



"""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__     :   Optional[int]			   =  logging.get_logger(__name__)  # pylint: disable=invalid-name

lowercase__     :   Tuple			   =  256




class SCREAMING_SNAKE_CASE__					(		__lowerCamelCase      ):
						_lowerCAmelCase							=					['melgan']



						def __init__(self						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     ):





													'''simple docstring'''

													super().__init__()

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

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



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase=(-1.0, 1.0)						,     _lowercase=False							):





													'''simple docstring'''

													__a							,     __a		:		List[str]									=       output_range
													if clip:
																				__a		:		str									=       torch.clip(UpperCamelCase_						,     self.min_value						,     self.max_value							)
													# Scale to [0, 1].
													__a		:		int									=       (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						,     _lowercase						,     _lowercase=(-1.0, 1.0)						,     _lowercase=False							):





													'''simple docstring'''

													__a							,     __a		:		str									=       input_range
													__a		:		Tuple									=       torch.clip(UpperCamelCase_						,     UpperCamelCase_						,     UpperCamelCase_							) if clip else outputs
													# Scale to [0, 1].
													__a		:		int									=       (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						,     _lowercase						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		Dict									=       input_tokens > 0
													__a							,     __a		:		List[str]									=       self.notes_encoder(
													    encoder_input_tokens=UpperCamelCase_						,     encoder_inputs_mask=UpperCamelCase_							)

													__a							,     __a		:		List[str]									=       self.continuous_encoder(
													    encoder_inputs=UpperCamelCase_						,     encoder_inputs_mask=UpperCamelCase_							)

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



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

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

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

													__a		:		str									=       self.decoder(
													    encodings_and_masks=UpperCamelCase_						,     decoder_input_tokens=UpperCamelCase_						,     decoder_noise_time=UpperCamelCase_							)
													return logits





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





													'''simple docstring'''

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

													__a		:		List[Any]									=       np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]						,     dtype=np.floataa							)
													__a		:		Optional[Any]									=       np.zeros([1, 0, self.n_dims]						,     np.floataa							)
													__a		:		Dict									=       torch.ones((1, TARGET_FEATURE_LENGTH)						,     dtype=UpperCamelCase_						,     device=self.device							)

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

																				__a		:		Optional[int]									=       self.scale_features(
																				    UpperCamelCase_						,     output_range=[-1.0, 1.0]						,     clip=UpperCamelCase_							)

																				__a		:		Tuple									=       self.encode(
																				    input_tokens=torch.IntTensor([encoder_input_tokens]							).to(device=self.device							)						,     continuous_inputs=UpperCamelCase_						,     continuous_mask=UpperCamelCase_						,     )

																				# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
																				__a		:		List[Any]									=       randn_tensor(
																				    shape=encoder_continuous_inputs.shape						,     generator=UpperCamelCase_						,     device=self.device						,     dtype=self.decoder.dtype						,     )

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

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

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

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

																				__a		:		Optional[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(UpperCamelCase_						,     UpperCamelCase_							)

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

													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		:		int									=       self.melgan(input_features=full_pred_mel.astype(np.floataa							)							)
													else:
																				__a		:		Optional[Any]									=       full_pred_mel

													if not return_dict:
																				return (output,)

													return AudioPipelineOutput(audios=UpperCamelCase_							)






"""simple docstring"""

import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class SCREAMING_SNAKE_CASE__					(		__snake_case     ,					unittest.TestCase      ):
						_lowerCAmelCase							=					LEDTokenizer
						_lowerCAmelCase							=					LEDTokenizerFast
						_lowerCAmelCase							=					True



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													super().setUp()
													__a		:		str									=       [
													    """l""",
													    """o""",
													    """w""",
													    """e""",
													    """r""",
													    """s""",
													    """t""",
													    """i""",
													    """d""",
													    """n""",
													    """\u0120""",
													    """\u0120l""",
													    """\u0120n""",
													    """\u0120lo""",
													    """\u0120low""",
													    """er""",
													    """\u0120lowest""",
													    """\u0120newer""",
													    """\u0120wider""",
													    """<unk>""",
													]
													__a		:		int									=       dict(zip(_lowercase						,     range(len(_lowercase							)							)							)							)
													__a		:		Optional[int]									=       ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
													__a		:		List[Any]									=       {"""unk_token""": """<unk>"""}

													__a		:		Union[str, Any]									=       os.path.join(self.tmpdirname						,     VOCAB_FILES_NAMES["""vocab_file"""]							)
													__a		:		Union[str, Any]									=       os.path.join(self.tmpdirname						,     VOCAB_FILES_NAMES["""merges_file"""]							)
													with open(self.vocab_file						,     """w"""						,     encoding="""utf-8"""							) as fp:
																				fp.write(json.dumps(_lowercase							) + """\n"""							)
													with open(self.merges_file						,     """w"""						,     encoding="""utf-8"""							) as fp:
																				fp.write("""\n""".join(_lowercase							)							)



						def     lowerCAmelCase__(self						,     **_lowercase							):





