

"""simple docstring"""


from typing import List, Optional

import numpy as np

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





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      int         =  """EncodecFeatureExtractor"""
				SCREAMING_SNAKE_CASE_   :      Optional[int]         =  ("""T5Tokenizer""", """T5TokenizerFast""")

				def __init__(	self							:				Any     ,  UpperCamelCase__							:				List[Any]     ,  UpperCamelCase__							:				Optional[Any])->   str:



						'''simple docstring'''


						super().__init__(UpperCamelCase__     ,  UpperCamelCase__)
						__lowerCAmelCase:       int          =			self.feature_extractor
						__lowerCAmelCase:       Union[str, Any]          =			False

				def 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				str=None     ,  UpperCamelCase__							:				Any=None     ,  UpperCamelCase__							:				Tuple=True)->   List[str]:



						'''simple docstring'''


						return self.tokenizer.get_decoder_prompt_ids(task=UpperCamelCase__     ,  language=UpperCamelCase__     ,  no_timestamps=UpperCamelCase__)

				def __call__(	self							:				Dict     ,  *UpperCamelCase__							:				Tuple     ,  **UpperCamelCase__							:				List[str])->   Any:



						'''simple docstring'''


						if self._in_target_context_manager:
								return self.current_processor(*UpperCamelCase__     ,  **UpperCamelCase__)

						__lowerCAmelCase:       Optional[Any]          =			kwargs.pop("audio"     ,  UpperCamelCase__)
						__lowerCAmelCase:       Optional[int]          =			kwargs.pop("sampling_rate"     ,  UpperCamelCase__)
						__lowerCAmelCase:       Optional[int]          =			kwargs.pop("text"     ,  UpperCamelCase__)
						if len(UpperCamelCase__) > 0:
								__lowerCAmelCase:       Union[str, Any]          =			args[0]
								__lowerCAmelCase:       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[str]          =			self.tokenizer(UpperCamelCase__     ,  **UpperCamelCase__)

						if audio is not None:
								__lowerCAmelCase:       List[Any]          =			self.feature_extractor(UpperCamelCase__     ,  *UpperCamelCase__     ,  sampling_rate=UpperCamelCase__     ,  **UpperCamelCase__)

						if audio is None:
								return inputs

						elif text is None:
								return audio_inputs

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

				def 							lowercase_     (	self							:				Optional[Any]     ,  *UpperCamelCase__							:				List[str]     ,  **UpperCamelCase__							:				Optional[Any])->   List[str]:



						'''simple docstring'''


						__lowerCAmelCase:       Tuple          =			kwargs.pop("audio"     ,  UpperCamelCase__)
						__lowerCAmelCase:       Tuple          =			kwargs.pop("padding_mask"     ,  UpperCamelCase__)

						if len(UpperCamelCase__) > 0:
								__lowerCAmelCase:       Optional[Any]          =			args[0]
								__lowerCAmelCase:       str          =			args[1:]

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

				def 							lowercase_     (	self							:				Union[str, Any]     ,  *UpperCamelCase__							:				Optional[int]     ,  **UpperCamelCase__							:				Tuple)->   List[Any]:



						'''simple docstring'''


						return self.tokenizer.decode(*UpperCamelCase__     ,  **UpperCamelCase__)




				def 							lowercase_     (	self							:				List[str]     ,  UpperCamelCase__							:				Union[str, Any]     ,  UpperCamelCase__							:				Optional = None)->   List[np.ndarray]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			to_numpy(UpperCamelCase__)
						__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase:       Optional[Any]          =			audio_values.shape

						if padding_mask is None:
								return list(UpperCamelCase__)

						__lowerCAmelCase:       int          =			to_numpy(UpperCamelCase__)

						# 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:       Tuple          =			seq_len - padding_mask.shape[-1]
						__lowerCAmelCase:       Dict          =			1 - self.feature_extractor.padding_value
						__lowerCAmelCase:       Optional[int]          =			np.pad(UpperCamelCase__     ,  ((0, 0), (0, difference))     ,  "constant"     ,  constant_values=UpperCamelCase__)

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

						return audio_values





"""simple docstring"""


import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class     snake_case	(				__snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_   :      List[str]         =  None
				SCREAMING_SNAKE_CASE_   :      Tuple         =  BloomTokenizerFast
				SCREAMING_SNAKE_CASE_   :      str         =  BloomTokenizerFast
				SCREAMING_SNAKE_CASE_   :      int         =  True
				SCREAMING_SNAKE_CASE_   :      Tuple         =  False
				SCREAMING_SNAKE_CASE_   :      int         =  """tokenizer_file"""
				SCREAMING_SNAKE_CASE_   :      List[str]         =  {"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}

				def 							lowercase_     (	self							:				List[Any])->   Dict:



						'''simple docstring'''


						super().setUp()
						__lowerCAmelCase:       Optional[Any]          =			BloomTokenizerFast.from_pretrained("bigscience/tokenizer")
						tokenizer.save_pretrained(self.tmpdirname)

				def 							lowercase_     (	self							:				List[Any]     ,  **UpperCamelCase__							:				Union[str, Any])->   Optional[Any]:



						'''simple docstring'''


						kwargs.update(self.special_tokens_map)
						return BloomTokenizerFast.from_pretrained(self.tmpdirname     ,  **UpperCamelCase__)

				def 							lowercase_     (	self							:				Union[str, Any])->   Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:       str          =			self.get_rust_tokenizer()

						__lowerCAmelCase:       int          =			["The quick brown fox</s>", "jumps over the lazy dog</s>"]
						__lowerCAmelCase:       List[str]          =			[[2_1_7_5, 2_3_7_1_4, 7_3_1_7_3, 1_4_4_2_5_2, 2], [7_7, 1_3_2_6_1_9, 3_4_7_8, 3_6_8, 1_0_9_5_8_6, 3_5_4_3_3, 2]]

						__lowerCAmelCase:       List[str]          =			tokenizer.batch_encode_plus(UpperCamelCase__)["input_ids"]
						self.assertListEqual(UpperCamelCase__     ,  UpperCamelCase__)

						__lowerCAmelCase:       List[Any]          =			tokenizer.batch_decode(UpperCamelCase__)
						self.assertListEqual(UpperCamelCase__     ,  UpperCamelCase__)

				def 							lowercase_     (	self							:				Optional[Any]     ,  UpperCamelCase__							:				Tuple=6)->   Tuple:



						'''simple docstring'''


						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
										__lowerCAmelCase:       Optional[Any]          =			self.rust_tokenizer_class.from_pretrained(UpperCamelCase__     ,  **UpperCamelCase__)
										# tokenizer_r.pad_token = None # Hotfixing padding = None
										# Simple input
										__lowerCAmelCase:       Dict          =			"This is a simple input"
										__lowerCAmelCase:       str          =			["This is a simple input 1", "This is a simple input 2"]
										__lowerCAmelCase:       int          =			("This is a simple input", "This is a pair")
										__lowerCAmelCase:       Union[str, Any]          =			[
										    ("This is a simple input 1", "This is a simple input 2"),
										    ("This is a simple pair 1", "This is a simple pair 2"),
										]

										# Simple input tests
										try:
												tokenizer_r.encode(UpperCamelCase__     ,  max_length=UpperCamelCase__)
												tokenizer_r.encode_plus(UpperCamelCase__     ,  max_length=UpperCamelCase__)

												tokenizer_r.batch_encode_plus(UpperCamelCase__     ,  max_length=UpperCamelCase__)
												tokenizer_r.encode(UpperCamelCase__     ,  max_length=UpperCamelCase__)
												tokenizer_r.batch_encode_plus(UpperCamelCase__     ,  max_length=UpperCamelCase__)
										except ValueError:
												self.fail("Bloom Tokenizer should be able to deal with padding")

										__lowerCAmelCase:       Tuple          =			None  # Hotfixing padding = None
										self.assertRaises(UpperCamelCase__     ,  tokenizer_r.encode     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length")

										# Simple input
										self.assertRaises(UpperCamelCase__     ,  tokenizer_r.encode_plus     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length")

										# Simple input
										self.assertRaises(
										    UpperCamelCase__     ,  tokenizer_r.batch_encode_plus     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length"     ,  )

										# Pair input
										self.assertRaises(UpperCamelCase__     ,  tokenizer_r.encode     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length")

										# Pair input
										self.assertRaises(UpperCamelCase__     ,  tokenizer_r.encode_plus     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length")

										# Pair input
										self.assertRaises(
										    UpperCamelCase__     ,  tokenizer_r.batch_encode_plus     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length"     ,  )

				def 							lowercase_     (	self							:				Optional[Any])->   List[str]:



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			self.get_rust_tokenizer()
						__lowerCAmelCase:       List[str]          =			load_dataset("xnli"     ,  "all_languages"     ,  split="test"     ,  streaming=UpperCamelCase__)

						__lowerCAmelCase:       Union[str, Any]          =			next(iter(UpperCamelCase__))["premise"]  # pick up one data
						__lowerCAmelCase:       Any          =			list(sample_data.values())

						__lowerCAmelCase:       int          =			list(map(tokenizer.encode     ,  UpperCamelCase__))
						__lowerCAmelCase:       str          =			[tokenizer.decode(UpperCamelCase__     ,  clean_up_tokenization_spaces=UpperCamelCase__) for x in output_tokens]
						self.assertListEqual(UpperCamelCase__     ,  UpperCamelCase__)




				def 							lowercase_     (	self							:				Optional[int])->   str:



						'''simple docstring'''


						self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map)     ,  1)
						self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values())[0])     ,  1)


