'''simple docstring'''

from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)




class    lowerCamelCase__  (   __snake_case     ):



   def __init__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__ = 0.93					,      lowerCAmelCase__ = True					,      )				->     int:
        """simple docstring"""





        _UpperCamelCase  :int       =mixing_coeff
        _UpperCamelCase  :Tuple       =leapfrog_steps

        super().__init__()
        self.register_modules(
            vae=lowerCAmelCase__					,      text_encoder=lowerCAmelCase__					,      tokenizer=lowerCAmelCase__					,      unet=lowerCAmelCase__					,      scheduler=lowerCAmelCase__					,      )

        _UpperCamelCase  :Optional[Any]       =2 ** (len(self.vae.config.block_out_channels     ) - 1)
        _UpperCamelCase  :Optional[int]       =VaeImageProcessor(vae_scale_factor=self.vae_scale_factor     )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = False     )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase  :List[Any]       =self.tokenizer(
            lowerCAmelCase__					,      padding="""max_length"""					,      max_length=self.tokenizer.model_max_length					,      truncation=lowerCAmelCase__					,      return_tensors="""pt"""					,      )

        _UpperCamelCase  :Tuple       =self.text_encoder(text_inputs.input_ids.to(self.device     )     ).last_hidden_state

        _UpperCamelCase  :List[str]       =prompt_embeds.to(dtype=self.text_encoder.dtype					,      device=self.device     )

        if do_classifier_free_guidance:
             _UpperCamelCase  :List[Any]       ="""""" if negative_prompt is None else negative_prompt

             _UpperCamelCase  :Any       =self.tokenizer(
                 lowerCAmelCase__					,      padding="""max_length"""					,      max_length=self.tokenizer.model_max_length					,      truncation=lowerCAmelCase__					,      return_tensors="""pt"""					,      )

             _UpperCamelCase  :Optional[Any]       =self.text_encoder(uncond_input.input_ids.to(self.device     )     ).last_hidden_state

             _UpperCamelCase  :int       =torch.cat([negative_prompt_embeds, prompt_embeds]     )

        return prompt_embeds



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     List[Any]:
        """simple docstring"""





        _UpperCamelCase  :str       =self.mixing_coeff * x + (1 - self.mixing_coeff) * y
        _UpperCamelCase  :int       =self.mixing_coeff * y + (1 - self.mixing_coeff) * x

        return [x, y]



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     List[str]:
        """simple docstring"""





        _UpperCamelCase  :Optional[int]       =(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
        _UpperCamelCase  :Optional[int]       =(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

        return [x, y]



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase  :List[str]       =int(lowerCAmelCase__     )

        _UpperCamelCase  :Union[str, Any]       =self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

        return alpha_prod, 1 - alpha_prod



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      )				->     Union[str, Any]:
        """simple docstring"""





        _UpperCamelCase  :List[str]       =timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

        _UpperCamelCase       ,      _UpperCamelCase  :Tuple       =self._get_alpha_and_beta(lowerCAmelCase__     )
        _UpperCamelCase       ,      _UpperCamelCase  :Dict       =self._get_alpha_and_beta(lowerCAmelCase__     )

        _UpperCamelCase  :str       =(alpha_prod_t_prev / alpha_prod_t) ** 0.5
        _UpperCamelCase  :Optional[Any]       =-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

        _UpperCamelCase  :Union[str, Any]       =(base - b_t * model_output) / a_t

        return model_input, next_model_input.to(base.dtype     )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      )				->     Any:
        """simple docstring"""





        _UpperCamelCase  :List[Any]       =timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

        _UpperCamelCase       ,      _UpperCamelCase  :Tuple       =self._get_alpha_and_beta(lowerCAmelCase__     )
        _UpperCamelCase       ,      _UpperCamelCase  :Optional[Any]       =self._get_alpha_and_beta(lowerCAmelCase__     )

        _UpperCamelCase  :Union[str, Any]       =(alpha_prod_t_prev / alpha_prod_t) ** 0.5
        _UpperCamelCase  :List[str]       =-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
        _UpperCamelCase  :Optional[int]       =a_t * base + b_t * model_output

        return model_input, next_model_input.to(base.dtype     )



   @torch.no_grad()
   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     int:
        """simple docstring"""





        _UpperCamelCase  :List[str]       =1 / self.vae.config.scaling_factor * latents
        _UpperCamelCase  :str       =self.vae.decode(lowerCAmelCase__     ).sample
        _UpperCamelCase  :Optional[Any]       =(image / 2 + 0.5).clamp(0					,      1     )
        return image



