



'''simple docstring'''




import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin







class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	__SCREAMING_SNAKE_CASE   :		Optional[Any]					=					None
	__SCREAMING_SNAKE_CASE   :		int					=					None


	@property
	def     _a	(self							):
				return self.feat_extract_tester.prepare_feat_extract_dict()


	def     _a	(self							):
				A_	:						Union[str, Any]	=     self.feature_extraction_class(**self.feat_extract_dict							)
				self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,      """feature_size"""							)							)
				self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,      """sampling_rate"""							)							)
				self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,      """padding_value"""							)							)


	def     _a	(self							):
				A_	:						Any	=     self.feat_extract_tester.prepare_inputs_for_common()
				A_	:						Union[str, Any]	=     self.feature_extraction_class(**self.feat_extract_dict							)
				A_	:						List[str]	=     feat_extract.model_input_names[0]

				A_	:						List[str]	=     BatchFeature({input_name: speech_inputs}							)

				self.assertTrue(all(len(__SCREAMING_SNAKE_CASE							) == len(__SCREAMING_SNAKE_CASE							) for x, y in zip(__SCREAMING_SNAKE_CASE       ,      processed_features[input_name]							)							)							)

				A_	:						Union[str, Any]	=     self.feat_extract_tester.prepare_inputs_for_common(equal_length=__SCREAMING_SNAKE_CASE							)
				A_	:						List[str]	=     BatchFeature({input_name: speech_inputs}       ,      tensor_type="""np"""							)

				A_	:						Dict	=     processed_features[input_name]

				if len(batch_features_input.shape							) < 3:
							A_	:						Any	=     batch_features_input[:, :, None]

				self.assertTrue(
				    batch_features_input.shape
				    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]							), self.feat_extract_tester.feature_size)							)


	@require_torch
	def     _a	(self							):
				A_	:						Union[str, Any]	=     self.feat_extract_tester.prepare_inputs_for_common(equal_length=__SCREAMING_SNAKE_CASE							)
				A_	:						Tuple	=     self.feature_extraction_class(**self.feat_extract_dict							)
				A_	:						Tuple	=     feat_extract.model_input_names[0]

				A_	:						Union[str, Any]	=     BatchFeature({input_name: speech_inputs}       ,      tensor_type="""pt"""							)

				A_	:						List[Any]	=     processed_features[input_name]

				if len(batch_features_input.shape							) < 3:
							A_	:						Tuple	=     batch_features_input[:, :, None]

				self.assertTrue(
				    batch_features_input.shape
				    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]							), self.feat_extract_tester.feature_size)							)


	@require_tf
	def     _a	(self							):
				A_	:						Union[str, Any]	=     self.feat_extract_tester.prepare_inputs_for_common(equal_length=__SCREAMING_SNAKE_CASE							)
				A_	:						Any	=     self.feature_extraction_class(**self.feat_extract_dict							)
				A_	:						Optional[int]	=     feat_extract.model_input_names[0]

				A_	:						Dict	=     BatchFeature({input_name: speech_inputs}       ,      tensor_type="""tf"""							)

				A_	:						Any	=     processed_features[input_name]

				if len(batch_features_input.shape							) < 3:
							A_	:						List[str]	=     batch_features_input[:, :, None]

				self.assertTrue(
				    batch_features_input.shape
				    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]							), self.feat_extract_tester.feature_size)							)


	def     _a	(self       ,      lowercase=False							):




				def _inputs_have_equal_length(lowercase							):
							A_	:						Optional[Any]	=     len(input[0]							)
							for input_slice in input[1:]:
										if len(__SCREAMING_SNAKE_CASE							) != length:
													return False
							return True

				def _inputs_are_equal(lowercase       ,      lowercase							):
							if len(__SCREAMING_SNAKE_CASE							) != len(__SCREAMING_SNAKE_CASE							):
										return False

							for input_slice_a, input_slice_a in zip(__SCREAMING_SNAKE_CASE       ,      __SCREAMING_SNAKE_CASE							):
										if not np.allclose(np.asarray(__SCREAMING_SNAKE_CASE							)       ,      np.asarray(__SCREAMING_SNAKE_CASE							)       ,      atol=1E-3							):
													return False
							return True

