


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__     					=			256



class 						_UpperCAmelCase      (     lowercase							):
		lowerCamelCase_	:			Optional[int]       			=  ["""melgan"""]






		def __init__(						self				:     List[Any]   ,  UpperCAmelCase				:     SpectrogramNotesEncoder   ,  UpperCAmelCase				:     SpectrogramContEncoder   ,  UpperCAmelCase				:     TaFilmDecoder   ,  UpperCAmelCase				:     DDPMScheduler   ,  UpperCAmelCase				:     OnnxRuntimeModel if is_onnx_available() else Any   ,  ):
				super().__init__()

				# From MELGAN
				SCREAMING_SNAKE_CASE_				:int				     =						math.log(1E-5)  # Matches MelGAN training.
				SCREAMING_SNAKE_CASE_				:Tuple				     =						4.0  # Largest value for most examples
				SCREAMING_SNAKE_CASE_				:Tuple				     =						1_28

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






		def _snake_case		(						self				:     Any   ,  UpperCAmelCase				:     Any   ,  UpperCAmelCase				:     Dict=(-1.0, 1.0)   ,  UpperCAmelCase				:     int=False):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Tuple				     =						output_range
				if clip:
						SCREAMING_SNAKE_CASE_				:List[str]				     =						torch.clip(UpperCAmelCase   ,  self.min_value   ,  self.max_value)
				# Scale to [0, 1].
				SCREAMING_SNAKE_CASE_				:Optional[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 _snake_case		(						self				:     int   ,  UpperCAmelCase				:     str   ,  UpperCAmelCase				:     Any=(-1.0, 1.0)   ,  UpperCAmelCase				:     Optional[int]=False):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						input_range
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						torch.clip(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase) if clip else outputs
				# Scale to [0, 1].
				SCREAMING_SNAKE_CASE_				: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 _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     List[str]   ,  UpperCAmelCase				:     Union[str, Any]   ,  UpperCAmelCase				:     Optional[Any]):
				SCREAMING_SNAKE_CASE_				:str				     =						input_tokens > 0
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:List[str]				     =						self.notes_encoder(
				    encoder_input_tokens=UpperCAmelCase   ,  encoder_inputs_mask=UpperCAmelCase)

				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:List[Any]				     =						self.continuous_encoder(
				    encoder_inputs=UpperCAmelCase   ,  encoder_inputs_mask=UpperCAmelCase)

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






		def _snake_case		(						self				:     Dict   ,  UpperCAmelCase				:     Optional[int]   ,  UpperCAmelCase				:     Union[str, Any]   ,  UpperCAmelCase				:     List[Any]):
				SCREAMING_SNAKE_CASE_				:str				     =						noise_time
				if not torch.is_tensor(UpperCAmelCase):
						SCREAMING_SNAKE_CASE_				:Dict				     =						torch.tensor([timesteps]   ,  dtype=torch.long   ,  device=input_tokens.device)
				elif torch.is_tensor(UpperCAmelCase) and len(timesteps.shape) == 0:
						SCREAMING_SNAKE_CASE_				:Any				     =						timesteps[None].to(input_tokens.device)

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

				SCREAMING_SNAKE_CASE_				:List[str]				     =						self.decoder(
				    encodings_and_masks=UpperCAmelCase   ,  decoder_input_tokens=UpperCAmelCase   ,  decoder_noise_time=UpperCAmelCase)
				return logits







		@torch.no_grad()
		def __call__(						self				:     List[str]   ,  UpperCAmelCase				:     List[List[int]]   ,  UpperCAmelCase				:     Optional[torch.Generator] = None   ,  UpperCAmelCase				:     int = 1_00   ,  UpperCAmelCase				:     bool = True   ,  UpperCAmelCase				:     str = "numpy"   ,  UpperCAmelCase				:     Optional[Callable[[int, int, torch.FloatTensor], None]] = None   ,  UpperCAmelCase				:     int = 1   ,  ):
				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)}.")

