





"""simple docstring"""



import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
       from PIL import Image

       from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast






@require_vision
class 		__snake_case					(		unittest.TestCase     ):





 """simple docstring"""


 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						str						 =     tempfile.mkdtemp()

     __A    :						Dict						 =     BlipImageProcessor()
     __A    :						Dict						 =     BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-BertModel'''  )

     __A    :						Union[str, Any]						 =     BlipProcessor(__lowerCamelCase					,						__lowerCamelCase  )

     processor.save_pretrained(self.tmpdirname  )

 def 					UpperCamelCase__(    self					,						**__lowerCamelCase  ):



     '''simple docstring'''




     return AutoProcessor.from_pretrained(self.tmpdirname					,						**__lowerCamelCase  ).tokenizer

 def 					UpperCamelCase__(    self					,						**__lowerCamelCase  ):



     '''simple docstring'''




     return AutoProcessor.from_pretrained(self.tmpdirname					,						**__lowerCamelCase  ).image_processor

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     shutil.rmtree(self.tmpdirname  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Dict						 =     [np.random.randint(255					,						size=(3, 30, 400)					,						dtype=np.uinta  )]

     __A    :						Dict						 =     [Image.fromarray(np.moveaxis(__lowerCamelCase					,						0					,						-1  )  ) for x in image_inputs]

     return image_inputs

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						int						 =     BlipProcessor(tokenizer=self.get_tokenizer()					,						image_processor=self.get_image_processor()  )
     processor.save_pretrained(self.tmpdirname  )

     __A    :						Tuple						 =     self.get_tokenizer(bos_token='''(BOS)'''					,						eos_token='''(EOS)'''  )
     __A    :						List[str]						 =     self.get_image_processor(do_normalize=__lowerCamelCase					,						padding_value=1.0  )

     __A    :						Optional[int]						 =     BlipProcessor.from_pretrained(
         self.tmpdirname					,						bos_token='''(BOS)'''					,						eos_token='''(EOS)'''					,						do_normalize=__lowerCamelCase					,						padding_value=1.0  )

     self.assertEqual(processor.tokenizer.get_vocab()					,						tokenizer_add_kwargs.get_vocab()  )
     self.assertIsInstance(processor.tokenizer					,						__lowerCamelCase  )

     self.assertEqual(processor.image_processor.to_json_string()					,						image_processor_add_kwargs.to_json_string()  )
     self.assertIsInstance(processor.image_processor					,						__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						str						 =     self.get_image_processor()
     __A    :						Dict						 =     self.get_tokenizer()

     __A    :						Tuple						 =     BlipProcessor(tokenizer=__lowerCamelCase					,						image_processor=__lowerCamelCase  )

     __A    :						str						 =     self.prepare_image_inputs()

     __A    :						Tuple						 =     image_processor(__lowerCamelCase					,						return_tensors='''np'''  )
     __A    :						str						 =     processor(images=__lowerCamelCase					,						return_tensors='''np'''  )

     for key in input_feat_extract.keys():
         self.assertAlmostEqual(input_feat_extract[key].sum()					,						input_processor[key].sum()					,						delta=1e-2  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Dict						 =     self.get_image_processor()
     __A    :						Any						 =     self.get_tokenizer()

     __A    :						Dict						 =     BlipProcessor(tokenizer=__lowerCamelCase					,						image_processor=__lowerCamelCase  )

     __A    :						Any						 =     '''lower newer'''

     __A    :						Any						 =     processor(text=__lowerCamelCase  )

     __A    :						List[str]						 =     tokenizer(__lowerCamelCase					,						return_token_type_ids=__lowerCamelCase  )

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

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Optional[int]						 =     self.get_image_processor()
     __A    :						Dict						 =     self.get_tokenizer()

     __A    :						int						 =     BlipProcessor(tokenizer=__lowerCamelCase					,						image_processor=__lowerCamelCase  )

     __A    :						Union[str, Any]						 =     '''lower newer'''
     __A    :						Union[str, Any]						 =     self.prepare_image_inputs()

     __A    :						List[Any]						 =     processor(text=__lowerCamelCase					,						images=__lowerCamelCase  )

     self.assertListEqual(list(inputs.keys()  )					,						['''pixel_values''', '''input_ids''', '''attention_mask''']  )

     # test if it raises when no input is passed
     with pytest.raises(__lowerCamelCase  ):
         processor()

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						List[str]						 =     self.get_image_processor()
     __A    :						Union[str, Any]						 =     self.get_tokenizer()

