

"""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    lowerCamelCase						(unittest.TestCase			):

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      '''ylacombe/bark-small'''
						SCREAMING_SNAKE_CASE__								=      tempfile.mkdtemp()
						SCREAMING_SNAKE_CASE__								=      '''en_speaker_1'''
						SCREAMING_SNAKE_CASE__								=      '''This is a test string'''
						SCREAMING_SNAKE_CASE__								=      '''speaker_embeddings_path.json'''
						SCREAMING_SNAKE_CASE__								=      '''speaker_embeddings'''

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]    ,				**__UpperCAmelCase				:						int       )   ->    Tuple:
						return AutoTokenizer.from_pretrained(self.checkpoint    ,				**__lowerCamelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    Dict:
						shutil.rmtree(self.tmpdirname       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      self.get_tokenizer()

						SCREAMING_SNAKE_CASE__								=      BarkProcessor(tokenizer=__lowerCamelCase       )

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

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

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      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__								=      self.get_tokenizer(bos_token="""(BOS)"""    ,				eos_token="""(EOS)"""       )

						SCREAMING_SNAKE_CASE__								=      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 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    Dict:
						SCREAMING_SNAKE_CASE__								=      BarkProcessor.from_pretrained(
						    pretrained_processor_name_or_path=self.checkpoint    ,				speaker_embeddings_dict_path=self.speaker_embeddings_dict_path    ,				)

						SCREAMING_SNAKE_CASE__								=      3_5
						SCREAMING_SNAKE_CASE__								=      2
						SCREAMING_SNAKE_CASE__								=      8

						SCREAMING_SNAKE_CASE__								=      {
						    '''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__								=      processor(text=self.input_string    ,				voice_preset=__lowerCamelCase       )

						SCREAMING_SNAKE_CASE__								=      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__								=      os.path.join(self.tmpdirname    ,				"""file.npz"""       )
						np.savez(__lowerCamelCase    ,				**__lowerCamelCase       )
						SCREAMING_SNAKE_CASE__								=      processor(text=self.input_string    ,				voice_preset=__lowerCamelCase       )
						SCREAMING_SNAKE_CASE__								=      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__								=      processor(text=self.input_string    ,				voice_preset=self.voice_preset       )





			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      self.get_tokenizer()

						SCREAMING_SNAKE_CASE__								=      BarkProcessor(tokenizer=__lowerCamelCase       )

						SCREAMING_SNAKE_CASE__								=      processor(text=self.input_string       )

						SCREAMING_SNAKE_CASE__								=      tokenizer(
						    self.input_string    ,				padding="""max_length"""    ,				max_length=2_5_6    ,				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"""




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    lowerCamelCase						(unittest.TestCase			):

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Any:
						torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      UNetaDModel(
						    block_out_channels=(3_2, 6_4)    ,				layers_per_block=2    ,				sample_size=3_2    ,				in_channels=3    ,				out_channels=3    ,				down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")    ,				up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")    ,				)
						return model





			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      self.dummy_uncond_unet
						SCREAMING_SNAKE_CASE__								=      KarrasVeScheduler()

						SCREAMING_SNAKE_CASE__								=      KarrasVePipeline(unet=__UpperCAmelCase    ,				scheduler=__UpperCAmelCase       )
						pipe.to(__UpperCAmelCase       )
						pipe.set_progress_bar_config(disable=__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      pipe(num_inference_steps=2    ,				generator=__UpperCAmelCase    ,				output_type="""numpy"""       ).images

						SCREAMING_SNAKE_CASE__								=      torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      pipe(num_inference_steps=2    ,				generator=__UpperCAmelCase    ,				output_type="""numpy"""    ,				return_dict=__UpperCAmelCase       )[0]

						SCREAMING_SNAKE_CASE__								=      image[0, -3:, -3:, -1]
						SCREAMING_SNAKE_CASE__								=      image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 3_2, 3_2, 3)
						SCREAMING_SNAKE_CASE__								=      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    lowerCamelCase						(unittest.TestCase			):

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      """google/ncsnpp-celebahq-256"""
						SCREAMING_SNAKE_CASE__								=      UNetaDModel.from_pretrained(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      KarrasVeScheduler()

						SCREAMING_SNAKE_CASE__								=      KarrasVePipeline(unet=__UpperCAmelCase    ,				scheduler=__UpperCAmelCase       )
						pipe.to(__UpperCAmelCase       )
						pipe.set_progress_bar_config(disable=__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      pipe(num_inference_steps=2_0    ,				generator=__UpperCAmelCase    ,				output_type="""numpy"""       ).images

						SCREAMING_SNAKE_CASE__								=      image[0, -3:, -3:, -1]
						assert image.shape == (1, 2_5_6, 2_5_6, 3)
						SCREAMING_SNAKE_CASE__								=      np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586]       )

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