													'''simple docstring'''

													kwargs.update(self.special_tokens_map							)
													return self.tokenizer_class.from_pretrained(self.tmpdirname						,     **_lowercase							)



						def     lowerCAmelCase__(self						,     **_lowercase							):





													'''simple docstring'''

													kwargs.update(self.special_tokens_map							)
													return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,     **_lowercase							)



						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

													return "lower newer", "lower newer"



						@cached_property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return LEDTokenizer.from_pretrained("""allenai/led-base-16384"""							)



						@cached_property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return LEDTokenizerFast.from_pretrained("""allenai/led-base-16384"""							)



						@require_torch
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Any									=       ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
													__a		:		List[str]									=       [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																				__a		:		Optional[int]									=       tokenizer(_lowercase						,     max_length=len(_lowercase							)						,     padding=_lowercase						,     return_tensors="""pt"""							)
																				self.assertIsInstance(_lowercase						,     _lowercase							)

																				self.assertEqual((2, 9)						,     batch.input_ids.shape							)
																				self.assertEqual((2, 9)						,     batch.attention_mask.shape							)
																				__a		:		Dict									=       batch.input_ids.tolist()[0]
																				self.assertListEqual(_lowercase						,     _lowercase							)



						@require_torch
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Union[str, Any]									=       ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																				__a		:		Tuple									=       tokenizer(_lowercase						,     padding=_lowercase						,     return_tensors="""pt"""							)
																				self.assertIn("""input_ids"""						,     _lowercase							)
																				self.assertIn("""attention_mask"""						,     _lowercase							)
																				self.assertNotIn("""labels"""						,     _lowercase							)
																				self.assertNotIn("""decoder_attention_mask"""						,     _lowercase							)



						@require_torch
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[Any]									=       [
													    """Summary of the text.""",
													    """Another summary.""",
													]
													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																				__a		:		Dict									=       tokenizer(text_target=_lowercase						,     max_length=32						,     padding="""max_length"""						,     return_tensors="""pt"""							)
																				self.assertEqual(32						,     targets["""input_ids"""].shape[1]							)



						@require_torch
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																				__a		:		Optional[int]									=       tokenizer(
																				    ["""I am a small frog""" * 1024, """I am a small frog"""]						,     padding=_lowercase						,     truncation=_lowercase						,     return_tensors="""pt"""							)
																				self.assertIsInstance(_lowercase						,     _lowercase							)
																				self.assertEqual(batch.input_ids.shape						,     (2, 5122)							)



						@require_torch
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Tuple									=       ["""A long paragraph for summarization."""]
													__a		:		Dict									=       [
													    """Summary of the text.""",
													]
													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																				__a		:		int									=       tokenizer(_lowercase						,     return_tensors="""pt"""							)
																				__a		:		Dict									=       tokenizer(text_target=_lowercase						,     return_tensors="""pt"""							)
																				__a		:		List[str]									=       inputs["""input_ids"""]
																				__a		:		List[Any]									=       targets["""input_ids"""]
																				self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()							)
																				self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()							)
																				self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()							)
																				self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()							)



						@require_torch
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																				__a		:		Optional[Any]									=       ["""Summary of the text.""", """Another summary."""]
																				__a		:		List[Any]									=       [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

																				__a		:		Union[str, Any]									=       tokenizer(_lowercase						,     padding=_lowercase							)
																				__a		:		Tuple									=       [[0] * len(_lowercase							) for x in encoded_output["""input_ids"""]]
																				__a		:		Union[str, Any]									=       tokenizer.pad(_lowercase							)
																				self.assertSequenceEqual(outputs["""global_attention_mask"""]						,     _lowercase							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													pass





						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																				with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
																											__a		:		Dict									=       self.rust_tokenizer_class.from_pretrained(_lowercase						,     **_lowercase							)
																											__a		:		Union[str, Any]									=       self.tokenizer_class.from_pretrained(_lowercase						,     **_lowercase							)
																											__a		:		Union[str, Any]									=       """A, <mask> AllenNLP sentence."""
																											__a		:		Dict									=       tokenizer_r.encode_plus(_lowercase						,     add_special_tokens=_lowercase						,     return_token_type_ids=_lowercase							)
																											__a		:		Tuple									=       tokenizer_p.encode_plus(_lowercase						,     add_special_tokens=_lowercase						,     return_token_type_ids=_lowercase							)
																											self.assertEqual(sum(tokens_r["""token_type_ids"""]							)						,     sum(tokens_p["""token_type_ids"""]							)							)
																											self.assertEqual(
																											    sum(tokens_r["""attention_mask"""]							) / len(tokens_r["""attention_mask"""]							)						,     sum(tokens_p["""attention_mask"""]							) / len(tokens_p["""attention_mask"""]							)						,     )

																											__a		:		Tuple									=       tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]							)
																											__a		:		Any									=       tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]							)
																											self.assertSequenceEqual(tokens_p["""input_ids"""]						,     [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]							)
																											self.assertSequenceEqual(tokens_r["""input_ids"""]						,     [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]							)

																											self.assertSequenceEqual(
																											    _lowercase						,     ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]							)
																											self.assertSequenceEqual(
																											    _lowercase						,     ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]							)

