


import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					['''image_processor''', '''tokenizer''']
					A__     =					'''AutoImageProcessor'''
					A__     =					'''AutoTokenizer'''

					def __init__(self       :		List[str] ,   _UpperCAmelCase       :		Dict=None ,   _UpperCAmelCase       :		Union[str, Any]=None ,   **_UpperCAmelCase       :		Union[str, Any]						)		->  str:




							"""simple docstring"""


							lowercase__	     =						None
							if "feature_extractor" in kwargs:
									warnings.warn(
									    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
									    """ instead.""" ,   _UpperCAmelCase ,   )
									lowercase__	     =						kwargs.pop("""feature_extractor"""						)

							lowercase__	     =						image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
									raise ValueError("""You need to specify an `image_processor`."""						)
							if tokenizer is None:
									raise ValueError("""You need to specify a `tokenizer`."""						)

							super().__init__(_UpperCAmelCase ,   _UpperCAmelCase						)
							lowercase__	     =						self.image_processor
							lowercase__	     =						False

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




							"""simple docstring"""


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

							lowercase__	     =						kwargs.pop("""images""" ,   _UpperCAmelCase						)
							lowercase__	     =						kwargs.pop("""text""" ,   _UpperCAmelCase						)
							if len(_UpperCAmelCase						) > 0:
									lowercase__	     =						args[0]
									lowercase__	     =						args[1:]

							if images is None and text is None:
									raise ValueError("""You need to specify either an `images` or `text` input to process."""						)

							if images is not None:
									lowercase__	     =						self.image_processor(_UpperCAmelCase ,   *_UpperCAmelCase ,   **_UpperCAmelCase						)
							if text is not None:
									lowercase__	     =						self.tokenizer(_UpperCAmelCase ,   **_UpperCAmelCase						)

							if text is None:
									return inputs
							elif images is None:
									return encodings
							else:
									lowercase__	     =						encodings["""input_ids"""]
									return inputs

					def     lowerCamelCase__	(self       :		List[str] ,   *_UpperCAmelCase       :		Union[str, Any] ,   **_UpperCAmelCase       :		Dict						)		->  List[Any]:




							"""simple docstring"""


							return self.tokenizer.batch_decode(*_UpperCAmelCase ,   **_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		int ,   *_UpperCAmelCase       :		Union[str, Any] ,   **_UpperCAmelCase       :		str						)		->  Union[str, Any]:




							"""simple docstring"""


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

					@contextmanager
					def     lowerCamelCase__	(self       :		Union[str, Any]						)		->  Tuple:




							"""simple docstring"""


							warnings.warn(
							    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
							    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
							    """your images inputs, or in a separate call."""						)
							lowercase__	     =						True
							lowercase__	     =						self.tokenizer
							yield
							lowercase__	     =						self.image_processor
							lowercase__	     =						False

					def     lowerCamelCase__	(self       :		Dict ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		Optional[int]=False ,   _UpperCAmelCase       :		Tuple=None						)		->  int:




							"""simple docstring"""


							if added_vocab is None:
									lowercase__	     =						self.tokenizer.get_added_vocab()

							lowercase__	     =						{}

							while tokens:
									lowercase__	     =						re.search(r"""<s_(.*?)>""" ,   _UpperCAmelCase ,   re.IGNORECASE						)
									if start_token is None:
											break
									lowercase__	     =						start_token.group(1						)
									lowercase__	     =						re.search(rf'''</s_{key}>''' ,   _UpperCAmelCase ,   re.IGNORECASE						)
									lowercase__	     =						start_token.group()
									if end_token is None:
											lowercase__	     =						tokens.replace(_UpperCAmelCase ,   """"""						)
									else:
											lowercase__	     =						end_token.group()
											lowercase__	     =						re.escape(_UpperCAmelCase						)
											lowercase__	     =						re.escape(_UpperCAmelCase						)
											lowercase__	     =						re.search(f'''{start_token_escaped}(.*?){end_token_escaped}''' ,   _UpperCAmelCase ,   re.IGNORECASE						)
											if content is not None:
													lowercase__	     =						content.group(1						).strip()
													if r"<s_" in content and r"</s_" in content:  # non-leaf node
															lowercase__	     =						self.tokenajson(_UpperCAmelCase ,   is_inner_value=_UpperCAmelCase ,   added_vocab=_UpperCAmelCase						)
															if value:
																	if len(_UpperCAmelCase						) == 1:
																			lowercase__	     =						value[0]
																	lowercase__	     =						value
													else:  # leaf nodes
															lowercase__	     =						[]
															for leaf in content.split(r"""<sep/>"""						):
																	lowercase__	     =						leaf.strip()
																	if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																			lowercase__	     =						leaf[1:-2]  # for categorical special tokens
																	output[key].append(_UpperCAmelCase						)
															if len(output[key]						) == 1:
																	lowercase__	     =						output[key][0]

