



import argparse
import collections
import json
from pathlib import Path

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

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase		:				Dict      		=logging.get_logger(__name__)

def      lowerCAmelCase_					(  _lowercase    :       List[Any])							->	Any:







			"""simple docstring"""
			print("""Loading config file...""")

			def flatten_yaml_as_dict(_lowercase    :       Union[str, Any]						,       _lowercase    :       List[str]=""						,       _lowercase    :       int="."):
						a__       :						Optional[Any]							=  []
						for k, v in d.items():
									a__       :						Optional[Any]							=  parent_key + sep + k if parent_key else k
									if isinstance(_lowercase						,       collections.abc.MutableMapping):
												items.extend(flatten_yaml_as_dict(_lowercase						,       _lowercase						,       sep=_lowercase).items())
									else:
												items.append((new_key, v))
						return dict(_lowercase)

			a__       :						str							=  argparse.Namespace()
			with open(_lowercase						,       """r""") as yaml_file:
						try:
									a__       :						List[str]							=  yaml.load(_lowercase						,       Loader=yaml.FullLoader)

									a__       :						Dict							=  flatten_yaml_as_dict(_lowercase)
									for k, v in flat_cfg.items():
												setattr(_lowercase						,       _lowercase						,       _lowercase)
						except yaml.YAMLError as exc:
									logger.error("""Error while loading config file: {}. Error message: {}""".format(_lowercase						,       str(_lowercase)))
			return config

def      lowerCAmelCase_					(  _lowercase    :       Tuple						,       _lowercase    :       str)							->	Tuple:







			"""simple docstring"""
			a__       :						Dict							=  MobileViTVaConfig()

			a__       :						Any							=  False

			# dataset
			if task_name.startswith("""imagenet1k_"""):
						a__       :						int							=  1000
						if int(task_name.strip().split("""_""")[-1]) == 384:
									a__       :						Union[str, Any]							=  384
						else:
									a__       :						List[str]							=  256
						a__       :						str							=  """imagenet-1k-id2label.json"""
			elif task_name.startswith("""imagenet21k_to_1k_"""):
						a__       :						int							=  2_1000
						if int(task_name.strip().split("""_""")[-1]) == 384:
									a__       :						Any							=  384
						else:
									a__       :						Tuple							=  256
						a__       :						Union[str, Any]							=  """imagenet-22k-id2label.json"""
			elif task_name.startswith("""ade20k_"""):
						a__       :						List[str]							=  151
						a__       :						Optional[Any]							=  512
						a__       :						Union[str, Any]							=  """ade20k-id2label.json"""
						a__       :						Optional[Any]							=  True
			elif task_name.startswith("""voc_"""):
						a__       :						Optional[int]							=  21
						a__       :						Optional[Any]							=  512
						a__       :						List[Any]							=  """pascal-voc-id2label.json"""
						a__       :						Union[str, Any]							=  True

			# orig_config
			a__       :						Optional[int]							=  load_orig_config_file(_lowercase)
			assert getattr(_lowercase						,       """model.classification.name"""						,       -1) == "mobilevit_v2", "Invalid model"
			a__       :						str							=  getattr(_lowercase						,       """model.classification.mitv2.width_multiplier"""						,       1.0)
			assert (
			    getattr(_lowercase						,       """model.classification.mitv2.attn_norm_layer"""						,       -1) == "layer_norm_2d"
			), "Norm layers other than layer_norm_2d is not supported"
			a__       :						Optional[int]							=  getattr(_lowercase						,       """model.classification.activation.name"""						,       """swish""")
			# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

			if is_segmentation_model:
						a__       :						List[Any]							=  getattr(_lowercase						,       """model.segmentation.output_stride"""						,       16)
						if "_deeplabv3" in task_name:
									a__       :						Optional[int]							=  getattr(_lowercase						,       """model.segmentation.deeplabv3.aspp_rates"""						,       [12, 24, 36])
									a__       :						Tuple							=  getattr(_lowercase						,       """model.segmentation.deeplabv3.aspp_out_channels"""						,       512)
									a__       :						Union[str, Any]							=  getattr(_lowercase						,       """model.segmentation.deeplabv3.aspp_dropout"""						,       0.1)

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

			return config

def      lowerCAmelCase_					(  _lowercase    :       Dict						,       _lowercase    :       str						,       _lowercase    :       List[Any])							->	Any:







			"""simple docstring"""
			a__       :						Any							=  dct.pop(_lowercase)
			a__       :						int							=  val

def      lowerCAmelCase_					(  _lowercase    :       Any						,       _lowercase    :       Dict=False)							->	Dict:







			"""simple docstring"""
			if base_model:
						a__       :						List[str]							=  """"""
			else:
						a__       :						int							=  """mobilevitv2."""

