

import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor


class     lowercase__  (					unittest.TestCase		):






  def __init__(					self      :					Tuple		,						_lowercase      :					List[str]		,						_lowercase      :					int=7		,						_lowercase      :					Dict=3		,						_lowercase      :					List[Any]=18		,						_lowercase      :					List[Any]=30		,						_lowercase      :					str=4_00		,						_lowercase      :					Dict=True		,						_lowercase      :					Union[str, Any]=None		,						_lowercase      :					List[Any]=True		,						_lowercase      :					List[str]=False		,						_lowercase      :					List[str]=True		,						_lowercase      :					List[str]=True		,						_lowercase      :					Dict=[0.5, 0.5, 0.5]		,						_lowercase      :					Tuple=[0.5, 0.5, 0.5]		,						):




         """simple docstring"""







         UpperCAmelCase__										=     parent
         UpperCAmelCase__										=     batch_size
         UpperCAmelCase__										=     num_channels
         UpperCAmelCase__										=     image_size
         UpperCAmelCase__										=     min_resolution
         UpperCAmelCase__										=     max_resolution
         UpperCAmelCase__										=     do_resize
         UpperCAmelCase__										=     size if size is not None else {"height": 18, "width": 20}
         UpperCAmelCase__										=     do_thumbnail
         UpperCAmelCase__										=     do_align_axis
         UpperCAmelCase__										=     do_pad
         UpperCAmelCase__										=     do_normalize
         UpperCAmelCase__										=     image_mean
         UpperCAmelCase__										=     image_std






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_thumbnail": self.do_thumbnail,
             "do_align_long_axis": self.do_align_axis,
             "do_pad": self.do_pad,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
         }




@require_torch
@require_vision
class     lowercase__  (					__a					,							unittest.TestCase		):
  A__=  DonutImageProcessor if is_vision_available() else None






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__										=     DonutImageProcessingTester(self )






  @property
  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







         return self.image_processor_tester.prepare_image_processor_dict()






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.image_processing_class(**self.image_processor_dict )
         self.assertTrue(hasattr(A__		,						"do_resize" ) )
         self.assertTrue(hasattr(A__		,						"size" ) )
         self.assertTrue(hasattr(A__		,						"do_thumbnail" ) )
         self.assertTrue(hasattr(A__		,						"do_align_long_axis" ) )
         self.assertTrue(hasattr(A__		,						"do_pad" ) )
         self.assertTrue(hasattr(A__		,						"do_normalize" ) )
         self.assertTrue(hasattr(A__		,						"image_mean" ) )
         self.assertTrue(hasattr(A__		,						"image_std" ) )






  def 			_UpperCAmelCase  (					self      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.image_processing_class.from_dict(self.image_processor_dict )
         self.assertEqual(image_processor.size		,						{"height": 18, "width": 20} )

         UpperCAmelCase__										=     self.image_processing_class.from_dict(self.image_processor_dict		,						size=42 )
         self.assertEqual(image_processor.size		,						{"height": 42, "width": 42} )

         # Previous config had dimensions in (width, height) order
         UpperCAmelCase__										=     self.image_processing_class.from_dict(self.image_processor_dict		,						size=(42, 84) )
         self.assertEqual(image_processor.size		,						{"height": 84, "width": 42} )






  def 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







         pass






  @is_flaky()
  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.image_processing_class(**self.image_processor_dict )
         # create random PIL images
         UpperCAmelCase__										=     prepare_image_inputs(self.image_processor_tester		,						equal_resolution=A__ )
         for image in image_inputs:
                self.assertIsInstance(A__		,						Image.Image )

         # Test not batched input
         UpperCAmelCase__										=     image_processing(image_inputs[0]		,						return_tensors="pt" ).pixel_values
         self.assertEqual(
             encoded_images.shape		,						(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["height"],
                 self.image_processor_tester.size["width"],
             )		,						)

         # Test batched
         UpperCAmelCase__										=     image_processing(A__		,						return_tensors="pt" ).pixel_values
         self.assertEqual(
             encoded_images.shape		,						(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["height"],
                 self.image_processor_tester.size["width"],
             )		,						)






  @is_flaky()
  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.image_processing_class(**self.image_processor_dict )
         # create random numpy tensors
         UpperCAmelCase__										=     prepare_image_inputs(self.image_processor_tester		,						equal_resolution=A__		,						numpify=A__ )
         for image in image_inputs:
                self.assertIsInstance(A__		,						np.ndarray )

         # Test not batched input
         UpperCAmelCase__										=     image_processing(image_inputs[0]		,						return_tensors="pt" ).pixel_values
         self.assertEqual(
             encoded_images.shape		,						(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["height"],
                 self.image_processor_tester.size["width"],
             )		,						)

         # Test batched
         UpperCAmelCase__										=     image_processing(A__		,						return_tensors="pt" ).pixel_values
         self.assertEqual(
             encoded_images.shape		,						(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["height"],
                 self.image_processor_tester.size["width"],
             )		,						)






  @is_flaky()
  def 			_UpperCAmelCase  (					self      :					Tuple ):




         """simple docstring"""







         UpperCAmelCase__										=     self.image_processing_class(**self.image_processor_dict )
         # create random PyTorch tensors
         UpperCAmelCase__										=     prepare_image_inputs(self.image_processor_tester		,						equal_resolution=A__		,						torchify=A__ )
         for image in image_inputs:
                self.assertIsInstance(A__		,						torch.Tensor )

         # Test not batched input
         UpperCAmelCase__										=     image_processing(image_inputs[0]		,						return_tensors="pt" ).pixel_values
         self.assertEqual(
             encoded_images.shape		,						(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["height"],
                 self.image_processor_tester.size["width"],
             )		,						)

         # Test batched
         UpperCAmelCase__										=     image_processing(A__		,						return_tensors="pt" ).pixel_values
         self.assertEqual(
             encoded_images.shape		,						(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["height"],
                 self.image_processor_tester.size["width"],
             )		,						)









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		):






  def 			_UpperCAmelCase  (					self      :					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 			_UpperCAmelCase  (					self      :					List[str]		,						**_lowercase      :					str ):




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         shutil.rmtree(self.tmpdirname )






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.get_tokenizer()

         UpperCAmelCase__										=     BarkProcessor(tokenizer=_lowercase )

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

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






  @slow
  def 			_UpperCAmelCase  (					self      :					str ):




         """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 			_UpperCAmelCase  (					self      :					Optional[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(_lowercase ),
             "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=_lowercase )

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

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

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

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






  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.get_tokenizer()

         UpperCAmelCase__										=     BarkProcessor(tokenizer=_lowercase )

         UpperCAmelCase__										=     processor(text=self.input_string )

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

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





