




"""simple docstring"""


import argparse
import json

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

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation



def 	lowercase_				(			__UpperCAmelCase				)						->    Any:
      lowerCAmelCase__      :			Union[str, Any]   =	384
      lowerCAmelCase__      :			Dict   =	7
      if "tiny" in model_name:
            lowerCAmelCase__      :			Union[str, Any]   =	96
            lowerCAmelCase__      :			List[Any]   =	(2, 2, 6, 2)
            lowerCAmelCase__      :			Optional[int]   =	(3, 6, 12, 24)
      elif "small" in model_name:
            lowerCAmelCase__      :			str   =	96
            lowerCAmelCase__      :			Optional[int]   =	(2, 2, 18, 2)
            lowerCAmelCase__      :			Dict   =	(3, 6, 12, 24)
      elif "base" in model_name:
            lowerCAmelCase__      :			Dict   =	128
            lowerCAmelCase__      :			int   =	(2, 2, 18, 2)
            lowerCAmelCase__      :			Dict   =	(4, 8, 16, 32)
            lowerCAmelCase__      :			Optional[int]   =	12
            lowerCAmelCase__      :			List[Any]   =	512
      elif "large" in model_name:
            lowerCAmelCase__      :			Tuple   =	192
            lowerCAmelCase__      :			Optional[int]   =	(2, 2, 18, 2)
            lowerCAmelCase__      :			Tuple   =	(6, 12, 24, 48)
            lowerCAmelCase__      :			List[str]   =	12
            lowerCAmelCase__      :			int   =	768

      # set label information
      lowerCAmelCase__      :			str   =	150
      lowerCAmelCase__      :			List[str]   =	"""huggingface/label-files"""
      lowerCAmelCase__      :			int   =	"""ade20k-id2label.json"""
      lowerCAmelCase__      :			Optional[Any]   =	json.load(open(hf_hub_download(__UpperCAmelCase    ,		__UpperCAmelCase    ,		repo_type="""dataset"""				)    ,		"""r"""				)				)
      lowerCAmelCase__      :			List[Any]   =	{int(__UpperCAmelCase				): v for k, v in idalabel.items()}
      lowerCAmelCase__      :			Tuple   =	{v: k for k, v in idalabel.items()}

      lowerCAmelCase__      :			Optional[Any]   =	SwinConfig(
          embed_dim=__UpperCAmelCase    ,		depths=__UpperCAmelCase    ,		num_heads=__UpperCAmelCase    ,		window_size=__UpperCAmelCase    ,		out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]    ,		)
      lowerCAmelCase__      :			str   =	UperNetConfig(
          backbone_config=__UpperCAmelCase    ,		auxiliary_in_channels=__UpperCAmelCase    ,		num_labels=__UpperCAmelCase    ,		idalabel=__UpperCAmelCase    ,		labelaid=__UpperCAmelCase    ,		)

      return config



def 	lowercase_				(			__UpperCAmelCase				)						->    str:
      lowerCAmelCase__      :			int   =	[]

      # fmt: off
      # stem
      rename_keys.append(("""backbone.patch_embed.projection.weight""", """backbone.embeddings.patch_embeddings.projection.weight""")				)
      rename_keys.append(("""backbone.patch_embed.projection.bias""", """backbone.embeddings.patch_embeddings.projection.bias""")				)
      rename_keys.append(("""backbone.patch_embed.norm.weight""", """backbone.embeddings.norm.weight""")				)
      rename_keys.append(("""backbone.patch_embed.norm.bias""", """backbone.embeddings.norm.bias""")				)
      # stages
      for i in range(len(config.backbone_config.depths				)				):
            for j in range(config.backbone_config.depths[i]				):
                  rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")				)
                  rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")				)
                  rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")				)
                  rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")				)
                  rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")				)
                  rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")				)
                  rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")				)
                  rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")				)
                  rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")				)
                  rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")				)
                  rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight""")				)
                  rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias""")				)

            if i < 3:
                  rename_keys.append((f"""backbone.stages.{i}.downsample.reduction.weight""", f"""backbone.encoder.layers.{i}.downsample.reduction.weight""")				)
                  rename_keys.append((f"""backbone.stages.{i}.downsample.norm.weight""", f"""backbone.encoder.layers.{i}.downsample.norm.weight""")				)
                  rename_keys.append((f"""backbone.stages.{i}.downsample.norm.bias""", f"""backbone.encoder.layers.{i}.downsample.norm.bias""")				)
            rename_keys.append((f"""backbone.norm{i}.weight""", f"""backbone.hidden_states_norms.stage{i+1}.weight""")				)
            rename_keys.append((f"""backbone.norm{i}.bias""", f"""backbone.hidden_states_norms.stage{i+1}.bias""")				)

