

'''simple docstring'''


import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__										= logging.get_logger(__name__)

lowerCAmelCase__										= {
    'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/config.json',
    'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/config.json',
}



class 					lowercase						(  snake_case__ ):
							_lowerCamelCase		:      int=   """xlnet"""
							_lowerCamelCase		:      Optional[Any]=   ["""mems"""]
							_lowerCamelCase		:      List[Any]=   {
							    """n_token""": """vocab_size""",  # Backward compatibility
							    """hidden_size""": """d_model""",
							    """num_attention_heads""": """n_head""",
							    """num_hidden_layers""": """n_layer""",
							}


							def __init__(			self    ,			_snake_case=3_2000    ,			_snake_case=1024    ,			_snake_case=24    ,			_snake_case=16    ,			_snake_case=4096    ,			_snake_case="gelu"    ,			_snake_case=True    ,			_snake_case="bi"    ,			_snake_case=0.02    ,			_snake_case=1e-12    ,			_snake_case=0.1    ,			_snake_case=512    ,			_snake_case=None    ,			_snake_case=True    ,			_snake_case=False    ,			_snake_case=False    ,			_snake_case=-1    ,			_snake_case=False    ,			_snake_case="last"    ,			_snake_case=True    ,			_snake_case="tanh"    ,			_snake_case=0.1    ,			_snake_case=5    ,			_snake_case=5    ,			_snake_case=5    ,			_snake_case=1    ,			_snake_case=2    ,			**_snake_case    ,			)							->				List[str]:
								UpperCAmelCase_		:      Tuple       	=					vocab_size
								UpperCAmelCase_		:      Union[str, Any]       	=					d_model
								UpperCAmelCase_		:      Optional[Any]       	=					n_layer
								UpperCAmelCase_		:      Tuple       	=					n_head
								if d_model % n_head != 0:
									raise ValueError(F"""'d_model % n_head' ({d_model % n_head}) should be equal to 0""")
								if "d_head" in kwargs:
									if kwargs["d_head"] != d_model // n_head:
										raise ValueError(
										    F"""`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})""")
								UpperCAmelCase_		:      int       	=					d_model // n_head
								UpperCAmelCase_		:      Optional[int]       	=					ff_activation
								UpperCAmelCase_		:      Dict       	=					d_inner
								UpperCAmelCase_		:      List[str]       	=					untie_r
								UpperCAmelCase_		:      List[Any]       	=					attn_type

								UpperCAmelCase_		:      int       	=					initializer_range
								UpperCAmelCase_		:      Tuple       	=					layer_norm_eps

								UpperCAmelCase_		:      List[Any]       	=					dropout
								UpperCAmelCase_		:      Optional[int]       	=					mem_len
								UpperCAmelCase_		:      int       	=					reuse_len
								UpperCAmelCase_		:      Any       	=					bi_data
								UpperCAmelCase_		:      Optional[int]       	=					clamp_len
								UpperCAmelCase_		:      str       	=					same_length

								UpperCAmelCase_		:      List[str]       	=					summary_type
								UpperCAmelCase_		:      List[Any]       	=					summary_use_proj
								UpperCAmelCase_		:      Union[str, Any]       	=					summary_activation
								UpperCAmelCase_		:      Optional[int]       	=					summary_last_dropout
								UpperCAmelCase_		:      Optional[Any]       	=					start_n_top
								UpperCAmelCase_		:      Union[str, Any]       	=					end_n_top

								UpperCAmelCase_		:      List[str]       	=					bos_token_id
								UpperCAmelCase_		:      Optional[Any]       	=					pad_token_id
								UpperCAmelCase_		:      List[str]       	=					eos_token_id

								if "use_cache" in kwargs:
									warnings.warn(
									    'The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'
									    ' instead.'    ,			UpperCAmelCase_    ,			)
									UpperCAmelCase_		:      Dict       	=					kwargs['use_cache']

								UpperCAmelCase_		:      str       	=					use_mems_eval
								UpperCAmelCase_		:      Union[str, Any]       	=					use_mems_train
								super().__init__(pad_token_id=UpperCAmelCase_    ,			bos_token_id=UpperCAmelCase_    ,			eos_token_id=UpperCAmelCase_    ,			**UpperCAmelCase_)


							@property
							def 						_snake_case							(			self)							->				Union[str, Any]:
								logger.info(F"""The model {self.model_type} is one of the few models that has no sequence length limit.""")
								return -1





							@max_position_embeddings.setter
							def 						_snake_case							(			self    ,			_snake_case)							->				str:
								raise NotImplementedError(
								    F"""The model {self.model_type} is one of the few models that has no sequence length limit.""")









