






'''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


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

# General docstring
SCREAMING_SNAKE_CASE							=						'UperNetConfig'
class UpperCAmelCase_       (     nn.Module					):




      """simple docstring"""




      def __init__(			self      :		Tuple  ,   UpperCAmelCase      :		int  ,   UpperCAmelCase      :		int  ,   UpperCAmelCase      :		Union[int, Tuple[int, int]]  ,   UpperCAmelCase      :		Union[int, Tuple[int, int], str] = 0  ,   UpperCAmelCase      :		bool = False  ,   UpperCAmelCase      :		Union[int, Tuple[int, int]] = 1  ,   )	->	None:


       '''simple docstring'''

       super().__init__()
       lowercase       : List[Any]						=nn.Convad(
           in_channels=UpperCAmelCase  ,   out_channels=UpperCAmelCase  ,   kernel_size=UpperCAmelCase  ,   padding=UpperCAmelCase  ,   bias=UpperCAmelCase  ,   dilation=UpperCAmelCase  ,   )
       lowercase       : Dict						=nn.BatchNormad(UpperCAmelCase		)
       lowercase       : Any						=nn.ReLU()






      def 					A__   (			self      :		List[Any]  ,   UpperCAmelCase      :		torch.Tensor		)	->	torch.Tensor:


       '''simple docstring'''

       lowercase       : Tuple						=self.conv(UpperCAmelCase		)
       lowercase       : Tuple						=self.batch_norm(UpperCAmelCase		)
       lowercase       : Any						=self.activation(UpperCAmelCase		)

       return output





class UpperCAmelCase_       (     nn.Module					):




      """simple docstring"""




      def __init__(			self      :		List[Any]  ,   UpperCAmelCase      :		int  ,   UpperCAmelCase      :		int  ,   UpperCAmelCase      :		int		)	->	None:


       '''simple docstring'''

       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 					A__   (			self      :		Union[str, Any]  ,   UpperCAmelCase      :		torch.Tensor		)	->	torch.Tensor:


       '''simple docstring'''

       lowercase       : Tuple						=input
       for layer in self.layers:
        lowercase       : int						=layer(UpperCAmelCase		)
       return hidden_state





class UpperCAmelCase_       (     nn.Module					):




      """simple docstring"""




      def __init__(			self      :		Dict  ,   UpperCAmelCase      :		Tuple[int, ...]  ,   UpperCAmelCase      :		int  ,   UpperCAmelCase      :		int  ,   UpperCAmelCase      :		bool		)	->	None:


       '''simple docstring'''

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






      def 					A__   (			self      :		List[Any]  ,   UpperCAmelCase      :		torch.Tensor		)	->	List[torch.Tensor]:


       '''simple docstring'''

       lowercase       : Optional[int]						=[]
       for ppm in self.blocks:
        lowercase       : Optional[Any]						=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      :		List[Any]  ,   UpperCAmelCase      :		Tuple  ,   UpperCAmelCase      :		Union[str, Any]		)	->	List[str]:


       '''simple docstring'''

       super().__init__()

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

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

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






      def 					A__   (			self      :		str		)	->	str:


       '''simple docstring'''

       self.apply(self._init_weights		)






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


       '''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 					A__   (			self      :		List[Any]  ,   UpperCAmelCase      :		Any		)	->	Any:


       '''simple docstring'''

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

       return output






      def 					A__   (			self      :		Optional[Any]  ,   UpperCAmelCase      :		torch.Tensor		)	->	torch.Tensor:


       '''simple docstring'''

       lowercase       : 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
       lowercase       : Any						=len(UpperCAmelCase		)
       for i in range(used_backbone_levels - 1  ,   0  ,   -1		):
        lowercase       : Optional[int]						=laterals[i - 1].shape[2:]
        lowercase       : Tuple						=laterals[i - 1] + nn.functional.interpolate(
            laterals[i]  ,   size=UpperCAmelCase  ,   mode='''bilinear'''  ,   align_corners=self.align_corners		)

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

       return output





class UpperCAmelCase_       (     nn.Module					):




      """simple docstring"""




      def __init__(			self      :		int  ,   UpperCAmelCase      :		str  ,   UpperCAmelCase      :		int = 2  ,   UpperCAmelCase      :		int = 3  ,   UpperCAmelCase      :		Union[int, Tuple[int, int]] = 1		)	->	None:


       '''simple docstring'''

       super().__init__()

       lowercase       : int						=config
       lowercase       : Any						=config.auxiliary_in_channels
       lowercase       : int						=config.auxiliary_channels
       lowercase       : Dict						=config.auxiliary_num_convs
       lowercase       : List[Any]						=config.auxiliary_concat_input
       lowercase       : List[str]						=in_index

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

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






      def 					A__   (			self      :		List[str]		)	->	Tuple:


       '''simple docstring'''

       self.apply(self._init_weights		)






      def 					A__   (			self      :		Tuple  ,   UpperCAmelCase      :		str		)	->	Dict:


       '''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 					A__   (			self      :		List[str]  ,   UpperCAmelCase      :		torch.Tensor		)	->	torch.Tensor:


       '''simple docstring'''

       lowercase       : Dict						=encoder_hidden_states[self.in_index]
       lowercase       : List[str]						=self.convs(UpperCAmelCase		)
       if self.concat_input:
        lowercase       : str						=self.conv_cat(torch.cat([hidden_states, output]  ,   dim=1		)		)
       lowercase       : List[str]						=self.classifier(UpperCAmelCase		)
       return output





class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      UpperCamelCase_             =      UperNetConfig
      UpperCamelCase_             =      '''pixel_values'''
      UpperCamelCase_             =      True






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


       '''simple docstring'''

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






      def 					A__   (			self      :		Optional[int]		)	->	List[Any]:


       '''simple docstring'''

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






      def 					A__   (			self      :		Tuple  ,   UpperCAmelCase      :		Any  ,   UpperCAmelCase      :		Optional[Any]=False		)	->	List[str]:


       '''simple docstring'''

       if isinstance(UpperCAmelCase  ,   UpperCAmelCase		):
        lowercase       : List[Any]						=value





SCREAMING_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'

SCREAMING_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.'''					,							__A					,							)
class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      def __init__(			self      :		Union[str, Any]  ,   UpperCAmelCase      :		str		)	->	Optional[int]:


       '''simple docstring'''

       super().__init__(UpperCAmelCase		)

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

       # Semantic segmentation head(s)
       lowercase       : Optional[Any]						=UperNetHead(UpperCAmelCase  ,   in_channels=self.backbone.channels		)
       lowercase       : Optional[int]						=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 					A__   (			self      :		Dict  ,   UpperCAmelCase      :		Optional[torch.Tensor] = None  ,   UpperCAmelCase      :		Optional[bool] = None  ,   UpperCAmelCase      :		Optional[bool] = None  ,   UpperCAmelCase      :		Optional[torch.Tensor] = None  ,   UpperCAmelCase      :		Optional[bool] = None  ,   )	->	Union[tuple, SemanticSegmenterOutput]:


       '''simple docstring'''

       lowercase       : Tuple						=return_dict if return_dict is not None else self.config.use_return_dict
       lowercase       : List[str]						=(
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       lowercase       : Any						=output_attentions if output_attentions is not None else self.config.output_attentions

       lowercase       : Union[str, Any]						=self.backbone.forward_with_filtered_kwargs(
           UpperCAmelCase  ,   output_hidden_states=UpperCAmelCase  ,   output_attentions=UpperCAmelCase		)
       lowercase       : List[Any]						=outputs.feature_maps

       lowercase       : Dict						=self.decode_head(UpperCAmelCase		)
       lowercase       : List[Any]						=nn.functional.interpolate(UpperCAmelCase  ,   size=pixel_values.shape[2:]  ,   mode='''bilinear'''  ,   align_corners=UpperCAmelCase		)

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

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

       if not return_dict:
        if output_hidden_states:
         lowercase       : Tuple						=(logits,) + outputs[1:]
        else:
         lowercase       : Tuple						=(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 math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


a    :							Any										=    logging.get_logger(__name__)






class 		a							(  _lowerCamelCase	):
				snake_case_           =						"linear"
				snake_case_           =						"cosine"
				snake_case_           =						"cosine_with_restarts"
				snake_case_           =						"polynomial"
				snake_case_           =						"constant"
				snake_case_           =						"constant_with_warmup"
				snake_case_           =						"piecewise_constant"







def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase = -1      )						->   Dict:

					'''simple docstring'''

					return LambdaLR(__UpperCAmelCase, lambda __UpperCAmelCase      : 1, last_epoch=__UpperCAmelCase      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = -1      )						->   Dict:

					'''simple docstring'''

					def lr_lambda(__UpperCAmelCase      ):
										if current_step < num_warmup_steps:
															return float(__UpperCAmelCase      ) / float(max(1.0, __UpperCAmelCase      )      )
										return 1.0

					return LambdaLR(__UpperCAmelCase, __UpperCAmelCase, last_epoch=__UpperCAmelCase      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = -1      )						->   Any:

					'''simple docstring'''

					snake_case_		     =							{}
					snake_case_		     =							step_rules.split(''','''      )
					for rule_str in rule_list[:-1]:
										snake_case_     ,snake_case_		     =							rule_str.split(''':'''      )
										snake_case_		     =							int(__UpperCAmelCase      )
										snake_case_		     =							float(__UpperCAmelCase      )
										snake_case_		     =							value
					snake_case_		     =							float(rule_list[-1]      )

					def create_rules_function(__UpperCAmelCase, __UpperCAmelCase      ):
										def rule_func(__UpperCAmelCase      ) -> float:
															snake_case_		     =							sorted(rules_dict.keys()      )
															for i, sorted_step in enumerate(__UpperCAmelCase      ):
																				if steps < sorted_step:
																									return rules_dict[sorted_steps[i]]
															return last_lr_multiple

										return rule_func

					snake_case_		     =							create_rules_function(__UpperCAmelCase, __UpperCAmelCase      )

					return LambdaLR(__UpperCAmelCase, __UpperCAmelCase, last_epoch=__UpperCAmelCase      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=-1      )						->   List[Any]:

