

from typing import List, Optional

import numpy as np

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






class 	A_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  Union[str, Any]										=				'''EncodecFeatureExtractor'''
							_UpperCAmelCase   :  Dict										=				('''T5Tokenizer''', '''T5TokenizerFast''')
							def __init__(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]):
								super().__init__(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    str 	=			self.feature_extractor
								__lowerCamelCase    :    List[str] 	=			False
							def    lowerCAmelCase					(		self		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      str=None       ,SCREAMING_SNAKE_CASE__		:      Any=None       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=True):
								return self.tokenizer.get_decoder_prompt_ids(task=SCREAMING_SNAKE_CASE__       ,language=SCREAMING_SNAKE_CASE__       ,no_timestamps=SCREAMING_SNAKE_CASE__)
							def __call__(		self		:      str       ,*SCREAMING_SNAKE_CASE__		:      Dict       ,**SCREAMING_SNAKE_CASE__		:      Optional[int]):
								# For backward compatibility
								if self._in_target_context_manager:
									return self.current_processor(*SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Any 	=			kwargs.pop('audio'       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    str 	=			kwargs.pop('sampling_rate'       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[int] 	=			kwargs.pop('text'       ,SCREAMING_SNAKE_CASE__)
								if len(SCREAMING_SNAKE_CASE__) > 0:
									__lowerCamelCase    :    Dict 	=			args[0]
									__lowerCamelCase    :    Union[str, Any] 	=			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:
									__lowerCamelCase    :    List[Any] 	=			self.tokenizer(SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)

								if audio is not None:
									__lowerCamelCase    :    int 	=			self.feature_extractor(SCREAMING_SNAKE_CASE__       ,*SCREAMING_SNAKE_CASE__       ,sampling_rate=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)

								if audio is None:
									return inputs

								elif text is None:
									return audio_inputs

								else:
									__lowerCamelCase    :    Optional[Any] 	=			audio_inputs['input_values']
									if "padding_mask" in audio_inputs:
										__lowerCamelCase    :    Union[str, Any] 	=			audio_inputs['padding_mask']
									return inputs
							def    lowerCAmelCase					(		self		:      Tuple       ,*SCREAMING_SNAKE_CASE__		:      List[Any]       ,**SCREAMING_SNAKE_CASE__		:      List[Any]):
								__lowerCamelCase    :    Union[str, Any] 	=			kwargs.pop('audio'       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Union[str, Any] 	=			kwargs.pop('padding_mask'       ,SCREAMING_SNAKE_CASE__)

								if len(SCREAMING_SNAKE_CASE__) > 0:
									__lowerCamelCase    :    Optional[Any] 	=			args[0]
									__lowerCamelCase    :    Optional[int] 	=			args[1:]

								if audio_values is not None:
									return self._decode_audio(SCREAMING_SNAKE_CASE__       ,padding_mask=SCREAMING_SNAKE_CASE__)
								else:
									return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      int       ,*SCREAMING_SNAKE_CASE__		:      List[str]       ,**SCREAMING_SNAKE_CASE__		:      str):
								return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)


							def    lowerCAmelCase					(		self		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      Optional = None):
								__lowerCamelCase    :    List[str] 	=			to_numpy(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    List[str] 	=			audio_values.shape

								if padding_mask is None:
									return list(SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Any 	=			to_numpy(SCREAMING_SNAKE_CASE__)

								# 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)
								__lowerCamelCase    :    Optional[Any] 	=			seq_len - padding_mask.shape[-1]
								__lowerCamelCase    :    Optional[int] 	=			1 - self.feature_extractor.padding_value
								__lowerCamelCase    :    Dict 	=			np.pad(SCREAMING_SNAKE_CASE__       ,((0, 0), (0, difference))       ,'constant'       ,constant_values=SCREAMING_SNAKE_CASE__)

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

								return audio_values







from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
					import numpy as np
					import tensorflow as tf

					from transformers import TFCamembertModel






@require_tf
@require_sentencepiece
@require_tokenizers
class 	A_		(	unittest.TestCase    ):
							@slow
							def    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase    :    Tuple 	=			TFCamembertModel.from_pretrained('jplu/tf-camembert-base')

								__lowerCamelCase    :    str 	=			tf.convert_to_tensor(
								    [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]       ,dtype=tf.intaa       ,)  # J'aime le camembert !"

								__lowerCamelCase    :    int 	=			model(SCREAMING_SNAKE_CASE__)['last_hidden_state']
								__lowerCamelCase    :    Dict 	=			tf.TensorShape((1, 1_0, 7_6_8))
								self.assertEqual(output.shape       ,SCREAMING_SNAKE_CASE__)
								# compare the actual values for a slice.
								__lowerCamelCase    :    Any 	=			tf.convert_to_tensor(
								    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]       ,dtype=tf.floataa       ,)
								# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
								# camembert.eval()
								# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

								self.assertTrue(np.allclose(output[:, :3, :3].numpy()       ,expected_slice.numpy()       ,atol=1E-4))




