



'''simple docstring'''






from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_torch_available():
     import torch

     from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

lowerCAmelCase: Optional[Any]							=						logging.get_logger(__name__)

@add_end_docstrings(_a     )
class 							a__( _a     ):







    def __init__(				self			:  Optional[Any]						,      *__snake_case			:  Optional[Any]						,      **__snake_case			:  Tuple     ):
      super().__init__(*__snake_case						,      **__snake_case     )
      requires_backends(self						,      'vision'     )
      self.check_model_type(__snake_case     )







    def __call__(				self			:  List[Any]						,      __snake_case			:  Union[str, Any]						,      **__snake_case			:  Any     ):
      return super().__call__(__snake_case						,      **__snake_case     )







    def       lowercase_	(				self			:  Optional[Any]						,      **__snake_case			:  Dict     ):
      return {}, {}, {}







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  List[str]     ):
      a						: Union[str, Any]					       =					load_image(__snake_case     )
      a						: Optional[Any]					       =					image.size
      a						: Any					       =					self.image_processor(images=__snake_case						,      return_tensors=self.framework     )
      return model_inputs







    def       lowercase_	(				self			:  int						,      __snake_case			:  str     ):
      a						: List[Any]					       =					self.model(**__snake_case     )
      return model_outputs







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  int     ):
      a						: str					       =					model_outputs.predicted_depth
      a						: int					       =					torch.nn.functional.interpolate(
          predicted_depth.unsqueeze(1     )						,      size=self.image_size[::-1]						,      mode='bicubic'						,      align_corners=__snake_case     )
      a						: int					       =					prediction.squeeze().cpu().numpy()
      a						: Union[str, Any]					       =					(output * 2_55 / np.max(__snake_case     )).astype('uint8'     )
      a						: List[str]					       =					Image.fromarray(__snake_case     )
      a						: str					       =					{}
      a						: Optional[Any]					       =					predicted_depth
      a						: str					       =					depth
      return output_dict




'''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: List[Any]							=						logging.get_logger(__name__)

# General docstring
lowerCAmelCase: Optional[int]							=						'RegNetConfig'

# Base docstring
lowerCAmelCase: Tuple							=						'facebook/regnet-y-040'
lowerCAmelCase: str							=						[1, 1_0_8_8, 7, 7]

# Image classification docstring
lowerCAmelCase: Any							=						'facebook/regnet-y-040'
lowerCAmelCase: List[Any]							=						'tabby, tabby cat'

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

class 							a__( nn.Module     ):







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







    def       lowercase_	(				self			:  Dict						,      __snake_case			:  int     ):
      a						: int					       =					self.convolution(__snake_case     )
      a						: int					       =					self.normalization(__snake_case     )
      a						: Any					       =					self.activation(__snake_case     )
      return hidden_state

class 							a__( nn.Module     ):







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







    def       lowercase_	(				self			:  int						,      __snake_case			:  List[str]     ):
      a						: 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.'     )
      a						: List[str]					       =					self.embedder(__snake_case     )
      return hidden_state

class 							a__( nn.Module     ):







    def __init__(				self			:  Tuple						,      __snake_case			:  int						,      __snake_case			:  int						,      __snake_case			:  int = 2     ):
      super().__init__()
      a						: Optional[int]					       =					nn.Convad(__snake_case						,      __snake_case						,      kernel_size=1						,      stride=__snake_case						,      bias=__snake_case     )
      a						: Optional[Any]					       =					nn.BatchNormad(__snake_case     )







    def       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  Tensor     ):
      a						: Any					       =					self.convolution(__snake_case     )
      a						: Dict					       =					self.normalization(__snake_case     )
      return hidden_state

class 							a__( nn.Module     ):







    def __init__(				self			:  Union[str, Any]						,      __snake_case			:  int						,      __snake_case			:  int     ):
      super().__init__()

      a						: Any					       =					nn.AdaptiveAvgPoolad((1, 1)     )
      a						: Dict					       =					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       lowercase_	(				self			:  List[str]						,      __snake_case			:  List[Any]     ):
      # b c h w -> b c 1 1
      a						: Any					       =					self.pooler(__snake_case     )
      a						: Optional[Any]					       =					self.attention(__snake_case     )
      a						: Tuple					       =					hidden_state * attention
      return hidden_state

class 							a__( nn.Module     ):







    def __init__(				self			:  List[str]						,      __snake_case			:  RegNetConfig						,      __snake_case			:  int						,      __snake_case			:  int						,      __snake_case			:  int = 1     ):
      super().__init__()
      a						: Union[str, Any]					       =					in_channels != out_channels or stride != 1
      a						: Dict					       =					max(1						,      out_channels // config.groups_width     )
      a						: List[str]					       =					(
          RegNetShortCut(__snake_case						,      __snake_case						,      stride=__snake_case     ) if should_apply_shortcut else nn.Identity()
      )
      a						: 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     )						,      )
      a						: int					       =					ACTaFN[config.hidden_act]







