





import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
a__      :     str         =		logging.get_logger(__name__)

def      UpperCAmelCase_    (    _UpperCAmelCase							:str				)	->				Tuple:
     '''simple docstring'''


     A_          =       SwinConfig.from_pretrained(
         '''microsoft/swin-tiny-patch4-window7-224'''  ,						out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']				)
     A_          =       MaskFormerConfig(backbone_config=_UpperCAmelCase				)

     A_          =       'huggingface/label-files'
     if "ade20k-full" in model_name:
          # this should be ok
          A_          =       847
          A_          =       'maskformer-ade20k-full-id2label.json'
     elif "ade" in model_name:
          # this should be ok
          A_          =       150
          A_          =       'ade20k-id2label.json'
     elif "coco-stuff" in model_name:
          # this should be ok
          A_          =       171
          A_          =       'maskformer-coco-stuff-id2label.json'
     elif "coco" in model_name:
          # TODO
          A_          =       133
          A_          =       'coco-panoptic-id2label.json'
     elif "cityscapes" in model_name:
          # this should be ok
          A_          =       19
          A_          =       'cityscapes-id2label.json'
     elif "vistas" in model_name:
          # this should be ok
          A_          =       65
          A_          =       'mapillary-vistas-id2label.json'

     A_          =       json.load(open(hf_hub_download(_UpperCAmelCase  ,						_UpperCAmelCase  ,						repo_type='''dataset'''				)  ,						'''r'''				)				)
     A_          =       {int(_UpperCAmelCase				): v for k, v in idalabel.items()}

     return config

def      UpperCAmelCase_    (    _UpperCAmelCase							:int				)	->				Union[str, Any]:
     '''simple docstring'''


     A_          =       []
     # stem
     # fmt: off
     rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight''')				)
     rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias''')				)
     rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight''')				)
     rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.model.embeddings.norm.bias''')				)
     # stages
     for i in range(len(config.backbone_config.depths				)				):
          for j in range(config.backbone_config.depths[i]				):
               rename_keys.append((f'backbone.layers.{i}.blocks.{j}.norm1.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')				)
               rename_keys.append((f'backbone.layers.{i}.blocks.{j}.norm1.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')				)
               rename_keys.append((f'backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')				)
               rename_keys.append((f'backbone.layers.{i}.blocks.{j}.attn.relative_position_index', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')				)
               rename_keys.append((f'backbone.layers.{i}.blocks.{j}.attn.proj.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')				)
               rename_keys.append((f'backbone.layers.{i}.blocks.{j}.attn.proj.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')				)
               rename_keys.append((f'backbone.layers.{i}.blocks.{j}.norm2.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')				)
               rename_keys.append((f'backbone.layers.{i}.blocks.{j}.norm2.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')				)
               rename_keys.append((f'backbone.layers.{i}.blocks.{j}.mlp.fc1.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')				)
               rename_keys.append((f'backbone.layers.{i}.blocks.{j}.mlp.fc1.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')				)
               rename_keys.append((f'backbone.layers.{i}.blocks.{j}.mlp.fc2.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')				)
               rename_keys.append((f'backbone.layers.{i}.blocks.{j}.mlp.fc2.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')				)

          if i < 3:
               rename_keys.append((f'backbone.layers.{i}.downsample.reduction.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight')				)
               rename_keys.append((f'backbone.layers.{i}.downsample.norm.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight')				)
               rename_keys.append((f'backbone.layers.{i}.downsample.norm.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias')				)
          rename_keys.append((f'backbone.norm{i}.weight', f'model.pixel_level_module.encoder.hidden_states_norms.{i}.weight')				)
          rename_keys.append((f'backbone.norm{i}.bias', f'model.pixel_level_module.encoder.hidden_states_norms.{i}.bias')				)

     # FPN
     rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight''')				)
     rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight''')				)
     rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias''')				)
     for source_index, target_index in zip(range(3  ,						0  ,						-1				)  ,						range(0  ,						3				)				):
          rename_keys.append((f'sem_seg_head.adapter_{source_index}.weight', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight')				)
          rename_keys.append((f'sem_seg_head.adapter_{source_index}.norm.weight', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight')				)
          rename_keys.append((f'sem_seg_head.adapter_{source_index}.norm.bias', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias')				)
          rename_keys.append((f'sem_seg_head.layer_{source_index}.weight', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight')				)
          rename_keys.append((f'sem_seg_head.layer_{source_index}.norm.weight', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight')				)
          rename_keys.append((f'sem_seg_head.layer_{source_index}.norm.bias', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias')				)
     rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight''')				)
     rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias''')				)

