


from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
       from ..models.auto import AutoTokenizer


class   a__	:

       def 						UpperCAmelCase(   self      :     Optional[int]      ,							lowerCamelCase_      :     Optional[int]    ):
            raise NotImplementedError()

       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            raise NotImplementedError()


class   a__	(						lowerCAmelCase_    ):

       def __init__(   self      :     int      ,							lowerCamelCase_      :     "AutoTokenizer"      ,							lowerCamelCase_      :     bool = False      ,							**lowerCamelCase_      :     int    ):
            a_    :     List[Any]										=    tokenizer
            a_    :     Optional[Any]										=    skip_prompt
            a_    :     List[Any]										=    decode_kwargs

            # variables used in the streaming process
            a_    :     Dict										=    []
            a_    :     Union[str, Any]										=    0
            a_    :     str										=    True

       def 						UpperCAmelCase(   self      :     Optional[int]      ,							lowerCamelCase_      :     Optional[int]    ):
            if len(value.shape    ) > 1 and value.shape[0] > 1:
                 raise ValueError("""TextStreamer only supports batch size 1"""    )
            elif len(value.shape    ) > 1:
                 a_    :     List[Any]										=    value[0]

            if self.skip_prompt and self.next_tokens_are_prompt:
                 a_    :     List[Any]										=    False
                 return

            # Add the new token to the cache and decodes the entire thing.
            self.token_cache.extend(value.tolist()    )
            a_    :     Any										=    self.tokenizer.decode(self.token_cache      ,							**self.decode_kwargs    )

            # After the symbol for a new line, we flush the cache.
            if text.endswith("""\n"""    ):
                 a_    :     str										=    text[self.print_len :]
                 a_    :     Optional[Any]										=    []
                 a_    :     Union[str, Any]										=    0
            # If the last token is a CJK character, we print the characters.
            elif len(lowerCamelCase_    ) > 0 and self._is_chinese_char(ord(text[-1]    )    ):
                 a_    :     List[str]										=    text[self.print_len :]
                 self.print_len += len(lowerCamelCase_    )
            # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
            # which may change with the subsequent token -- there are probably smarter ways to do this!)
            else:
                 a_    :     Optional[Any]										=    text[self.print_len : text.rfind(""" """    ) + 1]
                 self.print_len += len(lowerCamelCase_    )

            self.on_finalized_text(lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     List[str]    ):
            # Flush the cache, if it exists
            if len(self.token_cache    ) > 0:
                 a_    :     Union[str, Any]										=    self.tokenizer.decode(self.token_cache      ,							**self.decode_kwargs    )
                 a_    :     str										=    text[self.print_len :]
                 a_    :     Optional[int]										=    []
                 a_    :     List[Any]										=    0
            else:
                 a_    :     Union[str, Any]										=    """"""

            a_    :     Any										=    True
            self.on_finalized_text(lowerCamelCase_      ,							stream_end=lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     str      ,							lowerCamelCase_      :     str      ,							lowerCamelCase_      :     bool = False    ):
            print(lowerCamelCase_      ,							flush=lowerCamelCase_      ,							end="""""" if not stream_end else None    )

       def 						UpperCAmelCase(   self      :     Any      ,							lowerCamelCase_      :     str    ):
            # This defines a "chinese character" as anything in the CJK Unicode block:
            #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
            #
            # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
            # despite its name. The modern Korean Hangul alphabet is a different block,
            # as is Japanese Hiragana and Katakana. Those alphabets are used to write
            # space-separated words, so they are not treated specially and handled
            # like the all of the other languages.
            if (
                (cp >= 0X4E00 and cp <= 0X9FFF)
                or (cp >= 0X3400 and cp <= 0X4DBF)  #
                or (cp >= 0X20000 and cp <= 0X2A6DF)  #
                or (cp >= 0X2A700 and cp <= 0X2B73F)  #
                or (cp >= 0X2B740 and cp <= 0X2B81F)  #
                or (cp >= 0X2B820 and cp <= 0X2CEAF)  #
                or (cp >= 0XF900 and cp <= 0XFAFF)
                or (cp >= 0X2F800 and cp <= 0X2FA1F)  #
            ):  #
                 return True

            return False


class   a__	(						lowerCAmelCase_    ):

       def __init__(   self      :     Optional[int]      ,							lowerCamelCase_      :     "AutoTokenizer"      ,							lowerCamelCase_      :     bool = False      ,							lowerCamelCase_      :     Optional[float] = None      ,							**lowerCamelCase_      :     Union[str, Any]    ):
            super().__init__(lowerCamelCase_      ,							lowerCamelCase_      ,							**lowerCamelCase_    )
            a_    :     Optional[int]										=    Queue()
            a_    :     str										=    None
            a_    :     int										=    timeout

       def 						UpperCAmelCase(   self      :     Any      ,							lowerCamelCase_      :     str      ,							lowerCamelCase_      :     bool = False    ):
            self.text_queue.put(lowerCamelCase_      ,							timeout=self.timeout    )
            if stream_end:
                 self.text_queue.put(self.stop_signal      ,							timeout=self.timeout    )

       def __iter__(   self      :     Optional[int]    ):
            return self

       def 						UpperCAmelCase(   self      :     str    ):
            a_    :     Dict										=    self.text_queue.get(timeout=self.timeout    )
            if value == self.stop_signal:
                 raise StopIteration()
            else:
                 return value







