
import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class      lowercase__    (							__A     ,					unittest.TestCase  ):
  __UpperCamelCase	     =  RobertaTokenizer
  __UpperCamelCase	     =  RobertaTokenizerFast
  __UpperCamelCase	     =  True
  __UpperCamelCase	     =  {"""cls_token""": """<s>"""}







  def 	UpperCAmelCase__		(	self						):
       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       lowerCAmelCase_					:       str    =		[
           """l""",
           """o""",
           """w""",
           """e""",
           """r""",
           """s""",
           """t""",
           """i""",
           """d""",
           """n""",
           """\u0120""",
           """\u0120l""",
           """\u0120n""",
           """\u0120lo""",
           """\u0120low""",
           """er""",
           """\u0120lowest""",
           """\u0120newer""",
           """\u0120wider""",
           """<unk>""",
       ]
       lowerCAmelCase_					:       int    =		dict(zip(_lowercase    ,						range(len(_lowercase						)						)						)						)
       lowerCAmelCase_					:       Tuple    =		["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
       lowerCAmelCase_					:       List[Any]    =		{"""unk_token""": """<unk>"""}

       lowerCAmelCase_					:       Any    =		os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["""vocab_file"""]						)
       lowerCAmelCase_					:       Dict    =		os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["""merges_file"""]						)
       with open(self.vocab_file    ,						"""w"""    ,						encoding="""utf-8"""						) as fp:
            fp.write(json.dumps(_lowercase						) + """\n"""						)
       with open(self.merges_file    ,						"""w"""    ,						encoding="""utf-8"""						) as fp:
            fp.write("""\n""".join(_lowercase						)						)







  def 	UpperCAmelCase__		(	self    ,						**_lowercase						):
       kwargs.update(self.special_tokens_map						)
       return self.tokenizer_class.from_pretrained(self.tmpdirname    ,						**_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						**_lowercase						):
       kwargs.update(self.special_tokens_map						)
       return RobertaTokenizerFast.from_pretrained(self.tmpdirname    ,						**_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       lowerCAmelCase_					:       int    =		"""lower newer"""
       lowerCAmelCase_					:       Union[str, Any]    =		"""lower newer"""
       return input_text, output_text







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       int    =		self.tokenizer_class(self.vocab_file    ,						self.merges_file    ,						**self.special_tokens_map						)
       lowerCAmelCase_					:       Tuple    =		"""lower newer"""
       lowerCAmelCase_					:       List[Any]    =		["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
       lowerCAmelCase_					:       List[str]    =		tokenizer.tokenize(_lowercase						)  # , add_prefix_space=True)
       self.assertListEqual(_lowercase    ,						_lowercase						)

       lowerCAmelCase_					:       Optional[int]    =		tokens + [tokenizer.unk_token]
       lowerCAmelCase_					:       Optional[int]    =		[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase						)    ,						_lowercase						)







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Any    =		self.get_tokenizer()

       self.assertListEqual(tokenizer.encode("""Hello world!"""    ,						add_special_tokens=_lowercase						)    ,						[0, 31_414, 232, 328, 2]						)
       self.assertListEqual(
           tokenizer.encode("""Hello world! cécé herlolip 418"""    ,						add_special_tokens=_lowercase						)    ,						[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]    ,						)







  @slow
  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       str    =		self.tokenizer_class.from_pretrained("""roberta-base"""						)

       lowerCAmelCase_					:       Tuple    =		tokenizer.encode("""sequence builders"""    ,						add_special_tokens=_lowercase						)
       lowerCAmelCase_					:       List[str]    =		tokenizer.encode("""multi-sequence build"""    ,						add_special_tokens=_lowercase						)

       lowerCAmelCase_					:       Any    =		tokenizer.encode(
           """sequence builders"""    ,						add_special_tokens=_lowercase    ,						add_prefix_space=_lowercase						)
       lowerCAmelCase_					:       Dict    =		tokenizer.encode(
           """sequence builders"""    ,						"""multi-sequence build"""    ,						add_special_tokens=_lowercase    ,						add_prefix_space=_lowercase						)

       lowerCAmelCase_					:       Tuple    =		tokenizer.build_inputs_with_special_tokens(_lowercase						)
       lowerCAmelCase_					:       int    =		tokenizer.build_inputs_with_special_tokens(_lowercase    ,						_lowercase						)

       assert encoded_sentence == encoded_text_from_decode
       assert encoded_pair == encoded_pair_from_decode