   @torch.no_grad()
   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__ = None					,      )				->     Dict:
        """simple docstring"""





        _UpperCamelCase  :Union[str, Any]       =guidance_scale > 1.0

        _UpperCamelCase  :str       =image.to(device=self.device					,      dtype=text_embeds.dtype     )
        _UpperCamelCase  :List[str]       =self.vae.encode(lowerCAmelCase__     ).latent_dist.sample(lowerCAmelCase__     )

        _UpperCamelCase  :Any       =self.vae.config.scaling_factor * latent

        _UpperCamelCase  :Union[str, Any]       =[latent.clone(), latent.clone()]

        for i, t in tqdm(enumerate(lowerCAmelCase__     )					,      total=len(lowerCAmelCase__     )     ):
             _UpperCamelCase  :str       =self.noise_mixing_layer(x=coupled_latents[0]					,      y=coupled_latents[1]     )

             # j - model_input index, k - base index
             for j in range(2     ):
                  _UpperCamelCase  :Union[str, Any]       =j ^ 1

                  if self.leapfrog_steps:
                       if i % 2 == 0:
                            _UpperCamelCase       ,      _UpperCamelCase  :Optional[int]       =j, k

                  _UpperCamelCase  :Union[str, Any]       =coupled_latents[j]
                  _UpperCamelCase  :List[Any]       =coupled_latents[k]

                  _UpperCamelCase  :int       =torch.cat([model_input] * 2     ) if do_classifier_free_guidance else model_input

                  _UpperCamelCase  :Tuple       =self.unet(lowerCAmelCase__					,      lowerCAmelCase__					,      encoder_hidden_states=lowerCAmelCase__     ).sample

                  if do_classifier_free_guidance:
                       _UpperCamelCase       ,      _UpperCamelCase  :Dict       =noise_pred.chunk(2     )
                       _UpperCamelCase  :int       =noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                  _UpperCamelCase       ,      _UpperCamelCase  :List[str]       =self.noise_step(
                      base=lowerCAmelCase__					,      model_input=lowerCAmelCase__					,      model_output=lowerCAmelCase__					,      timestep=lowerCAmelCase__					,      )

                  _UpperCamelCase  :Any       =model_input

        return coupled_latents




   @torch.no_grad()
   def __call__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__ = 3.0					,      lowerCAmelCase__ = 50					,      lowerCAmelCase__ = 0.8					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = "pil"					,      )				->     int:
        """simple docstring"""





        _UpperCamelCase  :str       =guidance_scale > 1.0

        _UpperCamelCase  :Dict       =self.image_processor.preprocess(lowerCAmelCase__     )

        _UpperCamelCase  :Any       =self._encode_prompt(lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )
        _UpperCamelCase  :Union[str, Any]       =self._encode_prompt(lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )

        self.scheduler.set_timesteps(lowerCAmelCase__					,      self.device     )

        _UpperCamelCase  :Optional[Any]       =num_inference_steps - int(num_inference_steps * strength     )
        _UpperCamelCase  :Tuple       =self.scheduler.timesteps[t_limit:]
        _UpperCamelCase  :int       =fwd_timesteps.flip(0     )

        _UpperCamelCase  :Any       =self.prepare_latents(lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )

        for i, t in tqdm(enumerate(lowerCAmelCase__     )					,      total=len(lowerCAmelCase__     )     ):
             # j - model_input index, k - base index
             for k in range(2     ):
                  _UpperCamelCase  :Union[str, Any]       =k ^ 1

                  if self.leapfrog_steps:
                       if i % 2 == 1:
                            _UpperCamelCase       ,      _UpperCamelCase  :Optional[Any]       =j, k

                  _UpperCamelCase  :Union[str, Any]       =coupled_latents[j]
                  _UpperCamelCase  :Union[str, Any]       =coupled_latents[k]

                  _UpperCamelCase  :int       =torch.cat([model_input] * 2     ) if do_classifier_free_guidance else model_input

                  _UpperCamelCase  :str       =self.unet(lowerCAmelCase__					,      lowerCAmelCase__					,      encoder_hidden_states=lowerCAmelCase__     ).sample

                  if do_classifier_free_guidance:
                       _UpperCamelCase       ,      _UpperCamelCase  :List[str]       =noise_pred.chunk(2     )
                       _UpperCamelCase  :Optional[Any]       =noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                  _UpperCamelCase       ,      _UpperCamelCase  :str       =self.denoise_step(
                      base=lowerCAmelCase__					,      model_input=lowerCAmelCase__					,      model_output=lowerCAmelCase__					,      timestep=lowerCAmelCase__					,      )

