

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


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

_UpperCamelCase 			=		256







class   __lowercase      (lowerCAmelCase_ ):
 _UpperCamelCase			  =  ["""melgan"""]
 def __init__(							self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							)					->Optional[Any]:



       '''simple docstring'''





       super().__init__()

       # From MELGAN
       __lowerCAmelCase					:						Optional[int]					=					math.log(1e-5					)  # Matches MelGAN training.
       __lowerCAmelCase					:						Any					=					4.0  # Largest value for most examples
       __lowerCAmelCase					:						str					=					128

       self.register_modules(
           notes_encoder=snake_case_      ,							continuous_encoder=snake_case_      ,							decoder=snake_case_      ,							scheduler=snake_case_      ,							melgan=snake_case_      ,							)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_=(-1.0, 1.0)      ,							A_=False					)					->Dict:



       '''simple docstring'''





       __lowerCAmelCase,							__lowerCAmelCase					:						Optional[Any]					=					output_range
       if clip:
             __lowerCAmelCase					:						Any					=					torch.clip(snake_case_      ,							self.min_value      ,							self.max_value					)
       # Scale to [0, 1].
       __lowerCAmelCase					:						List[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      ,							A_      ,							A_=(-1.0, 1.0)      ,							A_=False					)					->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase,							__lowerCAmelCase					:						List[Any]					=					input_range
       __lowerCAmelCase					:						str					=					torch.clip(snake_case_      ,							snake_case_      ,							snake_case_					) if clip else outputs
       # Scale to [0, 1].
       __lowerCAmelCase					:						Tuple					=					(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      ,							A_      ,							A_      ,							A_					)					->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[Any]					=					input_tokens > 0
       __lowerCAmelCase,							__lowerCAmelCase					:						List[str]					=					self.notes_encoder(
           encoder_input_tokens=snake_case_      ,							encoder_inputs_mask=snake_case_					)

       __lowerCAmelCase,							__lowerCAmelCase					:						List[str]					=					self.continuous_encoder(
           encoder_inputs=snake_case_      ,							encoder_inputs_mask=snake_case_					)

       return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_					)					->Any:



       '''simple docstring'''





       __lowerCAmelCase					:						List[str]					=					noise_time
       if not torch.is_tensor(snake_case_					):
             __lowerCAmelCase					:						Union[str, Any]					=					torch.tensor([timesteps]      ,							dtype=torch.long      ,							device=input_tokens.device					)
       elif torch.is_tensor(snake_case_					) and len(timesteps.shape					) == 0:
             __lowerCAmelCase					:						int					=					timesteps[None].to(input_tokens.device					)

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

       __lowerCAmelCase					:						Union[str, Any]					=					self.decoder(
           encodings_and_masks=snake_case_      ,							decoder_input_tokens=snake_case_      ,							decoder_noise_time=snake_case_					)
       return logits





 @torch.no_grad()
 def __call__(							self      ,							A_      ,							A_ = None      ,							A_ = 100      ,							A_ = True      ,							A_ = "numpy"      ,							A_ = None      ,							A_ = 1      ,							)					->Tuple:



       '''simple docstring'''





       if (callback_steps is None) or (
           callback_steps is not None and (not isinstance(snake_case_      ,							snake_case_					) or callback_steps <= 0)
       ):
             raise ValueError(
                 f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
                 f""" {type(snake_case_					)}."""					)

       __lowerCAmelCase					:						Tuple					=					np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]      ,							dtype=np.floataa					)
       __lowerCAmelCase					:						List[str]					=					np.zeros([1, 0, self.n_dims]      ,							np.floataa					)
       __lowerCAmelCase					:						List[str]					=					torch.ones((1, TARGET_FEATURE_LENGTH)      ,							dtype=snake_case_      ,							device=self.device					)

       for i, encoder_input_tokens in enumerate(snake_case_					):
             if i == 0:
                   __lowerCAmelCase					:						str					=					torch.from_numpy(pred_mel[:1].copy()					).to(
                       device=self.device      ,							dtype=self.decoder.dtype					)
                   # The first chunk has no previous context.
                   __lowerCAmelCase					:						Tuple					=					torch.zeros((1, TARGET_FEATURE_LENGTH)      ,							dtype=snake_case_      ,							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.
                   __lowerCAmelCase					:						Optional[int]					=					ones

