'''simple docstring'''




import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE       :     Any     =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE       :     List[Any]     =  {
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn.grep_linear""": """encoder.layers.*.attention.gru_rel_pos_linear""",
    """self_attn.relative_attention_bias""": """encoder.layers.*.attention.rel_attn_embed""",
    """self_attn.grep_a""": """encoder.layers.*.attention.gru_rel_pos_const""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """ctc_proj""",
    """mask_emb""": """masked_spec_embed""",
}
__SCREAMING_SNAKE_CASE       :     Union[str, Any]     =  [
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]

def 	UpperCamelCase_							(					_UpperCAmelCase  :      List[Any]			,	_UpperCAmelCase  :      Optional[Any]			,	_UpperCAmelCase  :      List[str]			,	_UpperCAmelCase  :      Dict			,	_UpperCAmelCase  :      List[Any]    )    ->    Optional[Any]:


       """simple docstring"""







       for attribute in key.split("."    ):
              _UpperCAmelCase     :  List[Any]							      =    getattr(_UpperCAmelCase			,	_UpperCAmelCase    )

       if weight_type is not None:
              _UpperCAmelCase     :  Optional[Any]							      =    getattr(_UpperCAmelCase			,	_UpperCAmelCase    ).shape
       else:
              _UpperCAmelCase     :  Optional[int]							      =    hf_pointer.shape

       assert hf_shape == value.shape, (
           F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
           F""" {value.shape} for {full_name}"""
       )

       if weight_type == "weight":
              _UpperCAmelCase     :  Optional[int]							      =    value
       elif weight_type == "weight_g":
              _UpperCAmelCase     :  Union[str, Any]							      =    value
       elif weight_type == "weight_v":
              _UpperCAmelCase     :  List[str]							      =    value
       elif weight_type == "bias":
              _UpperCAmelCase     :  str							      =    value
       else:
              _UpperCAmelCase     :  Optional[Any]							      =    value

       logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""    )

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


       """simple docstring"""







       _UpperCAmelCase     :  int							      =    []
       _UpperCAmelCase     :  Optional[Any]							      =    fairseq_model.state_dict()

       _UpperCAmelCase     :  Dict							      =    hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              _UpperCAmelCase     :  Optional[int]							      =    False
              if "conv_layers" in name:
                     load_conv_layer(
                         _UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	hf_model.config.feat_extract_norm == "group"			,	)
                     _UpperCAmelCase     :  Dict							      =    True
              else:
                     for key, mapped_key in MAPPING.items():
                            if key in name or key.split("w2v_model."    )[-1] == name.split("."    )[0]:
                                   _UpperCAmelCase     :  Dict							      =    True
                                   if "*" in mapped_key:
                                          _UpperCAmelCase     :  Union[str, Any]							      =    name.split(_UpperCAmelCase    )[0].split("."    )[-2]
                                          _UpperCAmelCase     :  Dict							      =    mapped_key.replace("*"			,	_UpperCAmelCase    )
                                   if "weight_g" in name:
                                          _UpperCAmelCase     :  Any							      =    "weight_g"
                                   elif "weight_v" in name:
                                          _UpperCAmelCase     :  str							      =    "weight_v"
                                   elif "bias" in name and "relative_attention_bias" not in name:
                                          _UpperCAmelCase     :  Optional[int]							      =    "bias"
                                   elif "weight" in name:
                                          # TODO: don't match quantizer.weight_proj
                                          _UpperCAmelCase     :  Optional[Any]							      =    "weight"
                                   else:
                                          _UpperCAmelCase     :  Tuple							      =    None

                                   set_recursively(_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase    )
                            continue
              if not is_used:
                     unused_weights.append(_UpperCAmelCase    )

       logger.warning(F"""Unused weights: {unused_weights}"""    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      List[str]			,	_UpperCAmelCase  :      Union[str, Any]			,	_UpperCAmelCase  :      Optional[Any]			,	_UpperCAmelCase  :      List[Any]			,	_UpperCAmelCase  :      Dict    )    ->    Optional[Any]:


       """simple docstring"""







       _UpperCAmelCase     :  List[str]							      =    full_name.split("conv_layers."    )[-1]
       _UpperCAmelCase     :  str							      =    name.split("."    )
       _UpperCAmelCase     :  Any							      =    int(items[0]    )
       _UpperCAmelCase     :  Optional[int]							      =    int(items[1]    )

       if type_id == 0:
              if "bias" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                         F"""{full_name} has size {value.shape}, but"""
                         F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
                     )
                     _UpperCAmelCase     :  Optional[int]							      =    value
                     logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""    )
              elif "weight" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                         F"""{full_name} has size {value.shape}, but"""
                         F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
                     )
                     _UpperCAmelCase     :  Optional[Any]							      =    value
                     logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""    )
       elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
              if "bias" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                         F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
                         " found."
                     )
                     _UpperCAmelCase     :  List[Any]							      =    value
                     logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""    )
              elif "weight" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                         F"""{full_name} has size {value.shape}, but"""
                         F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
                     )
                     _UpperCAmelCase     :  List[str]							      =    value
                     logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""    )
       else:
              unused_weights.append(_UpperCAmelCase    )

@torch.no_grad()
def 	UpperCamelCase_							(					_UpperCAmelCase  :      List[Any]			,	_UpperCAmelCase  :      Optional[Any]			,	_UpperCAmelCase  :      List[str]=None    )    ->    Any:


       """simple docstring"""







       _UpperCAmelCase     :  int							      =    torch.load(_UpperCAmelCase    )
       _UpperCAmelCase     :  Tuple							      =    WavLMConfigOrig(checkpoint["cfg"]    )
       _UpperCAmelCase     :  Tuple							      =    WavLMOrig(_UpperCAmelCase    )
       model.load_state_dict(checkpoint["model"]    )
       model.eval()

       if config_path is not None:
              _UpperCAmelCase     :  List[str]							      =    WavLMConfig.from_pretrained(_UpperCAmelCase    )
       else:
              _UpperCAmelCase     :  List[Any]							      =    WavLMConfig()

       _UpperCAmelCase     :  Optional[Any]							      =    WavLMModel(_UpperCAmelCase    )

       recursively_load_weights(_UpperCAmelCase			,	_UpperCAmelCase    )

       hf_wavlm.save_pretrained(_UpperCAmelCase    )


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE       :     int     =  argparse.ArgumentParser()
  parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
  parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
  parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
  __SCREAMING_SNAKE_CASE       :     Tuple     =  parser.parse_args()
  convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)


'''simple docstring'''




import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


__SCREAMING_SNAKE_CASE       :     Union[str, Any]     =  get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
  from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

__SCREAMING_SNAKE_CASE       :     Optional[int]     =  256_047
__SCREAMING_SNAKE_CASE       :     Optional[int]     =  256_145

@require_sentencepiece
@require_tokenizers
class 	lowerCamelCase_	(snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  int						=						NllbTokenizer
      __UpperCamelCase:  Tuple						=						NllbTokenizerFast
      __UpperCamelCase:  Union[str, Any]						=						True
      __UpperCamelCase:  Dict						=						True
      __UpperCamelCase:  Optional[Any]						=						{}




      def      _A	(       self						:				Union[str, Any]       ):
             super().setUp()

             # We have a SentencePiece fixture for testing
             _UpperCAmelCase     :  Tuple							      =    NllbTokenizer(A						,					keep_accents=A       )
             tokenizer.save_pretrained(self.tmpdirname       )




      def      _A	(       self						:				Dict       ):
             _UpperCAmelCase     :  Tuple							      =    NllbTokenizer(A						,					keep_accents=A       )

             _UpperCAmelCase     :  Optional[Any]							      =    tokenizer.tokenize("This is a test"       )
             self.assertListEqual(A						,					["▁This", "▁is", "▁a", "▁t", "est"]       )

             self.assertListEqual(
                 tokenizer.convert_tokens_to_ids(A       )						,					[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]						,					)