      # decode head
      rename_keys.extend(
          [
              ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
              ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
              ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
              ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
          ]				)
      # fmt: on

      return rename_keys



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Optional[int]:
      lowerCAmelCase__      :			List[Any]   =	dct.pop(__UpperCAmelCase				)
      lowerCAmelCase__      :			List[str]   =	val



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



def 	lowercase_				(			__UpperCAmelCase				)						->    List[str]:
      lowerCAmelCase__      :			Optional[int]   =	x.shape
      lowerCAmelCase__      :			Tuple   =	x.reshape(__UpperCAmelCase    ,		4    ,		in_channel // 4				)
      lowerCAmelCase__      :			List[Any]   =	x[:, [0, 2, 1, 3], :].transpose(1    ,		2				).reshape(__UpperCAmelCase    ,		__UpperCAmelCase				)
      return x



def 	lowercase_				(			__UpperCAmelCase				)						->    Dict:
      lowerCAmelCase__      :			List[Any]   =	x.shape
      lowerCAmelCase__      :			List[str]   =	x.reshape(__UpperCAmelCase    ,		in_channel // 4    ,		4				)
      lowerCAmelCase__      :			Dict   =	x[:, :, [0, 2, 1, 3]].transpose(1    ,		2				).reshape(__UpperCAmelCase    ,		__UpperCAmelCase				)

      return x



def 	lowercase_				(			__UpperCAmelCase				)						->    int:
      lowerCAmelCase__      :			Tuple   =	x.shape[0]
      lowerCAmelCase__      :			Any   =	x.reshape(4    ,		in_channel // 4				)
      lowerCAmelCase__      :			List[Any]   =	x[[0, 2, 1, 3], :].transpose(0    ,		1				).reshape(__UpperCAmelCase				)
      return x



def 	lowercase_				(			__UpperCAmelCase				)						->    List[str]:
      lowerCAmelCase__      :			List[Any]   =	x.shape[0]
      lowerCAmelCase__      :			Tuple   =	x.reshape(in_channel // 4    ,		4				)
      lowerCAmelCase__      :			Union[str, Any]   =	x[:, [0, 2, 1, 3]].transpose(0    ,		1				).reshape(__UpperCAmelCase				)
      return x



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    List[str]:
      lowerCAmelCase__      :			int   =	{
          """upernet-swin-tiny""": """https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth""",
          """upernet-swin-small""": """https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth""",
          """upernet-swin-base""": """https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth""",
          """upernet-swin-large""": """https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth""",
      }
      lowerCAmelCase__      :			str   =	model_name_to_url[model_name]
      lowerCAmelCase__      :			Optional[Any]   =	torch.hub.load_state_dict_from_url(__UpperCAmelCase    ,		map_location="""cpu"""    ,		file_name=__UpperCAmelCase				)[
          """state_dict"""
      ]

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

      lowerCAmelCase__      :			List[Any]   =	get_upernet_config(__UpperCAmelCase				)
      lowerCAmelCase__      :			Any   =	UperNetForSemanticSegmentation(__UpperCAmelCase				)
      model.eval()

      # replace "bn" => "batch_norm"
      for key in state_dict.copy().keys():
            lowerCAmelCase__      :			Tuple   =	state_dict.pop(__UpperCAmelCase				)
            if "bn" in key:
                  lowerCAmelCase__      :			Optional[Any]   =	key.replace("""bn"""    ,		"""batch_norm"""				)
            lowerCAmelCase__      :			Dict   =	val

      # rename keys
      lowerCAmelCase__      :			List[str]   =	create_rename_keys(__UpperCAmelCase				)
      for src, dest in rename_keys:
            rename_key(__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)
      read_in_q_k_v(__UpperCAmelCase    ,		config.backbone_config				)