     __A    :						Union[str, Any]						 =     BlipProcessor(tokenizer=__lowerCamelCase					,						image_processor=__lowerCamelCase  )

     __A    :						str						 =     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     __A    :						Optional[Any]						 =     processor.batch_decode(__lowerCamelCase  )
     __A    :						str						 =     tokenizer.batch_decode(__lowerCamelCase  )

     self.assertListEqual(__lowerCamelCase					,						__lowerCamelCase  )







 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Any						 =     self.get_image_processor()
     __A    :						int						 =     self.get_tokenizer()

     __A    :						Tuple						 =     BlipProcessor(tokenizer=__lowerCamelCase					,						image_processor=__lowerCamelCase  )

     __A    :						List[Any]						 =     '''lower newer'''
     __A    :						Any						 =     self.prepare_image_inputs()

     __A    :						List[str]						 =     processor(text=__lowerCamelCase					,						images=__lowerCamelCase  )

     # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
     self.assertListEqual(list(inputs.keys()  )					,						['''pixel_values''', '''input_ids''', '''attention_mask''']  )











"""simple docstring"""



from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     ["""vqvae"""]

 def __init__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						):



     '''simple docstring'''




     super().__init__()
     self.register_modules(unet=__lowerCamelCase					,						scheduler=__lowerCamelCase					,						mel=__lowerCamelCase					,						vqvae=__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return 50 if isinstance(self.scheduler					,						__lowerCamelCase  ) else 1000

 @torch.no_grad()
 def __call__(    self					,						__lowerCamelCase = 1					,						__lowerCamelCase = None					,						__lowerCamelCase = None					,						__lowerCamelCase = 0					,						__lowerCamelCase = 0					,						__lowerCamelCase = None					,						__lowerCamelCase = None					,						__lowerCamelCase = 0					,						__lowerCamelCase = 0					,						__lowerCamelCase = None					,						__lowerCamelCase = 0					,						__lowerCamelCase = None					,						__lowerCamelCase = None					,						__lowerCamelCase=True					,						):



     '''simple docstring'''




     __A    :						Union[str, Any]						 =     steps or self.get_default_steps()
     self.scheduler.set_timesteps(__lowerCamelCase  )
     __A    :						Any						 =     step_generator or generator
     # For backwards compatibility
     if type(self.unet.config.sample_size  ) == int:
         __A    :						Optional[Any]						 =     (self.unet.config.sample_size, self.unet.config.sample_size)
     if noise is None:
         __A    :						Dict						 =     randn_tensor(
             (
                 batch_size,
                 self.unet.config.in_channels,
                 self.unet.config.sample_size[0],
                 self.unet.config.sample_size[1],
             )					,						generator=__lowerCamelCase					,						device=self.device					,						)
     __A    :						Optional[int]						 =     noise
     __A    :						int						 =     None

     if audio_file is not None or raw_audio is not None:
         self.mel.load_audio(__lowerCamelCase					,						__lowerCamelCase  )
         __A    :						int						 =     self.mel.audio_slice_to_image(__lowerCamelCase  )
         __A    :						Optional[Any]						 =     np.frombuffer(input_image.tobytes()					,						dtype='''uint8'''  ).reshape(
             (input_image.height, input_image.width)  )
         __A    :						Union[str, Any]						 =     (input_image / 255) * 2 - 1
         __A    :						Any						 =     torch.tensor(input_image[np.newaxis, :, :]					,						dtype=torch.float  ).to(self.device  )

         if self.vqvae is not None:
             __A    :						Any						 =     self.vqvae.encode(torch.unsqueeze(__lowerCamelCase					,						0  )  ).latent_dist.sample(
                 generator=__lowerCamelCase  )[0]
             __A    :						Optional[Any]						 =     self.vqvae.config.scaling_factor * input_images

         if start_step > 0:
             __A    :						Union[str, Any]						 =     self.scheduler.add_noise(__lowerCamelCase					,						__lowerCamelCase					,						self.scheduler.timesteps[start_step - 1]  )

