from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
    from ..models.auto import AutoTokenizer


class 	lowerCamelCase_     :






    '''simple docstring'''






    def 			UpperCamelCase__   (	self , __lowercase)       ->	Optional[Any]:
        raise NotImplementedError()







    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        raise NotImplementedError()


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    def __init__(	self , __lowercase , __lowercase = False , **__lowercase)       ->	Optional[Any]:
        __UpperCamelCase     :Dict			  =		tokenizer
        __UpperCamelCase     :str			  =		skip_prompt
        __UpperCamelCase     :str			  =		decode_kwargs

        # variables used in the streaming process
        __UpperCamelCase     :List[str]			  =		[]
        __UpperCamelCase     :Any			  =		0
        __UpperCamelCase     :List[str]			  =		True







    def 			UpperCamelCase__   (	self , __lowercase)       ->	Optional[Any]:
        if len(value.shape) > 1 and value.shape[0] > 1:
            raise ValueError('''TextStreamer only supports batch size 1''')
        elif len(value.shape) > 1:
            __UpperCamelCase     :List[str]			  =		value[0]

        if self.skip_prompt and self.next_tokens_are_prompt:
            __UpperCamelCase     :int			  =		False
            return

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

        # After the symbol for a new line, we flush the cache.
        if text.endswith('''\n'''):
            __UpperCamelCase     :Optional[Any]			  =		text[self.print_len :]
            __UpperCamelCase     :Tuple			  =		[]
            __UpperCamelCase     :str			  =		0
        # If the last token is a CJK character, we print the characters.
        elif len(__lowercase) > 0 and self._is_chinese_char(ord(text[-1])):
            __UpperCamelCase     :List[Any]			  =		text[self.print_len :]
            self.print_len += len(__lowercase)
        # 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:
            __UpperCamelCase     :Optional[int]			  =		text[self.print_len : text.rfind(''' ''') + 1]
            self.print_len += len(__lowercase)

        self.on_finalized_text(__lowercase)







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

        __UpperCamelCase     :List[Any]			  =		True
        self.on_finalized_text(__lowercase , stream_end=__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = False)       ->	Union[str, Any]:
        print(__lowercase , flush=__lowercase , end='''''' if not stream_end else None)







    def 			UpperCamelCase__   (	self , __lowercase)       ->	Union[str, Any]:
        # 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 >= 0x4_E00 and cp <= 0x9_FFF)
            or (cp >= 0x3_400 and cp <= 0x4_DBF)  #
            or (cp >= 0x20_000 and cp <= 0x2A_6DF)  #
            or (cp >= 0x2A_700 and cp <= 0x2B_73F)  #
            or (cp >= 0x2B_740 and cp <= 0x2B_81F)  #
            or (cp >= 0x2B_820 and cp <= 0x2C_EAF)  #
            or (cp >= 0xF_900 and cp <= 0xF_AFF)
            or (cp >= 0x2F_800 and cp <= 0x2F_A1F)  #
        ):  #
            return True

        return False


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    def __init__(	self , __lowercase , __lowercase = False , __lowercase = None , **__lowercase)       ->	Dict:
        super().__init__(__lowercase , __lowercase , **__lowercase)
        __UpperCamelCase     :str			  =		Queue()
        __UpperCamelCase     :Dict			  =		None
        __UpperCamelCase     :Dict			  =		timeout







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = False)       ->	str:
        self.text_queue.put(__lowercase , timeout=self.timeout)
        if stream_end:
            self.text_queue.put(self.stop_signal , timeout=self.timeout)







    def __iter__(	self)       ->	Tuple:
        return self







    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        __UpperCamelCase     :Optional[Any]			  =		self.text_queue.get(timeout=self.timeout)
        if value == self.stop_signal:
            raise StopIteration()
        else:
            return value






import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
    from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


__lowercase							 =					logging.get_logger(__name__)  # pylint: disable=invalid-name

__lowercase							 =					256


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Tuple						=      ["""melgan"""]







    def __init__(	self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , )       ->	None:
        super().__init__()

        # From MELGAN
        __UpperCamelCase     :int			  =		math.log(1E-5)  # Matches MelGAN training.
        __UpperCamelCase     :int			  =		4.0  # Largest value for most examples
        __UpperCamelCase     :str			  =		128

        self.register_modules(
            notes_encoder=__lowercase , continuous_encoder=__lowercase , decoder=__lowercase , scheduler=__lowercase , melgan=__lowercase , )







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase=(-1.0, 1.0) , __lowercase=False)       ->	Dict:
        __UpperCamelCase  ,       __UpperCamelCase     :str			  =		output_range
        if clip:
            __UpperCamelCase     :Union[str, Any]			  =		torch.clip(__lowercase , self.min_value , self.max_value)
        # Scale to [0, 1].
        __UpperCamelCase     :Union[str, Any]			  =		(features - self.min_value) / (self.max_value - self.min_value)
        # Scale to [min_out, max_out].
        return zero_one * (max_out - min_out) + min_out







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase=(-1.0, 1.0) , __lowercase=False)       ->	Optional[int]:
        __UpperCamelCase  ,       __UpperCamelCase     :int			  =		input_range
        __UpperCamelCase     :Optional[int]			  =		torch.clip(__lowercase , __lowercase , __lowercase) if clip else outputs
        # Scale to [0, 1].
        __UpperCamelCase     :List[str]			  =		(outputs - min_out) / (max_out - min_out)
        # Scale to [self.min_value, self.max_value].
        return zero_one * (self.max_value - self.min_value) + self.min_value