             _UpperCAmelCase     :  List[str]							      =    tokenizer.tokenize("I was born in 92000, and this is falsé."       )
             self.assertListEqual(
                 A						,					[
                     SPIECE_UNDERLINE + "I",
                     SPIECE_UNDERLINE + "was",
                     SPIECE_UNDERLINE + "b",
                     "or",
                     "n",
                     SPIECE_UNDERLINE + "in",
                     SPIECE_UNDERLINE + "",
                     "9",
                     "2",
                     "0",
                     "0",
                     "0",
                     ",",
                     SPIECE_UNDERLINE + "and",
                     SPIECE_UNDERLINE + "this",
                     SPIECE_UNDERLINE + "is",
                     SPIECE_UNDERLINE + "f",
                     "al",
                     "s",
                     "é",
                     ".",
                 ]						,					)
             _UpperCAmelCase     :  Optional[Any]							      =    tokenizer.convert_tokens_to_ids(A       )
             self.assertListEqual(
                 A						,					[
                     value + tokenizer.fairseq_offset
                     for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                 ]						,					)

             _UpperCAmelCase     :  Union[str, Any]							      =    tokenizer.convert_ids_to_tokens(A       )
             self.assertListEqual(
                 A						,					[
                     SPIECE_UNDERLINE + "I",
                     SPIECE_UNDERLINE + "was",
                     SPIECE_UNDERLINE + "b",
                     "or",
                     "n",
                     SPIECE_UNDERLINE + "in",
                     SPIECE_UNDERLINE + "",
                     "<unk>",
                     "2",
                     "0",
                     "0",
                     "0",
                     ",",
                     SPIECE_UNDERLINE + "and",
                     SPIECE_UNDERLINE + "this",
                     SPIECE_UNDERLINE + "is",
                     SPIECE_UNDERLINE + "f",
                     "al",
                     "s",
                     "<unk>",
                     ".",
                 ]						,					)




      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  Any							      =    (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-nllb", {})
             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
                           _UpperCAmelCase     :  Dict							      =    self.rust_tokenizer_class.from_pretrained(A						,					**A       )
                           _UpperCAmelCase     :  str							      =    self.tokenizer_class.from_pretrained(A						,					**A       )

                           _UpperCAmelCase     :  Optional[int]							      =    tempfile.mkdtemp()

                           _UpperCAmelCase     :  Dict							      =    tokenizer_r.save_pretrained(A       )
                           _UpperCAmelCase     :  Dict							      =    tokenizer_p.save_pretrained(A       )

                           # Checks it save with the same files + the tokenizer.json file for the fast one
                           self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files       )       )
                           _UpperCAmelCase     :  Optional[int]							      =    tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f       )
                           self.assertSequenceEqual(A						,					A       )

                           # Checks everything loads correctly in the same way
                           _UpperCAmelCase     :  List[Any]							      =    tokenizer_r.from_pretrained(A       )
                           _UpperCAmelCase     :  List[str]							      =    tokenizer_p.from_pretrained(A       )

                           # Check special tokens are set accordingly on Rust and Python
                           for key in tokenizer_pp.special_tokens_map:
                                  self.assertTrue(hasattr(A						,					A       )       )

                           shutil.rmtree(A       )

                           # Save tokenizer rust, legacy_format=True
                           _UpperCAmelCase     :  Optional[Any]							      =    tempfile.mkdtemp()

                           _UpperCAmelCase     :  str							      =    tokenizer_r.save_pretrained(A						,					legacy_format=A       )
                           _UpperCAmelCase     :  str							      =    tokenizer_p.save_pretrained(A       )

                           # Checks it save with the same files
                           self.assertSequenceEqual(A						,					A       )

                           # Checks everything loads correctly in the same way
                           _UpperCAmelCase     :  Optional[int]							      =    tokenizer_r.from_pretrained(A       )
                           _UpperCAmelCase     :  Dict							      =    tokenizer_p.from_pretrained(A       )