'''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


lowerCAmelCase__										= logging.get_logger(__name__)

# General docstring
lowerCAmelCase__										= "RegNetConfig"

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

# Image classification docstring
lowerCAmelCase__										= "facebook/regnet-y-040"
lowerCAmelCase__										= "tabby, tabby cat"

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



class 					lowercase						(  nn.Module ):


							def __init__(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case = 3    ,			_snake_case = 1    ,			_snake_case = 1    ,			_snake_case = "relu"    ,			)							->				int:
								super().__init__()
								UpperCAmelCase_		:      str       	=					nn.Convad(
								    _snake_case    ,			_snake_case    ,			kernel_size=_snake_case    ,			stride=_snake_case    ,			padding=kernel_size // 2    ,			groups=_snake_case    ,			bias=_snake_case    ,			)
								UpperCAmelCase_		:      List[Any]       	=					nn.BatchNormad(_snake_case)
								UpperCAmelCase_		:      Tuple       	=					ACTaFN[activation] if activation is not None else nn.Identity()





							def 						_snake_case							(			self    ,			_snake_case)							->				Tuple:
								UpperCAmelCase_		:      Optional[int]       	=					self.convolution(_snake_case)
								UpperCAmelCase_		:      int       	=					self.normalization(_snake_case)
								UpperCAmelCase_		:      Optional[int]       	=					self.activation(_snake_case)
								return hidden_state



class 					lowercase						(  nn.Module ):


							def __init__(			self    ,			_snake_case)							->				List[Any]:
								super().__init__()
								UpperCAmelCase_		:      Tuple       	=					RegNetConvLayer(
								    config.num_channels    ,			config.embedding_size    ,			kernel_size=3    ,			stride=2    ,			activation=config.hidden_act)
								UpperCAmelCase_		:      Optional[Any]       	=					config.num_channels





							def 						_snake_case							(			self    ,			_snake_case)							->				Dict:
								UpperCAmelCase_		:      List[Any]       	=					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.')
								UpperCAmelCase_		:      Any       	=					self.embedder(_snake_case)
								return hidden_state



class 					lowercase						(  nn.Module ):


							def __init__(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case = 2)							->				Optional[Any]:
								super().__init__()
								UpperCAmelCase_		:      Any       	=					nn.Convad(_snake_case    ,			_snake_case    ,			kernel_size=1    ,			stride=_snake_case    ,			bias=_snake_case)
								UpperCAmelCase_		:      Optional[Any]       	=					nn.BatchNormad(_snake_case)





							def 						_snake_case							(			self    ,			_snake_case)							->				Tensor:
								UpperCAmelCase_		:      Optional[Any]       	=					self.convolution(_snake_case)
								UpperCAmelCase_		:      Dict       	=					self.normalization(_snake_case)
								return hidden_state



class 					lowercase						(  nn.Module ):


							def __init__(			self    ,			_snake_case    ,			_snake_case)							->				Any:
								super().__init__()

								UpperCAmelCase_		:      Tuple       	=					nn.AdaptiveAvgPoolad((1, 1))
								UpperCAmelCase_		:      int       	=					nn.Sequential(
								    nn.Convad(_snake_case    ,			_snake_case    ,			kernel_size=1)    ,			nn.ReLU()    ,			nn.Convad(_snake_case    ,			_snake_case    ,			kernel_size=1)    ,			nn.Sigmoid()    ,			)





							def 						_snake_case							(			self    ,			_snake_case)							->				Any:
								# b c h w -> b c 1 1
								UpperCAmelCase_		:      Union[str, Any]       	=					self.pooler(_snake_case)
								UpperCAmelCase_		:      Any       	=					self.attention(_snake_case)
								UpperCAmelCase_		:      Optional[Any]       	=					hidden_state * attention
								return hidden_state



class 					lowercase						(  nn.Module ):


							def __init__(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case = 1)							->				str:
								super().__init__()
								UpperCAmelCase_		:      Optional[Any]       	=					in_channels != out_channels or stride != 1
								UpperCAmelCase_		:      Any       	=					max(1    ,			out_channels // config.groups_width)
								UpperCAmelCase_		:      str       	=					(
								    RegNetShortCut(_snake_case    ,			_snake_case    ,			stride=_snake_case) if should_apply_shortcut else nn.Identity()
								)
								UpperCAmelCase_		:      Optional[int]       	=					nn.Sequential(
								    RegNetConvLayer(_snake_case    ,			_snake_case    ,			kernel_size=1    ,			activation=config.hidden_act)    ,			RegNetConvLayer(_snake_case    ,			_snake_case    ,			stride=_snake_case    ,			groups=_snake_case    ,			activation=config.hidden_act)    ,			RegNetConvLayer(_snake_case    ,			_snake_case    ,			kernel_size=1    ,			activation=_snake_case)    ,			)
								UpperCAmelCase_		:      int       	=					ACTaFN[config.hidden_act]





