'''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


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

__lowercase          =  2_5_6






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     Dict      =       ['''melgan''']



			def __init__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    ):




					"""simple docstring"""


					super().__init__()

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

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



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase=(-1.0, 1.0)       ,    __lowerCAmelCase=False):




					"""simple docstring"""


					lowerCAmelCase					,     lowerCAmelCase   					=  output_range
					if clip:
							lowerCAmelCase   					=  torch.clip(__lowerCAmelCase       ,    self.min_value       ,    self.max_value)
					# Scale to [0, 1].
					lowerCAmelCase   					=  (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 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase=(-1.0, 1.0)       ,    __lowerCAmelCase=False):




					"""simple docstring"""


					lowerCAmelCase					,     lowerCAmelCase   					=  input_range
					lowerCAmelCase   					=  torch.clip(__lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase) if clip else outputs
					# Scale to [0, 1].
					lowerCAmelCase   					=  (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 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  input_tokens > 0
					lowerCAmelCase					,     lowerCAmelCase   					=  self.notes_encoder(
					    encoder_input_tokens=__lowerCAmelCase       ,    encoder_inputs_mask=__lowerCAmelCase)

					lowerCAmelCase					,     lowerCAmelCase   					=  self.continuous_encoder(
					    encoder_inputs=__lowerCAmelCase       ,    encoder_inputs_mask=__lowerCAmelCase)

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



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


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

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

					lowerCAmelCase   					=  self.decoder(
					    encodings_and_masks=__lowerCAmelCase       ,    decoder_input_tokens=__lowerCAmelCase       ,    decoder_noise_time=__lowerCAmelCase)
					return logits







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




					"""simple docstring"""


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

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

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

							lowerCAmelCase   					=  self.scale_features(
							    __lowerCAmelCase       ,    output_range=[-1.0, 1.0]       ,    clip=__lowerCAmelCase)

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

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

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

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

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

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

							lowerCAmelCase   					=  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(__lowerCAmelCase       ,    __lowerCAmelCase)

							logger.info("""Generated segment"""       ,    __lowerCAmelCase)

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

					if not return_dict:
							return (output,)

					return AudioPipelineOutput(audios=__lowerCAmelCase)





'''simple docstring'''




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
						import torch


if is_vision_available():
						from PIL import Image

						from transformers import MgpstrProcessor, ViTImageProcessor






@require_torch
@require_vision
class 				a__(					unittest.TestCase	):


			'''simple docstring'''






			UpperCAmelCase_			:     Dict      =       ViTImageProcessor if is_vision_available() else None



			@property
			def 							a_       (   self):




					"""simple docstring"""


					return self.image_processor_tester.prepare_image_processor_dict()



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  (3, 32, 128)
					lowerCAmelCase   					=  tempfile.mkdtemp()

					# fmt: off
					lowerCAmelCase   					=  ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
					# fmt: on
					lowerCAmelCase   					=  dict(zip(__lowerCAmelCase       ,    range(len(__lowerCAmelCase))))

					lowerCAmelCase   					=  os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES["""vocab_file"""])
					with open(self.vocab_file       ,    """w"""       ,    encoding="""utf-8""") as fp:
							fp.write(json.dumps(__lowerCAmelCase) + """\n""")

					lowerCAmelCase   					=  {
					    """do_normalize""": False,
					    """do_resize""": True,
					    """image_processor_type""": """ViTImageProcessor""",
					    """resample""": 3,
					    """size""": {"""height""": 32, """width""": 128},
					}
					lowerCAmelCase   					=  os.path.join(self.tmpdirname       ,    __lowerCAmelCase)
					with open(self.image_processor_file       ,    """w"""       ,    encoding="""utf-8""") as fp:
							json.dump(__lowerCAmelCase       ,    __lowerCAmelCase)



			def 							a_       (   self       ,    **__lowerCAmelCase):




					"""simple docstring"""


					return MgpstrTokenizer.from_pretrained(self.tmpdirname       ,    **__lowerCAmelCase)



			def 							a_       (   self       ,    **__lowerCAmelCase):




					"""simple docstring"""


					return ViTImageProcessor.from_pretrained(self.tmpdirname       ,    **__lowerCAmelCase)



			def 							a_       (   self):




