from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


a_							=      logging.get_logger(__name__)  # pylint: disable=invalid-name

a_							=      """
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")


        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""




def       __lowerCAmelCase      (						A_   : int						,	A_   : Optional[int]						,	A_   : int=8					)    ->  List[Any]:
  __UpperCAmelCase						    =       height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  __UpperCAmelCase						    =       width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor





class 			UpperCAmelCase__    (		snake_case					):



  """simple docstring"""






  def __init__(				self:			Union[str, Any]				,				__lowerCAmelCase:			UNetaDConditionModel				,				__lowerCAmelCase:			DDPMScheduler				,				__lowerCAmelCase:			VQModel				,				) -> int:




    '''simple docstring'''




    super().__init__()

    self.register_modules(
        unet=__lowerCAmelCase				,				scheduler=__lowerCAmelCase				,				movq=__lowerCAmelCase				,				)
    __UpperCAmelCase						    =       2 ** (len(self.movq.config.block_out_channels   ) - 1)






  def 	_UpperCAmelCase				(				self:			str				,				__lowerCAmelCase:			List[Any]				,				__lowerCAmelCase:			int				,				__lowerCAmelCase:			Any				,				__lowerCAmelCase:			Tuple				,				__lowerCAmelCase:			List[Any]				,				__lowerCAmelCase:			Any   ) -> List[str]:




    '''simple docstring'''




    if latents is None:
      __UpperCAmelCase						    =       randn_tensor(__lowerCAmelCase				,				generator=__lowerCAmelCase				,				device=__lowerCAmelCase				,				dtype=__lowerCAmelCase   )
    else:
      if latents.shape != shape:
        raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''   )
      __UpperCAmelCase						    =       latents.to(__lowerCAmelCase   )

    __UpperCAmelCase						    =       latents * scheduler.init_noise_sigma
    return latents






  def 	_UpperCAmelCase				(				self:			int				,				__lowerCAmelCase:			Optional[int]=0   ) -> Optional[Any]:




    '''simple docstring'''




    if is_accelerate_available():
      from accelerate import cpu_offload
    else:
      raise ImportError("Please install accelerate via `pip install accelerate`"   )

    __UpperCAmelCase						    =       torch.device(F'''cuda:{gpu_id}'''   )

    __UpperCAmelCase						    =       [
        self.unet,
        self.movq,
    ]
    for cpu_offloaded_model in models:
      if cpu_offloaded_model is not None:
        cpu_offload(__lowerCAmelCase				,				__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			List[str]				,				__lowerCAmelCase:			Tuple=0   ) -> str:




    '''simple docstring'''




    if is_accelerate_available() and is_accelerate_version(">="				,				"0.17.0.dev0"   ):
      from accelerate import cpu_offload_with_hook
    else:
      raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."   )

    __UpperCAmelCase						    =       torch.device(F'''cuda:{gpu_id}'''   )

    if self.device.type != "cpu":
      self.to("cpu"				,				silence_dtype_warnings=__lowerCAmelCase   )
      torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

    __UpperCAmelCase						    =       None
    for cpu_offloaded_model in [self.unet, self.movq]:
      __UpperCAmelCase    ,		__UpperCAmelCase						    =       cpu_offload_with_hook(__lowerCAmelCase				,				__lowerCAmelCase				,				prev_module_hook=__lowerCAmelCase   )

    # We'll offload the last model manually.
    __UpperCAmelCase						    =       hook






  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def 	_UpperCAmelCase				(				self:			Tuple   ) -> Optional[int]:




    '''simple docstring'''




    if not hasattr(self.unet				,				"_hf_hook"   ):
      return self.device
    for module in self.unet.modules():
      if (
          hasattr(__lowerCAmelCase				,				"_hf_hook"   )
          and hasattr(module._hf_hook				,				"execution_device"   )
          and module._hf_hook.execution_device is not None
      ):
        return torch.device(module._hf_hook.execution_device   )
    return self.device






