"""simple docstring"""






from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


__A			   = logging.get_logger(__name__)


class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Dict						    = ["input_values", "padding_mask"]






    def __init__(   self    ,			_UpperCAmelCase = 1    ,			_UpperCAmelCase = 24000    ,			_UpperCAmelCase = 0.0    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			**_UpperCAmelCase    ,			):
       super().__init__(feature_size=_UpperCAmelCase    ,			sampling_rate=_UpperCAmelCase    ,			padding_value=_UpperCAmelCase    ,			**_UpperCAmelCase							)
       lowercase__:   str					=      chunk_length_s
       lowercase__:   str					=      overlap






    @property
    def _snake_case			(   self							):
       if self.chunk_length_s is None:
          return None
       else:
          return int(self.chunk_length_s * self.sampling_rate							)






    @property
    def _snake_case			(   self							):
       if self.chunk_length_s is None or self.overlap is None:
          return None
       else:
          return max(1    ,			int((1.0 - self.overlap) * self.chunk_length							)							)






    def __call__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = False    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			):
       if sampling_rate is not None:
          if sampling_rate != self.sampling_rate:
             raise ValueError(
                 F"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
                 F""" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"""
                 F""" {self.sampling_rate} and not {sampling_rate}."""							)
       else:
          logger.warning(
              '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
              '''Failing to do so can result in silent errors that might be hard to debug.'''							)

       if padding and truncation:
          raise ValueError('''Both padding and truncation were set. Make sure you only set one.'''							)
       elif padding is None:
          # by default let's pad the inputs
          lowercase__:   Tuple					=      True

       lowercase__:   Any					=      bool(
           isinstance(_UpperCAmelCase    ,			(list, tuple)							) and (isinstance(raw_audio[0]    ,			(np.ndarray, tuple, list)							))							)

       if is_batched:
          lowercase__:   int					=      [np.asarray(_UpperCAmelCase    ,			dtype=np.floataa							).T for audio in raw_audio]
       elif not is_batched and not isinstance(_UpperCAmelCase    ,			np.ndarray							):
          lowercase__:   Optional[Any]					=      np.asarray(_UpperCAmelCase    ,			dtype=np.floataa							)
       elif isinstance(_UpperCAmelCase    ,			np.ndarray							) and raw_audio.dtype is np.dtype(np.floataa							):
          lowercase__:   Union[str, Any]					=      raw_audio.astype(np.floataa							)

       # always return batch
       if not is_batched:
          lowercase__:   Optional[Any]					=      [np.asarray(_UpperCAmelCase							).T]

       # verify inputs are valid
       for idx, example in enumerate(_UpperCAmelCase							):
          if example.ndim > 2:
             raise ValueError(F"""Expected input shape (channels, length) but got shape {example.shape}"""							)
          if self.feature_size == 1 and example.ndim != 1:
             raise ValueError(F"""Expected mono audio but example has {example.shape[-1]} channels"""							)
          if self.feature_size == 2 and example.shape[-1] != 2:
             raise ValueError(F"""Expected stereo audio but example has {example.shape[-1]} channels"""							)

       lowercase__:   Any					=      None
       lowercase__:   Any					=      BatchFeature({'''input_values''': raw_audio}							)
       if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
          if truncation:
             lowercase__:   Any					=      min(array.shape[0] for array in raw_audio							)
             lowercase__:   Dict					=      int(np.floor(max_length / self.chunk_stride							)							)
             lowercase__:   Union[str, Any]					=      (nb_step - 1) * self.chunk_stride + self.chunk_length
          elif padding:
             lowercase__:   int					=      max(array.shape[0] for array in raw_audio							)
             lowercase__:   str					=      int(np.ceil(max_length / self.chunk_stride							)							)
             lowercase__:   Any					=      (nb_step - 1) * self.chunk_stride + self.chunk_length
             lowercase__:   List[Any]					=      '''max_length'''
          else:
             lowercase__:   List[str]					=      input_values

        # normal padding on batch
       if padded_inputs is None:
          lowercase__:   Tuple					=      self.pad(
              _UpperCAmelCase    ,			max_length=_UpperCAmelCase    ,			truncation=_UpperCAmelCase    ,			padding=_UpperCAmelCase    ,			return_attention_mask=_UpperCAmelCase    ,			)
          if padding:
             lowercase__:   Tuple					=      padded_inputs.pop('''attention_mask'''							)

       lowercase__:   List[Any]					=      []
       for example in padded_inputs.pop('''input_values'''							):
          if self.feature_size == 1:
             lowercase__:   Any					=      example[..., None]
          input_values.append(example.T							)

       lowercase__:   Optional[Any]					=      input_values
       if return_tensors is not None:
          lowercase__:   Optional[Any]					=      padded_inputs.convert_to_tensors(_UpperCAmelCase							)

       return padded_inputs

"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch
from torch import nn
from torch.nn import CrossEntropyLoss

from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig


__A			   = [
    "openmmlab/upernet-convnext-tiny",
    # See all UperNet models at https://huggingface.co/models?filter=upernet
]

