





from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       __snake_case 			=    ['image_processor', 'tokenizer']
       __snake_case 			=    'BlipImageProcessor'
       __snake_case 			=    ('BertTokenizer', 'BertTokenizerFast')




       def __init__(      self    ,						__lowerCamelCase    ,						__lowerCamelCase							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	int						    =							False
            super().__init__(__lowerCamelCase    ,						__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.image_processor




       def __call__(      self    ,						__lowerCamelCase = None    ,						__lowerCamelCase = None    ,						__lowerCamelCase = True    ,						__lowerCamelCase = False    ,						__lowerCamelCase = None    ,						__lowerCamelCase = None    ,						__lowerCamelCase = 0    ,						__lowerCamelCase = None    ,						__lowerCamelCase = None    ,						__lowerCamelCase = False    ,						__lowerCamelCase = False    ,						__lowerCamelCase = False    ,						__lowerCamelCase = False    ,						__lowerCamelCase = False    ,						__lowerCamelCase = True    ,						__lowerCamelCase = None    ,						**__lowerCamelCase    ,						)			->     BatchEncoding:
            if images is None and text is None:
                 raise ValueError("You have to specify either images or text."							)

            # Get only text
            if images is None:
                 _SCREAMING_SNAKE_CASE       :	Dict						    =							self.tokenizer
                 _SCREAMING_SNAKE_CASE       :	Tuple						    =							self.tokenizer(
                     text=__lowerCamelCase    ,						add_special_tokens=__lowerCamelCase    ,						padding=__lowerCamelCase    ,						truncation=__lowerCamelCase    ,						max_length=__lowerCamelCase    ,						stride=__lowerCamelCase    ,						pad_to_multiple_of=__lowerCamelCase    ,						return_attention_mask=__lowerCamelCase    ,						return_overflowing_tokens=__lowerCamelCase    ,						return_special_tokens_mask=__lowerCamelCase    ,						return_offsets_mapping=__lowerCamelCase    ,						return_token_type_ids=__lowerCamelCase    ,						return_length=__lowerCamelCase    ,						verbose=__lowerCamelCase    ,						return_tensors=__lowerCamelCase    ,						**__lowerCamelCase    ,						)
                 return text_encoding

            # add pixel_values
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.image_processor(__lowerCamelCase    ,						return_tensors=__lowerCamelCase							)

            if text is not None:
                 _SCREAMING_SNAKE_CASE       :	str						    =							self.tokenizer(
                     text=__lowerCamelCase    ,						add_special_tokens=__lowerCamelCase    ,						padding=__lowerCamelCase    ,						truncation=__lowerCamelCase    ,						max_length=__lowerCamelCase    ,						stride=__lowerCamelCase    ,						pad_to_multiple_of=__lowerCamelCase    ,						return_attention_mask=__lowerCamelCase    ,						return_overflowing_tokens=__lowerCamelCase    ,						return_special_tokens_mask=__lowerCamelCase    ,						return_offsets_mapping=__lowerCamelCase    ,						return_token_type_ids=__lowerCamelCase    ,						return_length=__lowerCamelCase    ,						verbose=__lowerCamelCase    ,						return_tensors=__lowerCamelCase    ,						**__lowerCamelCase    ,						)
            else:
                 _SCREAMING_SNAKE_CASE       :	List[Any]						    =							None

            if text_encoding is not None:
                 encoding_image_processor.update(__lowerCamelCase							)

            return encoding_image_processor




       def     UpperCamelCase_			(      self    ,						*__lowerCamelCase    ,						**__lowerCamelCase							)			->     Dict:
            return self.tokenizer.batch_decode(*__lowerCamelCase    ,						**__lowerCamelCase							)




       def     UpperCamelCase_			(      self    ,						*__lowerCamelCase    ,						**__lowerCamelCase							)			->     List[Any]:
            return self.tokenizer.decode(*__lowerCamelCase    ,						**__lowerCamelCase							)







       @property
       def     UpperCamelCase_			(      self							)			->     List[str]:
            _SCREAMING_SNAKE_CASE       :	str						    =							self.tokenizer.model_input_names
            _SCREAMING_SNAKE_CASE       :	int						    =							self.image_processor.model_input_names
            return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)






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 			lowerCAmelCase__( unittest.TestCase    ):






       '''simple docstring'''


       def     UpperCamelCase_			(      self							)			->     Dict:
            _SCREAMING_SNAKE_CASE       :	Dict						    =							"ylacombe/bark-small"
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							tempfile.mkdtemp()
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							"en_speaker_1"
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							"This is a test string"
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							"speaker_embeddings_path.json"
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							"speaker_embeddings"




       def     UpperCamelCase_			(      self    ,						**__lowerCamelCase							)			->     str:
            return AutoTokenizer.from_pretrained(self.checkpoint    ,						**__lowerCamelCase							)




       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            shutil.rmtree(self.tmpdirname							)




       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	Any						    =							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE       :	Dict						    =							BarkProcessor(tokenizer=__lowerCamelCase							)

            processor.save_pretrained(self.tmpdirname							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							BarkProcessor.from_pretrained(self.tmpdirname							)

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




       @slow
       def     UpperCamelCase_			(      self							)			->     Optional[Any]:
            _SCREAMING_SNAKE_CASE       :	Dict						    =							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    ,						)

            _SCREAMING_SNAKE_CASE       :	Dict						    =							self.get_tokenizer(bos_token="(BOS)"    ,						eos_token="(EOS)"							)

            _SCREAMING_SNAKE_CASE       :	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     UpperCamelCase_			(      self							)			->     str:
            _SCREAMING_SNAKE_CASE       :	Any						    =							BarkProcessor.from_pretrained(
                pretrained_processor_name_or_path=self.checkpoint    ,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path    ,						)

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							3_5
            _SCREAMING_SNAKE_CASE       :	Any						    =							2
            _SCREAMING_SNAKE_CASE       :	int						    =							8

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							{
                "semantic_prompt": np.ones(__lowerCamelCase							),
                "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len)							),
                "fine_prompt": np.ones((nb_codebooks_total, seq_len)							),
            }

            # test providing already loaded voice_preset
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							processor(text=self.input_string    ,						voice_preset=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							inputs["history_prompt"]
            for key in voice_preset:
                 self.assertListEqual(voice_preset[key].tolist()    ,						processed_voice_preset.get(__lowerCamelCase    ,						np.array([]							)							).tolist()							)

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

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

            # test loading voice preset from the hub
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							processor(text=self.input_string    ,						voice_preset=self.voice_preset							)







       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							BarkProcessor(tokenizer=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	str						    =							processor(text=self.input_string							)

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							tokenizer(
                self.input_string    ,						padding="max_length"    ,						max_length=2_5_6    ,						add_special_tokens=__lowerCamelCase    ,						return_attention_mask=__lowerCamelCase    ,						return_token_type_ids=__lowerCamelCase    ,						)

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