			a__       :						Optional[Any]							=  []
			for k in state_dict.keys():
						if k[:8] == "encoder.":
									a__       :						Any							=  k[8:]
						else:
									a__       :						List[str]							=  k

						if ".block." in k:
									a__       :						List[Any]							=  k_new.replace(""".block."""						,       """.""")
						if ".conv." in k:
									a__       :						Optional[Any]							=  k_new.replace(""".conv."""						,       """.convolution.""")
						if ".norm." in k:
									a__       :						Union[str, Any]							=  k_new.replace(""".norm."""						,       """.normalization.""")

						if "conv_1." in k:
									a__       :						List[Any]							=  k_new.replace("""conv_1."""						,       F'''{model_prefix}conv_stem.''')
						for i in [1, 2]:
									if F'''layer_{i}.''' in k:
												a__       :						List[Any]							=  k_new.replace(F'''layer_{i}.'''						,       F'''{model_prefix}encoder.layer.{i-1}.layer.''')
						if ".exp_1x1." in k:
									a__       :						int							=  k_new.replace(""".exp_1x1."""						,       """.expand_1x1.""")
						if ".red_1x1." in k:
									a__       :						str							=  k_new.replace(""".red_1x1."""						,       """.reduce_1x1.""")

						for i in [3, 4, 5]:
									if F'''layer_{i}.0.''' in k:
												a__       :						Any							=  k_new.replace(F'''layer_{i}.0.'''						,       F'''{model_prefix}encoder.layer.{i-1}.downsampling_layer.''')
									if F'''layer_{i}.1.local_rep.0.''' in k:
												a__       :						Any							=  k_new.replace(F'''layer_{i}.1.local_rep.0.'''						,       F'''{model_prefix}encoder.layer.{i-1}.conv_kxk.''')
									if F'''layer_{i}.1.local_rep.1.''' in k:
												a__       :						str							=  k_new.replace(F'''layer_{i}.1.local_rep.1.'''						,       F'''{model_prefix}encoder.layer.{i-1}.conv_1x1.''')

						for i in [3, 4, 5]:
									if i == 3:
												a__       :						Dict							=  [0, 1]
									elif i == 4:
												a__       :						Tuple							=  [0, 1, 2, 3]
									elif i == 5:
												a__       :						Optional[int]							=  [0, 1, 2]

									for j in j_in:
												if F'''layer_{i}.1.global_rep.{j}.''' in k:
															a__       :						Tuple							=  k_new.replace(
															    F'''layer_{i}.1.global_rep.{j}.'''						,       F'''{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.''')
									if F'''layer_{i}.1.global_rep.{j+1}.''' in k:
												a__       :						Dict							=  k_new.replace(
												    F'''layer_{i}.1.global_rep.{j+1}.'''						,       F'''{model_prefix}encoder.layer.{i-1}.layernorm.''')

									if F'''layer_{i}.1.conv_proj.''' in k:
												a__       :						str							=  k_new.replace(F'''layer_{i}.1.conv_proj.'''						,       F'''{model_prefix}encoder.layer.{i-1}.conv_projection.''')

						if "pre_norm_attn.0." in k:
									a__       :						Optional[Any]							=  k_new.replace("""pre_norm_attn.0."""						,       """layernorm_before.""")
						if "pre_norm_attn.1." in k:
									a__       :						List[Any]							=  k_new.replace("""pre_norm_attn.1."""						,       """attention.""")
						if "pre_norm_ffn.0." in k:
									a__       :						int							=  k_new.replace("""pre_norm_ffn.0."""						,       """layernorm_after.""")
						if "pre_norm_ffn.1." in k:
									a__       :						Tuple							=  k_new.replace("""pre_norm_ffn.1."""						,       """ffn.conv1.""")
						if "pre_norm_ffn.3." in k:
									a__       :						str							=  k_new.replace("""pre_norm_ffn.3."""						,       """ffn.conv2.""")

						if "classifier.1." in k:
									a__       :						Optional[Any]							=  k_new.replace("""classifier.1."""						,       """classifier.""")

						if "seg_head." in k:
									a__       :						Dict							=  k_new.replace("""seg_head."""						,       """segmentation_head.""")
						if ".aspp_layer." in k:
									a__       :						List[Any]							=  k_new.replace(""".aspp_layer."""						,       """.""")
						if ".aspp_pool." in k:
									a__       :						Optional[Any]							=  k_new.replace(""".aspp_pool."""						,       """.""")

						rename_keys.append((k, k_new))
			return rename_keys

def      lowerCAmelCase_					(  _lowercase    :       Union[str, Any])							->	Optional[int]:







			"""simple docstring"""
			a__       :						List[Any]							=  []
			for k in state_dict.keys():
						if k.startswith("""seg_head.aux_head."""):
									keys_to_ignore.append(_lowercase)
			for k in keys_to_ignore:
						state_dict.pop(_lowercase						,       _lowercase)

def      lowerCAmelCase_					(  )							->	Any:







			"""simple docstring"""
			a__       :						Tuple							=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
			# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
			a__       :						Optional[Any]							=  Image.open(requests.get(_lowercase						,       stream=_lowercase).raw)
			return im

@torch.no_grad()
def      lowerCAmelCase_					(  _lowercase    :       Any						,       _lowercase    :       Optional[int]						,       _lowercase    :       Optional[Any]						,       _lowercase    :       Tuple)							->	Any:







			"""simple docstring"""
			a__       :						List[Any]							=  get_mobilevitva_config(_lowercase						,       _lowercase)

			# load original state_dict
			a__       :						str							=  torch.load(_lowercase						,       map_location="""cpu""")

			# load huggingface model
			if task_name.startswith("""ade20k_""") or task_name.startswith("""voc_"""):
						a__       :						Dict							=  MobileViTVaForSemanticSegmentation(_lowercase).eval()
						a__       :						str							=  False
			else:
						a__       :						str							=  MobileViTVaForImageClassification(_lowercase).eval()
						a__       :						str							=  False

			# remove and rename some keys of load the original model
			a__       :						Union[str, Any]							=  checkpoint
			remove_unused_keys(_lowercase)
			a__       :						str							=  create_rename_keys(_lowercase						,       base_model=_lowercase)
			for rename_key_src, rename_key_dest in rename_keys:
						rename_key(_lowercase						,       _lowercase						,       _lowercase)

			# load modified state_dict
			model.load_state_dict(_lowercase)

			# Check outputs on an image, prepared by MobileViTImageProcessor
			a__       :						Union[str, Any]							=  MobileViTImageProcessor(crop_size=config.image_size						,       size=config.image_size + 32)
			a__       :						str							=  image_processor(images=prepare_img()						,       return_tensors="""pt""")
			a__       :						str							=  model(**_lowercase)

			# verify classification model
			if task_name.startswith("""imagenet"""):
						a__       :						List[str]							=  outputs.logits
						a__       :						List[Any]							=  logits.argmax(-1).item()
						print("""Predicted class:"""						,       model.config.idalabel[predicted_class_idx])
						if task_name.startswith("""imagenet1k_256""") and config.width_multiplier == 1.0:
									# expected_logits for base variant
									a__       :						Optional[int]							=  torch.tensor([-1.6_3_3_6e0_0, -7.3_2_0_4e-0_2, -5.1_8_8_3e-0_1])
									assert torch.allclose(logits[0, :3]						,       _lowercase						,       atol=1e-4)

			Path(_lowercase).mkdir(exist_ok=_lowercase)
			print(F'''Saving model {task_name} to {pytorch_dump_folder_path}''')
			model.save_pretrained(_lowercase)
			print(F'''Saving image processor to {pytorch_dump_folder_path}''')
			image_processor.save_pretrained(_lowercase)


if __name__ == "__main__":
		_lowercase		:				List[Any]      		=argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--task",
		    default="imagenet1k_256",
		    type=str,
		    help=(
		        "Name of the task for which the MobileViTV2 model you'd like to convert is trained on . "
		        "\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            "
		    ),
		    choices=[
		        "imagenet1k_256",
		        "imagenet1k_384",
		        "imagenet21k_to_1k_256",
		        "imagenet21k_to_1k_384",
		        "ade20k_deeplabv3",
		        "voc_deeplabv3",
		    ],
		)

		parser.add_argument(
		    "--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
		)
		parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
		parser.add_argument(
		    "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
		)

		_lowercase		:				Optional[int]      		=parser.parse_args()
		convert_mobilevitva_checkpoint(
		    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
		)









import os
import unittest

from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin

class        snake_case__    (A__ ,     unittest.TestCase		):







						"""simple docstring"""




						__lowerCAmelCase					:Union[str, Any]      =					ProphetNetTokenizer
						__lowerCAmelCase					:Any      =					False

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Union[str, Any]:
									"""simple docstring"""




									super().setUp()

									a__       :						Optional[Any]							=  [
									    """[UNK]""",
									    """[CLS]""",
									    """[SEP]""",
									    """[PAD]""",
									    """[MASK]""",
									    """want""",
									    """##want""",
									    """##ed""",
									    """wa""",
									    """un""",
									    """runn""",
									    """##ing""",
									    """,""",
									    """low""",
									    """lowest""",
									]
									a__       :						List[str]							=  os.path.join(self.tmpdirname ,       VOCAB_FILES_NAMES["""vocab_file"""]  )
									with open(self.vocab_file ,       """w""" ,       encoding="""utf-8"""  ) as vocab_writer:
												vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]  )  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				str:
									"""simple docstring"""




									a__       :						Any							=  """UNwant\u00E9d,running"""
									a__       :						Dict							=  """unwanted, running"""
									return input_text, output_text

