






"""simple docstring"""







import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
	__a       :	List[Any]             = 'pt'
elif is_tf_available():
	__a       :	Dict             = 'tf'
else:
	__a       :	Any             = 'jax'







class        _SCREAMING_SNAKE_CASE						(  UpperCAmelCase__       , unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =ByTaTokenizer
							_SCREAMING_SNAKE_CASE    =False




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												super().setUp()
												a__          =  ByTaTokenizer()
												tokenizer.save_pretrained(self.tmpdirname    )




							@cached_property
							def  lowercase		(   self:     Optional[Any]    ):

												'''simple docstring'''

												return ByTaTokenizer.from_pretrained('''google/byt5-small'''    )




							def  lowercase		(   self:     List[Any]				,		**__A:     Any    ):

												'''simple docstring'''

												return self.tokenizer_class.from_pretrained(self.tmpdirname				,		**__A    )




							def  lowercase		(   self:     str				,		__A:     List[Any]				,		__A:     Any=False				,		__A:     Optional[Any]=20				,		__A:     Any=5    ):

												'''simple docstring'''

												a__          =  []
												for i in range(len(__A    )    ):
																	try:
																						a__          =  tokenizer.decode([i]				,		clean_up_tokenization_spaces=__A    )
																	except UnicodeDecodeError:
																						pass
																	toks.append((i, tok)    )

												a__          =  list(filter(lambda __A    : re.match(R'''^[ a-zA-Z]+$'''				,		t[1]    )				,		__A    )    )
												a__          =  list(filter(lambda __A    : [t[0]] == tokenizer.encode(t[1]				,		add_special_tokens=__A    )				,		__A    )    )
												if max_length is not None and len(__A    ) > max_length:
																	a__          =  toks[:max_length]
												if min_length is not None and len(__A    ) < min_length and len(__A    ) > 0:
																	while len(__A    ) < min_length:
																						a__          =  toks + toks
																	# toks_str = [t[1] for t in toks]
												a__          =  [t[0] for t in toks]

												# Ensure consistency
												a__          =  tokenizer.decode(__A				,		clean_up_tokenization_spaces=__A    )
												if " " not in output_txt and len(__A    ) > 1:
																	a__          =  (
																	    tokenizer.decode([toks_ids[0]]				,		clean_up_tokenization_spaces=__A    )
																	    + """ """
																	    + tokenizer.decode(toks_ids[1:]				,		clean_up_tokenization_spaces=__A    )
																	)
												if with_prefix_space:
																	a__          =  """ """ + output_txt
												a__          =  tokenizer.encode(__A				,		add_special_tokens=__A    )
												return output_txt, output_ids




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  self.ta_base_tokenizer
												a__          =  tokenizer(['''hi</s>''', '''I went to the gym</s>''', '''</s>''']    )
												a__          =  tokenizer(['''hi''', '''I went to the gym''', '''''']    )
												self.assertListEqual(batch_with_eos_added['''input_ids''']				,		batch_without_eos_added['''input_ids''']    )




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__          =  self.ta_base_tokenizer
												a__          =  """Unicode €."""
												a__          =  tokenizer(__A    )
												a__          =  [88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
												self.assertEqual(encoded['''input_ids''']				,		__A    )

												# decoding
												a__          =  tokenizer.decode(__A    )
												self.assertEqual(__A				,		'''Unicode €.</s>'''    )

												a__          =  tokenizer('''e è é ê ë'''    )
												a__          =  [104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
												self.assertEqual(encoded['''input_ids''']				,		__A    )
												# decoding
												a__          =  tokenizer.decode(__A    )
												self.assertEqual(__A				,		'''e è é ê ë</s>'''    )

												# encode/decode, but with `encode` instead of `__call__`
												self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë'''    )    )				,		'''e è é ê ë</s>'''    )




