






'''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    _SCREAMING_SNAKE_CASE							(		unittest.TestCase	):


			'''simple docstring'''





			def __init__(   self							:						Any ,      lowercase							:						Tuple ,      lowercase							:						Optional[Any]=1_3 ,      lowercase							:						Any=7 ,      lowercase							:						Dict=True ,      lowercase							:						Optional[Any]=True ,      lowercase							:						Dict=True ,      lowercase							:						int=True ,      lowercase							:						Dict=9_9 ,      lowercase							:						List[str]=3_2 ,      lowercase							:						Optional[int]=5 ,      lowercase							:						Tuple=4 ,      lowercase							:						Optional[Any]=3_7 ,      lowercase							:						List[Any]="gelu" ,      lowercase							:						List[str]=0.1 ,      lowercase							:						Union[str, Any]=0.1 ,      lowercase							:						Tuple=5_1_2 ,      lowercase							:						Dict=1_6 ,      lowercase							:						List[str]=2 ,      lowercase							:						int=0.0_2 ,      lowercase							:						List[Any]=4 ,      )							->    List[Any]:

									'''simple docstring'''







									UpperCamelCase__         = parent
									UpperCamelCase__         = batch_size
									UpperCamelCase__         = seq_length
									UpperCamelCase__         = is_training
									UpperCamelCase__         = use_attention_mask
									UpperCamelCase__         = use_token_type_ids
									UpperCamelCase__         = use_labels
									UpperCamelCase__         = vocab_size
									UpperCamelCase__         = hidden_size
									UpperCamelCase__         = num_hidden_layers
									UpperCamelCase__         = num_attention_heads
									UpperCamelCase__         = intermediate_size
									UpperCamelCase__         = hidden_act
									UpperCamelCase__         = hidden_dropout_prob
									UpperCamelCase__         = attention_probs_dropout_prob
									UpperCamelCase__         = max_position_embeddings
									UpperCamelCase__         = type_vocab_size
									UpperCamelCase__         = type_sequence_label_size
									UpperCamelCase__         = initializer_range
									UpperCamelCase__         = num_choices






			def 				A      (   self							:						str			)							->    Tuple:

									'''simple docstring'''







									UpperCamelCase__         = ids_tensor([self.batch_size, self.seq_length] ,      self.vocab_size			)

									UpperCamelCase__         = None
									if self.use_attention_mask:
															UpperCamelCase__         = random_attention_mask([self.batch_size, self.seq_length]			)

									UpperCamelCase__         = None
									if self.use_token_type_ids:
															UpperCamelCase__         = ids_tensor([self.batch_size, self.seq_length] ,      self.type_vocab_size			)

									UpperCamelCase__         = 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=lowercase ,      initializer_range=self.initializer_range ,      )

									return config, input_ids, token_type_ids, attention_mask







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

									'''simple docstring'''







									UpperCamelCase__         = self.prepare_config_and_inputs()
									UpperCamelCase__    ,   UpperCamelCase__    ,   UpperCamelCase__    ,   UpperCamelCase__         = config_and_inputs
									UpperCamelCase__         = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
									return config, inputs_dict
@require_flax
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE       ,unittest.TestCase	):


			'''simple docstring'''





			__a							:							str					     =					True

			__a							:							List[str]					     =					(
			    (
			        FlaxRoFormerModel,
			        FlaxRoFormerForMaskedLM,
			        FlaxRoFormerForSequenceClassification,
			        FlaxRoFormerForTokenClassification,
			        FlaxRoFormerForMultipleChoice,
			        FlaxRoFormerForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)






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

									'''simple docstring'''







									UpperCamelCase__         = FlaxRoFormerModelTester(self			)







			@slow
			def 				A      (   self							:						int			)							->    Union[str, Any]:

