





'''simple docstring'''

import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import (
      TFEfficientFormerForImageClassification,
      TFEfficientFormerForImageClassificationWithTeacher,
      TFEfficientFormerModel,
  )
  from transformers.models.efficientformer.modeling_tf_efficientformer import (
      TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  )


if is_vision_available():
  from PIL import Image

  from transformers import EfficientFormerImageProcessor







class __a :





   def __init__(					self				:			Union[str, Any]				,lowerCamelCase				:			str				,lowerCamelCase				:			int = 13				,lowerCamelCase				:			int = 64				,lowerCamelCase				:			int = 2				,lowerCamelCase				:			int = 3				,lowerCamelCase				:			int = 3				,lowerCamelCase				:			bool = True				,lowerCamelCase				:			bool = True				,lowerCamelCase				:			int = 128				,lowerCamelCase				:			int=[16, 32, 64, 128]				,lowerCamelCase				:			int = 7				,lowerCamelCase				:			int = 4				,lowerCamelCase				:			int = 37				,lowerCamelCase				:			str = "gelu"				,lowerCamelCase				:			float = 0.1				,lowerCamelCase				:			float = 0.1				,lowerCamelCase				:			int = 10				,lowerCamelCase				:			float = 0.02				,lowerCamelCase				:			int = 2				,lowerCamelCase				:			int = 1				,lowerCamelCase				:			int = 128				,lowerCamelCase				:			List[int] = [2, 2, 2, 2]				,lowerCamelCase				:			int = 2				,lowerCamelCase				:			int = 2				,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       parent
      __SCREAMING_SNAKE_CASE      							=       batch_size
      __SCREAMING_SNAKE_CASE      							=       image_size
      __SCREAMING_SNAKE_CASE      							=       patch_size
      __SCREAMING_SNAKE_CASE      							=       num_channels
      __SCREAMING_SNAKE_CASE      							=       is_training
      __SCREAMING_SNAKE_CASE      							=       use_labels
      __SCREAMING_SNAKE_CASE      							=       hidden_size
      __SCREAMING_SNAKE_CASE      							=       num_hidden_layers
      __SCREAMING_SNAKE_CASE      							=       num_attention_heads
      __SCREAMING_SNAKE_CASE      							=       intermediate_size
      __SCREAMING_SNAKE_CASE      							=       hidden_act
      __SCREAMING_SNAKE_CASE      							=       hidden_dropout_prob
      __SCREAMING_SNAKE_CASE      							=       attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE      							=       type_sequence_label_size
      __SCREAMING_SNAKE_CASE      							=       initializer_range
      __SCREAMING_SNAKE_CASE      							=       encoder_stride
      __SCREAMING_SNAKE_CASE      							=       num_attention_outputs
      __SCREAMING_SNAKE_CASE      							=       embed_dim
      __SCREAMING_SNAKE_CASE      							=       embed_dim + 1
      __SCREAMING_SNAKE_CASE      							=       resolution
      __SCREAMING_SNAKE_CASE      							=       depths
      __SCREAMING_SNAKE_CASE      							=       hidden_sizes
      __SCREAMING_SNAKE_CASE      							=       dim
      __SCREAMING_SNAKE_CASE      							=       mlp_expansion_ratio





   def      UpperCAmelCase__     (					self				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

      __SCREAMING_SNAKE_CASE      							=       None
      if self.use_labels:
         __SCREAMING_SNAKE_CASE      							=       ids_tensor([self.batch_size]				,self.type_sequence_label_size    )

      __SCREAMING_SNAKE_CASE      							=       self.get_config()

      return config, pixel_values, labels





   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      return EfficientFormerConfig(
          image_size=self.image_size				,patch_size=self.patch_size				,num_channels=self.num_channels				,hidden_size=self.hidden_size				,num_hidden_layers=self.num_hidden_layers				,num_attention_heads=self.num_attention_heads				,intermediate_size=self.intermediate_size				,hidden_act=self.hidden_act				,hidden_dropout_prob=self.hidden_dropout_prob				,attention_probs_dropout_prob=self.attention_probs_dropout_prob				,is_decoder=A_				,initializer_range=self.initializer_range				,encoder_stride=self.encoder_stride				,resolution=self.resolution				,depths=self.depths				,hidden_sizes=self.hidden_sizes				,dim=self.dim				,mlp_expansion_ratio=self.mlp_expansion_ratio				,)





   def      UpperCAmelCase__     (					self				:			Tuple				,lowerCamelCase				:			int				,lowerCamelCase				:			int				,lowerCamelCase				:			int    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       TFEfficientFormerModel(config=A_    )
      __SCREAMING_SNAKE_CASE      							=       model(A_				,training=A_    )
      self.parent.assertEqual(result.last_hidden_state.shape				,(self.batch_size, self.seq_length, self.hidden_size)    )





