

'''simple docstring'''

from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)







class lowerCamelCase   (    __lowerCAmelCase				):

				def __init__(       self,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						)    ->							Tuple:
					super().__init__()

					self.register_modules(vae=lowercase_,						text_encoder=lowercase_,						tokenizer=lowercase_,						unet=lowercase_,						scheduler=lowercase_ )

				def 					_lowerCamelCase    (       self,						lowercase_ )    ->							List[Any]:
					with torch.no_grad():
						snake_case							=					self.vae.encode(tfms.ToTensor()(lowercase_ ).unsqueeze(0 ).to(self.device ) * 2 - 1 )
						snake_case							=					0.18_215 * latent.latent_dist.sample()
					return latent

				def 					_lowerCamelCase    (       self,						lowercase_ )    ->							List[Any]:
					snake_case							=					(1 / 0.18_215) * latent
					with torch.no_grad():
						snake_case							=					self.vae.decode(lowercase_ ).sample
					snake_case							=					(img / 2 + 0.5).clamp(0,						1 )
					snake_case							=					img.detach().cpu().permute(0,						2,						3,						1 ).numpy()
					snake_case							=					(img * 255).round().astype('uint8' )
					return Image.fromarray(img[0] )

				def 					_lowerCamelCase    (       self,						lowercase_ )    ->							Dict:
					snake_case							=					self.tokenizer(
					    lowercase_,						padding='max_length',						max_length=self.tokenizer.model_max_length,						truncation=lowercase_,						return_tensors='pt',						)

					snake_case							=					self.text_encoder(text_input.input_ids.to(self.device ) )[0]

					snake_case							=					self.tokenizer(
					    '',						padding='max_length',						max_length=self.tokenizer.model_max_length,						truncation=lowercase_,						return_tensors='pt',						)

					snake_case							=					self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]

					return torch.cat([uncond_embedding, text_embedding] )






				def __call__(       self,						lowercase_,						lowercase_,						lowercase_ = 0.3,						lowercase_ = 0.6,						lowercase_ = 0.5,						lowercase_ = 42,						lowercase_ = 50,						lowercase_ = 7.5,						)    ->							Image.Image:
					snake_case							=					steps - int(kmin * steps )
					snake_case							=					steps - int(kmax * steps )

					snake_case							=					self.prep_text(lowercase_ )

					self.scheduler.set_timesteps(lowercase_ )

					snake_case			,			snake_case							=					img.size
					snake_case							=					self.encode(lowercase_ )

					torch.manual_seed(lowercase_ )
					snake_case							=					torch.randn(
					    (1, self.unet.config.in_channels, height // 8, width // 8),						).to(self.device )

					snake_case							=					self.scheduler.add_noise(
					    lowercase_,						lowercase_,						timesteps=self.scheduler.timesteps[tmax],						)

					snake_case							=					torch.cat([latents] * 2 )

					snake_case							=					self.scheduler.scale_model_input(lowercase_,						self.scheduler.timesteps[tmax] )

					with torch.no_grad():
						snake_case							=					self.unet(
						    lowercase_,						self.scheduler.timesteps[tmax],						encoder_hidden_states=lowercase_,						).sample

					snake_case			,			snake_case							=					pred.chunk(2 )
					snake_case							=					pred_uncond + guidance_scale * (pred_text - pred_uncond)

					snake_case							=					self.scheduler.step(lowercase_,						self.scheduler.timesteps[tmax],						lowercase_ ).prev_sample

					for i, t in enumerate(tqdm(self.scheduler.timesteps ) ):
						if i > tmax:
							if i < tmin:  # layout generation phase
								snake_case							=					self.scheduler.add_noise(
								    lowercase_,						lowercase_,						timesteps=lowercase_,						)

								snake_case							=					(mix_factor * latents) + (
								    1 - mix_factor
								) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
								snake_case							=					torch.cat([input] * 2 )

							else:  # content generation phase
								snake_case							=					torch.cat([latents] * 2 )

							snake_case							=					self.scheduler.scale_model_input(lowercase_,						lowercase_ )

							with torch.no_grad():
								snake_case							=					self.unet(
								    lowercase_,						lowercase_,						encoder_hidden_states=lowercase_,						).sample

							snake_case			,			snake_case							=					pred.chunk(2 )
							snake_case							=					pred_uncond + guidance_scale * (pred_text - pred_uncond)

							snake_case							=					self.scheduler.step(lowercase_,						lowercase_,						lowercase_ ).prev_sample

					return self.decode(lowercase_ )












"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list




class 					_a		( unittest.TestCase							):






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




										'''simple docstring'''



										_UpperCamelCase     :   List[Any]      =       '''<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'''.split(''' '''				)
										_UpperCamelCase     :   Tuple      =       dict(zip(lowerCAmelCase__,					range(len(lowerCAmelCase__				)				)				)				)

										_UpperCamelCase     :   Any      =       {
										    '''pad_token''': '''<pad>''',
										    '''unk_token''': '''<unk>''',
										    '''bos_token''': '''<s>''',
										    '''eos_token''': '''</s>''',
										}
										_UpperCamelCase     :   Optional[int]      =       {
										    '''feature_size''': 1,
										    '''padding_value''': 0.0,
										    '''sampling_rate''': 1_6_0_0_0,
										    '''return_attention_mask''': False,
										    '''do_normalize''': True,
										}