                           # Check special tokens are set accordingly on Rust and Python
                           for key in tokenizer_pp.special_tokens_map:
                                  self.assertTrue(hasattr(A						,					A       )       )

                           shutil.rmtree(A       )

                           # Save tokenizer rust, legacy_format=False
                           _UpperCAmelCase     :  Optional[int]							      =    tempfile.mkdtemp()

                           _UpperCAmelCase     :  Optional[int]							      =    tokenizer_r.save_pretrained(A						,					legacy_format=A       )
                           _UpperCAmelCase     :  Dict							      =    tokenizer_p.save_pretrained(A       )

                           # Checks it saved the tokenizer.json file
                           self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files       )       )

                           # Checks everything loads correctly in the same way
                           _UpperCAmelCase     :  List[Any]							      =    tokenizer_r.from_pretrained(A       )
                           _UpperCAmelCase     :  Optional[int]							      =    tokenizer_p.from_pretrained(A       )

                           # Check special tokens are set accordingly on Rust and Python
                           for key in tokenizer_pp.special_tokens_map:
                                  self.assertTrue(hasattr(A						,					A       )       )

                           shutil.rmtree(A       )




      @require_torch
      def      _A	(       self						:				Tuple       ):
             if not self.test_seqaseq:
                    return

             _UpperCAmelCase     :  Union[str, Any]							      =    self.get_tokenizers()
             for tokenizer in tokenizers:
                    with self.subTest(F"""{tokenizer.__class__.__name__}"""       ):
                           # Longer text that will definitely require truncation.
                           _UpperCAmelCase     :  Optional[Any]							      =    [
                               " UN Chief Says There Is No Military Solution in Syria",
                               " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"
                               " Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"
                               " will only worsen the violence and misery for millions of people.",
                           ]
                           _UpperCAmelCase     :  Optional[Any]							      =    [
                               "Şeful ONU declară că nu există o soluţie militară în Siria",
                               "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"
                               " Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"
                               " că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
                           ]
                           try:
                                  _UpperCAmelCase     :  Optional[int]							      =    tokenizer.prepare_seqaseq_batch(
                                      src_texts=A						,					tgt_texts=A						,					max_length=3						,					max_target_length=10						,					return_tensors="pt"						,					src_lang="eng_Latn"						,					tgt_lang="ron_Latn"						,					)
                           except NotImplementedError:
                                  return
                           self.assertEqual(batch.input_ids.shape[1]						,					3       )
                           self.assertEqual(batch.labels.shape[1]						,					10       )
                           # max_target_length will default to max_length if not specified
                           _UpperCAmelCase     :  Tuple							      =    tokenizer.prepare_seqaseq_batch(
                               A						,					tgt_texts=A						,					max_length=3						,					return_tensors="pt"       )
                           self.assertEqual(batch.input_ids.shape[1]						,					3       )
                           self.assertEqual(batch.labels.shape[1]						,					3       )

                           _UpperCAmelCase     :  Union[str, Any]							      =    tokenizer.prepare_seqaseq_batch(
                               src_texts=A						,					max_length=3						,					max_target_length=10						,					return_tensors="pt"       )
                           self.assertEqual(batch_encoder_only.input_ids.shape[1]						,					3       )
                           self.assertEqual(batch_encoder_only.attention_mask.shape[1]						,					3       )
                           self.assertNotIn("decoder_input_ids"						,					A       )




      @unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece."       )
      def      _A	(       self						:				List[Any]       ):
             pass





      def      _A	(       self						:				Union[str, Any]       ):
             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
                           _UpperCAmelCase     :  Any							      =    [AddedToken("<special>"						,					lstrip=A       )]

                           _UpperCAmelCase     :  List[Any]							      =    self.rust_tokenizer_class.from_pretrained(
                               A						,					additional_special_tokens=A						,					**A       )
                           _UpperCAmelCase     :  Dict							      =    tokenizer_r.encode("Hey this is a <special> token"       )

