCamelCase      ):


				'''simple docstring'''

				def __init__(					self     :     str				,     __lowerCamelCase     :     Optional[Any]     )							->							str:

					'''simple docstring'''




					super().__init__(__lowerCamelCase     )
					lowerCamelCase__  =     config.num_labels
					lowerCamelCase__  =     RegNetModel(__lowerCamelCase     )
					# classification head
					lowerCamelCase__  =     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(__lowerCamelCase     )
				@add_code_sample_docstrings(
				    checkpoint=_IMAGE_CLASS_CHECKPOINT				,     output_type=__lowerCamelCase				,     config_class=_CONFIG_FOR_DOC				,     expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT				,     )
				def    a__				(					self     :     Optional[Any]				,     __lowerCamelCase     :     Optional[torch.FloatTensor] = None				,     __lowerCamelCase     :     Optional[torch.LongTensor] = None				,     __lowerCamelCase     :     Optional[bool] = None				,     __lowerCamelCase     :     Optional[bool] = None				,     )							->							ImageClassifierOutputWithNoAttention:

					'''simple docstring'''




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

					lowerCamelCase__  =     self.regnet(__lowerCamelCase				,     output_hidden_states=__lowerCamelCase				,     return_dict=__lowerCamelCase     )

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

					lowerCamelCase__  =     self.classifier(__lowerCamelCase     )

					lowerCamelCase__  =     None

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

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

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









'''simple docstring'''





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							:							Union[str, Any]  							=		logging.get_logger(__name__)

__A							:							Tuple  							=		{
    """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 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

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

    def __init__(					self     :     str				,     __lowerCamelCase     :     Optional[int]=50257				,     __lowerCamelCase     :     Optional[int]=2048				,     __lowerCamelCase     :     Optional[int]=2048				,     __lowerCamelCase     :     Any=24				,     __lowerCamelCase     :     List[Any]=[[["global", "local"], 12]]				,     __lowerCamelCase     :     Any=16				,     __lowerCamelCase     :     Any=None				,     __lowerCamelCase     :     Optional[Any]=256				,     __lowerCamelCase     :     List[Any]="gelu_new"				,     __lowerCamelCase     :     Any=0.0				,     __lowerCamelCase     :     List[Any]=0.0				,     __lowerCamelCase     :     Union[str, Any]=0.0				,     __lowerCamelCase     :     Any=0.1				,     __lowerCamelCase     :     Dict=1E-5				,     __lowerCamelCase     :     List[Any]=0.0_2				,     __lowerCamelCase     :     List[Any]=True				,     __lowerCamelCase     :     Optional[int]=50256				,     __lowerCamelCase     :     Tuple=50256				,     **__lowerCamelCase     :     Optional[int]				,     )							->							str:

     '''simple docstring'''




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

     lowerCamelCase__  =     bos_token_id
     lowerCamelCase__  =     eos_token_id

     lowerCamelCase__  =     attention_types
     lowerCamelCase__  =     self.expand_attention_types_params(__lowerCamelCase     )

     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=__lowerCamelCase				,     eos_token_id=__lowerCamelCase				,     **__lowerCamelCase     )




    @staticmethod
    def    a__				(					__lowerCamelCase     :     int     )							->							str:

     '''simple docstring'''




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






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__				,     lowercase__):
 import torch

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

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

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

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

 return sliced.permute(lowercase__)






def    lowerCamelCase_     (    lowercase__				,     lowercase__):
 import torch

 lowerCamelCase__  =     torch.arange(1				,     lowercase__)
 lowerCamelCase__  =     torch.remainder(lowercase__				,     lowercase__)
 lowerCamelCase__  =     remainders == 0
 lowerCamelCase__  =     candidates[divisor_indices]
 lowerCamelCase__  =     torch.max(lowercase__)
 return largest_divisor, torch.div(lowercase__				,     lowercase__				,     rounding_mode="floor")
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    @property
    def    a__				(					self     :     Any     )							->							Mapping[str, Mapping[int, str]]:

     '''simple docstring'''




     lowerCamelCase__  =     OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}     )
     if self.use_past:
      self.fill_with_past_key_values_(__lowerCamelCase				,     direction="inputs"     )
      lowerCamelCase__  =     {0: "batch", 1: "past_sequence + sequence"}
     else:
      lowerCamelCase__  =     {0: "batch", 1: "sequence"}

     return common_inputs

    @property
    def    a__				(					self     :     Any     )							->							int:

     '''simple docstring'''




     return self._config.num_heads

    def    a__				(					self     :     Optional[Any]				,     __lowerCamelCase     :     PreTrainedTokenizer				,     __lowerCamelCase     :     int = -1				,     __lowerCamelCase     :     int = -1				,     __lowerCamelCase     :     bool = False				,     __lowerCamelCase     :     Optional[TensorType] = None				,     )							->							Mapping[str, Any]:

     '''simple docstring'''




     lowerCamelCase__  =     super(__lowerCamelCase				,     self     ).generate_dummy_inputs(
         __lowerCamelCase				,     batch_size=__lowerCamelCase				,     seq_length=__lowerCamelCase				,     is_pair=__lowerCamelCase				,     framework=__lowerCamelCase     )

     # We need to order the input in the way they appears in the forward()
     lowerCamelCase__  =     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

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

     lowerCamelCase__  =     common_inputs["attention_mask"]
     if self.use_past:
      lowerCamelCase__  =     ordered_inputs["attention_mask"].dtype
      lowerCamelCase__  =     torch.cat(
          [ordered_inputs["attention_mask"], torch.ones(__lowerCamelCase				,     __lowerCamelCase				,     dtype=__lowerCamelCase     )]				,     dim=1     )

     return ordered_inputs




    @property
    def    a__				(					self     :     Optional[Any]     )							->							int:

     '''simple docstring'''




     return 13