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Tuple    =		self.get_tokenizer()

       lowerCAmelCase_					:       Tuple    =		"""Encode this sequence."""
       lowerCAmelCase_					:       List[str]    =		tokenizer.byte_encoder[""" """.encode("""utf-8"""						)[0]]

       # Testing encoder arguments
       lowerCAmelCase_					:       Optional[int]    =		tokenizer.encode(_lowercase    ,						add_special_tokens=_lowercase    ,						add_prefix_space=_lowercase						)
       lowerCAmelCase_					:       Any    =		tokenizer.convert_ids_to_tokens(encoded[0]						)[0]
       self.assertNotEqual(_lowercase    ,						_lowercase						)

       lowerCAmelCase_					:       Any    =		tokenizer.encode(_lowercase    ,						add_special_tokens=_lowercase    ,						add_prefix_space=_lowercase						)
       lowerCAmelCase_					:       Optional[int]    =		tokenizer.convert_ids_to_tokens(encoded[0]						)[0]
       self.assertEqual(_lowercase    ,						_lowercase						)

       tokenizer.add_special_tokens({"""bos_token""": """<s>"""}						)
       lowerCAmelCase_					:       Optional[int]    =		tokenizer.encode(_lowercase    ,						add_special_tokens=_lowercase						)
       lowerCAmelCase_					:       List[Any]    =		tokenizer.convert_ids_to_tokens(encoded[1]						)[0]
       self.assertNotEqual(_lowercase    ,						_lowercase						)

       # Testing spaces after special tokens
       lowerCAmelCase_					:       Optional[Any]    =		"""<mask>"""
       tokenizer.add_special_tokens(
           {"""mask_token""": AddedToken(_lowercase    ,						lstrip=_lowercase    ,						rstrip=_lowercase						)}						)  # mask token has a left space
       lowerCAmelCase_					:       Dict    =		tokenizer.convert_tokens_to_ids(_lowercase						)

       lowerCAmelCase_					:       Optional[Any]    =		"""Encode <mask> sequence"""
       lowerCAmelCase_					:       Union[str, Any]    =		"""Encode <mask>sequence"""

       lowerCAmelCase_					:       Dict    =		tokenizer.encode(_lowercase						)
       lowerCAmelCase_					:       List[str]    =		encoded.index(_lowercase						)
       lowerCAmelCase_					:       Tuple    =		tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]						)[0]
       self.assertEqual(_lowercase    ,						_lowercase						)

       lowerCAmelCase_					:       Dict    =		tokenizer.encode(_lowercase						)
       lowerCAmelCase_					:       Dict    =		encoded.index(_lowercase						)
       lowerCAmelCase_					:       Union[str, Any]    =		tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]						)[0]
       self.assertNotEqual(_lowercase    ,						_lowercase						)







  def 	UpperCAmelCase__		(	self						):
       pass







  def 	UpperCAmelCase__		(	self						):
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'						):
                 lowerCAmelCase_					:       Union[str, Any]    =		self.rust_tokenizer_class.from_pretrained(_lowercase    ,						**_lowercase						)
                 lowerCAmelCase_					:       Union[str, Any]    =		self.tokenizer_class.from_pretrained(_lowercase    ,						**_lowercase						)
                 lowerCAmelCase_					:       List[Any]    =		"""A, <mask> AllenNLP sentence."""
                 lowerCAmelCase_					:       List[Any]    =		tokenizer_r.encode_plus(_lowercase    ,						add_special_tokens=_lowercase    ,						return_token_type_ids=_lowercase						)
                 lowerCAmelCase_					:       List[str]    =		tokenizer_p.encode_plus(_lowercase    ,						add_special_tokens=_lowercase    ,						return_token_type_ids=_lowercase						)