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase)       ->	List[Any]:
        __UpperCamelCase     :List[str]			  =		input_tokens > 0
        __UpperCamelCase  ,       __UpperCamelCase     :Union[str, Any]			  =		self.notes_encoder(
            encoder_input_tokens=__lowercase , encoder_inputs_mask=__lowercase)

        __UpperCamelCase  ,       __UpperCamelCase     :Union[str, Any]			  =		self.continuous_encoder(
            encoder_inputs=__lowercase , encoder_inputs_mask=__lowercase)

        return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase)       ->	str:
        __UpperCamelCase     :Optional[int]			  =		noise_time
        if not torch.is_tensor(__lowercase):
            __UpperCamelCase     :str			  =		torch.tensor([timesteps] , dtype=torch.long , device=input_tokens.device)
        elif torch.is_tensor(__lowercase) and len(timesteps.shape) == 0:
            __UpperCamelCase     :Dict			  =		timesteps[None].to(input_tokens.device)

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        __UpperCamelCase     :List[str]			  =		timesteps * torch.ones(input_tokens.shape[0] , dtype=timesteps.dtype , device=timesteps.device)

        __UpperCamelCase     :Tuple			  =		self.decoder(
            encodings_and_masks=__lowercase , decoder_input_tokens=__lowercase , decoder_noise_time=__lowercase)
        return logits







    @torch.no_grad()
    def __call__(	self , __lowercase , __lowercase = None , __lowercase = 100 , __lowercase = True , __lowercase = "numpy" , __lowercase = None , __lowercase = 1 , )       ->	Union[AudioPipelineOutput, Tuple]:
        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(__lowercase , __lowercase) or callback_steps <= 0)
        ):
            raise ValueError(
                f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
                f""" {type(__lowercase)}.""")

        __UpperCamelCase     :Union[str, Any]			  =		np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims] , dtype=np.floataa)
        __UpperCamelCase     :Union[str, Any]			  =		np.zeros([1, 0, self.n_dims] , np.floataa)
        __UpperCamelCase     :Union[str, Any]			  =		torch.ones((1, TARGET_FEATURE_LENGTH) , dtype=__lowercase , device=self.device)

        for i, encoder_input_tokens in enumerate(__lowercase):
            if i == 0:
                __UpperCamelCase     :int			  =		torch.from_numpy(pred_mel[:1].copy()).to(
                    device=self.device , dtype=self.decoder.dtype)
                # The first chunk has no previous context.
                __UpperCamelCase     :int			  =		torch.zeros((1, TARGET_FEATURE_LENGTH) , dtype=__lowercase , device=self.device)
            else:
                # The full song pipeline does not feed in a context feature, so the mask
                # will be all 0s after the feature converter. Because we know we're
                # feeding in a full context chunk from the previous prediction, set it
                # to all 1s.
                __UpperCamelCase     :Tuple			  =		ones

            __UpperCamelCase     :Optional[Any]			  =		self.scale_features(
                __lowercase , output_range=[-1.0, 1.0] , clip=__lowercase)

            __UpperCamelCase     :int			  =		self.encode(
                input_tokens=torch.IntTensor([encoder_input_tokens]).to(device=self.device) , continuous_inputs=__lowercase , continuous_mask=__lowercase , )

            # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
            __UpperCamelCase     :int			  =		randn_tensor(
                shape=encoder_continuous_inputs.shape , generator=__lowercase , device=self.device , dtype=self.decoder.dtype , )

            # set step values
            self.scheduler.set_timesteps(__lowercase)

            # Denoising diffusion loop
            for j, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
                __UpperCamelCase     :Optional[int]			  =		self.decode(
                    encodings_and_masks=__lowercase , input_tokens=__lowercase , noise_time=t / self.scheduler.config.num_train_timesteps , )

                # Compute previous output: x_t -> x_t-1
                __UpperCamelCase     :int			  =		self.scheduler.step(__lowercase , __lowercase , __lowercase , generator=__lowercase).prev_sample

            __UpperCamelCase     :Tuple			  =		self.scale_to_features(__lowercase , input_range=[-1.0, 1.0])
            __UpperCamelCase     :List[Any]			  =		mel[:1]
            __UpperCamelCase     :Optional[Any]			  =		mel.cpu().float().numpy()

            __UpperCamelCase     :Any			  =		np.concatenate([full_pred_mel, pred_mel[:1]] , axis=1)

            # call the callback, if provided
            if callback is not None and i % callback_steps == 0:
                callback(__lowercase , __lowercase)

            logger.info('''Generated segment''' , __lowercase)

        if output_type == "numpy" and not is_onnx_available():
            raise ValueError(
                '''Cannot return output in \'np\' format if ONNX is not available. Make sure to have ONNX installed or set \'output_type\' to \'mel\'.''')
        elif output_type == "numpy" and self.melgan is None:
            raise ValueError(
                '''Cannot return output in \'np\' format if melgan component is not defined. Make sure to define `self.melgan` or set \'output_type\' to \'mel\'.''')

        if output_type == "numpy":
            __UpperCamelCase     :Optional[Any]			  =		self.melgan(input_features=full_pred_mel.astype(np.floataa))
        else:
            __UpperCamelCase     :List[str]			  =		full_pred_mel

        if not return_dict:
            return (output,)

        return AudioPipelineOutput(audios=__lowercase)