      # fix downsample parameters
      for key, value in state_dict.items():
            if "downsample" in key:
                  if "reduction" in key:
                        lowerCAmelCase__      :			int   =	reverse_correct_unfold_reduction_order(__UpperCAmelCase				)
                  if "norm" in key:
                        lowerCAmelCase__      :			int   =	reverse_correct_unfold_norm_order(__UpperCAmelCase				)

      model.load_state_dict(__UpperCAmelCase				)

      # verify on image
      lowerCAmelCase__      :			Any   =	"""https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
      lowerCAmelCase__      :			Optional[int]   =	Image.open(requests.get(__UpperCAmelCase    ,		stream=__UpperCAmelCase				).raw				).convert("""RGB"""				)

      lowerCAmelCase__      :			Tuple   =	SegformerImageProcessor()
      lowerCAmelCase__      :			List[Any]   =	processor(__UpperCAmelCase    ,		return_tensors="""pt"""				).pixel_values

      with torch.no_grad():
            lowerCAmelCase__      :			Tuple   =	model(__UpperCAmelCase				)
            lowerCAmelCase__      :			Tuple   =	outputs.logits

      print(logits.shape				)
      print("""First values of logits:"""    ,		logits[0, 0, :3, :3]				)
      # assert values
      if model_name == "upernet-swin-tiny":
            lowerCAmelCase__      :			List[str]   =	torch.tensor(
                [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]				)
      elif model_name == "upernet-swin-small":
            lowerCAmelCase__      :			List[str]   =	torch.tensor(
                [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]]				)
      elif model_name == "upernet-swin-base":
            lowerCAmelCase__      :			List[str]   =	torch.tensor(
                [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]]				)
      elif model_name == "upernet-swin-large":
            lowerCAmelCase__      :			List[Any]   =	torch.tensor(
                [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]]				)
      print("""Logits:"""    ,		outputs.logits[0, 0, :3, :3]				)
      assert torch.allclose(outputs.logits[0, 0, :3, :3]    ,		__UpperCAmelCase    ,		atol=1E-4				)
      print("""Looks ok!"""				)

      if pytorch_dump_folder_path is not None:
            print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""				)
            model.save_pretrained(__UpperCAmelCase				)
            print(f"""Saving processor to {pytorch_dump_folder_path}"""				)
            processor.save_pretrained(__UpperCAmelCase				)

      if push_to_hub:
            print(f"""Pushing model and processor for {model_name} to hub"""				)
            model.push_to_hub(f"""openmmlab/{model_name}"""				)
            processor.push_to_hub(f"""openmmlab/{model_name}"""				)


if __name__ == "__main__":
  _A													=      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--model_name""",
      default="""upernet-swin-tiny""",
      type=str,
      choices=[f"""upernet-swin-{size}""" for size in ["""tiny""", """small""", """base""", """large"""]],
      help="""Name of the Swin + UperNet model you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )
  parser.add_argument(
      """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
  )

  _A													=      parser.parse_args()
  convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)











"""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


_A													=      get_tests_dir("""fixtures/test_sentencepiece.model""")


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

_A													=      2_5_6_0_4_7
_A													=      2_5_6_1_4_5


@require_sentencepiece
@require_tokenizers
class _lowerCamelCase    (					a_       ,	unittest.TestCase						):
   _lowerCamelCase    :Any     				=     NllbTokenizer
   _lowerCamelCase    :Dict     				=     NllbTokenizerFast
   _lowerCamelCase    :str     				=     True
   _lowerCamelCase    :Optional[Any]     				=     True
   _lowerCamelCase    :Union[str, Any]     				=     {}






   def 	_lowerCAmelCase   (		self       :		List[str]   )			->						int:

         """simple docstring"""
         super().setUp()

         # We have a SentencePiece fixture for testing
         lowerCAmelCase__      :			Optional[int]   =	NllbTokenizer(UpperCamelCase   ,     keep_accents=UpperCamelCase   )
         tokenizer.save_pretrained(self.tmpdirname   )