                 # token_type_ids should put 0 everywhere
                 self.assertEqual(sum(tokens_r["""token_type_ids"""]						)    ,						sum(tokens_p["""token_type_ids"""]						)						)

                 # attention_mask should put 1 everywhere, so sum over length should be 1
                 self.assertEqual(
                     sum(tokens_r["""attention_mask"""]						) / len(tokens_r["""attention_mask"""]						)    ,						sum(tokens_p["""attention_mask"""]						) / len(tokens_p["""attention_mask"""]						)    ,						)

                 lowerCAmelCase_					:       Optional[int]    =		tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]						)
                 lowerCAmelCase_					:       str    =		tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]						)

                 # Rust correctly handles the space before the mask while python doesnt
                 self.assertSequenceEqual(tokens_p["""input_ids"""]    ,						[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]						)
                 self.assertSequenceEqual(tokens_r["""input_ids"""]    ,						[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]						)

                 self.assertSequenceEqual(
                     _lowercase    ,						["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]						)
                 self.assertSequenceEqual(
                     _lowercase    ,						["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]						)







  def 	UpperCAmelCase__		(	self						):
       for trim_offsets, add_prefix_space in itertools.product([True, False]    ,						repeat=2						):
            lowerCAmelCase_					:       str    =		self.rust_tokenizer_class.from_pretrained(
                self.tmpdirname    ,						use_fast=_lowercase    ,						add_prefix_space=_lowercase    ,						trim_offsets=_lowercase						)

            lowerCAmelCase_					:       Tuple    =		json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()						)
            lowerCAmelCase_					:       str    =		json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()						)

            self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]    ,						_lowercase						)

            self.assertEqual(post_processor_state["""add_prefix_space"""]    ,						_lowercase						)
            self.assertEqual(post_processor_state["""trim_offsets"""]    ,						_lowercase						)







  def 	UpperCAmelCase__		(	self						):
       # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
       # `trim_offsets`
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'						):
                 lowerCAmelCase_					:       Tuple    =		"""hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
                 lowerCAmelCase_					:       List[str]    =		F'{text_of_1_token} {text_of_1_token}'

                 lowerCAmelCase_					:       List[str]    =		self.rust_tokenizer_class.from_pretrained(
                     _lowercase    ,						use_fast=_lowercase    ,						add_prefix_space=_lowercase    ,						trim_offsets=_lowercase						)
                 lowerCAmelCase_					:       Optional[Any]    =		tokenizer_r(_lowercase    ,						return_offsets_mapping=_lowercase    ,						add_special_tokens=_lowercase						)
                 self.assertEqual(encoding.offset_mapping[0]    ,						(0, len(_lowercase						))						)
                 self.assertEqual(
                     encoding.offset_mapping[1]    ,						(len(_lowercase						) + 1, len(_lowercase						) + 1 + len(_lowercase						))    ,						)

                 lowerCAmelCase_					:       List[str]    =		self.rust_tokenizer_class.from_pretrained(
                     _lowercase    ,						use_fast=_lowercase    ,						add_prefix_space=_lowercase    ,						trim_offsets=_lowercase						)
                 lowerCAmelCase_					:       Tuple    =		tokenizer_r(_lowercase    ,						return_offsets_mapping=_lowercase    ,						add_special_tokens=_lowercase						)
                 self.assertEqual(encoding.offset_mapping[0]    ,						(0, len(_lowercase						))						)
                 self.assertEqual(
                     encoding.offset_mapping[1]    ,						(len(_lowercase						) + 1, len(_lowercase						) + 1 + len(_lowercase						))    ,						)

