

'''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        A__   (   unittest.TestCase      ):



    def __init__(       self		,					UpperCamelCase__		,					UpperCamelCase__=13		,					UpperCamelCase__=7		,					UpperCamelCase__=True		,					UpperCamelCase__=True		,					UpperCamelCase__=True		,					UpperCamelCase__=True		,					UpperCamelCase__=99		,					UpperCamelCase__=32		,					UpperCamelCase__=5		,					UpperCamelCase__=4		,					UpperCamelCase__=37		,					UpperCamelCase__="gelu"		,					UpperCamelCase__=0.1		,					UpperCamelCase__=0.1		,					UpperCamelCase__=512		,					UpperCamelCase__=16		,					UpperCamelCase__=2		,					UpperCamelCase__=0.02		,					UpperCamelCase__=4		,					)    ->   Optional[Any]:





           '''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        snake_case_      (       self							)    ->   List[str]:





           '''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=UpperCamelCase__		,					initializer_range=self.initializer_range		,					)

           return config, input_ids, token_type_ids, attention_mask



    def        snake_case_      (       self							)    ->   Optional[int]:





           '''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        snake_case_      (       self							)    ->   List[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        A__   (   _snake_case    ,    unittest.TestCase      ):
    lowercase						       =		True

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



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





           '''simple docstring'''
           A_						       =   FlaxRobertaPreLayerNormModelTester(self							)







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





           '''simple docstring'''
           for model_class_name in self.all_model_classes:
                  A_						       =   model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""		,					from_pt=UpperCamelCase__							)
                  A_						       =   model(np.ones((1, 1)							)							)
                  self.assertIsNotNone(UpperCamelCase__							)


@require_flax
class        A__   (   unittest.TestCase      ):



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





           '''simple docstring'''
           A_						       =   FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""		,					from_pt=UpperCamelCase__							)

           A_						       =   np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]		,					dtype=jnp.intaa							)
           A_						       =   model(UpperCamelCase__							)[0]
           A_						       =   [1, 11, 50265]
           self.assertEqual(list(output.shape							)		,					UpperCamelCase__							)
           # 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]		,					UpperCamelCase__		,					atol=1e-4							)							)







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





           '''simple docstring'''
           A_						       =   FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""		,					from_pt=UpperCamelCase__							)

           A_						       =   np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]		,					dtype=jnp.intaa							)
           A_						       =   model(UpperCamelCase__							)[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]		,					UpperCamelCase__		,					atol=1e-4							)							)



'''simple docstring'''







from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
      import tensorflow as tf

      from transformers import TFBlipTextModel
      from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST


class        A__   :



    def __init__(       self		,					UpperCamelCase__		,					UpperCamelCase__=12		,					UpperCamelCase__=7		,					UpperCamelCase__=True		,					UpperCamelCase__=True		,					UpperCamelCase__=True		,					UpperCamelCase__=99		,					UpperCamelCase__=32		,					UpperCamelCase__=32		,					UpperCamelCase__=2		,					UpperCamelCase__=4		,					UpperCamelCase__=37		,					UpperCamelCase__=0.1		,					UpperCamelCase__=0.1		,					UpperCamelCase__=512		,					UpperCamelCase__=0.02		,					UpperCamelCase__=0		,					UpperCamelCase__=None		,					)    ->   Union[str, Any]:





           '''simple docstring'''
           A_						       =   parent
           A_						       =   batch_size
           A_						       =   seq_length
           A_						       =   is_training
           A_						       =   use_input_mask
           A_						       =   use_labels
           A_						       =   vocab_size
           A_						       =   hidden_size
           A_						       =   projection_dim
           A_						       =   num_hidden_layers
           A_						       =   num_attention_heads
           A_						       =   intermediate_size
           A_						       =   dropout
           A_						       =   attention_dropout
           A_						       =   max_position_embeddings
           A_						       =   initializer_range
           A_						       =   scope
           A_						       =   bos_token_id



    def        snake_case_      (       self							)    ->   Dict:





