


'''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 UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= """ylacombe/bark-small"""
											_SCREAMING_SNAKE_CASE					:			Tuple							= tempfile.mkdtemp()
											_SCREAMING_SNAKE_CASE					:			str							= """en_speaker_1"""
											_SCREAMING_SNAKE_CASE					:			Dict							= """This is a test string"""
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= """speaker_embeddings_path.json"""
											_SCREAMING_SNAKE_CASE					:			Tuple							= """speaker_embeddings"""






							def UpperCAmelCase_      (       self       ,   **__snake_case    ):
											return AutoTokenizer.from_pretrained(self.checkpoint       ,   **_lowerCAmelCase    )






							def UpperCAmelCase_      (       self    ):
											shutil.rmtree(self.tmpdirname    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			str							= self.get_tokenizer()

											_SCREAMING_SNAKE_CASE					:			Optional[int]							= BarkProcessor(tokenizer=_lowerCAmelCase    )

											processor.save_pretrained(self.tmpdirname    )
											_SCREAMING_SNAKE_CASE					:			List[Any]							= BarkProcessor.from_pretrained(self.tmpdirname    )

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






							@slow
							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			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       ,   )

											_SCREAMING_SNAKE_CASE					:			Dict							= self.get_tokenizer(bos_token="""(BOS)"""       ,   eos_token="""(EOS)"""    )

											_SCREAMING_SNAKE_CASE					:			List[str]							= 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 UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			str							= BarkProcessor.from_pretrained(
											    pretrained_processor_name_or_path=self.checkpoint       ,   speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,   )

											_SCREAMING_SNAKE_CASE					:			List[Any]							= 35
											_SCREAMING_SNAKE_CASE					:			str							= 2
											_SCREAMING_SNAKE_CASE					:			str							= 8

											_SCREAMING_SNAKE_CASE					:			List[Any]							= {
											    """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
											_SCREAMING_SNAKE_CASE					:			List[Any]							= processor(text=self.input_string       ,   voice_preset=_lowerCAmelCase    )

											_SCREAMING_SNAKE_CASE					:			Optional[int]							= 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
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= os.path.join(self.tmpdirname       ,   """file.npz"""    )
											np.savez(_lowerCAmelCase       ,   **_lowerCAmelCase    )
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= processor(text=self.input_string       ,   voice_preset=_lowerCAmelCase    )
											_SCREAMING_SNAKE_CASE					:			int							= 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
											_SCREAMING_SNAKE_CASE					:			List[str]							= processor(text=self.input_string       ,   voice_preset=self.voice_preset    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= self.get_tokenizer()

											_SCREAMING_SNAKE_CASE					:			List[str]							= BarkProcessor(tokenizer=_lowerCAmelCase    )

											_SCREAMING_SNAKE_CASE					:			List[str]							= processor(text=self.input_string    )

											_SCREAMING_SNAKE_CASE					:			int							= tokenizer(
											    self.input_string       ,   padding="""max_length"""       ,   max_length=256       ,   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()    )











'''simple docstring'''


from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				int								=       ["pixel_values"]




 def __init__(						self      ,  _lowerCAmelCase = True      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = PILImageResampling.BICUBIC      ,  _lowerCAmelCase = True      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = True      ,  _lowerCAmelCase = 1 / 255      ,  _lowerCAmelCase = True      ,  _lowerCAmelCase = IMAGENET_DEFAULT_MEAN      ,  _lowerCAmelCase = IMAGENET_DEFAULT_STD      ,  **_lowerCAmelCase      ,  )     ->				None:
     super().__init__(**_lowerCAmelCase		)
     _lowerCAmelCase						      =   size if size is not None else {"shortest_edge": 224}
     _lowerCAmelCase						      =   get_size_dict(_lowerCAmelCase      ,  default_to_square=_lowerCAmelCase		)
     _lowerCAmelCase						      =   crop_size if crop_size is not None else {"height": 224, "width": 224}
     _lowerCAmelCase						      =   get_size_dict(_lowerCAmelCase      ,  param_name="crop_size"		)

     _lowerCAmelCase						      =   do_resize
     _lowerCAmelCase						      =   size
     _lowerCAmelCase						      =   resample
     _lowerCAmelCase						      =   do_center_crop
     _lowerCAmelCase						      =   crop_size
     _lowerCAmelCase						      =   do_rescale
     _lowerCAmelCase						      =   rescale_factor
     _lowerCAmelCase						      =   do_normalize
     _lowerCAmelCase						      =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
     _lowerCAmelCase						      =   image_std if image_std is not None else IMAGENET_DEFAULT_STD




