import argparse

import torch
from datasets import load_dataset
from donut import DonutModel

from transformers import (
    DonutImageProcessor,
    DonutProcessor,
    DonutSwinConfig,
    DonutSwinModel,
    MBartConfig,
    MBartForCausalLM,
    VisionEncoderDecoderModel,
    XLMRobertaTokenizerFast,
)




def       __lowerCAmelCase      (						A_   : Any					)    ->  Dict:
  __UpperCAmelCase						    =       model.config

  __UpperCAmelCase						    =       DonutSwinConfig(
      image_size=original_config.input_size						,	patch_size=4						,	depths=original_config.encoder_layer						,	num_heads=[4, 8, 16, 32]						,	window_size=original_config.window_size						,	embed_dim=1_28						,	)
  __UpperCAmelCase						    =       MBartConfig(
      is_decoder=A_						,	is_encoder_decoder=A_						,	add_cross_attention=A_						,	decoder_layers=original_config.decoder_layer						,	max_position_embeddings=original_config.max_position_embeddings						,	vocab_size=len(
          model.decoder.tokenizer					)						,	scale_embedding=A_						,	add_final_layer_norm=A_						,	)

  return encoder_config, decoder_config




def       __lowerCAmelCase      (						A_   : Dict					)    ->  List[Any]:
  if "encoder.model" in name:
    __UpperCAmelCase						    =       name.replace("encoder.model"						,	"encoder"					)
  if "decoder.model" in name:
    __UpperCAmelCase						    =       name.replace("decoder.model"						,	"decoder"					)
  if "patch_embed.proj" in name:
    __UpperCAmelCase						    =       name.replace("patch_embed.proj"						,	"embeddings.patch_embeddings.projection"					)
  if "patch_embed.norm" in name:
    __UpperCAmelCase						    =       name.replace("patch_embed.norm"						,	"embeddings.norm"					)
  if name.startswith("encoder"					):
    if "layers" in name:
      __UpperCAmelCase						    =       "encoder." + name
    if "attn.proj" in name:
      __UpperCAmelCase						    =       name.replace("attn.proj"						,	"attention.output.dense"					)
    if "attn" in name and "mask" not in name:
      __UpperCAmelCase						    =       name.replace("attn"						,	"attention.self"					)
    if "norm1" in name:
      __UpperCAmelCase						    =       name.replace("norm1"						,	"layernorm_before"					)
    if "norm2" in name:
      __UpperCAmelCase						    =       name.replace("norm2"						,	"layernorm_after"					)
    if "mlp.fc1" in name:
      __UpperCAmelCase						    =       name.replace("mlp.fc1"						,	"intermediate.dense"					)
    if "mlp.fc2" in name:
      __UpperCAmelCase						    =       name.replace("mlp.fc2"						,	"output.dense"					)

    if name == "encoder.norm.weight":
      __UpperCAmelCase						    =       "encoder.layernorm.weight"
    if name == "encoder.norm.bias":
      __UpperCAmelCase						    =       "encoder.layernorm.bias"

  return name




def       __lowerCAmelCase      (						A_   : Optional[int]						,	A_   : str					)    ->  Union[str, Any]:
  for key in orig_state_dict.copy().keys():
    __UpperCAmelCase						    =       orig_state_dict.pop(A_					)

    if "qkv" in key:
      __UpperCAmelCase						    =       key.split("."					)
      __UpperCAmelCase						    =       int(key_split[3]					)
      __UpperCAmelCase						    =       int(key_split[5]					)
      __UpperCAmelCase						    =       model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

      if "weight" in key:
        __UpperCAmelCase						    =       val[:dim, :]
        __UpperCAmelCase						    =       val[dim : dim * 2, :]
        __UpperCAmelCase						    =       val[-dim:, :]
      else:
        __UpperCAmelCase						    =       val[:dim]
        __UpperCAmelCase						    =       val[dim : dim * 2]
        __UpperCAmelCase						    =       val[-dim:]
    elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
      # HuggingFace implementation doesn't use attn_mask buffer
      # and model doesn't use final LayerNorms for the encoder
      pass
    else:
      __UpperCAmelCase						    =       val

  return orig_state_dict




def       __lowerCAmelCase      (						A_   : Tuple						,	A_   : List[str]=None						,	A_   : Tuple=False					)    ->  Optional[Any]:
  # load original model
  __UpperCAmelCase						    =       DonutModel.from_pretrained(A_					).eval()