                           _UpperCAmelCase     :  Any							      =    tokenizer_r.encode("<special>"						,					add_special_tokens=A       )[0]

                           self.assertTrue(special_token_id in r_output       )

                           if self.test_slow_tokenizer:
                                  _UpperCAmelCase     :  Dict							      =    self.rust_tokenizer_class.from_pretrained(
                                      A						,					additional_special_tokens=A						,					**A						,					)
                                  _UpperCAmelCase     :  Optional[int]							      =    self.tokenizer_class.from_pretrained(
                                      A						,					additional_special_tokens=A						,					**A       )

                                  _UpperCAmelCase     :  Union[str, Any]							      =    tokenizer_p.encode("Hey this is a <special> token"       )

                                  _UpperCAmelCase     :  Any							      =    tokenizer_cr.encode("Hey this is a <special> token"       )

                                  self.assertEqual(A						,					A       )
                                  self.assertEqual(A						,					A       )
                                  self.assertTrue(special_token_id in p_output       )
                                  self.assertTrue(special_token_id in cr_output       )

@require_torch
@require_sentencepiece
@require_tokenizers
class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Dict						=						"facebook/nllb-200-distilled-600M"
      __UpperCamelCase:  Optional[int]						=						[
          " UN Chief Says There Is No Military Solution in Syria",
          " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
      ]
      __UpperCamelCase:  str						=						[
          "Şeful ONU declară că nu există o soluţie militară în Siria",
          "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
          " pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
          " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
      ]
      __UpperCamelCase:  str						=						[
          2_5_6_0_4_7,
          1_6_2_9_7,
          1_3_4_4_0_8,
          8_1_6_5,
          2_4_8_0_6_6,
          1_4_7_3_4,
          9_5_0,
          1_1_3_5,
          1_0_5_7_2_1,
          3_5_7_3,
          8_3,
          2_7_3_5_2,
          1_0_8,
          4_9_4_8_6,
          2,
      ]




      @classmethod
      def      _A	(       cls						:				int       ):
             _UpperCAmelCase     :  NllbTokenizer							      =    NllbTokenizer.from_pretrained(
                 cls.checkpoint_name						,					src_lang="eng_Latn"						,					tgt_lang="ron_Latn"       )
             _UpperCAmelCase     :  Union[str, Any]							      =    1
             return cls




      def      _A	(       self						:				Any       ):
             self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"]						,					256001       )
             self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"]						,					256002       )
             self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"]						,					256057       )




      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  Optional[int]							      =    self.tokenizer.batch_encode_plus(self.src_text       ).input_ids[0]
             self.assertListEqual(self.expected_src_tokens						,					A       )




      def      _A	(       self						:				Tuple       ):
             self.assertIn(A						,					self.tokenizer.all_special_ids       )
             # fmt: off
             _UpperCAmelCase     :  List[Any]							      =    [RO_CODE, 4254, 98068, 112923, 39072, 3909, 713, 102767, 26, 17314, 35642, 14683, 33118, 2022, 66987, 2, 256047]
             # fmt: on

             _UpperCAmelCase     :  Tuple							      =    self.tokenizer.decode(A						,					skip_special_tokens=A       )
             _UpperCAmelCase     :  Optional[Any]							      =    self.tokenizer.decode(generated_ids[1:]						,					skip_special_tokens=A       )
             self.assertEqual(A						,					A       )
             self.assertNotIn(self.tokenizer.eos_token						,					A       )




      def      _A	(       self						:				Optional[int]       ):
             _UpperCAmelCase     :  List[Any]							      =    ["this is gunna be a long sentence " * 20]
             assert isinstance(src_text[0]						,					A       )
             _UpperCAmelCase     :  Dict							      =    10
             _UpperCAmelCase     :  Tuple							      =    self.tokenizer(A						,					max_length=A						,					truncation=A       ).input_ids[0]
             self.assertEqual(ids[-1]						,					2       )
             self.assertEqual(ids[0]						,					A       )
             self.assertEqual(len(A       )						,					A       )




