




"""simple docstring"""





import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A   =       """▁"""

A   =       get_tests_dir("""fixtures/test_sentencepiece.model""")



@require_sentencepiece
class      a__				(	__magic_name__							,    unittest.TestCase   ):
			lowercase_    =	BertGenerationTokenizer
			lowercase_    =	False
			lowercase_    =	True


			def       a_     (      self      :    str):


						"""simple docstring"""




						super().setUp()

						__UpperCAmelCase	: str		=					BertGenerationTokenizer(UpperCamelCase_      ,  keep_accents=UpperCamelCase_)
						tokenizer.save_pretrained(self.tmpdirname)


			def       a_     (      self      :    List[str]):


						"""simple docstring"""




						__UpperCAmelCase	: List[Any]		=					"<s>"
						__UpperCAmelCase	: Union[str, Any]		=					1

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase_)      ,  UpperCamelCase_)
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase_)      ,  UpperCamelCase_)


			def       a_     (      self      :    str):


						"""simple docstring"""




						__UpperCAmelCase	: Any		=					list(self.get_tokenizer().get_vocab().keys())

						self.assertEqual(vocab_keys[0]      ,  "<unk>")
						self.assertEqual(vocab_keys[1]      ,  "<s>")
						self.assertEqual(vocab_keys[-1]      ,  "<pad>")
						self.assertEqual(len(UpperCamelCase_)      ,  1002)


			def       a_     (      self      :    List[str]):


						"""simple docstring"""




						self.assertEqual(self.get_tokenizer().vocab_size      ,  1000)


			def       a_     (      self      :    Tuple):


						"""simple docstring"""




						__UpperCAmelCase	: Union[str, Any]		=					BertGenerationTokenizer(UpperCamelCase_      ,  keep_accents=UpperCamelCase_)

						__UpperCAmelCase	: Tuple		=					tokenizer.tokenize("This is a test")
						self.assertListEqual(UpperCamelCase_      ,  ["▁This", "▁is", "▁a", "▁t", "est"])

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(UpperCamelCase_)      ,  [285, 46, 10, 170, 382]      ,  )

						__UpperCAmelCase	: Any		=					tokenizer.tokenize("I was born in 92000, and this is falsé.")
						self.assertListEqual(
						    UpperCamelCase_      ,  [
						        SPIECE_UNDERLINE + "I",
						        SPIECE_UNDERLINE + "was",
						        SPIECE_UNDERLINE + "b",
						        "or",
						        "n",
						        SPIECE_UNDERLINE + "in",
						        SPIECE_UNDERLINE + "",
						        "9",
						        "2",
						        "0",
						        "0",
						        "0",
						        ",",
						        SPIECE_UNDERLINE + "and",
						        SPIECE_UNDERLINE + "this",
						        SPIECE_UNDERLINE + "is",
						        SPIECE_UNDERLINE + "f",
						        "al",
						        "s",
						        "é",
						        ".",
						    ]      ,  )
						__UpperCAmelCase	: Dict		=					tokenizer.convert_tokens_to_ids(UpperCamelCase_)
						self.assertListEqual(
						    UpperCamelCase_      ,  [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]      ,  )

						__UpperCAmelCase	: List[str]		=					tokenizer.convert_ids_to_tokens(UpperCamelCase_)
						self.assertListEqual(
						    UpperCamelCase_      ,  [
						        SPIECE_UNDERLINE + "I",
						        SPIECE_UNDERLINE + "was",
						        SPIECE_UNDERLINE + "b",
						        "or",
						        "n",
						        SPIECE_UNDERLINE + "in",
						        SPIECE_UNDERLINE + "",
						        "<unk>",
						        "2",
						        "0",
						        "0",
						        "0",
						        ",",
						        SPIECE_UNDERLINE + "and",
						        SPIECE_UNDERLINE + "this",
						        SPIECE_UNDERLINE + "is",
						        SPIECE_UNDERLINE + "f",
						        "al",
						        "s",
						        "<unk>",
						        ".",
						    ]      ,  )


