






import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
     from transformers.models.rag.configuration_rag import RagConfig
     from transformers.models.rag.tokenization_rag import RagTokenizer







@require_faiss
@require_torch
class     __lowerCAmelCase	(   UpperCamelCase__):







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



        '''simple docstring'''




        a__     : Optional[Any]  =tempfile.mkdtemp()
        a__     : Union[str, Any]  =8

        # DPR tok
        a__     : Optional[Any]  =[
            "[UNK]",
            "[CLS]",
            "[SEP]",
            "[PAD]",
            "[MASK]",
            "want",
            "##want",
            "##ed",
            "wa",
            "un",
            "runn",
            "##ing",
            ",",
            "low",
            "lowest",
        ]
        a__     : List[str]  =os.path.join(self.tmpdirname		,				"dpr_tokenizer"							)
        os.makedirs(lowerCAmelCase__		,				exist_ok=lowerCAmelCase__							)
        a__     : Any  =os.path.join(lowerCAmelCase__		,				DPR_VOCAB_FILES_NAMES["vocab_file"]							)
        with open(self.vocab_file		,				"w"		,				encoding="utf-8"							) as vocab_writer:
          vocab_writer.write("".join([x + "\n" for x in vocab_tokens]							)							)

        # BART tok
        a__     : List[str]  =[
            "l",
            "o",
            "w",
            "e",
            "r",
            "s",
            "t",
            "i",
            "d",
            "n",
            "\u0120",
            "\u0120l",
            "\u0120n",
            "\u0120lo",
            "\u0120low",
            "er",
            "\u0120lowest",
            "\u0120newer",
            "\u0120wider",
            "<unk>",
        ]
        a__     : int  =dict(zip(lowerCAmelCase__		,				range(len(lowerCAmelCase__							)							)							)							)
        a__     : Dict  =["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
        a__     : Dict  ={"unk_token": "<unk>"}

        a__     : int  =os.path.join(self.tmpdirname		,				"bart_tokenizer"							)
        os.makedirs(lowerCAmelCase__		,				exist_ok=lowerCAmelCase__							)
        a__     : Any  =os.path.join(lowerCAmelCase__		,				BART_VOCAB_FILES_NAMES["vocab_file"]							)
        a__     : str  =os.path.join(lowerCAmelCase__		,				BART_VOCAB_FILES_NAMES["merges_file"]							)
        with open(self.vocab_file		,				"w"		,				encoding="utf-8"							) as fp:
          fp.write(json.dumps(lowerCAmelCase__							) + "\n"							)
        with open(self.merges_file		,				"w"		,				encoding="utf-8"							) as fp:
          fp.write("\n".join(lowerCAmelCase__							)							)







      def        _lowercase  (   self							)       ->						DPRQuestionEncoderTokenizer:



        '''simple docstring'''




        return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname		,				"dpr_tokenizer"							)							)







      def        _lowercase  (   self							)       ->						BartTokenizer:



        '''simple docstring'''




        return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname		,				"bart_tokenizer"							)							)







      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        shutil.rmtree(self.tmpdirname							)







      @require_tokenizers
      def        _lowercase  (   self							)       ->						List[str]:



        '''simple docstring'''




        a__     : int  =os.path.join(self.tmpdirname		,				"rag_tokenizer"							)
        a__     : Optional[int]  =RagConfig(question_encoder=DPRConfig().to_dict()		,				generator=BartConfig().to_dict()							)
        a__     : int  =RagTokenizer(question_encoder=self.get_dpr_tokenizer()		,				generator=self.get_bart_tokenizer()							)
        rag_config.save_pretrained(lowerCAmelCase__							)
        rag_tokenizer.save_pretrained(lowerCAmelCase__							)
        a__     : Any  =RagTokenizer.from_pretrained(lowerCAmelCase__		,				config=lowerCAmelCase__							)
        self.assertIsInstance(new_rag_tokenizer.question_encoder		,				lowerCAmelCase__							)
        self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()		,				rag_tokenizer.question_encoder.get_vocab()							)
        self.assertIsInstance(new_rag_tokenizer.generator		,				lowerCAmelCase__							)
        self.assertEqual(new_rag_tokenizer.generator.get_vocab()		,				rag_tokenizer.generator.get_vocab()							)







      @slow
      def        _lowercase  (   self							)       ->						Optional[int]:



        '''simple docstring'''




        a__     : List[str]  =RagTokenizer.from_pretrained("facebook/rag-token-nq"							)
        a__     : Any  =[
            "who got the first nobel prize in physics",
            "when is the next deadpool movie being released",
            "which mode is used for short wave broadcast service",
            "who is the owner of reading football club",
            "when is the next scandal episode coming out",
            "when is the last time the philadelphia won the superbowl",
            "what is the most current adobe flash player version",
            "how many episodes are there in dragon ball z",
            "what is the first step in the evolution of the eye",
            "where is gall bladder situated in human body",
            "what is the main mineral in lithium batteries",
            "who is the president of usa right now",
            "where do the greasers live in the outsiders",
            "panda is a national animal of which country",
            "what is the name of manchester united stadium",
        ]
        a__     : Optional[int]  =tokenizer(lowerCAmelCase__							)
        self.assertIsNotNone(lowerCAmelCase__							)







