

'''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


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

UpperCAmelCase					 =	256


class 	lowerCAmelCase  (   __snake_case ):
       lowerCAmelCase_												=			['melgan']







       def __init__(				self      :			Dict    ,   __lowercase      :			Optional[int]    ,   __lowercase      :			str    ,   __lowercase      :			Tuple    ,   __lowercase      :			Dict    ,   __lowercase      :			List[Any]    ,   ):







            """simple docstring"""







            super().__init__()

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

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







       def       snake_case (				self      :			Any    ,   __lowercase      :			Optional[int]    ,   __lowercase      :			List[str]=(-1.0, 1.0)    ,   __lowercase      :			Any=False   ):







            """simple docstring"""







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







            """simple docstring"""







            __lowercase      		=input_range
            __lowercase      		=torch.clip(A_    ,   A_    ,   A_   ) if clip else outputs
            # Scale to [0, 1].
            __lowercase      		=(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       snake_case (				self      :			Optional[int]    ,   __lowercase      :			List[Any]    ,   __lowercase      :			int    ,   __lowercase      :			Optional[Any]   ):







            """simple docstring"""







            __lowercase      		=input_tokens > 0
            __lowercase      		=self.notes_encoder(
                encoder_input_tokens=A_    ,   encoder_inputs_mask=A_   )

            __lowercase      		=self.continuous_encoder(
                encoder_inputs=A_    ,   encoder_inputs_mask=A_   )

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







       def       snake_case (				self      :			int    ,   __lowercase      :			List[str]    ,   __lowercase      :			List[str]    ,   __lowercase      :			str   ):







            """simple docstring"""







            __lowercase      		=noise_time
            if not torch.is_tensor(A_   ):
                 __lowercase      		=torch.tensor([timesteps]    ,   dtype=torch.long    ,   device=input_tokens.device   )
            elif torch.is_tensor(A_   ) and len(timesteps.shape   ) == 0:
                 __lowercase      		=timesteps[None].to(input_tokens.device   )

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

            __lowercase      		=self.decoder(
                encodings_and_masks=A_    ,   decoder_input_tokens=A_    ,   decoder_noise_time=A_   )
            return logits







       @torch.no_grad()
       def __call__(				self      :			Union[str, Any]    ,   __lowercase      :			Tuple    ,   __lowercase      :			Optional[Any] = None    ,   __lowercase      :			Optional[int] = 100    ,   __lowercase      :			int = True    ,   __lowercase      :			str = "numpy"    ,   __lowercase      :			List[str] = None    ,   __lowercase      :			List[Any] = 1    ,   ):







            """simple docstring"""







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

            __lowercase      		=np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]    ,   dtype=np.floataa   )
            __lowercase      		=np.zeros([1, 0, self.n_dims]    ,   np.floataa   )
            __lowercase      		=torch.ones((1, TARGET_FEATURE_LENGTH)    ,   dtype=A_    ,   device=self.device   )

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

                 __lowercase      		=self.scale_features(
                     A_    ,   output_range=[-1.0, 1.0]    ,   clip=A_   )

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

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

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

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

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

                 __lowercase      		=self.scale_to_features(A_    ,   input_range=[-1.0, 1.0]   )
                 __lowercase      		=mel[:1]
                 __lowercase      		=mel.cpu().float().numpy()

                 __lowercase      		=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(A_    ,   A_   )

                 logger.info('Generated segment'    ,   A_   )

            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":
                 __lowercase      		=self.melgan(input_features=full_pred_mel.astype(np.floataa   )   )
            else:
                 __lowercase      		=full_pred_mel

            if not return_dict:
                 return (output,)

            return AudioPipelineOutput(audios=A_   )









import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format



class    A__  :
      _UpperCAmelCase      :Union[str, Any]		       =  None





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   int			      =  self.feature_extraction_class(**self.feat_extract_dict  )
       UpperCamelCase      :   Union[str, Any]			      =  json.loads(feat_extract.to_json_string()  )
       for key, value in self.feat_extract_dict.items():
        self.assertEqual(obj[key]			,				A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   str			      =  self.feature_extraction_class(**self.feat_extract_dict  )

       with tempfile.TemporaryDirectory() as tmpdirname:
        UpperCamelCase      :   List[Any]			      =  os.path.join(A_			,				"feat_extract.json"  )
        feat_extract_first.to_json_file(A_  )
        UpperCamelCase      :   str			      =  self.feature_extraction_class.from_json_file(A_  )

       self.assertEqual(feat_extract_second.to_dict()			,				feat_extract_first.to_dict()  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Union[str, Any]			      =  self.feature_extraction_class(**self.feat_extract_dict  )

       with tempfile.TemporaryDirectory() as tmpdirname:
        UpperCamelCase      :   int			      =  feat_extract_first.save_pretrained(A_  )[0]
        check_json_file_has_correct_format(A_  )
        UpperCamelCase      :   Optional[Any]			      =  self.feature_extraction_class.from_pretrained(A_  )

       self.assertEqual(feat_extract_second.to_dict()			,				feat_extract_first.to_dict()  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Any			      =  self.feature_extraction_class()
       self.assertIsNotNone(A_  )
