


'''simple docstring'''




import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


_lowerCAmelCase  :		Union[str, Any]		     =	logging.get_logger(__name__)



class  lowerCAmelCase				(				a							):
	_lowerCamelCase   :					int			      = """AutoTokenizer"""
	_lowerCamelCase   :					Tuple			      = ["""tokenizer"""]

	_lowerCamelCase   :					str			      = {
	    """semantic_prompt""": 1,
	    """coarse_prompt""": 2,
	    """fine_prompt""": 2,
	}



	def __init__(					self ,					snake_case__ ,					snake_case__=None    ):
		super().__init__(snake_case__    )

		lowerCAmelCase					:    List[str]      							= speaker_embeddings



	@classmethod
	def 				lowercase     (					cls ,					snake_case__ ,					snake_case__="speaker_embeddings_path.json" ,					**snake_case__    ):

		if speaker_embeddings_dict_path is not None:
			lowerCAmelCase					:    Optional[int]      							= get_file_from_repo(
			    snake_case__ ,					snake_case__ ,					subfolder=kwargs.pop('subfolder' ,					snake_case__    ) ,					cache_dir=kwargs.pop('cache_dir' ,					snake_case__    ) ,					force_download=kwargs.pop('force_download' ,					snake_case__    ) ,					proxies=kwargs.pop('proxies' ,					snake_case__    ) ,					resume_download=kwargs.pop('resume_download' ,					snake_case__    ) ,					local_files_only=kwargs.pop('local_files_only' ,					snake_case__    ) ,					use_auth_token=kwargs.pop('use_auth_token' ,					snake_case__    ) ,					revision=kwargs.pop('revision' ,					snake_case__    ) ,					)
			if speaker_embeddings_path is None:
				logger.warning(
				    f"`{os.path.join(snake_case__ ,					snake_case__    )}` does not exists\n                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json\n                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`."    )
				lowerCAmelCase					:    Optional[int]      							= None
			else:
				with open(snake_case__    ) as speaker_embeddings_json:
					lowerCAmelCase					:    Optional[Any]      							= json.load(snake_case__    )
		else:
			lowerCAmelCase					:    Optional[Any]      							= None

		lowerCAmelCase					:    List[str]      							= AutoTokenizer.from_pretrained(snake_case__ ,					**snake_case__    )

		return cls(tokenizer=snake_case__ ,					speaker_embeddings=snake_case__    )



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__="speaker_embeddings_path.json" ,					snake_case__="speaker_embeddings" ,					snake_case__ = False ,					**snake_case__ ,					):
		if self.speaker_embeddings is not None:
			os.makedirs(os.path.join(snake_case__ ,					snake_case__ ,					'v2'    ) ,					exist_ok=snake_case__    )

			lowerCAmelCase					:    str      							= {}

			lowerCAmelCase					:    Optional[Any]      							= save_directory

			for prompt_key in self.speaker_embeddings:
				if prompt_key != "repo_or_path":
					lowerCAmelCase					:    Union[str, Any]      							= self._load_voice_preset(snake_case__    )

					lowerCAmelCase					:    Dict      							= {}
					for key in self.speaker_embeddings[prompt_key]:
						np.save(
						    os.path.join(
						        embeddings_dict['repo_or_path'] ,					snake_case__ ,					f"{prompt_key}_{key}"    ) ,					voice_preset[key] ,					allow_pickle=snake_case__ ,					)
						lowerCAmelCase					:    Optional[Any]      							= os.path.join(snake_case__ ,					f"{prompt_key}_{key}.npy"    )

					lowerCAmelCase					:    Optional[int]      							= tmp_dict

			with open(os.path.join(snake_case__ ,					snake_case__    ) ,					'w'    ) as fp:
				json.dump(snake_case__ ,					snake_case__    )

		super().save_pretrained(snake_case__ ,					snake_case__ ,					**snake_case__    )



	def 				lowercase     (					self ,					snake_case__ = None ,					**snake_case__    ):
		lowerCAmelCase					:    Optional[Any]      							= self.speaker_embeddings[voice_preset]

