import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


snake_case__				= get_tests_dir('fixtures/spiece.model')



@require_sentencepiece
@require_tokenizers
class  UpperCamelCase				( __lowercase   ,  unittest.TestCase ):

     '''simple docstring'''

     A_										=  AlbertTokenizer
     A_										=  AlbertTokenizerFast
     A_										=  True
     A_										=  True
     A_										=  True



     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           super().setUp()

           # We have a SentencePiece fixture for testing
           _lowerCamelCase			    =				AlbertTokenizer(A_						)
           tokenizer.save_pretrained(self.tmpdirname						)



     def 		UpperCamelCase_						(					self				,				A_						) ->				List[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				'''this is a test'''
           _lowerCamelCase			    =				'''this is a test'''
           return input_text, output_text



     def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				'''<pad>'''
           _lowerCamelCase			    =				0

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_						)				,				A_						)
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_						)				,				A_						)



     def 		UpperCamelCase_						(					self						) ->				List[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				list(self.get_tokenizer().get_vocab().keys()						)

           self.assertEqual(vocab_keys[0]				,				'''<pad>'''						)
           self.assertEqual(vocab_keys[1]				,				'''<unk>'''						)
           self.assertEqual(vocab_keys[-1]				,				'''▁eloquent'''						)
           self.assertEqual(len(A_						)				,				3_00_00						)



     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           self.assertEqual(self.get_tokenizer().vocab_size				,				3_00_00						)



     def 		UpperCamelCase_						(					self						) ->				Optional[int]:






           """simple docstring"""

           if not self.test_rust_tokenizer:
                 return

           _lowerCamelCase			    =				self.get_tokenizer()
           _lowerCamelCase			    =				self.get_rust_tokenizer()

           _lowerCamelCase			    =				'''I was born in 92000, and this is falsé.'''

           _lowerCamelCase			    =				tokenizer.tokenize(A_						)
           _lowerCamelCase			    =				rust_tokenizer.tokenize(A_						)
           self.assertListEqual(A_				,				A_						)

           _lowerCamelCase			    =				tokenizer.encode(A_				,				add_special_tokens=A_						)
           _lowerCamelCase			    =				rust_tokenizer.encode(A_				,				add_special_tokens=A_						)
           self.assertListEqual(A_				,				A_						)

           _lowerCamelCase			    =				self.get_rust_tokenizer()
           _lowerCamelCase			    =				tokenizer.encode(A_						)
           _lowerCamelCase			    =				rust_tokenizer.encode(A_						)
           self.assertListEqual(A_				,				A_						)



     def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				AlbertTokenizer(A_				,				keep_accents=A_						)

           _lowerCamelCase			    =				tokenizer.tokenize('''This is a test'''						)
           self.assertListEqual(A_				,				['''▁this''', '''▁is''', '''▁a''', '''▁test''']						)

           self.assertListEqual(tokenizer.convert_tokens_to_ids(A_						)				,				[48, 25, 21, 12_89]						)

           _lowerCamelCase			    =				tokenizer.tokenize('''I was born in 92000, and this is falsé.'''						)
           self.assertListEqual(
               A_				,				['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''']						)
           _lowerCamelCase			    =				tokenizer.convert_tokens_to_ids(A_						)
           self.assertListEqual(A_				,				[31, 23, 3_86, 19, 5_61, 30_50, 15, 17, 48, 25, 82_56, 18, 1, 9]						)

           _lowerCamelCase			    =				tokenizer.convert_ids_to_tokens(A_						)
           self.assertListEqual(
               A_				,				['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''']				,				)



     def 		UpperCamelCase_						(					self						) ->				Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase			    =				AlbertTokenizer(A_						)

           _lowerCamelCase			    =				tokenizer.encode('''sequence builders'''						)
           _lowerCamelCase			    =				tokenizer.encode('''multi-sequence build'''						)

           _lowerCamelCase			    =				tokenizer.build_inputs_with_special_tokens(A_						)
           _lowerCamelCase			    =				tokenizer.build_inputs_with_special_tokens(A_				,				A_						)

           assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
           assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
               tokenizer.sep_token_id
           ]





