

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



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any       ,						**lowerCAmelCase__  :					Optional[Any]):
									return AutoTokenizer.from_pretrained(self.checkpoint       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]):
									shutil.rmtree(self.tmpdirname)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.get_tokenizer()

									SCREAMING_SNAKE_CASE_:			List[str]  		=	BarkProcessor(tokenizer=lowerCAmelCase__)

									processor.save_pretrained(self.tmpdirname)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	BarkProcessor.from_pretrained(self.tmpdirname)

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



			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]):
									SCREAMING_SNAKE_CASE_:			List[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_:			Union[str, Any]  		=	self.get_tokenizer(bos_token="(BOS)"       ,						eos_token="(EOS)")

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

									SCREAMING_SNAKE_CASE_:			str  		=	35
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	2
									SCREAMING_SNAKE_CASE_:			str  		=	8

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




			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									SCREAMING_SNAKE_CASE_:			Dict  		=	self.get_tokenizer()

									SCREAMING_SNAKE_CASE_:			str  		=	BarkProcessor(tokenizer=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	processor(text=self.input_string)

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	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())









import torch

from diffusers import StableDiffusionPipeline


lowerCAmelCase							:  Any            =						"""path-to-your-trained-model"""
lowerCAmelCase							:  int            =						StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")

lowerCAmelCase							:  Union[str, Any]            =						"""A photo of sks dog in a bucket"""
lowerCAmelCase							:  Any            =						pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("""dog-bucket.png""")