# General docstring
__A			   = "UperNetConfig"


class     UpperCAmelCase						(nn.Module  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase = 0    ,			_UpperCAmelCase = False    ,			_UpperCAmelCase = 1    ,			):
       super().__init__()
       lowercase__:   List[Any]					=      nn.Convad(
           in_channels=_UpperCAmelCase    ,			out_channels=_UpperCAmelCase    ,			kernel_size=_UpperCAmelCase    ,			padding=_UpperCAmelCase    ,			bias=_UpperCAmelCase    ,			dilation=_UpperCAmelCase    ,			)
       lowercase__:   List[Any]					=      nn.BatchNormad(_UpperCAmelCase							)
       lowercase__:   int					=      nn.ReLU()






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__:   Dict					=      self.conv(_UpperCAmelCase							)
       lowercase__:   Optional[int]					=      self.batch_norm(_UpperCAmelCase							)
       lowercase__:   List[Any]					=      self.activation(_UpperCAmelCase							)

       return output



class     UpperCAmelCase						(nn.Module  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       super().__init__()
       lowercase__:   int					=      [
           nn.AdaptiveAvgPoolad(_UpperCAmelCase							),
           UperNetConvModule(_UpperCAmelCase    ,			_UpperCAmelCase    ,			kernel_size=1							),
       ]
       for i, layer in enumerate(self.layers							):
          self.add_module(str(_UpperCAmelCase							)    ,			_UpperCAmelCase							)






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__:   Any					=      input
       for layer in self.layers:
          lowercase__:   Any					=      layer(_UpperCAmelCase							)
       return hidden_state



class     UpperCAmelCase						(nn.Module  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       super().__init__()
       lowercase__:   int					=      pool_scales
       lowercase__:   Optional[Any]					=      align_corners
       lowercase__:   Optional[int]					=      in_channels
       lowercase__:   Optional[Any]					=      channels
       lowercase__:   List[Any]					=      []
       for i, pool_scale in enumerate(_UpperCAmelCase							):
          lowercase__:   Optional[int]					=      UperNetPyramidPoolingBlock(pool_scale=_UpperCAmelCase    ,			in_channels=_UpperCAmelCase    ,			channels=_UpperCAmelCase							)
          self.blocks.append(_UpperCAmelCase							)
          self.add_module(str(_UpperCAmelCase							)    ,			_UpperCAmelCase							)






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__:   Union[str, Any]					=      []
       for ppm in self.blocks:
          lowercase__:   Tuple					=      ppm(_UpperCAmelCase							)
          lowercase__:   Any					=      nn.functional.interpolate(
              _UpperCAmelCase    ,			size=x.size()[2:]    ,			mode='''bilinear'''    ,			align_corners=self.align_corners							)
          ppm_outs.append(_UpperCAmelCase							)
       return ppm_outs



class     UpperCAmelCase						(nn.Module  ):



    """simple docstring"""

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

       lowercase__:   Optional[int]					=      config
       lowercase__:   int					=      config.pool_scales  # e.g. (1, 2, 3, 6)
       lowercase__:   Optional[int]					=      in_channels
       lowercase__:   List[str]					=      config.hidden_size
       lowercase__:   List[str]					=      False
       lowercase__:   List[str]					=      nn.Convad(self.channels    ,			config.num_labels    ,			kernel_size=1							)

       # PSP Module
       lowercase__:   Dict					=      UperNetPyramidPoolingModule(
           self.pool_scales    ,			self.in_channels[-1]    ,			self.channels    ,			align_corners=self.align_corners    ,			)
       lowercase__:   int					=      UperNetConvModule(
           self.in_channels[-1] + len(self.pool_scales							) * self.channels    ,			self.channels    ,			kernel_size=3    ,			padding=1    ,			)
       # FPN Module
       lowercase__:   List[Any]					=      nn.ModuleList()
       lowercase__:   Union[str, Any]					=      nn.ModuleList()
       for in_channels in self.in_channels[:-1]:  # skip the top layer
          lowercase__:   int					=      UperNetConvModule(_UpperCAmelCase    ,			self.channels    ,			kernel_size=1							)
          lowercase__:   Dict					=      UperNetConvModule(self.channels    ,			self.channels    ,			kernel_size=3    ,			padding=1							)
          self.lateral_convs.append(_UpperCAmelCase							)
          self.fpn_convs.append(_UpperCAmelCase							)

       lowercase__:   Any					=      UperNetConvModule(
           len(self.in_channels							) * self.channels    ,			self.channels    ,			kernel_size=3    ,			padding=1    ,			)






    def _snake_case			(   self							):
       self.apply(self._init_weights							)






