



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,
)


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

snake_case_    					=	"""
    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				(       lowercase_		,     lowercase_		,     lowercase_=8						):
						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 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""






						def __init__( self			:List[Any]       ,  lowercase_			:UNetaDConditionModel       ,  lowercase_			:DDPMScheduler       ,  lowercase_			:VQModel       ,  )    ->      Optional[Any]:
												super().__init__()

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






						def      UpperCAmelCase__					( self			:Optional[Any]       ,  lowercase_			:Optional[Any]       ,  lowercase_			:List[str]       ,  lowercase_			:Dict       ,  lowercase_			:Union[str, Any]       ,  lowercase_			:int       ,  lowercase_			:Dict )    ->      Any:
												if latents is None:
																		UpperCAmelCase					=			randn_tensor(lowercase_       ,  generator=lowercase_       ,  device=lowercase_       ,  dtype=lowercase_ )
												else:
																		if latents.shape != shape:
																								raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}""" )
																		UpperCAmelCase					=			latents.to(lowercase_ )

												UpperCAmelCase					=			latents * scheduler.init_noise_sigma
												return latents






						def      UpperCAmelCase__					( self			:Tuple       ,  lowercase_			:List[str]=0 )    ->      List[str]:
												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(lowercase_       ,  lowercase_ )






						def      UpperCAmelCase__					( self			:Tuple       ,  lowercase_			:List[Any]=0 )    ->      Tuple:
												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=lowercase_ )
																		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(lowercase_       ,  lowercase_       ,  prev_module_hook=lowercase_ )

												# 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			:int )    ->      List[Any]:
												if not hasattr(self.unet       ,  '_hf_hook' ):
																		return self.device
												for module in self.unet.modules():
																		if (
																		    hasattr(lowercase_       ,  '_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(lowercase_ )
						def __call__( self			:List[str]       ,  lowercase_			:Union[torch.FloatTensor, List[torch.FloatTensor]]       ,  lowercase_			:Union[torch.FloatTensor, List[torch.FloatTensor]]       ,  lowercase_			:torch.FloatTensor       ,  lowercase_			:int = 5_12       ,  lowercase_			:int = 5_12       ,  lowercase_			:int = 1_00       ,  lowercase_			:float = 4.0       ,  lowercase_			:int = 1       ,  lowercase_			:Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,  lowercase_			:Optional[torch.FloatTensor] = None       ,  lowercase_			:Optional[str] = "pil"       ,  lowercase_			:bool = True       ,  )    ->      List[str]:
												UpperCAmelCase					=			self._execution_device

												UpperCAmelCase					=			guidance_scale > 1.0

												if isinstance(lowercase_       ,  lowercase_ ):
																		UpperCAmelCase					=			torch.cat(lowercase_       ,  dim=0 )
												if isinstance(lowercase_       ,  lowercase_ ):
																		UpperCAmelCase					=			torch.cat(lowercase_       ,  dim=0 )
												if isinstance(lowercase_       ,  lowercase_ ):
																		UpperCAmelCase					=			torch.cat(lowercase_       ,  dim=0 )

												UpperCAmelCase					=			image_embeds.shape[0] * num_images_per_prompt

												if do_classifier_free_guidance:
																		UpperCAmelCase					=			image_embeds.repeat_interleave(lowercase_       ,  dim=0 )
																		UpperCAmelCase					=			negative_image_embeds.repeat_interleave(lowercase_       ,  dim=0 )
																		UpperCAmelCase					=			hint.repeat_interleave(lowercase_       ,  dim=0 )

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

												UpperCAmelCase					=			self.movq.config.latent_channels

												UpperCAmelCase				,   UpperCAmelCase					=			downscale_height_and_width(lowercase_       ,  lowercase_       ,  self.movq_scale_factor )

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

												for i, t in enumerate(self.progress_bar(lowercase_ ) ):
																		# 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=lowercase_       ,  timestep=lowercase_       ,  encoder_hidden_states=lowercase_       ,  added_cond_kwargs=lowercase_       ,  return_dict=lowercase_       ,  )[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(
																		    lowercase_       ,  lowercase_       ,  lowercase_       ,  generator=lowercase_       ,  )[0]
												# post-processing
												UpperCAmelCase					=			self.movq.decode(lowercase_       ,  force_not_quantize=lowercase_ )['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(lowercase_ )