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												a__          =  self.ta_base_tokenizer
												a__          =  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
												# fmt: off
												a__          =  [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
												# fmt: on
												a__          =  tokenizer(__A				,		padding=__A				,		return_tensors=__A    )
												self.assertIsInstance(__A				,		__A    )

												if FRAMEWORK != "jax":
																	a__          =  list(batch.input_ids.numpy()[0]    )
												else:
																	a__          =  list(batch.input_ids.tolist()[0]    )

												self.assertListEqual(__A				,		__A    )

												self.assertEqual((2, 37)				,		batch.input_ids.shape    )
												self.assertEqual((2, 37)				,		batch.attention_mask.shape    )




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  self.ta_base_tokenizer
												a__          =  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
												a__          =  tokenizer(__A				,		padding=__A				,		return_tensors=__A    )
												# check if input_ids are returned and no decoder_input_ids
												self.assertIn('''input_ids'''				,		__A    )
												self.assertIn('''attention_mask'''				,		__A    )
												self.assertNotIn('''decoder_input_ids'''				,		__A    )
												self.assertNotIn('''decoder_attention_mask'''				,		__A    )




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  self.ta_base_tokenizer
												a__          =  [
												    """Summary of the text.""",
												    """Another summary.""",
												]
												a__          =  tokenizer(
												    text_target=__A				,		max_length=32				,		padding='''max_length'''				,		truncation=__A				,		return_tensors=__A    )
												self.assertEqual(32				,		targets['''input_ids'''].shape[1]    )




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__          =  self.ta_base_tokenizer
												a__          =  ["""A long paragraph for summarization. </s>"""]
												a__          =  ["""Summary of the text. </s>"""]
												# fmt: off
												a__          =  [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
												a__          =  [86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
												# fmt: on

												a__          =  tokenizer(__A				,		text_target=__A    )

												self.assertEqual(__A				,		batch['''input_ids'''][0]    )
												self.assertEqual(__A				,		batch['''labels'''][0]    )




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												a__          =  self.get_tokenizers()
												for tokenizer in tokenizers:
																	with self.subTest(F'{tokenizer.__class__.__name__}'    ):
																						self.assertNotEqual(tokenizer.model_max_length				,		42    )

																	# Now let's start the test
												a__          =  self.get_tokenizers()
												for tokenizer in tokenizers:
																	with self.subTest(F'{tokenizer.__class__.__name__}'    ):
																						# Isolate this from the other tests because we save additional tokens/etc
																						a__          =  tempfile.mkdtemp()

																						a__          =  """ He is very happy, UNwant\u00E9d,running"""
																						a__          =  tokenizer.encode(__A				,		add_special_tokens=__A    )
																						tokenizer.save_pretrained(__A    )

																						a__          =  tokenizer.__class__.from_pretrained(__A    )
																						a__          =  after_tokenizer.encode(__A				,		add_special_tokens=__A    )
																						self.assertListEqual(__A				,		__A    )

																						shutil.rmtree(__A    )

												a__          =  self.get_tokenizers(model_max_length=42    )
												for tokenizer in tokenizers:
																	with self.subTest(F'{tokenizer.__class__.__name__}'    ):
																						# Isolate this from the other tests because we save additional tokens/etc
																						a__          =  tempfile.mkdtemp()

																						a__          =  """ He is very happy, UNwant\u00E9d,running"""
																						tokenizer.add_tokens(['''bim''', '''bambam''']    )
																						a__          =  tokenizer.additional_special_tokens
																						additional_special_tokens.append('''new_additional_special_token'''    )
																						tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}    )
																						a__          =  tokenizer.encode(__A				,		add_special_tokens=__A    )
																						tokenizer.save_pretrained(__A    )

																						a__          =  tokenizer.__class__.from_pretrained(__A    )
																						a__          =  after_tokenizer.encode(__A				,		add_special_tokens=__A    )
																						self.assertListEqual(__A				,		__A    )
																						self.assertIn('''new_additional_special_token'''				,		after_tokenizer.additional_special_tokens    )
																						self.assertEqual(after_tokenizer.model_max_length				,		42    )

																						a__          =  tokenizer.__class__.from_pretrained(__A				,		model_max_length=43    )
																						self.assertEqual(tokenizer.model_max_length				,		43    )

																						shutil.rmtree(__A    )




