




'''simple docstring'''





import argparse
import os

import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints

from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder


UpperCamelCase_						:      Optional[int]       =						"""base_with_context"""





def      __a     (       _UpperCamelCase:   Union[str, Any] ,						_UpperCamelCase:   List[str]   )						->			int:

     """simple docstring"""




     _snake_case							=				nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"]   )   )
     _snake_case							=				nn.Parameter(
         torch.FloatTensor(weights["Embed_0"]["embedding"]   ) ,						requires_grad=SCREAMING_SNAKE_CASE_   )
     for lyr_num, lyr in enumerate(model.encoders   ):
          _snake_case							=				weights[F"""layers_{lyr_num}"""]
          _snake_case							=				nn.Parameter(
              torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]   )   )

          _snake_case							=				ly_weight["attention"]
          _snake_case							=				nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T   )   )
          _snake_case							=				nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T   )   )
          _snake_case							=				nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T   )   )
          _snake_case							=				nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T   )   )

          _snake_case							=				nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]   )   )

          _snake_case							=				nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T   )   )
          _snake_case							=				nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T   )   )
          _snake_case							=				nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T   )   )

     _snake_case							=				nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]   )   )
     return model





def      __a     (       _UpperCamelCase:   Optional[int] ,						_UpperCamelCase:   Union[str, Any]   )						->			Any:

     """simple docstring"""




     _snake_case							=				nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T   )   )

     _snake_case							=				nn.Parameter(
         torch.FloatTensor(weights["Embed_0"]["embedding"]   ) ,						requires_grad=SCREAMING_SNAKE_CASE_   )

     for lyr_num, lyr in enumerate(model.encoders   ):
          _snake_case							=				weights[F"""layers_{lyr_num}"""]
          _snake_case							=				ly_weight["attention"]

          _snake_case							=				nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T   )   )
          _snake_case							=				nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T   )   )
          _snake_case							=				nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T   )   )
          _snake_case							=				nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T   )   )
          _snake_case							=				nn.Parameter(
              torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]   )   )

          _snake_case							=				nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T   )   )
          _snake_case							=				nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T   )   )
          _snake_case							=				nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T   )   )
          _snake_case							=				nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]   )   )

     _snake_case							=				nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]   )   )

     return model





def      __a     (       _UpperCamelCase:   Optional[int] ,						_UpperCamelCase:   Dict   )						->			Tuple:

     """simple docstring"""




     _snake_case							=				nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T   )   )
     _snake_case							=				nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T   )   )

     _snake_case							=				nn.Parameter(
         torch.FloatTensor(weights["Embed_0"]["embedding"]   ) ,						requires_grad=SCREAMING_SNAKE_CASE_   )

     _snake_case							=				nn.Parameter(
         torch.FloatTensor(weights["continuous_inputs_projection"]["kernel"].T   )   )

     for lyr_num, lyr in enumerate(model.decoders   ):
          _snake_case							=				weights[F"""layers_{lyr_num}"""]
          _snake_case							=				nn.Parameter(
              torch.FloatTensor(ly_weight["pre_self_attention_layer_norm"]["scale"]   )   )

          _snake_case							=				nn.Parameter(
              torch.FloatTensor(ly_weight["FiLMLayer_0"]["DenseGeneral_0"]["kernel"].T   )   )

          _snake_case							=				ly_weight["self_attention"]
          _snake_case							=				nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T   )   )
          _snake_case							=				nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T   )   )
          _snake_case							=				nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T   )   )
          _snake_case							=				nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T   )   )

          _snake_case							=				ly_weight["MultiHeadDotProductAttention_0"]
          _snake_case							=				nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T   )   )
          _snake_case							=				nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T   )   )
          _snake_case							=				nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T   )   )
          _snake_case							=				nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T   )   )
          _snake_case							=				nn.Parameter(
              torch.FloatTensor(ly_weight["pre_cross_attention_layer_norm"]["scale"]   )   )

          _snake_case							=				nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]   )   )
          _snake_case							=				nn.Parameter(
              torch.FloatTensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T   )   )
          _snake_case							=				nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T   )   )
          _snake_case							=				nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T   )   )
          _snake_case							=				nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T   )   )

     _snake_case							=				nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"]   )   )

     _snake_case							=				nn.Parameter(torch.FloatTensor(weights["spec_out_dense"]["kernel"].T   )   )

     return model





def      __a     (       _UpperCamelCase:   List[Any]   )						->			Optional[Any]:

     """simple docstring"""




     _snake_case							=				checkpoints.load_tax_checkpoint(args.checkpoint_path   )
     _snake_case							=				jnp.tree_util.tree_map(onp.array ,						SCREAMING_SNAKE_CASE_   )

