


"""simple docstring"""


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 	_a   (   unittest.TestCase			):
    def   _UpperCamelCase							(			self				:       Dict				,       SCREAMING_SNAKE_CASE__				:       Any				,       SCREAMING_SNAKE_CASE__				:       Union[str, Any]				,       SCREAMING_SNAKE_CASE__				:       Union[str, Any]					):
          self.assertEqual(len(__A					)				,       len(__A					)					)
          for a, b in zip(__A				,       __A					):
                self.assertAlmostEqual(__A				,       __A				,       delta=__A					)
    def   _UpperCamelCase							(			self				:       Optional[Any]					):
          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(__A					):
                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   _UpperCamelCase							(			self				:       List[Any]					):
          lowerCamelCase__							     =		None
          ops.enable_eager_execution_internal()
          lowerCamelCase__							     =		tf.config.list_physical_devices('CPU'					)
          if len(__A					) == 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__							     =		create_optimizer(5e-5				,       10				,       5					)
                lowerCamelCase__							     =		tf.Variable([0.0, 0.0]				,       trainable=__A					)

          def accumulate_on_replica(SCREAMING_SNAKE_CASE__				:       Optional[Any]					):
                accumulator([gradient]					)

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

          @tf.function
          def accumulate(SCREAMING_SNAKE_CASE__				:       Union[str, Any]				,       SCREAMING_SNAKE_CASE__				:       List[str]					):
                with strategy.scope():
                      lowerCamelCase__							     =		strategy.experimental_local_results(__A					)
                      local_variables[0].assign(__A					)
                      local_variables[1].assign(__A					)
                      strategy.run(__A				,       args=(gradient_placeholder,)					)

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

          def _check_local_values(SCREAMING_SNAKE_CASE__				:       List[Any]				,       SCREAMING_SNAKE_CASE__				:       Tuple					):
                lowerCamelCase__							     =		strategy.experimental_local_results(accumulator._gradients[0]					)
                self.assertListAlmostEqual(values[0].value()				,       __A				,       tol=1e-2					)
                self.assertListAlmostEqual(values[1].value()				,       __A				,       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 List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   Union[str, Any]     					=				'''EncodecFeatureExtractor'''
	_lowercase		:   Any     					=				('''T5Tokenizer''', '''T5TokenizerFast''')




	def __init__(     self						:     List[Any]					,      __A						:     Any					,      __A						:     Tuple							):
		super().__init__(__A					,      __A							)
		__A :			Dict		    =			self.feature_extractor
		__A :			List[str]		    =			False




	def        lowerCAmelCase_       (     self						:     Union[str, Any]					,      __A						:     str=None					,      __A						:     Tuple=None					,      __A						:     Dict=True							):
		return self.tokenizer.get_decoder_prompt_ids(task=__A					,      language=__A					,      no_timestamps=__A							)




	def __call__(     self						:     Optional[Any]					,      *__A						:     Tuple					,      **__A						:     Tuple							):
		# For backward compatibility
		if self._in_target_context_manager:
			return self.current_processor(*__A					,      **__A							)

		__A :			str		    =			kwargs.pop("""audio"""					,      __A							)
		__A :			Optional[Any]		    =			kwargs.pop("""sampling_rate"""					,      __A							)
		__A :			int		    =			kwargs.pop("""text"""					,      __A							)
		if len(__A							) > 0:
			__A :			int		    =			args[0]
			__A :			Dict		    =			args[1:]

		if audio is None and text is None:
			raise ValueError("""You need to specify either an `audio` or `text` input to process."""							)

		if text is not None:
			__A :			Dict		    =			self.tokenizer(__A					,      **__A							)

		if audio is not None:
			__A :			Optional[int]		    =			self.feature_extractor(__A					,      *__A					,      sampling_rate=__A					,      **__A							)

		if audio is None:
			return inputs

		elif text is None:
			return audio_inputs

		else:
			__A :			List[Any]		    =			audio_inputs["""input_values"""]
			if "padding_mask" in audio_inputs:
				__A :			int		    =			audio_inputs["""padding_mask"""]
			return inputs




	def        lowerCAmelCase_       (     self						:     List[str]					,      *__A						:     int					,      **__A						:     Tuple							):
		__A :			Optional[int]		    =			kwargs.pop("""audio"""					,      __A							)
		__A :			List[str]		    =			kwargs.pop("""padding_mask"""					,      __A							)

		if len(__A							) > 0:
			__A :			Dict		    =			args[0]
			__A :			Optional[int]		    =			args[1:]

		if audio_values is not None:
			return self._decode_audio(__A					,      padding_mask=__A							)
		else:
			return self.tokenizer.batch_decode(*__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      *__A						:     Dict					,      **__A						:     Any							):
		return self.tokenizer.decode(*__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     Tuple					,      __A						:     Union[str, Any]					,      __A						:     Optional = None							):
		__A :			List[str]		    =			to_numpy(__A							)
		__A    ,				__A    ,				__A :			Tuple		    =			audio_values.shape

		if padding_mask is None:
			return list(__A							)

		__A :			Union[str, Any]		    =			to_numpy(__A							)

		# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
		# token (so that the generated audio values are **not** treated as padded tokens)
		__A :			List[str]		    =			seq_len - padding_mask.shape[-1]
		__A :			Tuple		    =			1 - self.feature_extractor.padding_value
		__A :			Optional[int]		    =			np.pad(__A					,      ((0, 0), (0, difference))					,      """constant"""					,      constant_values=__A							)

		__A :			int		    =			audio_values.tolist()
		for i in range(__A							):
			__A :			str		    =			np.asarray(audio_values[i]							)[
			    padding_mask[i][None, :] != self.feature_extractor.padding_value
			]
			__A :			List[Any]		    =			sliced_audio.reshape(__A					,      -1							)

		return audio_values