				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]   ,  dtype=np.floataa)
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						np.zeros([1, 0, self.n_dims]   ,  np.floataa)
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						torch.ones((1, TARGET_FEATURE_LENGTH)   ,  dtype=UpperCAmelCase   ,  device=self.device)

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

						SCREAMING_SNAKE_CASE_				:str				     =						self.scale_features(
						    UpperCAmelCase   ,  output_range=[-1.0, 1.0]   ,  clip=UpperCAmelCase)

						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						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
						SCREAMING_SNAKE_CASE_				:Union[str, 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)):
								SCREAMING_SNAKE_CASE_				:Optional[int]				     =						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
								SCREAMING_SNAKE_CASE_				:Tuple				     =						self.scheduler.step(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase   ,  generator=UpperCAmelCase).prev_sample

						SCREAMING_SNAKE_CASE_				:Any				     =						self.scale_to_features(UpperCAmelCase   ,  input_range=[-1.0, 1.0])
						SCREAMING_SNAKE_CASE_				:int				     =						mel[:1]
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						mel.cpu().float().numpy()

						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						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":
						SCREAMING_SNAKE_CASE_				:Dict				     =						self.melgan(input_features=full_pred_mel.astype(np.floataa))
				else:
						SCREAMING_SNAKE_CASE_				:Dict				     =						full_pred_mel

				if not return_dict:
						return (output,)

				return AudioPipelineOutput(audios=UpperCAmelCase)










import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
SCREAMING_SNAKE_CASE__     					=			(720, 1_280)  # Height, Width
SCREAMING_SNAKE_CASE__     					=			(0.4, 0.6)  # if height or width lower than this scale, drop it.
SCREAMING_SNAKE_CASE__     					=			1 / 100
SCREAMING_SNAKE_CASE__     					=			""
SCREAMING_SNAKE_CASE__     					=			""
SCREAMING_SNAKE_CASE__     					=			""
SCREAMING_SNAKE_CASE__     					=			250


def     lowercase	(    ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:str				     =						get_dataset(a     , a )
		for index in range(a ):
				SCREAMING_SNAKE_CASE_				:Any				     =						random.sample(range(len(a ) )     , 4 )
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						update_image_and_anno(
				    a     , a     , a     , a     , a     , filter_scale=a     , )

				# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
				SCREAMING_SNAKE_CASE_				:int				     =						random_chars(32 )
				SCREAMING_SNAKE_CASE_				:Dict				     =						path.split(os.sep )[-1].rsplit("."     , 1 )[0]
				SCREAMING_SNAKE_CASE_				:Dict				     =						F"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
				cva.imwrite(F"{file_root}.jpg"     , a     , [cva.IMWRITE_JPEG_QUALITY, 85] )
				print(F"Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}" )
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						[]
				for anno in new_annos:
						SCREAMING_SNAKE_CASE_				:Any				     =						anno[3] - anno[1]
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						anno[4] - anno[2]
						SCREAMING_SNAKE_CASE_				:Any				     =						anno[1] + width / 2
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						anno[2] + height / 2
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						F"{anno[0]} {x_center} {y_center} {width} {height}"
						annos_list.append(a )
				with open(F"{file_root}.txt"     , "w" ) as outfile:
						outfile.write("\n".join(line for line in annos_list ) )


def     lowercase	(    a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						[]
		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						[]
		for label_file in glob.glob(os.path.join(a     , "*.txt" ) ):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						label_file.split(os.sep )[-1].rsplit("."     , 1 )[0]
				with open(a ) as in_file:
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						in_file.readlines()
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						os.path.join(a     , F"{label_name}.jpg" )

