






'''simple docstring'''
from typing import Optional, Union

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig


UpperCamelCase__ =			logging.get_logger(__name__)


# General docstring
UpperCamelCase__ =			'MobileNetV1Config'

# Base docstring
UpperCamelCase__ =			'google/mobilenet_v1_1.0_224'
UpperCamelCase__ =			[1, 1024, 7, 7]

# Image classification docstring
UpperCamelCase__ =			'google/mobilenet_v1_1.0_224'
UpperCamelCase__ =			'tabby, tabby cat'


UpperCamelCase__ =			[
    'google/mobilenet_v1_1.0_224',
    'google/mobilenet_v1_0.75_192',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=None							):


   """simple docstring"""




   lowercase_	:    str           =     {}

   if isinstance(_UpperCamelCase						,		_UpperCamelCase							):
      lowercase_	:    str           =     model.mobilenet_va
   else:
      lowercase_	:    List[str]           =     model

   lowercase_	:    Any           =     "MobilenetV1/Conv2d_0/"
   lowercase_	:    Tuple           =     backbone.conv_stem.convolution.weight
   lowercase_	:    int           =     backbone.conv_stem.normalization.bias
   lowercase_	:    Optional[Any]           =     backbone.conv_stem.normalization.weight
   lowercase_	:    Optional[int]           =     backbone.conv_stem.normalization.running_mean
   lowercase_	:    str           =     backbone.conv_stem.normalization.running_var

   for i in range(13							):
      lowercase_	:    Tuple           =     i + 1
      lowercase_	:    str           =     i * 2

      lowercase_	:    Tuple           =     backbone.layer[pt_index]
      lowercase_	:    Union[str, Any]           =     F"""MobilenetV1/Conv2d_{tf_index}_depthwise/"""
      lowercase_	:    List[str]           =     pointer.convolution.weight
      lowercase_	:    Optional[int]           =     pointer.normalization.bias
      lowercase_	:    str           =     pointer.normalization.weight
      lowercase_	:    List[Any]           =     pointer.normalization.running_mean
      lowercase_	:    List[Any]           =     pointer.normalization.running_var

      lowercase_	:    Union[str, Any]           =     backbone.layer[pt_index + 1]
      lowercase_	:    List[Any]           =     F"""MobilenetV1/Conv2d_{tf_index}_pointwise/"""
      lowercase_	:    Optional[int]           =     pointer.convolution.weight
      lowercase_	:    List[str]           =     pointer.normalization.bias
      lowercase_	:    Optional[int]           =     pointer.normalization.weight
      lowercase_	:    Any           =     pointer.normalization.running_mean
      lowercase_	:    Union[str, Any]           =     pointer.normalization.running_var

   if isinstance(_UpperCamelCase						,		_UpperCamelCase							):
      lowercase_	:    Optional[Any]           =     "MobilenetV1/Logits/Conv2d_1c_1x1/"
      lowercase_	:    Dict           =     model.classifier.weight
      lowercase_	:    List[Any]           =     model.classifier.bias

   return tf_to_pt_map




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   try:
      import numpy as np
      import tensorflow as tf
   except ImportError:
      logger.error(
          "Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
          "https://www.tensorflow.org/install/ for installation instructions."							)
      raise

   # Load weights from TF model
   lowercase_	:    Tuple           =     tf.train.list_variables(_UpperCamelCase							)
   lowercase_	:    Any           =     {}
   for name, shape in init_vars:
      logger.info(F"""Loading TF weight {name} with shape {shape}"""							)
      lowercase_	:    Optional[int]           =     tf.train.load_variable(_UpperCamelCase						,		_UpperCamelCase							)
      lowercase_	:    int           =     array

