




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					: List[Any] ,       snake_case					: Optional[int] ,       snake_case					: int=13 ,       snake_case					: int=7 ,       snake_case					: Optional[int]=True ,       snake_case					: Dict=True ,       snake_case					: Dict=True ,       snake_case					: Union[str, Any]=True ,       snake_case					: List[Any]=99 ,       snake_case					: Tuple=32 ,       snake_case					: List[str]=5 ,       snake_case					: Dict=4 ,       snake_case					: Union[str, Any]=37 ,       snake_case					: Optional[int]="gelu" ,       snake_case					: Optional[int]=0.1 ,       snake_case					: Optional[Any]=0.1 ,       snake_case					: Tuple=512 ,       snake_case					: Union[str, Any]=16 ,       snake_case					: Optional[int]=2 ,       snake_case					: List[str]=0.02 ,       snake_case					: List[str]=4 ,       ):
          __UpperCamelCase  		=						parent
          __UpperCamelCase  		=						batch_size
          __UpperCamelCase  		=						seq_length
          __UpperCamelCase  		=						is_training
          __UpperCamelCase  		=						use_attention_mask
          __UpperCamelCase  		=						use_token_type_ids
          __UpperCamelCase  		=						use_labels
          __UpperCamelCase  		=						vocab_size
          __UpperCamelCase  		=						hidden_size
          __UpperCamelCase  		=						num_hidden_layers
          __UpperCamelCase  		=						num_attention_heads
          __UpperCamelCase  		=						intermediate_size
          __UpperCamelCase  		=						hidden_act
          __UpperCamelCase  		=						hidden_dropout_prob
          __UpperCamelCase  		=						attention_probs_dropout_prob
          __UpperCamelCase  		=						max_position_embeddings
          __UpperCamelCase  		=						type_vocab_size
          __UpperCamelCase  		=						type_sequence_label_size
          __UpperCamelCase  		=						initializer_range
          __UpperCamelCase  		=						num_choices







      def       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size				)

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

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

          __UpperCamelCase  		=						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       snake_case	(     self					: str				):
          __UpperCamelCase  		=						self.prepare_config_and_inputs()
          __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						config_and_inputs
          __UpperCamelCase  		=						{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
          return config, inputs_dict







      def       snake_case	(     self					: str				):
          __UpperCamelCase  		=						self.prepare_config_and_inputs()
          __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						config_and_inputs

          __UpperCamelCase  		=						True
          __UpperCamelCase  		=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
          __UpperCamelCase  		=						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						(			_SCREAMING_SNAKE_CASE							,			unittest.TestCase   ):

      """simple docstring"""

      lowerCAmelCase__			: Tuple    =					True

      lowerCAmelCase__			: str    =					(
          (
              FlaxRobertaPreLayerNormModel,
              FlaxRobertaPreLayerNormForCausalLM,
              FlaxRobertaPreLayerNormForMaskedLM,
              FlaxRobertaPreLayerNormForSequenceClassification,
              FlaxRobertaPreLayerNormForTokenClassification,
              FlaxRobertaPreLayerNormForMultipleChoice,
              FlaxRobertaPreLayerNormForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )







      def       snake_case	(     self					: List[str]				):
          __UpperCamelCase  		=						FlaxRobertaPreLayerNormModelTester(self				)







      @slow
      def       snake_case	(     self					: str				):
          for model_class_name in self.all_model_classes:
              __UpperCamelCase  		=						model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''' ,       from_pt=snake_case				)
              __UpperCamelCase  		=						model(np.ones((1, 1)				)				)
              self.assertIsNotNone(snake_case				)







@require_flax
class  _lowerCamelCase						(			unittest.TestCase   ):

      """simple docstring"""

      @slow
      def       snake_case	(     self					: List[str]				):
          __UpperCamelCase  		=						FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''' ,       from_pt=snake_case				)