  @torch.no_grad()
  @replace_example_docstring(__lowerCAmelCase   )
  def __call__(				self:			Optional[int]				,				__lowerCAmelCase:			Union[torch.FloatTensor, List[torch.FloatTensor]]				,				__lowerCAmelCase:			Union[torch.FloatTensor, List[torch.FloatTensor]]				,				__lowerCAmelCase:			torch.FloatTensor				,				__lowerCAmelCase:			int = 512				,				__lowerCAmelCase:			int = 512				,				__lowerCAmelCase:			int = 100				,				__lowerCAmelCase:			float = 4.0				,				__lowerCAmelCase:			int = 1				,				__lowerCAmelCase:			Optional[Union[torch.Generator, List[torch.Generator]]] = None				,				__lowerCAmelCase:			Optional[torch.FloatTensor] = None				,				__lowerCAmelCase:			Optional[str] = "pil"				,				__lowerCAmelCase:			bool = True				,				) -> Union[str, Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self._execution_device

    __UpperCAmelCase						    =       guidance_scale > 1.0

    if isinstance(__lowerCAmelCase				,				__lowerCAmelCase   ):
      __UpperCAmelCase						    =       torch.cat(__lowerCAmelCase				,				dim=0   )
    if isinstance(__lowerCAmelCase				,				__lowerCAmelCase   ):
      __UpperCAmelCase						    =       torch.cat(__lowerCAmelCase				,				dim=0   )
    if isinstance(__lowerCAmelCase				,				__lowerCAmelCase   ):
      __UpperCAmelCase						    =       torch.cat(__lowerCAmelCase				,				dim=0   )

    __UpperCAmelCase						    =       image_embeds.shape[0] * num_images_per_prompt

    if do_classifier_free_guidance:
      __UpperCAmelCase						    =       image_embeds.repeat_interleave(__lowerCAmelCase				,				dim=0   )
      __UpperCAmelCase						    =       negative_image_embeds.repeat_interleave(__lowerCAmelCase				,				dim=0   )
      __UpperCAmelCase						    =       hint.repeat_interleave(__lowerCAmelCase				,				dim=0   )

    __UpperCAmelCase						    =       torch.cat([negative_image_embeds, image_embeds]				,				dim=0   ).to(dtype=self.unet.dtype				,				device=__lowerCAmelCase   )
    __UpperCAmelCase						    =       torch.cat([hint, hint]				,				dim=0   ).to(dtype=self.unet.dtype				,				device=__lowerCAmelCase   )
    self.scheduler.set_timesteps(__lowerCAmelCase				,				device=__lowerCAmelCase   )
    __UpperCAmelCase						    =       self.scheduler.timesteps

    __UpperCAmelCase						    =       self.movq.config.latent_channels

    __UpperCAmelCase    ,		__UpperCAmelCase						    =       downscale_height_and_width(__lowerCAmelCase				,				__lowerCAmelCase				,				self.movq_scale_factor   )

    # create initial latent
    __UpperCAmelCase						    =       self.prepare_latents(
        (batch_size, num_channels_latents, height, width)				,				image_embeds.dtype				,				__lowerCAmelCase				,				__lowerCAmelCase				,				__lowerCAmelCase				,				self.scheduler				,				)

    for i, t in enumerate(self.progress_bar(__lowerCAmelCase   )   ):
      # expand the latents if we are doing classifier free guidance
      __UpperCAmelCase						    =       torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

      __UpperCAmelCase						    =       {"image_embeds": image_embeds, "hint": hint}
      __UpperCAmelCase						    =       self.unet(
          sample=__lowerCAmelCase				,				timestep=__lowerCAmelCase				,				encoder_hidden_states=__lowerCAmelCase				,				added_cond_kwargs=__lowerCAmelCase				,				return_dict=__lowerCAmelCase				,				)[0]

      if do_classifier_free_guidance:
        __UpperCAmelCase    ,		__UpperCAmelCase						    =       noise_pred.split(latents.shape[1]				,				dim=1   )
        __UpperCAmelCase    ,		__UpperCAmelCase						    =       noise_pred.chunk(2   )
        __UpperCAmelCase    ,		__UpperCAmelCase						    =       variance_pred.chunk(2   )
        __UpperCAmelCase						    =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
        __UpperCAmelCase						    =       torch.cat([noise_pred, variance_pred_text]				,				dim=1   )

      if not (
          hasattr(self.scheduler.config				,				"variance_type"   )
          and self.scheduler.config.variance_type in ["learned", "learned_range"]
      ):
        __UpperCAmelCase    ,		__UpperCAmelCase						    =       noise_pred.split(latents.shape[1]				,				dim=1   )