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=lowercase_ )










"""simple docstring"""


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 				A_      (					SCREAMING_SNAKE_CASE_     ,       unittest.TestCase    ):



						"""simple docstring"""
						__UpperCamelCase		    =							WavaVecaPhonemeCTCTokenizer
						__UpperCamelCase		    =							False






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      int:
												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(lowercase_       ,  range(len(lowercase_ ) ) ) )

												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(lowercase_ ) + '\n' )






						def      UpperCAmelCase__					( self			:Dict       ,  lowercase_			:Any       ,  lowercase_			:Union[str, Any]=False       ,  lowercase_			:int=20       ,  lowercase_			:Dict=5 )    ->      Tuple[str, list]:
												UpperCAmelCase					=			[(i, tokenizer.decode([i]       ,  clean_up_tokenization_spaces=lowercase_ )) for i in range(len(lowercase_ ) )]
												UpperCAmelCase					=			list(filter(lambda lowercase_ : [t[0]] == tokenizer.encode(t[1]       ,  do_phonemize=lowercase_ )       ,  lowercase_ ) )
												if max_length is not None and len(lowercase_ ) > max_length:
																		UpperCAmelCase					=			toks[:max_length]
												if min_length is not None and len(lowercase_ ) < min_length and len(lowercase_ ) > 0:
																		while len(lowercase_ ) < 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(lowercase_       ,  clean_up_tokenization_spaces=lowercase_ )
												if " " not in output_txt and len(lowercase_ ) > 1:
																		UpperCAmelCase					=			(
																		    tokenizer.decode([toks_ids[0]]       ,  clean_up_tokenization_spaces=lowercase_ )
																		    + ' '
																		    + tokenizer.decode(toks_ids[1:]       ,  clean_up_tokenization_spaces=lowercase_ )
																		)
												if with_prefix_space:
																		UpperCAmelCase					=			' ' + output_txt
												UpperCAmelCase					=			tokenizer.encode(lowercase_       ,  add_special_tokens=lowercase_ )
												return output_txt, output_ids






						def      UpperCAmelCase__					( self			:Union[str, Any]       ,  **lowercase_			:Union[str, Any] )    ->      Optional[Any]:
												kwargs.update(self.special_tokens_map )
												return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname       ,  **lowercase_ )






						def      UpperCAmelCase__					( self			:int )    ->      str:
												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=lowercase_ ).input_ids
												self.assertEqual(lowercase_       ,  [13, 3_92, 17] )  # xxx should be last token

												tokenizer.add_tokens(['aaa', 'bbb', 'ccc'] )
												UpperCAmelCase					=			tokenizer('m aaa ɪ ccc'       ,  do_phonemize=lowercase_ ).input_ids
												self.assertEqual(lowercase_       ,  [13, 3_93, 17, 3_95] )  # aaa and ccc should be after xxx and 2 after aaa

												UpperCAmelCase					=			tokenizer('maɪ c'       ,  do_phonemize=lowercase_ ).input_ids
												self.assertEqual(lowercase_       ,  [3, 2_00] )  # mai should be <unk> (=3)






						def      UpperCAmelCase__					( self			:Tuple )    ->      Union[str, Any]:
												UpperCAmelCase					=			self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft' )

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






						def      UpperCAmelCase__					( self			:Dict )    ->      int:
												UpperCAmelCase					=			self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft' )

												UpperCAmelCase					=			'Hello how are you'
												UpperCAmelCase					=			tokenizer.phonemize(lowercase_       ,  phonemizer_lang='en-us' )
												self.assertEqual(tokenizer(lowercase_ ).input_ids       ,  tokenizer(lowercase_       ,  do_phonemize=lowercase_ ).input_ids )






						def      UpperCAmelCase__					( self			:Optional[Any] )    ->      Dict:
												UpperCAmelCase					=			self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft' )
												UpperCAmelCase					=			'Hello how are you'
												UpperCAmelCase					=			tokenizer.phonemize(lowercase_       ,  phonemizer_lang='en-us' )