         __A    :						Optional[int]						 =     (
             self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
         )
         __A    :						int						 =     int(mask_start_secs * pixels_per_second  )
         __A    :						Dict						 =     int(mask_end_secs * pixels_per_second  )
         __A    :						int						 =     self.scheduler.add_noise(__lowerCamelCase					,						__lowerCamelCase					,						torch.tensor(self.scheduler.timesteps[start_step:]  )  )

     for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]  )  ):
         if isinstance(self.unet					,						__lowerCamelCase  ):
             __A    :						List[str]						 =     self.unet(__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  )['''sample''']
         else:
             __A    :						List[str]						 =     self.unet(__lowerCamelCase					,						__lowerCamelCase  )['''sample''']

         if isinstance(self.scheduler					,						__lowerCamelCase  ):
             __A    :						Optional[Any]						 =     self.scheduler.step(
                 model_output=__lowerCamelCase					,						timestep=__lowerCamelCase					,						sample=__lowerCamelCase					,						eta=__lowerCamelCase					,						generator=__lowerCamelCase					,						)['''prev_sample''']
         else:
             __A    :						Optional[Any]						 =     self.scheduler.step(
                 model_output=__lowerCamelCase					,						timestep=__lowerCamelCase					,						sample=__lowerCamelCase					,						generator=__lowerCamelCase					,						)['''prev_sample''']

         if mask is not None:
             if mask_start > 0:
                 __A    :						Optional[Any]						 =     mask[:, step, :, :mask_start]
             if mask_end > 0:
                 __A    :						Tuple						 =     mask[:, step, :, -mask_end:]

     if self.vqvae is not None:
         # 0.18215 was scaling factor used in training to ensure unit variance
         __A    :						Union[str, Any]						 =     1 / self.vqvae.config.scaling_factor * images
         __A    :						List[str]						 =     self.vqvae.decode(__lowerCamelCase  )['''sample''']

     __A    :						Any						 =     (images / 2 + 0.5).clamp(0					,						1  )
     __A    :						Any						 =     images.cpu().permute(0					,						2					,						3					,						1  ).numpy()
     __A    :						Any						 =     (images * 255).round().astype('''uint8'''  )
     __A    :						List[Any]						 =     list(
         (Image.fromarray(_[:, :, 0]  ) for _ in images)
         if images.shape[3] == 1
         else (Image.fromarray(__lowerCamelCase					,						mode='''RGB'''  ).convert('''L'''  ) for _ in images)  )

     __A    :						Dict						 =     [self.mel.image_to_audio(__lowerCamelCase  ) for _ in images]
     if not return_dict:
         return images, (self.mel.get_sample_rate(), audios)

     return BaseOutput(**AudioPipelineOutput(np.array(__lowerCamelCase  )[:, np.newaxis, :]  )					,						**ImagePipelineOutput(__lowerCamelCase  )  )

 @torch.no_grad()
 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase = 50  ):



     '''simple docstring'''




     assert isinstance(self.scheduler					,						__lowerCamelCase  )
     self.scheduler.set_timesteps(__lowerCamelCase  )
     __A    :						str						 =     np.array(
         [np.frombuffer(image.tobytes()					,						dtype='''uint8'''  ).reshape((1, image.height, image.width)  ) for image in images]  )
     __A    :						Any						 =     (sample / 255) * 2 - 1
     __A    :						Dict						 =     torch.Tensor(__lowerCamelCase  ).to(self.device  )

     for t in self.progress_bar(torch.flip(self.scheduler.timesteps					,						(0,)  )  ):
         __A    :						Tuple						 =     t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
         __A    :						List[Any]						 =     self.scheduler.alphas_cumprod[t]
         __A    :						Optional[int]						 =     (
             self.scheduler.alphas_cumprod[prev_timestep]
             if prev_timestep >= 0
             else self.scheduler.final_alpha_cumprod
         )
         __A    :						List[Any]						 =     1 - alpha_prod_t
         __A    :						int						 =     self.unet(__lowerCamelCase					,						__lowerCamelCase  )['''sample''']
         __A    :						Union[str, Any]						 =     (1 - alpha_prod_t_prev) ** 0.5 * model_output
         __A    :						List[Any]						 =     (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
         __A    :						Union[str, Any]						 =     sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

     return sample







 @staticmethod
 def 					UpperCamelCase__(    __lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Tuple						 =     acos(torch.dot(torch.flatten(__lowerCamelCase  )					,						torch.flatten(__lowerCamelCase  )  ) / torch.norm(__lowerCamelCase  ) / torch.norm(__lowerCamelCase  )  )
     return sin((1 - alpha) * theta  ) * xa / sin(__lowerCamelCase  ) + sin(alpha * theta  ) * xa / sin(__lowerCamelCase  )