     _snake_case							=				[
         "from __gin__ import dynamic_registration",
         "from music_spectrogram_diffusion.models.diffusion import diffusion_utils",
         "diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0",
         "diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()",
     ]

     _snake_case							=				os.path.join(args.checkpoint_path ,						".." ,						"config.gin"   )
     _snake_case							=				inference.parse_training_gin_file(SCREAMING_SNAKE_CASE_ ,						SCREAMING_SNAKE_CASE_   )
     _snake_case							=				inference.InferenceModel(args.checkpoint_path ,						SCREAMING_SNAKE_CASE_   )

     _snake_case							=				DDPMScheduler(beta_schedule="squaredcos_cap_v2" ,						variance_type="fixed_large"   )

     _snake_case							=				SpectrogramNotesEncoder(
         max_length=synth_model.sequence_length["inputs"] ,						vocab_size=synth_model.model.module.config.vocab_size ,						d_model=synth_model.model.module.config.emb_dim ,						dropout_rate=synth_model.model.module.config.dropout_rate ,						num_layers=synth_model.model.module.config.num_encoder_layers ,						num_heads=synth_model.model.module.config.num_heads ,						d_kv=synth_model.model.module.config.head_dim ,						d_ff=synth_model.model.module.config.mlp_dim ,						feed_forward_proj="gated-gelu" ,						)

     _snake_case							=				SpectrogramContEncoder(
         input_dims=synth_model.audio_codec.n_dims ,						targets_context_length=synth_model.sequence_length["targets_context"] ,						d_model=synth_model.model.module.config.emb_dim ,						dropout_rate=synth_model.model.module.config.dropout_rate ,						num_layers=synth_model.model.module.config.num_encoder_layers ,						num_heads=synth_model.model.module.config.num_heads ,						d_kv=synth_model.model.module.config.head_dim ,						d_ff=synth_model.model.module.config.mlp_dim ,						feed_forward_proj="gated-gelu" ,						)

     _snake_case							=				TaFilmDecoder(
         input_dims=synth_model.audio_codec.n_dims ,						targets_length=synth_model.sequence_length["targets_context"] ,						max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time ,						d_model=synth_model.model.module.config.emb_dim ,						num_layers=synth_model.model.module.config.num_decoder_layers ,						num_heads=synth_model.model.module.config.num_heads ,						d_kv=synth_model.model.module.config.head_dim ,						d_ff=synth_model.model.module.config.mlp_dim ,						dropout_rate=synth_model.model.module.config.dropout_rate ,						)

     _snake_case							=				load_notes_encoder(ta_checkpoint["target"]["token_encoder"] ,						SCREAMING_SNAKE_CASE_   )
     _snake_case							=				load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"] ,						SCREAMING_SNAKE_CASE_   )
     _snake_case							=				load_decoder(ta_checkpoint["target"]["decoder"] ,						SCREAMING_SNAKE_CASE_   )

     _snake_case							=				OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder"   )

     _snake_case							=				SpectrogramDiffusionPipeline(
         notes_encoder=SCREAMING_SNAKE_CASE_ ,						continuous_encoder=SCREAMING_SNAKE_CASE_ ,						decoder=SCREAMING_SNAKE_CASE_ ,						scheduler=SCREAMING_SNAKE_CASE_ ,						melgan=SCREAMING_SNAKE_CASE_ ,						)
     if args.save:
          pipe.save_pretrained(args.output_path   )


if __name__ == "__main__":
   UpperCamelCase_						:      Tuple       =						argparse.ArgumentParser()

   parser.add_argument('''--output_path''', default=None, type=str, required=True, help='''Path to the converted model.''')
   parser.add_argument(
       '''--save''', default=True, type=bool, required=False, help='''Whether to save the converted model or not.'''
   )
   parser.add_argument(
       '''--checkpoint_path''',
       default=F'{MODEL}/checkpoint_500000',
       type=str,
       required=False,
       help='''Path to the original jax model checkpoint.''',
   )
   UpperCamelCase_						:      Optional[int]       =						parser.parse_args()

   main(args)








'''simple docstring'''





import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, 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


UpperCamelCase_						:      List[str]       =						get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
   from transformers.models.mbart.modeling_mbart import shift_tokens_right

UpperCamelCase_						:      Optional[Any]       =						250004
UpperCamelCase_						:      Union[str, Any]       =						250020

@require_sentencepiece
@require_tokenizers
class        _a    (						__lowerCAmelCase , unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:   Optional[int]       =						MBartTokenizer
     SCREAMING_SNAKE_CASE_							:   List[str]       =						MBartTokenizerFast
     SCREAMING_SNAKE_CASE_							:   Union[str, Any]       =						True
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						True






     def     _lowercase     (			self   )							->      Optional[Any]:
          super().setUp()

