
'''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_											=						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    UpperCAmelCase_				(	nn.Module					):





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





 def        snake_case__		(    self ,       lowercase_):
     snake_case_ :    Optional[Any]     			=						self.convolution(lowercase_)
     snake_case_ :    Union[str, Any]     			=						self.normalization(lowercase_)
     snake_case_ :    Optional[Any]     			=						self.activation(lowercase_)
     return hidden_state






class    UpperCAmelCase_				(	nn.Module					):





 def __init__(    self ,       lowercase_):
     super().__init__()
     snake_case_ :    int     			=						RegNetConvLayer(
         config.num_channels ,       config.embedding_size ,       kernel_size=3 ,       stride=2 ,       activation=config.hidden_act)
     snake_case_ :    List[str]     			=						config.num_channels





 def        snake_case__		(    self ,       lowercase_):
     snake_case_ :    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.")
     snake_case_ :    Tuple     			=						self.embedder(lowercase_)
     return hidden_state






class    UpperCAmelCase_				(	nn.Module					):





 def __init__(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ = 2):
     super().__init__()
     snake_case_ :    Union[str, Any]     			=						nn.Convad(lowercase_ ,       lowercase_ ,       kernel_size=1 ,       stride=lowercase_ ,       bias=lowercase_)
     snake_case_ :    List[str]     			=						nn.BatchNormad(lowercase_)





 def        snake_case__		(    self ,       lowercase_):
     snake_case_ :    Optional[Any]     			=						self.convolution(lowercase_)
     snake_case_ :    List[Any]     			=						self.normalization(lowercase_)
     return hidden_state






class    UpperCAmelCase_				(	nn.Module					):





 def __init__(    self ,       lowercase_ ,       lowercase_):
     super().__init__()

     snake_case_ :    Tuple     			=						nn.AdaptiveAvgPoolad((1, 1))
     snake_case_ :    Dict     			=						nn.Sequential(
         nn.Convad(lowercase_ ,       lowercase_ ,       kernel_size=1) ,       nn.ReLU() ,       nn.Convad(lowercase_ ,       lowercase_ ,       kernel_size=1) ,       nn.Sigmoid() ,       )





 def        snake_case__		(    self ,       lowercase_):
     # b c h w -> b c 1 1
     snake_case_ :    Tuple     			=						self.pooler(lowercase_)
     snake_case_ :    str     			=						self.attention(lowercase_)
     snake_case_ :    Union[str, Any]     			=						hidden_state * attention
     return hidden_state






class    UpperCAmelCase_				(	nn.Module					):





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





 def        snake_case__		(    self ,       lowercase_):
     snake_case_ :    Dict     			=						hidden_state
     snake_case_ :    List[Any]     			=						self.layer(lowercase_)
     snake_case_ :    Optional[int]     			=						self.shortcut(lowercase_)
     hidden_state += residual
     snake_case_ :    int     			=						self.activation(lowercase_)
     return hidden_state






class    UpperCAmelCase_				(	nn.Module					):





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





 def        snake_case__		(    self ,       lowercase_):
     snake_case_ :    List[Any]     			=						hidden_state
     snake_case_ :    int     			=						self.layer(lowercase_)
     snake_case_ :    Tuple     			=						self.shortcut(lowercase_)
     hidden_state += residual
     snake_case_ :    Optional[int]     			=						self.activation(lowercase_)
     return hidden_state






class    UpperCAmelCase_				(	nn.Module					):





 def __init__(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ = 2 ,       lowercase_ = 2 ,       ):
     super().__init__()

     snake_case_ :    Optional[Any]     			=						RegNetXLayer if config.layer_type == "x" else RegNetYLayer

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





 def        snake_case__		(    self ,       lowercase_):
     snake_case_ :    List[Any]     			=						self.layers(lowercase_)
     return hidden_state






class    UpperCAmelCase_				(	nn.Module					):





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





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ = False ,       lowercase_ = True):
     snake_case_ :    Tuple     			=						() if output_hidden_states else None

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

         snake_case_ :    Dict     			=						stage_module(lowercase_)

     if output_hidden_states:
         snake_case_ :    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=lowercase_ ,       hidden_states=lowercase_)






class    UpperCAmelCase_				(	snake_case__					):
 UpperCAmelCase_		    =						RegNetConfig
 UpperCAmelCase_		    =						"""regnet"""
 UpperCAmelCase_		    =						"""pixel_values"""
 UpperCAmelCase_		    =						True





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





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_=False):
     if isinstance(lowercase_ ,       lowercase_):
         snake_case_ :    List[str]     			=						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    UpperCAmelCase_				(	snake_case__					):





 def __init__(    self ,       lowercase_):
     super().__init__(lowercase_)
     snake_case_ :    Optional[Any]     			=						config
     snake_case_ :    Dict     			=						RegNetEmbeddings(lowercase_)
     snake_case_ :    Dict     			=						RegNetEncoder(lowercase_)
     snake_case_ :    Dict     			=						nn.AdaptiveAvgPoolad((1, 1))
     # Initialize weights and apply final processing
     self.post_init()





