


"""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 			__snake_case   (unittest.TestCase  ):




							def   __a   (     self:    Dict   ):
										__lowerCamelCase          =      """ylacombe/bark-small"""
										__lowerCamelCase          =      tempfile.mkdtemp()
										__lowerCamelCase          =      """en_speaker_1"""
										__lowerCamelCase          =      """This is a test string"""
										__lowerCamelCase          =      """speaker_embeddings_path.json"""
										__lowerCamelCase          =      """speaker_embeddings"""




							def   __a   (     self:    Union[str, Any]     ,							**A_:    List[Any]   ):
										return AutoTokenizer.from_pretrained(self.checkpoint     ,							**SCREAMING_SNAKE_CASE_   )




							def   __a   (     self:    int   ):
										shutil.rmtree(self.tmpdirname   )




							def   __a   (     self:    Optional[int]   ):
										__lowerCamelCase          =      self.get_tokenizer()

										__lowerCamelCase          =      BarkProcessor(tokenizer=SCREAMING_SNAKE_CASE_   )

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

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




							@slow
							def   __a   (     self:    Optional[Any]   ):
										__lowerCamelCase          =      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     ,							)

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

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

										__lowerCamelCase          =      35
										__lowerCamelCase          =      2
										__lowerCamelCase          =      8

										__lowerCamelCase          =      {
										    """semantic_prompt""": np.ones(SCREAMING_SNAKE_CASE_   ),
										    """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)   ),
										    """fine_prompt""": np.ones((nb_codebooks_total, seq_len)   ),
										}

										# test providing already loaded voice_preset
										__lowerCamelCase          =      processor(text=self.input_string     ,							voice_preset=SCREAMING_SNAKE_CASE_   )

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

										# test loading voice preset from npz file
										__lowerCamelCase          =      os.path.join(self.tmpdirname     ,							"""file.npz"""   )
										np.savez(SCREAMING_SNAKE_CASE_     ,							**SCREAMING_SNAKE_CASE_   )
										__lowerCamelCase          =      processor(text=self.input_string     ,							voice_preset=SCREAMING_SNAKE_CASE_   )
										__lowerCamelCase          =      inputs["""history_prompt"""]

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

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




							def   __a   (     self:    Dict   ):
										__lowerCamelCase          =      self.get_tokenizer()

										__lowerCamelCase          =      BarkProcessor(tokenizer=SCREAMING_SNAKE_CASE_   )

										__lowerCamelCase          =      processor(text=self.input_string   )

										__lowerCamelCase          =      tokenizer(
										    self.input_string     ,							padding="""max_length"""     ,							max_length=2_56     ,							add_special_tokens=SCREAMING_SNAKE_CASE_     ,							return_attention_mask=SCREAMING_SNAKE_CASE_     ,							return_token_type_ids=SCREAMING_SNAKE_CASE_     ,							)

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






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import ViTImageProcessor



class    _snake_case (						unittest.TestCase				):


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=13       ,	SCREAMING_SNAKE_CASE_=3       ,	SCREAMING_SNAKE_CASE_=2_24       ,	SCREAMING_SNAKE_CASE_=30       ,	SCREAMING_SNAKE_CASE_=4_00       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]       ,	SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]       ,	):


   '''simple docstring'''




   lowercase__	: List[str]     		=   size if size is not None else {"""height""": 18, """width""": 18}
   lowercase__	: int     		=   parent
   lowercase__	: Union[str, Any]     		=   batch_size
   lowercase__	: List[str]     		=   num_channels
   lowercase__	: str     		=   image_size
   lowercase__	: int     		=   min_resolution
   lowercase__	: Dict     		=   max_resolution
   lowercase__	: Tuple     		=   do_resize
   lowercase__	: Union[str, Any]     		=   size
   lowercase__	: Any     		=   do_normalize
   lowercase__	: Tuple     		=   image_mean
   lowercase__	: str     		=   image_std



 def   lowercase__  (       self):


   '''simple docstring'''




   return {
       "image_mean": self.image_mean,
       "image_std": self.image_std,
       "do_normalize": self.do_normalize,
       "do_resize": self.do_resize,
       "size": self.size,
   }



@require_torch
@require_vision
class    _snake_case (						UpperCAmelCase_	,    unittest.TestCase				):
 __lowerCAmelCase	:  Optional[Any]	   =      ViTImageProcessor if is_vision_available() else None


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   EfficientFormerImageProcessorTester(self)


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   return self.image_proc_tester.prepare_image_processor_dict()


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Any     		=   self.image_processing_class(**self.image_processor_dict)
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""image_mean"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""image_std"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""do_normalize"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""do_resize"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""size"""))


 def   lowercase__  (       self):


   '''simple docstring'''




   pass


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   self.image_processing_class(**self.image_processor_dict)
   # create random PIL images
   lowercase__	: List[Any]     		=   prepare_image_inputs(self.image_proc_tester       ,	equal_resolution=SCREAMING_SNAKE_CASE_)
   for image in image_inputs:
     self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,	Image.Image)

   # Test not batched input
   lowercase__	: int     		=   image_processor(image_inputs[0]       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           1,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)

   # Test batched
   lowercase__	: str     		=   image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           self.image_proc_tester.batch_size,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Tuple     		=   self.image_processing_class(**self.image_processor_dict)
   # create random numpy tensors
   lowercase__	: str     		=   prepare_image_inputs(self.image_proc_tester       ,	equal_resolution=SCREAMING_SNAKE_CASE_       ,	numpify=SCREAMING_SNAKE_CASE_)
   for image in image_inputs:
     self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,	np.ndarray)

   # Test not batched input
   lowercase__	: Optional[int]     		=   image_processor(image_inputs[0]       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           1,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)

   # Test batched
   lowercase__	: Dict     		=   image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           self.image_proc_tester.batch_size,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)



 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   self.image_processing_class(**self.image_processor_dict)
   # create random PyTorch tensors
   lowercase__	: Dict     		=   prepare_image_inputs(self.image_proc_tester       ,	equal_resolution=SCREAMING_SNAKE_CASE_       ,	torchify=SCREAMING_SNAKE_CASE_)
   for image in image_inputs:
     self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,	torch.Tensor)

   # Test not batched input
   lowercase__	: int     		=   image_processor(image_inputs[0]       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           1,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)

   # Test batched
   lowercase__	: Any     		=   image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           self.image_proc_tester.batch_size,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)