		lowerCAmelCase					:    Union[str, Any]      							= {}
		for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
			if key not in voice_preset_paths:
				raise ValueError(
				    f"Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}]."    )

			lowerCAmelCase					:    str      							= get_file_from_repo(
			    self.speaker_embeddings.get('repo_or_path' ,					'/'    ) ,					voice_preset_paths[key] ,					subfolder=kwargs.pop('subfolder' ,					snake_case__    ) ,					cache_dir=kwargs.pop('cache_dir' ,					snake_case__    ) ,					force_download=kwargs.pop('force_download' ,					snake_case__    ) ,					proxies=kwargs.pop('proxies' ,					snake_case__    ) ,					resume_download=kwargs.pop('resume_download' ,					snake_case__    ) ,					local_files_only=kwargs.pop('local_files_only' ,					snake_case__    ) ,					use_auth_token=kwargs.pop('use_auth_token' ,					snake_case__    ) ,					revision=kwargs.pop('revision' ,					snake_case__    ) ,					)
			if path is None:
				raise ValueError(
				    f"`{os.path.join(self.speaker_embeddings.get('repo_or_path' ,					'/'    ) ,					voice_preset_paths[key]    )}` does not exists\n                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n                    embeddings."    )

			lowerCAmelCase					:    Any      							= np.load(snake_case__    )

		return voice_preset_dict



	def 				lowercase     (					self ,					snake_case__ = None    ):
		for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
			if key not in voice_preset:
				raise ValueError(f"Voice preset unrecognized, missing {key} as a key."    )

			if not isinstance(voice_preset[key] ,					np.ndarray    ):
				raise ValueError(f"{key} voice preset must be a {str(self.preset_shape[key]    )}D ndarray."    )

			if len(voice_preset[key].shape    ) != self.preset_shape[key]:
				raise ValueError(f"{key} voice preset must be a {str(self.preset_shape[key]    )}D ndarray."    )



	def __call__(					self ,					snake_case__=None ,					snake_case__=None ,					snake_case__="pt" ,					snake_case__=256 ,					snake_case__=False ,					snake_case__=True ,					snake_case__=False ,					**snake_case__ ,					):
		if voice_preset is not None and not isinstance(snake_case__ ,					snake_case__    ):
			if (
			    isinstance(snake_case__ ,					snake_case__    )
			    and self.speaker_embeddings is not None
			    and voice_preset in self.speaker_embeddings
			):
				lowerCAmelCase					:    Any      							= self._load_voice_preset(snake_case__    )

			else:
				if isinstance(snake_case__ ,					snake_case__    ) and not voice_preset.endswith('.npz'    ):
					lowerCAmelCase					:    List[str]      							= voice_preset + '.npz'

				lowerCAmelCase					:    Tuple      							= np.load(snake_case__    )

		if voice_preset is not None:
			self._validate_voice_preset_dict(snake_case__ ,					**snake_case__    )
			lowerCAmelCase					:    Union[str, Any]      							= BatchFeature(data=snake_case__ ,					tensor_type=snake_case__    )

		lowerCAmelCase					:    List[Any]      							= self.tokenizer(
		    snake_case__ ,					return_tensors=snake_case__ ,					padding='max_length' ,					max_length=snake_case__ ,					return_attention_mask=snake_case__ ,					return_token_type_ids=snake_case__ ,					add_special_tokens=snake_case__ ,					**snake_case__ ,					)

		if voice_preset is not None:
			lowerCAmelCase					:    Tuple      							= voice_preset

		return encoded_text




'''simple docstring'''




import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCAmelCase  :		Union[str, Any]		     =	get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model')



@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase				(				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					Tuple			      = GPTSwaTokenizer
	_lowerCamelCase   :					str			      = False
	_lowerCamelCase   :					Dict			      = True
	_lowerCamelCase   :					Optional[Any]			      = False



	def 				lowercase     (					self    ):
		super().setUp()

