




"""simple docstring"""





from typing import TYPE_CHECKING

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


A   =       {
    "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:
					A   =       ["FunnelTokenizerFast"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A   =       [
					    "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:
					A   =       [
					    "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

					A   =       _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       a_     (      self      :    Optional[int]):


						"""simple docstring"""




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


			def       a_     (      self      :    Optional[int]      ,  **UpperCamelCase_      :    Optional[int]):


						"""simple docstring"""




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


			def       a_     (      self      :    Dict):


						"""simple docstring"""




						shutil.rmtree(self.tmpdirname)


			def       a_     (      self      :    int):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					self.get_tokenizer()

						__UpperCAmelCase	: Optional[Any]		=					BarkProcessor(tokenizer=UpperCamelCase_)

						processor.save_pretrained(self.tmpdirname)
						__UpperCAmelCase	: Optional[int]		=					BarkProcessor.from_pretrained(self.tmpdirname)

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


			@slow
			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					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      ,  )

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

						__UpperCAmelCase	: 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       a_     (      self      :    Union[str, Any]):


						"""simple docstring"""




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

						__UpperCAmelCase	: Optional[int]		=					35
						__UpperCAmelCase	: Union[str, Any]		=					2
						__UpperCAmelCase	: List[Any]		=					8

						__UpperCAmelCase	: str		=					{
						    "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
						__UpperCAmelCase	: Union[str, Any]		=					processor(text=self.input_string      ,  voice_preset=UpperCamelCase_)

						__UpperCAmelCase	: 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
						__UpperCAmelCase	: int		=					os.path.join(self.tmpdirname      ,  "file.npz")
						np.savez(UpperCamelCase_      ,  **UpperCamelCase_)
						__UpperCAmelCase	: List[Any]		=					processor(text=self.input_string      ,  voice_preset=UpperCamelCase_)
						__UpperCAmelCase	: 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
						__UpperCAmelCase	: Tuple		=					processor(text=self.input_string      ,  voice_preset=self.voice_preset)


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: Tuple		=					self.get_tokenizer()

						__UpperCAmelCase	: Any		=					BarkProcessor(tokenizer=UpperCamelCase_)

						__UpperCAmelCase	: List[Any]		=					processor(text=self.input_string)

						__UpperCAmelCase	: List[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())


