





import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
				from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

				from .test_feature_extraction_speechta import floats_list


__lowerCAmelCase 	=      get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")




@require_torch
class 	_UpperCAmelCase						( unittest.TestCase       ):




					def 						snake_case_		(    self							)    ->	Dict:
							UpperCamelCase__								=						tempfile.mkdtemp()

							UpperCamelCase__								=						SpeechTaTokenizer(__lowerCAmelCase							)
							tokenizer.save_pretrained(self.tmpdirname							)

							UpperCamelCase__								=						{
							    'feature_size': 1,
							    'padding_value': 0.0,
							    'sampling_rate': 16_000,
							    'do_normalize': False,
							    'num_mel_bins': 80,
							    'hop_length': 16,
							    'win_length': 64,
							    'win_function': 'hann_window',
							    'fmin': 80,
							    'fmax': 7_600,
							    'mel_floor': 1E-1_0,
							    'reduction_factor': 2,
							    'return_attention_mask': True,
							}

							UpperCamelCase__								=						os.path.join(self.tmpdirname     ,   __lowerCAmelCase							)
							with open(self.feature_extraction_file     ,   """w"""     ,   encoding="""utf-8"""							) as fp:
									fp.write(json.dumps(__lowerCAmelCase							) + """\n"""							)




					def 						snake_case_		(    self     ,   **__lowerCAmelCase							)    ->	List[Any]:
							return SpeechTaTokenizer.from_pretrained(self.tmpdirname     ,   **__lowerCAmelCase							)




					def 						snake_case_		(    self     ,   **__lowerCAmelCase							)    ->	int:
							return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname     ,   **__lowerCAmelCase							)




					def 						snake_case_		(    self							)    ->	Optional[int]:
							shutil.rmtree(self.tmpdirname							)




					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							UpperCamelCase__								=						self.get_tokenizer()
							UpperCamelCase__								=						self.get_feature_extractor()

							UpperCamelCase__								=						SpeechTaProcessor(tokenizer=__lowerCAmelCase     ,   feature_extractor=__lowerCAmelCase							)

							processor.save_pretrained(self.tmpdirname							)
							UpperCamelCase__								=						SpeechTaProcessor.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							)    ->	List[str]:
							UpperCamelCase__								=						SpeechTaProcessor(tokenizer=self.get_tokenizer()     ,   feature_extractor=self.get_feature_extractor()							)
							processor.save_pretrained(self.tmpdirname							)

							UpperCamelCase__								=						self.get_tokenizer(bos_token="""(BOS)"""     ,   eos_token="""(EOS)"""							)
							UpperCamelCase__								=						self.get_feature_extractor(do_normalize=__lowerCAmelCase     ,   padding_value=1.0							)

							UpperCamelCase__								=						SpeechTaProcessor.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							)    ->	Any:
							UpperCamelCase__								=						self.get_feature_extractor()
							UpperCamelCase__								=						self.get_tokenizer()

							UpperCamelCase__								=						SpeechTaProcessor(tokenizer=__lowerCAmelCase     ,   feature_extractor=__lowerCAmelCase							)

							UpperCamelCase__								=						floats_list((3, 1_000)							)

							UpperCamelCase__								=						feature_extractor(audio=__lowerCAmelCase     ,   return_tensors="""np"""							)
							UpperCamelCase__								=						processor(audio=__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							)    ->	Dict:
							UpperCamelCase__								=						self.get_feature_extractor()
							UpperCamelCase__								=						self.get_tokenizer()

							UpperCamelCase__								=						SpeechTaProcessor(tokenizer=__lowerCAmelCase     ,   feature_extractor=__lowerCAmelCase							)

							UpperCamelCase__								=						floats_list((3, 1_000)							)

							UpperCamelCase__								=						feature_extractor(audio_target=__lowerCAmelCase     ,   return_tensors="""np"""							)
							UpperCamelCase__								=						processor(audio_target=__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							)    ->	List[Any]:
							UpperCamelCase__								=						self.get_feature_extractor()
							UpperCamelCase__								=						self.get_tokenizer()

							UpperCamelCase__								=						SpeechTaProcessor(tokenizer=__lowerCAmelCase     ,   feature_extractor=__lowerCAmelCase							)

							UpperCamelCase__								=						'This is a test string'

							UpperCamelCase__								=						processor(text=__lowerCAmelCase							)
							UpperCamelCase__								=						tokenizer(__lowerCAmelCase							)

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




					def 						snake_case_		(    self							)    ->	Optional[int]:
							UpperCamelCase__								=						self.get_feature_extractor()
							UpperCamelCase__								=						self.get_tokenizer()

							UpperCamelCase__								=						SpeechTaProcessor(tokenizer=__lowerCAmelCase     ,   feature_extractor=__lowerCAmelCase							)

							UpperCamelCase__								=						'This is a test string'

