"""simple docstring"""




import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class 						__A							( unittest.TestCase		):






     @property
     def     __A  (		self  ):
       torch.manual_seed(0  )
       _lowerCAmelCase						:       Tuple  			=     UNetaDModel(
           block_out_channels=(32, 64)				,     layers_per_block=2				,     sample_size=32				,     in_channels=3				,     out_channels=3				,     down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")				,     up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")				,     )
       return model






     def     __A  (		self  ):
       _lowerCAmelCase						:       Dict  			=     self.dummy_uncond_unet
       _lowerCAmelCase						:       List[str]  			=     KarrasVeScheduler()

       _lowerCAmelCase						:       List[Any]  			=     KarrasVePipeline(unet=a__				,     scheduler=a__  )
       pipe.to(a__  )
       pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       str  			=     torch.manual_seed(0  )
       _lowerCAmelCase						:       Tuple  			=     pipe(num_inference_steps=2				,     generator=a__				,     output_type="""numpy"""  ).images

       _lowerCAmelCase						:       Tuple  			=     torch.manual_seed(0  )
       _lowerCAmelCase						:       Optional[int]  			=     pipe(num_inference_steps=2				,     generator=a__				,     output_type="""numpy"""				,     return_dict=a__  )[0]

       _lowerCAmelCase						:       Dict  			=     image[0, -3:, -3:, -1]
       _lowerCAmelCase						:       Tuple  			=     image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       _lowerCAmelCase						:       Dict  			=     np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]  )

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1e-2







@slow
@require_torch
class 						__A							( unittest.TestCase		):






     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     """google/ncsnpp-celebahq-256"""
       _lowerCAmelCase						:       str  			=     UNetaDModel.from_pretrained(a__  )
       _lowerCAmelCase						:       Optional[Any]  			=     KarrasVeScheduler()

       _lowerCAmelCase						:       Dict  			=     KarrasVePipeline(unet=a__				,     scheduler=a__  )
       pipe.to(a__  )
       pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       Tuple  			=     torch.manual_seed(0  )
       _lowerCAmelCase						:       Tuple  			=     pipe(num_inference_steps=20				,     generator=a__				,     output_type="""numpy"""  ).images

       _lowerCAmelCase						:       List[str]  			=     image[0, -3:, -3:, -1]
       assert image.shape == (1, 256, 256, 3)
       _lowerCAmelCase						:       List[Any]  			=     np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6]  )

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2





"""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 						__A							( unittest.TestCase		):






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     """ylacombe/bark-small"""
       _lowerCAmelCase						:       Optional[Any]  			=     tempfile.mkdtemp()
       _lowerCAmelCase						:       int  			=     """en_speaker_1"""
       _lowerCAmelCase						:       List[Any]  			=     """This is a test string"""
       _lowerCAmelCase						:       Any  			=     """speaker_embeddings_path.json"""
       _lowerCAmelCase						:       List[Any]  			=     """speaker_embeddings"""






     def     __A  (		self				,     **a__  ):
       return AutoTokenizer.from_pretrained(self.checkpoint				,     **a__  )






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






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[Any]  			=     self.get_tokenizer()

       _lowerCAmelCase						:       int  			=     BarkProcessor(tokenizer=a__  )

       processor.save_pretrained(self.tmpdirname  )
       _lowerCAmelCase						:       str  			=     BarkProcessor.from_pretrained(self.tmpdirname  )

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






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     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						:       Tuple  			=     self.get_tokenizer(bos_token="""(BOS)"""				,     eos_token="""(EOS)"""  )

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

       _lowerCAmelCase						:       Union[str, Any]  			=     35
       _lowerCAmelCase						:       Union[str, Any]  			=     2
       _lowerCAmelCase						:       Optional[int]  			=     8

       _lowerCAmelCase						:       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
       _lowerCAmelCase						:       Dict  			=     processor(text=self.input_string				,     voice_preset=a__  )

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






     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     self.get_tokenizer()

       _lowerCAmelCase						:       List[Any]  			=     BarkProcessor(tokenizer=a__  )

       _lowerCAmelCase						:       Dict  			=     processor(text=self.input_string  )

       _lowerCAmelCase						:       Tuple  			=     tokenizer(
           self.input_string				,     padding="""max_length"""				,     max_length=256				,     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()  )




