


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


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

# General docstring
__snake_case							   =  'UperNetConfig'
class      lowercase			(							nn.Module      ):

		"""simple docstring"""

		def __init__(	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_ = 0					,     UpperCamelCase_ = False					,     UpperCamelCase_ = 1					,     ):

							'''simple docstring'''


							super().__init__()
							UpperCamelCase__     :Union[str, Any]							      =				nn.Convad(
							    in_channels=UpperCamelCase__					,     out_channels=UpperCamelCase__					,     kernel_size=UpperCamelCase__					,     padding=UpperCamelCase__					,     bias=UpperCamelCase__					,     dilation=UpperCamelCase__					,     )
							UpperCamelCase__     :Optional[int]							      =				nn.BatchNormad(UpperCamelCase__							)
							UpperCamelCase__     :Optional[int]							      =				nn.ReLU()




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				self.conv(UpperCamelCase__							)
							UpperCamelCase__     :Union[str, Any]							      =				self.batch_norm(UpperCamelCase__							)
							UpperCamelCase__     :List[str]							      =				self.activation(UpperCamelCase__							)

							return output



class      lowercase			(							nn.Module      ):

		"""simple docstring"""

		def __init__(	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


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




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :List[str]							      =				input
							for layer in self.layers:
												UpperCamelCase__     :int							      =				layer(UpperCamelCase__							)
							return hidden_state



class      lowercase			(							nn.Module      ):

		"""simple docstring"""

		def __init__(	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							super().__init__()
							UpperCamelCase__     :Any							      =				pool_scales
							UpperCamelCase__     :List[Any]							      =				align_corners
							UpperCamelCase__     :List[Any]							      =				in_channels
							UpperCamelCase__     :str							      =				channels
							UpperCamelCase__     :List[Any]							      =				[]
							for i, pool_scale in enumerate(UpperCamelCase__							):
												UpperCamelCase__     :Union[str, Any]							      =				UperNetPyramidPoolingBlock(pool_scale=UpperCamelCase__					,     in_channels=UpperCamelCase__					,     channels=UpperCamelCase__							)
												self.blocks.append(UpperCamelCase__							)
												self.add_module(str(UpperCamelCase__							)					,     UpperCamelCase__							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				[]
							for ppm in self.blocks:
												UpperCamelCase__     :Tuple							      =				ppm(UpperCamelCase__							)
												UpperCamelCase__     :Any							      =				nn.functional.interpolate(
												    UpperCamelCase__					,     size=x.size()[2:]					,     mode='''bilinear'''					,     align_corners=self.align_corners							)
												ppm_outs.append(UpperCamelCase__							)
							return ppm_outs



class      lowercase			(							nn.Module      ):

		"""simple docstring"""

		def __init__(	self					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							super().__init__()

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

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

							UpperCamelCase__     :Optional[Any]							      =				UperNetConvModule(
							    len(self.in_channels							) * self.channels					,     self.channels					,     kernel_size=3					,     padding=1					,     )




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							self.apply(self._init_weights							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							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 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				inputs[-1]
							UpperCamelCase__     :List[Any]							      =				[x]
							psp_outs.extend(self.psp_modules(UpperCamelCase__							)							)
							UpperCamelCase__     :Optional[Any]							      =				torch.cat(UpperCamelCase__					,     dim=1							)
							UpperCamelCase__     :List[str]							      =				self.bottleneck(UpperCamelCase__							)

							return output




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Union[str, Any]							      =				[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
							UpperCamelCase__     :List[Any]							      =				len(UpperCamelCase__							)
							for i in range(used_backbone_levels - 1					,     0					,     -1							):
												UpperCamelCase__     :List[Any]							      =				laterals[i - 1].shape[2:]
												UpperCamelCase__     :str							      =				laterals[i - 1] + nn.functional.interpolate(
												    laterals[i]					,     size=UpperCamelCase__					,     mode='''bilinear'''					,     align_corners=self.align_corners							)

