'''simple docstring'''






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


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

__a										=					256





class     A__							(       UpperCAmelCase_   ):





   """simple docstring"""





   UpperCamelCase_       :						Tuple    				=     ["""melgan"""]




   def __init__( self     :		List[Any]						,     lowerCAmelCase__     :		Optional[Any]						,     lowerCAmelCase__     :		Any						,     lowerCAmelCase__     :		Union[str, Any]						,     lowerCAmelCase__     :		Union[str, Any]						,     lowerCAmelCase__     :		str						,     )       ->    None:







      """simple docstring"""




      super().__init__()

      # From MELGAN
      _UpperCAmelCase							:	int							=   math.log(1e-5			)  # Matches MelGAN training.
      _UpperCAmelCase							:	int							=   4.0  # Largest value for most examples
      _UpperCAmelCase							:	str							=   1_2_8

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




   def 			_lowerCAmelCase		( self     :		Dict						,     lowerCAmelCase__     :		Optional[Any]						,     lowerCAmelCase__     :		Union[str, Any]=(-1.0, 1.0)						,     lowerCAmelCase__     :		str=False			)       ->    Dict:







      """simple docstring"""




      _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 			_lowerCAmelCase		( self     :		Optional[int]						,     lowerCAmelCase__     :		Tuple						,     lowerCAmelCase__     :		str=(-1.0, 1.0)						,     lowerCAmelCase__     :		Union[str, Any]=False			)       ->    Optional[int]:







      """simple docstring"""




      _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 			_lowerCAmelCase		( self     :		Optional[int]						,     lowerCAmelCase__     :		Dict						,     lowerCAmelCase__     :		Dict						,     lowerCAmelCase__     :		Optional[int]			)       ->    List[Any]:







      """simple docstring"""




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

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

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




   def 			_lowerCAmelCase		( self     :		Tuple						,     lowerCAmelCase__     :		Dict						,     lowerCAmelCase__     :		int						,     lowerCAmelCase__     :		Any			)       ->    str:







      """simple docstring"""




      _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     :		int						,     lowerCAmelCase__     :		Dict						,     lowerCAmelCase__     :		int = None						,     lowerCAmelCase__     :		int = 1_0_0						,     lowerCAmelCase__     :		Dict = True						,     lowerCAmelCase__     :		str = "numpy"						,     lowerCAmelCase__     :		int = None						,     lowerCAmelCase__     :		Tuple = 1						,     )       ->    Union[AudioPipelineOutput, Tuple]:







      """simple docstring"""




      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			)
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


__lowercase							 =					logging.get_logger(__name__)

__lowercase							 =					{'''tokenizer_file''': '''tokenizer.json'''}

__lowercase							 =					{
    '''tokenizer_file''': {
        '''bigscience/tokenizer''': '''https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json''',
        '''bigscience/bloom-560m''': '''https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json''',
        '''bigscience/bloom-1b1''': '''https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json''',
        '''bigscience/bloom-1b7''': '''https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json''',
        '''bigscience/bloom-3b''': '''https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json''',
        '''bigscience/bloom-7b1''': '''https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json''',
        '''bigscience/bloom''': '''https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json''',
    },
}


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						int						=      VOCAB_FILES_NAMES
    a__	:						Tuple						=      PRETRAINED_VOCAB_FILES_MAP
    a__	:						List[str]						=      ["""input_ids""", """attention_mask"""]
    a__	:						int						=      None







    def __init__(	self , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase="<unk>" , __lowercase="<s>" , __lowercase="</s>" , __lowercase="<pad>" , __lowercase=False , __lowercase=False , **__lowercase , )       ->	List[str]:
        super().__init__(
            __lowercase , __lowercase , tokenizer_file=__lowercase , unk_token=__lowercase , bos_token=__lowercase , eos_token=__lowercase , pad_token=__lowercase , add_prefix_space=__lowercase , clean_up_tokenization_spaces=__lowercase , **__lowercase , )
        __UpperCamelCase     :int			  =		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
        if pre_tok_state.get('''add_prefix_space''' , __lowercase) != add_prefix_space:
            __UpperCamelCase     :Any			  =		getattr(__lowercase , pre_tok_state.pop('''type'''))
            __UpperCamelCase     :str			  =		add_prefix_space
            __UpperCamelCase     :List[str]			  =		pre_tok_class(**__lowercase)

        __UpperCamelCase     :Tuple			  =		add_prefix_space







    def 			UpperCamelCase__   (	self , *__lowercase , **__lowercase)       ->	BatchEncoding:
        __UpperCamelCase     :Tuple			  =		kwargs.get('''is_split_into_words''' , __lowercase)
        if not (self.add_prefix_space or not is_split_into_words):
            raise Exception(
                f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
                ''' pretokenized inputs.''')

        return super()._batch_encode_plus(*__lowercase , **__lowercase)







    def 			UpperCamelCase__   (	self , *__lowercase , **__lowercase)       ->	BatchEncoding:
        __UpperCamelCase     :List[str]			  =		kwargs.get('''is_split_into_words''' , __lowercase)

        if not (self.add_prefix_space or not is_split_into_words):
            raise Exception(
                f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
                ''' pretokenized inputs.''')

        return super()._encode_plus(*__lowercase , **__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = None)       ->	Tuple[str]:
        __UpperCamelCase     :Optional[Any]			  =		self._tokenizer.model.save(__lowercase , name=__lowercase)
        return tuple(__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase)       ->	List[int]:
        __UpperCamelCase     :str			  =		[]
        for is_user, text in conversation.iter_texts():
            input_ids.extend(self.encode(__lowercase , add_special_tokens=__lowercase) + [self.eos_token_id])

        if len(__lowercase) > self.model_max_length:
            __UpperCamelCase     :Any			  =		input_ids[-self.model_max_length :]
        return input_ids