     @slow
     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           _lowerCamelCase			    =				{'''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''input_ids''': [[2, 2_19_70, 13, 5, 60_92, 1_67, 28, 71_03, 21_53, 6_73, 8, 70_28, 1_20_51, 18, 17, 71_03, 21_53, 6_73, 8, 35_15, 1_86_84, 8, 44_61, 6, 19_27, 2_97, 8, 1_20_60, 26_07, 18, 13, 5, 44_61, 15, 1_05_38, 38, 8, 1_35, 15, 8_22, 58, 15, 9_93, 1_03_63, 15, 14_60, 80_05, 44_61, 15, 9_93, 2_55, 23_28, 9, 9, 9, 6, 26, 11_12, 8_16, 32_60, 13, 5, 1_03, 23_77, 6, 17, 11_12, 8_16, 27_82, 13, 5, 1_03, 1_06_41, 6, 29, 84, 25_12, 24_30, 7_82, 1_86_84, 27_61, 19, 8_08, 24_30, 25_56, 17, 8_55, 14_80, 94_77, 40_91, 1_28, 1_17_12, 15, 71_03, 21_53, 6_73, 17, 2_48_83, 99_90, 9, 3], [2, 1_15_02, 25, 10_06, 20, 7_82, 8, 1_18_09, 8_55, 17_32, 1_93_93, 1_86_67, 37, 3_67, 2_10_18, 69, 18_54, 34, 1_18_60, 1_91_24, 27, 1_56, 2_25, 17, 1_93, 41_41, 19, 65, 91_24, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 14, 22_31, 8_86, 23_85, 1_76_59, 84, 14, 1_67_92, 19_52, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
           # fmt: on

           self.tokenizer_integration_test_util(
               expected_encoding=A_				,				model_name='''albert-base-v2'''				,				revision='''6b6560eaf5ff2e250b00c50f380c5389a9c2d82e'''				,				)
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  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  ['vqvae']



     def __init__(					self				,				A_				,				A_				,				A_				,				A_				,				) ->				Tuple:






           """simple docstring"""
           super().__init__()
           self.register_modules(unet=A_				,				scheduler=A_				,				mel=A_				,				vqvae=A_						)



     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           return 50 if isinstance(self.scheduler				,				A_						) else 10_00



     @torch.no_grad()
     def __call__(					self				,				A_ = 1				,				A_ = None				,				A_ = None				,				A_ = 0				,				A_ = 0				,				A_ = None				,				A_ = None				,				A_ = 0				,				A_ = 0				,				A_ = None				,				A_ = 0				,				A_ = None				,				A_ = None				,				A_=True				,				) ->				Union[
         Union[AudioPipelineOutput, ImagePipelineOutput],
         Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
     ]:






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

           if audio_file is not None or raw_audio is not None:
                 self.mel.load_audio(A_				,				A_						)
                 _lowerCamelCase			    =				self.mel.audio_slice_to_image(A_						)
                 _lowerCamelCase			    =				np.frombuffer(input_image.tobytes()				,				dtype='''uint8'''						).reshape(
                     (input_image.height, input_image.width)						)
                 _lowerCamelCase			    =				(input_image / 2_55) * 2 - 1
                 _lowerCamelCase			    =				torch.tensor(input_image[np.newaxis, :, :]				,				dtype=torch.float						).to(self.device						)

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

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

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

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

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

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

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

           _lowerCamelCase			    =				(images / 2 + 0.5).clamp(0				,				1						)
           _lowerCamelCase			    =				images.cpu().permute(0				,				2				,				3				,				1						).numpy()
           _lowerCamelCase			    =				(images * 2_55).round().astype('''uint8'''						)
           _lowerCamelCase			    =				list(
               (Image.fromarray(_[:, :, 0]						) for _ in images)
               if images.shape[3] == 1
               else (Image.fromarray(A_				,				mode='''RGB'''						).convert('''L'''						) for _ in images)						)

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

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



     @torch.no_grad()
     def 		UpperCamelCase_						(					self				,				A_				,				A_ = 50						) ->				np.ndarray:






           """simple docstring"""
           assert isinstance(self.scheduler				,				A_						)
           self.scheduler.set_timesteps(A_						)
           _lowerCamelCase			    =				np.array(
               [np.frombuffer(image.tobytes()				,				dtype='''uint8'''						).reshape((1, image.height, image.width)						) for image in images]						)
           _lowerCamelCase			    =				(sample / 2_55) * 2 - 1
           _lowerCamelCase			    =				torch.Tensor(A_						).to(self.device						)

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

           return sample





     @staticmethod
     def 		UpperCamelCase_						(					A_				,				A_				,				A_						) ->				torch.Tensor:






           """simple docstring"""
           _lowerCamelCase			    =				acos(torch.dot(torch.flatten(A_						)				,				torch.flatten(A_						)						) / torch.norm(A_						) / torch.norm(A_						)						)
           return sin((1 - alpha) * theta						) * xa / sin(A_						) + sin(alpha * theta						) * xa / sin(A_						)