				SCREAMING_SNAKE_CASE_				:Dict				     =						[]
				for obj_list in obj_lists:
						SCREAMING_SNAKE_CASE_				:Dict				     =						obj_list.rstrip("\n" ).split(" " )
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						float(obj[1] ) - float(obj[3] ) / 2
						SCREAMING_SNAKE_CASE_				:Dict				     =						float(obj[2] ) - float(obj[4] ) / 2
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						float(obj[1] ) + float(obj[3] ) / 2
						SCREAMING_SNAKE_CASE_				:List[str]				     =						float(obj[2] ) + float(obj[4] ) / 2

						boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
				if not boxes:
						continue
				img_paths.append(a )
				labels.append(a )
		return img_paths, labels


def     lowercase	(    a     , a     , a     , a     , a     , a = 0.0     , ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						np.zeros([output_size[0], output_size[1], 3]     , dtype=np.uinta )
		SCREAMING_SNAKE_CASE_				:Tuple				     =						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
		SCREAMING_SNAKE_CASE_				:List[str]				     =						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
		SCREAMING_SNAKE_CASE_				:Any				     =						int(scale_x * output_size[1] )
		SCREAMING_SNAKE_CASE_				:List[Any]				     =						int(scale_y * output_size[0] )

		SCREAMING_SNAKE_CASE_				:Any				     =						[]
		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						[]
		for i, index in enumerate(a ):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						all_img_list[index]
				path_list.append(a )
				SCREAMING_SNAKE_CASE_				:Tuple				     =						all_annos[index]
				SCREAMING_SNAKE_CASE_				:Any				     =						cva.imread(a )
				if i == 0:  # top-left
						SCREAMING_SNAKE_CASE_				:int				     =						cva.resize(a     , (divid_point_x, divid_point_y) )
						SCREAMING_SNAKE_CASE_				:Any				     =						img
						for bbox in img_annos:
								SCREAMING_SNAKE_CASE_				:Tuple				     =						bbox[1] * scale_x
								SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						bbox[2] * scale_y
								SCREAMING_SNAKE_CASE_				:List[Any]				     =						bbox[3] * scale_x
								SCREAMING_SNAKE_CASE_				:Tuple				     =						bbox[4] * scale_y
								new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
				elif i == 1:  # top-right
						SCREAMING_SNAKE_CASE_				:Dict				     =						cva.resize(a     , (output_size[1] - divid_point_x, divid_point_y) )
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						img
						for bbox in img_annos:
								SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						scale_x + bbox[1] * (1 - scale_x)
								SCREAMING_SNAKE_CASE_				:Dict				     =						bbox[2] * scale_y
								SCREAMING_SNAKE_CASE_				:Optional[int]				     =						scale_x + bbox[3] * (1 - scale_x)
								SCREAMING_SNAKE_CASE_				:Tuple				     =						bbox[4] * scale_y
								new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
				elif i == 2:  # bottom-left
						SCREAMING_SNAKE_CASE_				:List[str]				     =						cva.resize(a     , (divid_point_x, output_size[0] - divid_point_y) )
						SCREAMING_SNAKE_CASE_				:int				     =						img
						for bbox in img_annos:
								SCREAMING_SNAKE_CASE_				:Tuple				     =						bbox[1] * scale_x
								SCREAMING_SNAKE_CASE_				:Dict				     =						scale_y + bbox[2] * (1 - scale_y)
								SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						bbox[3] * scale_x
								SCREAMING_SNAKE_CASE_				:List[str]				     =						scale_y + bbox[4] * (1 - scale_y)
								new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
				else:  # bottom-right
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						cva.resize(
						    a     , (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						img
						for bbox in img_annos:
								SCREAMING_SNAKE_CASE_				:Optional[int]				     =						scale_x + bbox[1] * (1 - scale_x)
								SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						scale_y + bbox[2] * (1 - scale_y)
								SCREAMING_SNAKE_CASE_				:Dict				     =						scale_x + bbox[3] * (1 - scale_x)
								SCREAMING_SNAKE_CASE_				:List[Any]				     =						scale_y + bbox[4] * (1 - scale_y)
								new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )

    # Remove bounding box small than scale of filter
		if filter_scale > 0:
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						[
				    anno
				    for anno in new_anno
				    if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
				]

		return output_img, new_anno, path_list[0]


def     lowercase	(    a ):



		'''simple docstring'''




		assert number_char > 1, "The number of character should greater than 1"
		SCREAMING_SNAKE_CASE_				:Dict				     =						ascii_lowercase + digits
		return "".join(random.choice(a ) for _ in range(a ) )


if __name__ == "__main__":
		main()
		print("DONE ✅")