   def      UpperCAmelCase__     (					self				:			List[str]				,lowerCamelCase				:			int				,lowerCamelCase				:			List[Any]				,lowerCamelCase				:			List[str]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.type_sequence_label_size
      __SCREAMING_SNAKE_CASE      							=       TFEfficientFormerForImageClassification(A_    )
      __SCREAMING_SNAKE_CASE      							=       model(A_				,labels=A_				,training=A_    )
      self.parent.assertEqual(result.logits.shape				,(self.batch_size, self.type_sequence_label_size)    )

      # test greyscale images
      __SCREAMING_SNAKE_CASE      							=       1
      __SCREAMING_SNAKE_CASE      							=       TFEfficientFormerForImageClassification(A_    )

      __SCREAMING_SNAKE_CASE      							=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
      __SCREAMING_SNAKE_CASE      							=       model(A_				,labels=A_    )
      self.parent.assertEqual(result.logits.shape				,(self.batch_size, self.type_sequence_label_size)    )





   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.prepare_config_and_inputs()
      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       config_and_inputs
      __SCREAMING_SNAKE_CASE      							=       {"""pixel_values""": pixel_values}
      return config, inputs_dict







@require_tf
class __a (   _UpperCAmelCase,   _UpperCAmelCase,   unittest.TestCase			):
   __UpperCamelCase   : Optional[Any]	      =	(
       (
           TFEfficientFormerModel,
           TFEfficientFormerForImageClassificationWithTeacher,
           TFEfficientFormerForImageClassification,
       )
       if is_tf_available()
       else ()
   )
   __UpperCamelCase   : List[Any]	      =	(
       {
           '''feature-extraction''': TFEfficientFormerModel,
           '''image-classification''': (
               TFEfficientFormerForImageClassification,
               TFEfficientFormerForImageClassificationWithTeacher,
           ),
       }
       if is_tf_available()
       else {}
   )

   __UpperCamelCase   : Union[str, Any]	      =	False

   __UpperCamelCase   : Optional[Any]	      =	False
   __UpperCamelCase   : Tuple	      =	False
   __UpperCamelCase   : Union[str, Any]	      =	False
   __UpperCamelCase   : Optional[Any]	      =	False





   def      UpperCAmelCase__     (					self				:			List[str]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       TFEfficientFormerModelTester(self    )
      __SCREAMING_SNAKE_CASE      							=       ConfigTester(
          self				,config_class=A_				,has_text_modality=A_				,hidden_size=37    )





   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      self.config_tester.run_common_tests()





   @unittest.skip(reason="""EfficientFormer does not use inputs_embeds"""    )
   def      UpperCAmelCase__     (					self				:			int    ):

      '''simple docstring'''

      pass





   @unittest.skip(reason="""EfficientFormer does not support input and output embeddings"""    )
   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      pass





   def      UpperCAmelCase__     (					self				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         __SCREAMING_SNAKE_CASE      							=       model_class(A_    )
         __SCREAMING_SNAKE_CASE      							=       inspect.signature(model.call    )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         __SCREAMING_SNAKE_CASE      							=       [*signature.parameters.keys()]

         __SCREAMING_SNAKE_CASE      							=       ["""pixel_values"""]
         self.assertListEqual(arg_names[:1]				,A_    )





   def      UpperCAmelCase__     (					self				:			Optional[Any]    ):

      '''simple docstring'''

      def check_hidden_states_output(lowerCamelCase				:			Dict				,lowerCamelCase				:			int				,lowerCamelCase				:			List[str]    ):
         __SCREAMING_SNAKE_CASE      							=       model_class(A_    )

         __SCREAMING_SNAKE_CASE      							=       model(**self._prepare_for_class(A_				,A_    )				,training=A_    )
         __SCREAMING_SNAKE_CASE      							=       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

