
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


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

_A  =	2_56





class       _lowerCAmelCase   (      __a			):
				_lowercase     						=['''melgan''']




				def __init__(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							)							-> None:
							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=_UpperCamelCase							,							continuous_encoder=_UpperCamelCase							,							decoder=_UpperCamelCase							,							scheduler=_UpperCamelCase							,							melgan=_UpperCamelCase							,							)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase=(-1.0, 1.0)							,							_UpperCamelCase=False	)							-> Union[str, Any]:
							lowerCAmelCase_      ,    lowerCAmelCase_         =      output_range
							if clip:
										lowerCAmelCase_         =      torch.clip(_UpperCamelCase							,							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							,							_UpperCamelCase							,							_UpperCamelCase=(-1.0, 1.0)							,							_UpperCamelCase=False	)							-> str:
							lowerCAmelCase_      ,    lowerCAmelCase_         =      input_range
							lowerCAmelCase_         =      torch.clip(_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	) 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							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> str:
							lowerCAmelCase_         =      input_tokens > 0
							lowerCAmelCase_      ,    lowerCAmelCase_         =      self.notes_encoder(
							    encoder_input_tokens=_UpperCamelCase							,							encoder_inputs_mask=_UpperCamelCase	)

							lowerCAmelCase_      ,    lowerCAmelCase_         =      self.continuous_encoder(
							    encoder_inputs=_UpperCamelCase							,							encoder_inputs_mask=_UpperCamelCase	)

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




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> List[str]:
							lowerCAmelCase_         =      noise_time
							if not torch.is_tensor(_UpperCamelCase	):
										lowerCAmelCase_         =      torch.tensor([timesteps]							,							dtype=torch.long							,							device=input_tokens.device	)
							elif torch.is_tensor(_UpperCamelCase	) 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=_UpperCamelCase							,							decoder_input_tokens=_UpperCamelCase							,							decoder_noise_time=_UpperCamelCase	)
							return logits




				@torch.no_grad()
				def __call__(   self							,							_UpperCamelCase							,							_UpperCamelCase = None							,							_UpperCamelCase = 100							,							_UpperCamelCase = True							,							_UpperCamelCase = "numpy"							,							_UpperCamelCase = None							,							_UpperCamelCase = 1							,							)							-> Union[AudioPipelineOutput, Tuple]:
							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	)}."""	)

							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=_UpperCamelCase							,							device=self.device	)

							for i, encoder_input_tokens in enumerate(_UpperCamelCase	):
										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=_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.
													lowerCAmelCase_         =      ones

										lowerCAmelCase_         =      self.scale_features(
										    _UpperCamelCase							,							output_range=[-1.0, 1.0]							,							clip=_UpperCamelCase	)

										lowerCAmelCase_         =      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
										lowerCAmelCase_         =      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	)	):
													lowerCAmelCase_         =      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
													lowerCAmelCase_         =      self.scheduler.step(_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							generator=_UpperCamelCase	).prev_sample

										lowerCAmelCase_         =      self.scale_to_features(_UpperCamelCase							,							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(_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":
										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=_UpperCamelCase	)





import argparse
import struct
import unittest





class       _lowerCAmelCase   :




				def __init__(   self							,							_UpperCamelCase	)							-> None:
							lowerCAmelCase_         =      data

							# Initialize hash values
							lowerCAmelCase_         =      [
							    0x6_A_0_9_E_6_6_7,
							    0xB_B_6_7_A_E_8_5,
							    0x3_C_6_E_F_3_7_2,
							    0xA_5_4_F_F_5_3_A,
							    0x5_1_0_E_5_2_7_F,
							    0x9_B_0_5_6_8_8_C,
							    0x1_F_8_3_D_9_A_B,
							    0x5_B_E_0_C_D_1_9,
							]