                 lowerCAmelCase_					:       Optional[Any]    =		self.rust_tokenizer_class.from_pretrained(
                     _lowercase    ,						use_fast=_lowercase    ,						add_prefix_space=_lowercase    ,						trim_offsets=_lowercase						)
                 lowerCAmelCase_					:       Optional[Any]    =		tokenizer_r(_lowercase    ,						return_offsets_mapping=_lowercase    ,						add_special_tokens=_lowercase						)
                 self.assertEqual(encoding.offset_mapping[0]    ,						(0, len(_lowercase						))						)
                 self.assertEqual(
                     encoding.offset_mapping[1]    ,						(len(_lowercase						), len(_lowercase						) + 1 + len(_lowercase						))    ,						)

                 lowerCAmelCase_					:       Optional[Any]    =		self.rust_tokenizer_class.from_pretrained(
                     _lowercase    ,						use_fast=_lowercase    ,						add_prefix_space=_lowercase    ,						trim_offsets=_lowercase						)
                 lowerCAmelCase_					:       Union[str, Any]    =		tokenizer_r(_lowercase    ,						return_offsets_mapping=_lowercase    ,						add_special_tokens=_lowercase						)
                 self.assertEqual(encoding.offset_mapping[0]    ,						(0, len(_lowercase						))						)
                 self.assertEqual(
                     encoding.offset_mapping[1]    ,						(len(_lowercase						), len(_lowercase						) + 1 + len(_lowercase						))    ,						)

                 lowerCAmelCase_					:       List[Any]    =		F' {text}'

                 # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                 #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                 # )
                 # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                 # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                 # self.assertEqual(
                 #     encoding.offset_mapping[1],
                 #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                 # )

                 lowerCAmelCase_					:       Union[str, Any]    =		self.rust_tokenizer_class.from_pretrained(
                     _lowercase    ,						use_fast=_lowercase    ,						add_prefix_space=_lowercase    ,						trim_offsets=_lowercase						)
                 lowerCAmelCase_					:       str    =		tokenizer_r(_lowercase    ,						return_offsets_mapping=_lowercase    ,						add_special_tokens=_lowercase						)
                 self.assertEqual(encoding.offset_mapping[0]    ,						(1, 1 + len(_lowercase						))						)
                 self.assertEqual(
                     encoding.offset_mapping[1]    ,						(1 + len(_lowercase						) + 1, 1 + len(_lowercase						) + 1 + len(_lowercase						))    ,						)

                 lowerCAmelCase_					:       str    =		self.rust_tokenizer_class.from_pretrained(
                     _lowercase    ,						use_fast=_lowercase    ,						add_prefix_space=_lowercase    ,						trim_offsets=_lowercase						)
                 lowerCAmelCase_					:       str    =		tokenizer_r(_lowercase    ,						return_offsets_mapping=_lowercase    ,						add_special_tokens=_lowercase						)
                 self.assertEqual(encoding.offset_mapping[0]    ,						(0, 1 + len(_lowercase						))						)
                 self.assertEqual(
                     encoding.offset_mapping[1]    ,						(1 + len(_lowercase						), 1 + len(_lowercase						) + 1 + len(_lowercase						))    ,						)

                 lowerCAmelCase_					:       int    =		self.rust_tokenizer_class.from_pretrained(
                     _lowercase    ,						use_fast=_lowercase    ,						add_prefix_space=_lowercase    ,						trim_offsets=_lowercase						)
                 lowerCAmelCase_					:       int    =		tokenizer_r(_lowercase    ,						return_offsets_mapping=_lowercase    ,						add_special_tokens=_lowercase						)
                 self.assertEqual(encoding.offset_mapping[0]    ,						(0, 1 + len(_lowercase						))						)
                 self.assertEqual(
                     encoding.offset_mapping[1]    ,						(1 + len(_lowercase						), 1 + len(_lowercase						) + 1 + len(_lowercase						))    ,						)







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      lowercase__    (							__A  ):
  __UpperCamelCase	     =  ["""vqvae"""]







  def __init__(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						):
       super().__init__()
       self.register_modules(unet=_lowercase    ,						scheduler=_lowercase    ,						mel=_lowercase    ,						vqvae=_lowercase						)







  def 	UpperCAmelCase__		(	self						):
       return 50 if isinstance(self.scheduler    ,						_lowercase						) else 1_000