             __lowerCAmelCase					:						List[Any]					=					self.scale_features(
                 snake_case_      ,							output_range=[-1.0, 1.0]      ,							clip=snake_case_					)

             __lowerCAmelCase					:						List[str]					=					self.encode(
                 input_tokens=torch.IntTensor([encoder_input_tokens]					).to(device=self.device					)      ,							continuous_inputs=snake_case_      ,							continuous_mask=snake_case_      ,							)

             # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
             __lowerCAmelCase					:						str					=					randn_tensor(
                 shape=encoder_continuous_inputs.shape      ,							generator=snake_case_      ,							device=self.device      ,							dtype=self.decoder.dtype      ,							)

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

             # Denoising diffusion loop
             for j, t in enumerate(self.progress_bar(self.scheduler.timesteps					)					):
                   __lowerCAmelCase					:						List[str]					=					self.decode(
                       encodings_and_masks=snake_case_      ,							input_tokens=snake_case_      ,							noise_time=t / self.scheduler.config.num_train_timesteps      ,							)

                   # Compute previous output: x_t -> x_t-1
                   __lowerCAmelCase					:						Optional[int]					=					self.scheduler.step(snake_case_      ,							snake_case_      ,							snake_case_      ,							generator=snake_case_					).prev_sample

             __lowerCAmelCase					:						str					=					self.scale_to_features(snake_case_      ,							input_range=[-1.0, 1.0]					)
             __lowerCAmelCase					:						Union[str, Any]					=					mel[:1]
             __lowerCAmelCase					:						Optional[Any]					=					mel.cpu().float().numpy()

             __lowerCAmelCase					:						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(snake_case_      ,							snake_case_					)

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

       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":
             __lowerCAmelCase					:						Union[str, Any]					=					self.melgan(input_features=full_pred_mel.astype(np.floataa					)					)
       else:
             __lowerCAmelCase					:						Optional[int]					=					full_pred_mel

       if not return_dict:
             return (output,)

       return AudioPipelineOutput(audios=snake_case_					)









'''simple docstring'''




from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


__SCREAMING_SNAKE_CASE     :Dict       =				logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     :int       =				{
    '''deepmind/language-perceiver''': '''https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json''',
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : int           =							"""perceiver"""




 def __init__(					self       :    Any	,			snake_case_       :    List[Any]=2_5_6	,			snake_case_       :    str=1_2_8_0	,			snake_case_       :    Optional[int]=7_6_8	,			snake_case_       :    int=1	,			snake_case_       :    List[Any]=2_6	,			snake_case_       :    Dict=8	,			snake_case_       :    List[Any]=8	,			snake_case_       :    Tuple=None	,			snake_case_       :    Tuple=None	,			snake_case_       :    Any="kv"	,			snake_case_       :    Any=1	,			snake_case_       :    List[str]=1	,			snake_case_       :    Optional[int]="gelu"	,			snake_case_       :    List[Any]=0.1	,			snake_case_       :    Dict=0.0_2	,			snake_case_       :    int=1e-12	,			snake_case_       :    List[str]=True	,			snake_case_       :    str=2_6_2	,			snake_case_       :    Optional[Any]=2_0_4_8	,			snake_case_       :    Union[str, Any]=5_6	,			snake_case_       :    Dict=[3_6_8, 4_9_6]	,			snake_case_       :    Tuple=1_6	,			snake_case_       :    Union[str, Any]=1_9_2_0	,			snake_case_       :    List[Any]=1_6	,			snake_case_       :    Tuple=[1, 1_6, 2_2_4, 2_2_4]	,			**snake_case_       :    List[Any]	,			):
      super().__init__(**snake_case_    )

