






'''simple docstring'''







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  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



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





       '''simple docstring'''

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

  def __a						(					self     :							int						):





       '''simple docstring'''

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

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

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

       __a       =   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=__lowercase				,							initializer_range=self.initializer_range				,							)

       return config, input_ids, token_type_ids, attention_mask

  def __a						(					self     :							str						):





       '''simple docstring'''

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


  def __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       __a       =   self.prepare_config_and_inputs()
       __a ,						__a ,						__a ,						__a       =   config_and_inputs

       __a       =   True
       __a       =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
       __a       =   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  lowerCAmelCase_						(       lowercase__		,    unittest.TestCase      ):





  """simple docstring"""



  a_       :int												=True

  a_       :Union[str, Any]												=(
      (
          FlaxRobertaPreLayerNormModel,
          FlaxRobertaPreLayerNormForCausalLM,
          FlaxRobertaPreLayerNormForMaskedLM,
          FlaxRobertaPreLayerNormForSequenceClassification,
          FlaxRobertaPreLayerNormForTokenClassification,
          FlaxRobertaPreLayerNormForMultipleChoice,
          FlaxRobertaPreLayerNormForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )

  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       __a       =   FlaxRobertaPreLayerNormModelTester(self						)


  @slow
  def __a						(					self     :							Tuple						):





       '''simple docstring'''

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


@require_flax
class  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



  @slow
  def __a						(					self     :							str						):





       '''simple docstring'''

       __a       =   FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""				,							from_pt=__lowercase						)

       __a       =   np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]				,							dtype=jnp.intaa						)
       __a       =   model(__lowercase						)[0]
       __a       =   [1, 1_1, 5_0_2_6_5]
       self.assertEqual(list(output.shape						)				,							__lowercase						)
       # compare the actual values for a slice.
       __a       =   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]				,							__lowercase				,							atol=1E-4						)						)


  @slow
  def __a						(					self     :							Union[str, Any]						):





       '''simple docstring'''

       __a       =   FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""				,							from_pt=__lowercase						)

       __a       =   np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]				,							dtype=jnp.intaa						)
       __a       =   model(__lowercase						)[0]
       # compare the actual values for a slice.
       __a       =   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]				,							__lowercase				,							atol=1E-4						)						)










'''simple docstring'''







from ...processing_utils import ProcessorMixin


class  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



  a_       :Dict												=["""image_processor""", """feature_extractor"""]
  a_       :str												="""TvltImageProcessor"""
  a_       :str												="""TvltFeatureExtractor"""

  def __init__(					self     :							Dict				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]						):





       '''simple docstring'''

       super().__init__(image_processor=SCREAMING_SNAKE_CASE__				,							feature_extractor=SCREAMING_SNAKE_CASE__						)

       __a       =   image_processor
       __a       =   feature_extractor

  def __call__(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							str=None				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=None				,							SCREAMING_SNAKE_CASE__     :							Tuple=None				,							SCREAMING_SNAKE_CASE__     :							List[Any]=None				,							SCREAMING_SNAKE_CASE__     :							Any=False				,							SCREAMING_SNAKE_CASE__     :							List[str]=False				,							*SCREAMING_SNAKE_CASE__     :							str				,							**SCREAMING_SNAKE_CASE__     :							Optional[int]				,							):





       '''simple docstring'''

       if images is None and audio is None:
            raise ValueError("""You need to specify either an `images` or `audio` input to process."""						)

       __a       =   None
       if images is not None:
            __a       =   self.image_processor(SCREAMING_SNAKE_CASE__				,							mask_pixel=SCREAMING_SNAKE_CASE__				,							*SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)
       if images_mixed is not None:
            __a       =   self.image_processor(SCREAMING_SNAKE_CASE__				,							is_mixed=SCREAMING_SNAKE_CASE__				,							*SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)
       if audio is not None:
            __a       =   self.feature_extractor(
                SCREAMING_SNAKE_CASE__				,							*SCREAMING_SNAKE_CASE__				,							sampling_rate=SCREAMING_SNAKE_CASE__				,							mask_audio=SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)

       __a       =   {}
       if audio is not None:
            output_dict.update(SCREAMING_SNAKE_CASE__						)
       if images is not None:
            output_dict.update(SCREAMING_SNAKE_CASE__						)
       if images_mixed_dict is not None:
            output_dict.update(SCREAMING_SNAKE_CASE__						)
       return output_dict


  @property
  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       __a       =   self.image_processor.model_input_names
       __a       =   self.feature_extractor.model_input_names
       return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names						)						)