          # We have a SentencePiece fixture for testing
          _snake_case							=				MBartTokenizer(_SCREAMING_SNAKE_CASE ,keep_accents=_SCREAMING_SNAKE_CASE   )
          tokenizer.save_pretrained(self.tmpdirname   )






     def     _lowercase     (			self   )							->      Dict:
          _snake_case							=				MBartTokenizer(_SCREAMING_SNAKE_CASE ,keep_accents=_SCREAMING_SNAKE_CASE   )

          _snake_case							=				tokenizer.tokenize("This is a test"   )
          self.assertListEqual(_SCREAMING_SNAKE_CASE ,["▁This", "▁is", "▁a", "▁t", "est"]   )

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

          _snake_case							=				tokenizer.tokenize("I was born in 92000, and this is falsé."   )
          self.assertListEqual(
              _SCREAMING_SNAKE_CASE ,[
                  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",
                  ".",
              ] ,)
          _snake_case							=				tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE   )
          self.assertListEqual(
              _SCREAMING_SNAKE_CASE ,[
                  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]
                  #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
              ] ,)

          _snake_case							=				tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE   )
          self.assertListEqual(
              _SCREAMING_SNAKE_CASE ,[
                  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     _lowercase     (			self   )							->      List[str]:
          if not self.test_slow_tokenizer:
               # as we don't have a slow version, we can't compare the outputs between slow and fast versions
               return

          _snake_case							=				(self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart", {})
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""   ):
                    _snake_case							=				self.rust_tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				self.tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )

                    _snake_case							=				tempfile.mkdtemp()

                    _snake_case							=				tokenizer_r.save_pretrained(_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				tokenizer_p.save_pretrained(_SCREAMING_SNAKE_CASE   )

                    # 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   )   )
                    _snake_case							=				tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f   )
                    self.assertSequenceEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )

                    # Checks everything loads correctly in the same way
                    _snake_case							=				tokenizer_r.from_pretrained(_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				tokenizer_p.from_pretrained(_SCREAMING_SNAKE_CASE   )

                    # Check special tokens are set accordingly on Rust and Python
                    for key in tokenizer_pp.special_tokens_map:
                         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )   )
                         # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                         # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                    shutil.rmtree(_SCREAMING_SNAKE_CASE   )

                    # Save tokenizer rust, legacy_format=True
                    _snake_case							=				tempfile.mkdtemp()

                    _snake_case							=				tokenizer_r.save_pretrained(_SCREAMING_SNAKE_CASE ,legacy_format=_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				tokenizer_p.save_pretrained(_SCREAMING_SNAKE_CASE   )

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

                    # Checks everything loads correctly in the same way
                    _snake_case							=				tokenizer_r.from_pretrained(_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				tokenizer_p.from_pretrained(_SCREAMING_SNAKE_CASE   )

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

                    shutil.rmtree(_SCREAMING_SNAKE_CASE   )

                    # Save tokenizer rust, legacy_format=False
                    _snake_case							=				tempfile.mkdtemp()

                    _snake_case							=				tokenizer_r.save_pretrained(_SCREAMING_SNAKE_CASE ,legacy_format=_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				tokenizer_p.save_pretrained(_SCREAMING_SNAKE_CASE   )

                    # 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
                    _snake_case							=				tokenizer_r.from_pretrained(_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				tokenizer_p.from_pretrained(_SCREAMING_SNAKE_CASE   )

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

                    shutil.rmtree(_SCREAMING_SNAKE_CASE   )







@require_torch
@require_sentencepiece
@require_tokenizers
class        _a    (						unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:   Optional[Any]       =						"""facebook/mbart-large-en-ro"""
     SCREAMING_SNAKE_CASE_							:   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.""",
     ]
     SCREAMING_SNAKE_CASE_							:   Dict       =						[
         """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.""",
     ]
     SCREAMING_SNAKE_CASE_							:   Optional[int]       =						[82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2, EN_CODE]






