




'''simple docstring'''





import argparse
import json
from pathlib import Path

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

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase_						:      int       =						logging.get_logger(__name__)





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

     """simple docstring"""




     _snake_case							=				MobileViTConfig()

     # size of the architecture
     if "mobilevit_s" in mobilevit_name:
          _snake_case							=				[144, 192, 240]
          _snake_case							=				[16, 32, 64, 96, 128, 160, 640]
     elif "mobilevit_xs" in mobilevit_name:
          _snake_case							=				[96, 120, 144]
          _snake_case							=				[16, 32, 48, 64, 80, 96, 384]
     elif "mobilevit_xxs" in mobilevit_name:
          _snake_case							=				[64, 80, 96]
          _snake_case							=				[16, 16, 24, 48, 64, 80, 320]
          _snake_case							=				0.05
          _snake_case							=				2.0

     if mobilevit_name.startswith("deeplabv3_"   ):
          _snake_case							=				512
          _snake_case							=				16
          _snake_case							=				21
          _snake_case							=				"pascal-voc-id2label.json"
     else:
          _snake_case							=				1_000
          _snake_case							=				"imagenet-1k-id2label.json"

     _snake_case							=				"huggingface/label-files"
     _snake_case							=				json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_ ,						SCREAMING_SNAKE_CASE_ ,						repo_type="dataset"   ) ,						"r"   )   )
     _snake_case							=				{int(SCREAMING_SNAKE_CASE_   ): v for k, v in idalabel.items()}
     _snake_case							=				idalabel
     _snake_case							=				{v: k for k, v in idalabel.items()}

     return config





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

     """simple docstring"""




     for i in range(1 ,						6   ):
          if F"""layer_{i}.""" in name:
               _snake_case							=				name.replace(F"""layer_{i}.""" ,						F"""encoder.layer.{i - 1}."""   )

     if "conv_1." in name:
          _snake_case							=				name.replace("conv_1." ,						"conv_stem."   )
     if ".block." in name:
          _snake_case							=				name.replace(".block." ,						"."   )

     if "exp_1x1" in name:
          _snake_case							=				name.replace("exp_1x1" ,						"expand_1x1"   )
     if "red_1x1" in name:
          _snake_case							=				name.replace("red_1x1" ,						"reduce_1x1"   )
     if ".local_rep.conv_3x3." in name:
          _snake_case							=				name.replace(".local_rep.conv_3x3." ,						".conv_kxk."   )
     if ".local_rep.conv_1x1." in name:
          _snake_case							=				name.replace(".local_rep.conv_1x1." ,						".conv_1x1."   )
     if ".norm." in name:
          _snake_case							=				name.replace(".norm." ,						".normalization."   )
     if ".conv." in name:
          _snake_case							=				name.replace(".conv." ,						".convolution."   )
     if ".conv_proj." in name:
          _snake_case							=				name.replace(".conv_proj." ,						".conv_projection."   )

     for i in range(0 ,						2   ):
          for j in range(0 ,						4   ):
               if F""".{i}.{j}.""" in name:
                    _snake_case							=				name.replace(F""".{i}.{j}.""" ,						F""".{i}.layer.{j}."""   )

     for i in range(2 ,						6   ):
          for j in range(0 ,						4   ):
               if F""".{i}.{j}.""" in name:
                    _snake_case							=				name.replace(F""".{i}.{j}.""" ,						F""".{i}."""   )
                    if "expand_1x1" in name:
                         _snake_case							=				name.replace("expand_1x1" ,						"downsampling_layer.expand_1x1"   )
                    if "conv_3x3" in name:
                         _snake_case							=				name.replace("conv_3x3" ,						"downsampling_layer.conv_3x3"   )
                    if "reduce_1x1" in name:
                         _snake_case							=				name.replace("reduce_1x1" ,						"downsampling_layer.reduce_1x1"   )

     for i in range(2 ,						5   ):
          if F""".global_rep.{i}.weight""" in name:
               _snake_case							=				name.replace(F""".global_rep.{i}.weight""" ,						".layernorm.weight"   )
          if F""".global_rep.{i}.bias""" in name:
               _snake_case							=				name.replace(F""".global_rep.{i}.bias""" ,						".layernorm.bias"   )