							# Initialize round constants
							lowerCAmelCase_         =      [
							    0x4_2_8_A_2_F_9_8,
							    0x7_1_3_7_4_4_9_1,
							    0xB_5_C_0_F_B_C_F,
							    0xE_9_B_5_D_B_A_5,
							    0x3_9_5_6_C_2_5_B,
							    0x5_9_F_1_1_1_F_1,
							    0x9_2_3_F_8_2_A_4,
							    0xA_B_1_C_5_E_D_5,
							    0xD_8_0_7_A_A_9_8,
							    0x1_2_8_3_5_B_0_1,
							    0x2_4_3_1_8_5_B_E,
							    0x5_5_0_C_7_D_C_3,
							    0x7_2_B_E_5_D_7_4,
							    0x8_0_D_E_B_1_F_E,
							    0x9_B_D_C_0_6_A_7,
							    0xC_1_9_B_F_1_7_4,
							    0xE_4_9_B_6_9_C_1,
							    0xE_F_B_E_4_7_8_6,
							    0x0_F_C_1_9_D_C_6,
							    0x2_4_0_C_A_1_C_C,
							    0x2_D_E_9_2_C_6_F,
							    0x4_A_7_4_8_4_A_A,
							    0x5_C_B_0_A_9_D_C,
							    0x7_6_F_9_8_8_D_A,
							    0x9_8_3_E_5_1_5_2,
							    0xA_8_3_1_C_6_6_D,
							    0xB_0_0_3_2_7_C_8,
							    0xB_F_5_9_7_F_C_7,
							    0xC_6_E_0_0_B_F_3,
							    0xD_5_A_7_9_1_4_7,
							    0x0_6_C_A_6_3_5_1,
							    0x1_4_2_9_2_9_6_7,
							    0x2_7_B_7_0_A_8_5,
							    0x2_E_1_B_2_1_3_8,
							    0x4_D_2_C_6_D_F_C,
							    0x5_3_3_8_0_D_1_3,
							    0x6_5_0_A_7_3_5_4,
							    0x7_6_6_A_0_A_B_B,
							    0x8_1_C_2_C_9_2_E,
							    0x9_2_7_2_2_C_8_5,
							    0xA_2_B_F_E_8_A_1,
							    0xA_8_1_A_6_6_4_B,
							    0xC_2_4_B_8_B_7_0,
							    0xC_7_6_C_5_1_A_3,
							    0xD_1_9_2_E_8_1_9,
							    0xD_6_9_9_0_6_2_4,
							    0xF_4_0_E_3_5_8_5,
							    0x1_0_6_A_A_0_7_0,
							    0x1_9_A_4_C_1_1_6,
							    0x1_E_3_7_6_C_0_8,
							    0x2_7_4_8_7_7_4_C,
							    0x3_4_B_0_B_C_B_5,
							    0x3_9_1_C_0_C_B_3,
							    0x4_E_D_8_A_A_4_A,
							    0x5_B_9_C_C_A_4_F,
							    0x6_8_2_E_6_F_F_3,
							    0x7_4_8_F_8_2_E_E,
							    0x7_8_A_5_6_3_6_F,
							    0x8_4_C_8_7_8_1_4,
							    0x8_C_C_7_0_2_0_8,
							    0x9_0_B_E_F_F_F_A,
							    0xA_4_5_0_6_C_E_B,
							    0xB_E_F_9_A_3_F_7,
							    0xC_6_7_1_7_8_F_2,
							]

							lowerCAmelCase_         =      self.preprocessing(self.data	)
							self.final_hash()




				@staticmethod
				def 	__a		(   _UpperCamelCase	)							-> bytes:
							lowerCAmelCase_         =      B"\x80" + (B"\x00" * (63 - (len(_UpperCamelCase	) + 8) % 64))
							lowerCAmelCase_         =      struct.pack(">Q"							,							(len(_UpperCamelCase	) * 8)	)
							return data + padding + big_endian_integer




				def 	__a		(   self	)							-> None:
							# Convert into blocks of 64 bytes
							lowerCAmelCase_         =      [
							    self.preprocessed_data[x : x + 64]
							    for x in range(0							,							len(self.preprocessed_data	)							,							64	)
							]