   # Build TF to PyTorch weights loading map
   lowercase_	:    Optional[Any]           =     _build_tf_to_pytorch_map(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)

   for name, pointer in tf_to_pt_map.items():
      logger.info(F"""Importing {name}"""							)
      if name not in tf_weights:
         logger.info(F"""{name} not in tf pre-trained weights, skipping"""							)
         continue

      lowercase_	:    Optional[int]           =     tf_weights[name]

      if "depthwise_weights" in name:
         logger.info("Transposing depthwise"							)
         lowercase_	:    Optional[Any]           =     np.transpose(_UpperCamelCase						,		(2, 3, 0, 1)							)
      elif "weights" in name:
         logger.info("Transposing"							)
         if len(pointer.shape							) == 2:  # copying into linear layer
            lowercase_	:    str           =     array.squeeze().transpose()
         else:
            lowercase_	:    Union[str, Any]           =     np.transpose(_UpperCamelCase						,		(3, 2, 0, 1)							)

      if pointer.shape != array.shape:
         raise ValueError(F"""Pointer shape {pointer.shape} and array shape {array.shape} mismatched"""							)

      logger.info(F"""Initialize PyTorch weight {name} {array.shape}"""							)
      lowercase_	:    List[Any]           =     torch.from_numpy(_UpperCamelCase							)

      tf_weights.pop(_UpperCamelCase						,		_UpperCamelCase							)
      tf_weights.pop(name + "/RMSProp"						,		_UpperCamelCase							)
      tf_weights.pop(name + "/RMSProp_1"						,		_UpperCamelCase							)
      tf_weights.pop(name + "/ExponentialMovingAverage"						,		_UpperCamelCase							)

   logger.info(F"""Weights not copied to PyTorch model: {", ".join(tf_weights.keys()							)}"""							)
   return model




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_    ,		lowercase_	:    Dict           =     features.shape[-2:]
   lowercase_    ,		lowercase_	:    List[str]           =     conv_layer.stride
   lowercase_    ,		lowercase_	:    List[Any]           =     conv_layer.kernel_size

   if in_height % stride_height == 0:
      lowercase_	:    Tuple           =     max(kernel_height - stride_height						,		0							)
   else:
      lowercase_	:    List[str]           =     max(kernel_height - (in_height % stride_height)						,		0							)

   if in_width % stride_width == 0:
      lowercase_	:    str           =     max(kernel_width - stride_width						,		0							)
   else:
      lowercase_	:    Any           =     max(kernel_width - (in_width % stride_width)						,		0							)

   lowercase_	:    Dict           =     pad_along_width // 2
   lowercase_	:    str           =     pad_along_width - pad_left
   lowercase_	:    List[Any]           =     pad_along_height // 2
   lowercase_	:    Optional[int]           =     pad_along_height - pad_top

   lowercase_	:    Union[str, Any]           =     (pad_left, pad_right, pad_top, pad_bottom)
   return nn.functional.pad(_UpperCamelCase						,		_UpperCamelCase						,		"constant"						,		0.0							)


class        _UpperCAmelCase						(		nn.Module			):




    def __init__(			self  :       Optional[int]  ,     a  :       MobileNetVaConfig  ,     a  :       int  ,     a  :       int  ,     a  :       int  ,     a  :       Optional[int] = 1  ,     a  :       Optional[int] = 1  ,     a  :       bool = False  ,     a  :       Optional[bool] = True  ,     a  :       Optional[bool or str] = True  ,     ):






       '''simple docstring'''






       super().__init__()
       lowercase_	:    str           =     config

       if in_channels % groups != 0:
          raise ValueError(f"""Input channels ({in_channels}) are not divisible by {groups} groups."""			)
       if out_channels % groups != 0:
          raise ValueError(f"""Output channels ({out_channels}) are not divisible by {groups} groups."""			)

       lowercase_	:    List[str]           =     0 if config.tf_padding else int((kernel_size - 1) / 2			)

       lowercase_	:    str           =     nn.Convad(
           in_channels=a  ,     out_channels=a  ,     kernel_size=a  ,     stride=a  ,     padding=a  ,     groups=a  ,     bias=a  ,     padding_mode="zeros"  ,     )