           '''simple docstring'''
           A_						       =   ids_tensor([self.batch_size, self.seq_length]		,					self.vocab_size							)

           A_						       =   None
           if self.use_input_mask:
                  A_						       =   random_attention_mask([self.batch_size, self.seq_length]							)

           if input_mask is not None:
                  A_						       =   input_mask.numpy()
                  A_       ,      A_						       =   input_mask.shape
                  A_						       =   np.random.randint(1		,					seq_length - 1		,					size=(batch_size,)							)
                  for batch_idx, start_index in enumerate(UpperCamelCase__							):
                         A_						       =   1
                         A_						       =   0

           A_						       =   self.get_config()

           return config, input_ids, tf.convert_to_tensor(UpperCamelCase__							)



    def        snake_case_      (       self							)    ->   Optional[int]:





           '''simple docstring'''
           return BlipTextConfig(
               vocab_size=self.vocab_size		,					hidden_size=self.hidden_size		,					projection_dim=self.projection_dim		,					num_hidden_layers=self.num_hidden_layers		,					num_attention_heads=self.num_attention_heads		,					intermediate_size=self.intermediate_size		,					dropout=self.dropout		,					attention_dropout=self.attention_dropout		,					max_position_embeddings=self.max_position_embeddings		,					initializer_range=self.initializer_range		,					bos_token_id=self.bos_token_id		,					)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   int:





           '''simple docstring'''
           A_						       =   TFBlipTextModel(config=UpperCamelCase__							)
           A_						       =   model(UpperCamelCase__		,					attention_mask=UpperCamelCase__		,					training=UpperCamelCase__							)
           A_						       =   model(UpperCamelCase__		,					training=UpperCamelCase__							)
           self.parent.assertEqual(result.last_hidden_state.shape		,					(self.batch_size, self.seq_length, self.hidden_size)							)
           self.parent.assertEqual(result.pooler_output.shape		,					(self.batch_size, self.hidden_size)							)







    def        snake_case_      (       self							)    ->   List[Any]:





           '''simple docstring'''
           A_						       =   self.prepare_config_and_inputs()
           A_       ,      A_       ,      A_						       =   config_and_inputs
           A_						       =   {"""input_ids""": input_ids, """attention_mask""": input_mask}
           return config, inputs_dict


@require_tf
class        A__   (   _snake_case    ,    unittest.TestCase      ):
    lowercase						       =		(TFBlipTextModel,) if is_tf_available() else ()
    lowercase						       =		False
    lowercase						       =		False
    lowercase						       =		False



    def        snake_case_      (       self							)    ->   Optional[int]:





           '''simple docstring'''
           A_						       =   BlipTextModelTester(self							)
           A_						       =   ConfigTester(self		,					config_class=UpperCamelCase__		,					hidden_size=37							)



    def        snake_case_      (       self							)    ->   Tuple:





           '''simple docstring'''
           self.config_tester.run_common_tests()



    def        snake_case_      (       self							)    ->   str:





           '''simple docstring'''
           A_						       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCamelCase__							)



    def        snake_case_      (       self							)    ->   List[Any]:





           '''simple docstring'''
           pass



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





           '''simple docstring'''
           pass



    @unittest.skip(reason="""Blip does not use inputs_embeds"""							)
    def        snake_case_      (       self							)    ->   Union[str, Any]:





           '''simple docstring'''
           pass



    @unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING"""							)
    def        snake_case_      (       self							)    ->   List[Any]:





           '''simple docstring'''
           pass



    @unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING"""							)
    def        snake_case_      (       self							)    ->   Any:





           '''simple docstring'''
           pass



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





           '''simple docstring'''
           for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A_						       =   TFBlipTextModel.from_pretrained(UpperCamelCase__							)
                  self.assertIsNotNone(UpperCamelCase__							)







    def        snake_case_      (       self		,					UpperCamelCase__=True							)    ->   str:





           '''simple docstring'''
           super().test_pt_tf_model_equivalence(allow_missing_keys=UpperCamelCase__							)