										_UpperCamelCase     :   List[str]      =       tempfile.mkdtemp()
										_UpperCamelCase     :   Any      =       os.path.join(self.tmpdirname,					VOCAB_FILES_NAMES['''vocab_file''']				)
										_UpperCamelCase     :   Optional[int]      =       os.path.join(self.tmpdirname,					lowerCAmelCase__				)
										with open(self.vocab_file,					'''w''',					encoding='''utf-8'''				) as fp:
																fp.write(json.dumps(lowerCAmelCase__				) + '''\n'''				)

										with open(self.feature_extraction_file,					'''w''',					encoding='''utf-8'''				) as fp:
																fp.write(json.dumps(lowerCAmelCase__				) + '''\n'''				)






				def 							snake_case      (					self						:       List[Any],					**lowerCAmelCase__						:       Any				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   str      =       self.add_kwargs_tokens_map.copy()
										kwargs.update(lowerCAmelCase__				)
										return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname,					**lowerCAmelCase__				)






				def 							snake_case      (					self						:       Any,					**lowerCAmelCase__						:       Dict				)      -> Optional[int]:




										'''simple docstring'''



										return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname,					**lowerCAmelCase__				)






				def 							snake_case      (					self						:       Dict				)      -> Optional[int]:




										'''simple docstring'''



										shutil.rmtree(self.tmpdirname				)






				def 							snake_case      (					self						:       Optional[Any]				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   Tuple      =       self.get_tokenizer()
										_UpperCamelCase     :   str      =       self.get_feature_extractor()

										_UpperCamelCase     :   str      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										processor.save_pretrained(self.tmpdirname				)
										_UpperCamelCase     :   Optional[Any]      =       WavaVecaProcessor.from_pretrained(self.tmpdirname				)

										self.assertEqual(processor.tokenizer.get_vocab(),					tokenizer.get_vocab()				)
										self.assertIsInstance(processor.tokenizer,					lowerCAmelCase__				)

										self.assertEqual(processor.feature_extractor.to_json_string(),					feature_extractor.to_json_string()				)
										self.assertIsInstance(processor.feature_extractor,					lowerCAmelCase__				)






				def 							snake_case      (					self						:       str				)      -> str:




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       WavaVecaProcessor(tokenizer=self.get_tokenizer(),					feature_extractor=self.get_feature_extractor()				)
										processor.save_pretrained(self.tmpdirname				)

										_UpperCamelCase     :   Tuple      =       self.get_tokenizer(bos_token='''(BOS)''',					eos_token='''(EOS)'''				)
										_UpperCamelCase     :   Any      =       self.get_feature_extractor(do_normalize=lowerCAmelCase__,					padding_value=1.0				)

										_UpperCamelCase     :   int      =       WavaVecaProcessor.from_pretrained(
										    self.tmpdirname,					bos_token='''(BOS)''',					eos_token='''(EOS)''',					do_normalize=lowerCAmelCase__,					padding_value=1.0				)

										self.assertEqual(processor.tokenizer.get_vocab(),					tokenizer_add_kwargs.get_vocab()				)
										self.assertIsInstance(processor.tokenizer,					lowerCAmelCase__				)

										self.assertEqual(processor.feature_extractor.to_json_string(),					feature_extractor_add_kwargs.to_json_string()				)
										self.assertIsInstance(processor.feature_extractor,					lowerCAmelCase__				)






				def 							snake_case      (					self						:       Optional[Any]				)      -> Tuple:




										'''simple docstring'''



										_UpperCamelCase     :   Union[str, Any]      =       self.get_feature_extractor()
										_UpperCamelCase     :   str      =       self.get_tokenizer()

										_UpperCamelCase     :   Union[str, Any]      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										_UpperCamelCase     :   Any      =       floats_list((3, 1_0_0_0)				)

										_UpperCamelCase     :   int      =       feature_extractor(lowerCAmelCase__,					return_tensors='''np'''				)
										_UpperCamelCase     :   List[Any]      =       processor(lowerCAmelCase__,					return_tensors='''np'''				)

										for key in input_feat_extract.keys():
																self.assertAlmostEqual(input_feat_extract[key].sum(),					input_processor[key].sum(),					delta=1e-2				)






				def 							snake_case      (					self						:       Tuple				)      -> Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   int      =       self.get_feature_extractor()
										_UpperCamelCase     :   Tuple      =       self.get_tokenizer()

										_UpperCamelCase     :   Optional[Any]      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										_UpperCamelCase     :   List[Any]      =       '''This is a test string'''

										_UpperCamelCase     :   List[Any]      =       processor(text=lowerCAmelCase__				)

										_UpperCamelCase     :   Tuple      =       tokenizer(lowerCAmelCase__				)

										for key in encoded_tok.keys():
																self.assertListEqual(encoded_tok[key],					encoded_processor[key]				)






				def 							snake_case      (					self						:       Dict				)      -> List[str]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       self.get_feature_extractor()
										_UpperCamelCase     :   Dict      =       self.get_tokenizer()

										_UpperCamelCase     :   List[Any]      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										_UpperCamelCase     :   List[str]      =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										_UpperCamelCase     :   List[str]      =       processor.batch_decode(lowerCAmelCase__				)
										_UpperCamelCase     :   Any      =       tokenizer.batch_decode(lowerCAmelCase__				)

										self.assertListEqual(lowerCAmelCase__,					lowerCAmelCase__				)






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




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       self.get_feature_extractor()
										_UpperCamelCase     :   List[Any]      =       self.get_tokenizer()

										_UpperCamelCase     :   Optional[int]      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										self.assertListEqual(
										    processor.model_input_names,					feature_extractor.model_input_names,					msg='''`processor` and `feature_extractor` model input names do not match''',					)