				A_	:						List[Any]	=     self.feature_extraction_class(**self.feat_extract_dict							)
				A_	:						Optional[int]	=     self.feat_extract_tester.prepare_inputs_for_common(numpify=__SCREAMING_SNAKE_CASE							)
				A_	:						Tuple	=     feat_extract.model_input_names[0]

				A_	:						List[Any]	=     BatchFeature({input_name: speech_inputs}							)

				A_	:						Union[str, Any]	=     self.feat_extract_tester.seq_length_diff
				A_	:						Union[str, Any]	=     self.feat_extract_tester.max_seq_length + pad_diff
				A_	:						Optional[Any]	=     self.feat_extract_tester.min_seq_length
				A_	:						Dict	=     self.feat_extract_tester.batch_size
				A_	:						int	=     self.feat_extract_tester.feature_size

				# test padding for List[int] + numpy
				A_	:						Dict	=     feat_extract.pad(__SCREAMING_SNAKE_CASE       ,      padding=__SCREAMING_SNAKE_CASE							)
				A_	:						Optional[int]	=     input_a[input_name]

				A_	:						Any	=     feat_extract.pad(__SCREAMING_SNAKE_CASE       ,      padding="""longest"""							)
				A_	:						Union[str, Any]	=     input_a[input_name]

				A_	:						Union[str, Any]	=     feat_extract.pad(__SCREAMING_SNAKE_CASE       ,      padding="""max_length"""       ,      max_length=len(speech_inputs[-1]							)							)
				A_	:						Dict	=     input_a[input_name]

				A_	:						Any	=     feat_extract.pad(__SCREAMING_SNAKE_CASE       ,      padding="""longest"""       ,      return_tensors="""np"""							)
				A_	:						Dict	=     input_a[input_name]

				# max_length parameter has to be provided when setting `padding="max_length"`
				with self.assertRaises(__SCREAMING_SNAKE_CASE							):
							feat_extract.pad(__SCREAMING_SNAKE_CASE       ,      padding="""max_length"""							)[input_name]

				A_	:						Union[str, Any]	=     feat_extract.pad(
				    __SCREAMING_SNAKE_CASE       ,      padding="""max_length"""       ,      max_length=__SCREAMING_SNAKE_CASE       ,      return_tensors="""np"""							)
				A_	:						Optional[int]	=     input_a[input_name]

				self.assertFalse(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE							)							)
				self.assertTrue(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE							)							)
				self.assertTrue(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE							)							)
				self.assertTrue(_inputs_are_equal(__SCREAMING_SNAKE_CASE       ,      __SCREAMING_SNAKE_CASE							)							)
				self.assertTrue(len(input_a[0]							) == pad_min_length							)
				self.assertTrue(len(input_a[1]							) == pad_min_length + pad_diff							)
				self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]							))							)
				self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)							)

				if feature_size > 1:
							self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size							)

				# test padding for `pad_to_multiple_of` for List[int] + numpy
				A_	:						List[Any]	=     feat_extract.pad(__SCREAMING_SNAKE_CASE       ,      pad_to_multiple_of=10							)
				A_	:						List[Any]	=     input_a[input_name]

				A_	:						Tuple	=     feat_extract.pad(__SCREAMING_SNAKE_CASE       ,      padding="""longest"""       ,      pad_to_multiple_of=10							)
				A_	:						str	=     input_a[input_name]

				A_	:						Dict	=     feat_extract.pad(
				    __SCREAMING_SNAKE_CASE       ,      padding="""max_length"""       ,      pad_to_multiple_of=10       ,      max_length=__SCREAMING_SNAKE_CASE							)
				A_	:						int	=     input_a[input_name]

				A_	:						Optional[Any]	=     feat_extract.pad(
				    __SCREAMING_SNAKE_CASE       ,      padding="""max_length"""       ,      pad_to_multiple_of=10       ,      max_length=__SCREAMING_SNAKE_CASE       ,      return_tensors="""np"""       ,      )
				A_	:						Tuple	=     input_a[input_name]

				self.assertTrue(all(len(__SCREAMING_SNAKE_CASE							) % 10 == 0 for x in input_a							)							)
				self.assertTrue(_inputs_are_equal(__SCREAMING_SNAKE_CASE       ,      __SCREAMING_SNAKE_CASE							)							)

