




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowercase  =			{
    """configuration_rembert""": ["""REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RemBertConfig""", """RemBertOnnxConfig"""]
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase  =			["""RemBertTokenizer"""]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase  =			["""RemBertTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase  =			[
	    """REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """RemBertForCausalLM""",
	    """RemBertForMaskedLM""",
	    """RemBertForMultipleChoice""",
	    """RemBertForQuestionAnswering""",
	    """RemBertForSequenceClassification""",
	    """RemBertForTokenClassification""",
	    """RemBertLayer""",
	    """RemBertModel""",
	    """RemBertPreTrainedModel""",
	    """load_tf_weights_in_rembert""",
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase  =			[
	    """TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFRemBertForCausalLM""",
	    """TFRemBertForMaskedLM""",
	    """TFRemBertForMultipleChoice""",
	    """TFRemBertForQuestionAnswering""",
	    """TFRemBertForSequenceClassification""",
	    """TFRemBertForTokenClassification""",
	    """TFRemBertLayer""",
	    """TFRemBertModel""",
	    """TFRemBertPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_rembert import RemBertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_rembert_fast import RemBertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_rembert import (
		    REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RemBertForCausalLM,
		    RemBertForMaskedLM,
		    RemBertForMultipleChoice,
		    RemBertForQuestionAnswering,
		    RemBertForSequenceClassification,
		    RemBertForTokenClassification,
		    RemBertLayer,
		    RemBertModel,
		    RemBertPreTrainedModel,
		    load_tf_weights_in_rembert,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_rembert import (
		    TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRemBertForCausalLM,
		    TFRemBertForMaskedLM,
		    TFRemBertForMultipleChoice,
		    TFRemBertForQuestionAnswering,
		    TFRemBertForSequenceClassification,
		    TFRemBertForTokenClassification,
		    TFRemBertLayer,
		    TFRemBertModel,
		    TFRemBertPreTrainedModel,
		)


else:
	import sys

	_lowercase  =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







'''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   lowercase__   (       self     :  Optional[Any]      ):
							__snake_case						 =   'ylacombe/bark-small'
							__snake_case						 =   tempfile.mkdtemp()
							__snake_case						 =   'en_speaker_1'
							__snake_case						 =   'This is a test string'
							__snake_case						 =   'speaker_embeddings_path.json'
							__snake_case						 =   'speaker_embeddings'




	def   lowercase__   (       self     :  int       ,		**__lowerCAmelCase     :  str      ):
							return AutoTokenizer.from_pretrained(self.checkpoint       ,		**__lowerCAmelCase      )




	def   lowercase__   (       self     :  List[Any]      ):
							shutil.rmtree(self.tmpdirname      )




	def   lowercase__   (       self     :  Dict      ):
							__snake_case						 =   self.get_tokenizer()

							__snake_case						 =   BarkProcessor(tokenizer=__lowerCAmelCase      )

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

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




	@slow
	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   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       ,		)

							__snake_case						 =   self.get_tokenizer(bos_token='(BOS)'       ,		eos_token='(EOS)'      )

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

							__snake_case						 =   3_5
							__snake_case						 =   2
							__snake_case						 =   8

							__snake_case						 =   {
							    '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
							__snake_case						 =   processor(text=self.input_string       ,		voice_preset=__lowerCAmelCase      )

							__snake_case						 =   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
							__snake_case						 =   os.path.join(self.tmpdirname       ,		'file.npz'      )
							np.savez(__lowerCAmelCase       ,		**__lowerCAmelCase      )
							__snake_case						 =   processor(text=self.input_string       ,		voice_preset=__lowerCAmelCase      )
							__snake_case						 =   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
							__snake_case						 =   processor(text=self.input_string       ,		voice_preset=self.voice_preset      )




	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   self.get_tokenizer()

							__snake_case						 =   BarkProcessor(tokenizer=__lowerCAmelCase      )

							__snake_case						 =   processor(text=self.input_string      )

							__snake_case						 =   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()      )