     # Transformer decoder
     for idx in range(config.decoder_config.decoder_layers				):
          # self-attention out projection
          rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight', f'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight')				)
          rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias', f'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias')				)
          # cross-attention out projection
          rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight', f'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight')				)
          rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias', f'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias')				)
          # MLP 1
          rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight', f'model.transformer_module.decoder.layers.{idx}.fc1.weight')				)
          rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias', f'model.transformer_module.decoder.layers.{idx}.fc1.bias')				)
          # MLP 2
          rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight', f'model.transformer_module.decoder.layers.{idx}.fc2.weight')				)
          rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias', f'model.transformer_module.decoder.layers.{idx}.fc2.bias')				)
          # layernorm 1 (self-attention layernorm)
          rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight', f'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight')				)
          rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias', f'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias')				)
          # layernorm 2 (cross-attention layernorm)
          rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight', f'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight')				)
          rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias', f'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias')				)
          # layernorm 3 (final layernorm)
          rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight', f'model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight')				)
          rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias', f'model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias')				)

     rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight''')				)
     rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias''')				)

     # heads on top
     rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight''')				)

     rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight''')				)
     rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias''')				)

     rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight''')				)
     rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias''')				)

     for i in range(3				):
          rename_keys.append((f'sem_seg_head.predictor.mask_embed.layers.{i}.weight', f'mask_embedder.{i}.0.weight')				)
          rename_keys.append((f'sem_seg_head.predictor.mask_embed.layers.{i}.bias', f'mask_embedder.{i}.0.bias')				)
     # fmt: on

     return rename_keys

def      UpperCAmelCase_    (    _UpperCAmelCase							:Any  ,						_UpperCAmelCase							:List[Any]  ,						_UpperCAmelCase							:Optional[Any]				)	->				int:
     '''simple docstring'''


     A_          =       dct.pop(_UpperCAmelCase				)
     A_          =       val

def      UpperCAmelCase_    (    _UpperCAmelCase							:Optional[int]  ,						_UpperCAmelCase							:Optional[int]				)	->				Union[str, Any]:
     '''simple docstring'''


     A_          =       [int(backbone_config.embed_dim * 2**i				) for i in range(len(backbone_config.depths				)				)]
     for i in range(len(backbone_config.depths				)				):
          A_          =       num_features[i]
          for j in range(backbone_config.depths[i]				):
               # fmt: off
               # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
               A_          =       state_dict.pop(f'backbone.layers.{i}.blocks.{j}.attn.qkv.weight'				)
               A_          =       state_dict.pop(f'backbone.layers.{i}.blocks.{j}.attn.qkv.bias'				)
               # next, add query, keys and values (in that order) to the state dict
               A_          =       in_proj_weight[:dim, :]
               A_          =       in_proj_bias[: dim]
               A_          =       in_proj_weight[
                   dim : dim * 2, :
               ]
               A_          =       in_proj_bias[
                   dim : dim * 2
               ]
               A_          =       in_proj_weight[
                   -dim :, :
               ]
               A_          =       in_proj_bias[-dim :]
               # fmt: on

def      UpperCAmelCase_    (    _UpperCAmelCase							:Union[str, Any]  ,						_UpperCAmelCase							:int				)	->				Union[str, Any]:
     '''simple docstring'''