				A_	:						List[Any]	=     pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
				self.assertTrue(all(len(__SCREAMING_SNAKE_CASE							) == expected_mult_pad_length for x in input_a							)							)
				self.assertEqual(input_a.shape[:2]       ,      (batch_size, expected_mult_pad_length)							)

				if feature_size > 1:
							self.assertTrue(input_a.shape[2] == feature_size							)

				# Check padding value is correct
				A_	:						Tuple	=     (np.ones(self.feat_extract_tester.feature_size							) * feat_extract.padding_value).sum()
				self.assertTrue(
				    abs(np.asarray(input_a[0]							)[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)							)
				    < 1E-3							)
				self.assertTrue(
				    abs(
				        np.asarray(input_a[1]							)[pad_min_length + pad_diff :].sum()
				        - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)							)
				    < 1E-3							)
				self.assertTrue(
				    abs(
				        np.asarray(input_a[2]							)[pad_min_length + 2 * pad_diff :].sum()
				        - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)							)
				    < 1E-3							)
				self.assertTrue(
				    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)							) < 1E-3							)
				self.assertTrue(
				    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)							)
				    < 1E-3							)


	def     _a	(self       ,      lowercase=False							):




				def _inputs_have_equal_length(lowercase							):
							A_	:						str	=     len(input[0]							)
							for input_slice in input[1:]:
										if len(__SCREAMING_SNAKE_CASE							) != length:
													return False
							return True

				def _inputs_are_equal(lowercase       ,      lowercase							):
							if len(__SCREAMING_SNAKE_CASE							) != len(__SCREAMING_SNAKE_CASE							):
										return False

							for input_slice_a, input_slice_a in zip(__SCREAMING_SNAKE_CASE       ,      __SCREAMING_SNAKE_CASE							):
										if not np.allclose(np.asarray(__SCREAMING_SNAKE_CASE							)       ,      np.asarray(__SCREAMING_SNAKE_CASE							)       ,      atol=1E-3							):
													return False
							return True

				A_	:						Optional[Any]	=     self.feature_extraction_class(**self.feat_extract_dict							)
				A_	:						Optional[int]	=     self.feat_extract_tester.prepare_inputs_for_common(numpify=__SCREAMING_SNAKE_CASE							)
				A_	:						Tuple	=     feat_extract.model_input_names[0]

				A_	:						Union[str, Any]	=     BatchFeature({input_name: speech_inputs}							)

				# truncate to smallest
				A_	:						Optional[int]	=     feat_extract.pad(
				    __SCREAMING_SNAKE_CASE       ,      padding="""max_length"""       ,      max_length=len(speech_inputs[0]							)       ,      truncation=__SCREAMING_SNAKE_CASE							)
				A_	:						Optional[int]	=     input_a[input_name]

				A_	:						Tuple	=     feat_extract.pad(__SCREAMING_SNAKE_CASE       ,      padding="""max_length"""       ,      max_length=len(speech_inputs[0]							)							)
				A_	:						int	=     input_a[input_name]

				self.assertTrue(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE							)							)
				self.assertFalse(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE							)							)

				# truncate to smallest with np
				A_	:						Optional[int]	=     feat_extract.pad(
				    __SCREAMING_SNAKE_CASE       ,      padding="""max_length"""       ,      max_length=len(speech_inputs[0]							)       ,      return_tensors="""np"""       ,      truncation=__SCREAMING_SNAKE_CASE       ,      )
				A_	:						int	=     input_a[input_name]

				A_	:						Dict	=     feat_extract.pad(
				    __SCREAMING_SNAKE_CASE       ,      padding="""max_length"""       ,      max_length=len(speech_inputs[0]							)       ,      return_tensors="""np"""							)
				A_	:						Dict	=     input_a[input_name]

				self.assertTrue(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE							)							)
				self.assertTrue(input_a.shape[1] == len(speech_inputs[0]							)							)