							# build outputs
							UpperCamelCase__     :List[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							):
												UpperCamelCase__     :List[Any]							      =				nn.functional.interpolate(
												    fpn_outs[i]					,     size=fpn_outs[0].shape[2:]					,     mode='''bilinear'''					,     align_corners=self.align_corners							)
							UpperCamelCase__     :Any							      =				torch.cat(UpperCamelCase__					,     dim=1							)
							UpperCamelCase__     :Optional[Any]							      =				self.fpn_bottleneck(UpperCamelCase__							)
							UpperCamelCase__     :List[Any]							      =				self.classifier(UpperCamelCase__							)

							return output



class      lowercase			(							nn.Module      ):

		"""simple docstring"""

		def __init__(	self					,     UpperCamelCase_					,     UpperCamelCase_ = 2					,     UpperCamelCase_ = 3					,     UpperCamelCase_ = 1							):

							'''simple docstring'''


							super().__init__()

							UpperCamelCase__     :Dict							      =				config
							UpperCamelCase__     :List[str]							      =				config.auxiliary_in_channels
							UpperCamelCase__     :Optional[Any]							      =				config.auxiliary_channels
							UpperCamelCase__     :int							      =				config.auxiliary_num_convs
							UpperCamelCase__     :Union[str, Any]							      =				config.auxiliary_concat_input
							UpperCamelCase__     :List[str]							      =				in_index

							UpperCamelCase__     :Tuple							      =				(kernel_size // 2) * dilation
							UpperCamelCase__     :str							      =				[]
							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:
												UpperCamelCase__     :Optional[int]							      =				nn.Identity()
							else:
												UpperCamelCase__     :List[Any]							      =				nn.Sequential(*UpperCamelCase__							)
							if self.concat_input:
												UpperCamelCase__     :Any							      =				UperNetConvModule(
												    self.in_channels + self.channels					,     self.channels					,     kernel_size=UpperCamelCase__					,     padding=kernel_size // 2							)

							UpperCamelCase__     :List[Any]							      =				nn.Convad(self.channels					,     config.num_labels					,     kernel_size=1							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							self.apply(self._init_weights							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							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 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				encoder_hidden_states[self.in_index]
							UpperCamelCase__     :Union[str, Any]							      =				self.convs(UpperCamelCase__							)
							if self.concat_input:
												UpperCamelCase__     :Optional[Any]							      =				self.conv_cat(torch.cat([hidden_states, output]					,     dim=1							)							)
							UpperCamelCase__     :Optional[int]							      =				self.classifier(UpperCamelCase__							)
							return output



class      lowercase			(							UpperCamelCase_      ):

		"""simple docstring"""

		_a									=       UperNetConfig
		_a									=       'pixel_values'
		_a									=       True




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							if isinstance(UpperCamelCase__					,     UpperCamelCase__							):
												module.backbone.init_weights()
												module.decode_head.init_weights()
												module.auxiliary_head.init_weights()




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							self.backbone.init_weights()
							self.decode_head.init_weights()
							self.auxiliary_head.init_weights()




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_=False							):

							'''simple docstring'''


							if isinstance(UpperCamelCase__					,     UpperCamelCase__							):
												UpperCamelCase__     :Optional[Any]							      =				value



__snake_case							   =  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'

__snake_case							   =  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      lowercase			(							UpperCamelCase_      ):

		"""simple docstring"""

		def __init__(	self					,     UpperCamelCase_							):

							'''simple docstring'''


							super().__init__(UpperCamelCase__							)

							UpperCamelCase__     :Optional[Any]							      =				AutoBackbone.from_config(config.backbone_config							)

							# Semantic segmentation head(s)
							UpperCamelCase__     :List[Any]							      =				UperNetHead(UpperCamelCase__					,     in_channels=self.backbone.channels							)
							UpperCamelCase__     :List[Any]							      =				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 					lowerCAmelCase__       (	self					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     ):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				return_dict if return_dict is not None else self.config.use_return_dict
							UpperCamelCase__     :str							      =				(
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							UpperCamelCase__     :Dict							      =				output_attentions if output_attentions is not None else self.config.output_attentions