      # compute the previous noisy sample x_t -> x_t-1
      __UpperCAmelCase						    =       self.scheduler.step(
          __lowerCAmelCase				,				__lowerCAmelCase				,				__lowerCAmelCase				,				generator=__lowerCAmelCase				,				)[0]
    # post-processing
    __UpperCAmelCase						    =       self.movq.decode(__lowerCAmelCase				,				force_not_quantize=__lowerCAmelCase   )["sample"]

    if output_type not in ["pt", "np", "pil"]:
      raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''   )

    if output_type in ["np", "pil"]:
      __UpperCAmelCase						    =       image * 0.5 + 0.5
      __UpperCAmelCase						    =       image.clamp(0				,				1   )
      __UpperCAmelCase						    =       image.cpu().permute(0				,				2				,				3				,				1   ).float().numpy()

    if output_type == "pil":
      __UpperCAmelCase						    =       self.numpy_to_pil(__lowerCAmelCase   )

    if not return_dict:
      return (image,)

    return ImagePipelineOutput(images=__lowerCAmelCase   )




import json
import os
import unittest
from typing import Tuple

from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizerOutput
from transformers.testing_utils import require_phonemizer

from ...test_tokenization_common import TokenizerTesterMixin


@require_phonemizer
class 			UpperCAmelCase__    (		snake_case		,	unittest.TestCase					):



  """simple docstring"""






  lowerCAmelCase__		:	Any    		= WavaVecaPhonemeCTCTokenizer
  lowerCAmelCase__		:	List[str]    		= False






  def 	_UpperCAmelCase				(				self:			Any   ) -> List[str]:




    '''simple docstring'''




    super().setUp()

    __UpperCAmelCase						    =       (
        "<s> <pad> </s> <unk> n s t ə l a i k d m ɛ ɾ e ɪ p o ɐ z ð f j v b ɹ ʁ ʊ iː r w ʌ u ɡ æ aɪ ʃ h ɔ ɑː "
        "ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː "
        "ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
        "oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
        "pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
        "yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
        "əʊ S ɡʲ onɡ2 u\" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ "
        "ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ "
        "ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ "
        "uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ "
        "ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ "
        "ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
        "ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"
    ).split(" "   )
    __UpperCAmelCase						    =       dict(zip(__lowerCAmelCase				,				range(len(__lowerCAmelCase   )   )   )   )

    __UpperCAmelCase						    =       {"pad_token": "<pad>", "unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}

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






  def 	_UpperCAmelCase				(				self:			List[Any]				,				__lowerCAmelCase:			Tuple				,				__lowerCAmelCase:			Dict=False				,				__lowerCAmelCase:			Union[str, Any]=20				,				__lowerCAmelCase:			Tuple=5   ) -> Tuple[str, list]:




    '''simple docstring'''




    __UpperCAmelCase						    =       [(i, tokenizer.decode([i]				,				clean_up_tokenization_spaces=__lowerCAmelCase   )) for i in range(len(__lowerCAmelCase   )   )]
    __UpperCAmelCase						    =       list(filter(lambda __lowerCAmelCase   : [t[0]] == tokenizer.encode(t[1]				,				do_phonemize=__lowerCAmelCase   )				,				__lowerCAmelCase   )   )
    if max_length is not None and len(__lowerCAmelCase   ) > max_length:
      __UpperCAmelCase						    =       toks[:max_length]
    if min_length is not None and len(__lowerCAmelCase   ) < min_length and len(__lowerCAmelCase   ) > 0:
      while len(__lowerCAmelCase   ) < min_length:
        __UpperCAmelCase						    =       toks + toks
        # toks_str = [t[1] for t in toks]
    __UpperCAmelCase						    =       [t[0] for t in toks]