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__	(						lowerCAmelCase_    ):
       lowerCamelCase__:     List[str]							    =     ["""vqvae"""]

       def __init__(   self      :     str      ,							lowerCamelCase_      :     AutoencoderKL      ,							lowerCamelCase_      :     UNetaDConditionModel      ,							lowerCamelCase_      :     Mel      ,							lowerCamelCase_      :     Union[DDIMScheduler, DDPMScheduler]      ,							):
            super().__init__()
            self.register_modules(unet=lowerCamelCase_      ,							scheduler=lowerCamelCase_      ,							mel=lowerCamelCase_      ,							vqvae=lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Any    ):
            return 5_0 if isinstance(self.scheduler      ,							lowerCamelCase_    ) else 1_0_0_0

       @torch.no_grad()
       def __call__(   self      :     List[str]      ,							lowerCamelCase_      :     int = 1      ,							lowerCamelCase_      :     str = None      ,							lowerCamelCase_      :     np.ndarray = None      ,							lowerCamelCase_      :     int = 0      ,							lowerCamelCase_      :     int = 0      ,							lowerCamelCase_      :     int = None      ,							lowerCamelCase_      :     torch.Generator = None      ,							lowerCamelCase_      :     float = 0      ,							lowerCamelCase_      :     float = 0      ,							lowerCamelCase_      :     torch.Generator = None      ,							lowerCamelCase_      :     float = 0      ,							lowerCamelCase_      :     torch.Tensor = None      ,							lowerCamelCase_      :     torch.Tensor = None      ,							lowerCamelCase_      :     List[Any]=True      ,							):
            a_    :     Optional[Any]										=    steps or self.get_default_steps()
            self.scheduler.set_timesteps(lowerCamelCase_    )
            a_    :     Optional[int]										=    step_generator or generator
            # For backwards compatibility
            if type(self.unet.config.sample_size    ) == int:
                 a_    :     str										=    (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_    :     Tuple										=    noise
            a_    :     Union[str, Any]										=    None

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

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

                 if start_step > 0:
                      a_    :     Tuple										=    self.scheduler.add_noise(lowerCamelCase_      ,							lowerCamelCase_      ,							self.scheduler.timesteps[start_step - 1]    )

                 a_    :     Union[str, Any]										=    (
                     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_    :     Tuple										=    int(mask_end_secs * pixels_per_second    )
                 a_    :     Tuple										=    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_    :     Dict										=    self.unet(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_    )["""sample"""]
                 else:
                      a_    :     Dict										=    self.unet(lowerCamelCase_      ,							lowerCamelCase_    )["""sample"""]

                 if isinstance(self.scheduler      ,							lowerCamelCase_    ):
                      a_    :     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_    :     Any										=    mask[:, step, :, :mask_start]
                      if mask_end > 0:
                           a_    :     Dict										=    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_    :     List[Any]										=    self.vqvae.decode(lowerCamelCase_    )["""sample"""]

            a_    :     Union[str, Any]										=    (images / 2 + 0.5).clamp(0      ,							1    )
            a_    :     Dict										=    images.cpu().permute(0      ,							2      ,							3      ,							1    ).numpy()
            a_    :     Any										=    (images * 2_5_5).round().astype("""uint8"""    )
            a_    :     int										=    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 						UpperCAmelCase(   self      :     List[str]      ,							lowerCamelCase_      :     List[Image.Image]      ,							lowerCamelCase_      :     int = 5_0    ):
            assert isinstance(self.scheduler      ,							lowerCamelCase_    )
            self.scheduler.set_timesteps(lowerCamelCase_    )
            a_    :     Any										=    np.array(
                [np.frombuffer(image.tobytes()      ,							dtype="""uint8"""    ).reshape((1, image.height, image.width)    ) for image in images]    )
            a_    :     Union[str, Any]										=    (sample / 2_5_5) * 2 - 1
            a_    :     Optional[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_    :     Any										=    self.scheduler.alphas_cumprod[t]
                 a_    :     Dict										=    (
                     self.scheduler.alphas_cumprod[prev_timestep]
                     if prev_timestep >= 0
                     else self.scheduler.final_alpha_cumprod
                 )
                 a_    :     Optional[Any]										=    1 - alpha_prod_t
                 a_    :     Optional[int]										=    self.unet(lowerCamelCase_      ,							lowerCamelCase_    )["""sample"""]
                 a_    :     Tuple										=    (1 - alpha_prod_t_prev) ** 0.5 * model_output
                 a_    :     Tuple										=    (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
                 a_    :     Tuple										=    sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

            return sample

       @staticmethod
       def 						UpperCAmelCase(   lowerCamelCase_      :     torch.Tensor      ,							lowerCamelCase_      :     torch.Tensor      ,							lowerCamelCase_      :     float    ):
            a_    :     Dict										=    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_    )