							UpperCamelCase__     :Optional[Any]							      =				self.backbone.forward_with_filtered_kwargs(
							    UpperCamelCase__					,     output_hidden_states=UpperCamelCase__					,     output_attentions=UpperCamelCase__							)
							UpperCamelCase__     :Optional[int]							      =				outputs.feature_maps

							UpperCamelCase__     :Optional[int]							      =				self.decode_head(UpperCamelCase__							)
							UpperCamelCase__     :List[Any]							      =				nn.functional.interpolate(UpperCamelCase__					,     size=pixel_values.shape[2:]					,     mode='''bilinear'''					,     align_corners=UpperCamelCase__							)

							UpperCamelCase__     :Optional[int]							      =				None
							if self.auxiliary_head is not None:
												UpperCamelCase__     :Any							      =				self.auxiliary_head(UpperCamelCase__							)
												UpperCamelCase__     :Optional[Any]							      =				nn.functional.interpolate(
												    UpperCamelCase__					,     size=pixel_values.shape[2:]					,     mode='''bilinear'''					,     align_corners=UpperCamelCase__							)

							UpperCamelCase__     :Optional[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
																	UpperCamelCase__     :str							      =				CrossEntropyLoss(ignore_index=self.config.loss_ignore_index							)
																	UpperCamelCase__     :Union[str, Any]							      =				loss_fct(UpperCamelCase__					,     UpperCamelCase__							)
																	UpperCamelCase__     :Union[str, Any]							      =				loss_fct(UpperCamelCase__					,     UpperCamelCase__							)
																	UpperCamelCase__     :List[str]							      =				main_loss + self.config.auxiliary_loss_weight * auxiliary_loss

							if not return_dict:
												if output_hidden_states:
																	UpperCamelCase__     :Optional[Any]							      =				(logits,) + outputs[1:]
												else:
																	UpperCamelCase__     :Optional[Any]							      =				(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					,     )



'''simple docstring'''
import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
		import torch
@require_torch
@require_optimum
@slow
class      lowercase			(							unittest.TestCase      ):

		"""simple docstring"""

		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				'''hf-internal-testing/tiny-random-t5'''
							UpperCamelCase__     :int							      =				AutoTokenizer.from_pretrained(UpperCamelCase_							)
							UpperCamelCase__     :int							      =				AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_							)

							UpperCamelCase__     :int							      =				tokenizer('''This is me'''					,     return_tensors='''pt'''							)

							UpperCamelCase__     :Optional[int]							      =				model.to_bettertransformer()

							self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()							)							)

							UpperCamelCase__     :int							      =				model.generate(**UpperCamelCase_							)

							UpperCamelCase__     :Optional[Any]							      =				model.reverse_bettertransformer()

							self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()							)							)

							with tempfile.TemporaryDirectory() as tmpdirname:
												model.save_pretrained(UpperCamelCase_							)

												UpperCamelCase__     :Tuple							      =				AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_							)

												self.assertFalse(
												    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()							)							)

												UpperCamelCase__     :Tuple							      =				model_reloaded.generate(**UpperCamelCase_							)
												self.assertTrue(torch.allclose(UpperCamelCase_					,     UpperCamelCase_							)							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				'''hf-internal-testing/tiny-random-t5'''
							UpperCamelCase__     :Dict							      =				AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_							)

							UpperCamelCase__     :Union[str, Any]							      =				model.to_bettertransformer()

							with tempfile.TemporaryDirectory() as tmpdirname:
												with self.assertRaises(UpperCamelCase_							):
																	model.save_pretrained(UpperCamelCase_							)

												UpperCamelCase__     :Tuple							      =				model.reverse_bettertransformer()
												model.save_pretrained(UpperCamelCase_							)