
from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Any          = ["vqvae"]



	def __init__(  self     :   List[Any]      ,  lowercase     :   AutoencoderKL      ,  lowercase     :   UNetaDConditionModel      ,  lowercase     :   Mel      ,  lowercase     :   Union[DDIMScheduler, DDPMScheduler]      ,  ):


				'''simple docstring'''


				super().__init__()
				self.register_modules(unet=lowercase      ,  scheduler=lowercase      ,  mel=lowercase      ,  vqvae=lowercase     )



	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				return 50 if isinstance(self.scheduler      ,  lowercase     ) else 1_000



	@torch.no_grad()
	def __call__(  self     :   Tuple      ,  lowercase     :   int = 1      ,  lowercase     :   str = None      ,  lowercase     :   np.ndarray = None      ,  lowercase     :   int = 0      ,  lowercase     :   int = 0      ,  lowercase     :   int = None      ,  lowercase     :   torch.Generator = None      ,  lowercase     :   float = 0      ,  lowercase     :   float = 0      ,  lowercase     :   torch.Generator = None      ,  lowercase     :   float = 0      ,  lowercase     :   torch.Tensor = None      ,  lowercase     :   torch.Tensor = None      ,  lowercase     :   Optional[Any]=True      ,  ):


				'''simple docstring'''


				_snake_case          =      steps or self.get_default_steps()
				self.scheduler.set_timesteps(lowercase     )
				_snake_case          =      step_generator or generator
				# For backwards compatibility
				if type(self.unet.config.sample_size     ) == int:
							_snake_case          =      (self.unet.config.sample_size, self.unet.config.sample_size)
				if noise is None:
							_snake_case          =      randn_tensor(
							    (
							        batch_size,
							        self.unet.config.in_channels,
							        self.unet.config.sample_size[0],
							        self.unet.config.sample_size[1],
							    )      ,  generator=lowercase      ,  device=self.device      ,  )
				_snake_case          =      noise
				_snake_case          =      None

				if audio_file is not None or raw_audio is not None:
							self.mel.load_audio(lowercase      ,  lowercase     )
							_snake_case          =      self.mel.audio_slice_to_image(lowercase     )
							_snake_case          =      np.frombuffer(input_image.tobytes()      ,  dtype='uint8'     ).reshape(
							    (input_image.height, input_image.width)     )
							_snake_case          =      (input_image / 255) * 2 - 1
							_snake_case          =      torch.tensor(input_image[np.newaxis, :, :]      ,  dtype=torch.float     ).to(self.device     )

							if self.vqvae is not None:
										_snake_case          =      self.vqvae.encode(torch.unsqueeze(lowercase      ,  0     )     ).latent_dist.sample(
										    generator=lowercase     )[0]
										_snake_case          =      self.vqvae.config.scaling_factor * input_images

							if start_step > 0:
										_snake_case          =      self.scheduler.add_noise(lowercase      ,  lowercase      ,  self.scheduler.timesteps[start_step - 1]     )

							_snake_case          =      (
							    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
							)
							_snake_case          =      int(mask_start_secs * pixels_per_second     )
							_snake_case          =      int(mask_end_secs * pixels_per_second     )
							_snake_case          =      self.scheduler.add_noise(lowercase      ,  lowercase      ,  torch.tensor(self.scheduler.timesteps[start_step:]     )     )

				for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]     )     ):
							if isinstance(self.unet      ,  lowercase     ):
										_snake_case          =      self.unet(lowercase      ,  lowercase      ,  lowercase     )['sample']
							else:
										_snake_case          =      self.unet(lowercase      ,  lowercase     )['sample']

							if isinstance(self.scheduler      ,  lowercase     ):
										_snake_case          =      self.scheduler.step(
										    model_output=lowercase      ,  timestep=lowercase      ,  sample=lowercase      ,  eta=lowercase      ,  generator=lowercase      ,  )['prev_sample']
							else:
										_snake_case          =      self.scheduler.step(
										    model_output=lowercase      ,  timestep=lowercase      ,  sample=lowercase      ,  generator=lowercase      ,  )['prev_sample']

							if mask is not None:
										if mask_start > 0:
													_snake_case          =      mask[:, step, :, :mask_start]
										if mask_end > 0:
													_snake_case          =      mask[:, step, :, -mask_end:]

				if self.vqvae is not None:
							# 0.18215 was scaling factor used in training to ensure unit variance
							_snake_case          =      1 / self.vqvae.config.scaling_factor * images
							_snake_case          =      self.vqvae.decode(lowercase     )['sample']

				_snake_case          =      (images / 2 + 0.5).clamp(0      ,  1     )
				_snake_case          =      images.cpu().permute(0      ,  2      ,  3      ,  1     ).numpy()
				_snake_case          =      (images * 255).round().astype('uint8'     )
				_snake_case          =      list(
				    (Image.fromarray(_[:, :, 0]     ) for _ in images)
				    if images.shape[3] == 1
				    else (Image.fromarray(lowercase      ,  mode='RGB'     ).convert('L'     ) for _ in images)     )

				_snake_case          =      [self.mel.image_to_audio(lowercase     ) for _ in images]
				if not return_dict:
							return images, (self.mel.get_sample_rate(), audios)

				return BaseOutput(**AudioPipelineOutput(np.array(lowercase     )[:, np.newaxis, :]     )      ,  **ImagePipelineOutput(lowercase     )     )