         __SCREAMING_SNAKE_CASE      							=       getattr(
             self.model_tester				,"""expected_num_hidden_layers"""				,self.model_tester.num_hidden_layers + 1    )
         self.assertEqual(len(A_    )				,A_    )

         if hasattr(self.model_tester				,"""encoder_seq_length"""    ):
            __SCREAMING_SNAKE_CASE      							=       self.model_tester.encoder_seq_length
            if hasattr(self.model_tester				,"""chunk_length"""    ) and self.model_tester.chunk_length > 1:
               __SCREAMING_SNAKE_CASE      							=       seq_length * self.model_tester.chunk_length
         else:
            __SCREAMING_SNAKE_CASE      							=       self.model_tester.seq_length

         self.assertListEqual(
             list(hidden_states[-1].shape[-2:]    )				,[seq_length, self.model_tester.hidden_size]				,)

         if config.is_encoder_decoder:
            __SCREAMING_SNAKE_CASE      							=       outputs.decoder_hidden_states

            self.asseretIsInstance(A_				,(list, tuple)    )
            self.assertEqual(len(A_    )				,A_    )
            __SCREAMING_SNAKE_CASE      							=       getattr(self.model_tester				,"""seq_length"""				,A_    )
            __SCREAMING_SNAKE_CASE      							=       getattr(self.model_tester				,"""decoder_seq_length"""				,A_    )

            self.assertListEqual(
                list(hidden_states[-1].shape[-2:]    )				,[decoder_seq_length, self.model_tester.hidden_size]				,)

      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         __SCREAMING_SNAKE_CASE      							=       True
         check_hidden_states_output(A_				,A_				,A_    )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         __SCREAMING_SNAKE_CASE      							=       True

         check_hidden_states_output(A_				,A_				,A_    )





   def      UpperCAmelCase__     (					self				:			Union[str, Any]				,lowerCamelCase				:			Tuple				,lowerCamelCase				:			Optional[int]				,lowerCamelCase				:			Dict=False    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       super()._prepare_for_class(A_				,A_				,return_labels=A_    )

      if return_labels:
         if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
            del inputs_dict["labels"]

      return inputs_dict





   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A_    )





   @unittest.skip(reason="""EfficientFormer does not implement masked image modeling yet"""    )
   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_image_modeling(*A_    )





   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*A_    )





   @slow
   def      UpperCAmelCase__     (					self				:			Dict    ):

      '''simple docstring'''

      for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __SCREAMING_SNAKE_CASE      							=       TFEfficientFormerModel.from_pretrained(A_    )
         self.assertIsNotNone(A_    )





   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       self.model_tester.prepare_config_and_inputs_for_common()
      __SCREAMING_SNAKE_CASE      							=       True

      __SCREAMING_SNAKE_CASE      							=       getattr(self.model_tester				,"""seq_length"""				,A_    )
      __SCREAMING_SNAKE_CASE      							=       getattr(self.model_tester				,"""encoder_seq_length"""				,A_    )
      __SCREAMING_SNAKE_CASE      							=       getattr(self.model_tester				,"""key_length"""				,A_    )
      __SCREAMING_SNAKE_CASE      							=       getattr(self.model_tester				,"""chunk_length"""				,A_    )

      if chunk_length is not None and hasattr(self.model_tester				,"""num_hashes"""    ):
         __SCREAMING_SNAKE_CASE      							=       encoder_seq_length * self.model_tester.num_hashes

      for model_class in self.all_model_classes:
         __SCREAMING_SNAKE_CASE      							=       True
         __SCREAMING_SNAKE_CASE      							=       False
         __SCREAMING_SNAKE_CASE      							=       True
         __SCREAMING_SNAKE_CASE      							=       model_class(A_    )

         __SCREAMING_SNAKE_CASE      							=       model(**self._prepare_for_class(A_				,A_    )				,training=A_    )
         __SCREAMING_SNAKE_CASE      							=       outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
         self.assertEqual(len(A_    )				,self.model_tester.num_attention_outputs    )

         # check that output_attentions also work using config
         del inputs_dict["output_attentions"]
         __SCREAMING_SNAKE_CASE      							=       True
         __SCREAMING_SNAKE_CASE      							=       model_class(A_    )
         __SCREAMING_SNAKE_CASE      							=       model(**self._prepare_for_class(A_				,A_    )				,training=A_    )

         __SCREAMING_SNAKE_CASE      							=       outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
         self.assertEqual(len(A_    )				,self.model_tester.num_attention_outputs    )

         if chunk_length is not None:
            self.assertListEqual(
                list(attentions[0].shape[-4:]    )				,[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]				,)
         else:
            self.assertListEqual(
                list(attentions[0].shape[-3:]    )				,[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]				,)





   def      UpperCAmelCase__     (					self				:			Dict    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         # Prepare our model
         __SCREAMING_SNAKE_CASE      							=       model_class(A_    )
         # These are maximally general inputs for the model, with multiple None dimensions
         # Hopefully this will catch any conditionals that fail for flexible shapes
         __SCREAMING_SNAKE_CASE      							=       {
             key: tf.keras.Input(shape=val.shape[1:]				,dtype=val.dtype				,name=A_    )
             for key, val in model.input_signature.items()
             if key in model.dummy_inputs
         }
         __SCREAMING_SNAKE_CASE      							=       model(A_    )
         self.assertTrue(outputs_dict is not None    )





def 						__magic_name__       (							)						->					Dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
   return image