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       if isinstance(_UpperCAmelCase    ,			nn.Convad							):
          module.weight.data.normal_(mean=0.0    ,			std=self.config.initializer_range							)
          if module.bias is not None:
             module.bias.data.zero_()






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__:   List[str]					=      inputs[-1]
       lowercase__:   str					=      [x]
       psp_outs.extend(self.psp_modules(_UpperCAmelCase							)							)
       lowercase__:   Dict					=      torch.cat(_UpperCAmelCase    ,			dim=1							)
       lowercase__:   Tuple					=      self.bottleneck(_UpperCAmelCase							)

       return output






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       # build laterals
       lowercase__:   Dict					=      [lateral_conv(encoder_hidden_states[i]							) for i, lateral_conv in enumerate(self.lateral_convs							)]

       laterals.append(self.psp_forward(_UpperCAmelCase							)							)

       # build top-down path
       lowercase__:   int					=      len(_UpperCAmelCase							)
       for i in range(used_backbone_levels - 1    ,			0    ,			-1							):
          lowercase__:   str					=      laterals[i - 1].shape[2:]
          lowercase__:   Optional[int]					=      laterals[i - 1] + nn.functional.interpolate(
              laterals[i]    ,			size=_UpperCAmelCase    ,			mode='''bilinear'''    ,			align_corners=self.align_corners							)

       # build outputs
       lowercase__:   str					=      [self.fpn_convs[i](laterals[i]							) for i in range(used_backbone_levels - 1							)]
       # append psp feature
       fpn_outs.append(laterals[-1]							)

       for i in range(used_backbone_levels - 1    ,			0    ,			-1							):
          lowercase__:   Any					=      nn.functional.interpolate(
              fpn_outs[i]    ,			size=fpn_outs[0].shape[2:]    ,			mode='''bilinear'''    ,			align_corners=self.align_corners							)
       lowercase__:   int					=      torch.cat(_UpperCAmelCase    ,			dim=1							)
       lowercase__:   Tuple					=      self.fpn_bottleneck(_UpperCAmelCase							)
       lowercase__:   Dict					=      self.classifier(_UpperCAmelCase							)

       return output



class     UpperCAmelCase						(nn.Module  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = 2    ,			_UpperCAmelCase = 3    ,			_UpperCAmelCase = 1							):
       super().__init__()

       lowercase__:   Optional[Any]					=      config
       lowercase__:   Optional[Any]					=      config.auxiliary_in_channels
       lowercase__:   List[Any]					=      config.auxiliary_channels
       lowercase__:   Tuple					=      config.auxiliary_num_convs
       lowercase__:   Any					=      config.auxiliary_concat_input
       lowercase__:   Optional[int]					=      in_index

       lowercase__:   Tuple					=      (kernel_size // 2) * dilation
       lowercase__:   Tuple					=      []
       convs.append(
           UperNetConvModule(
               self.in_channels    ,			self.channels    ,			kernel_size=_UpperCAmelCase    ,			padding=_UpperCAmelCase    ,			dilation=_UpperCAmelCase							)							)
       for i in range(self.num_convs - 1							):
          convs.append(
              UperNetConvModule(
                  self.channels    ,			self.channels    ,			kernel_size=_UpperCAmelCase    ,			padding=_UpperCAmelCase    ,			dilation=_UpperCAmelCase							)							)
       if self.num_convs == 0:
          lowercase__:   List[Any]					=      nn.Identity()
       else:
          lowercase__:   Union[str, Any]					=      nn.Sequential(*_UpperCAmelCase							)
       if self.concat_input:
          lowercase__:   Dict					=      UperNetConvModule(
              self.in_channels + self.channels    ,			self.channels    ,			kernel_size=_UpperCAmelCase    ,			padding=kernel_size // 2							)

       lowercase__:   Union[str, Any]					=      nn.Convad(self.channels    ,			config.num_labels    ,			kernel_size=1							)






    def _snake_case			(   self							):
       self.apply(self._init_weights							)






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       if isinstance(_UpperCAmelCase    ,			nn.Convad							):
          module.weight.data.normal_(mean=0.0    ,			std=self.config.initializer_range							)
          if module.bias is not None:
             module.bias.data.zero_()






