
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 itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase							,unittest.TestCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Tuple          = RobertaTokenizer
	_UpperCAmelCase	:		Dict          = RobertaTokenizerFast
	_UpperCAmelCase	:		List[Any]          = True
	_UpperCAmelCase	:		Any          = {"cls_token": "<s>"}



	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				super().setUp()

				# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
				_snake_case          =      [
				    'l',
				    'o',
				    'w',
				    'e',
				    'r',
				    's',
				    't',
				    'i',
				    'd',
				    'n',
				    '\u0120',
				    '\u0120l',
				    '\u0120n',
				    '\u0120lo',
				    '\u0120low',
				    'er',
				    '\u0120lowest',
				    '\u0120newer',
				    '\u0120wider',
				    '<unk>',
				]
				_snake_case          =      dict(zip(lowercase      ,  range(len(lowercase     )     )     )     )
				_snake_case          =      ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
				_snake_case          =      {'unk_token': '<unk>'}

				_snake_case          =      os.path.join(self.tmpdirname      ,  VOCAB_FILES_NAMES['vocab_file']     )
				_snake_case          =      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 			A							(  self     :   List[str]      ,  **lowercase     :   List[str]     ):


				'''simple docstring'''


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



	def 			A							(  self     :   List[str]      ,  **lowercase     :   int     ):


				'''simple docstring'''


				kwargs.update(self.special_tokens_map     )
				return RobertaTokenizerFast.from_pretrained(self.tmpdirname      ,  **lowercase     )



	def 			A							(  self     :   Optional[Any]      ,  lowercase     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      'lower newer'
				_snake_case          =      'lower newer'
				return input_text, output_text



	def 			A							(  self     :   str     ):


				'''simple docstring'''


				_snake_case          =      self.tokenizer_class(self.vocab_file      ,  self.merges_file      ,  **self.special_tokens_map     )
				_snake_case          =      'lower newer'
				_snake_case          =      ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
				_snake_case          =      tokenizer.tokenize(lowercase     )  # , add_prefix_space=True)
				self.assertListEqual(lowercase      ,  lowercase     )

				_snake_case          =      tokens + [tokenizer.unk_token]
				_snake_case          =      [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
				self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase     )      ,  lowercase     )



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      self.get_tokenizer()

				self.assertListEqual(tokenizer.encode('Hello world!'      ,  add_special_tokens=lowercase     )      ,  [0, 31_414, 232, 328, 2]     )
				self.assertListEqual(
				    tokenizer.encode('Hello world! cécé herlolip 418'      ,  add_special_tokens=lowercase     )      ,  [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]      ,  )



	@slow
	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


				_snake_case          =      self.tokenizer_class.from_pretrained('roberta-base'     )

				_snake_case          =      tokenizer.encode('sequence builders'      ,  add_special_tokens=lowercase     )
				_snake_case          =      tokenizer.encode('multi-sequence build'      ,  add_special_tokens=lowercase     )

				_snake_case          =      tokenizer.encode(
				    'sequence builders'      ,  add_special_tokens=lowercase      ,  add_prefix_space=lowercase     )
				_snake_case          =      tokenizer.encode(
				    'sequence builders'      ,  'multi-sequence build'      ,  add_special_tokens=lowercase      ,  add_prefix_space=lowercase     )

				_snake_case          =      tokenizer.build_inputs_with_special_tokens(lowercase     )
				_snake_case          =      tokenizer.build_inputs_with_special_tokens(lowercase      ,  lowercase     )

				assert encoded_sentence == encoded_text_from_decode
				assert encoded_pair == encoded_pair_from_decode



	def 			A							(  self     :   int     ):


				'''simple docstring'''


				_snake_case          =      self.get_tokenizer()

				_snake_case          =      'Encode this sequence.'
				_snake_case          =      tokenizer.byte_encoder[' '.encode('utf-8'     )[0]]

				# Testing encoder arguments
				_snake_case          =      tokenizer.encode(lowercase      ,  add_special_tokens=lowercase      ,  add_prefix_space=lowercase     )
				_snake_case          =      tokenizer.convert_ids_to_tokens(encoded[0]     )[0]
				self.assertNotEqual(lowercase      ,  lowercase     )