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase = PILImageResampling.BICUBIC      ,  _lowerCAmelCase = None      ,  **_lowerCAmelCase      ,  )     ->				np.ndarray:
     _lowerCAmelCase						      =   get_size_dict(_lowerCAmelCase      ,  default_to_square=_lowerCAmelCase		)
     # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
     if "shortest_edge" in size:
         _lowerCAmelCase						      =   int((256 / 224) * size["shortest_edge"]		)
         _lowerCAmelCase						      =   get_resize_output_image_size(_lowerCAmelCase      ,  size=_lowerCAmelCase      ,  default_to_square=_lowerCAmelCase		)
         _lowerCAmelCase						      =   {"height": output_size[0], "width": output_size[1]}
     if "height" not in size_dict or "width" not in size_dict:
         raise ValueError(
             f'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'''		)
     return resize(
         _lowerCAmelCase      ,  size=(size_dict["height"], size_dict["width"])      ,  resample=_lowerCAmelCase      ,  data_format=_lowerCAmelCase      ,  **_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None      ,  **_lowerCAmelCase      ,  )     ->				np.ndarray:
     _lowerCAmelCase						      =   get_size_dict(_lowerCAmelCase		)
     if "height" not in size or "width" not in size:
         raise ValueError(f'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'''		)
     return center_crop(_lowerCAmelCase      ,  size=(size["height"], size["width"])      ,  data_format=_lowerCAmelCase      ,  **_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None      ,  **_lowerCAmelCase      ,  )     ->				np.ndarray:
     return rescale(_lowerCAmelCase      ,  scale=_lowerCAmelCase      ,  data_format=_lowerCAmelCase      ,  **_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None      ,  **_lowerCAmelCase      ,  )     ->				np.ndarray:
     return normalize(_lowerCAmelCase      ,  mean=_lowerCAmelCase      ,  std=_lowerCAmelCase      ,  data_format=_lowerCAmelCase      ,  **_lowerCAmelCase		)





 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = ChannelDimension.FIRST      ,  **_lowerCAmelCase      ,  )     ->				BatchFeature:
     _lowerCAmelCase						      =   do_resize if do_resize is not None else self.do_resize
     _lowerCAmelCase						      =   resample if resample is not None else self.resample
     _lowerCAmelCase						      =   do_center_crop if do_center_crop is not None else self.do_center_crop
     _lowerCAmelCase						      =   do_rescale if do_rescale is not None else self.do_rescale
     _lowerCAmelCase						      =   rescale_factor if rescale_factor is not None else self.rescale_factor
     _lowerCAmelCase						      =   do_normalize if do_normalize is not None else self.do_normalize
     _lowerCAmelCase						      =   image_mean if image_mean is not None else self.image_mean
     _lowerCAmelCase						      =   image_std if image_std is not None else self.image_std

     _lowerCAmelCase						      =   size if size is not None else self.size
     _lowerCAmelCase						      =   get_size_dict(_lowerCAmelCase      ,  default_to_square=_lowerCAmelCase		)
     _lowerCAmelCase						      =   crop_size if crop_size is not None else self.crop_size
     _lowerCAmelCase						      =   get_size_dict(_lowerCAmelCase      ,  param_name="crop_size"		)

     _lowerCAmelCase						      =   make_list_of_images(_lowerCAmelCase		)

     if not valid_images(_lowerCAmelCase		):
         raise ValueError(
             "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
             "torch.Tensor, tf.Tensor or jax.ndarray."		)

     if do_resize and size is None:
         raise ValueError("Size must be specified if do_resize is True."		)

     if do_center_crop and crop_size is None:
         raise ValueError("Crop size must be specified if do_center_crop is True."		)

     if do_rescale and rescale_factor is None:
         raise ValueError("Rescale factor must be specified if do_rescale is True."		)

     if do_normalize and (image_mean is None or image_std is None):
         raise ValueError("Image mean and std must be specified if do_normalize is True."		)

     # All transformations expect numpy arrays.
     _lowerCAmelCase						      =   [to_numpy_array(_lowerCAmelCase		) for image in images]

     if do_resize:
         _lowerCAmelCase						      =   [self.resize(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		) for image in images]

     if do_center_crop:
         _lowerCAmelCase						      =   [self.center_crop(_lowerCAmelCase      ,  _lowerCAmelCase		) for image in images]

     if do_rescale:
         _lowerCAmelCase						      =   [self.rescale(_lowerCAmelCase      ,  _lowerCAmelCase		) for image in images]

     if do_normalize:
         _lowerCAmelCase						      =   [self.normalize(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		) for image in images]

     _lowerCAmelCase						      =   [to_channel_dimension_format(_lowerCAmelCase      ,  _lowerCAmelCase		) for image in images]

     _lowerCAmelCase						      =   {"pixel_values": images}
     return BatchFeature(data=_lowerCAmelCase      ,  tensor_type=_lowerCAmelCase		)


