






'''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 UpperCAmelCase_       (     unittest.TestCase					):




      """simple docstring"""




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


       '''simple docstring'''

       lowercase       : Tuple						=parent
       lowercase       : Any						=batch_size
       lowercase       : Union[str, Any]						=seq_length
       lowercase       : Optional[Any]						=is_training
       lowercase       : List[str]						=use_attention_mask
       lowercase       : Dict						=use_token_type_ids
       lowercase       : List[Any]						=use_labels
       lowercase       : List[Any]						=vocab_size
       lowercase       : List[str]						=hidden_size
       lowercase       : Dict						=num_hidden_layers
       lowercase       : List[str]						=num_attention_heads
       lowercase       : Dict						=intermediate_size
       lowercase       : List[Any]						=hidden_act
       lowercase       : List[str]						=hidden_dropout_prob
       lowercase       : Dict						=attention_probs_dropout_prob
       lowercase       : Tuple						=max_position_embeddings
       lowercase       : List[str]						=type_vocab_size
       lowercase       : Union[str, Any]						=type_sequence_label_size
       lowercase       : List[Any]						=initializer_range
       lowercase       : List[str]						=num_choices






      def 					A__   (			self      :		Tuple		)	->	Optional[int]:


       '''simple docstring'''

       lowercase       : int						=ids_tensor([self.batch_size, self.seq_length]  ,   self.vocab_size		)

       lowercase       : Dict						=None
       if self.use_attention_mask:
        lowercase       : str						=random_attention_mask([self.batch_size, self.seq_length]		)

       lowercase       : Optional[int]						=None
       if self.use_token_type_ids:
        lowercase       : Dict						=ids_tensor([self.batch_size, self.seq_length]  ,   self.type_vocab_size		)

       lowercase       : Optional[int]						=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=UpperCAmelCase  ,   initializer_range=self.initializer_range  ,   )

       return config, input_ids, token_type_ids, attention_mask






      def 					A__   (			self      :		str		)	->	Optional[int]:


       '''simple docstring'''

       lowercase       : Tuple						=self.prepare_config_and_inputs()
       lowercase	,		lowercase	,		lowercase	,		lowercase       : Tuple						=config_and_inputs
       lowercase       : Tuple						={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict






      def 					A__   (			self      :		str		)	->	int:


       '''simple docstring'''

       lowercase       : int						=self.prepare_config_and_inputs()
       lowercase	,		lowercase	,		lowercase	,		lowercase       : Any						=config_and_inputs

       lowercase       : Optional[Any]						=True
       lowercase       : str						=floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
       lowercase       : str						=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 UpperCAmelCase_       (     __A					,							unittest.TestCase					):




      """simple docstring"""




      UpperCamelCase_             =      True

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






      def 					A__   (			self      :		List[str]		)	->	List[str]:


       '''simple docstring'''

       lowercase       : Optional[Any]						=FlaxRobertaPreLayerNormModelTester(self		)






      @slow
      def 					A__   (			self      :		Union[str, Any]		)	->	List[str]:


       '''simple docstring'''

       for model_class_name in self.all_model_classes:
        lowercase       : Any						=model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''  ,   from_pt=UpperCAmelCase		)
        lowercase       : int						=model(np.ones((1, 1)		)		)
        self.assertIsNotNone(UpperCAmelCase		)





@require_flax
class UpperCAmelCase_       (     unittest.TestCase					):




      """simple docstring"""




      @slow
      def 					A__   (			self      :		Optional[int]		)	->	List[Any]:


       '''simple docstring'''

       lowercase       : Optional[Any]						=FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''  ,   from_pt=UpperCAmelCase		)

       lowercase       : Any						=np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]  ,   dtype=jnp.intaa		)
       lowercase       : List[Any]						=model(UpperCAmelCase		)[0]
       lowercase       : str						=[1, 11, 5_0265]
       self.assertEqual(list(output.shape		)  ,   UpperCAmelCase		)
       # compare the actual values for a slice.
       lowercase       : Optional[Any]						=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]  ,   UpperCAmelCase  ,   atol=1e-4		)		)






      @slow
      def 					A__   (			self      :		Tuple		)	->	Optional[Any]:


       '''simple docstring'''

       lowercase       : int						=FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''  ,   from_pt=UpperCAmelCase		)

       lowercase       : Optional[Any]						=np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]  ,   dtype=jnp.intaa		)
       lowercase       : str						=model(UpperCAmelCase		)[0]
       # compare the actual values for a slice.
       lowercase       : List[str]						=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]  ,   UpperCAmelCase  ,   atol=1e-4		)		)











'''simple docstring'''



SCREAMING_SNAKE_CASE							=						frozenset(
    [
        'prompt',
        'height',
        'width',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
        'cross_attention_kwargs',
    ]
)

SCREAMING_SNAKE_CASE							=						frozenset(['prompt', 'negative_prompt'])

SCREAMING_SNAKE_CASE							=						frozenset([])

SCREAMING_SNAKE_CASE							=						frozenset(['image'])

SCREAMING_SNAKE_CASE							=						frozenset(
    [
        'image',
        'height',
        'width',
        'guidance_scale',
    ]
)

SCREAMING_SNAKE_CASE							=						frozenset(['image'])

SCREAMING_SNAKE_CASE							=						frozenset(
    [
        'prompt',
        'image',
        'height',
        'width',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
    ]
)

SCREAMING_SNAKE_CASE							=						frozenset(['prompt', 'image', 'negative_prompt'])

SCREAMING_SNAKE_CASE							=						frozenset(
    [
        # Text guided image variation with an image mask
        'prompt',
        'image',
        'mask_image',
        'height',
        'width',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
    ]
)

SCREAMING_SNAKE_CASE							=						frozenset(['prompt', 'image', 'mask_image', 'negative_prompt'])

SCREAMING_SNAKE_CASE							=						frozenset(
    [
        # image variation with an image mask
        'image',
        'mask_image',
        'height',
        'width',
        'guidance_scale',
    ]
)

SCREAMING_SNAKE_CASE							=						frozenset(['image', 'mask_image'])

SCREAMING_SNAKE_CASE							=						frozenset(
    [
        'example_image',
        'image',
        'mask_image',
        'height',
        'width',
        'guidance_scale',
    ]
)

SCREAMING_SNAKE_CASE							=						frozenset(['example_image', 'image', 'mask_image'])

SCREAMING_SNAKE_CASE							=						frozenset(['class_labels'])

SCREAMING_SNAKE_CASE							=						frozenset(['class_labels'])

SCREAMING_SNAKE_CASE							=						frozenset(['batch_size'])

SCREAMING_SNAKE_CASE							=						frozenset([])

SCREAMING_SNAKE_CASE							=						frozenset(['batch_size'])

SCREAMING_SNAKE_CASE							=						frozenset([])

SCREAMING_SNAKE_CASE							=						frozenset(
    [
        'prompt',
        'audio_length_in_s',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
        'cross_attention_kwargs',
    ]
)

SCREAMING_SNAKE_CASE							=						frozenset(['prompt', 'negative_prompt'])
SCREAMING_SNAKE_CASE							=						frozenset(['input_tokens'])

SCREAMING_SNAKE_CASE							=						frozenset(['input_tokens'])