				_snake_case          =      tokenizer.encode(lowercase      ,  add_special_tokens=lowercase      ,  add_prefix_space=lowercase     )
				_snake_case          =      tokenizer.convert_ids_to_tokens(encoded[0]     )[0]
				self.assertEqual(lowercase      ,  lowercase     )

				tokenizer.add_special_tokens({'bos_token': '<s>'}     )
				_snake_case          =      tokenizer.encode(lowercase      ,  add_special_tokens=lowercase     )
				_snake_case          =      tokenizer.convert_ids_to_tokens(encoded[1]     )[0]
				self.assertNotEqual(lowercase      ,  lowercase     )

				# Testing spaces after special tokens
				_snake_case          =      '<mask>'
				tokenizer.add_special_tokens(
				    {'mask_token': AddedToken(lowercase      ,  lstrip=lowercase      ,  rstrip=lowercase     )}     )  # mask token has a left space
				_snake_case          =      tokenizer.convert_tokens_to_ids(lowercase     )

				_snake_case          =      'Encode <mask> sequence'
				_snake_case          =      'Encode <mask>sequence'

				_snake_case          =      tokenizer.encode(lowercase     )
				_snake_case          =      encoded.index(lowercase     )
				_snake_case          =      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]     )[0]
				self.assertEqual(lowercase      ,  lowercase     )

				_snake_case          =      tokenizer.encode(lowercase     )
				_snake_case          =      encoded.index(lowercase     )
				_snake_case          =      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]     )[0]
				self.assertNotEqual(lowercase      ,  lowercase     )



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				pass



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
										_snake_case          =      self.rust_tokenizer_class.from_pretrained(lowercase      ,  **lowercase     )
										_snake_case          =      self.tokenizer_class.from_pretrained(lowercase      ,  **lowercase     )
										_snake_case          =      'A, <mask> AllenNLP sentence.'
										_snake_case          =      tokenizer_r.encode_plus(lowercase      ,  add_special_tokens=lowercase      ,  return_token_type_ids=lowercase     )
										_snake_case          =      tokenizer_p.encode_plus(lowercase      ,  add_special_tokens=lowercase      ,  return_token_type_ids=lowercase     )

										# token_type_ids should put 0 everywhere
										self.assertEqual(sum(tokens_r['token_type_ids']     )      ,  sum(tokens_p['token_type_ids']     )     )

										# attention_mask should put 1 everywhere, so sum over length should be 1
										self.assertEqual(
										    sum(tokens_r['attention_mask']     ) / len(tokens_r['attention_mask']     )      ,  sum(tokens_p['attention_mask']     ) / len(tokens_p['attention_mask']     )      ,  )

										_snake_case          =      tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']     )
										_snake_case          =      tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']     )

										# Rust correctly handles the space before the mask while python doesnt
										self.assertSequenceEqual(tokens_p['input_ids']      ,  [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]     )
										self.assertSequenceEqual(tokens_r['input_ids']      ,  [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]     )

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



	def 			A							(  self     :   str     ):


				'''simple docstring'''


				for trim_offsets, add_prefix_space in itertools.product([True, False]      ,  repeat=2     ):
							_snake_case          =      self.rust_tokenizer_class.from_pretrained(
							    self.tmpdirname      ,  use_fast=lowercase      ,  add_prefix_space=lowercase      ,  trim_offsets=lowercase     )

							_snake_case          =      json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()     )
							_snake_case          =      json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()     )

							self.assertEqual(pre_tokenizer_state['add_prefix_space']      ,  lowercase     )

							self.assertEqual(post_processor_state['add_prefix_space']      ,  lowercase     )
							self.assertEqual(post_processor_state['trim_offsets']      ,  lowercase     )





	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
										_snake_case          =      'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
										_snake_case          =      f'''{text_of_1_token} {text_of_1_token}'''

										_snake_case          =      self.rust_tokenizer_class.from_pretrained(
										    lowercase      ,  use_fast=lowercase      ,  add_prefix_space=lowercase      ,  trim_offsets=lowercase     )
										_snake_case          =      tokenizer_r(lowercase      ,  return_offsets_mapping=lowercase      ,  add_special_tokens=lowercase     )
										self.assertEqual(encoding.offset_mapping[0]      ,  (0, len(lowercase     ))     )
										self.assertEqual(
										    encoding.offset_mapping[1]      ,  (len(lowercase     ) + 1, len(lowercase     ) + 1 + len(lowercase     ))      ,  )