    # Ensure consistency
    __UpperCAmelCase						    =       tokenizer.decode(__lowerCAmelCase				,				clean_up_tokenization_spaces=__lowerCAmelCase   )
    if " " not in output_txt and len(__lowerCAmelCase   ) > 1:
      __UpperCAmelCase						    =       (
          tokenizer.decode([toks_ids[0]]				,				clean_up_tokenization_spaces=__lowerCAmelCase   )
          + " "
          + tokenizer.decode(toks_ids[1:]				,				clean_up_tokenization_spaces=__lowerCAmelCase   )
      )
    if with_prefix_space:
      __UpperCAmelCase						    =       " " + output_txt
    __UpperCAmelCase						    =       tokenizer.encode(__lowerCAmelCase				,				add_special_tokens=__lowerCAmelCase   )
    return output_txt, output_ids






  def 	_UpperCAmelCase				(				self:			str				,				**__lowerCAmelCase:			List[Any]   ) -> str:




    '''simple docstring'''




    kwargs.update(self.special_tokens_map   )
    return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname				,				**__lowerCAmelCase   )






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




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"   )

    # check adding a single token
    tokenizer.add_tokens("xxx"   )
    __UpperCAmelCase						    =       tokenizer("m xxx ɪ"				,				do_phonemize=__lowerCAmelCase   ).input_ids
    self.assertEqual(__lowerCAmelCase				,				[13, 392, 17]   )  # xxx should be last token

    tokenizer.add_tokens(["aaa", "bbb", "ccc"]   )
    __UpperCAmelCase						    =       tokenizer("m aaa ɪ ccc"				,				do_phonemize=__lowerCAmelCase   ).input_ids
    self.assertEqual(__lowerCAmelCase				,				[13, 393, 17, 395]   )  # aaa and ccc should be after xxx and 2 after aaa

    __UpperCAmelCase						    =       tokenizer("maɪ c"				,				do_phonemize=__lowerCAmelCase   ).input_ids
    self.assertEqual(__lowerCAmelCase				,				[3, 200]   )  # mai should be <unk> (=3)






  def 	_UpperCAmelCase				(				self:			int   ) -> int:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"   )

    __UpperCAmelCase						    =       "Hello how are you"
    __UpperCAmelCase						    =       tokenizer.phonemize(__lowerCAmelCase				,				phonemizer_lang="en-us"   )
    self.assertEqual(__lowerCAmelCase				,				"h ə l oʊ h aʊ ɑːɹ j uː"   )






  def 	_UpperCAmelCase				(				self:			List[str]   ) -> List[str]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"   )

    __UpperCAmelCase						    =       "Hello how are you"
    __UpperCAmelCase						    =       tokenizer.phonemize(__lowerCAmelCase				,				phonemizer_lang="en-us"   )
    self.assertEqual(tokenizer(__lowerCAmelCase   ).input_ids				,				tokenizer(__lowerCAmelCase				,				do_phonemize=__lowerCAmelCase   ).input_ids   )






  def 	_UpperCAmelCase				(				self:			Any   ) -> Any:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"   )
    __UpperCAmelCase						    =       "Hello how are you"
    __UpperCAmelCase						    =       tokenizer.phonemize(__lowerCAmelCase				,				phonemizer_lang="en-us"   )

    __UpperCAmelCase						    =       tokenizer.decode(tokenizer(__lowerCAmelCase   ).input_ids   )

    self.assertEqual(__lowerCAmelCase				,				__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			str   ) -> Any:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"   )

    __UpperCAmelCase						    =       [
        [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
        [24, 22, 5, 24, 22, 5, 77],
    ]
    __UpperCAmelCase						    =       tokenizer.decode(sample_ids[0]   )
    __UpperCAmelCase						    =       tokenizer.batch_decode(__lowerCAmelCase   )
    self.assertEqual(__lowerCAmelCase				,				batch_tokens[0]   )
    self.assertEqual(__lowerCAmelCase				,				["k s ɾ ɾ l ɭʲ", "j ð s j ð s oːɹ"]   )






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




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer_class.from_pretrained(
        "facebook/wav2vec2-lv-60-espeak-cv-ft"				,				word_delimiter_token="|"   )
    tokenizer.add_tokens("|"   )