												UpperCAmelCase					=			tokenizer.decode(tokenizer(lowercase_ ).input_ids )

												self.assertEqual(lowercase_       ,  lowercase_ )






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      str:
												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(lowercase_ )
												self.assertEqual(lowercase_       ,  batch_tokens[0] )
												self.assertEqual(lowercase_       ,  ['k s ɾ ɾ l ɭʲ', 'j ð s j ð s oːɹ'] )






						def      UpperCAmelCase__					( self			:Any )    ->      str:
												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(lowercase_       ,  phonemizer_lang='en-us' )
												self.assertEqual(lowercase_       ,  'h ə l oʊ | h aʊ | ɑːɹ | j uː |' )






						def      UpperCAmelCase__					( self			:Any )    ->      Any:
												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(lowercase_       ,  phonemizer_lang='en-us' )
												self.assertEqual(tokenizer(lowercase_ ).input_ids       ,  tokenizer(lowercase_       ,  do_phonemize=lowercase_ ).input_ids )






						def      UpperCAmelCase__					( self			:Dict )    ->      Union[str, Any]:
												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(lowercase_ )
												self.assertEqual(lowercase_       ,  batch_tokens[0] )
												self.assertEqual(lowercase_       ,  ['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=lowercase_ )
												UpperCAmelCase					=			tokenizer.batch_decode(lowercase_       ,  filter_word_delimiter_token=lowercase_ )
												self.assertEqual(lowercase_       ,  batch_tokens[0] )
												self.assertEqual(lowercase_       ,  ['k s ɾ | ɾ l | ɭʲ', '| j ð | s j ð s oːɹ'] )






						def      UpperCAmelCase__					( self			:int )    ->      int:
												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(lowercase_       ,  phonemizer_lang='en-us' )

												UpperCAmelCase					=			tokenizer.decode(tokenizer(lowercase_ ).input_ids       ,  filter_word_delimiter_token=lowercase_ )

												self.assertEqual(lowercase_       ,  lowercase_ )






						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      Optional[Any]:
												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(lowercase_       ,  phonemizer_lang='en-us' )

												UpperCAmelCase					=			tokenizer.decode(tokenizer(lowercase_ ).input_ids       ,  filter_word_delimiter_token=lowercase_ )

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






						def      UpperCAmelCase__					( self			:int )    ->      Optional[Any]:
												UpperCAmelCase					=			self.tokenizer_class.from_pretrained(
												    'facebook/wav2vec2-lv-60-espeak-cv-ft'       ,  word_delimiter_token=lowercase_ )
												UpperCAmelCase					=			'Hello how are you'

												UpperCAmelCase					=			tokenizer(lowercase_       ,  phonemizer_lang='en-us' ).input_ids
												UpperCAmelCase					=			tokenizer(lowercase_       ,  phonemizer_lang='fr-fr' ).input_ids

												self.assertNotEqual(lowercase_       ,  lowercase_ )

												UpperCAmelCase					=			tokenizer.decode(lowercase_ )
												UpperCAmelCase					=			tokenizer.decode(lowercase_ )

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






						def      UpperCAmelCase__					( self			:int )    ->      List[Any]:
												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(lowercase_ ).input_ids
												UpperCAmelCase					=			tokenizer(lowercase_ ).input_ids

												self.assertEqual(lowercase_       ,  lowercase_ )






						def      UpperCAmelCase__					( self			:Optional[Any] )    ->      int:
												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, 3_92, 3_92, 3_93, 3_92, 3_92, 3_93, 3_94, 3_94],
												    [24, 22, 5, 24, 22, 5, 77, tokenizer.pad_token_id, 3_94, 3_94],
												]
												# fmt: on

												UpperCAmelCase					=			tokenizer.batch_decode(lowercase_ )
												self.assertEqual(lowercase_       ,  ['k s ɾ ɾ l ɭʲ!?!? $$$', 'j ð s j ð s oːɹ $$$'] )