     if ".global_rep." in name:
          _snake_case							=				name.replace(".global_rep." ,						".transformer."   )
     if ".pre_norm_mha.0." in name:
          _snake_case							=				name.replace(".pre_norm_mha.0." ,						".layernorm_before."   )
     if ".pre_norm_mha.1.out_proj." in name:
          _snake_case							=				name.replace(".pre_norm_mha.1.out_proj." ,						".attention.output.dense."   )
     if ".pre_norm_ffn.0." in name:
          _snake_case							=				name.replace(".pre_norm_ffn.0." ,						".layernorm_after."   )
     if ".pre_norm_ffn.1." in name:
          _snake_case							=				name.replace(".pre_norm_ffn.1." ,						".intermediate.dense."   )
     if ".pre_norm_ffn.4." in name:
          _snake_case							=				name.replace(".pre_norm_ffn.4." ,						".output.dense."   )
     if ".transformer." in name:
          _snake_case							=				name.replace(".transformer." ,						".transformer.layer."   )

     if ".aspp_layer." in name:
          _snake_case							=				name.replace(".aspp_layer." ,						"."   )
     if ".aspp_pool." in name:
          _snake_case							=				name.replace(".aspp_pool." ,						"."   )
     if "seg_head." in name:
          _snake_case							=				name.replace("seg_head." ,						"segmentation_head."   )
     if "segmentation_head.classifier.classifier." in name:
          _snake_case							=				name.replace("segmentation_head.classifier.classifier." ,						"segmentation_head.classifier."   )

     if "classifier.fc." in name:
          _snake_case							=				name.replace("classifier.fc." ,						"classifier."   )
     elif (not base_model) and ("segmentation_head." not in name):
          _snake_case							=				"mobilevit." + name

     return name





def      __a     (       _UpperCamelCase:   List[str] ,						_UpperCamelCase:   Optional[Any] ,						_UpperCamelCase:   List[str]=False   )						->			Any:

     """simple docstring"""




     if base_model:
          _snake_case							=				""
     else:
          _snake_case							=				"mobilevit."

     for key in orig_state_dict.copy().keys():
          _snake_case							=				orig_state_dict.pop(SCREAMING_SNAKE_CASE_   )

          if key[:8] == "encoder.":
               _snake_case							=				key[8:]

          if "qkv" in key:
               _snake_case							=				key.split("."   )
               _snake_case							=				int(key_split[0][6:]   ) - 1
               _snake_case							=				int(key_split[3]   )
               _snake_case							=				model.get_submodule(F"""{model_prefix}encoder.layer.{layer_num}"""   )
               _snake_case							=				layer.transformer.layer[transformer_num].attention.attention.all_head_size
               _snake_case							=				(
                   F"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
               )
               if "weight" in key:
                    _snake_case							=				val[:dim, :]
                    _snake_case							=				val[dim : dim * 2, :]
                    _snake_case							=				val[-dim:, :]
               else:
                    _snake_case							=				val[:dim]
                    _snake_case							=				val[dim : dim * 2]
                    _snake_case							=				val[-dim:]
          else:
               _snake_case							=				val

     return orig_state_dict





def      __a     (       )						->			Optional[Any]:

     """simple docstring"""




     _snake_case							=				"http://images.cocodataset.org/val2017/000000039769.jpg"
     _snake_case							=				Image.open(requests.get(SCREAMING_SNAKE_CASE_ ,						stream=SCREAMING_SNAKE_CASE_   ).raw   )
     return im





@torch.no_grad()
def      __a     (       _UpperCamelCase:   Optional[int] ,						_UpperCamelCase:   Any ,						_UpperCamelCase:   Optional[Any] ,						_UpperCamelCase:   List[Any]=False   )						->			List[str]:

     """simple docstring"""




     _snake_case							=				get_mobilevit_config(SCREAMING_SNAKE_CASE_   )