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



        '''simple docstring'''




        a__     : List[Any]  =RagTokenizer.from_pretrained("facebook/rag-sequence-nq"							)
        a__     : Optional[Any]  =[
            "who got the first nobel prize in physics",
            "when is the next deadpool movie being released",
            "which mode is used for short wave broadcast service",
            "who is the owner of reading football club",
            "when is the next scandal episode coming out",
            "when is the last time the philadelphia won the superbowl",
            "what is the most current adobe flash player version",
            "how many episodes are there in dragon ball z",
            "what is the first step in the evolution of the eye",
            "where is gall bladder situated in human body",
            "what is the main mineral in lithium batteries",
            "who is the president of usa right now",
            "where do the greasers live in the outsiders",
            "panda is a national animal of which country",
            "what is the name of manchester united stadium",
        ]
        a__     : Optional[Any]  =tokenizer(lowerCAmelCase__							)
        self.assertIsNotNone(lowerCAmelCase__							)













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     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : str            =      ["""vqvae"""]







      def __init__(   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				)       ->						Union[str, Any]:



        '''simple docstring'''




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







      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        return 5_0 if isinstance(self.scheduler		,				lowerCAmelCase__							) else 1_0_0_0







      @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		,				)       ->						Union[
          Union[AudioPipelineOutput, ImagePipelineOutput],
          Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
      ]:



        '''simple docstring'''




        a__     : List[Any]  =steps or self.get_default_steps()
        self.scheduler.set_timesteps(lowerCAmelCase__							)
        a__     : Tuple  =step_generator or generator
        # For backwards compatibility
        if type(self.unet.config.sample_size							) == int:
          a__     : List[str]  =(self.unet.config.sample_size, self.unet.config.sample_size)
        if noise is None:
          a__     : Optional[Any]  =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__     : List[str]  =noise
        a__     : Optional[Any]  =None

        if audio_file is not None or raw_audio is not None:
          self.mel.load_audio(lowerCAmelCase__		,				lowerCAmelCase__							)
          a__     : Tuple  =self.mel.audio_slice_to_image(lowerCAmelCase__							)
          a__     : List[Any]  =np.frombuffer(input_image.tobytes()		,				dtype="uint8"							).reshape(
              (input_image.height, input_image.width)							)
          a__     : Optional[Any]  =(input_image / 2_5_5) * 2 - 1
          a__     : Dict  =torch.tensor(input_image[np.newaxis, :, :]		,				dtype=torch.float							).to(self.device							)

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

          if start_step > 0:
            a__     : Optional[int]  =self.scheduler.add_noise(lowerCAmelCase__		,				lowerCAmelCase__		,				self.scheduler.timesteps[start_step - 1]							)

          a__     : Tuple  =(
              self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
          )
          a__     : Union[str, Any]  =int(mask_start_secs * pixels_per_second							)
          a__     : List[str]  =int(mask_end_secs * pixels_per_second							)
          a__     : Optional[Any]  =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__     : Optional[Any]  =self.unet(lowerCAmelCase__		,				lowerCAmelCase__							)["sample"]

          if isinstance(self.scheduler		,				lowerCAmelCase__							):
            a__     : int  =self.scheduler.step(
                model_output=lowerCAmelCase__		,				timestep=lowerCAmelCase__		,				sample=lowerCAmelCase__		,				eta=lowerCAmelCase__		,				generator=lowerCAmelCase__		,				)["prev_sample"]
          else:
            a__     : str  =self.scheduler.step(
                model_output=lowerCAmelCase__		,				timestep=lowerCAmelCase__		,				sample=lowerCAmelCase__		,				generator=lowerCAmelCase__		,				)["prev_sample"]

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

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

        a__     : str  =(images / 2 + 0.5).clamp(0		,				1							)
        a__     : int  =images.cpu().permute(0		,				2		,				3		,				1							).numpy()
        a__     : List[Any]  =(images * 2_5_5).round().astype("uint8"							)
        a__     : Dict  =list(
            (Image.fromarray(_[:, :, 0]							) for _ in images)
            if images.shape[3] == 1
            else (Image.fromarray(lowerCAmelCase__		,				mode="RGB"							).convert("L"							) for _ in images)							)

        a__     : str  =[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        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = 5_0							)       ->						np.ndarray:



        '''simple docstring'''




        assert isinstance(self.scheduler		,				lowerCAmelCase__							)
        self.scheduler.set_timesteps(lowerCAmelCase__							)
        a__     : Union[str, Any]  =np.array(
            [np.frombuffer(image.tobytes()		,				dtype="uint8"							).reshape((1, image.height, image.width)							) for image in images]							)
        a__     : Tuple  =(sample / 2_5_5) * 2 - 1
        a__     : List[Any]  =torch.Tensor(lowerCAmelCase__							).to(self.device							)

        for t in self.progress_bar(torch.flip(self.scheduler.timesteps		,				(0,)							)							):
          a__     : str  =t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
          a__     : Dict  =self.scheduler.alphas_cumprod[t]
          a__     : Optional[Any]  =(
              self.scheduler.alphas_cumprod[prev_timestep]
              if prev_timestep >= 0
              else self.scheduler.final_alpha_cumprod
          )
          a__     : Optional[Any]  =1 - alpha_prod_t
          a__     : str  =self.unet(lowerCAmelCase__		,				lowerCAmelCase__							)["sample"]
          a__     : Optional[Any]  =(1 - alpha_prod_t_prev) ** 0.5 * model_output
          a__     : List[str]  =(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
          a__     : Optional[Any]  =sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

        return sample







      @staticmethod
      def        _lowercase  (   lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						torch.Tensor:



        '''simple docstring'''




        a__     : Any  =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__							)