       if use_normalization:
          lowercase_	:    Optional[int]           =     nn.BatchNormad(
              num_features=a  ,     eps=config.layer_norm_eps  ,     momentum=0.9997  ,     affine=a  ,     track_running_stats=a  ,     )
       else:
          lowercase_	:    str           =     None

       if use_activation:
          if isinstance(a  ,     a			):
             lowercase_	:    Union[str, Any]           =     ACTaFN[use_activation]
          elif isinstance(config.hidden_act  ,     a			):
             lowercase_	:    List[str]           =     ACTaFN[config.hidden_act]
          else:
             lowercase_	:    int           =     config.hidden_act
       else:
          lowercase_	:    Optional[int]           =     None





    def 				lowerCAmelCase__				(			self  :       Optional[Any]  ,     a  :       torch.Tensor			):






       '''simple docstring'''






       if self.config.tf_padding:
          lowercase_	:    Dict           =     apply_tf_padding(a  ,     self.convolution			)
       lowercase_	:    List[Any]           =     self.convolution(a			)
       if self.normalization is not None:
          lowercase_	:    str           =     self.normalization(a			)
       if self.activation is not None:
          lowercase_	:    Any           =     self.activation(a			)
       return features


class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   Optional[Any]		       =    MobileNetVaConfig
    __lowerCamelCase:   Any		       =    load_tf_weights_in_mobilenet_va
    __lowerCamelCase:   str		       =    'mobilenet_v1'
    __lowerCamelCase:   Union[str, Any]		       =    'pixel_values'
    __lowerCamelCase:   Union[str, Any]		       =    False




    def 				lowerCAmelCase__				(			self  :       Optional[int]  ,     a  :       Union[nn.Linear, nn.Convad]			):






       '''simple docstring'''






       if isinstance(a  ,     (nn.Linear, nn.Convad)			):
          module.weight.data.normal_(mean=0.0  ,     std=self.config.initializer_range			)
          if module.bias is not None:
             module.bias.data.zero_()
       elif isinstance(a  ,     nn.BatchNormad			):
          module.bias.data.zero_()
          module.weight.data.fill_(1.0			)


UpperCamelCase__ =			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 ([`MobileNetV1Config`]): 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'

UpperCamelCase__ =			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            [`MobileNetV1ImageProcessor.__call__`] for details.\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 [`~utils.ModelOutput`] instead of a plain tuple.\n'


@add_start_docstrings(
    'The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.'				,					snake_case				,					)
class        _UpperCAmelCase						(		snake_case			):




    def __init__(			self  :       Optional[Any]  ,     a  :       MobileNetVaConfig  ,     a  :       bool = True			):






       '''simple docstring'''






       super().__init__(a			)
       lowercase_	:    Optional[int]           =     config

       lowercase_	:    Tuple           =     3_2
       lowercase_	:    Optional[int]           =     max(int(depth * config.depth_multiplier			)  ,     config.min_depth			)

       lowercase_	:    int           =     MobileNetVaConvLayer(
           a  ,     in_channels=config.num_channels  ,     out_channels=a  ,     kernel_size=3  ,     stride=2  ,     )

       lowercase_	:    List[str]           =     [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

       lowercase_	:    str           =     nn.ModuleList()
       for i in range(1_3			):
          lowercase_	:    List[Any]           =     out_channels

          if strides[i] == 2 or i == 0:
             depth *= 2
             lowercase_	:    str           =     max(int(depth * config.depth_multiplier			)  ,     config.min_depth			)

          self.layer.append(
              MobileNetVaConvLayer(
                  a  ,     in_channels=a  ,     out_channels=a  ,     kernel_size=3  ,     stride=strides[i]  ,     groups=a  ,     )			)

          self.layer.append(
              MobileNetVaConvLayer(
                  a  ,     in_channels=a  ,     out_channels=a  ,     kernel_size=1  ,     )			)

       lowercase_	:    Dict           =     nn.AdaptiveAvgPoolad((1, 1)			) if add_pooling_layer else None