							def 						_snake_case							(			self    ,			_snake_case)							->				Union[str, Any]:
								UpperCAmelCase_		:      str       	=					hidden_state
								UpperCAmelCase_		:      List[Any]       	=					self.layer(_snake_case)
								UpperCAmelCase_		:      Dict       	=					self.shortcut(_snake_case)
								hidden_state += residual
								UpperCAmelCase_		:      Any       	=					self.activation(_snake_case)
								return hidden_state



class 					lowercase						(  nn.Module ):


							def __init__(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case = 1)							->				int:
								super().__init__()
								UpperCAmelCase_		:      List[Any]       	=					in_channels != out_channels or stride != 1
								UpperCAmelCase_		:      Optional[Any]       	=					max(1    ,			out_channels // config.groups_width)
								UpperCAmelCase_		:      Optional[int]       	=					(
								    RegNetShortCut(_snake_case    ,			_snake_case    ,			stride=_snake_case) if should_apply_shortcut else nn.Identity()
								)
								UpperCAmelCase_		:      Optional[Any]       	=					nn.Sequential(
								    RegNetConvLayer(_snake_case    ,			_snake_case    ,			kernel_size=1    ,			activation=config.hidden_act)    ,			RegNetConvLayer(_snake_case    ,			_snake_case    ,			stride=_snake_case    ,			groups=_snake_case    ,			activation=config.hidden_act)    ,			RegNetSELayer(_snake_case    ,			reduced_channels=int(round(in_channels / 4)))    ,			RegNetConvLayer(_snake_case    ,			_snake_case    ,			kernel_size=1    ,			activation=_snake_case)    ,			)
								UpperCAmelCase_		:      Any       	=					ACTaFN[config.hidden_act]





							def 						_snake_case							(			self    ,			_snake_case)							->				Union[str, Any]:
								UpperCAmelCase_		:      List[str]       	=					hidden_state
								UpperCAmelCase_		:      int       	=					self.layer(_snake_case)
								UpperCAmelCase_		:      Any       	=					self.shortcut(_snake_case)
								hidden_state += residual
								UpperCAmelCase_		:      Any       	=					self.activation(_snake_case)
								return hidden_state



class 					lowercase						(  nn.Module ):


							def __init__(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case = 2    ,			_snake_case = 2    ,			)							->				Optional[int]:
								super().__init__()

								UpperCAmelCase_		:      str       	=					RegNetXLayer if config.layer_type == 'x' else RegNetYLayer

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





							def 						_snake_case							(			self    ,			_snake_case)							->				Dict:
								UpperCAmelCase_		:      Optional[Any]       	=					self.layers(_snake_case)
								return hidden_state



class 					lowercase						(  nn.Module ):


							def __init__(			self    ,			_snake_case)							->				List[Any]:
								super().__init__()
								UpperCAmelCase_		:      List[str]       	=					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(
								        _snake_case    ,			config.embedding_size    ,			config.hidden_sizes[0]    ,			stride=2 if config.downsample_in_first_stage else 1    ,			depth=config.depths[0]    ,			))
								UpperCAmelCase_		:      Dict       	=					zip(config.hidden_sizes    ,			config.hidden_sizes[1:])
								for (in_channels, out_channels), depth in zip(_snake_case    ,			config.depths[1:]):
									self.stages.append(RegNetStage(_snake_case    ,			_snake_case    ,			_snake_case    ,			depth=_snake_case))





							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case = False    ,			_snake_case = True)							->				BaseModelOutputWithNoAttention:
								UpperCAmelCase_		:      List[Any]       	=					() if output_hidden_states else None

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

									UpperCAmelCase_		:      Tuple       	=					stage_module(_snake_case)

								if output_hidden_states:
									UpperCAmelCase_		:      Union[str, Any]       	=					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=_snake_case    ,			hidden_states=_snake_case)



class 					lowercase						(  a_ ):
							_lowerCamelCase		:      str=   RegNetConfig
							_lowerCamelCase		:      Optional[int]=   "regnet"
							_lowerCamelCase		:      Union[str, Any]=   "pixel_values"
							_lowerCamelCase		:      List[str]=   True


							def 						_snake_case							(			self    ,			_snake_case)							->				List[Any]:
								if isinstance(_snake_case    ,			nn.Convad):
									nn.init.kaiming_normal_(module.weight    ,			mode='fan_out'    ,			nonlinearity='relu')
								elif isinstance(_snake_case    ,			(nn.BatchNormad, nn.GroupNorm)):
									nn.init.constant_(module.weight    ,			1)
									nn.init.constant_(module.bias    ,			0)