     A_          =       config.decoder_config.hidden_size
     for idx in range(config.decoder_config.decoder_layers				):
          # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
          A_          =       state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'				)
          A_          =       state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias'				)
          # next, add query, keys and values (in that order) to the state dict
          A_          =       in_proj_weight[: hidden_size, :]
          A_          =       in_proj_bias[:config.hidden_size]
          A_          =       in_proj_weight[hidden_size : hidden_size * 2, :]
          A_          =       in_proj_bias[hidden_size : hidden_size * 2]
          A_          =       in_proj_weight[-hidden_size :, :]
          A_          =       in_proj_bias[-hidden_size :]
          # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
          A_          =       state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'				)
          A_          =       state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'				)
          # next, add query, keys and values (in that order) to the state dict
          A_          =       in_proj_weight[: hidden_size, :]
          A_          =       in_proj_bias[:config.hidden_size]
          A_          =       in_proj_weight[hidden_size : hidden_size * 2, :]
          A_          =       in_proj_bias[hidden_size : hidden_size * 2]
          A_          =       in_proj_weight[-hidden_size :, :]
          A_          =       in_proj_bias[-hidden_size :]
     # fmt: on

def      UpperCAmelCase_    (    )	->				torch.Tensor:
     '''simple docstring'''


     A_          =       'http://images.cocodataset.org/val2017/000000039769.jpg'
     A_          =       Image.open(requests.get(_UpperCAmelCase  ,						stream=_UpperCAmelCase				).raw				)
     return im

@torch.no_grad()
def      UpperCAmelCase_    (    _UpperCAmelCase							:str  ,						_UpperCAmelCase							:str  ,						_UpperCAmelCase							:str  ,						_UpperCAmelCase							:bool = False				)	->				List[Any]:
     '''simple docstring'''


     A_          =       get_maskformer_config(_UpperCAmelCase				)

     # load original state_dict
     with open(_UpperCAmelCase  ,						'''rb'''				) as f:
          A_          =       pickle.load(_UpperCAmelCase				)
     A_          =       data['model']

     # for name, param in state_dict.items():
     #     print(name, param.shape)

     # rename keys
     A_          =       create_rename_keys(_UpperCAmelCase				)
     for src, dest in rename_keys:
          rename_key(_UpperCAmelCase  ,						_UpperCAmelCase  ,						_UpperCAmelCase				)
     read_in_swin_q_k_v(_UpperCAmelCase  ,						config.backbone_config				)
     read_in_decoder_q_k_v(_UpperCAmelCase  ,						_UpperCAmelCase				)

     # update to torch tensors
     for key, value in state_dict.items():
          A_          =       torch.from_numpy(_UpperCAmelCase				)

     # load 🤗 model
     A_          =       MaskFormerForInstanceSegmentation(_UpperCAmelCase				)
     model.eval()

     for name, param in model.named_parameters():
          print(_UpperCAmelCase  ,						param.shape				)

     A_          =       model.load_state_dict(_UpperCAmelCase  ,						strict=_UpperCAmelCase				)
     assert missing_keys == [
         "model.pixel_level_module.encoder.model.layernorm.weight",
         "model.pixel_level_module.encoder.model.layernorm.bias",
     ]
     assert len(_UpperCAmelCase				) == 0, f'Unexpected keys: {unexpected_keys}'

     # verify results
     A_          =       prepare_img()
     if "vistas" in model_name:
          A_          =       65
     elif "cityscapes" in model_name:
          A_          =       65535
     else:
          A_          =       255
     A_          =       True if 'ade' in model_name else False
     A_          =       MaskFormerImageProcessor(ignore_index=_UpperCAmelCase  ,						reduce_labels=_UpperCAmelCase				)

     A_          =       image_processor(_UpperCAmelCase  ,						return_tensors='''pt'''				)

     A_          =       model(**_UpperCAmelCase				)

     print('''Logits:'''  ,						outputs.class_queries_logits[0, :3, :3]				)

     if model_name == "maskformer-swin-tiny-ade":
          A_          =       torch.tensor(
              [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]				)
     assert torch.allclose(outputs.class_queries_logits[0, :3, :3]  ,						_UpperCAmelCase  ,						atol=1e-4				)
     print('''Looks ok!'''				)

     if pytorch_dump_folder_path is not None:
          print(f'Saving model and image processor to {pytorch_dump_folder_path}'				)
          Path(_UpperCAmelCase				).mkdir(exist_ok=_UpperCAmelCase				)
          model.save_pretrained(_UpperCAmelCase				)
          image_processor.save_pretrained(_UpperCAmelCase				)

     if push_to_hub:
          print('''Pushing model and image processor to the hub...'''				)
          model.push_to_hub(f'nielsr/{model_name}'				)
          image_processor.push_to_hub(f'nielsr/{model_name}'				)


if __name__ == "__main__":
      a__      :     int         =		argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--model_name',
          default='maskformer-swin-tiny-ade',
          type=str,
          help=('Name of the MaskFormer model you\'d like to convert',),
      )
      parser.add_argument(
          '--checkpoint_path',
          default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
          type=str,
          help='Path to the original state dict (.pth file).',
      )
      parser.add_argument(
          '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
      )
      parser.add_argument(
          '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
      )

      a__      :     Dict         =		parser.parse_args()
      convert_maskformer_checkpoint(
          args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )








'''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  snake_case__						(    SCREAMING_SNAKE_CASE_  ,     unittest.TestCase				):
 A__				= WavaVecaPhonemeCTCTokenizer
 A__				= False
 def A_   (   self						:							Any  )      ->   Optional[Any]:







       '''simple docstring'''
       super().setUp()

       __snake_case :   Optional[Any]													=    (
           '<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(' '  )
       __snake_case :   Tuple													=    dict(zip(__a      ,     range(len(__a  )  )  )  )

       __snake_case :   List[str]													=    {'pad_token': '<pad>', 'unk_token': '<unk>', 'bos_token': '<s>', 'eos_token': '</s>'}

       __snake_case :   int													=    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(__a  ) + '\n'  )
 def A_   (   self						:							Tuple      ,     __a						:							Any      ,     __a						:							str=False      ,     __a						:							Tuple=20      ,     __a						:							int=5  )      ->   Tuple[str, list]:







       '''simple docstring'''
       __snake_case :   Any													=    [(i, tokenizer.decode([i]      ,     clean_up_tokenization_spaces=__a  )) for i in range(len(__a  )  )]
       __snake_case :   Optional[int]													=    list(filter(lambda __a  : [t[0]] == tokenizer.encode(t[1]      ,     do_phonemize=__a  )      ,     __a  )  )
       if max_length is not None and len(__a  ) > max_length:
             __snake_case :   List[str]													=    toks[:max_length]
       if min_length is not None and len(__a  ) < min_length and len(__a  ) > 0:
             while len(__a  ) < min_length:
                   __snake_case :   Optional[int]													=    toks + toks
        # toks_str = [t[1] for t in toks]
       __snake_case :   List[Any]													=    [t[0] for t in toks]

       # Ensure consistency
       __snake_case :   Optional[Any]													=    tokenizer.decode(__a      ,     clean_up_tokenization_spaces=__a  )
       if " " not in output_txt and len(__a  ) > 1:
             __snake_case :   Tuple													=    (
                 tokenizer.decode([toks_ids[0]]      ,     clean_up_tokenization_spaces=__a  )
                 + ' '
                 + tokenizer.decode(toks_ids[1:]      ,     clean_up_tokenization_spaces=__a  )
             )
       if with_prefix_space:
             __snake_case :   Tuple													=    ' ' + output_txt
       __snake_case :   Optional[Any]													=    tokenizer.encode(__a      ,     add_special_tokens=__a  )
       return output_txt, output_ids
 def A_   (   self						:							Union[str, Any]      ,     **__a						:							str  )      ->   int:







       '''simple docstring'''
       kwargs.update(self.special_tokens_map  )
       return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname      ,     **__a  )
 def A_   (   self						:							List[Any]  )      ->   Union[str, Any]:







       '''simple docstring'''
       __snake_case :   Optional[Any]													=    self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'  )

       # check adding a single token
       tokenizer.add_tokens('xxx'  )
       __snake_case :   Optional[Any]													=    tokenizer('m xxx ɪ'      ,     do_phonemize=__a  ).input_ids
       self.assertEqual(__a      ,     [13, 392, 17]  )  # xxx should be last token

       tokenizer.add_tokens(['aaa', 'bbb', 'ccc']  )
       __snake_case :   Union[str, Any]													=    tokenizer('m aaa ɪ ccc'      ,     do_phonemize=__a  ).input_ids
       self.assertEqual(__a      ,     [13, 393, 17, 395]  )  # aaa and ccc should be after xxx and 2 after aaa

       __snake_case :   Dict													=    tokenizer('maɪ c'      ,     do_phonemize=__a  ).input_ids
       self.assertEqual(__a      ,     [3, 200]  )  # mai should be <unk> (=3)
 def A_   (   self						:							Any  )      ->   str:







       '''simple docstring'''
       __snake_case :   List[Any]													=    self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'  )

       __snake_case :   List[str]													=    'Hello how are you'
       __snake_case :   Dict													=    tokenizer.phonemize(__a      ,     phonemizer_lang='en-us'  )
       self.assertEqual(__a      ,     'h ə l oʊ h aʊ ɑːɹ j uː'  )
 def A_   (   self						:							Union[str, Any]  )      ->   Any:







       '''simple docstring'''
       __snake_case :   Dict													=    self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'  )

       __snake_case :   Optional[Any]													=    'Hello how are you'
       __snake_case :   List[str]													=    tokenizer.phonemize(__a      ,     phonemizer_lang='en-us'  )
       self.assertEqual(tokenizer(__a  ).input_ids      ,     tokenizer(__a      ,     do_phonemize=__a  ).input_ids  )
 def A_   (   self						:							Optional[Any]  )      ->   Dict:







       '''simple docstring'''
       __snake_case :   Optional[int]													=    self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'  )
       __snake_case :   Tuple													=    'Hello how are you'
       __snake_case :   Tuple													=    tokenizer.phonemize(__a      ,     phonemizer_lang='en-us'  )

       __snake_case :   str													=    tokenizer.decode(tokenizer(__a  ).input_ids  )

       self.assertEqual(__a      ,     __a  )
 def A_   (   self						:							Union[str, Any]  )      ->   List[str]:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'  )

       __snake_case :   Union[str, Any]													=    [
           [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
           [24, 22, 5, 24, 22, 5, 77],
       ]
       __snake_case :   Tuple													=    tokenizer.decode(sample_ids[0]  )
       __snake_case :   str													=    tokenizer.batch_decode(__a  )
       self.assertEqual(__a      ,     batch_tokens[0]  )
       self.assertEqual(__a      ,     ['k s ɾ ɾ l ɭʲ', 'j ð s j ð s oːɹ']  )
 def A_   (   self						:							Tuple  )      ->   str:







       '''simple docstring'''
       __snake_case :   Optional[Any]													=    self.tokenizer_class.from_pretrained(
           'facebook/wav2vec2-lv-60-espeak-cv-ft'      ,     word_delimiter_token='|'  )
       tokenizer.add_tokens('|'  )

       __snake_case :   Optional[Any]													=    'Hello how are you'
       __snake_case :   Union[str, Any]													=    tokenizer.phonemize(__a      ,     phonemizer_lang='en-us'  )
       self.assertEqual(__a      ,     'h ə l oʊ | h aʊ | ɑːɹ | j uː |'  )
 def A_   (   self						:							Union[str, Any]  )      ->   Dict:







       '''simple docstring'''
       __snake_case :   Any													=    self.tokenizer_class.from_pretrained(
           'facebook/wav2vec2-lv-60-espeak-cv-ft'      ,     word_delimiter_token='|'  )
       tokenizer.add_tokens('|'  )

       __snake_case :   Tuple													=    'Hello how are you'
       __snake_case :   List[str]													=    tokenizer.phonemize(__a      ,     phonemizer_lang='en-us'  )
       self.assertEqual(tokenizer(__a  ).input_ids      ,     tokenizer(__a      ,     do_phonemize=__a  ).input_ids  )
 def A_   (   self						:							Tuple  )      ->   Dict:







       '''simple docstring'''
       __snake_case :   List[Any]													=    self.tokenizer_class.from_pretrained(
           'facebook/wav2vec2-lv-60-espeak-cv-ft'      ,     word_delimiter_token='|'  )
       tokenizer.add_tokens('|'  )

       # fmt: off
       __snake_case :   int													=    [
           [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
       __snake_case :   Dict													=    tokenizer.decode(sample_ids[0]  )
       __snake_case :   Tuple													=    tokenizer.batch_decode(__a  )
       self.assertEqual(__a      ,     batch_tokens[0]  )
       self.assertEqual(__a      ,     ['k s ɾ ɾ l ɭʲ', 'j ð s j ð s oːɹ']  )