										_snake_case          =      self.rust_tokenizer_class.from_pretrained(
										    lowercase      ,  use_fast=lowercase      ,  add_prefix_space=lowercase      ,  trim_offsets=lowercase     )
										_snake_case          =      tokenizer_r(lowercase      ,  return_offsets_mapping=lowercase      ,  add_special_tokens=lowercase     )
										self.assertEqual(encoding.offset_mapping[0]      ,  (0, len(lowercase     ))     )
										self.assertEqual(
										    encoding.offset_mapping[1]      ,  (len(lowercase     ) + 1, len(lowercase     ) + 1 + len(lowercase     ))      ,  )

										_snake_case          =      self.rust_tokenizer_class.from_pretrained(
										    lowercase      ,  use_fast=lowercase      ,  add_prefix_space=lowercase      ,  trim_offsets=lowercase     )
										_snake_case          =      tokenizer_r(lowercase      ,  return_offsets_mapping=lowercase      ,  add_special_tokens=lowercase     )
										self.assertEqual(encoding.offset_mapping[0]      ,  (0, len(lowercase     ))     )
										self.assertEqual(
										    encoding.offset_mapping[1]      ,  (len(lowercase     ), len(lowercase     ) + 1 + len(lowercase     ))      ,  )

										_snake_case          =      self.rust_tokenizer_class.from_pretrained(
										    lowercase      ,  use_fast=lowercase      ,  add_prefix_space=lowercase      ,  trim_offsets=lowercase     )
										_snake_case          =      tokenizer_r(lowercase      ,  return_offsets_mapping=lowercase      ,  add_special_tokens=lowercase     )
										self.assertEqual(encoding.offset_mapping[0]      ,  (0, len(lowercase     ))     )
										self.assertEqual(
										    encoding.offset_mapping[1]      ,  (len(lowercase     ), len(lowercase     ) + 1 + len(lowercase     ))      ,  )

										_snake_case          =      f''' {text}'''

										# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
										#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
										# )
										# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
										# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
										# self.assertEqual(
										#     encoding.offset_mapping[1],
										#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
										# )

										_snake_case          =      self.rust_tokenizer_class.from_pretrained(
										    lowercase      ,  use_fast=lowercase      ,  add_prefix_space=lowercase      ,  trim_offsets=lowercase     )
										_snake_case          =      tokenizer_r(lowercase      ,  return_offsets_mapping=lowercase      ,  add_special_tokens=lowercase     )
										self.assertEqual(encoding.offset_mapping[0]      ,  (1, 1 + len(lowercase     ))     )
										self.assertEqual(
										    encoding.offset_mapping[1]      ,  (1 + len(lowercase     ) + 1, 1 + len(lowercase     ) + 1 + len(lowercase     ))      ,  )

										_snake_case          =      self.rust_tokenizer_class.from_pretrained(
										    lowercase      ,  use_fast=lowercase      ,  add_prefix_space=lowercase      ,  trim_offsets=lowercase     )
										_snake_case          =      tokenizer_r(lowercase      ,  return_offsets_mapping=lowercase      ,  add_special_tokens=lowercase     )
										self.assertEqual(encoding.offset_mapping[0]      ,  (0, 1 + len(lowercase     ))     )
										self.assertEqual(
										    encoding.offset_mapping[1]      ,  (1 + len(lowercase     ), 1 + len(lowercase     ) + 1 + len(lowercase     ))      ,  )

										_snake_case          =      self.rust_tokenizer_class.from_pretrained(
										    lowercase      ,  use_fast=lowercase      ,  add_prefix_space=lowercase      ,  trim_offsets=lowercase     )
										_snake_case          =      tokenizer_r(lowercase      ,  return_offsets_mapping=lowercase      ,  add_special_tokens=lowercase     )
										self.assertEqual(encoding.offset_mapping[0]      ,  (0, 1 + len(lowercase     ))     )
										self.assertEqual(
										    encoding.offset_mapping[1]      ,  (1 + len(lowercase     ), 1 + len(lowercase     ) + 1 + len(lowercase     ))      ,  )