@require_tf
@require_vision
class __a (   unittest.TestCase			):





   @cached_property
   def      UpperCAmelCase__     (					self				:			Optional[Any]    ):

      '''simple docstring'''

      return (
          EfficientFormerImageProcessor.from_pretrained("""snap-research/efficientformer-l1-300"""    )
          if is_vision_available()
          else None
      )





   @slow
   def      UpperCAmelCase__     (					self				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       TFEfficientFormerForImageClassification.from_pretrained("""snap-research/efficientformer-l1-300"""    )
      __SCREAMING_SNAKE_CASE      							=       self.default_image_processor
      __SCREAMING_SNAKE_CASE      							=       prepare_img()
      __SCREAMING_SNAKE_CASE      							=       image_processor(images=A_				,return_tensors="""tf"""    )
      # forward pass
      __SCREAMING_SNAKE_CASE      							=       model(**A_				,training=A_    )
      # verify the logits
      __SCREAMING_SNAKE_CASE      							=       tf.TensorShape((1, 1000)    )
      self.assertEqual(outputs.logits.shape				,A_    )
      __SCREAMING_SNAKE_CASE      							=       tf.constant([-0.0_555, 0.4_825, -0.0_852]    )
      self.assertTrue(np.allclose(outputs.logits[0, :3]				,A_				,atol=1E-4    )    )





   @slow
   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
          """snap-research/efficientformer-l1-300"""    )
      __SCREAMING_SNAKE_CASE      							=       self.default_image_processor
      __SCREAMING_SNAKE_CASE      							=       prepare_img()
      __SCREAMING_SNAKE_CASE      							=       image_processor(images=A_				,return_tensors="""tf"""    )
      # forward pass
      __SCREAMING_SNAKE_CASE      							=       model(**A_				,training=A_    )
      # verify the logits
      __SCREAMING_SNAKE_CASE      							=       tf.TensorShape((1, 1000)    )
      self.assertEqual(outputs.logits.shape				,A_    )
      __SCREAMING_SNAKE_CASE      							=       tf.constant([-0.1_312, 0.4_353, -1.0_499]    )
      self.assertTrue(np.allclose(outputs.logits[0, :3]				,A_				,atol=1E-4    )    )












'''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        =						logging.get_logger(__name__)







class __a (   _snake_case			):
   __UpperCamelCase   : int	      =	'linear'
   __UpperCamelCase   : Tuple	      =	'cosine'
   __UpperCamelCase   : Tuple	      =	'cosine_with_restarts'
   __UpperCamelCase   : List[Any]	      =	'polynomial'
   __UpperCamelCase   : Optional[Any]	      =	'constant'
   __UpperCamelCase   : Optional[int]	      =	'constant_with_warmup'
   __UpperCamelCase   : List[Any]	      =	'piecewise_constant'





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

   '''simple docstring'''







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

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

   '''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      )						->					int:

   '''simple docstring'''







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

   def create_rules_function(__UpperCAmelCase							,	__UpperCAmelCase      ):
      def rule_func(__UpperCAmelCase      ) -> float:
         __SCREAMING_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

   __SCREAMING_SNAKE_CASE      							=       create_rules_function(__UpperCAmelCase							,	__UpperCAmelCase      )

   return LambdaLR(__UpperCAmelCase							,	__UpperCAmelCase							,	last_epoch=__UpperCAmelCase      )

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

   '''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      )						->					Dict:

   '''simple docstring'''







   def lr_lambda(__UpperCAmelCase      ):
      if current_step < num_warmup_steps:
         return float(__UpperCAmelCase      ) / float(max(1							,	__UpperCAmelCase      )      )
      __SCREAMING_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      )						->					Tuple:

   '''simple docstring'''







   def lr_lambda(__UpperCAmelCase      ):
      if current_step < num_warmup_steps:
         return float(__UpperCAmelCase      ) / float(max(1							,	__UpperCAmelCase      )      )
      __SCREAMING_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      )						->					Tuple:

   '''simple docstring'''







   __SCREAMING_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:
         __SCREAMING_SNAKE_CASE      							=       lr_init - lr_end
         __SCREAMING_SNAKE_CASE      							=       num_training_steps - num_warmup_steps
         __SCREAMING_SNAKE_CASE      							=       1 - (current_step - num_warmup_steps) / decay_steps
         __SCREAMING_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        =						{
    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							,	)						->					str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       SchedulerType(__UpperCAmelCase      )
   __SCREAMING_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      )





