

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 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




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





   def      _snake_case		(       self					:			str			):
       SCREAMING_SNAKE_CASE										=   ids_tensor([self.batch_size, self.seq_length]     , self.vocab_size			)

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

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

       SCREAMING_SNAKE_CASE										=   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=__lowerCamelCase     , initializer_range=self.initializer_range     , )

       return config, input_ids, token_type_ids, attention_mask





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   config_and_inputs
       SCREAMING_SNAKE_CASE										=   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
       return config, inputs_dict





@require_flax
class 			_SCREAMING_SNAKE_CASE (   __snake_case					,       unittest.TestCase		):







   '''simple docstring'''




   lowerCamelCase__				=      (
       (
           FlaxAlbertModel,
           FlaxAlbertForPreTraining,
           FlaxAlbertForMaskedLM,
           FlaxAlbertForMultipleChoice,
           FlaxAlbertForQuestionAnswering,
           FlaxAlbertForSequenceClassification,
           FlaxAlbertForTokenClassification,
           FlaxAlbertForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )





   def      _snake_case		(       self					:			List[str]			):
       SCREAMING_SNAKE_CASE										=   FlaxAlbertModelTester(self			)





   @slow
   def      _snake_case		(       self					:			Any			):
       for model_class_name in self.all_model_classes:
           SCREAMING_SNAKE_CASE										=   model_class_name.from_pretrained("albert-base-v2"			)
           SCREAMING_SNAKE_CASE										=   model(np.ones((1, 1)			)			)
           self.assertIsNotNone(__lowerCamelCase			)





@require_flax
class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   @slow
   def      _snake_case		(       self					:			str			):
       SCREAMING_SNAKE_CASE										=   FlaxAlbertModel.from_pretrained("albert-base-v2"			)
       SCREAMING_SNAKE_CASE										=   np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]			)
       SCREAMING_SNAKE_CASE										=   np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]			)
       SCREAMING_SNAKE_CASE										=   model(__lowerCamelCase     , attention_mask=__lowerCamelCase			)[0]
       SCREAMING_SNAKE_CASE										=   (1, 11, 768)
       self.assertEqual(output.shape     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   np.array(
           [[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]]			)

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

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_snake_case      :							Union[str, Any]     	=    {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case      :							Union[str, Any]     	=    [
						    "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "WavLMForAudioFrameClassification",
						    "WavLMForCTC",
						    "WavLMForSequenceClassification",
						    "WavLMForXVector",
						    "WavLMModel",
						    "WavLMPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_wavlm import (
												    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    WavLMForAudioFrameClassification,
												    WavLMForCTC,
												    WavLMForSequenceClassification,
												    WavLMForXVector,
												    WavLMModel,
												    WavLMPreTrainedModel,
												)

else:
						import sys

						_snake_case      :							str     	=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
