


"""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   ):




      '''simple docstring'''


      def __init__(    self:	Tuple						,				a:	Tuple						,				a:	Optional[Any]=13						,				a:	int=7						,				a:	Any=True						,				a:	List[Any]=True						,				a:	Optional[int]=True						,				a:	Optional[int]=True						,				a:	Union[str, Any]=99						,				a:	Any=32						,				a:	List[Any]=5						,				a:	List[Any]=4						,				a:	str=37						,				a:	List[str]="gelu"						,				a:	Tuple=0.1						,				a:	Optional[int]=0.1						,				a:	Optional[int]=512						,				a:	Optional[int]=16						,				a:	Optional[Any]=2						,				a:	int=0.0_2						,				a:	Optional[int]=4						,				):
       __lowerCamelCase		:							Optional[int]		 =						parent
       __lowerCamelCase		:							List[Any]		 =						batch_size
       __lowerCamelCase		:							List[Any]		 =						seq_length
       __lowerCamelCase		:							str		 =						is_training
       __lowerCamelCase		:							Dict		 =						use_attention_mask
       __lowerCamelCase		:							Optional[Any]		 =						use_token_type_ids
       __lowerCamelCase		:							str		 =						use_labels
       __lowerCamelCase		:							Optional[int]		 =						vocab_size
       __lowerCamelCase		:							Tuple		 =						hidden_size
       __lowerCamelCase		:							Tuple		 =						num_hidden_layers
       __lowerCamelCase		:							List[Any]		 =						num_attention_heads
       __lowerCamelCase		:							Tuple		 =						intermediate_size
       __lowerCamelCase		:							Union[str, Any]		 =						hidden_act
       __lowerCamelCase		:							Dict		 =						hidden_dropout_prob
       __lowerCamelCase		:							Any		 =						attention_probs_dropout_prob
       __lowerCamelCase		:							str		 =						max_position_embeddings
       __lowerCamelCase		:							Optional[Any]		 =						type_vocab_size
       __lowerCamelCase		:							int		 =						type_sequence_label_size
       __lowerCamelCase		:							List[str]		 =						initializer_range
       __lowerCamelCase		:							str		 =						num_choices




      def 					_snake_case     (    self:	Dict    ):
       __lowerCamelCase		:							Optional[Any]		 =						ids_tensor([self.batch_size, self.seq_length]						,				self.vocab_size    )

       __lowerCamelCase		:							Tuple		 =						None
       if self.use_attention_mask:
        __lowerCamelCase		:							int		 =						random_attention_mask([self.batch_size, self.seq_length]    )

       __lowerCamelCase		:							Tuple		 =						None
       if self.use_token_type_ids:
        __lowerCamelCase		:							List[Any]		 =						ids_tensor([self.batch_size, self.seq_length]						,				self.type_vocab_size    )

       __lowerCamelCase		:							Optional[Any]		 =						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=a_						,				initializer_range=self.initializer_range						,				)

       return config, input_ids, token_type_ids, attention_mask




      def 					_snake_case     (    self:	Dict    ):
       __lowerCamelCase		:							List[str]		 =						self.prepare_config_and_inputs()
       __lowerCamelCase		:							str		 =						config_and_inputs
       __lowerCamelCase		:							Tuple		 =						{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict




      def 					_snake_case     (    self:	Optional[int]    ):
       __lowerCamelCase		:							int		 =						self.prepare_config_and_inputs()
       __lowerCamelCase		:							Dict		 =						config_and_inputs

       __lowerCamelCase		:							Optional[Any]		 =						True
       __lowerCamelCase		:							Dict		 =						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
       __lowerCamelCase		:							int		 =						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   ):




      '''simple docstring'''


      __snake_case 	=       True

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




      def 					_snake_case     (    self:	List[str]    ):
       __lowerCamelCase		:							Optional[Any]		 =						FlaxRobertaPreLayerNormModelTester(self    )




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

       for model_class_name in self.all_model_classes:
        __lowerCamelCase		:							Union[str, Any]		 =						model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'						,				from_pt=a_    )
        __lowerCamelCase		:							int		 =						model(np.ones((1, 1)    )    )
        self.assertIsNotNone(a_    )



@require_flax
class 	A_ (	unittest.TestCase   ):




      '''simple docstring'''


      @slow
      def 					_snake_case     (    self:	Tuple    ):
       __lowerCamelCase		:							str		 =						FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'						,				from_pt=a_    )

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




      @slow
      def 					_snake_case     (    self:	int    ):
       __lowerCamelCase		:							int		 =						FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'						,				from_pt=a_    )

