





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


class 			__UpperCamelCase							(		a__		):
							lowerCamelCase  :   int    ="""linear"""
							lowerCamelCase  :   Dict    ="""cosine"""
							lowerCamelCase  :   Any    ="""cosine_with_restarts"""
							lowerCamelCase  :   Optional[Any]    ="""polynomial"""
							lowerCamelCase  :   List[str]    ="""constant"""
							lowerCamelCase  :   List[str]    ="""constant_with_warmup"""
							lowerCamelCase  :   Dict    ="""piecewise_constant"""






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Optimizer       ,			_lowercase		:       int = -1   )					->Tuple:






	'''simple docstring'''






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






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Optimizer       ,			_lowercase		:       int       ,			_lowercase		:       int = -1   )					->List[Any]:






	'''simple docstring'''






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

	return LambdaLR(_lowercase       ,			_lowercase       ,			last_epoch=_lowercase   )






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Optimizer       ,			_lowercase		:       str       ,			_lowercase		:       int = -1   )					->Any:






	'''simple docstring'''






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

	def create_rules_function(_lowercase		:       Any       ,			_lowercase		:       Dict   ):
		def rule_func(_lowercase		:       int   ) -> float:
			a :						Union[str, Any]      =							sorted(rules_dict.keys()   )
			for i, sorted_step in enumerate(_lowercase   ):
				if steps < sorted_step:
					return rules_dict[sorted_steps[i]]
			return last_lr_multiple

		return rule_func

	a :						int      =							create_rules_function(_lowercase       ,			_lowercase   )

	return LambdaLR(_lowercase       ,			_lowercase       ,			last_epoch=_lowercase   )






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Dict       ,			_lowercase		:       Union[str, Any]       ,			_lowercase		:       Any       ,			_lowercase		:       List[str]=-1   )					->Union[str, Any]:






	'''simple docstring'''






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

	return LambdaLR(_lowercase       ,			_lowercase       ,			_lowercase   )






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Optimizer       ,			_lowercase		:       int       ,			_lowercase		:       int       ,			_lowercase		:       float = 0.5       ,			_lowercase		:       int = -1   )					->Union[str, Any]:






	'''simple docstring'''






	def lr_lambda(_lowercase		:       List[Any]   ):
		if current_step < num_warmup_steps:
			return float(_lowercase   ) / float(max(1       ,			_lowercase   )   )
		a :						List[Any]      =							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(_lowercase   ) * 2.0 * progress   ))   )

	return LambdaLR(_lowercase       ,			_lowercase       ,			_lowercase   )






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Optimizer       ,			_lowercase		:       int       ,			_lowercase		:       int       ,			_lowercase		:       int = 1       ,			_lowercase		:       int = -1   )					->str:






	'''simple docstring'''






	def lr_lambda(_lowercase		:       Union[str, Any]   ):
		if current_step < num_warmup_steps:
			return float(_lowercase   ) / float(max(1       ,			_lowercase   )   )
		a :						str      =							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(_lowercase   ) * progress) % 1.0)   ))   )

	return LambdaLR(_lowercase       ,			_lowercase       ,			_lowercase   )






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       int       ,			_lowercase		:       Tuple       ,			_lowercase		:       Union[str, Any]       ,			_lowercase		:       str=1E-7       ,			_lowercase		:       Optional[Any]=1.0       ,			_lowercase		:       Any=-1   )					->Optional[int]:






	'''simple docstring'''






	a :						str      =							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(_lowercase		:       int   ):
		if current_step < num_warmup_steps:
			return float(_lowercase   ) / float(max(1       ,			_lowercase   )   )
		elif current_step > num_training_steps:
			return lr_end / lr_init  # as LambdaLR multiplies by lr_init
		else:
			a :						List[Any]      =							lr_init - lr_end
			a :						List[str]      =							num_training_steps - num_warmup_steps
			a :						Dict      =							1 - (current_step - num_warmup_steps) / decay_steps
			a :						Tuple      =							lr_range * pct_remaining**power + lr_end
			return decay / lr_init  # as LambdaLR multiplies by lr_init

	return LambdaLR(_lowercase       ,			_lowercase       ,			_lowercase   )


a		:					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  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Union[str, SchedulerType]       ,			_lowercase		:       Optimizer       ,			_lowercase		:       Optional[str] = None       ,			_lowercase		:       Optional[int] = None       ,			_lowercase		:       Optional[int] = None       ,			_lowercase		:       int = 1       ,			_lowercase		:       float = 1.0       ,			_lowercase		:       int = -1       ,			)					->Optional[Any]:






	'''simple docstring'''






	a :						str      =							SchedulerType(_lowercase   )
	a :						int      =							TYPE_TO_SCHEDULER_FUNCTION[name]
	if name == SchedulerType.CONSTANT:
		return schedule_func(_lowercase       ,			last_epoch=_lowercase   )

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

	# 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(_lowercase       ,			num_warmup_steps=_lowercase       ,			last_epoch=_lowercase   )