							UpperCamelCase__								=						processor(text_target=__lowerCAmelCase							)
							UpperCamelCase__								=						tokenizer(__lowerCAmelCase							)

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




					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							UpperCamelCase__								=						self.get_feature_extractor()
							UpperCamelCase__								=						self.get_tokenizer()

							UpperCamelCase__								=						SpeechTaProcessor(tokenizer=__lowerCAmelCase     ,   feature_extractor=__lowerCAmelCase							)

							UpperCamelCase__								=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							UpperCamelCase__								=						processor.batch_decode(__lowerCAmelCase							)
							UpperCamelCase__								=						tokenizer.batch_decode(__lowerCAmelCase							)

							self.assertListEqual(__lowerCAmelCase     ,   __lowerCAmelCase							)




					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							UpperCamelCase__								=						self.get_feature_extractor()
							UpperCamelCase__								=						self.get_tokenizer()

							UpperCamelCase__								=						SpeechTaProcessor(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"""     ,   )






"""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   __snake_case			(	__lowerCAmelCase							):



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





        '''simple docstring'''





        super().__init__()

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



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     int:





        '''simple docstring'''





        with torch.no_grad():
          a__:	List[str]     				=      self.vae.encode(tfms.ToTensor()(lowercase).unsqueeze(0).to(self.device) * 2 - 1)
          a__:	Optional[Any]     				=      0.18215 * latent.latent_dist.sample()
        return latent



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     List[str]:





        '''simple docstring'''





        a__:	Optional[Any]     				=      (1 / 0.18215) * latent
        with torch.no_grad():
          a__:	Optional[int]     				=      self.vae.decode(lowercase).sample
        a__:	Dict     				=      (img / 2 + 0.5).clamp(0       ,     1)
        a__:	List[Any]     				=      img.detach().cpu().permute(0       ,     2       ,     3       ,     1).numpy()
        a__:	Tuple     				=      (img * 2_55).round().astype('uint8')
        return Image.fromarray(img[0])



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Tuple:





        '''simple docstring'''





        a__:	Any     				=      self.tokenizer(
            lowercase       ,     padding='max_length'       ,     max_length=self.tokenizer.model_max_length       ,     truncation=lowercase       ,     return_tensors='pt'       ,     )

        a__:	Optional[Any]     				=      self.text_encoder(text_input.input_ids.to(self.device))[0]

        a__:	str     				=      self.tokenizer(
            ''       ,     padding='max_length'       ,     max_length=self.tokenizer.model_max_length       ,     truncation=lowercase       ,     return_tensors='pt'       ,     )

        a__:	Optional[int]     				=      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:





        '''simple docstring'''





        a__:	Optional[Any]     				=      steps - int(kmin * steps)
        a__:	Optional[int]     				=      steps - int(kmax * steps)

        a__:	Optional[int]     				=      self.prep_text(lowercase)

        self.scheduler.set_timesteps(lowercase)

        a__				,       a__:	Union[str, Any]     				=      img.size
        a__:	str     				=      self.encode(lowercase)

        torch.manual_seed(lowercase)
        a__:	Optional[int]     				=      torch.randn(
            (1, self.unet.config.in_channels, height // 8, width // 8)       ,     ).to(self.device)

        a__:	Tuple     				=      self.scheduler.add_noise(
            lowercase       ,     lowercase       ,     timesteps=self.scheduler.timesteps[tmax]       ,     )

        a__:	List[Any]     				=      torch.cat([latents] * 2)

        a__:	List[str]     				=      self.scheduler.scale_model_input(lowercase       ,     self.scheduler.timesteps[tmax])

        with torch.no_grad():
          a__:	Optional[Any]     				=      self.unet(
              lowercase       ,     self.scheduler.timesteps[tmax]       ,     encoder_hidden_states=lowercase       ,     ).sample

        a__				,       a__:	List[str]     				=      pred.chunk(2)
        a__:	Union[str, Any]     				=      pred_uncond + guidance_scale * (pred_text - pred_uncond)

        a__:	int     				=      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
              a__:	List[Any]     				=      self.scheduler.add_noise(
                  lowercase       ,     lowercase       ,     timesteps=lowercase       ,     )

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

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

            a__:	Union[str, Any]     				=      self.scheduler.scale_model_input(lowercase       ,     lowercase)

            with torch.no_grad():
              a__:	Union[str, Any]     				=      self.unet(
                  lowercase       ,     lowercase       ,     encoder_hidden_states=lowercase       ,     ).sample

            a__				,       a__:	Union[str, Any]     				=      pred.chunk(2)
            a__:	Optional[Any]     				=      pred_uncond + guidance_scale * (pred_text - pred_uncond)

            a__:	List[str]     				=      self.scheduler.step(lowercase       ,     lowercase       ,     lowercase).prev_sample

        return self.decode(lowercase)


