






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 			snake_case__   (							unittest.TestCase):

       '''simple docstring'''



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





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



       def 					__lowercase						(							self			,						**a__    ) ->      List[str]:






            '''simple docstring'''





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



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






            '''simple docstring'''





            shutil.rmtree(self.tmpdirname    )



       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            __snake_case			:Tuple          =						self.get_tokenizer()

            __snake_case			:str          =						BarkProcessor(tokenizer=a__    )

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

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



       @slow
       def 					__lowercase						(							self    ) ->      List[Any]:






            '''simple docstring'''





            __snake_case			: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			,						)

            __snake_case			:List[str]          =						self.get_tokenizer(bos_token="""(BOS)"""			,						eos_token="""(EOS)"""    )

            __snake_case			:Optional[int]          =						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    ) ->      Optional[Any]:






            '''simple docstring'''





            __snake_case			:Union[str, Any]          =						BarkProcessor.from_pretrained(
                pretrained_processor_name_or_path=self.checkpoint			,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path			,						)

            __snake_case			:Dict          =						35
            __snake_case			:str          =						2
            __snake_case			:Tuple          =						8

            __snake_case			:Dict          =						{
                """semantic_prompt""": np.ones(a__    ),
                """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			:Optional[int]          =						processor(text=self.input_string			,						voice_preset=a__    )

            __snake_case			:int          =						inputs["""history_prompt"""]
            for key in voice_preset:
                 self.assertListEqual(voice_preset[key].tolist()			,						processed_voice_preset.get(a__			,						np.array([]    )    ).tolist()    )

            # test loading voice preset from npz file
            __snake_case			:List[Any]          =						os.path.join(self.tmpdirname			,						"""file.npz"""    )
            np.savez(a__			,						**a__    )
            __snake_case			:str          =						processor(text=self.input_string			,						voice_preset=a__    )
            __snake_case			:List[Any]          =						inputs["""history_prompt"""]

            for key in voice_preset:
                 self.assertListEqual(voice_preset[key].tolist()			,						processed_voice_preset.get(a__			,						np.array([]    )    ).tolist()    )

            # test loading voice preset from the hub
            __snake_case			:Any          =						processor(text=self.input_string			,						voice_preset=self.voice_preset    )



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






            '''simple docstring'''





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

            __snake_case			:Any          =						BarkProcessor(tokenizer=a__    )

            __snake_case			:Union[str, Any]          =						processor(text=self.input_string    )

            __snake_case			:str          =						tokenizer(
                self.input_string			,						padding="""max_length"""			,						max_length=2_56			,						add_special_tokens=a__			,						return_attention_mask=a__			,						return_token_type_ids=a__			,						)

            for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]			,						encoded_processor[key].squeeze().tolist()    )







from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


lowerCamelCase__							=     {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPTConfig"""]}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__							=     ["""DPTFeatureExtractor"""]
     lowerCamelCase__							=     ["""DPTImageProcessor"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__							=     [
         """DPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """DPTForDepthEstimation""",
         """DPTForSemanticSegmentation""",
         """DPTModel""",
         """DPTPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_dpt import DPTFeatureExtractor
          from .image_processing_dpt import DPTImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_dpt import (
              DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              DPTForDepthEstimation,
              DPTForSemanticSegmentation,
              DPTModel,
              DPTPreTrainedModel,
          )


else:
     import sys

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


