


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     _lowercase				(							unittest.TestCase):


       """simple docstring"""




       def __init__(     self							:    Any      ,   __lowerCamelCase							:    int      ,   __lowerCamelCase							:    str=13      ,   __lowerCamelCase							:    List[Any]=7      ,   __lowerCamelCase							:    str=True      ,   __lowerCamelCase							:    int=True      ,   __lowerCamelCase							:    Any=True      ,   __lowerCamelCase							:    Optional[int]=True      ,   __lowerCamelCase							:    Union[str, Any]=99      ,   __lowerCamelCase							:    Optional[int]=32      ,   __lowerCamelCase							:    Any=5      ,   __lowerCamelCase							:    Dict=4      ,   __lowerCamelCase							:    Optional[Any]=37      ,   __lowerCamelCase							:    int="gelu"      ,   __lowerCamelCase							:    Union[str, Any]=0.1      ,   __lowerCamelCase							:    Optional[int]=0.1      ,   __lowerCamelCase							:    int=512      ,   __lowerCamelCase							:    List[Any]=16      ,   __lowerCamelCase							:    int=2      ,   __lowerCamelCase							:    List[Any]=0.0_2      ,   __lowerCamelCase							:    Optional[Any]=4      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						parent
             lowerCamelCase__	:       Optional[int]					=						batch_size
             lowerCamelCase__	:       List[Any]					=						seq_length
             lowerCamelCase__	:       str					=						is_training
             lowerCamelCase__	:       Dict					=						use_attention_mask
             lowerCamelCase__	:       List[str]					=						use_token_type_ids
             lowerCamelCase__	:       Tuple					=						use_labels
             lowerCamelCase__	:       List[str]					=						vocab_size
             lowerCamelCase__	:       Optional[Any]					=						hidden_size
             lowerCamelCase__	:       Optional[Any]					=						num_hidden_layers
             lowerCamelCase__	:       List[Any]					=						num_attention_heads
             lowerCamelCase__	:       Dict					=						intermediate_size
             lowerCamelCase__	:       int					=						hidden_act
             lowerCamelCase__	:       Optional[int]					=						hidden_dropout_prob
             lowerCamelCase__	:       str					=						attention_probs_dropout_prob
             lowerCamelCase__	:       Tuple					=						max_position_embeddings
             lowerCamelCase__	:       Dict					=						type_vocab_size
             lowerCamelCase__	:       Optional[Any]					=						type_sequence_label_size
             lowerCamelCase__	:       Any					=						initializer_range
             lowerCamelCase__	:       Union[str, Any]					=						num_choices





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


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						ids_tensor([self.batch_size, self.seq_length]      ,   self.vocab_size )

             lowerCamelCase__	:       Dict					=						None
             if self.use_attention_mask:
                   lowerCamelCase__	:       List[str]					=						random_attention_mask([self.batch_size, self.seq_length] )

             lowerCamelCase__	:       Optional[int]					=						None
             if self.use_token_type_ids:
                   lowerCamelCase__	:       Tuple					=						ids_tensor([self.batch_size, self.seq_length]      ,   self.type_vocab_size )

             lowerCamelCase__	:       Optional[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=__lowerCamelCase      ,   initializer_range=self.initializer_range      ,   )

             return config, input_ids, token_type_ids, attention_mask





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						self.prepare_config_and_inputs()
             lowerCamelCase__					,     lowerCamelCase__					,     lowerCamelCase__					,     lowerCamelCase__	:       List[Any]					=						config_and_inputs
             lowerCamelCase__	:       List[str]					=						{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
             return config, inputs_dict







@require_flax
class     _lowercase				(							lowercase__							,						unittest.TestCase):


       """simple docstring"""




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





       def 		lowerCAmelCase  (     self							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						FlaxAlbertModelTester(self )





       @slow
       def 		lowerCAmelCase  (     self							:    Optional[Any] ):


             '''simple docstring'''



             for model_class_name in self.all_model_classes:
                   lowerCamelCase__	:       Union[str, Any]					=						model_class_name.from_pretrained("albert-base-v2" )
                   lowerCamelCase__	:       Dict					=						model(np.ones((1, 1) ) )
                   self.assertIsNotNone(__lowerCamelCase )







@require_flax
class     _lowercase				(							unittest.TestCase):


       """simple docstring"""




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


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						FlaxAlbertModel.from_pretrained("albert-base-v2" )
             lowerCamelCase__	:       Optional[int]					=						np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
             lowerCamelCase__	:       List[str]					=						np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
             lowerCamelCase__	:       Union[str, Any]					=						model(__lowerCamelCase      ,   attention_mask=__lowerCamelCase )[0]
             lowerCamelCase__	:       Tuple					=						(1, 11, 768)
             self.assertEqual(output.shape      ,   __lowerCamelCase )
             lowerCamelCase__	:       Any					=						np.array(
                 [[[-0.6_5_1_3, 1.5_0_3_5, -0.2_7_6_6], [-0.6_5_1_5, 1.5_0_4_6, -0.2_7_8_0], [-0.6_5_1_2, 1.5_0_4_9, -0.2_7_8_4]]] )

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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


A   :					Optional[int]	=      {
    "configuration_speecht5": [
        "SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP",
        "SpeechT5Config",
        "SpeechT5HifiGanConfig",
    ],
    "feature_extraction_speecht5": ["SpeechT5FeatureExtractor"],
    "processing_speecht5": ["SpeechT5Processor"],
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A   :					List[Any]	=      ["SpeechT5Tokenizer"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A   :					List[str]	=      [
        "SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST",
        "SpeechT5ForSpeechToText",
        "SpeechT5ForSpeechToSpeech",
        "SpeechT5ForTextToSpeech",
        "SpeechT5Model",
        "SpeechT5PreTrainedModel",
        "SpeechT5HifiGan",
    ]

if TYPE_CHECKING:
    from .configuration_speechta import (
        SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
        SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
        SpeechTaConfig,
        SpeechTaHifiGanConfig,
    )
    from .feature_extraction_speechta import SpeechTaFeatureExtractor
    from .processing_speechta import SpeechTaProcessor

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_speechta import SpeechTaTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_speechta import (
            SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
            SpeechTaForSpeechToSpeech,
            SpeechTaForSpeechToText,
            SpeechTaForTextToSpeech,
            SpeechTaHifiGan,
            SpeechTaModel,
            SpeechTaPreTrainedModel,
        )

else:
    import sys

    A   :					Any	=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


