




'''simple docstring'''



import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_							:	Dict	     =		logging.get_logger(__name__)

UpperCAmelCase_							:	Optional[Any]	     =		{
    '''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}





class      lowerCAmelCase (			__lowerCAmelCase):
       __lowercase	:   int										=      '''wavlm'''


       def __init__( self     ,	__SCREAMING_SNAKE_CASE=32     ,	__SCREAMING_SNAKE_CASE=768     ,	__SCREAMING_SNAKE_CASE=12     ,	__SCREAMING_SNAKE_CASE=12     ,	__SCREAMING_SNAKE_CASE=3072     ,	__SCREAMING_SNAKE_CASE="gelu"     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE=0.0     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE=0.02     ,	__SCREAMING_SNAKE_CASE=1E-5     ,	__SCREAMING_SNAKE_CASE="group"     ,	__SCREAMING_SNAKE_CASE="gelu"     ,	__SCREAMING_SNAKE_CASE=(512, 512, 512, 512, 512, 512, 512)     ,	__SCREAMING_SNAKE_CASE=(5, 2, 2, 2, 2, 2, 2)     ,	__SCREAMING_SNAKE_CASE=(10, 3, 3, 3, 3, 2, 2)     ,	__SCREAMING_SNAKE_CASE=False     ,	__SCREAMING_SNAKE_CASE=128     ,	__SCREAMING_SNAKE_CASE=16     ,	__SCREAMING_SNAKE_CASE=320     ,	__SCREAMING_SNAKE_CASE=800     ,	__SCREAMING_SNAKE_CASE=False     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=0.05     ,	__SCREAMING_SNAKE_CASE=10     ,	__SCREAMING_SNAKE_CASE=2     ,	__SCREAMING_SNAKE_CASE=0.0     ,	__SCREAMING_SNAKE_CASE=10     ,	__SCREAMING_SNAKE_CASE=320     ,	__SCREAMING_SNAKE_CASE=2     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE=100     ,	__SCREAMING_SNAKE_CASE=256     ,	__SCREAMING_SNAKE_CASE=256     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE="mean"     ,	__SCREAMING_SNAKE_CASE=False     ,	__SCREAMING_SNAKE_CASE=False     ,	__SCREAMING_SNAKE_CASE=256     ,	__SCREAMING_SNAKE_CASE=(512, 512, 512, 512, 1500)     ,	__SCREAMING_SNAKE_CASE=(5, 3, 3, 1, 1)     ,	__SCREAMING_SNAKE_CASE=(1, 2, 3, 1, 1)     ,	__SCREAMING_SNAKE_CASE=512     ,	__SCREAMING_SNAKE_CASE=80     ,	__SCREAMING_SNAKE_CASE=0     ,	__SCREAMING_SNAKE_CASE=1     ,	__SCREAMING_SNAKE_CASE=2     ,	__SCREAMING_SNAKE_CASE=False     ,	__SCREAMING_SNAKE_CASE=3     ,	__SCREAMING_SNAKE_CASE=2     ,	__SCREAMING_SNAKE_CASE=3     ,	__SCREAMING_SNAKE_CASE=None     ,	**__SCREAMING_SNAKE_CASE     ,	)   ->	int:



            '''simple docstring'''




            super().__init__(**__SCREAMING_SNAKE_CASE     ,	pad_token_id=__SCREAMING_SNAKE_CASE     ,	bos_token_id=__SCREAMING_SNAKE_CASE     ,	eos_token_id=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	hidden_size
            __snake_case            =	feat_extract_norm
            __snake_case            =	feat_extract_activation
            __snake_case            =	list(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	list(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	list(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	conv_bias
            __snake_case            =	num_buckets
            __snake_case            =	max_bucket_distance
            __snake_case            =	num_conv_pos_embeddings
            __snake_case            =	num_conv_pos_embedding_groups
            __snake_case            =	len(self.conv_dim	)
            __snake_case            =	num_hidden_layers
            __snake_case            =	intermediate_size
            __snake_case            =	hidden_act
            __snake_case            =	num_attention_heads
            __snake_case            =	hidden_dropout
            __snake_case            =	attention_dropout
            __snake_case            =	activation_dropout
            __snake_case            =	feat_proj_dropout
            __snake_case            =	final_dropout
            __snake_case            =	layerdrop
            __snake_case            =	layer_norm_eps
            __snake_case            =	initializer_range
            __snake_case            =	num_ctc_classes
            __snake_case            =	vocab_size
            __snake_case            =	do_stable_layer_norm
            __snake_case            =	use_weighted_layer_sum
            __snake_case            =	classifier_proj_size

            if (
                (len(self.conv_stride	) != self.num_feat_extract_layers)
                or (len(self.conv_kernel	) != self.num_feat_extract_layers)
                or (len(self.conv_dim	) != self.num_feat_extract_layers)
            ):
                 raise ValueError(
                     '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
                     ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
                     F''' {len(self.conv_dim	)}`, `len(config.conv_stride) = {len(self.conv_stride	)}`,'''
                     F''' `len(config.conv_kernel) = {len(self.conv_kernel	)}`.'''	)

            # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
            __snake_case            =	apply_spec_augment
            __snake_case            =	mask_time_prob
            __snake_case            =	mask_time_length
            __snake_case            =	mask_time_min_masks
            __snake_case            =	mask_feature_prob
            __snake_case            =	mask_feature_length

            # parameters for pretraining with codevector quantized representations
            __snake_case            =	num_codevectors_per_group
            __snake_case            =	num_codevector_groups
            __snake_case            =	contrastive_logits_temperature
            __snake_case            =	num_negatives
            __snake_case            =	codevector_dim
            __snake_case            =	proj_codevector_dim
            __snake_case            =	diversity_loss_weight

            # ctc loss
            __snake_case            =	ctc_loss_reduction
            __snake_case            =	ctc_zero_infinity

            # adapter
            __snake_case            =	add_adapter
            __snake_case            =	adapter_kernel_size
            __snake_case            =	adapter_stride
            __snake_case            =	num_adapter_layers
            __snake_case            =	output_hidden_size or hidden_size

            # SequenceClassification-specific parameter. Feel free to ignore for other classes.
            __snake_case            =	classifier_proj_size

            # XVector-specific parameters. Feel free to ignore for other classes.
            __snake_case            =	list(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	list(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	list(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	xvector_output_dim




       @property
       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            return functools.reduce(operator.mul     ,	self.conv_stride     ,	1	)












'''simple docstring'''



import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
     import jax.numpy as jnp

     from transformers.models.roformer.modeling_flax_roformer import (
         FlaxRoFormerForMaskedLM,
         FlaxRoFormerForMultipleChoice,
         FlaxRoFormerForQuestionAnswering,
         FlaxRoFormerForSequenceClassification,
         FlaxRoFormerForTokenClassification,
         FlaxRoFormerModel,
     )





class      lowerCAmelCase (			unittest.TestCase):


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



            '''simple docstring'''




            __snake_case            =	parent
            __snake_case            =	batch_size
            __snake_case            =	seq_length
            __snake_case            =	is_training
            __snake_case            =	use_attention_mask
            __snake_case            =	use_token_type_ids
            __snake_case            =	use_labels
            __snake_case            =	vocab_size
            __snake_case            =	hidden_size
            __snake_case            =	num_hidden_layers
            __snake_case            =	num_attention_heads
            __snake_case            =	intermediate_size
            __snake_case            =	hidden_act
            __snake_case            =	hidden_dropout_prob
            __snake_case            =	attention_probs_dropout_prob
            __snake_case            =	max_position_embeddings
            __snake_case            =	type_vocab_size
            __snake_case            =	type_sequence_label_size
            __snake_case            =	initializer_range
            __snake_case            =	num_choices


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



            '''simple docstring'''




            __snake_case            =	ids_tensor([self.batch_size, self.seq_length]     ,	self.vocab_size	)

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

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

            __snake_case            =	RoFormerConfig(
                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=__SCREAMING_SNAKE_CASE     ,	initializer_range=self.initializer_range     ,	)

            return config, input_ids, token_type_ids, attention_mask




       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




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





@require_flax
class      lowerCAmelCase (			__lowerCAmelCase					, unittest.TestCase):
       __lowercase	:   Tuple										=      True

       __lowercase	:   Optional[int]										=      (
           (
               FlaxRoFormerModel,
               FlaxRoFormerForMaskedLM,
               FlaxRoFormerForSequenceClassification,
               FlaxRoFormerForTokenClassification,
               FlaxRoFormerForMultipleChoice,
               FlaxRoFormerForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )


       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	FlaxRoFormerModelTester(self	)




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



            '''simple docstring'''




            for model_class_name in self.all_model_classes:
                 __snake_case            =	model_class_name.from_pretrained('''junnyu/roformer_chinese_small'''     ,	from_pt=__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	model(np.ones((1, 1)	)	)
                 self.assertIsNotNone(__SCREAMING_SNAKE_CASE	)





@require_flax
class      lowerCAmelCase (			unittest.TestCase):


       @slow
       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''	)
            __snake_case            =	jnp.array([[0, 1, 2, 3, 4, 5]]	)
            __snake_case            =	model(__SCREAMING_SNAKE_CASE	)[0]

            __snake_case            =	5_0000

            __snake_case            =	(1, 6, vocab_size)
            self.assertEqual(output.shape     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	jnp.array(
                [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]]	)

            self.assertTrue(jnp.allclose(output[:, :3, :3]     ,	__SCREAMING_SNAKE_CASE     ,	atol=1E-4	)	)