 @add_start_docstrings_to_model_forward(lowercase_)
 @add_code_sample_docstrings(
     checkpoint=_CHECKPOINT_FOR_DOC ,       output_type=lowercase_ ,       config_class=_CONFIG_FOR_DOC ,       modality="vision" ,       expected_output=_EXPECTED_OUTPUT_SHAPE ,       )
 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ = None ,       lowercase_ = None):
     snake_case_ :    Optional[int]     			=						(
         output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
     )
     snake_case_ :    int     			=						return_dict if return_dict is not None else self.config.use_return_dict

     snake_case_ :    Union[str, Any]     			=						self.embedder(lowercase_)

     snake_case_ :    List[str]     			=						self.encoder(
         lowercase_ ,       output_hidden_states=lowercase_ ,       return_dict=lowercase_)

     snake_case_ :    Union[str, Any]     			=						encoder_outputs[0]

     snake_case_ :    int     			=						self.pooler(lowercase_)

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

     return BaseModelOutputWithPoolingAndNoAttention(
         last_hidden_state=lowercase_ ,       pooler_output=lowercase_ ,       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    UpperCAmelCase_				(	snake_case__					):





 def __init__(    self ,       lowercase_):
     super().__init__(lowercase_)
     snake_case_ :    str     			=						config.num_labels
     snake_case_ :    Tuple     			=						RegNetModel(lowercase_)
     # classification head
     snake_case_ :    Tuple     			=						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(lowercase_)
 @add_code_sample_docstrings(
     checkpoint=_IMAGE_CLASS_CHECKPOINT ,       output_type=lowercase_ ,       config_class=_CONFIG_FOR_DOC ,       expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,       )
 def        snake_case__		(    self ,       lowercase_ = None ,       lowercase_ = None ,       lowercase_ = None ,       lowercase_ = None ,       ):
     snake_case_ :    Optional[int]     			=						return_dict if return_dict is not None else self.config.use_return_dict

     snake_case_ :    List[Any]     			=						self.regnet(lowercase_ ,       output_hidden_states=lowercase_ ,       return_dict=lowercase_)

     snake_case_ :    Union[str, Any]     			=						outputs.pooler_output if return_dict else outputs[1]

     snake_case_ :    int     			=						self.classifier(lowercase_)

     snake_case_ :    List[str]     			=						None

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

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

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






'''simple docstring'''
import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


a_											=						logging.get_logger(__name__)






class    UpperCAmelCase_				:
 UpperCAmelCase_		    =						None


@experimental
def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    if ParallelBackendConfig.backend_name is None:
        return _map_with_multiprocessing_pool(
            __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

    return _map_with_joblib(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    str     			=						num_proc if num_proc <= len(__SCREAMING_SNAKE_CASE       ) else len(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Optional[int]     			=						[]  # We organize the splits ourselve (contiguous splits)
    for index in range(__SCREAMING_SNAKE_CASE       ):
        snake_case_ :    List[str]     			=						len(__SCREAMING_SNAKE_CASE       ) // num_proc
        snake_case_ :    Optional[Any]     			=						len(__SCREAMING_SNAKE_CASE       ) % num_proc
        snake_case_ :    Optional[Any]     			=						div * index + min(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
        snake_case_ :    Union[str, Any]     			=						start + div + (1 if index < mod else 0)
        split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)       )

    if len(__SCREAMING_SNAKE_CASE       ) != sum(len(i[1]       ) for i in split_kwds       ):
        raise ValueError(
            f'Error dividing inputs iterable among processes. '
            f'Total number of objects {len(__SCREAMING_SNAKE_CASE       )}, '
            f'length: {sum(len(i[1]       ) for i in split_kwds       )}'       )

    logger.info(
        f'Spawning {num_proc} processes for {len(__SCREAMING_SNAKE_CASE       )} objects in slices of {[len(i[1]       ) for i in split_kwds]}'       )
    snake_case_			, snake_case_ :    Tuple     			=						None, None
    if not disable_tqdm:
        snake_case_			, snake_case_ :    int     			=						(RLock(),), tqdm.set_lock
    with Pool(__SCREAMING_SNAKE_CASE,		initargs=__SCREAMING_SNAKE_CASE,		initializer=__SCREAMING_SNAKE_CASE       ) as pool:
        snake_case_ :    Union[str, Any]     			=						pool.map(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    logger.info(f'Finished {num_proc} processes'       )
    snake_case_ :    str     			=						[obj for proc_res in mapped for obj in proc_res]
    logger.info(f'Unpacked {len(__SCREAMING_SNAKE_CASE       )} objects'       )

    return mapped

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    import joblib

    with joblib.parallel_backend(ParallelBackendConfig.backend_name,		n_jobs=__SCREAMING_SNAKE_CASE       ):
        return joblib.Parallel()(
            joblib.delayed(__SCREAMING_SNAKE_CASE       )((function, obj, types, None, True, None)       ) for obj in iterable       )


@experimental
@contextlib.contextmanager
def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Union[str, Any]     			=						backend_name

    if backend_name == "spark":
        from joblibspark import register_spark

        register_spark()

        # TODO: call create_cache_and_write_probe if "download" in steps
        # TODO: raise NotImplementedError when Dataset.map etc is called

    try:
        yield
    finally:
        snake_case_ :    Any     			=						None