											lowercase__	     =						tokens[tokens.find(_UpperCAmelCase						) + len(_UpperCAmelCase						) :].strip()
											if tokens[:6] == r"<sep/>":  # non-leaf nodes
													return [output] + self.tokenajson(tokens[6:] ,   is_inner_value=_UpperCAmelCase ,   added_vocab=_UpperCAmelCase						)

							if len(_UpperCAmelCase						):
									return [output] if is_inner_value else output
							else:
									return [] if is_inner_value else {"text_sequence": tokens}

					@property
					def     lowerCamelCase__	(self       :		Any						)		->  Dict:




							"""simple docstring"""


							warnings.warn(
							    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" ,   _UpperCAmelCase ,   )
							return self.image_processor_class


					@property
					def     lowerCamelCase__	(self       :		Union[str, Any]						)		->  Union[str, Any]:




							"""simple docstring"""


							warnings.warn(
							    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" ,   _UpperCAmelCase ,   )
							return self.image_processor





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				):
					'''simple docstring'''






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




							"""simple docstring"""


							lowercase__	     =						"""ylacombe/bark-small"""
							lowercase__	     =						tempfile.mkdtemp()
							lowercase__	     =						"""en_speaker_1"""
							lowercase__	     =						"""This is a test string"""
							lowercase__	     =						"""speaker_embeddings_path.json"""
							lowercase__	     =						"""speaker_embeddings"""

					def     lowerCamelCase__	(self       :		str ,   **_UpperCAmelCase       :		Optional[int]						)		->  str:




							"""simple docstring"""


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

					def     lowerCamelCase__	(self       :		str						)		->  List[str]:




							"""simple docstring"""


							shutil.rmtree(self.tmpdirname						)

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




							"""simple docstring"""


							lowercase__	     =						self.get_tokenizer()

							lowercase__	     =						BarkProcessor(tokenizer=_UpperCAmelCase						)

							processor.save_pretrained(self.tmpdirname						)
							lowercase__	     =						BarkProcessor.from_pretrained(self.tmpdirname						)

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

					@slow
					def     lowerCamelCase__	(self       :		str						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						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 ,   )

							lowercase__	     =						self.get_tokenizer(bos_token="""(BOS)""" ,   eos_token="""(EOS)"""						)

							lowercase__	     =						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     lowerCamelCase__	(self       :		List[str]						)		->  List[Any]:




							"""simple docstring"""


							lowercase__	     =						BarkProcessor.from_pretrained(
							    pretrained_processor_name_or_path=self.checkpoint ,   speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,   )

							lowercase__	     =						35
							lowercase__	     =						2
							lowercase__	     =						8

							lowercase__	     =						{
							    """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
							lowercase__	     =						processor(text=self.input_string ,   voice_preset=_UpperCAmelCase						)

							lowercase__	     =						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
							lowercase__	     =						os.path.join(self.tmpdirname ,   """file.npz"""						)
							np.savez(_UpperCAmelCase ,   **_UpperCAmelCase						)
							lowercase__	     =						processor(text=self.input_string ,   voice_preset=_UpperCAmelCase						)
							lowercase__	     =						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
							lowercase__	     =						processor(text=self.input_string ,   voice_preset=self.voice_preset						)


					def     lowerCamelCase__	(self       :		int						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						self.get_tokenizer()

							lowercase__	     =						BarkProcessor(tokenizer=_UpperCAmelCase						)

							lowercase__	     =						processor(text=self.input_string						)

							lowercase__	     =						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()						)