				# since truncation forces padding to be smaller than longest input
				# function can't return `np.ndarray`, but has to return list
				self.assertFalse(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE							)							)

				# truncate to middle
				A_	:						Tuple	=     feat_extract.pad(
				    __SCREAMING_SNAKE_CASE       ,      padding="""max_length"""       ,      max_length=len(speech_inputs[1]							)       ,      truncation=__SCREAMING_SNAKE_CASE       ,      return_tensors="""np"""       ,      )
				A_	:						Tuple	=     input_a[input_name]

				A_	:						str	=     feat_extract.pad(
				    __SCREAMING_SNAKE_CASE       ,      padding="""max_length"""       ,      max_length=len(speech_inputs[1]							)       ,      truncation=__SCREAMING_SNAKE_CASE							)
				A_	:						Union[str, Any]	=     input_a[input_name]

				A_	:						Optional[Any]	=     feat_extract.pad(
				    __SCREAMING_SNAKE_CASE       ,      padding="""max_length"""       ,      max_length=len(speech_inputs[1]							)       ,      return_tensors="""np"""							)
				A_	:						Any	=     input_a[input_name]

				self.assertTrue(input_a.shape[1] == len(speech_inputs[1]							)							)
				self.assertTrue(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE							)							)
				self.assertTrue(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE							)							)
				self.assertTrue(_inputs_are_equal(__SCREAMING_SNAKE_CASE       ,      __SCREAMING_SNAKE_CASE							)							)

				# since truncation forces padding to be smaller than longest input
				# function can't return `np.ndarray`, but has to return list
				self.assertFalse(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE							)							)
				self.assertTrue(len(input_a[-1]							) == len(speech_inputs[-1]							)							)

				# padding has to be max_length when setting `truncation=True`
				with self.assertRaises(__SCREAMING_SNAKE_CASE							):
							feat_extract.pad(__SCREAMING_SNAKE_CASE       ,      truncation=__SCREAMING_SNAKE_CASE							)[input_name]

				# padding has to be max_length when setting `truncation=True`
				with self.assertRaises(__SCREAMING_SNAKE_CASE							):
							feat_extract.pad(__SCREAMING_SNAKE_CASE       ,      padding="""longest"""       ,      truncation=__SCREAMING_SNAKE_CASE							)[input_name]

				# padding has to be max_length when setting `truncation=True`
				with self.assertRaises(__SCREAMING_SNAKE_CASE							):
							feat_extract.pad(__SCREAMING_SNAKE_CASE       ,      padding="""longest"""       ,      truncation=__SCREAMING_SNAKE_CASE							)[input_name]

				# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
				with self.assertRaises(__SCREAMING_SNAKE_CASE							):
							feat_extract.pad(__SCREAMING_SNAKE_CASE       ,      padding="""max_length"""       ,      truncation=__SCREAMING_SNAKE_CASE							)[input_name]

				# test truncation for `pad_to_multiple_of` for List[int] + numpy
				A_	:						Optional[int]	=     12
				A_	:						Optional[int]	=     feat_extract.pad(
				    __SCREAMING_SNAKE_CASE       ,      padding="""max_length"""       ,      max_length=len(speech_inputs[0]							)       ,      pad_to_multiple_of=__SCREAMING_SNAKE_CASE       ,      truncation=__SCREAMING_SNAKE_CASE       ,      )
				A_	:						Tuple	=     input_a[input_name]

				A_	:						Optional[int]	=     feat_extract.pad(
				    __SCREAMING_SNAKE_CASE       ,      padding="""max_length"""       ,      max_length=len(speech_inputs[0]							)       ,      pad_to_multiple_of=__SCREAMING_SNAKE_CASE       ,      )
				A_	:						Tuple	=     input_a[input_name]