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

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

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









"""simple docstring"""




import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
		import jax.numpy as jnp

		from transformers.models.roformer.modeling_flax_roformer import (
		    FlaxRoFormerForMaskedLM,
		    FlaxRoFormerForMultipleChoice,
		    FlaxRoFormerForQuestionAnswering,
		    FlaxRoFormerForSequenceClassification,
		    FlaxRoFormerForTokenClassification,
		    FlaxRoFormerModel,
		)


class 			__UpperCamelCase							(		unittest.TestCase		):






							def __init__(  self ,   lowerCAmelCase__ ,   lowerCAmelCase__=13 ,   lowerCAmelCase__=7 ,   lowerCAmelCase__=True ,   lowerCAmelCase__=True ,   lowerCAmelCase__=True ,   lowerCAmelCase__=True ,   lowerCAmelCase__=99 ,   lowerCAmelCase__=32 ,   lowerCAmelCase__=5 ,   lowerCAmelCase__=4 ,   lowerCAmelCase__=37 ,   lowerCAmelCase__="gelu" ,   lowerCAmelCase__=0.1 ,   lowerCAmelCase__=0.1 ,   lowerCAmelCase__=512 ,   lowerCAmelCase__=16 ,   lowerCAmelCase__=2 ,   lowerCAmelCase__=0.02 ,   lowerCAmelCase__=4 ,   )      ->					List[str]:
								a :						Any      =							parent
								a :						str      =							batch_size
								a :						str      =							seq_length
								a :						List[str]      =							is_training
								a :						Optional[int]      =							use_attention_mask
								a :						Optional[int]      =							use_token_type_ids
								a :						Union[str, Any]      =							use_labels
								a :						Optional[int]      =							vocab_size
								a :						Optional[Any]      =							hidden_size
								a :						List[Any]      =							num_hidden_layers
								a :						Optional[int]      =							num_attention_heads
								a :						Optional[int]      =							intermediate_size
								a :						Tuple      =							hidden_act
								a :						List[str]      =							hidden_dropout_prob
								a :						Optional[Any]      =							attention_probs_dropout_prob
								a :						List[Any]      =							max_position_embeddings
								a :						Optional[int]      =							type_vocab_size
								a :						Dict      =							type_sequence_label_size
								a :						int      =							initializer_range
								a :						Tuple      =							num_choices






							def        __a  (  self )      ->					List[Any]:
								a :						Dict      =							ids_tensor([self.batch_size, self.seq_length] ,   self.vocab_size )

								a :						List[str]      =							None
								if self.use_attention_mask:
									a :						Optional[Any]      =							random_attention_mask([self.batch_size, self.seq_length] )

								a :						List[str]      =							None
								if self.use_token_type_ids:
									a :						Dict      =							ids_tensor([self.batch_size, self.seq_length] ,   self.type_vocab_size )

								a :						Tuple      =							RoFormerConfig(
								    vocab_size=self.vocab_size ,   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 ,   max_position_embeddings=self.max_position_embeddings ,   type_vocab_size=self.type_vocab_size ,   is_decoder=lowerCAmelCase__ ,   initializer_range=self.initializer_range ,   )

								return config, input_ids, token_type_ids, attention_mask






							def        __a  (  self )      ->					List[str]:
								a :						Dict      =							self.prepare_config_and_inputs()
								a,			a,			a,			a :						int      =							config_and_inputs
								a :						Dict      =							{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
								return config, inputs_dict


@require_flax
class 			__UpperCamelCase							(		a__						,			unittest.TestCase		):
							lowerCamelCase  :   Union[str, Any]    =True

							lowerCamelCase  :   Union[str, Any]    =(
							    (
							        FlaxRoFormerModel,
							        FlaxRoFormerForMaskedLM,
							        FlaxRoFormerForSequenceClassification,
							        FlaxRoFormerForTokenClassification,
							        FlaxRoFormerForMultipleChoice,
							        FlaxRoFormerForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)






							def        __a  (  self )      ->					List[Any]:
								a :						Dict      =							FlaxRoFormerModelTester(self )






							@slow
							def        __a  (  self )      ->					List[str]:
								for model_class_name in self.all_model_classes:
									a :						Any      =							model_class_name.from_pretrained("junnyu/roformer_chinese_small" ,   from_pt=lowerCAmelCase__ )
									a :						Optional[int]      =							model(np.ones((1, 1) ) )
									self.assertIsNotNone(lowerCAmelCase__ )


@require_flax
class 			__UpperCamelCase							(		unittest.TestCase		):






							@slow
							def        __a  (  self )      ->					List[str]:
								a :						str      =							FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base" )
								a :						List[Any]      =							jnp.array([[0, 1, 2, 3, 4, 5]] )
								a :						Any      =							model(lowerCAmelCase__ )[0]

								a :						int      =							5_0000

								a :						Any      =							(1, 6, vocab_size)
								self.assertEqual(output.shape ,   lowerCAmelCase__ )

								a :						List[Any]      =							jnp.array(
								    [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]] )

								self.assertTrue(jnp.allclose(output[:, :3, :3] ,   lowerCAmelCase__ ,   atol=1E-4 ) )