							for block in self.blocks:
										# Convert the given block into a list of 4 byte integers
										lowerCAmelCase_         =      list(struct.unpack(">16L"							,							_UpperCamelCase	)	)
										# add 48 0-ed integers
										words += [0] * 48

										lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_         =      self.hashes

										for index in range(0							,							64	):
													if index > 15:
																# modify the zero-ed indexes at the end of the array
																lowerCAmelCase_         =      (
																    self.ror(words[index - 15]							,							7	)
																    ^ self.ror(words[index - 15]							,							18	)
																    ^ (words[index - 15] >> 3)
																)
																lowerCAmelCase_         =      (
																    self.ror(words[index - 2]							,							17	)
																    ^ self.ror(words[index - 2]							,							19	)
																    ^ (words[index - 2] >> 10)
																)

																lowerCAmelCase_         =      (
																    words[index - 16] + sa + words[index - 7] + sa
																) % 0x1_0_0_0_0_0_0_0_0

													# Compression
													lowerCAmelCase_         =      self.ror(_UpperCamelCase							,							6	) ^ self.ror(_UpperCamelCase							,							11	) ^ self.ror(_UpperCamelCase							,							25	)
													lowerCAmelCase_         =      (e & f) ^ ((~e & 0xF_F_F_F_F_F_F_F) & g)
													lowerCAmelCase_         =      (
													    h + sa + ch + self.round_constants[index] + words[index]
													) % 0x1_0_0_0_0_0_0_0_0
													lowerCAmelCase_         =      self.ror(_UpperCamelCase							,							2	) ^ self.ror(_UpperCamelCase							,							13	) ^ self.ror(_UpperCamelCase							,							22	)
													lowerCAmelCase_         =      (a & b) ^ (a & c) ^ (b & c)
													lowerCAmelCase_         =      (sa + maj) % 0x1_0_0_0_0_0_0_0_0

													lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_         =      (
													    g,
													    f,
													    e,
													    ((d + tempa) % 0x1_0_0_0_0_0_0_0_0),
													    c,
													    b,
													    a,
													    ((tempa + tempa) % 0x1_0_0_0_0_0_0_0_0),
													)

										lowerCAmelCase_         =      [a, b, c, d, e, f, g, h]

										# Modify final values
										lowerCAmelCase_         =      [
										    ((element + mutated_hash_values[index]) % 0x1_0_0_0_0_0_0_0_0)
										    for index, element in enumerate(self.hashes	)
										]

							lowerCAmelCase_         =      "".join([hex(_UpperCamelCase	)[2:].zfill(8	) for value in self.hashes]	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase	)							-> int:
							return 0xF_F_F_F_F_F_F_F & (value << (32 - rotations)) | (value >> rotations)





class       _lowerCAmelCase   (      unittest.TestCase			):




				def 	__a		(   self	)							-> None:
							import hashlib

							lowerCAmelCase_         =      bytes("Test String"							,							"utf-8"	)
							self.assertEqual(SHAaaa(_UpperCamelCase	).hash							,							hashlib.shaaaa(_UpperCamelCase	).hexdigest()	)






def   lowerCamelCase__							(							):




			"""simple docstring"""


			import doctest

			doctest.testmod()

			lowerCAmelCase_         =      argparse.ArgumentParser()
			parser.add_argument(
			    "-s"       ,				"--string"       ,				dest="input_string"       ,				default="Hello World!! Welcome to Cryptography"       ,				help="Hash the string"       ,				)
			parser.add_argument(
			    "-f"       ,				"--file"       ,				dest="input_file"       ,				help="Hash contents of a file"							)

			lowerCAmelCase_         =      parser.parse_args()

			lowerCAmelCase_         =      args.input_string

			# hash input should be a bytestring
			if args.input_file:
						with open(args.input_file       ,				"rb"							) as f:
									lowerCAmelCase_         =      f.read()
			else:
						lowerCAmelCase_         =      bytes(__lowerCAmelCase       ,				"utf-8"							)

			print(SHAaaa(__lowerCAmelCase							).hash							)


if __name__ == "__main__":
			main()