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       # just take the relevant feature maps
       lowercase__:   Dict					=      encoder_hidden_states[self.in_index]
       lowercase__:   Optional[int]					=      self.convs(_UpperCAmelCase							)
       if self.concat_input:
          lowercase__:   Optional[int]					=      self.conv_cat(torch.cat([hidden_states, output]    ,			dim=1							)							)
       lowercase__:   Dict					=      self.classifier(_UpperCAmelCase							)
       return output



class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Dict						    = UperNetConfig
    _UpperCAmelCase	:int						    = "pixel_values"
    _UpperCAmelCase	:Optional[Any]						    = True






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       if isinstance(_UpperCAmelCase    ,			_UpperCAmelCase							):
          module.backbone.init_weights()
          module.decode_head.init_weights()
          module.auxiliary_head.init_weights()






    def _snake_case			(   self							):
       self.backbone.init_weights()
       self.decode_head.init_weights()
       self.auxiliary_head.init_weights()






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=False							):
       if isinstance(_UpperCAmelCase    ,			_UpperCAmelCase							):
          lowercase__:   Any					=      value



__A			   = R"\n    Parameters:\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n        config ([`UperNetConfig`]): 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. Padding will be ignored by default should you provide it. Pixel values can be obtained using\n            [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.\n        output_attentions (`bool`, *optional*):\n            Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See\n            `attentions` under returned tensors for more detail.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under\n            returned tensors for more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"


@add_start_docstrings(
    "UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes."    ,_UpperCAmelCase    ,)
class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

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

       lowercase__:   Optional[int]					=      AutoBackbone.from_config(config.backbone_config							)

       # Semantic segmentation head(s)
       lowercase__:   Any					=      UperNetHead(_UpperCAmelCase    ,			in_channels=self.backbone.channels							)
       lowercase__:   Tuple					=      UperNetFCNHead(_UpperCAmelCase							) if config.use_auxiliary_head else None

       # Initialize weights and apply final processing
       self.post_init()






    @add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format('''batch_size, sequence_length'''							)							)
    @replace_return_docstrings(output_type=_UpperCAmelCase    ,			config_class=_CONFIG_FOR_DOC							)
    def _snake_case			(   self    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			):
       lowercase__:   Tuple					=      return_dict if return_dict is not None else self.config.use_return_dict
       lowercase__:   Any					=      (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       lowercase__:   str					=      output_attentions if output_attentions is not None else self.config.output_attentions

       lowercase__:   List[str]					=      self.backbone.forward_with_filtered_kwargs(
           _UpperCAmelCase    ,			output_hidden_states=_UpperCAmelCase    ,			output_attentions=_UpperCAmelCase							)
       lowercase__:   Tuple					=      outputs.feature_maps

       lowercase__:   Union[str, Any]					=      self.decode_head(_UpperCAmelCase							)
       lowercase__:   str					=      nn.functional.interpolate(_UpperCAmelCase    ,			size=pixel_values.shape[2:]    ,			mode='''bilinear'''    ,			align_corners=_UpperCAmelCase							)

       lowercase__:   Any					=      None
       if self.auxiliary_head is not None:
          lowercase__:   Union[str, Any]					=      self.auxiliary_head(_UpperCAmelCase							)
          lowercase__:   Tuple					=      nn.functional.interpolate(
              _UpperCAmelCase    ,			size=pixel_values.shape[2:]    ,			mode='''bilinear'''    ,			align_corners=_UpperCAmelCase							)

       lowercase__:   List[Any]					=      None
       if labels is not None:
          if self.config.num_labels == 1:
             raise ValueError('''The number of labels should be greater than one'''							)
          else:
             # compute weighted loss
             lowercase__:   List[str]					=      CrossEntropyLoss(ignore_index=self.config.loss_ignore_index							)
             lowercase__:   Optional[Any]					=      loss_fct(_UpperCAmelCase    ,			_UpperCAmelCase							)
             lowercase__:   Dict					=      loss_fct(_UpperCAmelCase    ,			_UpperCAmelCase							)
             lowercase__:   int					=      main_loss + self.config.auxiliary_loss_weight * auxiliary_loss

       if not return_dict:
          if output_hidden_states:
             lowercase__:   Tuple					=      (logits,) + outputs[1:]
          else:
             lowercase__:   Optional[int]					=      (logits,) + outputs[2:]
          return ((loss,) + output) if loss is not None else output

       return SemanticSegmenterOutput(
           loss=_UpperCAmelCase    ,			logits=_UpperCAmelCase    ,			hidden_states=outputs.hidden_states    ,			attentions=outputs.attentions    ,			)