          __UpperCamelCase  		=						np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] ,       dtype=jnp.intaa				)
          __UpperCamelCase  		=						model(snake_case				)[0]
          __UpperCamelCase  		=						[1, 11, 50265]
          self.assertEqual(list(output.shape				) ,       snake_case				)
          # compare the actual values for a slice.
          __UpperCamelCase  		=						np.array(
              [[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]] ,       dtype=np.floataa				)
          self.assertTrue(np.allclose(output[:, :3, :3] ,       snake_case ,       atol=1E-4				)				)







      @slow
      def       snake_case	(     self					: List[Any]				):
          __UpperCamelCase  		=						FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''' ,       from_pt=snake_case				)

          __UpperCamelCase  		=						np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] ,       dtype=jnp.intaa				)
          __UpperCamelCase  		=						model(snake_case				)[0]
          # compare the actual values for a slice.
          __UpperCamelCase  		=						np.array(
              [[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]] ,       dtype=np.floataa				)
          self.assertTrue(np.allclose(output[:, :3, :3] ,       snake_case ,       atol=1E-4				)				)












from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a_   		=     logging.get_logger(__name__)

a_   		=     {
    "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json",
    "google/bigbird-roberta-large": "https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json",
    "google/bigbird-base-trivia-itc": "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json",
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: str    =					"big_bird"







      def __init__(     self					: str ,       snake_case					: Optional[Any]=50358 ,       snake_case					: Union[str, Any]=768 ,       snake_case					: Dict=12 ,       snake_case					: List[Any]=12 ,       snake_case					: Tuple=3072 ,       snake_case					: Tuple="gelu_new" ,       snake_case					: Dict=0.1 ,       snake_case					: Any=0.1 ,       snake_case					: List[Any]=4096 ,       snake_case					: Union[str, Any]=2 ,       snake_case					: Union[str, Any]=0.02 ,       snake_case					: Union[str, Any]=1E-12 ,       snake_case					: List[Any]=True ,       snake_case					: List[Any]=0 ,       snake_case					: int=1 ,       snake_case					: int=2 ,       snake_case					: Any=66 ,       snake_case					: str="block_sparse" ,       snake_case					: Any=True ,       snake_case					: Tuple=False ,       snake_case					: List[Any]=64 ,       snake_case					: Dict=3 ,       snake_case					: Union[str, Any]=None ,       **snake_case					: Any ,       ):
          super().__init__(
              pad_token_id=snake_case ,       bos_token_id=snake_case ,       eos_token_id=snake_case ,       sep_token_id=snake_case ,       **snake_case ,       )

          __UpperCamelCase  		=						vocab_size
          __UpperCamelCase  		=						max_position_embeddings
          __UpperCamelCase  		=						hidden_size
          __UpperCamelCase  		=						num_hidden_layers
          __UpperCamelCase  		=						num_attention_heads
          __UpperCamelCase  		=						intermediate_size
          __UpperCamelCase  		=						hidden_act
          __UpperCamelCase  		=						hidden_dropout_prob
          __UpperCamelCase  		=						attention_probs_dropout_prob
          __UpperCamelCase  		=						initializer_range
          __UpperCamelCase  		=						type_vocab_size
          __UpperCamelCase  		=						layer_norm_eps
          __UpperCamelCase  		=						use_cache

          __UpperCamelCase  		=						rescale_embeddings
          __UpperCamelCase  		=						attention_type
          __UpperCamelCase  		=						use_bias
          __UpperCamelCase  		=						block_size
          __UpperCamelCase  		=						num_random_blocks
          __UpperCamelCase  		=						classifier_dropout







class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      @property
      def       snake_case	(     self					: int				):
          if self.task == "multiple-choice":
              __UpperCamelCase  		=						{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
          else:
              __UpperCamelCase  		=						{0: '''batch''', 1: '''sequence'''}
          return OrderedDict(
              [
                  ('''input_ids''', dynamic_axis),
                  ('''attention_mask''', dynamic_axis),
              ]				)