       # decode with no word_del_token filter
       __snake_case :   Union[str, Any]													=    tokenizer.decode(sample_ids[0]      ,     filter_word_delimiter_token=__a  )
       __snake_case :   Optional[int]													=    tokenizer.batch_decode(__a      ,     filter_word_delimiter_token=__a  )
       self.assertEqual(__a      ,     batch_tokens[0]  )
       self.assertEqual(__a      ,     ['k s ɾ | ɾ l | ɭʲ', '| j ð | s j ð s oːɹ']  )
 def A_   (   self						:							Optional[Any]  )      ->   Tuple:







       '''simple docstring'''
       __snake_case :   Dict													=    self.tokenizer_class.from_pretrained(
           'facebook/wav2vec2-lv-60-espeak-cv-ft'      ,     word_delimiter_token='|'  )
       tokenizer.add_tokens('|'  )

       __snake_case :   Any													=    'Hello how are you'
       __snake_case :   Optional[int]													=    tokenizer.phonemize(__a      ,     phonemizer_lang='en-us'  )

       __snake_case :   Union[str, Any]													=    tokenizer.decode(tokenizer(__a  ).input_ids      ,     filter_word_delimiter_token=__a  )

       self.assertEqual(__a      ,     __a  )
 def A_   (   self						:							Dict  )      ->   List[str]:







       '''simple docstring'''
       __snake_case :   Optional[Any]													=    self.tokenizer_class.from_pretrained(
           'facebook/wav2vec2-lv-60-espeak-cv-ft'      ,     word_delimiter_token='|'  )
       tokenizer.add_tokens('|'  )

       __snake_case :   Optional[int]													=    'Hello how are you'
       __snake_case :   List[Any]													=    tokenizer.phonemize(__a      ,     phonemizer_lang='en-us'  )

       __snake_case :   Union[str, Any]													=    tokenizer.decode(tokenizer(__a  ).input_ids      ,     filter_word_delimiter_token=__a  )

       self.assertEqual(' '.join([p.strip() for p in phonemes.split(' |'  )]  ).strip()      ,     __a  )
 def A_   (   self						:							int  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    self.tokenizer_class.from_pretrained(
           'facebook/wav2vec2-lv-60-espeak-cv-ft'      ,     word_delimiter_token=__a  )
       __snake_case :   Any													=    'Hello how are you'

       __snake_case :   Union[str, Any]													=    tokenizer(__a      ,     phonemizer_lang='en-us'  ).input_ids
       __snake_case :   Union[str, Any]													=    tokenizer(__a      ,     phonemizer_lang='fr-fr'  ).input_ids

       self.assertNotEqual(__a      ,     __a  )

       __snake_case :   str													=    tokenizer.decode(__a  )
       __snake_case :   int													=    tokenizer.decode(__a  )

       self.assertEqual(__a      ,     'h ə l oʊ h aʊ ɑːɹ j uː'  )
       self.assertEqual(__a      ,     'ɛ l o h aʊ a ʁ j u'  )
 def A_   (   self						:							str  )      ->   str:







       '''simple docstring'''
       __snake_case :   Dict													=    self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'  )
       __snake_case :   List[str]													=    'Hello how Are you'
       __snake_case :   Optional[Any]													=    'hello how are you'

       __snake_case :   Union[str, Any]													=    tokenizer(__a  ).input_ids
       __snake_case :   Any													=    tokenizer(__a  ).input_ids

       self.assertEqual(__a      ,     __a  )
 def A_   (   self						:							List[Any]  )      ->   Any:







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

       # fmt: off
       __snake_case :   List[Any]													=    [
           [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

       __snake_case :   str													=    tokenizer.batch_decode(__a  )
       self.assertEqual(__a      ,     ['k s ɾ ɾ l ɭʲ!?!? $$$', 'j ð s j ð s oːɹ $$$']  )
 @staticmethod
 def A_   (   __a						:							Any      ,     __a						:							Dict  )      ->   Tuple:







       '''simple docstring'''
       __snake_case :   str													=    [d[key] for d in offsets]
       return retrieved_list
 def A_   (   self						:							str  )      ->   Optional[int]:







       '''simple docstring'''
       __snake_case :   Optional[int]													=    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"
       __snake_case :   int													=    [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

       __snake_case :   Any													=    tokenizer.decode(__a      ,     output_char_offsets=__a      ,     filter_word_delimiter_token=__a  )
       # 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(__a      ,     __a  )  )

