
"""simple docstring"""

import unittest

from transformers import BigBirdConfig, 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

  from transformers.models.big_bird.modeling_flax_big_bird import (
      FlaxBigBirdForCausalLM,
      FlaxBigBirdForMaskedLM,
      FlaxBigBirdForMultipleChoice,
      FlaxBigBirdForPreTraining,
      FlaxBigBirdForQuestionAnswering,
      FlaxBigBirdForSequenceClassification,
      FlaxBigBirdForTokenClassification,
      FlaxBigBirdModel,
  )

class __lowerCAmelCase		(				unittest.TestCase      ):





     def __init__(     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=56     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=99     ,					__UpperCAmelCase=32     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=7     ,					__UpperCAmelCase="gelu_new"     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=512     ,					__UpperCAmelCase=16     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=0.0_2     ,					__UpperCAmelCase=4     ,					__UpperCAmelCase="block_sparse"     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=3     ,					):

      '''simple docstring'''




      __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

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





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __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      =	BigBirdConfig(
          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=__UpperCAmelCase     ,					initializer_range=self.initializer_range     ,					attention_type=self.attention_type     ,					block_size=self.block_size     ,					num_random_blocks=self.num_random_blocks     ,					use_bias=self.use_bias     ,					rescale_embeddings=self.rescale_embeddings     ,					)

      return config, input_ids, token_type_ids, attention_mask







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __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


@require_flax
class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ,  unittest.TestCase      ):
     lowercase						       =						(
         (
             FlaxBigBirdForCausalLM,
             FlaxBigBirdModel,
             FlaxBigBirdForPreTraining,
             FlaxBigBirdForMaskedLM,
             FlaxBigBirdForMultipleChoice,
             FlaxBigBirdForQuestionAnswering,
             FlaxBigBirdForSequenceClassification,
             FlaxBigBirdForTokenClassification,
         )
         if is_flax_available()
         else ()
     )

     lowercase						       =						False
     lowercase						       =						False





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	FlaxBigBirdModelTester(self				)





     @slow
     # copied from `test_modeling_flax_common` because it takes much longer than other models
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      super().test_from_pretrained_save_pretrained()





     @slow
     # copied from `test_modeling_flax_common` because it takes much longer than other models
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      super().test_from_pretrained_with_no_automatic_init()





     @slow
     # copied from `test_modeling_flax_common` because it takes much longer than other models
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      super().test_no_automatic_init()





     @slow
     # copied from `test_modeling_flax_common` because it takes much longer than other models
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      super().test_hidden_states_output()





     @slow
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      for model_class_name in self.all_model_classes:
       __UpperCamelCase      =	model_class_name.from_pretrained('google/bigbird-roberta-base'				)
       self.assertIsNotNone(__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      if self.test_attn_probs:
       super().test_attention_outputs()





     @slow
     # copied from `test_modeling_flax_common` because it takes much longer than other models
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase  ,     __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__				):
        __UpperCamelCase      =	self._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase				)
        __UpperCamelCase      =	model_class(__UpperCAmelCase				)

        @jax.jit
        def model_jitted(__UpperCAmelCase     ,					__UpperCAmelCase=None     ,					**__UpperCAmelCase				):
         return model(input_ids=__UpperCAmelCase     ,					attention_mask=__UpperCAmelCase     ,					**__UpperCAmelCase				)

        with self.subTest('JIT Enabled'				):
         __UpperCamelCase      =	model_jitted(**__UpperCAmelCase				).to_tuple()

        with self.subTest('JIT Disabled'				):
         with jax.disable_jit():
          __UpperCamelCase      =	model_jitted(**__UpperCAmelCase				).to_tuple()

        self.assertEqual(len(__UpperCAmelCase				)     ,					len(__UpperCAmelCase				)				)
        for jitted_output, output in zip(__UpperCAmelCase     ,					__UpperCAmelCase				):
         self.assertEqual(jitted_output.shape     ,					output.shape				)







     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase=1E-5     ,					__UpperCAmelCase="outputs"     ,					__UpperCAmelCase=None				):

