
'''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  A				(							__snake_case						):
     __magic_name__						=		['''vqvae''']





     def __init__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			) ->				str:






         """simple docstring"""






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





     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






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





     @torch.no_grad()
     def __call__(							self						,			SCREAMING_SNAKE_CASE = 1						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = 0						,			SCREAMING_SNAKE_CASE = 0						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = 0						,			SCREAMING_SNAKE_CASE = 0						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = 0						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE=True						,			) ->				Union[
         Union[AudioPipelineOutput, ImagePipelineOutput],
         Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
     ]:






         """simple docstring"""






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

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

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

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

             A       :   Optional[Any]					=						(
                 self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
             )
             A       :   str					=						int(mask_start_secs * pixels_per_second	)
             A       :   int					=						int(mask_end_secs * pixels_per_second	)
             A       :   List[Any]					=						self.scheduler.add_noise(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			torch.tensor(self.scheduler.timesteps[start_step:]	)	)

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

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

             if mask is not None:
                 if mask_start > 0:
                     A       :   str					=						mask[:, step, :, :mask_start]
                 if mask_end > 0:
                     A       :   List[str]					=						mask[:, step, :, -mask_end:]

         if self.vqvae is not None:
             # 0.18215 was scaling factor used in training to ensure unit variance
             A       :   str					=						1 / self.vqvae.config.scaling_factor * images
             A       :   Tuple					=						self.vqvae.decode(SCREAMING_SNAKE_CASE	)['''sample''']

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

         A       :   Any					=						[self.mel.image_to_audio(SCREAMING_SNAKE_CASE	) for _ in images]
         if not return_dict:
             return images, (self.mel.get_sample_rate(), audios)

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





     @torch.no_grad()
     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = 50	) ->				np.ndarray:






         """simple docstring"""






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

         for t in self.progress_bar(torch.flip(self.scheduler.timesteps						,			(0,)	)	):
             A       :   Union[str, Any]					=						t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
             A       :   Union[str, Any]					=						self.scheduler.alphas_cumprod[t]
             A       :   Tuple					=						(
                 self.scheduler.alphas_cumprod[prev_timestep]
                 if prev_timestep >= 0
                 else self.scheduler.final_alpha_cumprod
             )
             A       :   List[Any]					=						1 - alpha_prod_t
             A       :   Dict					=						self.unet(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)['''sample''']
             A       :   List[str]					=						(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 				__lowerCAmelCase							(							SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				torch.Tensor:






         """simple docstring"""






         A       :   List[str]					=						acos(torch.dot(torch.flatten(SCREAMING_SNAKE_CASE	)						,			torch.flatten(SCREAMING_SNAKE_CASE	)	) / torch.norm(SCREAMING_SNAKE_CASE	) / torch.norm(SCREAMING_SNAKE_CASE	)	)
         return sin((1 - alpha) * theta	) * xa / sin(SCREAMING_SNAKE_CASE	) + sin(alpha * theta	) * xa / sin(SCREAMING_SNAKE_CASE	)






'''simple docstring'''




import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
      import jax

      from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
      from transformers.models.bert.modeling_flax_bert import FlaxBertModel
      from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel





@require_flax
class  A				(							unittest.TestCase						):





     @slow
     def 				__lowerCAmelCase							(							self	) ->				List[Any]:






         """simple docstring"""






         for model_name in ["bert-base-cased", "bert-large-uncased"]:
             with self.subTest(SCREAMING_SNAKE_CASE	):
                 A       :   int					=						AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE	)
                 self.assertIsNotNone(SCREAMING_SNAKE_CASE	)
                 self.assertIsInstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

                 A       :   List[str]					=						FlaxAutoModel.from_pretrained(SCREAMING_SNAKE_CASE	)
                 self.assertIsNotNone(SCREAMING_SNAKE_CASE	)
                 self.assertIsInstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)





     @slow
     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         for model_name in ["roberta-base", "roberta-large"]:
             with self.subTest(SCREAMING_SNAKE_CASE	):
                 A       :   Any					=						AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE	)
                 self.assertIsNotNone(SCREAMING_SNAKE_CASE	)
                 self.assertIsInstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

                 A       :   Any					=						FlaxAutoModel.from_pretrained(SCREAMING_SNAKE_CASE	)
                 self.assertIsNotNone(SCREAMING_SNAKE_CASE	)
                 self.assertIsInstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)





     @slow
     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         for model_name in ["bert-base-cased", "bert-large-uncased"]:
             A       :   Optional[int]					=						AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE	)
             A       :   List[str]					=						FlaxBertModel.from_pretrained(SCREAMING_SNAKE_CASE	)
             A       :   Optional[Any]					=						tokenizer('''Do you support jax jitted function?'''						,			return_tensors=TensorType.JAX	)

             @jax.jit
             def eval(**SCREAMING_SNAKE_CASE	):
                 return model(**SCREAMING_SNAKE_CASE	)

             eval(**SCREAMING_SNAKE_CASE	).block_until_ready()





     @slow
     def 				__lowerCAmelCase							(							self	) ->				List[str]:






         """simple docstring"""






         for model_name in ["roberta-base", "roberta-large"]:
             A       :   List[str]					=						AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE	)
             A       :   Union[str, Any]					=						FlaxRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE	)
             A       :   int					=						tokenizer('''Do you support jax jitted function?'''						,			return_tensors=TensorType.JAX	)

             @jax.jit
             def eval(**SCREAMING_SNAKE_CASE	):
                 return model(**SCREAMING_SNAKE_CASE	)

             eval(**SCREAMING_SNAKE_CASE	).block_until_ready()





     def 				__lowerCAmelCase							(							self	) ->				List[str]:






         """simple docstring"""






         with self.assertRaisesRegex(
             SCREAMING_SNAKE_CASE						,			'''bert-base is not a local folder and is not a valid model identifier'''	):
             A       :   List[Any]					=						FlaxAutoModel.from_pretrained('''bert-base'''	)





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         with self.assertRaisesRegex(
             SCREAMING_SNAKE_CASE						,			R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''	):
             A       :   Optional[int]					=						FlaxAutoModel.from_pretrained(SCREAMING_SNAKE_CASE						,			revision='''aaaaaa'''	)





     def 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






         with self.assertRaisesRegex(
             SCREAMING_SNAKE_CASE						,			'''hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack'''						,			):
             A       :   List[str]					=						FlaxAutoModel.from_pretrained('''hf-internal-testing/config-no-model'''	)





     def 				__lowerCAmelCase							(							self	) ->				Optional[Any]:






         """simple docstring"""






         with self.assertRaisesRegex(SCREAMING_SNAKE_CASE						,			'''Use `from_pt=True` to load this model'''	):
             A       :   Any					=						FlaxAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only'''	)