      _UpperCAmelCase     							=   num_latents
      _UpperCAmelCase     							=   d_latents
      _UpperCAmelCase     							=   d_model
      _UpperCAmelCase     							=   num_blocks
      _UpperCAmelCase     							=   num_self_attends_per_block
      _UpperCAmelCase     							=   num_self_attention_heads
      _UpperCAmelCase     							=   num_cross_attention_heads
      _UpperCAmelCase     							=   qk_channels
      _UpperCAmelCase     							=   v_channels
      _UpperCAmelCase     							=   cross_attention_shape_for_attention
      _UpperCAmelCase     							=   self_attention_widening_factor
      _UpperCAmelCase     							=   cross_attention_widening_factor
      _UpperCAmelCase     							=   hidden_act
      _UpperCAmelCase     							=   attention_probs_dropout_prob
      _UpperCAmelCase     							=   initializer_range
      _UpperCAmelCase     							=   layer_norm_eps
      _UpperCAmelCase     							=   use_query_residual
      # masked language modeling attributes
      _UpperCAmelCase     							=   vocab_size
      _UpperCAmelCase     							=   max_position_embeddings
      # image classification attributes
      _UpperCAmelCase     							=   image_size
      # flow attributes
      _UpperCAmelCase     							=   train_size
      # multimodal autoencoding attributes
      _UpperCAmelCase     							=   num_frames
      _UpperCAmelCase     							=   audio_samples_per_frame
      _UpperCAmelCase     							=   samples_per_patch
      _UpperCAmelCase     							=   output_shape







class   A_	(  lowerCAmelCase_			):




 @property
 def      lowercase	(					self       :    int    ):
      if self.task == "multiple-choice":
           _UpperCAmelCase     							=   {0: "batch", 1: "choice", 2: "sequence"}
      else:
           _UpperCAmelCase     							=   {0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ("inputs", dynamic_axis),
              ("attention_mask", dynamic_axis),
          ]    )




 @property
 def      lowercase	(					self       :    Optional[Any]    ):
      return 1e-4




 def      lowercase	(					self       :    List[str]	,			snake_case_       :    Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]	,			snake_case_       :    int = -1	,			snake_case_       :    int = -1	,			snake_case_       :    int = -1	,			snake_case_       :    bool = False	,			snake_case_       :    Optional[TensorType] = None	,			snake_case_       :    int = 3	,			snake_case_       :    int = 4_0	,			snake_case_       :    int = 4_0	,			):
      # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

      if isinstance(snake_case_	,			snake_case_    ):
           # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
           _UpperCAmelCase     							=   compute_effective_axis_dimension(
               snake_case_	,			fixed_dimension=OnnxConfig.default_fixed_batch	,			num_token_to_add=0    )
           # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
           _UpperCAmelCase     							=   preprocessor.num_special_tokens_to_add(snake_case_    )
           _UpperCAmelCase     							=   compute_effective_axis_dimension(
               snake_case_	,			fixed_dimension=OnnxConfig.default_fixed_sequence	,			num_token_to_add=snake_case_    )
           # Generate dummy inputs according to compute batch and sequence
           _UpperCAmelCase     							=   [" ".join(["a"]    ) * seq_length] * batch_size
           _UpperCAmelCase     							=   dict(preprocessor(snake_case_	,			return_tensors=snake_case_    )    )
           _UpperCAmelCase     							=   inputs.pop("input_ids"    )
           return inputs
      elif isinstance(snake_case_	,			snake_case_    ) and preprocessor.model_input_names[0] == "pixel_values":
           # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
           _UpperCAmelCase     							=   compute_effective_axis_dimension(snake_case_	,			fixed_dimension=OnnxConfig.default_fixed_batch    )
           _UpperCAmelCase     							=   self._generate_dummy_images(snake_case_	,			snake_case_	,			snake_case_	,			snake_case_    )
           _UpperCAmelCase     							=   dict(preprocessor(images=snake_case_	,			return_tensors=snake_case_    )    )
           _UpperCAmelCase     							=   inputs.pop("pixel_values"    )
           return inputs
      else:
           raise ValueError(
               "Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."    )