									'''simple docstring'''







									for model_class_name in self.all_model_classes:
															UpperCamelCase__         = model_class_name.from_pretrained("""junnyu/roformer_chinese_small""" ,      from_pt=lowercase			)
															UpperCamelCase__         = model(np.ones((1, 1)			)			)
															self.assertIsNotNone(lowercase			)
@require_flax
class    _SCREAMING_SNAKE_CASE							(		unittest.TestCase	):


			'''simple docstring'''





			@slow
			def 				A      (   self							:						Union[str, Any]			)							->    Dict:

									'''simple docstring'''







									UpperCamelCase__         = FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""			)
									UpperCamelCase__         = jnp.array([[0, 1, 2, 3, 4, 5]]			)
									UpperCamelCase__         = model(lowercase			)[0]

									UpperCamelCase__         = 5_0_0_0_0

									UpperCamelCase__         = (1, 6, vocab_size)
									self.assertEqual(output.shape ,      lowercase			)

									UpperCamelCase__         = jnp.array(
									    [[[-0.1_2_0_5, -1.0_2_6_5, 0.2_9_2_2], [-1.5_1_3_4, 0.1_9_7_4, 0.1_5_1_9], [-5.0_1_3_5, -3.9_0_0_3, -0.8_4_0_4]]]			)

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








'''simple docstring'''


# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class    _SCREAMING_SNAKE_CASE							:


			'''simple docstring'''





			def __init__(   self							:						Optional[int] ,      lowercase							:						Optional[int] ,      lowercase							:						Optional[Any] ,      lowercase							:						bool = True ,      lowercase							:						bool = False			)							->    Optional[int]:

									'''simple docstring'''







									UpperCamelCase__         = scheduler
									UpperCamelCase__         = optimizers if isinstance(lowercase ,      (list, tuple)			) else [optimizers]
									UpperCamelCase__         = split_batches
									UpperCamelCase__         = step_with_optimizer
									UpperCamelCase__         = GradientState()






			def 				A      (   self							:						int ,      *lowercase							:						int ,      **lowercase							:						str			)							->    str:

									'''simple docstring'''







									if not self.step_with_optimizer:
															# No link between scheduler and optimizer -> just step
															self.scheduler.step(*lowercase ,      **lowercase			)
															return

									# Otherwise, first make sure the optimizer was stepped.
									if not self.gradient_state.sync_gradients:
															if self.gradient_state.adjust_scheduler:
																					self.scheduler._step_count += 1
															return

									for opt in self.optimizers:
															if opt.step_was_skipped:
																					return
									if self.split_batches:
															# Split batches -> the training dataloader batch size is not changed so one step per training step
															self.scheduler.step(*lowercase ,      **lowercase			)
									else:
															# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
															# num_processes steps per training step
															UpperCamelCase__         = AcceleratorState().num_processes
															for _ in range(lowercase			):
																					# Special case when using OneCycle and `drop_last` was not used
																					if hasattr(self.scheduler ,      """total_steps"""			):
																											if self.scheduler._step_count <= self.scheduler.total_steps:
																																	self.scheduler.step(*lowercase ,      **lowercase			)
																					else:
																											self.scheduler.step(*lowercase ,      **lowercase			)






			def 				A      (   self							:						List[str]			)							->    Optional[Any]:

									'''simple docstring'''







									return self.scheduler.get_last_lr()






			def 				A      (   self							:						Any			)							->    int:

									'''simple docstring'''







									return self.scheduler.state_dict()






			def 				A      (   self							:						Any ,      lowercase							:						int			)							->    Optional[int]:

									'''simple docstring'''







									self.scheduler.load_state_dict(lowercase			)






			def 				A      (   self							:						str			)							->    int:

									'''simple docstring'''







									return self.scheduler.get_lr()







			def 				A      (   self							:						Union[str, Any] ,      *lowercase							:						List[Any] ,      **lowercase							:						str			)							->    str:

									'''simple docstring'''







									return self.scheduler.print_lr(*lowercase ,      **lowercase			)