							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  []
												if self.test_slow_tokenizer:
																	tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())    )

												if self.test_rust_tokenizer:
																	tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())    )

												for tokenizer_class, tokenizer_utils in tokenizer_list:
																	with tempfile.TemporaryDirectory() as tmp_dir:
																						tokenizer_utils.save_pretrained(__A    )

																						with open(os.path.join(__A				,		'''special_tokens_map.json'''    )				,		encoding='''utf-8'''    ) as json_file:
																											a__          =  json.load(__A    )

																						with open(os.path.join(__A				,		'''tokenizer_config.json'''    )				,		encoding='''utf-8'''    ) as json_file:
																											a__          =  json.load(__A    )

																						a__          =  [F'<extra_id_{i}>' for i in range(125    )]

																						a__          =  added_tokens_extra_ids + [
																						    """an_additional_special_token"""
																						]
																						a__          =  added_tokens_extra_ids + [
																						    """an_additional_special_token"""
																						]

																						with open(os.path.join(__A				,		'''special_tokens_map.json'''    )				,		'''w'''				,		encoding='''utf-8'''    ) as outfile:
																											json.dump(__A				,		__A    )
																						with open(os.path.join(__A				,		'''tokenizer_config.json'''    )				,		'''w'''				,		encoding='''utf-8'''    ) as outfile:
																											json.dump(__A				,		__A    )

																						# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
																						# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
																						# "special_tokens_map.json" files
																						a__          =  tokenizer_class.from_pretrained(
																						    __A				,		)
																						self.assertIn(
																						    '''an_additional_special_token'''				,		tokenizer_without_change_in_init.additional_special_tokens    )
																						# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
																						self.assertEqual(
																						    ['''an_additional_special_token''']				,		tokenizer_without_change_in_init.convert_ids_to_tokens(
																						        tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token''']    )    )				,		)

																						# Now we test that we can change the value of additional_special_tokens in the from_pretrained
																						a__          =  added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''				,		lstrip=__A    )]
																						a__          =  tokenizer_class.from_pretrained(
																						    __A				,		additional_special_tokens=__A				,		)

																						self.assertIn('''a_new_additional_special_token'''				,		tokenizer.additional_special_tokens    )
																						self.assertEqual(
																						    ['''a_new_additional_special_token''']				,		tokenizer.convert_ids_to_tokens(
																						        tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token''']    )    )				,		)




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  []
												if self.test_slow_tokenizer:
																	tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())    )

												if self.test_rust_tokenizer:
																	tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())    )

												for tokenizer_class, tokenizer_utils in tokenizer_list:
																	with tempfile.TemporaryDirectory() as tmp_dir:
																						tokenizer_utils.save_pretrained(__A    )

																						a__          =  tokenizer_class.from_pretrained(__A    )

																						self.assertTrue(tokenizer.decode([255]    ) == ''''''    )




							def  lowercase		(   self:     Optional[Any]    ):

												'''simple docstring'''

												pass




							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												pass




							def  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												pass




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												pass




							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												a__          =  self.get_tokenizers(fast=__A				,		do_lower_case=__A    )
												for tokenizer in tokenizers:
																	with self.subTest(F'{tokenizer.__class__.__name__}'    ):
																						a__          =  ["""t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """x""", """t""", """</s>"""]
																						a__          =  tokenizer.convert_tokens_to_string(__A    )

																						self.assertIsInstance(__A				,		__A    )





							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  self.get_tokenizers()
												for tokenizer in tokenizers:
																	with self.subTest(F'{tokenizer.__class__.__name__}'    ):
																						a__          =  [
																						    """bos_token""",
																						    """eos_token""",
																						    """unk_token""",
																						    """sep_token""",
																						    """pad_token""",
																						    """cls_token""",
																						    """mask_token""",
																						]

																						a__          =  0
																						a__          =  tokenizer.convert_ids_to_tokens(
																						    __A				,		skip_special_tokens=__A    )

																						for attr in attributes_list:
																											setattr(__A				,		attr + '''_id'''				,		__A    )
																											self.assertEqual(getattr(__A				,		__A    )				,		__A    )
																											self.assertEqual(getattr(__A				,		attr + '''_id'''    )				,		__A    )