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						List[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Dict   =	NllbTokenizer(UpperCamelCase   ,     keep_accents=UpperCamelCase   )

         lowerCAmelCase__      :			List[str]   =	tokenizer.tokenize("""This is a test"""   )
         self.assertListEqual(UpperCamelCase   ,     ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]   )

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(UpperCamelCase   )   ,     [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]   ,     )

         lowerCAmelCase__      :			Dict   =	tokenizer.tokenize("""I was born in 92000, and this is falsé."""   )
         self.assertListEqual(
             UpperCamelCase   ,     [
                 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""",
                 """.""",
             ]   ,     )
         lowerCAmelCase__      :			Optional[Any]   =	tokenizer.convert_tokens_to_ids(UpperCamelCase   )
         self.assertListEqual(
             UpperCamelCase   ,     [
                 value + tokenizer.fairseq_offset
                 for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
             ]   ,     )

         lowerCAmelCase__      :			List[str]   =	tokenizer.convert_ids_to_tokens(UpperCamelCase   )
         self.assertListEqual(
             UpperCamelCase   ,     [
                 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 	_lowerCAmelCase   (		self       :		Any   )			->						Any:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	(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})"""   ):
                     lowerCAmelCase__      :			Union[str, Any]   =	self.rust_tokenizer_class.from_pretrained(UpperCamelCase   ,     **UpperCamelCase   )
                     lowerCAmelCase__      :			str   =	self.tokenizer_class.from_pretrained(UpperCamelCase   ,     **UpperCamelCase   )

                     lowerCAmelCase__      :			int   =	tempfile.mkdtemp()

                     lowerCAmelCase__      :			Tuple   =	tokenizer_r.save_pretrained(UpperCamelCase   )
                     lowerCAmelCase__      :			Optional[int]   =	tokenizer_p.save_pretrained(UpperCamelCase   )

                     # 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   )   )
                     lowerCAmelCase__      :			Dict   =	tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f   )
                     self.assertSequenceEqual(UpperCamelCase   ,     UpperCamelCase   )

                     # Checks everything loads correctly in the same way
                     lowerCAmelCase__      :			int   =	tokenizer_r.from_pretrained(UpperCamelCase   )
                     lowerCAmelCase__      :			Union[str, Any]   =	tokenizer_p.from_pretrained(UpperCamelCase   )

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

                     shutil.rmtree(UpperCamelCase   )

                     # Save tokenizer rust, legacy_format=True
                     lowerCAmelCase__      :			List[str]   =	tempfile.mkdtemp()

                     lowerCAmelCase__      :			Optional[Any]   =	tokenizer_r.save_pretrained(UpperCamelCase   ,     legacy_format=UpperCamelCase   )
                     lowerCAmelCase__      :			List[str]   =	tokenizer_p.save_pretrained(UpperCamelCase   )

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

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

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

                     shutil.rmtree(UpperCamelCase   )

                     # Save tokenizer rust, legacy_format=False
                     lowerCAmelCase__      :			List[Any]   =	tempfile.mkdtemp()

                     lowerCAmelCase__      :			int   =	tokenizer_r.save_pretrained(UpperCamelCase   ,     legacy_format=UpperCamelCase   )
                     lowerCAmelCase__      :			str   =	tokenizer_p.save_pretrained(UpperCamelCase   )

                     # 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
                     lowerCAmelCase__      :			Dict   =	tokenizer_r.from_pretrained(UpperCamelCase   )
                     lowerCAmelCase__      :			Optional[int]   =	tokenizer_p.from_pretrained(UpperCamelCase   )

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

                     shutil.rmtree(UpperCamelCase   )






   @require_torch
   def 	_lowerCAmelCase   (		self       :		Tuple   )			->						List[str]:

