
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 			_lowerCAmelCase      (			unittest.TestCase ):




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Optional[Any]:
							self.assertEqual(len(_UpperCamelCase  )       ,       len(_UpperCamelCase  )  )
							for a, b in zip(_UpperCamelCase       ,       _UpperCamelCase  ):
										self.assertAlmostEqual(_UpperCamelCase       ,       _UpperCamelCase       ,       delta=_UpperCamelCase  )




				def   __a		(      self  )       ->   int:
							lowerCAmelCase_      			=			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(_UpperCamelCase  ):
										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   __a		(      self  )       ->   List[str]:
							lowerCAmelCase_      			=			None
							ops.enable_eager_execution_internal()
							lowerCAmelCase_      			=			tf.config.list_physical_devices("CPU"  )
							if len(_UpperCamelCase  ) == 1:
										tf.config.set_logical_device_configuration(
										    physical_devices[0]       ,       [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]  )
							lowerCAmelCase_      			=			tf.config.list_logical_devices(device_type="CPU"  )
							lowerCAmelCase_      			=			tf.distribute.MirroredStrategy(devices=devices[:2]  )

							with strategy.scope():
										lowerCAmelCase_      			=			GradientAccumulator()
										lowerCAmelCase_      			=			tf.Variable([4.0, 3.0]  )
										lowerCAmelCase_			,						lowerCAmelCase_      			=			create_optimizer(5e-5       ,       10       ,       5  )
										lowerCAmelCase_      			=			tf.Variable([0.0, 0.0]       ,       trainable=_UpperCamelCase  )

							def accumulate_on_replica(_UpperCamelCase  ):
										accumulator([gradient]  )

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

							@tf.function
							def accumulate(_UpperCamelCase       ,       _UpperCamelCase  ):
										with strategy.scope():
													lowerCAmelCase_      			=			strategy.experimental_local_results(_UpperCamelCase  )
													local_variables[0].assign(_UpperCamelCase  )
													local_variables[1].assign(_UpperCamelCase  )
													strategy.run(_UpperCamelCase       ,       args=(gradient_placeholder,)  )

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

							def _check_local_values(_UpperCamelCase       ,       _UpperCamelCase  ):
										lowerCAmelCase_      			=			strategy.experimental_local_results(accumulator._gradients[0]  )
										self.assertListAlmostEqual(values[0].value()       ,       _UpperCamelCase       ,       tol=1e-2  )
										self.assertListAlmostEqual(values[1].value()       ,       _UpperCamelCase       ,       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 ...processing_utils import ProcessorMixin





class 			_lowerCAmelCase      (			__a ):
				_lowercase    	='''SpeechT5FeatureExtractor'''
				_lowercase    	='''SpeechT5Tokenizer'''




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   int:
							super().__init__(_UpperCamelCase       ,       _UpperCamelCase  )




				def __call__(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   Optional[int]:
							lowerCAmelCase_      			=			kwargs.pop("audio"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("text"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("text_target"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("audio_target"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("sampling_rate"       ,       _UpperCamelCase  )

							if audio is not None and text is not None:
										raise ValueError(
										    "Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"  )
							if audio_target is not None and text_target is not None:
										raise ValueError(
										    "Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"  )
							if audio is None and audio_target is None and text is None and text_target is None:
										raise ValueError(
										    "You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."  )

							if audio is not None:
										lowerCAmelCase_      			=			self.feature_extractor(_UpperCamelCase       ,       *_UpperCamelCase       ,       sampling_rate=_UpperCamelCase       ,       **_UpperCamelCase  )
							elif text is not None:
										lowerCAmelCase_      			=			self.tokenizer(_UpperCamelCase       ,       **_UpperCamelCase  )
							else:
										lowerCAmelCase_      			=			None

							if audio_target is not None:
										lowerCAmelCase_      			=			self.feature_extractor(audio_target=_UpperCamelCase       ,       *_UpperCamelCase       ,       sampling_rate=_UpperCamelCase       ,       **_UpperCamelCase  )
										lowerCAmelCase_      			=			targets["input_values"]
							elif text_target is not None:
										lowerCAmelCase_      			=			self.tokenizer(_UpperCamelCase       ,       **_UpperCamelCase  )
										lowerCAmelCase_      			=			targets["input_ids"]
							else:
										lowerCAmelCase_      			=			None

							if inputs is None:
										return targets

							if targets is not None:
										lowerCAmelCase_      			=			labels

										lowerCAmelCase_      			=			targets.get("attention_mask"  )
										if decoder_attention_mask is not None:
													lowerCAmelCase_      			=			decoder_attention_mask

							return inputs




				def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   str:
							lowerCAmelCase_      			=			kwargs.pop("input_values"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("input_ids"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("labels"       ,       _UpperCamelCase  )

							if input_values is not None and input_ids is not None:
										raise ValueError("Cannot process both `input_values` and `input_ids` inputs."  )
							if input_values is None and input_ids is None and labels is None:
										raise ValueError(
										    "You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."  )

							if input_values is not None:
										lowerCAmelCase_      			=			self.feature_extractor.pad(_UpperCamelCase       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )
							elif input_ids is not None:
										lowerCAmelCase_      			=			self.tokenizer.pad(_UpperCamelCase       ,       **_UpperCamelCase  )
							else:
										lowerCAmelCase_      			=			None

							if labels is not None:
										if "input_ids" in labels or (isinstance(_UpperCamelCase       ,       _UpperCamelCase  ) and "input_ids" in labels[0]):
													lowerCAmelCase_      			=			self.tokenizer.pad(_UpperCamelCase       ,       **_UpperCamelCase  )
													lowerCAmelCase_      			=			targets["input_ids"]
										else:
													lowerCAmelCase_      			=			self.feature_extractor.feature_size
													lowerCAmelCase_      			=			self.feature_extractor.num_mel_bins
													lowerCAmelCase_      			=			self.feature_extractor.pad(_UpperCamelCase       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )
													lowerCAmelCase_      			=			feature_size_hack
													lowerCAmelCase_      			=			targets["input_values"]
							else:
										lowerCAmelCase_      			=			None

							if inputs is None:
										return targets

							if targets is not None:
										lowerCAmelCase_      			=			labels

										lowerCAmelCase_      			=			targets.get("attention_mask"  )
										if decoder_attention_mask is not None:
													lowerCAmelCase_      			=			decoder_attention_mask

							return inputs




				def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   List[Any]:
							return self.tokenizer.batch_decode(*_UpperCamelCase       ,       **_UpperCamelCase  )




				def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   Optional[Any]:
							return self.tokenizer.decode(*_UpperCamelCase       ,       **_UpperCamelCase  )



