


import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
    import tensorflow as tf
    from tensorflow.python.eager import context
    from tensorflow.python.framework import ops

    from transformers import GradientAccumulator, create_optimizer







@require_tf
class     _lowercase				(							unittest.TestCase):


       """simple docstring"""




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


             '''simple docstring'''



             self.assertEqual(len(__lowerCamelCase )      ,   len(__lowerCamelCase ) )
             for a, b in zip(__lowerCamelCase      ,   __lowerCamelCase ):
                   self.assertAlmostEqual(__lowerCamelCase      ,   __lowerCamelCase      ,   delta=__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						GradientAccumulator()
             accumulator([tf.constant([1.0, 2.0] )] )
             accumulator([tf.constant([-2.0, 1.0] )] )
             accumulator([tf.constant([-1.0, 2.0] )] )
             with self.assertRaises(__lowerCamelCase ):
                   accumulator([tf.constant([1.0, 1.0] ), tf.constant([2.0, 2.0] )] )
             self.assertEqual(accumulator.step      ,   3 )
             self.assertEqual(len(accumulator.gradients )      ,   1 )
             self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()      ,   [-2.0, 5.0]      ,   tol=1E-2 )
             accumulator.reset()
             self.assertEqual(accumulator.step      ,   0 )
             self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()      ,   [0.0, 0.0]      ,   tol=1E-2 )





       def 		lowerCAmelCase  (     self							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						None
             ops.enable_eager_execution_internal()
             lowerCamelCase__	:       Optional[Any]					=						tf.config.list_physical_devices("CPU" )
             if len(__lowerCamelCase ) == 1:
                   tf.config.set_logical_device_configuration(
                       physical_devices[0]      ,   [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()] )
             lowerCamelCase__	:       Optional[Any]					=						tf.config.list_logical_devices(device_type="CPU" )
             lowerCamelCase__	:       str					=						tf.distribute.MirroredStrategy(devices=devices[:2] )

             with strategy.scope():
                   lowerCamelCase__	:       Tuple					=						GradientAccumulator()
                   lowerCamelCase__	:       Union[str, Any]					=						tf.Variable([4.0, 3.0] )
                   lowerCamelCase__					,     lowerCamelCase__	:       Union[str, Any]					=						create_optimizer(5E-5      ,   10      ,   5 )
                   lowerCamelCase__	:       str					=						tf.Variable([0.0, 0.0]      ,   trainable=__lowerCamelCase )

             def accumulate_on_replica(__lowerCamelCase							:    Optional[int] ):
                   accumulator([gradient] )

             def apply_on_replica():
                   optimizer.apply_gradients(list(zip(accumulator.gradients      ,   [variable] ) ) )

             @tf.function
             def accumulate(__lowerCamelCase							:    Optional[int]      ,   __lowerCamelCase							:    List[str] ):
                   with strategy.scope():
                         lowerCamelCase__	:       int					=						strategy.experimental_local_results(__lowerCamelCase )
                         local_variables[0].assign(__lowerCamelCase )
                         local_variables[1].assign(__lowerCamelCase )
                         strategy.run(__lowerCamelCase      ,   args=(gradient_placeholder,) )

             @tf.function
             def apply_grad():
                   with strategy.scope():
                         strategy.run(__lowerCamelCase )

             def _check_local_values(__lowerCamelCase							:    str      ,   __lowerCamelCase							:    Tuple ):
                   lowerCamelCase__	:       Optional[Any]					=						strategy.experimental_local_results(accumulator._gradients[0] )
                   self.assertListAlmostEqual(values[0].value()      ,   __lowerCamelCase      ,   tol=1E-2 )
                   self.assertListAlmostEqual(values[1].value()      ,   __lowerCamelCase      ,   tol=1E-2 )

             accumulate([1.0, 2.0]      ,   [-1.0, 1.0] )
             accumulate([3.0, -1.0]      ,   [-1.0, -1.0] )
             accumulate([-2.0, 2.0]      ,   [3.0, -2.0] )
             self.assertEqual(accumulator.step      ,   3 )
             _check_local_values([2.0, 3.0]      ,   [1.0, -2.0] )
             apply_grad()
             self.assertListAlmostEqual(variable.value()      ,   [4.0, 3.0]      ,   tol=1E-2 )
             accumulator.reset()
             self.assertEqual(accumulator.step      ,   0 )
             _check_local_values([0.0, 0.0]      ,   [0.0, 0.0] )






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__)