							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case=False)							->				List[Any]:
								if isinstance(_snake_case    ,			_snake_case):
									UpperCAmelCase_		:      str       	=					value


lowerCAmelCase__										= 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"

lowerCAmelCase__										= 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.",					a_,					)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 					lowercase						(  a_ ):


							def __init__(			self    ,			_snake_case)							->				List[str]:
								super().__init__(_snake_case)
								UpperCAmelCase_		:      str       	=					config
								UpperCAmelCase_		:      Optional[int]       	=					RegNetEmbeddings(_snake_case)
								UpperCAmelCase_		:      int       	=					RegNetEncoder(_snake_case)
								UpperCAmelCase_		:      Any       	=					nn.AdaptiveAvgPoolad((1, 1))
								# Initialize weights and apply final processing
								self.post_init()





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

								UpperCAmelCase_		:      int       	=					self.embedder(_snake_case)

								UpperCAmelCase_		:      str       	=					self.encoder(
								    _snake_case    ,			output_hidden_states=_snake_case    ,			return_dict=_snake_case)

								UpperCAmelCase_		:      List[Any]       	=					encoder_outputs[0]

								UpperCAmelCase_		:      str       	=					self.pooler(_snake_case)

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

								return BaseModelOutputWithPoolingAndNoAttention(
								    last_hidden_state=_snake_case    ,			pooler_output=_snake_case    ,			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    ",					a_,					)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 					lowercase						(  a_ ):


							def __init__(			self    ,			_snake_case)							->				Union[str, Any]:
								super().__init__(_snake_case)
								UpperCAmelCase_		:      List[str]       	=					config.num_labels
								UpperCAmelCase_		:      Optional[int]       	=					RegNetModel(_snake_case)
								# classification head
								UpperCAmelCase_		:      Optional[Any]       	=					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(_snake_case)
							@add_code_sample_docstrings(
							    checkpoint=_IMAGE_CLASS_CHECKPOINT    ,			output_type=_snake_case    ,			config_class=_CONFIG_FOR_DOC    ,			expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT    ,			)
							def 						_snake_case							(			self    ,			_snake_case = None    ,			_snake_case = None    ,			_snake_case = None    ,			_snake_case = None    ,			)							->				ImageClassifierOutputWithNoAttention:
								UpperCAmelCase_		:      str       	=					return_dict if return_dict is not None else self.config.use_return_dict

								UpperCAmelCase_		:      Optional[Any]       	=					self.regnet(_snake_case    ,			output_hidden_states=_snake_case    ,			return_dict=_snake_case)

								UpperCAmelCase_		:      Dict       	=					outputs.pooler_output if return_dict else outputs[1]

								UpperCAmelCase_		:      int       	=					self.classifier(_snake_case)

								UpperCAmelCase_		:      List[Any]       	=					None

								if labels is not None:
									if self.config.problem_type is None:
										if self.num_labels == 1:
											UpperCAmelCase_		:      Optional[int]       	=					'regression'
										elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
											UpperCAmelCase_		:      Union[str, Any]       	=					'single_label_classification'
										else:
											UpperCAmelCase_		:      Tuple       	=					'multi_label_classification'
									if self.config.problem_type == "regression":
										UpperCAmelCase_		:      int       	=					MSELoss()
										if self.num_labels == 1:
											UpperCAmelCase_		:      Dict       	=					loss_fct(logits.squeeze()    ,			labels.squeeze())
										else:
											UpperCAmelCase_		:      Union[str, Any]       	=					loss_fct(_snake_case    ,			_snake_case)
									elif self.config.problem_type == "single_label_classification":
										UpperCAmelCase_		:      Optional[int]       	=					CrossEntropyLoss()
										UpperCAmelCase_		:      Union[str, Any]       	=					loss_fct(logits.view(-1    ,			self.num_labels)    ,			labels.view(-1))
									elif self.config.problem_type == "multi_label_classification":
										UpperCAmelCase_		:      int       	=					BCEWithLogitsLoss()
										UpperCAmelCase_		:      Tuple       	=					loss_fct(_snake_case    ,			_snake_case)

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

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