  @torch.no_grad()
  def __call__(	self    ,						_lowercase = 1    ,						_lowercase = None    ,						_lowercase = None    ,						_lowercase = 0    ,						_lowercase = 0    ,						_lowercase = None    ,						_lowercase = None    ,						_lowercase = 0    ,						_lowercase = 0    ,						_lowercase = None    ,						_lowercase = 0    ,						_lowercase = None    ,						_lowercase = None    ,						_lowercase=True    ,						):
       lowerCAmelCase_					:       Tuple    =		steps or self.get_default_steps()
       self.scheduler.set_timesteps(_lowercase						)
       lowerCAmelCase_					:       Union[str, Any]    =		step_generator or generator
       # For backwards compatibility
       if type(self.unet.config.sample_size						) == int:
            lowerCAmelCase_					:       Optional[Any]    =		(self.unet.config.sample_size, self.unet.config.sample_size)
       if noise is None:
            lowerCAmelCase_					:       Tuple    =		randn_tensor(
                (
                    batch_size,
                    self.unet.config.in_channels,
                    self.unet.config.sample_size[0],
                    self.unet.config.sample_size[1],
                )    ,						generator=_lowercase    ,						device=self.device    ,						)
       lowerCAmelCase_					:       List[str]    =		noise
       lowerCAmelCase_					:       List[str]    =		None

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

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

            if start_step > 0:
                 lowerCAmelCase_					:       str    =		self.scheduler.add_noise(_lowercase    ,						_lowercase    ,						self.scheduler.timesteps[start_step - 1]						)

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

       for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]						)						):
            if isinstance(self.unet    ,						_lowercase						):
                 lowerCAmelCase_					:       List[Any]    =		self.unet(_lowercase    ,						_lowercase    ,						_lowercase						)["""sample"""]
            else:
                 lowerCAmelCase_					:       Any    =		self.unet(_lowercase    ,						_lowercase						)["""sample"""]

            if isinstance(self.scheduler    ,						_lowercase						):
                 lowerCAmelCase_					:       str    =		self.scheduler.step(
                     model_output=_lowercase    ,						timestep=_lowercase    ,						sample=_lowercase    ,						eta=_lowercase    ,						generator=_lowercase    ,						)["""prev_sample"""]
            else:
                 lowerCAmelCase_					:       List[str]    =		self.scheduler.step(
                     model_output=_lowercase    ,						timestep=_lowercase    ,						sample=_lowercase    ,						generator=_lowercase    ,						)["""prev_sample"""]

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

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

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

       lowerCAmelCase_					:       str    =		[self.mel.image_to_audio(_lowercase						) for _ in images]
       if not return_dict:
            return images, (self.mel.get_sample_rate(), audios)

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







  @torch.no_grad()
  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = 50						):
       assert isinstance(self.scheduler    ,						_lowercase						)
       self.scheduler.set_timesteps(_lowercase						)
       lowerCAmelCase_					:       List[Any]    =		np.array(
           [np.frombuffer(image.tobytes()    ,						dtype="""uint8"""						).reshape((1, image.height, image.width)						) for image in images]						)
       lowerCAmelCase_					:       List[str]    =		(sample / 255) * 2 - 1
       lowerCAmelCase_					:       Optional[Any]    =		torch.Tensor(_lowercase						).to(self.device						)

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

       return sample







  @staticmethod
  def 	UpperCAmelCase__		(	_lowercase    ,						_lowercase    ,						_lowercase						):
       lowerCAmelCase_					:       Optional[int]    =		acos(torch.dot(torch.flatten(_lowercase						)    ,						torch.flatten(_lowercase						)						) / torch.norm(_lowercase						) / torch.norm(_lowercase						)						)
       return sin((1 - alpha) * theta						) * xa / sin(_lowercase						) + sin(alpha * theta						) * xa / sin(_lowercase						)
