



"""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    lowercase__     (					unittest.TestCase   ):







       '''simple docstring'''


       def   lowercase__       (							self    :						Dict					)				->		Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_    				=							"ylacombe/bark-small"
             UpperCAmelCase_    				=							tempfile.mkdtemp()
             UpperCAmelCase_    				=							"en_speaker_1"
             UpperCAmelCase_    				=							"This is a test string"
             UpperCAmelCase_    				=							"speaker_embeddings_path.json"
             UpperCAmelCase_    				=							"speaker_embeddings"



       def   lowercase__       (							self    :						str	,       **_UpperCAmelCase    :						List[Any]					)				->		Optional[Any]:


             '''simple docstring'''




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



       def   lowercase__       (							self    :						List[Any]					)				->		Dict:


             '''simple docstring'''




             shutil.rmtree(self.tmpdirname					)



       def   lowercase__       (							self    :						Optional[Any]					)				->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.get_tokenizer()

             UpperCAmelCase_    				=							BarkProcessor(tokenizer=_UpperCAmelCase					)

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

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



       @slow
       def   lowercase__       (							self    :						Any					)				->		int:


             '''simple docstring'''




             UpperCAmelCase_    				=							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_    				=							self.get_tokenizer(bos_token="(BOS)"	,       eos_token="(EOS)"					)

             UpperCAmelCase_    				=							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    :						int					)				->		List[Any]:


             '''simple docstring'''




             UpperCAmelCase_    				=							BarkProcessor.from_pretrained(
                 pretrained_processor_name_or_path=self.checkpoint	,       speaker_embeddings_dict_path=self.speaker_embeddings_dict_path	,       )

             UpperCAmelCase_    				=							35
             UpperCAmelCase_    				=							2
             UpperCAmelCase_    				=							8

             UpperCAmelCase_    				=							{
                 "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_    				=							processor(text=self.input_string	,       voice_preset=_UpperCAmelCase					)

             UpperCAmelCase_    				=							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_    				=							os.path.join(self.tmpdirname	,       "file.npz"					)
             np.savez(_UpperCAmelCase	,       **_UpperCAmelCase					)
             UpperCAmelCase_    				=							processor(text=self.input_string	,       voice_preset=_UpperCAmelCase					)
             UpperCAmelCase_    				=							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_    				=							processor(text=self.input_string	,       voice_preset=self.voice_preset					)







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


             '''simple docstring'''




             UpperCAmelCase_    				=							self.get_tokenizer()

             UpperCAmelCase_    				=							BarkProcessor(tokenizer=_UpperCAmelCase					)

             UpperCAmelCase_    				=							processor(text=self.input_string					)

             UpperCAmelCase_    				=							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()					)









"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase      =		logging.get_logger(__name__)

lowerCamelCase      =		{
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  '''mobilenet_v1'''



       def __init__(							self    :						Tuple	,       _UpperCAmelCase    :						int=3	,       _UpperCAmelCase    :						Union[str, Any]=224	,       _UpperCAmelCase    :						Any=1.0	,       _UpperCAmelCase    :						Any=8	,       _UpperCAmelCase    :						List[Any]="relu6"	,       _UpperCAmelCase    :						List[str]=True	,       _UpperCAmelCase    :						Dict=0.999	,       _UpperCAmelCase    :						List[Any]=0.02	,       _UpperCAmelCase    :						List[Any]=0.001	,       **_UpperCAmelCase    :						str	,       )				->		Optional[int]:


             '''simple docstring'''




             super().__init__(**_UpperCAmelCase					)

             if depth_multiplier <= 0:
                   raise ValueError("depth_multiplier must be greater than zero."					)

             UpperCAmelCase_    				=							num_channels
             UpperCAmelCase_    				=							image_size
             UpperCAmelCase_    				=							depth_multiplier
             UpperCAmelCase_    				=							min_depth
             UpperCAmelCase_    				=							hidden_act
             UpperCAmelCase_    				=							tf_padding
             UpperCAmelCase_    				=							classifier_dropout_prob
             UpperCAmelCase_    				=							initializer_range
             UpperCAmelCase_    				=							layer_norm_eps



class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  version.parse('''1.11'''   )



       @property
       def   lowercase__       (							self    :						List[Any]					)				->		Mapping[str, Mapping[int, str]]:


             '''simple docstring'''




             return OrderedDict([("pixel_values", {0: "batch"})]					)



       @property
       def   lowercase__       (							self    :						List[Any]					)				->		Mapping[str, Mapping[int, str]]:


             '''simple docstring'''




             if self.task == "image-classification":
                   return OrderedDict([("logits", {0: "batch"})]					)
             else:
                   return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]					)







       @property
       def   lowercase__       (							self    :						Tuple					)				->		float:


             '''simple docstring'''




             return 1e-4