    __UpperCAmelCase						    =       "Hello how are you"
    __UpperCAmelCase						    =       tokenizer.phonemize(__lowerCAmelCase				,				phonemizer_lang="en-us"   )
    self.assertEqual(__lowerCAmelCase				,				"h ə l oʊ | h aʊ | ɑːɹ | j uː |"   )






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




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer_class.from_pretrained(
        "facebook/wav2vec2-lv-60-espeak-cv-ft"				,				word_delimiter_token="|"   )
    tokenizer.add_tokens("|"   )

    __UpperCAmelCase						    =       "Hello how are you"
    __UpperCAmelCase						    =       tokenizer.phonemize(__lowerCAmelCase				,				phonemizer_lang="en-us"   )
    self.assertEqual(tokenizer(__lowerCAmelCase   ).input_ids				,				tokenizer(__lowerCAmelCase				,				do_phonemize=__lowerCAmelCase   ).input_ids   )






  def 	_UpperCAmelCase				(				self:			List[str]   ) -> int:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer_class.from_pretrained(
        "facebook/wav2vec2-lv-60-espeak-cv-ft"				,				word_delimiter_token="|"   )
    tokenizer.add_tokens("|"   )

    # fmt: off
    __UpperCAmelCase						    =       [
        [11, 5, 15, tokenizer.pad_token_id, tokenizer.word_delimiter_token_id, 15, 8, tokenizer.word_delimiter_token_id, 98],
        [tokenizer.word_delimiter_token_id, 24, 22, tokenizer.word_delimiter_token_id, 5, 24, 22, 5, 77],
    ]
    # fmt: on

    # decode with word_del_token filter
    __UpperCAmelCase						    =       tokenizer.decode(sample_ids[0]   )
    __UpperCAmelCase						    =       tokenizer.batch_decode(__lowerCAmelCase   )
    self.assertEqual(__lowerCAmelCase				,				batch_tokens[0]   )
    self.assertEqual(__lowerCAmelCase				,				["k s ɾ ɾ l ɭʲ", "j ð s j ð s oːɹ"]   )

    # decode with no word_del_token filter
    __UpperCAmelCase						    =       tokenizer.decode(sample_ids[0]				,				filter_word_delimiter_token=__lowerCAmelCase   )
    __UpperCAmelCase						    =       tokenizer.batch_decode(__lowerCAmelCase				,				filter_word_delimiter_token=__lowerCAmelCase   )
    self.assertEqual(__lowerCAmelCase				,				batch_tokens[0]   )
    self.assertEqual(__lowerCAmelCase				,				["k s ɾ | ɾ l | ɭʲ", "| j ð | s j ð s oːɹ"]   )






  def 	_UpperCAmelCase				(				self:			Any   ) -> List[str]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer_class.from_pretrained(
        "facebook/wav2vec2-lv-60-espeak-cv-ft"				,				word_delimiter_token="|"   )
    tokenizer.add_tokens("|"   )

    __UpperCAmelCase						    =       "Hello how are you"
    __UpperCAmelCase						    =       tokenizer.phonemize(__lowerCAmelCase				,				phonemizer_lang="en-us"   )

    __UpperCAmelCase						    =       tokenizer.decode(tokenizer(__lowerCAmelCase   ).input_ids				,				filter_word_delimiter_token=__lowerCAmelCase   )

    self.assertEqual(__lowerCAmelCase				,				__lowerCAmelCase   )






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




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer_class.from_pretrained(
        "facebook/wav2vec2-lv-60-espeak-cv-ft"				,				word_delimiter_token="|"   )
    tokenizer.add_tokens("|"   )

    __UpperCAmelCase						    =       "Hello how are you"
    __UpperCAmelCase						    =       tokenizer.phonemize(__lowerCAmelCase				,				phonemizer_lang="en-us"   )

    __UpperCAmelCase						    =       tokenizer.decode(tokenizer(__lowerCAmelCase   ).input_ids				,				filter_word_delimiter_token=__lowerCAmelCase   )

    self.assertEqual(" ".join([p.strip() for p in phonemes.split(" |"   )]   ).strip()				,				__lowerCAmelCase   )