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


						"""simple docstring"""




						return BertGenerationTokenizer.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder")


			@slow
			def       a_     (      self      :    Tuple):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					"Hello World!"
						__UpperCAmelCase	: str		=					[18536, 2260, 101]

						self.assertListEqual(UpperCamelCase_      ,  self.big_tokenizer.encode(UpperCamelCase_))


			@slow
			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: List[str]		=					(
						    "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
						    " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
						)
						__UpperCAmelCase	: Optional[int]		=					[
						    871,
						    419,
						    358,
						    946,
						    991,
						    2521,
						    452,
						    358,
						    1357,
						    387,
						    7751,
						    3536,
						    112,
						    985,
						    456,
						    126,
						    865,
						    938,
						    5400,
						    5734,
						    458,
						    1368,
						    467,
						    786,
						    2462,
						    5246,
						    1159,
						    633,
						    865,
						    4519,
						    457,
						    582,
						    852,
						    2557,
						    427,
						    916,
						    508,
						    405,
						    34324,
						    497,
						    391,
						    408,
						    11342,
						    1244,
						    385,
						    100,
						    938,
						    985,
						    456,
						    574,
						    362,
						    12597,
						    3200,
						    3129,
						    1172,
						]

						self.assertListEqual(UpperCamelCase_      ,  self.big_tokenizer.encode(UpperCamelCase_))


			@require_torch
			@slow
			def       a_     (      self      :    Optional[Any]):


						"""simple docstring"""




						import torch

						from transformers import BertGenerationConfig, BertGenerationEncoder

						# Build sequence
						__UpperCAmelCase	: Any		=					list(self.big_tokenizer.get_vocab().keys())[:10]
						__UpperCAmelCase	: List[Any]		=					" ".join(UpperCamelCase_)
						__UpperCAmelCase	: Optional[Any]		=					self.big_tokenizer.encode_plus(UpperCamelCase_      ,  return_tensors="pt"      ,  return_token_type_ids=UpperCamelCase_)
						__UpperCAmelCase	: List[str]		=					self.big_tokenizer.batch_encode_plus(
						    [sequence + " " + sequence]      ,  return_tensors="pt"      ,  return_token_type_ids=UpperCamelCase_)

						__UpperCAmelCase	: Tuple		=					BertGenerationConfig()
						__UpperCAmelCase	: List[str]		=					BertGenerationEncoder(UpperCamelCase_)

						assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

						with torch.no_grad():
									model(**UpperCamelCase_)
									model(**UpperCamelCase_)


			@slow
			def       a_     (      self      :    Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase	: List[str]		=					{"input_ids": [[39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114], [448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
						# fmt: on

						self.tokenizer_integration_test_util(
						    expected_encoding=UpperCamelCase_      ,  model_name="google/bert_for_seq_generation_L-24_bbc_encoder"      ,  revision="c817d1fd1be2ffa69431227a1fe320544943d4db"      ,  )








"""simple docstring"""





import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow



@require_torch
class      a__				(	unittest.TestCase   ):


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: Union[str, Any]		=					"ylacombe/bark-small"
						__UpperCAmelCase	: Any		=					tempfile.mkdtemp()
						__UpperCAmelCase	: List[str]		=					"en_speaker_1"
						__UpperCAmelCase	: List[Any]		=					"This is a test string"
						__UpperCAmelCase	: List[str]		=					"speaker_embeddings_path.json"
						__UpperCAmelCase	: Tuple		=					"speaker_embeddings"


			def       a_     (      self      :    Optional[int]      ,  **UpperCamelCase_      :    Optional[int]):


						"""simple docstring"""




						return AutoTokenizer.from_pretrained(self.checkpoint      ,  **UpperCamelCase_)


			def       a_     (      self      :    Dict):


						"""simple docstring"""




						shutil.rmtree(self.tmpdirname)


			def       a_     (      self      :    int):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					self.get_tokenizer()

