




"""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							):




    def __init__(   self						:	List[str]     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Any=13     ,			snake_case_						:	Dict=7     ,			snake_case_						:	int=True     ,			snake_case_						:	Optional[Any]=True     ,			snake_case_						:	Tuple=True     ,			snake_case_						:	int=True     ,			snake_case_						:	int=99     ,			snake_case_						:	Tuple=32     ,			snake_case_						:	str=5     ,			snake_case_						:	Optional[Any]=4     ,			snake_case_						:	Optional[Any]=37     ,			snake_case_						:	Any="gelu"     ,			snake_case_						:	Union[str, Any]=0.1     ,			snake_case_						:	int=0.1     ,			snake_case_						:	Any=512     ,			snake_case_						:	str=16     ,			snake_case_						:	int=2     ,			snake_case_						:	int=0.02     ,			snake_case_						:	List[str]=4     ,			)				->							Optional[int]:




     '''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 							__magic_name__							(   self						:	Any				)				->							List[Any]:




     '''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=snake_case_     ,			initializer_range=self.initializer_range     ,			)

     return config, input_ids, token_type_ids, attention_mask




    def 							__magic_name__							(   self						:	Dict				)				->							List[Any]:




     '''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 							__magic_name__							(   self						:	Optional[Any]				)				->							int:




     '''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      UpperCAmelCase_  (						A_,       unittest.TestCase							):
    lowercase__						 =				True

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




    def 							__magic_name__							(   self						:	List[str]				)				->							Union[str, Any]:




     '''simple docstring'''

     A__	=    FlaxRobertaPreLayerNormModelTester(self				)




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




     '''simple docstring'''

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






@require_flax
class      UpperCAmelCase_  (						unittest.TestCase							):




    @slow
    def 							__magic_name__							(   self						:	List[str]				)				->							List[Any]:




     '''simple docstring'''

     A__	=    FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"     ,			from_pt=snake_case_				)

     A__	=    np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]     ,			dtype=jnp.intaa				)
     A__	=    model(snake_case_				)[0]
     A__	=    [1, 11, 50_265]
     self.assertEqual(list(output.shape				)     ,			snake_case_				)
     # compare the actual values for a slice.
     A__	=    np.array(
         [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]]     ,			dtype=np.floataa				)
     self.assertTrue(np.allclose(output[:, :3, :3]     ,			snake_case_     ,			atol=1e-4				)				)




    @slow
    def 							__magic_name__							(   self						:	Tuple				)				->							List[str]:




     '''simple docstring'''

     A__	=    FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"     ,			from_pt=snake_case_				)

     A__	=    np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]     ,			dtype=jnp.intaa				)
     A__	=    model(snake_case_				)[0]
     # compare the actual values for a slice.
     A__	=    np.array(
         [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]]     ,			dtype=np.floataa				)
     self.assertTrue(np.allclose(output[:, :3, :3]     ,			snake_case_     ,			atol=1e-4				)				)











"""simple docstring"""



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				['''image_processor''', '''tokenizer''']
    lowercase__						 =				'''ViTImageProcessor'''
    lowercase__						 =				('''CLIPTokenizer''', '''CLIPTokenizerFast''')




    def __init__(   self						:	Optional[Any]     ,			snake_case_						:	Union[str, Any]=None     ,			snake_case_						:	Dict=None     ,			**snake_case_						:	Optional[Any]				)				->							List[str]:




     '''simple docstring'''

     A__	=    None
     if "feature_extractor" in kwargs:
      warnings.warn(
          "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
          " instead."     ,			snake_case_     ,			)
      A__	=    kwargs.pop("feature_extractor"				)

     A__	=    image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
      raise ValueError("You need to specify an `image_processor`."				)
     if tokenizer is None:
      raise ValueError("You need to specify a `tokenizer`."				)

     super().__init__(snake_case_     ,			snake_case_				)




    def __call__(   self						:	int     ,			snake_case_						:	Union[str, Any]=None     ,			snake_case_						:	int=None     ,			snake_case_						:	Dict=None     ,			snake_case_						:	int=None     ,			**snake_case_						:	List[str]				)				->							Union[str, Any]:




     '''simple docstring'''

     if text is None and visual_prompt is None and images is None:
      raise ValueError("You have to specify either text, visual prompt or images."				)

     if text is not None and visual_prompt is not None:
      raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt."				)

     if text is not None:
      A__	=    self.tokenizer(snake_case_     ,			return_tensors=snake_case_     ,			**snake_case_				)

     if visual_prompt is not None:
      A__	=    self.image_processor(snake_case_     ,			return_tensors=snake_case_     ,			**snake_case_				)

     if images is not None:
      A__	=    self.image_processor(snake_case_     ,			return_tensors=snake_case_     ,			**snake_case_				)

     if visual_prompt is not None and images is not None:
      A__	=    {
          "pixel_values": image_features.pixel_values,
          "conditional_pixel_values": prompt_features.pixel_values,
      }
      return encoding
     elif text is not None and images is not None:
      A__	=    image_features.pixel_values
      return encoding
     elif text is not None:
      return encoding
     elif visual_prompt is not None:
      A__	=    {
          "conditional_pixel_values": prompt_features.pixel_values,
      }
      return encoding
     else:
      return BatchEncoding(data=dict(**snake_case_				)     ,			tensor_type=snake_case_				)




    def 							__magic_name__							(   self						:	Tuple     ,			*snake_case_						:	List[str]     ,			**snake_case_						:	Optional[int]				)				->							str:




     '''simple docstring'''

     return self.tokenizer.batch_decode(*snake_case_     ,			**snake_case_				)




    def 							__magic_name__							(   self						:	Optional[Any]     ,			*snake_case_						:	Any     ,			**snake_case_						:	List[Any]				)				->							List[str]:




     '''simple docstring'''

     return self.tokenizer.decode(*snake_case_     ,			**snake_case_				)




    @property
    def 							__magic_name__							(   self						:	int				)				->							Optional[int]:




     '''simple docstring'''

     warnings.warn(
         "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."     ,			snake_case_     ,			)
     return self.image_processor_class




    @property
    def 							__magic_name__							(   self						:	int				)				->							int:




     '''simple docstring'''

     warnings.warn(
         "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."     ,			snake_case_     ,			)
     return self.image_processor





