import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
     import jax.numpy as jnp

     from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
         FlaxRobertaPreLayerNormForCausalLM,
         FlaxRobertaPreLayerNormForMaskedLM,
         FlaxRobertaPreLayerNormForMultipleChoice,
         FlaxRobertaPreLayerNormForQuestionAnswering,
         FlaxRobertaPreLayerNormForSequenceClassification,
         FlaxRobertaPreLayerNormForTokenClassification,
         FlaxRobertaPreLayerNormModel,
     )
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   def __init__(     self						:						List[str]   ,   lowerCamelCase_						:						Union[str, Any]   ,   lowerCamelCase_						:						Any=13   ,   lowerCamelCase_						:						str=7   ,   lowerCamelCase_						:						Union[str, Any]=True   ,   lowerCamelCase_						:						Optional[int]=True   ,   lowerCamelCase_						:						Tuple=True   ,   lowerCamelCase_						:						Any=True   ,   lowerCamelCase_						:						List[Any]=99   ,   lowerCamelCase_						:						Union[str, Any]=32   ,   lowerCamelCase_						:						List[str]=5   ,   lowerCamelCase_						:						Any=4   ,   lowerCamelCase_						:						Tuple=37   ,   lowerCamelCase_						:						Optional[Any]="gelu"   ,   lowerCamelCase_						:						str=0.1   ,   lowerCamelCase_						:						Union[str, Any]=0.1   ,   lowerCamelCase_						:						List[Any]=512   ,   lowerCamelCase_						:						Dict=16   ,   lowerCamelCase_						:						Dict=2   ,   lowerCamelCase_						:						str=0.0_2   ,   lowerCamelCase_						:						Optional[int]=4   ,   ):





    """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 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """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			  =      RobertaPreLayerNormConfig(
        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 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self.prepare_config_and_inputs()
    UpperCamelCase			  =      config_and_inputs
    UpperCamelCase			  =      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
    return config, inputs_dict







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase			  =      self.prepare_config_and_inputs()
    UpperCamelCase			  =      config_and_inputs

    UpperCamelCase			  =      True
    UpperCamelCase			  =      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
    UpperCamelCase			  =      ids_tensor([self.batch_size, self.seq_length]   ,   vocab_size=2				)

    return (
        config,
        input_ids,
        token_type_ids,
        encoder_hidden_states,
        encoder_attention_mask,
    )





@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class SCREAMING_SNAKE_CASE_		(      _UpperCamelCase  ,     unittest.TestCase		):
   __lowerCAmelCase           =				True

   __lowerCAmelCase           =				(
       (
           FlaxRobertaPreLayerNormModel,
           FlaxRobertaPreLayerNormForCausalLM,
           FlaxRobertaPreLayerNormForMaskedLM,
           FlaxRobertaPreLayerNormForSequenceClassification,
           FlaxRobertaPreLayerNormForTokenClassification,
           FlaxRobertaPreLayerNormForMultipleChoice,
           FlaxRobertaPreLayerNormForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase			  =      FlaxRobertaPreLayerNormModelTester(self				)







   @slow
   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    for model_class_name in self.all_model_classes:
     UpperCamelCase			  =      model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""   ,   from_pt=lowerCamelCase_				)
     UpperCamelCase			  =      model(np.ones((1, 1)				)				)
     self.assertIsNotNone(lowerCamelCase_				)





@require_flax
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   @slow
   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""   ,   from_pt=lowerCamelCase_				)

    UpperCamelCase			  =      np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]   ,   dtype=jnp.intaa				)
    UpperCamelCase			  =      model(lowerCamelCase_				)[0]
    UpperCamelCase			  =      [1, 11, 5_0265]
    self.assertEqual(list(output.shape				)   ,   lowerCamelCase_				)
    # compare the actual values for a slice.
    UpperCamelCase			  =      np.array(
        [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]   ,   dtype=np.floataa				)
    self.assertTrue(np.allclose(output[:, :3, :3]   ,   lowerCamelCase_   ,   atol=1E-4				)				)







   @slow
   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""   ,   from_pt=lowerCamelCase_				)

    UpperCamelCase			  =      np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]   ,   dtype=jnp.intaa				)
    UpperCamelCase			  =      model(lowerCamelCase_				)[0]
    # compare the actual values for a slice.
    UpperCamelCase			  =      np.array(
        [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]   ,   dtype=np.floataa				)
    self.assertTrue(np.allclose(output[:, :3, :3]   ,   lowerCamelCase_   ,   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       UpperCAmelCase_    :



      def __init__(	self	:      Optional[Any]	,      A	:      List[str]	,      A	:      List[str]	,      A	:      bool = True	,      A	:      bool = False		):
           _UpperCAmelCase     :      Dict														=       scheduler
           _UpperCAmelCase     :      str														=       optimizers if isinstance(A	,      (list, tuple)		) else [optimizers]
           _UpperCAmelCase     :      List[str]														=       split_batches
           _UpperCAmelCase     :      Dict														=       step_with_optimizer
           _UpperCAmelCase     :      Tuple														=       GradientState()



      def 		snake_case_   (	self	:      Optional[int]	,      *A	:      List[Any]	,      **A	:      Optional[int]		):
           if not self.step_with_optimizer:
                # No link between scheduler and optimizer -> just step
                self.scheduler.step(*A	,      **A		)
                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(*A	,      **A		)
           else:
                # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
                # num_processes steps per training step
                _UpperCAmelCase     :      int														=       AcceleratorState().num_processes
                for _ in range(A		):
                     # 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(*A	,      **A		)
                     else:
                          self.scheduler.step(*A	,      **A		)



      def 		snake_case_   (	self	:      Union[str, Any]		):
           return self.scheduler.get_last_lr()



      def 		snake_case_   (	self	:      str		):
           return self.scheduler.state_dict()



      def 		snake_case_   (	self	:      str	,      A	:      Optional[Any]		):
           self.scheduler.load_state_dict(A		)



      def 		snake_case_   (	self	:      Tuple		):
           return self.scheduler.get_lr()



      def 		snake_case_   (	self	:      Optional[int]	,      *A	:      List[str]	,      **A	:      Optional[Any]		):
           return self.scheduler.print_lr(*A	,      **A		)