						__UpperCAmelCase	: Optional[Any]		=					BarkProcessor(tokenizer=UpperCamelCase_)

						processor.save_pretrained(self.tmpdirname)
						__UpperCAmelCase	: Optional[int]		=					BarkProcessor.from_pretrained(self.tmpdirname)

						self.assertEqual(processor.tokenizer.get_vocab()      ,  tokenizer.get_vocab())


			@slow
			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					BarkProcessor.from_pretrained(
						    pretrained_processor_name_or_path=self.checkpoint      ,  speaker_embeddings_dict_path=self.speaker_embeddings_dict_path      ,  )
						processor.save_pretrained(
						    self.tmpdirname      ,  speaker_embeddings_dict_path=self.speaker_embeddings_dict_path      ,  speaker_embeddings_directory=self.speaker_embeddings_directory      ,  )

						__UpperCAmelCase	: Optional[Any]		=					self.get_tokenizer(bos_token="(BOS)"      ,  eos_token="(EOS)")

						__UpperCAmelCase	: Optional[Any]		=					BarkProcessor.from_pretrained(
						    self.tmpdirname      ,  self.speaker_embeddings_dict_path      ,  bos_token="(BOS)"      ,  eos_token="(EOS)"      ,  )

						self.assertEqual(processor.tokenizer.get_vocab()      ,  tokenizer_add_kwargs.get_vocab())


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


						"""simple docstring"""




						__UpperCAmelCase	: List[Any]		=					BarkProcessor.from_pretrained(
						    pretrained_processor_name_or_path=self.checkpoint      ,  speaker_embeddings_dict_path=self.speaker_embeddings_dict_path      ,  )

						__UpperCAmelCase	: Optional[int]		=					35
						__UpperCAmelCase	: Union[str, Any]		=					2
						__UpperCAmelCase	: List[Any]		=					8

						__UpperCAmelCase	: str		=					{
						    "semantic_prompt": np.ones(UpperCamelCase_),
						    "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len)),
						    "fine_prompt": np.ones((nb_codebooks_total, seq_len)),
						}

						# test providing already loaded voice_preset
						__UpperCAmelCase	: Union[str, Any]		=					processor(text=self.input_string      ,  voice_preset=UpperCamelCase_)

						__UpperCAmelCase	: str		=					inputs["history_prompt"]
						for key in voice_preset:
									self.assertListEqual(voice_preset[key].tolist()      ,  processed_voice_preset.get(UpperCamelCase_      ,  np.array([])).tolist())

						# test loading voice preset from npz file
						__UpperCAmelCase	: int		=					os.path.join(self.tmpdirname      ,  "file.npz")
						np.savez(UpperCamelCase_      ,  **UpperCamelCase_)
						__UpperCAmelCase	: List[Any]		=					processor(text=self.input_string      ,  voice_preset=UpperCamelCase_)
						__UpperCAmelCase	: List[str]		=					inputs["history_prompt"]

						for key in voice_preset:
									self.assertListEqual(voice_preset[key].tolist()      ,  processed_voice_preset.get(UpperCamelCase_      ,  np.array([])).tolist())

						# test loading voice preset from the hub
						__UpperCAmelCase	: Tuple		=					processor(text=self.input_string      ,  voice_preset=self.voice_preset)


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: Tuple		=					self.get_tokenizer()

						__UpperCAmelCase	: Any		=					BarkProcessor(tokenizer=UpperCamelCase_)

						__UpperCAmelCase	: List[Any]		=					processor(text=self.input_string)

						__UpperCAmelCase	: List[Any]		=					tokenizer(
						    self.input_string      ,  padding="max_length"      ,  max_length=256      ,  add_special_tokens=UpperCamelCase_      ,  return_attention_mask=UpperCamelCase_      ,  return_token_type_ids=UpperCamelCase_      ,  )

						for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key]      ,  encoded_processor[key].squeeze().tolist())