		# We have a SentencePiece fixture for testing
		lowerCAmelCase					:    Tuple      							= GPTSwaTokenizer(snake_case__ ,					eos_token='<unk>' ,					bos_token='<unk>' ,					pad_token='<unk>'    )

		tokenizer.save_pretrained(self.tmpdirname    )



	def 				lowercase     (					self ,					snake_case__    ):
		lowerCAmelCase					:    List[Any]      							= 'This is a test'
		lowerCAmelCase					:    List[Any]      							= 'This is a test'
		return input_text, output_text



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Tuple      							= '<s>'
		lowerCAmelCase					:    Optional[int]      							= 1

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



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[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] ,					'j'    )
		self.assertEqual(len(snake_case__    ) ,					2000    )



	def 				lowercase     (					self    ):
		self.assertEqual(self.get_tokenizer().vocab_size ,					2000    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[Any]      							= GPTSwaTokenizer(snake_case__    )

		lowerCAmelCase					:    Optional[Any]      							= tokenizer.tokenize('This is a test'    )
		self.assertListEqual(snake_case__ ,					['▁This', '▁is', '▁a', '▁t', 'est']    )

		self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__    ) ,					[465, 287, 265, 631, 842]    )

		lowerCAmelCase					:    Tuple      							= tokenizer.tokenize('I was born in 92000, and this is falsé.'    )
		# fmt: off
		self.assertListEqual(
		    snake_case__ ,					['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.'] ,					)
		# fmt: on

		lowerCAmelCase					:    Optional[Any]      							= tokenizer.convert_tokens_to_ids(snake_case__    )
		self.assertListEqual(
		    snake_case__ ,					[262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260] ,					)

		lowerCAmelCase					:    int      							= tokenizer.convert_ids_to_tokens(snake_case__    )
		# fmt: off
		self.assertListEqual(
		    snake_case__ ,					['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']    )
		# fmt: on



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    str      							= GPTSwaTokenizer(snake_case__    )
		lowerCAmelCase					:    Optional[int]      							= ['This is a test', 'I was born in 92000, and this is falsé.']
		lowerCAmelCase					:    Tuple      							= [
		    [465, 287, 265, 631, 842],
		    [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
		]

		# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
		for text, expected_ids in zip(snake_case__ ,					snake_case__    ):
			self.assertListEqual(tokenizer.encode_fast(snake_case__    ) ,					snake_case__    )

		# Test that decode_fast returns the input text
		for text, token_ids in zip(snake_case__ ,					snake_case__    ):
			self.assertEqual(tokenizer.decode_fast(snake_case__    ) ,					snake_case__    )



	@slow
	def 				lowercase     (					self    ):
		lowerCAmelCase					:    str      							= [
		    '<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')',
		    'Hey there, how are you doing this fine day?',
		    'This is a text with a trailing spaces followed by a dot     .',
		    'Häj sväjs lillebrör! =)',
		    'Det är inget fel på Mr. Cool',
		]

		# fmt: off
		lowerCAmelCase					:    Tuple      							= {'input_ids': [[6_3423, 5, 6811, 1_4954, 282, 816, 3821, 6_3466, 6_3425, 6_3462, 18, 6_3978, 678, 301, 1320, 6_3423, 6_3455, 6_3458, 18, 6_3982, 4246, 3940, 1901, 4_7789, 5547, 1_8994], [1_9630, 1100, 6_3446, 1342, 633, 544, 4488, 593, 5102, 2416, 6_3495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 5_8593, 2_2413, 9106, 546, 268, 3_3213, 6_3979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5130, 6_3450, 924, 6_3449, 2249, 4062, 1558, 318, 6_3504, 2_1498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 6_3443, 2_6801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[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, 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, 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, 1, 1, 1, 1, 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], [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]]}
		# fmt: on
		self.tokenizer_integration_test_util(
		    expected_encoding=snake_case__ ,					model_name='AI-Sweden/gpt-sw3-126m' ,					sequences=snake_case__ ,					)