				# retrieve expected_length as multiple of pad_to_multiple_of
				A_	:						str	=     len(speech_inputs[0]							)
				if expected_length % pad_to_multiple_of != 0:
							A_	:						List[Any]	=     ((len(speech_inputs[0]							) // pad_to_multiple_of) + 1) * pad_to_multiple_of

				self.assertTrue(len(input_a[0]							) == expected_length							)
				self.assertTrue(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE							)							)
				self.assertFalse(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE							)							)


	def     _a	(self							):
				self._check_padding(numpify=__SCREAMING_SNAKE_CASE							)


	def     _a	(self							):
				self._check_padding(numpify=__SCREAMING_SNAKE_CASE							)


	def     _a	(self							):
				self._check_truncation(numpify=__SCREAMING_SNAKE_CASE							)


	def     _a	(self							):
				self._check_truncation(numpify=__SCREAMING_SNAKE_CASE							)


	@require_torch
	def     _a	(self							):
				A_	:						Any	=     self.feature_extraction_class(**self.feat_extract_dict							)
				A_	:						Optional[Any]	=     self.feat_extract_tester.prepare_inputs_for_common()
				A_	:						Any	=     feat_extract.model_input_names[0]

				A_	:						Any	=     BatchFeature({input_name: speech_inputs}							)

				A_	:						List[str]	=     feat_extract.pad(__SCREAMING_SNAKE_CASE       ,      padding="""longest"""       ,      return_tensors="""np"""							)[input_name]
				A_	:						Any	=     feat_extract.pad(__SCREAMING_SNAKE_CASE       ,      padding="""longest"""       ,      return_tensors="""pt"""							)[input_name]

				self.assertTrue(abs(input_np.astype(np.floataa							).sum() - input_pt.numpy().astype(np.floataa							).sum()							) < 1E-2							)


	@require_tf
	def     _a	(self							):
				A_	:						List[str]	=     self.feature_extraction_class(**self.feat_extract_dict							)
				A_	:						Tuple	=     self.feat_extract_tester.prepare_inputs_for_common()
				A_	:						str	=     feat_extract.model_input_names[0]

				A_	:						Optional[int]	=     BatchFeature({input_name: speech_inputs}							)

				A_	:						Optional[int]	=     feat_extract.pad(__SCREAMING_SNAKE_CASE       ,      padding="""longest"""       ,      return_tensors="""np"""							)[input_name]
				A_	:						Tuple	=     feat_extract.pad(__SCREAMING_SNAKE_CASE       ,      padding="""longest"""       ,      return_tensors="""tf"""							)[input_name]

				self.assertTrue(abs(input_np.astype(np.floataa							).sum() - input_tf.numpy().astype(np.floataa							).sum()							) < 1E-2							)


	def     _a	(self							):
				A_	:						Optional[Any]	=     self.feat_extract_dict
				A_	:						Optional[Any]	=     True
				A_	:						str	=     self.feature_extraction_class(**__SCREAMING_SNAKE_CASE							)
				A_	:						Any	=     self.feat_extract_tester.prepare_inputs_for_common()
				A_	:						Tuple	=     [len(__SCREAMING_SNAKE_CASE							) for x in speech_inputs]
				A_	:						Union[str, Any]	=     feat_extract.model_input_names[0]

				A_	:						List[Any]	=     BatchFeature({input_name: speech_inputs}							)

				A_	:						Any	=     feat_extract.pad(__SCREAMING_SNAKE_CASE       ,      padding="""longest"""       ,      return_tensors="""np"""							)
				self.assertIn("""attention_mask"""       ,      __SCREAMING_SNAKE_CASE							)
				self.assertListEqual(list(processed.attention_mask.shape							)       ,      list(processed[input_name].shape[:2]							)							)
				self.assertListEqual(processed.attention_mask.sum(-1							).tolist()       ,      __SCREAMING_SNAKE_CASE							)


	def     _a	(self							):
				A_	:						int	=     self.feat_extract_dict
				A_	:						Any	=     True
				A_	:						str	=     self.feature_extraction_class(**__SCREAMING_SNAKE_CASE							)
				A_	:						List[Any]	=     self.feat_extract_tester.prepare_inputs_for_common()
				A_	:						Tuple	=     [len(__SCREAMING_SNAKE_CASE							) for x in speech_inputs]
				A_	:						List[Any]	=     feat_extract.model_input_names[0]