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




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer_class.from_pretrained(
        "facebook/wav2vec2-lv-60-espeak-cv-ft"				,				word_delimiter_token=__lowerCAmelCase   )
    __UpperCAmelCase						    =       "Hello how are you"

    __UpperCAmelCase						    =       tokenizer(__lowerCAmelCase				,				phonemizer_lang="en-us"   ).input_ids
    __UpperCAmelCase						    =       tokenizer(__lowerCAmelCase				,				phonemizer_lang="fr-fr"   ).input_ids

    self.assertNotEqual(__lowerCAmelCase				,				__lowerCAmelCase   )

    __UpperCAmelCase						    =       tokenizer.decode(__lowerCAmelCase   )
    __UpperCAmelCase						    =       tokenizer.decode(__lowerCAmelCase   )

    self.assertEqual(__lowerCAmelCase				,				"h ə l oʊ h aʊ ɑːɹ j uː"   )
    self.assertEqual(__lowerCAmelCase				,				"ɛ l o h aʊ a ʁ j u"   )






  def 	_UpperCAmelCase				(				self:			Tuple   ) -> Tuple:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"   )
    __UpperCAmelCase						    =       "Hello how Are you"
    __UpperCAmelCase						    =       "hello how are you"

    __UpperCAmelCase						    =       tokenizer(__lowerCAmelCase   ).input_ids
    __UpperCAmelCase						    =       tokenizer(__lowerCAmelCase   ).input_ids

    self.assertEqual(__lowerCAmelCase				,				__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			List[str]   ) -> List[str]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"   )
    tokenizer.add_tokens(["!", "?"]   )
    tokenizer.add_special_tokens({"cls_token": "$$$"}   )

    # fmt: off
    __UpperCAmelCase						    =       [
        [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98, 392, 392, 393, 392, 392, 393, 394, 394],
        [24, 22, 5, 24, 22, 5, 77, tokenizer.pad_token_id, 394, 394],
    ]
    # fmt: on

    __UpperCAmelCase						    =       tokenizer.batch_decode(__lowerCAmelCase   )
    self.assertEqual(__lowerCAmelCase				,				["k s ɾ ɾ l ɭʲ!?!? $$$", "j ð s j ð s oːɹ $$$"]   )






  @staticmethod
  def 	_UpperCAmelCase				(				__lowerCAmelCase:			List[Any]				,				__lowerCAmelCase:			List[str]   ) -> Optional[int]:




    '''simple docstring'''




    __UpperCAmelCase						    =       [d[key] for d in offsets]
    return retrieved_list






  def 	_UpperCAmelCase				(				self:			int   ) -> Dict:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.get_tokenizer(word_delimiter_token="|"   )
    tokenizer.add_tokens("|"   )

    # fmt: off
    # ksssɾɾ|ɾɾ<pad>ɾɾ|<pad>ɾlll|ɭʲ -> k s ɾ ɾ | ɾ l | ɭʲ"
    __UpperCAmelCase						    =       [11, 5, 5, 5, 15, 15, tokenizer.pad_token_id, 15, 15, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 15, 8, 8, 8, tokenizer.word_delimiter_token_id, 98]
    # fmt: on

    __UpperCAmelCase						    =       tokenizer.decode(__lowerCAmelCase				,				output_char_offsets=__lowerCAmelCase				,				filter_word_delimiter_token=__lowerCAmelCase   )
    # check Wav2Vec2CTCTokenizerOutput keys for char
    self.assertEqual(len(outputs.keys()   )				,				2   )
    self.assertTrue("text" in outputs   )
    self.assertTrue("char_offsets" in outputs   )
    self.assertTrue(isinstance(__lowerCAmelCase				,				__lowerCAmelCase   )   )

    # check that order of chars is correct and identical for both outputs
    self.assertEqual(" ".join(self.get_from_offsets(outputs["char_offsets"]				,				"char"   )   )				,				outputs.text   )
    self.assertListEqual(
        self.get_from_offsets(outputs["char_offsets"]				,				"char"   )				,				["k", "s", "ɾ", "ɾ", "|", "ɾ", "l", "|", "ɭʲ"]   )