					'''simple docstring'''

					def lr_lambda(__UpperCAmelCase      ):
										if current_step < num_warmup_steps:
															return float(__UpperCAmelCase      ) / float(max(1, __UpperCAmelCase      )      )
										return max(
										    0.0, float(num_training_steps - current_step      ) / float(max(1, num_training_steps - num_warmup_steps      )      )      )

					return LambdaLR(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = 0.5, __UpperCAmelCase = -1      )						->   Optional[Any]:

					'''simple docstring'''

					def lr_lambda(__UpperCAmelCase      ):
										if current_step < num_warmup_steps:
															return float(__UpperCAmelCase      ) / float(max(1, __UpperCAmelCase      )      )
										snake_case_		     =							float(current_step - num_warmup_steps      ) / float(max(1, num_training_steps - num_warmup_steps      )      )
										return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(__UpperCAmelCase      ) * 2.0 * progress      ))      )

					return LambdaLR(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = 1, __UpperCAmelCase = -1      )						->   Dict:

					'''simple docstring'''

					def lr_lambda(__UpperCAmelCase      ):
										if current_step < num_warmup_steps:
															return float(__UpperCAmelCase      ) / float(max(1, __UpperCAmelCase      )      )
										snake_case_		     =							float(current_step - num_warmup_steps      ) / float(max(1, num_training_steps - num_warmup_steps      )      )
										if progress >= 1.0:
															return 0.0
										return max(0.0, 0.5 * (1.0 + math.cos(math.pi * ((float(__UpperCAmelCase      ) * progress) % 1.0)      ))      )

					return LambdaLR(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=1e-7, __UpperCAmelCase=1.0, __UpperCAmelCase=-1      )						->   str:

					'''simple docstring'''

					snake_case_		     =							optimizer.defaults['''lr''']
					if not (lr_init > lr_end):
										raise ValueError(F"lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"      )

					def lr_lambda(__UpperCAmelCase      ):
										if current_step < num_warmup_steps:
															return float(__UpperCAmelCase      ) / float(max(1, __UpperCAmelCase      )      )
										elif current_step > num_training_steps:
															return lr_end / lr_init  # as LambdaLR multiplies by lr_init
										else:
															snake_case_		     =							lr_init - lr_end
															snake_case_		     =							num_training_steps - num_warmup_steps
															snake_case_		     =							1 - (current_step - num_warmup_steps) / decay_steps
															snake_case_		     =							lr_range * pct_remaining**power + lr_end
															return decay / lr_init  # as LambdaLR multiplies by lr_init

					return LambdaLR(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )


a    :							Tuple										=    {
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = None, __UpperCAmelCase = None, __UpperCAmelCase = None, __UpperCAmelCase = 1, __UpperCAmelCase = 1.0, __UpperCAmelCase = -1, )						->   int:

					'''simple docstring'''

					snake_case_		     =							SchedulerType(__UpperCAmelCase      )
					snake_case_		     =							TYPE_TO_SCHEDULER_FUNCTION[name]
					if name == SchedulerType.CONSTANT:
										return schedule_func(__UpperCAmelCase, last_epoch=__UpperCAmelCase      )

					if name == SchedulerType.PIECEWISE_CONSTANT:
										return schedule_func(__UpperCAmelCase, step_rules=__UpperCAmelCase, last_epoch=__UpperCAmelCase      )

					# All other schedulers require `num_warmup_steps`
					if num_warmup_steps is None:
										raise ValueError(F"{name} requires `num_warmup_steps`, please provide that argument."      )

					if name == SchedulerType.CONSTANT_WITH_WARMUP:
										return schedule_func(__UpperCAmelCase, num_warmup_steps=__UpperCAmelCase, last_epoch=__UpperCAmelCase      )

					# All other schedulers require `num_training_steps`
					if num_training_steps is None:
										raise ValueError(F"{name} requires `num_training_steps`, please provide that argument."      )

					if name == SchedulerType.COSINE_WITH_RESTARTS:
										return schedule_func(
										    __UpperCAmelCase, num_warmup_steps=__UpperCAmelCase, num_training_steps=__UpperCAmelCase, num_cycles=__UpperCAmelCase, last_epoch=__UpperCAmelCase, )

					if name == SchedulerType.POLYNOMIAL:
										return schedule_func(
										    __UpperCAmelCase, num_warmup_steps=__UpperCAmelCase, num_training_steps=__UpperCAmelCase, power=__UpperCAmelCase, last_epoch=__UpperCAmelCase, )

					return schedule_func(
					    __UpperCAmelCase, num_warmup_steps=__UpperCAmelCase, num_training_steps=__UpperCAmelCase, last_epoch=__UpperCAmelCase      )