       # 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 A_   (   self						:							Union[str, Any]  )      ->   str:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    self.get_tokenizer(word_delimiter_token='|'  )

       def check_list_tuples_equal(__a						:							int      ,     __a						:							Union[str, Any]  ):
             self.assertTrue(isinstance(__a      ,     __a  )  )
             self.assertTrue(isinstance(outputs_list[0]      ,     __a  )  )

             # transform list to ModelOutput
             __snake_case :   Optional[int]													=    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(__a						:							Any      ,     __a						:							str  ):
                   if isinstance(__a      ,     __a  ):
                         [recursive_check(__a      ,     __a  ) for la, la in zip(__a      ,     __a  )]
                   self.assertEqual(__a      ,     __a  )

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

        # fmt: off
       __snake_case :   int													=    [
           [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
       __snake_case :   List[str]													=    tokenizer.batch_decode(__a      ,     output_char_offsets=__a  )
       __snake_case :   str													=    [tokenizer.decode(__a      ,     output_char_offsets=__a  ) for ids in sample_ids]
       check_list_tuples_equal(__a      ,     __a  )
 @unittest.skip('Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes'  )
 def A_   (   self						:							Any  )      ->   Optional[int]:







       '''simple docstring'''
       pass
 @unittest.skip('Wav2Vec2PhonemeTokenizer always puts spaces between phonemes'  )
 def A_   (   self						:							Union[str, Any]  )      ->   Union[str, Any]:







       '''simple docstring'''
       pass
 @unittest.skip('encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency'  )
 def A_   (   self						:							str  )      ->   Dict:







       '''simple docstring'''
       pass
 @unittest.skip('Wav2Vec2PhonemeModel has no max model length => no testing'  )
 def A_   (   self						:							Tuple  )      ->   Optional[Any]:







       '''simple docstring'''
       pass
 def A_   (   self						:							Optional[int]  )      ->   str:







       '''simple docstring'''
       __snake_case :   int													=    self.get_tokenizers(do_lower_case=__a  )
       for tokenizer in tokenizers:
             with self.subTest(f'''{tokenizer.__class__.__name__}'''  ):
                   __snake_case :   int													=    tokenizer.vocab_size
                   __snake_case :   List[Any]													=    len(__a  )

                   self.assertNotEqual(__a      ,     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)

                   __snake_case :   Optional[Any]													=    ['aaaaa bbbbbb', 'cccccccccdddddddd']
                   __snake_case :   Optional[int]													=    tokenizer.add_tokens(__a  )
                   __snake_case :   Optional[int]													=    tokenizer.vocab_size
                   __snake_case :   Tuple													=    len(__a  )

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

                   __snake_case :   Optional[int]													=    tokenizer.encode('aaaaa bbbbbb low cccccccccdddddddd l'      ,     add_special_tokens=__a  )

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

                   __snake_case :   Tuple													=    {'eos_token': '>>>>|||<||<<|<<', 'pad_token': '<<<<<|||>|>>>>|>'}
                   __snake_case :   Optional[Any]													=    tokenizer.add_special_tokens(__a  )
                   __snake_case :   int													=    tokenizer.vocab_size
                   __snake_case :   Any													=    len(__a  )

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

                   __snake_case :   List[str]													=    tokenizer.encode(
                       '>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l'      ,     add_special_tokens=__a  )

                   self.assertGreaterEqual(len(__a  )      ,     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 A_   (   self						:							Dict  )      ->   List[str]:







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







       '''simple docstring'''
       pass





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







       '''simple docstring'''
       # The default common tokenizer tests assumes that the output of `convert_tokens_to_string` is a string which
       # is not the case for Wav2Vec2PhonemeCTCTokenizer.
       __snake_case :   Optional[int]													=    self.get_tokenizers(fast=__a      ,     do_lower_case=__a  )
       for tokenizer in tokenizers:
             with self.subTest(f'''{tokenizer.__class__.__name__}'''  ):
                   __snake_case :   Union[str, Any]													=    ['ð', 'ɪ', 's', 'ɪ', 'z', 'ɐ', 't', 'ɛ', 'k', 's', 't']
                   __snake_case :   List[Any]													=    tokenizer.convert_tokens_to_string(__a  )

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