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




    def 				lowerCAmelCase__				(			self  :       List[str]  ,     a  :       str			):






       '''simple docstring'''






       raise NotImplementedError





    @add_start_docstrings_to_model_forward(a			)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC  ,     output_type=a  ,     config_class=_CONFIG_FOR_DOC  ,     modality="vision"  ,     expected_output=_EXPECTED_OUTPUT_SHAPE  ,     )
    def 				lowerCAmelCase__				(			self  :       List[str]  ,     a  :       Optional[torch.Tensor] = None  ,     a  :       Optional[bool] = None  ,     a  :       Optional[bool] = None  ,     ):






       '''simple docstring'''






       lowercase_	:    Dict           =     (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       lowercase_	:    Union[str, Any]           =     return_dict if return_dict is not None else self.config.use_return_dict

       if pixel_values is None:
          raise ValueError("You have to specify pixel_values"			)

       lowercase_	:    Optional[int]           =     self.conv_stem(a			)

       lowercase_	:    Union[str, Any]           =     () if output_hidden_states else None

       for i, layer_module in enumerate(self.layer			):
          lowercase_	:    Optional[Any]           =     layer_module(a			)

          if output_hidden_states:
             lowercase_	:    List[Any]           =     all_hidden_states + (hidden_states,)

       lowercase_	:    Tuple           =     hidden_states

       if self.pooler is not None:
          lowercase_	:    List[str]           =     torch.flatten(self.pooler(a			)  ,     start_dim=1			)
       else:
          lowercase_	:    Tuple           =     None

       if not return_dict:
          return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None			)

       return BaseModelOutputWithPoolingAndNoAttention(
           last_hidden_state=a  ,     pooler_output=a  ,     hidden_states=a  ,     )


@add_start_docstrings(
    '\n    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '				,					snake_case				,					)
class        _UpperCAmelCase						(		snake_case			):




    def __init__(			self  :       int  ,     a  :       MobileNetVaConfig			):






       '''simple docstring'''






       super().__init__(a			)

       lowercase_	:    Tuple           =     config.num_labels
       lowercase_	:    Optional[int]           =     MobileNetVaModel(a			)

       lowercase_	:    Any           =     self.mobilenet_va.layer[-1].convolution.out_channels

       # Classifier head
       lowercase_	:    str           =     nn.Dropout(config.classifier_dropout_prob  ,     inplace=a			)
       lowercase_	:    Optional[int]           =     nn.Linear(a  ,     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(a			)
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT  ,     output_type=a  ,     config_class=_CONFIG_FOR_DOC  ,     expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT  ,     )
    def 				lowerCAmelCase__				(			self  :       Optional[int]  ,     a  :       Optional[torch.Tensor] = None  ,     a  :       Optional[bool] = None  ,     a  :       Optional[torch.Tensor] = None  ,     a  :       Optional[bool] = None  ,     ):






       '''simple docstring'''






       lowercase_	:    int           =     return_dict if return_dict is not None else self.config.use_return_dict

       lowercase_	:    List[Any]           =     self.mobilenet_va(a  ,     output_hidden_states=a  ,     return_dict=a			)