    def       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  Tuple     ):
      a						: List[str]					       =					hidden_state
      a						: Dict					       =					self.layer(__snake_case     )
      a						: Tuple					       =					self.shortcut(__snake_case     )
      hidden_state += residual
      a						: Optional[int]					       =					self.activation(__snake_case     )
      return hidden_state

class 							a__( nn.Module     ):







    def __init__(				self			:  List[str]						,      __snake_case			:  RegNetConfig						,      __snake_case			:  int						,      __snake_case			:  int						,      __snake_case			:  int = 1     ):
      super().__init__()
      a						: Union[str, Any]					       =					in_channels != out_channels or stride != 1
      a						: Dict					       =					max(1						,      out_channels // config.groups_width     )
      a						: Dict					       =					(
          RegNetShortCut(__snake_case						,      __snake_case						,      stride=__snake_case     ) if should_apply_shortcut else nn.Identity()
      )
      a						: List[str]					       =					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     )						,      )
      a						: Union[str, Any]					       =					ACTaFN[config.hidden_act]







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

class 							a__( nn.Module     ):







    def __init__(				self			:  int						,      __snake_case			:  RegNetConfig						,      __snake_case			:  int						,      __snake_case			:  int						,      __snake_case			:  int = 2						,      __snake_case			:  int = 2						,      ):
      super().__init__()

      a						: int					       =					RegNetXLayer if config.layer_type == 'x' else RegNetYLayer

      a						: Any					       =					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       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  str     ):
      a						: Dict					       =					self.layers(__snake_case     )
      return hidden_state

class 							a__( nn.Module     ):







    def __init__(				self			:  List[Any]						,      __snake_case			:  RegNetConfig     ):
      super().__init__()
      a						: Dict					       =					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]						,      )     )
      a						: str					       =					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       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  Tensor						,      __snake_case			:  bool = False						,      __snake_case			:  bool = True     ):
      a						: Union[str, Any]					       =					() if output_hidden_states else None

      for stage_module in self.stages:
        if output_hidden_states:
          a						: List[str]					       =					hidden_states + (hidden_state,)

        a						: int					       =					stage_module(__snake_case     )

      if output_hidden_states:
        a						: 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 							a__( lowerCamelCase__     ):
    lowercase__			=   RegNetConfig
    lowercase__			=   """regnet"""
    lowercase__			=   """pixel_values"""
    lowercase__			=   True







    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  Optional[int]     ):
      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       lowercase_	(				self			:  Dict						,      __snake_case			:  List[str]						,      __snake_case			:  Dict=False     ):
      if isinstance(__snake_case						,      __snake_case     ):
        a						: Optional[Any]					       =					value
lowerCAmelCase: Union[str, Any]							=						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: int							=						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			:  Dict						,      __snake_case			:  Dict     ):
      super().__init__(__snake_case     )
      a						: str					       =					config
      a						: str					       =					RegNetEmbeddings(__snake_case     )
      a						: Dict					       =					RegNetEncoder(__snake_case     )
      a						: int					       =					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       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  Tensor						,      __snake_case			:  Optional[bool] = None						,      __snake_case			:  Optional[bool] = None     ):
      a						: Dict					       =					(
          output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
      )
      a						: List[Any]					       =					return_dict if return_dict is not None else self.config.use_return_dict

      a						: int					       =					self.embedder(__snake_case     )

      a						: Optional[int]					       =					self.encoder(
          __snake_case						,      output_hidden_states=__snake_case						,      return_dict=__snake_case     )

      a						: List[Any]					       =					encoder_outputs[0]

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







    def __init__(				self			:  int						,      __snake_case			:  Optional[int]     ):
      super().__init__(__snake_case     )
      a						: Optional[Any]					       =					config.num_labels
      a						: Tuple					       =					RegNetModel(__snake_case     )
      # classification head
      a						: List[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       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  Optional[torch.FloatTensor] = None						,      __snake_case			:  Optional[torch.LongTensor] = None						,      __snake_case			:  Optional[bool] = None						,      __snake_case			:  Optional[bool] = None						,      ):
      a						: str					       =					return_dict if return_dict is not None else self.config.use_return_dict

      a						: Optional[Any]					       =					self.regnet(__snake_case						,      output_hidden_states=__snake_case						,      return_dict=__snake_case     )

      a						: Any					       =					outputs.pooler_output if return_dict else outputs[1]

      a						: str					       =					self.classifier(__snake_case     )

      a						: Optional[int]					       =					None

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

      if not return_dict:
        a						: Optional[int]					       =					(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     )