     @classmethod
     def     _lowercase     (			cls   )							->      List[str]:
          _snake_case							=				MBartTokenizer.from_pretrained(
              cls.checkpoint_name ,src_lang="en_XX" ,tgt_lang="ro_RO"   )
          _snake_case							=				1
          return cls






     def     _lowercase     (			self   )							->      Dict:
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"] ,250_001   )
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"] ,250_004   )
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"] ,250_020   )






     def     _lowercase     (			self   )							->      Tuple:
          _snake_case							=				self.tokenizer.batch_encode_plus(self.src_text   ).input_ids[0]
          self.assertListEqual(self.expected_src_tokens ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      Optional[int]:
          self.assertIn(_SCREAMING_SNAKE_CASE ,self.tokenizer.all_special_ids   )
          _snake_case							=				[RO_CODE, 884, 9_019, 96, 9, 916, 86_792, 36, 18_743, 15_596, 5, 2]
          _snake_case							=				self.tokenizer.decode(_SCREAMING_SNAKE_CASE ,skip_special_tokens=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				self.tokenizer.decode(generated_ids[1:] ,skip_special_tokens=_SCREAMING_SNAKE_CASE   )
          self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          self.assertNotIn(self.tokenizer.eos_token ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      List[Any]:
          _snake_case							=				["this is gunna be a long sentence " * 20]
          assert isinstance(src_text[0] ,_SCREAMING_SNAKE_CASE   )
          _snake_case							=				10
          _snake_case							=				self.tokenizer(_SCREAMING_SNAKE_CASE ,max_length=_SCREAMING_SNAKE_CASE ,truncation=_SCREAMING_SNAKE_CASE   ).input_ids[0]
          self.assertEqual(ids[-2] ,2   )
          self.assertEqual(ids[-1] ,_SCREAMING_SNAKE_CASE   )
          self.assertEqual(len(_SCREAMING_SNAKE_CASE   ) ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      Optional[int]:
          self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]   ) ,[250_026, 250_001]   )






     def     _lowercase     (			self   )							->      str:
          _snake_case							=				tempfile.mkdtemp()
          _snake_case							=				self.tokenizer.fairseq_tokens_to_ids
          self.tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				MBartTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   )
          self.assertDictEqual(new_tok.fairseq_tokens_to_ids ,_SCREAMING_SNAKE_CASE   )






     @require_torch
     def     _lowercase     (			self   )							->      Dict:
          _snake_case							=				self.tokenizer(self.src_text ,text_target=self.tgt_text ,padding=_SCREAMING_SNAKE_CASE ,return_tensors="pt"   )
          _snake_case							=				shift_tokens_right(batch["labels"] ,self.tokenizer.pad_token_id   )

          # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
          assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
          assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
          assert batch.decoder_input_ids[1][-1] == 2
          assert batch.labels[1][-2:].tolist() == [2, RO_CODE]






     @require_torch
     def     _lowercase     (			self   )							->      Optional[int]:
          _snake_case							=				self.tokenizer(
              self.src_text ,text_target=self.tgt_text ,padding=_SCREAMING_SNAKE_CASE ,truncation=_SCREAMING_SNAKE_CASE ,max_length=len(self.expected_src_tokens   ) ,return_tensors="pt" ,)

          _snake_case							=				shift_tokens_right(batch["labels"] ,self.tokenizer.pad_token_id   )

          self.assertIsInstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )

          self.assertEqual((2, 14) ,batch.input_ids.shape   )
          self.assertEqual((2, 14) ,batch.attention_mask.shape   )
          _snake_case							=				batch.input_ids.tolist()[0]
          self.assertListEqual(self.expected_src_tokens ,_SCREAMING_SNAKE_CASE   )
          self.assertEqual(2 ,batch.decoder_input_ids[0, -1]   )  # EOS
          # Test that special tokens are reset
          self.assertEqual(self.tokenizer.prefix_tokens ,[]   )
          self.assertEqual(self.tokenizer.suffix_tokens ,[self.tokenizer.eos_token_id, EN_CODE]   )






     def     _lowercase     (			self   )							->      str:
          _snake_case							=				self.tokenizer(self.src_text ,padding=_SCREAMING_SNAKE_CASE ,truncation=_SCREAMING_SNAKE_CASE ,max_length=3 ,return_tensors="pt"   )
          _snake_case							=				self.tokenizer(
              text_target=self.tgt_text ,padding=_SCREAMING_SNAKE_CASE ,truncation=_SCREAMING_SNAKE_CASE ,max_length=10 ,return_tensors="pt"   )
          _snake_case							=				targets["input_ids"]
          _snake_case							=				shift_tokens_right(_SCREAMING_SNAKE_CASE ,self.tokenizer.pad_token_id   )

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






     @require_torch
     def     _lowercase     (			self   )							->      Any:
          _snake_case							=				self.tokenizer._build_translation_inputs(
              "A test" ,return_tensors="pt" ,src_lang="en_XX" ,tgt_lang="ar_AR"   )

          self.assertEqual(
              nested_simplify(_SCREAMING_SNAKE_CASE   ) ,{
                  # A, test, EOS, en_XX
                  "input_ids": [[62, 3_034, 2, 250_004]],
                  "attention_mask": [[1, 1, 1, 1]],
                  # ar_AR
                  "forced_bos_token_id": 250_001,
              } ,)