	@torch.no_grad()
	def 			A							(  self     :   Union[str, Any]      ,  lowercase     :   List[Image.Image]      ,  lowercase     :   int = 50     ):


				'''simple docstring'''


				assert isinstance(self.scheduler      ,  lowercase     )
				self.scheduler.set_timesteps(lowercase     )
				_snake_case          =      np.array(
				    [np.frombuffer(image.tobytes()      ,  dtype='uint8'     ).reshape((1, image.height, image.width)     ) for image in images]     )
				_snake_case          =      (sample / 255) * 2 - 1
				_snake_case          =      torch.Tensor(lowercase     ).to(self.device     )

				for t in self.progress_bar(torch.flip(self.scheduler.timesteps      ,  (0,)     )     ):
							_snake_case          =      t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
							_snake_case          =      self.scheduler.alphas_cumprod[t]
							_snake_case          =      (
							    self.scheduler.alphas_cumprod[prev_timestep]
							    if prev_timestep >= 0
							    else self.scheduler.final_alpha_cumprod
							)
							_snake_case          =      1 - alpha_prod_t
							_snake_case          =      self.unet(lowercase      ,  lowercase     )['sample']
							_snake_case          =      (1 - alpha_prod_t_prev) ** 0.5 * model_output
							_snake_case          =      (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
							_snake_case          =      sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

				return sample





	@staticmethod
	def 			A							(  lowercase     :   torch.Tensor      ,  lowercase     :   torch.Tensor      ,  lowercase     :   float     ):


				'''simple docstring'''


				_snake_case          =      acos(torch.dot(torch.flatten(lowercase     )      ,  torch.flatten(lowercase     )     ) / torch.norm(lowercase     ) / torch.norm(lowercase     )     )
				return sin((1 - alpha) * theta     ) * xa / sin(lowercase     ) + sin(alpha * theta     ) * xa / sin(lowercase     )

import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
_lowerCamelCase	:    Union[str, Any]          =      logging.getLogger()






def 	a_		(	__lowercase       :     Path	, __lowercase       :     list			)			->  Tuple:
			_snake_case          =      '\n'.join(__lowercase			)
			Path(__lowercase			).open('w'			).writelines(__lowercase			)


_lowerCamelCase	:    Any          =      '''patrickvonplaten/t5-tiny-random'''
_lowerCamelCase	:    List[Any]          =      '''sshleifer/bart-tiny-random'''
_lowerCamelCase	:    List[Any]          =      '''sshleifer/tiny-mbart'''

_lowerCamelCase	:    Union[str, Any]          =      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	def 			A							(  self     :   Optional[int]      ,  lowercase     :   int     ):


				'''simple docstring'''


				_snake_case          =      Path(self.get_auto_remove_tmp_dir()     ) / 'utest_input.source'
				_snake_case          =      input_file_name.parent / 'utest_output.txt'
				assert not output_file_name.exists()
				_snake_case          =      [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
				_dump_articles(lowercase      ,  lowercase     )

				_snake_case          =      str(Path(self.get_auto_remove_tmp_dir()     ) / 'scores.json'     )
				_snake_case          =      'translation_en_to_de' if model == T5_TINY else 'summarization'
				_snake_case          =      f'''
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            '''.split()

				with patch.object(lowercase      ,  'argv'      ,  lowercase     ):
							run_generate()
							assert Path(lowercase     ).exists()
							# os.remove(Path(output_file_name))



	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				self.run_eval_tester(lowercase     )



	@parameterized.expand([BART_TINY, MBART_TINY]     )
	@slow
	def 			A							(  self     :   Any      ,  lowercase     :   int     ):


				'''simple docstring'''


				self.run_eval_tester(lowercase     )





	@parameterized.expand([T5_TINY, MBART_TINY]     )
	@slow
	def 			A							(  self     :   Any      ,  lowercase     :   str     ):


				'''simple docstring'''


				_snake_case          =      Path(self.get_auto_remove_tmp_dir()     ) / 'utest_input.source'
				_snake_case          =      input_file_name.parent / 'utest_output.txt'
				assert not output_file_name.exists()

				_snake_case          =      {
				    'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
				    'de': [
				        'Maschinelles Lernen ist großartig, oder?',
				        'Ich esse gerne Bananen',
				        'Morgen ist wieder ein toller Tag!',
				    ],
				}

				_snake_case          =      Path(self.get_auto_remove_tmp_dir()     )
				_snake_case          =      str(tmp_dir / 'scores.json'     )
				_snake_case          =      str(tmp_dir / 'val.target'     )
				_dump_articles(lowercase      ,  text['en']     )
				_dump_articles(lowercase      ,  text['de']     )
				_snake_case          =      'translation_en_to_de' if model == T5_TINY else 'summarization'
				_snake_case          =      f'''
            run_eval_search.py
            {model}
            {str(lowercase     )}
            {str(lowercase     )}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            '''.split()
				testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']     )

				with patch.object(lowercase      ,  'argv'      ,  lowercase     ):
							with CaptureStdout() as cs:
										run_search()
							_snake_case          =      [' num_beams | length_penalty', model, 'Best score args']
							_snake_case          =      ['Info']
							if "translation" in task:
										expected_strings.append('bleu'     )
							else:
										expected_strings.extend(lowercase     )
							for w in expected_strings:
										assert w in cs.out
							for w in un_expected_strings:
										assert w not in cs.out
							assert Path(lowercase     ).exists()
							os.remove(Path(lowercase     )     )