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

       lowercase_	:    Union[str, Any]           =     self.classifier(self.dropout(a			)			)

       lowercase_	:    Tuple           =     None
       if labels is not None:
          if self.config.problem_type is None:
             if self.num_labels == 1:
                lowercase_	:    Optional[Any]           =     "regression"
             elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                lowercase_	:    Any           =     "single_label_classification"
             else:
                lowercase_	:    List[str]           =     "multi_label_classification"

          if self.config.problem_type == "regression":
             lowercase_	:    Optional[int]           =     MSELoss()
             if self.num_labels == 1:
                lowercase_	:    Dict           =     loss_fct(logits.squeeze()  ,     labels.squeeze()			)
             else:
                lowercase_	:    Optional[int]           =     loss_fct(a  ,     a			)
          elif self.config.problem_type == "single_label_classification":
             lowercase_	:    Any           =     CrossEntropyLoss()
             lowercase_	:    Tuple           =     loss_fct(logits.view(-1  ,     self.num_labels			)  ,     labels.view(-1			)			)
          elif self.config.problem_type == "multi_label_classification":
             lowercase_	:    Union[str, Any]           =     BCEWithLogitsLoss()
             lowercase_	:    Optional[int]           =     loss_fct(a  ,     a			)

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

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












'''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


UpperCamelCase__ =			logging.get_logger(__name__)


class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   Tuple		       =    'linear'
    __lowerCamelCase:   Any		       =    'cosine'
    __lowerCamelCase:   Optional[Any]		       =    'cosine_with_restarts'
    __lowerCamelCase:   Tuple		       =    'polynomial'
    __lowerCamelCase:   int		       =    'constant'
    __lowerCamelCase:   Optional[Any]		       =    'constant_with_warmup'
    __lowerCamelCase:   List[str]		       =    'piecewise_constant'




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase = -1							):


   """simple docstring"""




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




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = -1							):


   """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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = -1							):


   """simple docstring"""




   lowercase_	:    List[Any]           =     {}
   lowercase_	:    Dict           =     step_rules.split(","							)
   for rule_str in rule_list[:-1]:
      lowercase_    ,		lowercase_	:    Any           =     rule_str.split(":"							)
      lowercase_	:    List[Any]           =     int(_UpperCamelCase							)
      lowercase_	:    int           =     float(_UpperCamelCase							)
      lowercase_	:    Optional[int]           =     value
   lowercase_	:    Union[str, Any]           =     float(rule_list[-1]							)

   def create_rules_function(_UpperCamelCase						,		_UpperCamelCase							):
      def rule_func(_UpperCamelCase							) -> float:
         lowercase_	:    Optional[Any]           =     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

   lowercase_	:    Optional[int]           =     create_rules_function(_UpperCamelCase						,		_UpperCamelCase							)

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		last_epoch=_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=-1							):


   """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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = 0.5						,		_UpperCamelCase = -1							):


   """simple docstring"""




   def lr_lambda(_UpperCamelCase							):
      if current_step < num_warmup_steps:
         return float(_UpperCamelCase							) / float(max(1						,		_UpperCamelCase							)							)
      lowercase_	:    List[str]           =     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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = 1						,		_UpperCamelCase = -1							):


   """simple docstring"""




   def lr_lambda(_UpperCamelCase							):
      if current_step < num_warmup_steps:
         return float(_UpperCamelCase							) / float(max(1						,		_UpperCamelCase							)							)
      lowercase_	:    Dict           =     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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=1e-7						,		_UpperCamelCase=1.0						,		_UpperCamelCase=-1							):


   """simple docstring"""




   lowercase_	:    Dict           =     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:
         lowercase_	:    int           =     lr_init - lr_end
         lowercase_	:    Optional[int]           =     num_training_steps - num_warmup_steps
         lowercase_	:    Optional[Any]           =     1 - (current_step - num_warmup_steps) / decay_steps
         lowercase_	:    List[Any]           =     lr_range * pct_remaining**power + lr_end
         return decay / lr_init  # as LambdaLR multiplies by lr_init

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)


UpperCamelCase__ =			{
    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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = None						,		_UpperCamelCase = None						,		_UpperCamelCase = None						,		_UpperCamelCase = 1						,		_UpperCamelCase = 1.0						,		_UpperCamelCase = -1						,		):


   """simple docstring"""




   lowercase_	:    Any           =     SchedulerType(_UpperCamelCase							)
   lowercase_	:    List[Any]           =     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							)




