


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


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

SCREAMING_SNAKE_CASE							    =							2_5_6


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      ["""melgan"""]


     def __init__(	self  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A  ,       ):
        super().__init__()

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

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


     def 	snake_case_  (	self  ,       __A  ,       __A=(-1.0, 1.0)  ,       __A=False    ):
        __a	,    __a					=  output_range
        if clip:
           __a					=  torch.clip(__A  ,       self.min_value  ,       self.max_value    )
        # Scale to [0, 1].
        __a					=  (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  ,       __A  ,       __A=(-1.0, 1.0)  ,       __A=False    ):
        __a	,    __a					=  input_range
        __a					=  torch.clip(__A  ,       __A  ,       __A    ) if clip else outputs
        # Scale to [0, 1].
        __a					=  (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  ,       __A  ,       __A  ,       __A    ):
        __a					=  input_tokens > 0
        __a	,    __a					=  self.notes_encoder(
            encoder_input_tokens=__A  ,       encoder_inputs_mask=__A    )

        __a	,    __a					=  self.continuous_encoder(
            encoder_inputs=__A  ,       encoder_inputs_mask=__A    )

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


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A    ):
        __a					=  noise_time
        if not torch.is_tensor(__A    ):
           __a					=  torch.tensor([timesteps]  ,       dtype=torch.long  ,       device=input_tokens.device    )
        elif torch.is_tensor(__A    ) and len(timesteps.shape    ) == 0:
           __a					=  timesteps[None].to(input_tokens.device    )

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

        __a					=  self.decoder(
            encodings_and_masks=__A  ,       decoder_input_tokens=__A  ,       decoder_noise_time=__A    )
        return logits


     @torch.no_grad()
     def __call__(	self  ,       __A  ,       __A = None  ,       __A = 100  ,       __A = True  ,       __A = "numpy"  ,       __A = None  ,       __A = 1  ,       ):
        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    )}.'''    )

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

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

           __a					=  self.scale_features(
               __A  ,       output_range=[-1.0, 1.0]  ,       clip=__A    )

           __a					=  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
           __a					=  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    )    ):
              __a					=  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
              __a					=  self.scheduler.step(__A  ,       __A  ,       __A  ,       generator=__A    ).prev_sample

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

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

        if not return_dict:
           return (output,)

        return AudioPipelineOutput(audios=__A    )







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import ViTImageProcessor


class     __UpperCAmelCase							(       unittest.TestCase							):


     """simple docstring"""


     def __init__(	self  ,       __A  ,       __A=13  ,       __A=3  ,       __A=224  ,       __A=30  ,       __A=400  ,       __A=True  ,       __A=None  ,       __A=True  ,       __A=[0.5, 0.5, 0.5]  ,       __A=[0.5, 0.5, 0.5]  ,       ):
        __a					=  size if size is not None else {"""height""": 18, """width""": 18}
        __a					=  parent
        __a					=  batch_size
        __a					=  num_channels
        __a					=  image_size
        __a					=  min_resolution
        __a					=  max_resolution
        __a					=  do_resize
        __a					=  size
        __a					=  do_normalize
        __a					=  image_mean
        __a					=  image_std


     def 	snake_case_  (	self    ):
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
        }







@require_torch
@require_vision
class     __UpperCAmelCase							(       __A ,					unittest.TestCase							):


     """simple docstring"""
     _lowerCamelCase   =      ViTImageProcessor if is_vision_available() else None


     def 	snake_case_  (	self    ):
        __a					=  EfficientFormerImageProcessorTester(self    )


     @property
     def 	snake_case_  (	self    ):
        return self.image_proc_tester.prepare_image_processor_dict()


     def 	snake_case_  (	self    ):
        __a					=  self.image_processing_class(**self.image_processor_dict    )
        self.assertTrue(hasattr(__A  ,       """image_mean"""    )    )
        self.assertTrue(hasattr(__A  ,       """image_std"""    )    )
        self.assertTrue(hasattr(__A  ,       """do_normalize"""    )    )
        self.assertTrue(hasattr(__A  ,       """do_resize"""    )    )
        self.assertTrue(hasattr(__A  ,       """size"""    )    )


     def 	snake_case_  (	self    ):
        pass


     def 	snake_case_  (	self    ):
        # Initialize image_processor
        __a					=  self.image_processing_class(**self.image_processor_dict    )
        # create random PIL images
        __a					=  prepare_image_inputs(self.image_proc_tester  ,       equal_resolution=__A    )
        for image in image_inputs:
           self.assertIsInstance(__A  ,       Image.Image    )

        # Test not batched input
        __a					=  image_processor(image_inputs[0]  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                1,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )

        # Test batched
        __a					=  image_processor(__A  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                self.image_proc_tester.batch_size,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )


     def 	snake_case_  (	self    ):
        # Initialize image_processor
        __a					=  self.image_processing_class(**self.image_processor_dict    )
        # create random numpy tensors
        __a					=  prepare_image_inputs(self.image_proc_tester  ,       equal_resolution=__A  ,       numpify=__A    )
        for image in image_inputs:
           self.assertIsInstance(__A  ,       np.ndarray    )

        # Test not batched input
        __a					=  image_processor(image_inputs[0]  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                1,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )

        # Test batched
        __a					=  image_processor(__A  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                self.image_proc_tester.batch_size,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )


     def 	snake_case_  (	self    ):
        # Initialize image_processor
        __a					=  self.image_processing_class(**self.image_processor_dict    )
        # create random PyTorch tensors
        __a					=  prepare_image_inputs(self.image_proc_tester  ,       equal_resolution=__A  ,       torchify=__A    )
        for image in image_inputs:
           self.assertIsInstance(__A  ,       torch.Tensor    )

        # Test not batched input
        __a					=  image_processor(image_inputs[0]  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                1,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )

        # Test batched
        __a					=  image_processor(__A  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                self.image_proc_tester.batch_size,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )



