import unittest

import numpy as np

from transformers import AlbertConfig, 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.albert.modeling_flax_albert import (
        FlaxAlbertForMaskedLM,
        FlaxAlbertForMultipleChoice,
        FlaxAlbertForPreTraining,
        FlaxAlbertForQuestionAnswering,
        FlaxAlbertForSequenceClassification,
        FlaxAlbertForTokenClassification,
        FlaxAlbertModel,
    )


class 			lowerCamelCase_  (     unittest.TestCase				):






    '''simple docstring'''






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







    def     UpperCamelCase__     (			self) ->			Any:
        __UpperCamelCase  :int    						=	ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size)

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

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

        __UpperCamelCase  :Union[str, Any]    						=	AlbertConfig(
            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=__lowercase				,				initializer_range=self.initializer_range				,				)

        return config, input_ids, token_type_ids, attention_mask







    def     UpperCamelCase__     (			self) ->			int:
        __UpperCamelCase  :Union[str, Any]    						=	self.prepare_config_and_inputs()
        __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase  :List[str]    						=	config_and_inputs
        __UpperCamelCase  :Dict    						=	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
        return config, inputs_dict


@require_flax
class 			lowerCamelCase_  (     UpperCAmelCase_		,			unittest.TestCase				):






    '''simple docstring'''






    a__			:		List[str]      			=   (
        (
            FlaxAlbertModel,
            FlaxAlbertForPreTraining,
            FlaxAlbertForMaskedLM,
            FlaxAlbertForMultipleChoice,
            FlaxAlbertForQuestionAnswering,
            FlaxAlbertForSequenceClassification,
            FlaxAlbertForTokenClassification,
            FlaxAlbertForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )







    def     UpperCamelCase__     (			self) ->			Dict:
        __UpperCamelCase  :Tuple    						=	FlaxAlbertModelTester(self)







    @slow
    def     UpperCamelCase__     (			self) ->			str:
        for model_class_name in self.all_model_classes:
            __UpperCamelCase  :int    						=	model_class_name.from_pretrained('''albert-base-v2''')
            __UpperCamelCase  :Optional[Any]    						=	model(np.ones((1, 1)))
            self.assertIsNotNone(__lowercase)


@require_flax
class 			lowerCamelCase_  (     unittest.TestCase				):






    '''simple docstring'''






    @slow
    def     UpperCamelCase__     (			self) ->			Optional[Any]:
        __UpperCamelCase  :List[Any]    						=	FlaxAlbertModel.from_pretrained('''albert-base-v2''')
        __UpperCamelCase  :Optional[int]    						=	np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]])
        __UpperCamelCase  :Tuple    						=	np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
        __UpperCamelCase  :Union[str, Any]    						=	model(__lowercase				,				attention_mask=__lowercase)[0]
        __UpperCamelCase  :str    						=	(1, 11, 768)
        self.assertEqual(output.shape				,				__lowercase)
        __UpperCamelCase  :str    						=	np.array(
            [[[-0.65_13, 1.50_35, -0.27_66], [-0.65_15, 1.50_46, -0.27_80], [-0.65_12, 1.50_49, -0.27_84]]])

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






import functools
import operator

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


__lowercase											=      logging.get_logger(__name__)

__lowercase											=      {
    '''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_  (     UpperCAmelCase_				):






    '''simple docstring'''






    a__			:		List[Any]      			=   """wavlm"""