         """simple docstring"""
         if not self.test_seqaseq:
               return

         lowerCAmelCase__      :			int   =	self.get_tokenizers()
         for tokenizer in tokenizers:
               with self.subTest(f"""{tokenizer.__class__.__name__}"""   ):
                     # Longer text that will definitely require truncation.
                     lowerCAmelCase__      :			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.""",
                     ]
                     lowerCAmelCase__      :			Optional[int]   =	[
                         """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:
                           lowerCAmelCase__      :			Dict   =	tokenizer.prepare_seqaseq_batch(
                               src_texts=UpperCamelCase   ,     tgt_texts=UpperCamelCase   ,     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
                     lowerCAmelCase__      :			str   =	tokenizer.prepare_seqaseq_batch(
                         UpperCamelCase   ,     tgt_texts=UpperCamelCase   ,     max_length=3   ,     return_tensors="""pt"""   )
                     self.assertEqual(batch.input_ids.shape[1]   ,     3   )
                     self.assertEqual(batch.labels.shape[1]   ,     3   )

                     lowerCAmelCase__      :			int   =	tokenizer.prepare_seqaseq_batch(
                         src_texts=UpperCamelCase   ,     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"""   ,     UpperCamelCase   )






   @unittest.skip("""Unfortunately way too slow to build a BPE with SentencePiece."""   )
   def 	_lowerCAmelCase   (		self       :		List[str]   )			->						Dict:

         """simple docstring"""
         pass






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

         """simple docstring"""
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""   ):
                     lowerCAmelCase__      :			str   =	[AddedToken("""<special>"""   ,     lstrip=UpperCamelCase   )]

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

                     lowerCAmelCase__      :			Dict   =	tokenizer_r.encode("""<special>"""   ,     add_special_tokens=UpperCamelCase   )[0]

                     self.assertTrue(special_token_id in r_output   )

                     if self.test_slow_tokenizer:
                           lowerCAmelCase__      :			List[Any]   =	self.rust_tokenizer_class.from_pretrained(
                               UpperCamelCase   ,     additional_special_tokens=UpperCamelCase   ,     **UpperCamelCase   ,     )
                           lowerCAmelCase__      :			Dict   =	self.tokenizer_class.from_pretrained(
                               UpperCamelCase   ,     additional_special_tokens=UpperCamelCase   ,     **UpperCamelCase   )

                           lowerCAmelCase__      :			Optional[int]   =	tokenizer_p.encode("""Hey this is a <special> token"""   )

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

                           self.assertEqual(UpperCamelCase   ,     UpperCamelCase   )
                           self.assertEqual(UpperCamelCase   ,     UpperCamelCase   )
                           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						):
   _lowerCamelCase    :int     				=     "facebook/nllb-200-distilled-600M"
   _lowerCamelCase    :List[str]     				=     [
       " 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.",
   ]
   _lowerCamelCase    :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.",
   ]
   _lowerCamelCase    :Tuple     				=     [
       256047,
       16297,
       134408,
       8165,
       248066,
       14734,
       950,
       1135,
       105721,
       3573,
       83,
       27352,
       108,
       49486,
       2,
   ]






   @classmethod
   def 	_lowerCAmelCase   (		cls       :		Optional[Any]   )			->						Any:

         """simple docstring"""
         lowerCAmelCase__      :			NllbTokenizer   =	NllbTokenizer.from_pretrained(
             cls.checkpoint_name   ,     src_lang="""eng_Latn"""   ,     tgt_lang="""ron_Latn"""   )
         lowerCAmelCase__      :			Optional[Any]   =	1
         return cls






   def 	_lowerCAmelCase   (		self       :		Any   )			->						int:

         """simple docstring"""
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Arab"""]   ,     25_60_01   )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Latn"""]   ,     25_60_02   )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""fra_Latn"""]   ,     25_60_57   )






   def 	_lowerCAmelCase   (		self       :		List[str]   )			->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	self.tokenizer.batch_encode_plus(self.src_text   ).input_ids[0]
         self.assertListEqual(self.expected_src_tokens   ,     UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						Optional[int]:

         """simple docstring"""
         self.assertIn(UpperCamelCase   ,     self.tokenizer.all_special_ids   )
         # fmt: off
         lowerCAmelCase__      :			str   =	[RO_CODE, 42_54, 9_80_68, 11_29_23, 3_90_72, 39_09, 7_13, 10_27_67, 26, 1_73_14, 3_56_42, 1_46_83, 3_31_18, 20_22, 6_69_87, 2, 25_60_47]
         # fmt: on

         lowerCAmelCase__      :			Any   =	self.tokenizer.decode(UpperCamelCase   ,     skip_special_tokens=UpperCamelCase   )
         lowerCAmelCase__      :			Tuple   =	self.tokenizer.decode(generated_ids[1:]   ,     skip_special_tokens=UpperCamelCase   )
         self.assertEqual(UpperCamelCase   ,     UpperCamelCase   )
         self.assertNotIn(self.tokenizer.eos_token   ,     UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						List[str]:

         """simple docstring"""
         lowerCAmelCase__      :			List[Any]   =	["""this is gunna be a long sentence """ * 20]
         assert isinstance(src_text[0]   ,     UpperCamelCase   )
         lowerCAmelCase__      :			int   =	10
         lowerCAmelCase__      :			Any   =	self.tokenizer(UpperCamelCase   ,     max_length=UpperCamelCase   ,     truncation=UpperCamelCase   ).input_ids[0]
         self.assertEqual(ids[-1]   ,     2   )
         self.assertEqual(ids[0]   ,     UpperCamelCase   )
         self.assertEqual(len(UpperCamelCase   )   ,     UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Any   )			->						Any:

         """simple docstring"""
         self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]   )   ,     [25_62_03, 3]   )






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			List[Any]   =	tempfile.mkdtemp()
         lowerCAmelCase__      :			int   =	self.tokenizer.fairseq_tokens_to_ids
         self.tokenizer.save_pretrained(UpperCamelCase   )
         lowerCAmelCase__      :			Union[str, Any]   =	NllbTokenizer.from_pretrained(UpperCamelCase   )
         self.assertDictEqual(new_tok.fairseq_tokens_to_ids   ,     UpperCamelCase   )






   @require_torch
   def 	_lowerCAmelCase   (		self       :		int   )			->						List[str]:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	self.tokenizer(
             self.src_text   ,     text_target=self.tgt_text   ,     padding=UpperCamelCase   ,     truncation=UpperCamelCase   ,     max_length=len(self.expected_src_tokens   )   ,     return_tensors="""pt"""   ,     )
         lowerCAmelCase__      :			int   =	shift_tokens_right(
             batch["""labels"""]   ,     self.tokenizer.pad_token_id   ,     self.tokenizer.lang_code_to_id["""ron_Latn"""]   )

         self.assertIsInstance(UpperCamelCase   ,     UpperCamelCase   )

         self.assertEqual((2, 15)   ,     batch.input_ids.shape   )
         self.assertEqual((2, 15)   ,     batch.attention_mask.shape   )
         lowerCAmelCase__      :			Dict   =	batch.input_ids.tolist()[0]
         self.assertListEqual(self.expected_src_tokens   ,     UpperCamelCase   )
         self.assertEqual(UpperCamelCase   ,     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 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						int:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	self.tokenizer(self.src_text   ,     padding=UpperCamelCase   ,     truncation=UpperCamelCase   ,     max_length=3   ,     return_tensors="""pt"""   )
         lowerCAmelCase__      :			Any   =	self.tokenizer(
             text_target=self.tgt_text   ,     padding=UpperCamelCase   ,     truncation=UpperCamelCase   ,     max_length=10   ,     return_tensors="""pt"""   )
         lowerCAmelCase__      :			str   =	targets["""input_ids"""]
         lowerCAmelCase__      :			Any   =	shift_tokens_right(
             UpperCamelCase   ,     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 	_lowerCAmelCase   (		self       :		Dict   )			->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Any   =	self.tokenizer._build_translation_inputs(
             """A test"""   ,     return_tensors="""pt"""   ,     src_lang="""eng_Latn"""   ,     tgt_lang="""fra_Latn"""   )

         self.assertEqual(
             nested_simplify(UpperCamelCase   )   ,     {
                 # A, test, EOS, en_XX
                 """input_ids""": [[25_60_47, 70, 73_56, 2]],
                 """attention_mask""": [[1, 1, 1, 1]],
                 # ar_AR
                 """forced_bos_token_id""": 25_60_57,
             }   ,     )






   @require_torch
   def 	_lowerCAmelCase   (		self       :		Dict   )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	True
         lowerCAmelCase__      :			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   ,     [1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2, 25_60_47]   )

         lowerCAmelCase__      :			List[str]   =	False
         lowerCAmelCase__      :			Union[str, 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   ,     [25_60_47, 1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2]   )