						@staticmethod
						def      UpperCAmelCase__					( lowercase_			:List[str]       ,  lowercase_			:List[str] )    ->      List[str]:
												UpperCAmelCase					=			[d[key] for d in offsets]
												return retrieved_list






						def      UpperCAmelCase__					( self			:str )    ->      Optional[int]:
												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(lowercase_       ,  output_char_offsets=lowercase_       ,  filter_word_delimiter_token=lowercase_ )
												# 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(lowercase_       ,  lowercase_ ) )

												# 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			:Optional[int] )    ->      Optional[int]:
												UpperCAmelCase					=			self.get_tokenizer(word_delimiter_token='|' )

												def check_list_tuples_equal(lowercase_			:List[Any]       ,  lowercase_			:str ):
																		self.assertTrue(isinstance(lowercase_       ,  lowercase_ ) )
																		self.assertTrue(isinstance(outputs_list[0]       ,  lowercase_ ) )

																		# 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(lowercase_			:Any       ,  lowercase_			:str ):
																								if isinstance(lowercase_       ,  lowercase_ ):
																														[recursive_check(lowercase_       ,  lowercase_ ) for la, la in zip(lowercase_       ,  lowercase_ )]
																								self.assertEqual(lowercase_       ,  lowercase_ )

																		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(lowercase_       ,  output_char_offsets=lowercase_ )
												UpperCAmelCase					=			[tokenizer.decode(lowercase_       ,  output_char_offsets=lowercase_ ) for ids in sample_ids]
												check_list_tuples_equal(lowercase_       ,  lowercase_ )






						@unittest.skip('Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes' )
						def      UpperCAmelCase__					( self			:Any )    ->      str:
												pass






						@unittest.skip('Wav2Vec2PhonemeTokenizer always puts spaces between phonemes' )
						def      UpperCAmelCase__					( self			:str )    ->      List[str]:
												pass






						@unittest.skip('encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency' )
						def      UpperCAmelCase__					( self			:List[str] )    ->      int:
												pass






						@unittest.skip('Wav2Vec2PhonemeModel has no max model length => no testing' )
						def      UpperCAmelCase__					( self			:List[Any] )    ->      Optional[int]:
												pass






						def      UpperCAmelCase__					( self			:int )    ->      Optional[Any]:
												UpperCAmelCase					=			self.get_tokenizers(do_lower_case=lowercase_ )
												for tokenizer in tokenizers:
																		with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
																								UpperCAmelCase					=			tokenizer.vocab_size
																								UpperCAmelCase					=			len(lowercase_ )

																								self.assertNotEqual(lowercase_       ,  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(lowercase_ )
																								UpperCAmelCase					=			tokenizer.vocab_size
																								UpperCAmelCase					=			len(lowercase_ )

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

																								UpperCAmelCase					=			tokenizer.encode('aaaaa bbbbbb low cccccccccdddddddd l'       ,  add_special_tokens=lowercase_ )

																								self.assertGreaterEqual(len(lowercase_ )       ,  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(lowercase_ )
																								UpperCAmelCase					=			tokenizer.vocab_size
																								UpperCAmelCase					=			len(lowercase_ )

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

																								UpperCAmelCase					=			tokenizer.encode(
																								    '>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l'       ,  add_special_tokens=lowercase_ )

																								self.assertGreaterEqual(len(lowercase_ )       ,  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			:Tuple )    ->      Optional[Any]:
												pass






						@unittest.skip('The tokenizer shouldn\'t be used to encode input IDs (except for labels), only to decode.' )
						def      UpperCAmelCase__					( self			:int )    ->      Any:
												pass






						def      UpperCAmelCase__					( self			:Tuple )    ->      Dict:
												# The default common tokenizer tests assumes that the output of `convert_tokens_to_string` is a string which
												# is not the case for Wav2Vec2PhonemeCTCTokenizer.
												UpperCAmelCase					=			self.get_tokenizers(fast=lowercase_       ,  do_lower_case=lowercase_ )
												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(lowercase_ )

																								self.assertIsInstance(output['text']       ,  lowercase_ )