    def __init__(			self				,				__lowercase=32				,				__lowercase=768				,				__lowercase=12				,				__lowercase=12				,				__lowercase=3_072				,				__lowercase="gelu"				,				__lowercase=0.1				,				__lowercase=0.1				,				__lowercase=0.1				,				__lowercase=0.0				,				__lowercase=0.1				,				__lowercase=0.1				,				__lowercase=0.02				,				__lowercase=1E-5				,				__lowercase="group"				,				__lowercase="gelu"				,				__lowercase=(512, 512, 512, 512, 512, 512, 512)				,				__lowercase=(5, 2, 2, 2, 2, 2, 2)				,				__lowercase=(10, 3, 3, 3, 3, 2, 2)				,				__lowercase=False				,				__lowercase=128				,				__lowercase=16				,				__lowercase=320				,				__lowercase=800				,				__lowercase=False				,				__lowercase=True				,				__lowercase=0.05				,				__lowercase=10				,				__lowercase=2				,				__lowercase=0.0				,				__lowercase=10				,				__lowercase=320				,				__lowercase=2				,				__lowercase=0.1				,				__lowercase=100				,				__lowercase=256				,				__lowercase=256				,				__lowercase=0.1				,				__lowercase="mean"				,				__lowercase=False				,				__lowercase=False				,				__lowercase=256				,				__lowercase=(512, 512, 512, 512, 1_500)				,				__lowercase=(5, 3, 3, 1, 1)				,				__lowercase=(1, 2, 3, 1, 1)				,				__lowercase=512				,				__lowercase=80				,				__lowercase=0				,				__lowercase=1				,				__lowercase=2				,				__lowercase=False				,				__lowercase=3				,				__lowercase=2				,				__lowercase=3				,				__lowercase=None				,				**__lowercase				,				) ->			Dict:
        super().__init__(**__lowercase				,				pad_token_id=__lowercase				,				bos_token_id=__lowercase				,				eos_token_id=__lowercase)
        __UpperCamelCase  :Any    						=	hidden_size
        __UpperCamelCase  :Tuple    						=	feat_extract_norm
        __UpperCamelCase  :List[str]    						=	feat_extract_activation
        __UpperCamelCase  :int    						=	list(__lowercase)
        __UpperCamelCase  :List[Any]    						=	list(__lowercase)
        __UpperCamelCase  :Union[str, Any]    						=	list(__lowercase)
        __UpperCamelCase  :Optional[Any]    						=	conv_bias
        __UpperCamelCase  :Tuple    						=	num_buckets
        __UpperCamelCase  :Optional[int]    						=	max_bucket_distance
        __UpperCamelCase  :Union[str, Any]    						=	num_conv_pos_embeddings
        __UpperCamelCase  :Optional[Any]    						=	num_conv_pos_embedding_groups
        __UpperCamelCase  :List[Any]    						=	len(self.conv_dim)
        __UpperCamelCase  :Tuple    						=	num_hidden_layers
        __UpperCamelCase  :str    						=	intermediate_size
        __UpperCamelCase  :Union[str, Any]    						=	hidden_act
        __UpperCamelCase  :Optional[int]    						=	num_attention_heads
        __UpperCamelCase  :str    						=	hidden_dropout
        __UpperCamelCase  :int    						=	attention_dropout
        __UpperCamelCase  :Optional[int]    						=	activation_dropout
        __UpperCamelCase  :str    						=	feat_proj_dropout
        __UpperCamelCase  :List[Any]    						=	final_dropout
        __UpperCamelCase  :int    						=	layerdrop
        __UpperCamelCase  :List[Any]    						=	layer_norm_eps
        __UpperCamelCase  :Optional[int]    						=	initializer_range
        __UpperCamelCase  :Any    						=	num_ctc_classes
        __UpperCamelCase  :Optional[int]    						=	vocab_size
        __UpperCamelCase  :List[Any]    						=	do_stable_layer_norm
        __UpperCamelCase  :str    						=	use_weighted_layer_sum
        __UpperCamelCase  :Any    						=	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
        __UpperCamelCase  :Union[str, Any]    						=	apply_spec_augment
        __UpperCamelCase  :Optional[Any]    						=	mask_time_prob
        __UpperCamelCase  :Union[str, Any]    						=	mask_time_length
        __UpperCamelCase  :Optional[int]    						=	mask_time_min_masks
        __UpperCamelCase  :str    						=	mask_feature_prob
        __UpperCamelCase  :Tuple    						=	mask_feature_length

        # parameters for pretraining with codevector quantized representations
        __UpperCamelCase  :Optional[Any]    						=	num_codevectors_per_group
        __UpperCamelCase  :List[Any]    						=	num_codevector_groups
        __UpperCamelCase  :str    						=	contrastive_logits_temperature
        __UpperCamelCase  :Tuple    						=	num_negatives
        __UpperCamelCase  :Any    						=	codevector_dim
        __UpperCamelCase  :Union[str, Any]    						=	proj_codevector_dim
        __UpperCamelCase  :Tuple    						=	diversity_loss_weight

        # ctc loss
        __UpperCamelCase  :int    						=	ctc_loss_reduction
        __UpperCamelCase  :Any    						=	ctc_zero_infinity

        # adapter
        __UpperCamelCase  :List[Any]    						=	add_adapter
        __UpperCamelCase  :Dict    						=	adapter_kernel_size
        __UpperCamelCase  :Any    						=	adapter_stride
        __UpperCamelCase  :Optional[int]    						=	num_adapter_layers
        __UpperCamelCase  :Union[str, Any]    						=	output_hidden_size or hidden_size

        # SequenceClassification-specific parameter. Feel free to ignore for other classes.
        __UpperCamelCase  :int    						=	classifier_proj_size

        # XVector-specific parameters. Feel free to ignore for other classes.
        __UpperCamelCase  :Optional[Any]    						=	list(__lowercase)
        __UpperCamelCase  :Optional[Any]    						=	list(__lowercase)
        __UpperCamelCase  :List[str]    						=	list(__lowercase)
        __UpperCamelCase  :List[Any]    						=	xvector_output_dim







    @property
    def     UpperCamelCase__     (			self) ->			Any:
        return functools.reduce(operator.mul				,				self.conv_stride				,				1)