      def      _A	(       self						:				Dict       ):
             self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]       )						,					[256203, 3]       )




      def      _A	(       self						:				Optional[Any]       ):
             _UpperCAmelCase     :  Dict							      =    tempfile.mkdtemp()
             _UpperCAmelCase     :  str							      =    self.tokenizer.fairseq_tokens_to_ids
             self.tokenizer.save_pretrained(A       )
             _UpperCAmelCase     :  Tuple							      =    NllbTokenizer.from_pretrained(A       )
             self.assertDictEqual(new_tok.fairseq_tokens_to_ids						,					A       )




      @require_torch
      def      _A	(       self						:				Dict       ):
             _UpperCAmelCase     :  List[str]							      =    self.tokenizer(
                 self.src_text						,					text_target=self.tgt_text						,					padding=A						,					truncation=A						,					max_length=len(self.expected_src_tokens       )						,					return_tensors="pt"						,					)
             _UpperCAmelCase     :  Tuple							      =    shift_tokens_right(
                 batch["labels"]						,					self.tokenizer.pad_token_id						,					self.tokenizer.lang_code_to_id["ron_Latn"]       )

             self.assertIsInstance(A						,					A       )

             self.assertEqual((2, 15)						,					batch.input_ids.shape       )
             self.assertEqual((2, 15)						,					batch.attention_mask.shape       )
             _UpperCAmelCase     :  Dict							      =    batch.input_ids.tolist()[0]
             self.assertListEqual(self.expected_src_tokens						,					A       )
             self.assertEqual(A						,					batch.decoder_input_ids[0, 0]       )  # EOS
             # Test that special tokens are reset
             self.assertEqual(self.tokenizer.prefix_tokens						,					[EN_CODE]       )
             self.assertEqual(self.tokenizer.suffix_tokens						,					[self.tokenizer.eos_token_id]       )




      def      _A	(       self						:				str       ):
             _UpperCAmelCase     :  Optional[Any]							      =    self.tokenizer(self.src_text						,					padding=A						,					truncation=A						,					max_length=3						,					return_tensors="pt"       )
             _UpperCAmelCase     :  Dict							      =    self.tokenizer(
                 text_target=self.tgt_text						,					padding=A						,					truncation=A						,					max_length=10						,					return_tensors="pt"       )
             _UpperCAmelCase     :  List[Any]							      =    targets["input_ids"]
             _UpperCAmelCase     :  Union[str, Any]							      =    shift_tokens_right(
                 A						,					self.tokenizer.pad_token_id						,					decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]						,					)

             self.assertEqual(batch.input_ids.shape[1]						,					3       )
             self.assertEqual(batch.decoder_input_ids.shape[1]						,					10       )




      @require_torch
      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  str							      =    self.tokenizer._build_translation_inputs(
                 "A test"						,					return_tensors="pt"						,					src_lang="eng_Latn"						,					tgt_lang="fra_Latn"       )

             self.assertEqual(
                 nested_simplify(A       )						,					{
                     # A, test, EOS, en_XX
                     "input_ids": [[256047, 70, 7356, 2]],
                     "attention_mask": [[1, 1, 1, 1]],
                     # ar_AR
                     "forced_bos_token_id": 256057,
                 }						,					)





      @require_torch
      def      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  Dict							      =    True
             _UpperCAmelCase     :  Any							      =    self.tokenizer(
                 "UN Chief says there is no military solution in Syria"						,					src_lang="eng_Latn"						,					tgt_lang="fra_Latn"       )
             self.assertEqual(
                 inputs.input_ids						,					[16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2, 256047]       )

             _UpperCAmelCase     :  Optional[int]							      =    False
             _UpperCAmelCase     :  str							      =    self.tokenizer(
                 "UN Chief says there is no military solution in Syria"						,					src_lang="eng_Latn"						,					tgt_lang="fra_Latn"       )
             self.assertEqual(
                 inputs.input_ids						,					[256047, 16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2]       )