  # load HuggingFace model
  __UpperCAmelCase    ,		__UpperCAmelCase						    =       get_configs(A_					)
  __UpperCAmelCase						    =       DonutSwinModel(A_					)
  __UpperCAmelCase						    =       MBartForCausalLM(A_					)
  __UpperCAmelCase						    =       VisionEncoderDecoderModel(encoder=A_						,	decoder=A_					)
  model.eval()

  __UpperCAmelCase						    =       original_model.state_dict()
  __UpperCAmelCase						    =       convert_state_dict(A_						,	A_					)
  model.load_state_dict(A_					)

  # verify results on scanned document
  __UpperCAmelCase						    =       load_dataset("hf-internal-testing/example-documents"					)
  __UpperCAmelCase						    =       dataset["test"][0]["image"].convert("RGB"					)

  __UpperCAmelCase						    =       XLMRobertaTokenizerFast.from_pretrained(A_						,	from_slow=A_					)
  __UpperCAmelCase						    =       DonutImageProcessor(
      do_align_long_axis=original_model.config.align_long_axis						,	size=original_model.config.input_size[::-1]					)
  __UpperCAmelCase						    =       DonutProcessor(A_						,	A_					)
  __UpperCAmelCase						    =       processor(A_						,	return_tensors="pt"					).pixel_values

  if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
    __UpperCAmelCase						    =       "<s_docvqa><s_question>{user_input}</s_question><s_answer>"
    __UpperCAmelCase						    =       "When is the coffee break?"
    __UpperCAmelCase						    =       task_prompt.replace("{user_input}"						,	A_					)
  elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
    __UpperCAmelCase						    =       "<s_rvlcdip>"
  elif model_name in [
      "naver-clova-ix/donut-base-finetuned-cord-v1",
      "naver-clova-ix/donut-base-finetuned-cord-v1-2560",
  ]:
    __UpperCAmelCase						    =       "<s_cord>"
  elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
    __UpperCAmelCase						    =       "s_cord-v2>"
  elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
    __UpperCAmelCase						    =       "<s_zhtrainticket>"
  elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
    # use a random prompt
    __UpperCAmelCase						    =       "hello world"
  else:
    raise ValueError("Model name not supported"					)
  __UpperCAmelCase						    =       original_model.decoder.tokenizer(A_						,	add_special_tokens=A_						,	return_tensors="pt"					)[
      "input_ids"
  ]

  __UpperCAmelCase						    =       original_model.encoder.model.patch_embed(A_					)
  __UpperCAmelCase    ,		__UpperCAmelCase						    =       model.encoder.embeddings(A_					)
  assert torch.allclose(A_						,	A_						,	atol=1e-3					)

  # verify encoder hidden states
  __UpperCAmelCase						    =       original_model.encoder(A_					)
  __UpperCAmelCase						    =       model.encoder(A_					).last_hidden_state
  assert torch.allclose(A_						,	A_						,	atol=1e-2					)

  # verify decoder hidden states
  __UpperCAmelCase						    =       original_model(A_						,	A_						,	A_					).logits
  __UpperCAmelCase						    =       model(A_						,	decoder_input_ids=A_					).logits
  assert torch.allclose(A_						,	A_						,	atol=1e-3					)
  print("Looks ok!"					)

  if pytorch_dump_folder_path is not None:
    print(F'''Saving model and processor to {pytorch_dump_folder_path}'''					)
    model.save_pretrained(A_					)
    processor.save_pretrained(A_					)

  if push_to_hub:
    model.push_to_hub("nielsr/" + model_name.split("/"					)[-1]						,	commit_message="Update model"					)
    processor.push_to_hub("nielsr/" + model_name.split("/"					)[-1]						,	commit_message="Update model"					)


if __name__ == "__main__":
       a_							=      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--model_name""",
           default="""naver-clova-ix/donut-base-finetuned-docvqa""",
           required=False,
           type=str,
           help="""Name of the original model you'd like to convert.""",
       )
       parser.add_argument(
           """--pytorch_dump_folder_path""",
           default=None,
           required=False,
           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 and processor to the 🤗 hub.""",
       )

       a_							=      parser.parse_args()
       convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




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   )