     # load original state_dict
     _snake_case							=				torch.load(SCREAMING_SNAKE_CASE_ ,						map_location="cpu"   )

     # load 🤗 model
     if mobilevit_name.startswith("deeplabv3_"   ):
          _snake_case							=				MobileViTForSemanticSegmentation(SCREAMING_SNAKE_CASE_   ).eval()
     else:
          _snake_case							=				MobileViTForImageClassification(SCREAMING_SNAKE_CASE_   ).eval()

     _snake_case							=				convert_state_dict(SCREAMING_SNAKE_CASE_ ,						SCREAMING_SNAKE_CASE_   )
     model.load_state_dict(SCREAMING_SNAKE_CASE_   )

     # Check outputs on an image, prepared by MobileViTImageProcessor
     _snake_case							=				MobileViTImageProcessor(crop_size=config.image_size ,						size=config.image_size + 32   )
     _snake_case							=				image_processor(images=prepare_img() ,						return_tensors="pt"   )
     _snake_case							=				model(**SCREAMING_SNAKE_CASE_   )
     _snake_case							=				outputs.logits

     if mobilevit_name.startswith("deeplabv3_"   ):
          assert logits.shape == (1, 21, 32, 32)

          if mobilevit_name == "deeplabv3_mobilevit_s":
               _snake_case							=				torch.tensor(
                   [
                       [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
                       [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
                       [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
                   ]   )
          elif mobilevit_name == "deeplabv3_mobilevit_xs":
               _snake_case							=				torch.tensor(
                   [
                       [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
                       [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
                       [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
                   ]   )
          elif mobilevit_name == "deeplabv3_mobilevit_xxs":
               _snake_case							=				torch.tensor(
                   [
                       [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
                       [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
                       [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
                   ]   )
          else:
               raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""   )

          assert torch.allclose(logits[0, :3, :3, :3] ,						SCREAMING_SNAKE_CASE_ ,						atol=1E-4   )
     else:
          assert logits.shape == (1, 1_000)

          if mobilevit_name == "mobilevit_s":
               _snake_case							=				torch.tensor([-0.9866, 0.2392, -1.1241]   )
          elif mobilevit_name == "mobilevit_xs":
               _snake_case							=				torch.tensor([-2.4761, -0.9399, -1.9587]   )
          elif mobilevit_name == "mobilevit_xxs":
               _snake_case							=				torch.tensor([-1.9364, -1.2327, -0.4653]   )
          else:
               raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""   )

          assert torch.allclose(logits[0, :3] ,						SCREAMING_SNAKE_CASE_ ,						atol=1E-4   )

     Path(SCREAMING_SNAKE_CASE_   ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_   )
     print(F"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}"""   )
     model.save_pretrained(SCREAMING_SNAKE_CASE_   )
     print(F"""Saving image processor to {pytorch_dump_folder_path}"""   )
     image_processor.save_pretrained(SCREAMING_SNAKE_CASE_   )

     if push_to_hub:
          _snake_case							=				{
              "mobilevit_s": "mobilevit-small",
              "mobilevit_xs": "mobilevit-x-small",
              "mobilevit_xxs": "mobilevit-xx-small",
              "deeplabv3_mobilevit_s": "deeplabv3-mobilevit-small",
              "deeplabv3_mobilevit_xs": "deeplabv3-mobilevit-x-small",
              "deeplabv3_mobilevit_xxs": "deeplabv3-mobilevit-xx-small",
          }

          print("Pushing to the hub..."   )
          _snake_case							=				model_mapping[mobilevit_name]
          image_processor.push_to_hub(SCREAMING_SNAKE_CASE_ ,						organization="apple"   )
          model.push_to_hub(SCREAMING_SNAKE_CASE_ ,						organization="apple"   )


if __name__ == "__main__":
   UpperCamelCase_						:      Any       =						argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--mobilevit_name''',
       default='''mobilevit_s''',
       type=str,
       help=(
           '''Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','''
           ''' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'''
       ),
   )
   parser.add_argument(
       '''--checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
   )
   parser.add_argument(
       '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
   )

   UpperCamelCase_						:      Any       =						parser.parse_args()
   convert_movilevit_checkpoint(
       args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
   )








'''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,
              } ,)


