from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

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


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

a_							=      '''
    Examples:
        ```py
        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")
        >>> pipe_prior.to("cuda")

        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> negative_image_emb = out.negative_image_embeds

        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")
        >>> pipe.to("cuda")

        >>> image = pipe(
        ...     prompt,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=negative_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ... ).images

        >>> image[0].save("cat.png")
        ```
'''




def       __lowerCAmelCase      (						A_   : Union[str, Any]						,	A_   : str						,	A_   : str=8					)    ->  int:
      __UpperCAmelCase						    =       h // scale_factor**2
      if h % scale_factor**2 != 0:
            new_h += 1
      __UpperCAmelCase						    =       w // scale_factor**2
      if w % scale_factor**2 != 0:
            new_w += 1
      return new_h * scale_factor, new_w * scale_factor





class 			UpperCAmelCase__    (		__a					):



  """simple docstring"""






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




        '''simple docstring'''




        super().__init__()

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






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




        '''simple docstring'''




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

        __UpperCAmelCase						    =       latents * scheduler.init_noise_sigma
        return latents






  def 	_UpperCAmelCase				(				self:			Any				,				__lowerCAmelCase:			Union[str, Any]				,				__lowerCAmelCase:			Optional[Any]				,				__lowerCAmelCase:			str				,				__lowerCAmelCase:			Optional[int]				,				__lowerCAmelCase:			Optional[int]=None				,				) -> str:




        '''simple docstring'''




        __UpperCAmelCase						    =       len(snake_case__   ) if isinstance(snake_case__				,				snake_case__   ) else 1
        # get prompt text embeddings
        __UpperCAmelCase						    =       self.tokenizer(
            snake_case__				,				padding="max_length"				,				truncation=snake_case__				,				max_length=77				,				return_attention_mask=snake_case__				,				add_special_tokens=snake_case__				,				return_tensors="pt"				,				)

        __UpperCAmelCase						    =       text_inputs.input_ids
        __UpperCAmelCase						    =       self.tokenizer(snake_case__				,				padding="longest"				,				return_tensors="pt"   ).input_ids

        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(snake_case__				,				snake_case__   ):
              __UpperCAmelCase						    =       self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]   )
              logger.warning(
                  "The following part of your input was truncated because CLIP can only handle sequences up to"
                  F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''   )

        __UpperCAmelCase						    =       text_input_ids.to(snake_case__   )
        __UpperCAmelCase						    =       text_inputs.attention_mask.to(snake_case__   )

        __UpperCAmelCase    ,		__UpperCAmelCase						    =       self.text_encoder(
            input_ids=snake_case__				,				attention_mask=snake_case__   )

        __UpperCAmelCase						    =       prompt_embeds.repeat_interleave(snake_case__				,				dim=0   )
        __UpperCAmelCase						    =       text_encoder_hidden_states.repeat_interleave(snake_case__				,				dim=0   )
        __UpperCAmelCase						    =       text_mask.repeat_interleave(snake_case__				,				dim=0   )

        if do_classifier_free_guidance:
              __UpperCAmelCase						    =       42
              if negative_prompt is None:
                    __UpperCAmelCase						    =       [""] * batch_size
              elif type(snake_case__   ) is not type(snake_case__   ):
                    raise TypeError(
                        F'''`negative_prompt` should be the same type to `prompt`, but got {type(snake_case__   )} !='''
                        F''' {type(snake_case__   )}.'''   )
              elif isinstance(snake_case__				,				snake_case__   ):
                    __UpperCAmelCase						    =       [negative_prompt]
              elif batch_size != len(snake_case__   ):
                    raise ValueError(
                        F'''`negative_prompt`: {negative_prompt} has batch size {len(snake_case__   )}, but `prompt`:'''
                        F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
                        " the batch size of `prompt`."   )
              else:
                    __UpperCAmelCase						    =       negative_prompt

              __UpperCAmelCase						    =       self.tokenizer(
                  snake_case__				,				padding="max_length"				,				max_length=77				,				truncation=snake_case__				,				return_attention_mask=snake_case__				,				add_special_tokens=snake_case__				,				return_tensors="pt"				,				)
              __UpperCAmelCase						    =       uncond_input.input_ids.to(snake_case__   )
              __UpperCAmelCase						    =       uncond_input.attention_mask.to(snake_case__   )

              __UpperCAmelCase    ,		__UpperCAmelCase						    =       self.text_encoder(
                  input_ids=snake_case__				,				attention_mask=snake_case__   )

              # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

              __UpperCAmelCase						    =       negative_prompt_embeds.shape[1]
              __UpperCAmelCase						    =       negative_prompt_embeds.repeat(1				,				snake_case__   )
              __UpperCAmelCase						    =       negative_prompt_embeds.view(batch_size * num_images_per_prompt				,				snake_case__   )

              __UpperCAmelCase						    =       uncond_text_encoder_hidden_states.shape[1]
              __UpperCAmelCase						    =       uncond_text_encoder_hidden_states.repeat(1				,				snake_case__				,				1   )
              __UpperCAmelCase						    =       uncond_text_encoder_hidden_states.view(
                  batch_size * num_images_per_prompt				,				snake_case__				,				-1   )
              __UpperCAmelCase						    =       uncond_text_mask.repeat_interleave(snake_case__				,				dim=0   )

              # done duplicates

              # For classifier free guidance, we need to do two forward passes.
              # Here we concatenate the unconditional and text embeddings into a single batch
              # to avoid doing two forward passes
              __UpperCAmelCase						    =       torch.cat([negative_prompt_embeds, prompt_embeds]   )
              __UpperCAmelCase						    =       torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]   )

              __UpperCAmelCase						    =       torch.cat([uncond_text_mask, text_mask]   )

        return prompt_embeds, text_encoder_hidden_states, text_mask






  def 	_UpperCAmelCase				(				self:			Union[str, Any]				,				__lowerCAmelCase:			str=0   ) -> Union[str, 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.text_encoder,
            self.movq,
        ]
        for cpu_offloaded_model in models:
              if cpu_offloaded_model is not None:
                    cpu_offload(snake_case__				,				snake_case__   )






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




        '''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=snake_case__   )
              torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

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

        if self.safety_checker is not None:
              __UpperCAmelCase    ,		__UpperCAmelCase						    =       cpu_offload_with_hook(self.safety_checker				,				snake_case__				,				prev_module_hook=snake_case__   )

        # 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:			str   ) -> Any:




        '''simple docstring'''




        if not hasattr(self.unet				,				"_hf_hook"   ):
              return self.device
        for module in self.unet.modules():
              if (
                  hasattr(snake_case__				,				"_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(snake_case__   )
  def __call__(				self:			Union[str, Any]				,				__lowerCAmelCase:			Union[str, List[str]]				,				__lowerCAmelCase:			Union[torch.FloatTensor, List[torch.FloatTensor]]				,				__lowerCAmelCase:			Union[torch.FloatTensor, List[torch.FloatTensor]]				,				__lowerCAmelCase:			Optional[Union[str, List[str]]] = None				,				__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				,				) -> Any:




        '''simple docstring'''




        if isinstance(snake_case__				,				snake_case__   ):
              __UpperCAmelCase						    =       1
        elif isinstance(snake_case__				,				snake_case__   ):
              __UpperCAmelCase						    =       len(snake_case__   )
        else:
              raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(snake_case__   )}'''   )

        __UpperCAmelCase						    =       self._execution_device

        __UpperCAmelCase						    =       batch_size * num_images_per_prompt
        __UpperCAmelCase						    =       guidance_scale > 1.0

        __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase						    =       self._encode_prompt(
            snake_case__				,				snake_case__				,				snake_case__				,				snake_case__				,				snake_case__   )

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

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

        __UpperCAmelCase						    =       torch.cat([negative_image_embeds, image_embeds]				,				dim=0   ).to(
            dtype=prompt_embeds.dtype				,				device=snake_case__   )

        self.scheduler.set_timesteps(snake_case__				,				device=snake_case__   )
        __UpperCAmelCase						    =       self.scheduler.timesteps

        __UpperCAmelCase						    =       self.unet.config.in_channels

        __UpperCAmelCase    ,		__UpperCAmelCase						    =       get_new_h_w(snake_case__				,				snake_case__				,				self.movq_scale_factor   )

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

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

              __UpperCAmelCase						    =       {"text_embeds": prompt_embeds, "image_embeds": image_embeds}
              __UpperCAmelCase						    =       self.unet(
                  sample=snake_case__				,				timestep=snake_case__				,				encoder_hidden_states=snake_case__				,				added_cond_kwargs=snake_case__				,				return_dict=snake_case__				,				)[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(
                  snake_case__				,				snake_case__				,				snake_case__				,				generator=snake_case__				,				).prev_sample
        # post-processing
        __UpperCAmelCase						    =       self.movq.decode(snake_case__				,				force_not_quantize=snake_case__   )["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(snake_case__   )

        if not return_dict:
              return (image,)

        return ImagePipelineOutput(images=snake_case__   )




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   )