																											setattr(__A				,		attr + '''_id'''				,		__A    )
																											self.assertEqual(getattr(__A				,		__A    )				,		__A    )
																											self.assertEqual(getattr(__A				,		attr + '''_id'''    )				,		__A    )

																						setattr(__A				,		'''additional_special_tokens_ids'''				,		[]    )
																						self.assertListEqual(getattr(__A				,		'''additional_special_tokens'''    )				,		[]    )
																						self.assertListEqual(getattr(__A				,		'''additional_special_tokens_ids'''    )				,		[]    )

																						setattr(__A				,		'''additional_special_tokens_ids'''				,		[token_id_to_test_setters]    )
																						self.assertListEqual(getattr(__A				,		'''additional_special_tokens'''    )				,		[token_to_test_setters]    )
																						self.assertListEqual(getattr(__A				,		'''additional_special_tokens_ids'''    )				,		[token_id_to_test_setters]    )










"""simple docstring"""







from typing import List, Optional

import numpy as np

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







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='EncodecFeatureExtractor'
							_SCREAMING_SNAKE_CASE    =('T5Tokenizer', 'T5TokenizerFast')




							def __init__(   self:     List[Any]				,		__A:     Any				,		__A:     Dict    ):

												'''simple docstring'''

												super().__init__(__A				,		__A    )
												a__          =  self.feature_extractor
												a__          =  False




							def  lowercase		(   self:     Union[str, Any]				,		__A:     List[Any]=None				,		__A:     Optional[Any]=None				,		__A:     List[Any]=True    ):

												'''simple docstring'''

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




							def __call__(   self:     Union[str, Any]				,		*__A:     int				,		**__A:     Dict    ):

												'''simple docstring'''

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

												a__          =  kwargs.pop('''audio'''				,		__A    )
												a__          =  kwargs.pop('''sampling_rate'''				,		__A    )
												a__          =  kwargs.pop('''text'''				,		__A    )
												if len(__A    ) > 0:
																	a__          =  args[0]
																	a__          =  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__          =  self.tokenizer(__A				,		**__A    )

												if audio is not None:
																	a__          =  self.feature_extractor(__A				,		*__A				,		sampling_rate=__A				,		**__A    )

												if audio is None:
																	return inputs

												elif text is None:
																	return audio_inputs

												else:
																	a__          =  audio_inputs['''input_values''']
																	if "padding_mask" in audio_inputs:
																						a__          =  audio_inputs['''padding_mask''']
																	return inputs




							def  lowercase		(   self:     Union[str, Any]				,		*__A:     List[str]				,		**__A:     Tuple    ):

												'''simple docstring'''

												a__          =  kwargs.pop('''audio'''				,		__A    )
												a__          =  kwargs.pop('''padding_mask'''				,		__A    )

												if len(__A    ) > 0:
																	a__          =  args[0]
																	a__          =  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  lowercase		(   self:     Union[str, Any]				,		*__A:     Optional[int]				,		**__A:     Optional[Any]    ):

												'''simple docstring'''

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





							def  lowercase		(   self:     Union[str, Any]				,		__A:     Dict				,		__A:     Optional = None    ):

												'''simple docstring'''

												a__          =  to_numpy(__A    )
												a__       ,a__       ,a__          =  audio_values.shape

												if padding_mask is None:
																	return list(__A    )

												a__          =  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__          =  seq_len - padding_mask.shape[-1]
												a__          =  1 - self.feature_extractor.padding_value
												a__          =  np.pad(__A				,		((0, 0), (0, difference))				,		'''constant'''				,		constant_values=__A    )

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

												return audio_values


