






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					,						__UpperCamelCase					,						__UpperCamelCase=1_3					,						__UpperCamelCase=7					,						__UpperCamelCase=True					,						__UpperCamelCase=True					,						__UpperCamelCase=True					,						__UpperCamelCase=True					,						__UpperCamelCase=9_9					,						__UpperCamelCase=3_2					,						__UpperCamelCase=5					,						__UpperCamelCase=4					,						__UpperCamelCase=3_7					,						__UpperCamelCase="gelu"					,						__UpperCamelCase=0.1					,						__UpperCamelCase=0.1					,						__UpperCamelCase=5_1_2					,						__UpperCamelCase=1_6					,						__UpperCamelCase=2					,						__UpperCamelCase=0.0_2					,						__UpperCamelCase=4					,						)->	Optional[Any]:
           __lowerCAmelCase								=					parent
           __lowerCAmelCase								=					batch_size
           __lowerCAmelCase								=					seq_length
           __lowerCAmelCase								=					is_training
           __lowerCAmelCase								=					use_attention_mask
           __lowerCAmelCase								=					use_token_type_ids
           __lowerCAmelCase								=					use_labels
           __lowerCAmelCase								=					vocab_size
           __lowerCAmelCase								=					hidden_size
           __lowerCAmelCase								=					num_hidden_layers
           __lowerCAmelCase								=					num_attention_heads
           __lowerCAmelCase								=					intermediate_size
           __lowerCAmelCase								=					hidden_act
           __lowerCAmelCase								=					hidden_dropout_prob
           __lowerCAmelCase								=					attention_probs_dropout_prob
           __lowerCAmelCase								=					max_position_embeddings
           __lowerCAmelCase								=					type_vocab_size
           __lowerCAmelCase								=					type_sequence_label_size
           __lowerCAmelCase								=					initializer_range
           __lowerCAmelCase								=					num_choices


      def  __UpperCAmelCase		(	self	)->	Dict:
           __lowerCAmelCase								=					ids_tensor([self.batch_size, self.seq_length]					,						self.vocab_size	)

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

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

           __lowerCAmelCase								=					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=__lowerCamelCase					,						initializer_range=self.initializer_range					,						)

           return config, input_ids, token_type_ids, attention_mask


      def  __UpperCAmelCase		(	self	)->	Optional[int]:
           __lowerCAmelCase								=					self.prepare_config_and_inputs()
           __lowerCAmelCase								=					config_and_inputs
           __lowerCAmelCase								=					{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
           return config, inputs_dict


      def  __UpperCAmelCase		(	self	)->	Optional[int]:
           __lowerCAmelCase								=					self.prepare_config_and_inputs()
           __lowerCAmelCase								=					config_and_inputs

           __lowerCAmelCase								=					True
           __lowerCAmelCase								=					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
           __lowerCAmelCase								=					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			):
      snake_case_  =							True

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


      def  __UpperCAmelCase		(	self	)->	List[str]:
           __lowerCAmelCase								=					FlaxRobertaPreLayerNormModelTester(self	)


      @slow
      def  __UpperCAmelCase		(	self	)->	Any:
           for model_class_name in self.all_model_classes:
                __lowerCAmelCase								=					model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"					,						from_pt=__lowerCamelCase	)
                __lowerCAmelCase								=					model(np.ones((1, 1)	)	)
                self.assertIsNotNone(__lowerCamelCase	)



@require_flax
class 							_UpperCamelCase			(unittest.TestCase			):


      @slow
      def  __UpperCAmelCase		(	self	)->	Dict:
           __lowerCAmelCase								=					FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"					,						from_pt=__lowerCamelCase	)

           __lowerCAmelCase								=					np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]					,						dtype=jnp.intaa	)
           __lowerCAmelCase								=					model(__lowerCamelCase	)[0]
           __lowerCAmelCase								=					[1, 1_1, 5_0_2_6_5]
           self.assertEqual(list(output.shape	)					,						__lowerCamelCase	)
           # compare the actual values for a slice.
           __lowerCAmelCase								=					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]					,						__lowerCamelCase					,						atol=1e-4	)	)


      @slow
      def  __UpperCAmelCase		(	self	)->	Union[str, Any]:
           __lowerCAmelCase								=					FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"					,						from_pt=__lowerCamelCase	)

           __lowerCAmelCase								=					np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]					,						dtype=jnp.intaa	)
           __lowerCAmelCase								=					model(__lowerCamelCase	)[0]
           # compare the actual values for a slice.
           __lowerCAmelCase								=					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]					,						__lowerCamelCase					,						atol=1e-4	)	)







def       __lowerCAmelCase      (						__snake_case					, __snake_case       ):
     if not isinstance(__snake_case					, __snake_case       ):
          raise ValueError("iterations must be defined as integers"       )
     if not isinstance(__snake_case					, __snake_case       ) or not number >= 1:
          raise ValueError(
              "starting number must be\n                         and integer and be more than 0"       )
     if not iterations >= 1:
          raise ValueError("Iterations must be done more than 0 times to play FizzBuzz"       )

     __lowerCAmelCase								=					""
     while number <= iterations:
          if number % 3 == 0:
               out += "Fizz"
          if number % 5 == 0:
               out += "Buzz"
          if 0 not in (number % 3, number % 5):
               out += str(__snake_case       )

          # print(out)
          number += 1
          out += " "
     return out


if __name__ == "__main__":
 import doctest

 doctest.testmod()