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





import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
       from ... import PreTrainedTokenizerBase, TensorType

lowercase_	   =     logging.get_logger(__name__)


class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       """vision-encoder-decoder"""
      __snake_case 	=       True




      def __init__(    self:	Union[str, Any]						,				**a:	Optional[Any]    ):
       super().__init__(**a    )
       if "encoder" not in kwargs or "decoder" not in kwargs:
        raise ValueError(
            F'A configuraton of type {self.model_type} cannot be instantiated because '
            F'not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}'    )

       __lowerCamelCase		:							Dict		 =						kwargs.pop('encoder'    )
       __lowerCamelCase		:							int		 =						encoder_config.pop('model_type'    )
       __lowerCamelCase		:							Any		 =						kwargs.pop('decoder'    )
       __lowerCamelCase		:							Union[str, Any]		 =						decoder_config.pop('model_type'    )

       __lowerCamelCase		:							Optional[Any]		 =						AutoConfig.for_model(a						,				**a    )
       __lowerCamelCase		:							List[Any]		 =						AutoConfig.for_model(a						,				**a    )
       __lowerCamelCase		:							Tuple		 =						True




      @classmethod
      def 					_snake_case     (    cls:	Optional[Any]						,				a:	PretrainedConfig						,				a:	PretrainedConfig						,				**a:	Dict    ):
       logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'    )
       __lowerCamelCase		:							Optional[Any]		 =						True
       __lowerCamelCase		:							Optional[Any]		 =						True

       return cls(encoder=encoder_config.to_dict()						,				decoder=decoder_config.to_dict()						,				**a    )




      def 					_snake_case     (    self:	str    ):
       __lowerCamelCase		:							int		 =						copy.deepcopy(self.__dict__    )
       __lowerCamelCase		:							Dict		 =						self.encoder.to_dict()
       __lowerCamelCase		:							Union[str, Any]		 =						self.decoder.to_dict()
       __lowerCamelCase		:							Optional[int]		 =						self.__class__.model_type
       return output



class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       version.parse("""1.11"""   )




      @property
      def 					_snake_case     (    self:	Union[str, Any]    ):
       return OrderedDict(
           [
               ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
           ]    )




      @property
      def 					_snake_case     (    self:	Optional[Any]    ):
       return 1e-4




      @property
      def 					_snake_case     (    self:	List[str]    ):
       return OrderedDict({'last_hidden_state': {0: 'batch', 1: 'encoder_sequence'}}    )



class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      @property
      def 					_snake_case     (    self:	Tuple    ):
       __lowerCamelCase		:							Union[str, Any]		 =						OrderedDict()
       __lowerCamelCase		:							Union[str, Any]		 =						{0: 'batch', 1: 'past_decoder_sequence + sequence'}
       __lowerCamelCase		:							Optional[Any]		 =						{0: 'batch', 1: 'past_decoder_sequence + sequence'}
       __lowerCamelCase		:							Dict		 =						{0: 'batch', 1: 'encoder_sequence'}

       return common_inputs




      def 					_snake_case     (    self:	List[Any]						,				a:	"PreTrainedTokenizerBase"						,				a:	int = -1						,				a:	int = -1						,				a:	bool = False						,				a:	Optional["TensorType"] = None						,				):
       import torch

       __lowerCamelCase		:							str		 =						OrderedDict()

       __lowerCamelCase		:							List[Any]		 =						super().generate_dummy_inputs(
           a						,				batch_size=a						,				seq_length=a						,				is_pair=a						,				framework=a    )

       __lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						dummy_input['input_ids'].shape
       __lowerCamelCase		:							str		 =						(batch, encoder_sequence, self._config.encoder_hidden_size)
       __lowerCamelCase		:							str		 =						dummy_input.pop('input_ids'    )
       __lowerCamelCase		:							Union[str, Any]		 =						dummy_input.pop('attention_mask'    )
       __lowerCamelCase		:							str		 =						torch.zeros(a    )

       return common_inputs



class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      @property
      def 					_snake_case     (    self:	List[Any]    ):
       pass




      def 					_snake_case     (    self:	List[str]						,				a:	PretrainedConfig    ):
       return VisionEncoderDecoderEncoderOnnxConfig(a    )




      def 					_snake_case     (    self:	Optional[int]						,				a:	PretrainedConfig						,				a:	PretrainedConfig						,				a:	str = "default"    ):
       __lowerCamelCase		:							List[Any]		 =						encoder_config.hidden_size
       return VisionEncoderDecoderDecoderOnnxConfig(a						,				a    )