      '''simple docstring'''




      if name.startswith('outputs.attentions'				):
       return
      else:
       super().check_pt_flax_outputs(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)





"""simple docstring"""

import functools
import operator

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


UpperCamelCase     :    str       =			logging.get_logger(__name__)

UpperCamelCase     :    Dict       =			{
    "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		(				__SCREAMING_SNAKE_CASE      ):
     lowercase						       =						"wavlm"





     def __init__(     self     ,					__UpperCAmelCase=32     ,					__UpperCAmelCase=768     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=3072     ,					__UpperCAmelCase="gelu"     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.0     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.0_2     ,					__UpperCAmelCase=1E-5     ,					__UpperCAmelCase="group"     ,					__UpperCAmelCase="gelu"     ,					__UpperCAmelCase=(512, 512, 512, 512, 512, 512, 512)     ,					__UpperCAmelCase=(5, 2, 2, 2, 2, 2, 2)     ,					__UpperCAmelCase=(10, 3, 3, 3, 3, 2, 2)     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=128     ,					__UpperCAmelCase=16     ,					__UpperCAmelCase=320     ,					__UpperCAmelCase=800     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=0.0_5     ,					__UpperCAmelCase=10     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=0.0     ,					__UpperCAmelCase=10     ,					__UpperCAmelCase=320     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=100     ,					__UpperCAmelCase=256     ,					__UpperCAmelCase=256     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase="mean"     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=256     ,					__UpperCAmelCase=(512, 512, 512, 512, 1500)     ,					__UpperCAmelCase=(5, 3, 3, 1, 1)     ,					__UpperCAmelCase=(1, 2, 3, 1, 1)     ,					__UpperCAmelCase=512     ,					__UpperCAmelCase=80     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase=1     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=None     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      super().__init__(**__UpperCAmelCase     ,					pad_token_id=__UpperCAmelCase     ,					bos_token_id=__UpperCAmelCase     ,					eos_token_id=__UpperCAmelCase				)
      __UpperCamelCase      =	hidden_size
      __UpperCamelCase      =	feat_extract_norm
      __UpperCamelCase      =	feat_extract_activation
      __UpperCamelCase      =	list(__UpperCAmelCase				)
      __UpperCamelCase      =	list(__UpperCAmelCase				)
      __UpperCamelCase      =	list(__UpperCAmelCase				)
      __UpperCamelCase      =	conv_bias
      __UpperCamelCase      =	num_buckets
      __UpperCamelCase      =	max_bucket_distance
      __UpperCamelCase      =	num_conv_pos_embeddings
      __UpperCamelCase      =	num_conv_pos_embedding_groups
      __UpperCamelCase      =	len(self.conv_dim				)
      __UpperCamelCase      =	num_hidden_layers
      __UpperCamelCase      =	intermediate_size
      __UpperCamelCase      =	hidden_act
      __UpperCamelCase      =	num_attention_heads
      __UpperCamelCase      =	hidden_dropout
      __UpperCamelCase      =	attention_dropout
      __UpperCamelCase      =	activation_dropout
      __UpperCamelCase      =	feat_proj_dropout
      __UpperCamelCase      =	final_dropout
      __UpperCamelCase      =	layerdrop
      __UpperCamelCase      =	layer_norm_eps
      __UpperCamelCase      =	initializer_range
      __UpperCamelCase      =	num_ctc_classes
      __UpperCamelCase      =	vocab_size
      __UpperCamelCase      =	do_stable_layer_norm
      __UpperCamelCase      =	use_weighted_layer_sum
      __UpperCamelCase      =	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      =	apply_spec_augment
      __UpperCamelCase      =	mask_time_prob
      __UpperCamelCase      =	mask_time_length
      __UpperCamelCase      =	mask_time_min_masks
      __UpperCamelCase      =	mask_feature_prob
      __UpperCamelCase      =	mask_feature_length

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

      # ctc loss
      __UpperCamelCase      =	ctc_loss_reduction
      __UpperCamelCase      =	ctc_zero_infinity

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

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

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







     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




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



