


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       lowerCAmelCase				(  self				:	int							)			->  List[Any]:


												"""simple docstring"""

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



					def       lowerCAmelCase				(  self				:	Union[str, Any]		,     **__a				:	int							)			->  Union[str, Any]:


												"""simple docstring"""

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



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


												"""simple docstring"""

												shutil.rmtree(self.tmpdirname							)



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


												"""simple docstring"""

												__lowercase      :							Tuple				=     self.get_tokenizer()

												__lowercase      :							Optional[Any]				=     BarkProcessor(tokenizer=UpperCAmelCase_							)

												processor.save_pretrained(self.tmpdirname							)
												__lowercase      :							Dict				=     BarkProcessor.from_pretrained(self.tmpdirname							)

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



					@slow
					def       lowerCAmelCase				(  self				:	Optional[Any]							)			->  List[str]:


												"""simple docstring"""

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

												__lowercase      :							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       lowerCAmelCase				(  self				:	List[Any]							)			->  str:


												"""simple docstring"""

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

												__lowercase      :							Dict				=     35
												__lowercase      :							Optional[Any]				=     2
												__lowercase      :							int				=     8

												__lowercase      :							Any				=     {
												    """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      :							List[str]				=     processor(text=self.input_string		,     voice_preset=UpperCAmelCase_							)

												__lowercase      :							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
												__lowercase      :							Tuple				=     os.path.join(self.tmpdirname		,     """file.npz"""							)
												np.savez(UpperCAmelCase_		,     **UpperCAmelCase_							)
												__lowercase      :							Any				=     processor(text=self.input_string		,     voice_preset=UpperCAmelCase_							)
												__lowercase      :							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
												__lowercase      :							Any				=     processor(text=self.input_string		,     voice_preset=self.voice_preset							)



					def       lowerCAmelCase				(  self				:	List[Any]							)			->  int:


												"""simple docstring"""

												__lowercase      :							str				=     self.get_tokenizer()

												__lowercase      :							Union[str, Any]				=     BarkProcessor(tokenizer=UpperCAmelCase_							)

												__lowercase      :							Any				=     processor(text=self.input_string							)

												__lowercase      :							Optional[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()							)







from typing import TYPE_CHECKING

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


lowerCamelCase : int						   =       {
    '''configuration_funnel''': ['''FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FunnelConfig'''],
    '''convert_funnel_original_tf_checkpoint_to_pytorch''': [],
    '''tokenization_funnel''': ['''FunnelTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase : List[str]						   =       ['''FunnelTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase : Union[str, Any]						   =       [
	    '''FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''FunnelBaseModel''',
	    '''FunnelForMaskedLM''',
	    '''FunnelForMultipleChoice''',
	    '''FunnelForPreTraining''',
	    '''FunnelForQuestionAnswering''',
	    '''FunnelForSequenceClassification''',
	    '''FunnelForTokenClassification''',
	    '''FunnelModel''',
	    '''FunnelPreTrainedModel''',
	    '''load_tf_weights_in_funnel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase : str						   =       [
	    '''TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFFunnelBaseModel''',
	    '''TFFunnelForMaskedLM''',
	    '''TFFunnelForMultipleChoice''',
	    '''TFFunnelForPreTraining''',
	    '''TFFunnelForQuestionAnswering''',
	    '''TFFunnelForSequenceClassification''',
	    '''TFFunnelForTokenClassification''',
	    '''TFFunnelModel''',
	    '''TFFunnelPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
	from .tokenization_funnel import FunnelTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_funnel_fast import FunnelTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_funnel import (
		    FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FunnelBaseModel,
		    FunnelForMaskedLM,
		    FunnelForMultipleChoice,
		    FunnelForPreTraining,
		    FunnelForQuestionAnswering,
		    FunnelForSequenceClassification,
		    FunnelForTokenClassification,
		    FunnelModel,
		    FunnelPreTrainedModel,
		    load_tf_weights_in_funnel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_funnel import (
		    TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFFunnelBaseModel,
		    TFFunnelForMaskedLM,
		    TFFunnelForMultipleChoice,
		    TFFunnelForPreTraining,
		    TFFunnelForQuestionAnswering,
		    TFFunnelForSequenceClassification,
		    TFFunnelForTokenClassification,
		    TFFunnelModel,
		    TFFunnelPreTrainedModel,
		)

else:
	import sys

	lowerCamelCase : Any						   =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)