    # check that offsets are actually correct for char
    # 0-1 is 11, 1-4 is 5, 4-6 is first 15, 6-7 is <pad> (thus not shown), 7-9 is second 15, 9-10 is word_delimiter_token,
    # 10-11 is <pad> (thus not shown), 11-12 is third 15, 12-15 is 8, 15-16 is word_delimiter_token, 16-17 is 98
    self.assertListEqual(
        self.get_from_offsets(outputs["char_offsets"]				,				"start_offset"   )				,				[0, 1, 4, 7, 9, 11, 12, 15, 16]   )
    self.assertListEqual(
        self.get_from_offsets(outputs["char_offsets"]				,				"end_offset"   )				,				[1, 4, 6, 9, 10, 12, 15, 16, 17]   )






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




    '''simple docstring'''




    __UpperCAmelCase						    =       self.get_tokenizer(word_delimiter_token="|"   )

    def check_list_tuples_equal(__lowerCAmelCase:			List[Any]				,				__lowerCAmelCase:			Optional[int]   ):
      self.assertTrue(isinstance(__lowerCAmelCase				,				__lowerCAmelCase   )   )
      self.assertTrue(isinstance(outputs_list[0]				,				__lowerCAmelCase   )   )

      # transform list to ModelOutput
      __UpperCAmelCase						    =       WavaVecaPhonemeCTCTokenizerOutput(
          {k: [d[k] for d in outputs_list] for k in outputs_list[0]}   )

      self.assertListEqual(outputs_batch["text"]				,				outputs_batch_a["text"]   )

      def recursive_check(__lowerCAmelCase:			Optional[int]				,				__lowerCAmelCase:			List[Any]   ):
        if isinstance(__lowerCAmelCase				,				__lowerCAmelCase   ):
          [recursive_check(__lowerCAmelCase				,				__lowerCAmelCase   ) for la, la in zip(__lowerCAmelCase				,				__lowerCAmelCase   )]
        self.assertEqual(__lowerCAmelCase				,				__lowerCAmelCase   )

      if "char_offsets" in outputs_batch:
        recursive_check(outputs_batch["char_offsets"]				,				outputs_batch_a["char_offsets"]   )

        # fmt: off
    __UpperCAmelCase						    =       [
        [11, 5, 15, tokenizer.pad_token_id, 15, 4, 8, 98, 32, 32, 32, 32, 4, 33, tokenizer.word_delimiter_token_id, 32, 32, 33, 34, 34],
        [24, 22, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 24, 22, 22, 22, 4, 5, 77, tokenizer.pad_token_id, 22, 22, 4, 34, 34, 34, 34],
    ]
    # fmt: on

    # We assume that `decode` works as expected. All we will check now is
    # the output type is correct and the output is identical to `decode`

    # char
    __UpperCAmelCase						    =       tokenizer.batch_decode(__lowerCAmelCase				,				output_char_offsets=__lowerCAmelCase   )
    __UpperCAmelCase						    =       [tokenizer.decode(__lowerCAmelCase				,				output_char_offsets=__lowerCAmelCase   ) for ids in sample_ids]
    check_list_tuples_equal(__lowerCAmelCase				,				__lowerCAmelCase   )






  @unittest.skip("Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes"   )
  def 	_UpperCAmelCase				(				self:			Dict   ) -> Optional[int]:




    '''simple docstring'''




    pass






  @unittest.skip("Wav2Vec2PhonemeTokenizer always puts spaces between phonemes"   )
  def 	_UpperCAmelCase				(				self:			Dict   ) -> int:




    '''simple docstring'''




    pass






  @unittest.skip("encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency"   )
  def 	_UpperCAmelCase				(				self:			Union[str, Any]   ) -> Any:




    '''simple docstring'''




    pass






  @unittest.skip("Wav2Vec2PhonemeModel has no max model length => no testing"   )
  def 	_UpperCAmelCase				(				self:			List[Any]   ) -> List[Any]:




    '''simple docstring'''




    pass






  def 	_UpperCAmelCase				(				self:			str   ) -> List[str]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.get_tokenizers(do_lower_case=__lowerCAmelCase   )
    for tokenizer in tokenizers:
      with self.subTest(F'''{tokenizer.__class__.__name__}'''   ):
        __UpperCAmelCase						    =       tokenizer.vocab_size
        __UpperCAmelCase						    =       len(__lowerCAmelCase   )

        self.assertNotEqual(__lowerCAmelCase				,				0   )

        # We usually have added tokens from the start in tests because our vocab fixtures are
        # smaller than the original vocabs - let's not assert this
        # self.assertEqual(vocab_size, all_size)

        __UpperCAmelCase						    =       ["aaaaa bbbbbb", "cccccccccdddddddd"]
        __UpperCAmelCase						    =       tokenizer.add_tokens(__lowerCAmelCase   )
        __UpperCAmelCase						    =       tokenizer.vocab_size
        __UpperCAmelCase						    =       len(__lowerCAmelCase   )

        self.assertNotEqual(__lowerCAmelCase				,				0   )
        self.assertEqual(__lowerCAmelCase				,				__lowerCAmelCase   )
        self.assertEqual(__lowerCAmelCase				,				len(__lowerCAmelCase   )   )
        self.assertEqual(__lowerCAmelCase				,				all_size + len(__lowerCAmelCase   )   )

        __UpperCAmelCase						    =       tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l"				,				add_special_tokens=__lowerCAmelCase   )

        self.assertGreaterEqual(len(__lowerCAmelCase   )				,				4   )
        self.assertGreater(tokens[0]				,				tokenizer.vocab_size - 1   )
        self.assertGreater(tokens[-3]				,				tokenizer.vocab_size - 1   )

        __UpperCAmelCase						    =       {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
        __UpperCAmelCase						    =       tokenizer.add_special_tokens(__lowerCAmelCase   )
        __UpperCAmelCase						    =       tokenizer.vocab_size
        __UpperCAmelCase						    =       len(__lowerCAmelCase   )

        self.assertNotEqual(__lowerCAmelCase				,				0   )
        self.assertEqual(__lowerCAmelCase				,				__lowerCAmelCase   )
        self.assertEqual(__lowerCAmelCase				,				len(__lowerCAmelCase   )   )
        self.assertEqual(__lowerCAmelCase				,				all_size_a + len(__lowerCAmelCase   )   )

        __UpperCAmelCase						    =       tokenizer.encode(
            ">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l"				,				add_special_tokens=__lowerCAmelCase   )

        self.assertGreaterEqual(len(__lowerCAmelCase   )				,				6   )
        self.assertGreater(tokens[0]				,				tokenizer.vocab_size - 1   )
        self.assertGreater(tokens[0]				,				tokens[1]   )
        self.assertGreater(tokens[-3]				,				tokenizer.vocab_size - 1   )
        self.assertGreater(tokens[-3]				,				tokens[-4]   )
        self.assertEqual(tokens[0]				,				tokenizer.eos_token_id   )
        self.assertEqual(tokens[-3]				,				tokenizer.pad_token_id   )






  @unittest.skip("The tokenizer shouldn't be used to encode input IDs (except for labels), only to decode."   )
  def 	_UpperCAmelCase				(				self:			Any   ) -> List[Any]:




    '''simple docstring'''




    pass






  @unittest.skip("The tokenizer shouldn't be used to encode input IDs (except for labels), only to decode."   )
  def 	_UpperCAmelCase				(				self:			str   ) -> Optional[Any]:




    '''simple docstring'''




    pass






  def 	_UpperCAmelCase				(				self:			Dict   ) -> Dict:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.get_tokenizers(fast=__lowerCAmelCase				,				do_lower_case=__lowerCAmelCase   )
    for tokenizer in tokenizers:
      with self.subTest(F'''{tokenizer.__class__.__name__}'''   ):
        __UpperCAmelCase						    =       ["ð", "ɪ", "s", "ɪ", "z", "ɐ", "t", "ɛ", "k", "s", "t"]
        __UpperCAmelCase						    =       tokenizer.convert_tokens_to_string(__lowerCAmelCase   )

        self.assertIsInstance(output["text"]				,				__lowerCAmelCase   )



