



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			,    a__			,    a__=1_3			,    a__=7			,    a__=True			,    a__=True			,    a__=True			,    a__=True			,    a__=9_9			,    a__=3_2			,    a__=5			,    a__=4			,    a__=3_7			,    a__="gelu"			,    a__=0.1			,    a__=0.1			,    a__=5_1_2			,    a__=1_6			,    a__=2			,    a__=0.0_2			,    a__=4			,    ):
         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):
         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=a__			,    initializer_range=self.initializer_range			,    )

         return config, input_ids, token_type_ids, attention_mask




     def 		snake_case_		(					self):
         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):
         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  ):
     UpperCamelCase__		     =					True

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




     def 		snake_case_		(					self):
         A__					=       FlaxRobertaPreLayerNormModelTester(self)




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







@require_flax
class      _UpperCAmelCase      (							unittest.TestCase  ):




     @slow
     def 		snake_case_		(					self):
         A__					=       FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''			,    from_pt=a__)

         A__					=       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)
         A__					=       model(a__)[0]
         A__					=       [1, 1_1, 5_0_2_6_5]
         self.assertEqual(list(output.shape)			,    a__)
         # compare the actual values for a slice.
         A__					=       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):
         A__					=       FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''			,    from_pt=a__)

         A__					=       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)
         A__					=       model(a__)[0]
         # compare the actual values for a slice.
         A__					=       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))





from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase    	=		{
    "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
    "feature_extraction_mctct": ["MCTCTFeatureExtractor"],
    "processing_mctct": ["MCTCTProcessor"],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase    	=		[
       "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "MCTCTForCTC",
       "MCTCTModel",
       "MCTCTPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
   from .feature_extraction_mctct import MCTCTFeatureExtractor
   from .processing_mctct import MCTCTProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
   import sys

   _lowercase    	=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