					"""simple docstring"""


					shutil.rmtree(self.tmpdirname)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  np.random.randint(255       ,    size=(3, 30, 400)       ,    dtype=np.uinta)

					lowerCAmelCase   					=  Image.fromarray(np.moveaxis(__lowerCAmelCase       ,    0       ,    -1))

					return image_input



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_tokenizer()
					lowerCAmelCase   					=  self.get_image_processor()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)
					processor.save_pretrained(self.tmpdirname)
					lowerCAmelCase   					=  MgpstrProcessor.from_pretrained(self.tmpdirname       ,    use_fast=__lowerCAmelCase)

					self.assertEqual(processor.char_tokenizer.get_vocab()       ,    tokenizer.get_vocab())
					self.assertIsInstance(processor.char_tokenizer       ,    __lowerCAmelCase)

					self.assertEqual(processor.image_processor.to_json_string()       ,    image_processor.to_json_string())
					self.assertIsInstance(processor.image_processor       ,    __lowerCAmelCase)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_tokenizer()
					lowerCAmelCase   					=  self.get_image_processor()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)
					processor.save_pretrained(self.tmpdirname)

					lowerCAmelCase   					=  self.get_tokenizer(bos_token="""(BOS)"""       ,    eos_token="""(EOS)""")
					lowerCAmelCase   					=  self.get_image_processor(do_normalize=__lowerCAmelCase       ,    padding_value=1.0)

					lowerCAmelCase   					=  MgpstrProcessor.from_pretrained(
					    self.tmpdirname       ,    bos_token="""(BOS)"""       ,    eos_token="""(EOS)"""       ,    do_normalize=__lowerCAmelCase       ,    padding_value=1.0)

					self.assertEqual(processor.char_tokenizer.get_vocab()       ,    tokenizer_add_kwargs.get_vocab())
					self.assertIsInstance(processor.char_tokenizer       ,    __lowerCAmelCase)

					self.assertEqual(processor.image_processor.to_json_string()       ,    image_processor_add_kwargs.to_json_string())
					self.assertIsInstance(processor.image_processor       ,    __lowerCAmelCase)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()
					lowerCAmelCase   					=  self.get_tokenizer()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)

					lowerCAmelCase   					=  self.prepare_image_inputs()

					lowerCAmelCase   					=  image_processor(__lowerCAmelCase       ,    return_tensors="""np""")
					lowerCAmelCase   					=  processor(images=__lowerCAmelCase       ,    return_tensors="""np""")

					for key in input_image_proc.keys():
							self.assertAlmostEqual(input_image_proc[key].sum()       ,    input_processor[key].sum()       ,    delta=1E-2)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()
					lowerCAmelCase   					=  self.get_tokenizer()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)

					lowerCAmelCase   					=  """test"""

					lowerCAmelCase   					=  processor(text=__lowerCAmelCase)

					lowerCAmelCase   					=  tokenizer(__lowerCAmelCase)
					for key in encoded_tok.keys():
							self.assertListEqual(encoded_tok[key]       ,    encoded_processor[key])



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()
					lowerCAmelCase   					=  self.get_tokenizer()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)

					lowerCAmelCase   					=  """test"""
					lowerCAmelCase   					=  self.prepare_image_inputs()

					lowerCAmelCase   					=  processor(text=__lowerCAmelCase       ,    images=__lowerCAmelCase)

					self.assertListEqual(list(inputs.keys())       ,    ["""pixel_values""", """labels"""])

					# test if it raises when no input is passed
					with pytest.raises(__lowerCAmelCase):
							processor()



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()
					lowerCAmelCase   					=  self.get_tokenizer()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)

					lowerCAmelCase   					=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

					lowerCAmelCase   					=  processor.char_decode(__lowerCAmelCase)
					lowerCAmelCase   					=  tokenizer.batch_decode(__lowerCAmelCase)
					lowerCAmelCase   					=  [seq.replace(""" """       ,    """""") for seq in decoded_tok]

					self.assertListEqual(__lowerCAmelCase       ,    __lowerCAmelCase)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()
					lowerCAmelCase   					=  self.get_tokenizer()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)

					lowerCAmelCase   					=  None
					lowerCAmelCase   					=  self.prepare_image_inputs()

					lowerCAmelCase   					=  processor(text=__lowerCAmelCase       ,    images=__lowerCAmelCase)

					self.assertListEqual(list(inputs.keys())       ,    processor.model_input_names)







			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.get_image_processor()
					lowerCAmelCase   					=  self.get_tokenizer()

					lowerCAmelCase   					=  MgpstrProcessor(tokenizer=__lowerCAmelCase       ,    image_processor=__lowerCAmelCase)

					lowerCAmelCase   					=  torch.randn(1       ,    27       ,    38)
					lowerCAmelCase   					=  torch.randn(1       ,    27       ,    50257)
					lowerCAmelCase   					=  torch.randn(1       ,    27       ,    30522)

					lowerCAmelCase   					=  processor.batch_decode([char_input, bpe_input, wp_input])

					self.assertListEqual(list(results.keys())       ,    ["""generated_text""", """scores""", """char_preds""", """bpe_preds""", """wp_preds"""])