				A_	:						str	=     BatchFeature({input_name: speech_inputs}							)
				A_	:						List[Any]	=     min(__SCREAMING_SNAKE_CASE							)

				A_	:						Optional[Any]	=     feat_extract.pad(
				    __SCREAMING_SNAKE_CASE       ,      padding="""max_length"""       ,      max_length=__SCREAMING_SNAKE_CASE       ,      truncation=__SCREAMING_SNAKE_CASE       ,      return_tensors="""np"""							)
				self.assertIn("""attention_mask"""       ,      __SCREAMING_SNAKE_CASE							)
				self.assertListEqual(
				    list(processed_pad.attention_mask.shape							)       ,      [processed_pad[input_name].shape[0], max_length]							)
				self.assertListEqual(
				    processed_pad.attention_mask[:, :max_length].sum(-1							).tolist()       ,      [max_length for x in speech_inputs]							)


'''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 :       str			   =     logging.get_logger(__name__)







class 						lowerCAmelCase__   (      a					):



 """simple docstring"""



 lowerCAmelCase__  					=      "linear"
 lowerCAmelCase__  					=      "cosine"
 lowerCAmelCase__  					=      "cosine_with_restarts"
 lowerCAmelCase__  					=      "polynomial"
 lowerCAmelCase__  					=      "constant"
 lowerCAmelCase__  					=      "constant_with_warmup"
 lowerCAmelCase__  					=      "piecewise_constant"







def    a__       (  a__      ,    a__ = -1						):




     """simple docstring"""


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







def    a__       (  a__      ,    a__      ,    a__ = -1						):




     """simple docstring"""


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

     return LambdaLR(a__      ,    a__      ,    last_epoch=a__						)







def    a__       (  a__      ,    a__      ,    a__ = -1						):




     """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(a__						)
          __SCREAMING_SNAKE_CASE     =      float(a__						)
          __SCREAMING_SNAKE_CASE     =      value
     __SCREAMING_SNAKE_CASE     =      float(rule_list[-1]						)

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

          return rule_func

     __SCREAMING_SNAKE_CASE     =      create_rules_function(a__      ,    a__						)

     return LambdaLR(a__      ,    a__      ,    last_epoch=a__						)







def    a__       (  a__      ,    a__      ,    a__      ,    a__=-1						):




     """simple docstring"""


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

     return LambdaLR(a__      ,    a__      ,    a__						)







def    a__       (  a__      ,    a__      ,    a__      ,    a__ = 0.5      ,    a__ = -1						):




     """simple docstring"""


     def lr_lambda(a__						):
          if current_step < num_warmup_steps:
               return float(a__						) / float(max(1      ,    a__						)						)
          __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(a__						) * 2.0 * progress						))						)

     return LambdaLR(a__      ,    a__      ,    a__						)







def    a__       (  a__      ,    a__      ,    a__      ,    a__ = 1      ,    a__ = -1						):




     """simple docstring"""


     def lr_lambda(a__						):
          if current_step < num_warmup_steps:
               return float(a__						) / float(max(1      ,    a__						)						)
          __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(a__						) * progress) % 1.0)						))						)

     return LambdaLR(a__      ,    a__      ,    a__						)







def    a__       (  a__      ,    a__      ,    a__      ,    a__=1E-7      ,    a__=1.0      ,    a__=-1						):




     """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(a__						):
          if current_step < num_warmup_steps:
               return float(a__						) / float(max(1      ,    a__						)						)
          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(a__      ,    a__      ,    a__						)


UpperCAmelCase :       Optional[Any]			   =     {
    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    a__       (  a__      ,    a__      ,    a__ = None      ,    a__ = None      ,    a__ = None      ,    a__ = 1      ,    a__ = 1.0      ,    a__ = -1      ,    ):




     """simple docstring"""


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

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

     # 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(a__      ,    num_warmup_steps=a__      ,    last_epoch=a__						)

     # 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(
              a__      ,    num_warmup_steps=a__      ,    num_training_steps=a__      ,    num_cycles=a__      ,    last_epoch=a__      ,    )

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

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