                  _UpperCamelCase  :List[str]       =model_input

             _UpperCamelCase  :List[Any]       =self.denoise_mixing_layer(x=coupled_latents[0]					,      y=coupled_latents[1]     )

        # either one is fine
        _UpperCamelCase  :Optional[int]       =coupled_latents[0]

        if output_type not in ["latent", "pt", "np", "pil"]:
             _UpperCamelCase  :Tuple       =(
                 f'''the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '''
                 """`pil`, `np`, `pt`, `latent`"""
             )
             deprecate("""Unsupported output_type"""					,      """1.0.0"""					,      lowerCAmelCase__					,      standard_warn=lowerCAmelCase__     )
             _UpperCamelCase  :Dict       ="""np"""

        if output_type == "latent":
             _UpperCamelCase  :int       =final_latent
        else:
             _UpperCamelCase  :str       =self.decode_latents(lowerCAmelCase__     )
             _UpperCamelCase  :Tuple       =self.image_processor.postprocess(lowerCAmelCase__					,      output_type=lowerCAmelCase__     )

        return image
'''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    lowerCamelCase__  (   unittest.TestCase     ):



   def  _UpperCamelCase		(    self     )				->     str:
        """simple docstring"""





        _UpperCamelCase  :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  :Optional[Any]       =dict(zip(lowerCAmelCase__					,      range(len(lowerCAmelCase__     )     )     )     )

        _UpperCamelCase  :int       ={
            """pad_token""": """<pad>""",
            """unk_token""": """<unk>""",
            """bos_token""": """<s>""",
            """eos_token""": """</s>""",
        }
        _UpperCamelCase  :Union[str, Any]       ={
            """feature_size""": 1,
            """padding_value""": 0.0,
            """sampling_rate""": 16_000,
            """return_attention_mask""": False,
            """do_normalize""": True,
        }

        _UpperCamelCase  :List[str]       =tempfile.mkdtemp()
        _UpperCamelCase  :Optional[Any]       =os.path.join(self.tmpdirname					,      VOCAB_FILES_NAMES["""vocab_file"""]     )
        _UpperCamelCase  :Tuple       =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  _UpperCamelCase		(    self					,      **lowerCAmelCase__     )				->     List[str]:
        """simple docstring"""





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



   def  _UpperCamelCase		(    self					,      **lowerCAmelCase__     )				->     str:
        """simple docstring"""





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



   def  _UpperCamelCase		(    self     )				->     List[str]:
        """simple docstring"""





        shutil.rmtree(self.tmpdirname     )



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





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

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

        processor.save_pretrained(self.tmpdirname     )
        _UpperCamelCase  :Dict       =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  _UpperCamelCase		(    self     )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase  :Union[str, Any]       =WavaVecaProcessor(tokenizer=self.get_tokenizer()					,      feature_extractor=self.get_feature_extractor()     )
        processor.save_pretrained(self.tmpdirname     )

        _UpperCamelCase  :Optional[int]       =self.get_tokenizer(bos_token="""(BOS)"""					,      eos_token="""(EOS)"""     )
        _UpperCamelCase  :Optional[int]       =self.get_feature_extractor(do_normalize=lowerCAmelCase__					,      padding_value=1.0     )

        _UpperCamelCase  :Optional[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  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





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

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

        _UpperCamelCase  :Tuple       =floats_list((3, 1_000)     )

        _UpperCamelCase  :Tuple       =feature_extractor(lowerCAmelCase__					,      return_tensors="""np"""     )
        _UpperCamelCase  :Union[str, 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  _UpperCamelCase		(    self     )				->     Tuple:
        """simple docstring"""





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

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

        _UpperCamelCase  :Optional[Any]       ="""This is a test string"""

        _UpperCamelCase  :Union[str, Any]       =processor(text=lowerCAmelCase__     )

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

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



   def  _UpperCamelCase		(    self     )				->     Optional[Any]:
        """simple docstring"""





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

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

        _UpperCamelCase  :Optional[int]       =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

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

        self.assertListEqual(lowerCAmelCase__					,      lowerCAmelCase__     )




   def  _UpperCamelCase		(    self     )				->     Union[str, Any]:
        """simple docstring"""





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

        _UpperCamelCase  :List[str]       =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"""					,      )