






'''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


SCREAMING_SNAKE_CASE__											=     logging.get_logger(__name__)

# General docstring
SCREAMING_SNAKE_CASE__											=     'RegNetConfig'

# Base docstring
SCREAMING_SNAKE_CASE__											=     'facebook/regnet-y-040'
SCREAMING_SNAKE_CASE__											=     [1, 1_0_8_8, 7, 7]

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

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






class        a_		(    nn.Module       ):

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = 3    ,	_SCREAMING_SNAKE_CASE = 1    ,	_SCREAMING_SNAKE_CASE = 1    ,	_SCREAMING_SNAKE_CASE = "relu"    ,	)						->   int:



        """simple docstring"""






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


  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   Optional[int]:



        """simple docstring"""






        UpperCamelCase       						=      self.convolution(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      self.normalization(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      self.activation(_SCREAMING_SNAKE_CASE   )
        return hidden_state






class        a_		(    nn.Module       ):

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



        """simple docstring"""






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


  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   List[Any]:



        """simple docstring"""






        UpperCamelCase       						=      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       						=      self.embedder(_SCREAMING_SNAKE_CASE   )
        return hidden_state






class        a_		(    nn.Module       ):

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = 2   )						->   Dict:



        """simple docstring"""






        super().__init__()
        UpperCamelCase       						=      nn.Convad(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	kernel_size=1    ,	stride=_SCREAMING_SNAKE_CASE    ,	bias=_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      nn.BatchNormad(_SCREAMING_SNAKE_CASE   )


  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   Tensor:



        """simple docstring"""






        UpperCamelCase       						=      self.convolution(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      self.normalization(_SCREAMING_SNAKE_CASE   )
        return hidden_state






class        a_		(    nn.Module       ):

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   Dict:



        """simple docstring"""






        super().__init__()

        UpperCamelCase       						=      nn.AdaptiveAvgPoolad((1, 1)   )
        UpperCamelCase       						=      nn.Sequential(
            nn.Convad(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	kernel_size=1   )    ,	nn.ReLU()    ,	nn.Convad(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	kernel_size=1   )    ,	nn.Sigmoid()    ,	)


  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   Optional[Any]:



        """simple docstring"""






        UpperCamelCase       						=      self.pooler(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      self.attention(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      hidden_state * attention
        return hidden_state






class        a_		(    nn.Module       ):

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = 1   )						->   Tuple:



        """simple docstring"""






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


  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase       						=      hidden_state
        UpperCamelCase       						=      self.layer(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      self.shortcut(_SCREAMING_SNAKE_CASE   )
        hidden_state += residual
        UpperCamelCase       						=      self.activation(_SCREAMING_SNAKE_CASE   )
        return hidden_state






class        a_		(    nn.Module       ):

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = 1   )						->   Dict:



        """simple docstring"""






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


  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      hidden_state
        UpperCamelCase       						=      self.layer(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      self.shortcut(_SCREAMING_SNAKE_CASE   )
        hidden_state += residual
        UpperCamelCase       						=      self.activation(_SCREAMING_SNAKE_CASE   )
        return hidden_state






class        a_		(    nn.Module       ):

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = 2    ,	_SCREAMING_SNAKE_CASE = 2    ,	)						->   Optional[int]:



        """simple docstring"""






        super().__init__()

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

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


  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   Any:



        """simple docstring"""






        UpperCamelCase       						=      self.layers(_SCREAMING_SNAKE_CASE   )
        return hidden_state






class        a_		(    nn.Module       ):

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE   )						->   Dict:



        """simple docstring"""






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


  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = False    ,	_SCREAMING_SNAKE_CASE = True   )						->   BaseModelOutputWithNoAttention:



        """simple docstring"""






        UpperCamelCase       						=      () if output_hidden_states else None

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

              UpperCamelCase       						=      stage_module(_SCREAMING_SNAKE_CASE   )

        if output_hidden_states:
              UpperCamelCase       						=      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=_SCREAMING_SNAKE_CASE    ,	hidden_states=_SCREAMING_SNAKE_CASE   )






class        a_		(    lowerCAmelCase__       ):
  lowercase  	=   RegNetConfig
  lowercase  	=   "regnet"
  lowercase  	=   "pixel_values"
  lowercase  	=   True

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   Any:



        """simple docstring"""






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


  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=False   )						->   Tuple:



        """simple docstring"""






        if isinstance(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ):
              UpperCamelCase       						=      value


SCREAMING_SNAKE_CASE__											=     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'

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

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE   )						->   List[Any]:



        """simple docstring"""






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


  @add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE   )
  @add_code_sample_docstrings(
      checkpoint=_CHECKPOINT_FOR_DOC    ,	output_type=_SCREAMING_SNAKE_CASE    ,	config_class=_CONFIG_FOR_DOC    ,	modality="""vision"""    ,	expected_output=_EXPECTED_OUTPUT_SHAPE    ,	)
  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None   )						->   BaseModelOutputWithPoolingAndNoAttention:



        """simple docstring"""






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

        UpperCamelCase       						=      self.embedder(_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      self.encoder(
            _SCREAMING_SNAKE_CASE    ,	output_hidden_states=_SCREAMING_SNAKE_CASE    ,	return_dict=_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      encoder_outputs[0]

        UpperCamelCase       						=      self.pooler(_SCREAMING_SNAKE_CASE   )

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

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

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE   )						->   List[str]:



        """simple docstring"""






        super().__init__(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      config.num_labels
        UpperCamelCase       						=      RegNetModel(_SCREAMING_SNAKE_CASE   )
        # classification head
        UpperCamelCase       						=      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(_SCREAMING_SNAKE_CASE   )
  @add_code_sample_docstrings(
      checkpoint=_IMAGE_CLASS_CHECKPOINT    ,	output_type=_SCREAMING_SNAKE_CASE    ,	config_class=_CONFIG_FOR_DOC    ,	expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT    ,	)
  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	)						->   ImageClassifierOutputWithNoAttention:



        """simple docstring"""






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

        UpperCamelCase       						=      self.regnet(_SCREAMING_SNAKE_CASE    ,	output_hidden_states=_SCREAMING_SNAKE_CASE    ,	return_dict=_SCREAMING_SNAKE_CASE   )

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

        UpperCamelCase       						=      self.classifier(_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      None

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

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

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









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import _LazyModule


SCREAMING_SNAKE_CASE__											=     {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}


if TYPE_CHECKING:
     from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
     import sys

     SCREAMING_SNAKE_CASE__											=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