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									a__       :						Tuple							=  self.tokenizer_class(self.vocab_file  )

									a__       :						int							=  tokenizer.tokenize("""UNwant\u00E9d,running"""  )
									self.assertListEqual(__lowercase ,       ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]  )
									self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase  ) ,       [9, 6, 7, 1_2, 1_0, 1_1]  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Union[str, Any]:
									"""simple docstring"""




									a__       :						str							=  BasicTokenizer()

									self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz"""  ) ,       ["""ah""", """\u535A""", """\u63A8""", """zz"""]  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				int:
									"""simple docstring"""




									a__       :						int							=  BasicTokenizer(do_lower_case=__lowercase  )

									self.assertListEqual(
									    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """  ) ,       ["""hello""", """!""", """how""", """are""", """you""", """?"""]  )
									self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""  ) ,       ["""hello"""]  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[Any]:
									"""simple docstring"""




									a__       :						str							=  BasicTokenizer(do_lower_case=__lowercase ,       strip_accents=__lowercase  )

									self.assertListEqual(
									    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """  ) ,       ["""hällo""", """!""", """how""", """are""", """you""", """?"""]  )
									self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""  ) ,       ["""h\u00E9llo"""]  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									a__       :						List[str]							=  BasicTokenizer(do_lower_case=__lowercase ,       strip_accents=__lowercase  )

									self.assertListEqual(
									    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """  ) ,       ["""hallo""", """!""", """how""", """are""", """you""", """?"""]  )
									self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""  ) ,       ["""hello"""]  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[Any]:
									"""simple docstring"""




									a__       :						Optional[Any]							=  BasicTokenizer(do_lower_case=__lowercase  )

									self.assertListEqual(
									    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """  ) ,       ["""hallo""", """!""", """how""", """are""", """you""", """?"""]  )
									self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""  ) ,       ["""hello"""]  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




									a__       :						List[str]							=  BasicTokenizer(do_lower_case=__lowercase  )

									self.assertListEqual(
									    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """  ) ,       ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[str]:
									"""simple docstring"""




									a__       :						str							=  BasicTokenizer(do_lower_case=__lowercase ,       strip_accents=__lowercase  )

									self.assertListEqual(
									    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """  ) ,       ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""]  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									a__       :						List[str]							=  BasicTokenizer(do_lower_case=__lowercase ,       strip_accents=__lowercase  )

									self.assertListEqual(
									    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """  ) ,       ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""]  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[Any]:
									"""simple docstring"""




									a__       :						Union[str, Any]							=  BasicTokenizer(do_lower_case=__lowercase ,       never_split=["""[UNK]"""]  )

									self.assertListEqual(
									    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]"""  ) ,       ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""]  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									a__       :						Optional[Any]							=  ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

									a__       :						Dict							=  {}
									for i, token in enumerate(__lowercase  ):
												a__       :						Optional[Any]							=  i
									a__       :						str							=  WordpieceTokenizer(vocab=__lowercase ,       unk_token="""[UNK]"""  )

									self.assertListEqual(tokenizer.tokenize(""""""  ) ,       []  )

									self.assertListEqual(tokenizer.tokenize("""unwanted running"""  ) ,       ["""un""", """##want""", """##ed""", """runn""", """##ing"""]  )

									self.assertListEqual(tokenizer.tokenize("""unwantedX running"""  ) ,       ["""[UNK]""", """runn""", """##ing"""]  )

						@require_torch
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[Any]:
									"""simple docstring"""




									a__       :						List[Any]							=  self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased"""  )

									a__       :						List[Any]							=  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
									a__       :						Optional[Any]							=  [1_0_3_7, 2_1_4_6, 2_0_4_2_3, 2_0_0_5, 7_6_8_0, 7_8_4_9, 3_9_8_9, 1_0_1_2, 1_0_2]
									a__       :						List[Any]							=  tokenizer(__lowercase ,       padding=__lowercase ,       return_tensors="""pt"""  )
									self.assertIsInstance(__lowercase ,       __lowercase  )
									a__       :						Optional[int]							=  list(batch.input_ids.numpy()[0]  )
									self.assertListEqual(__lowercase ,       __lowercase  )

									self.assertEqual((2, 9) ,       batch.input_ids.shape  )
									self.assertEqual((2, 9) ,       batch.attention_mask.shape  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[str]:
									"""simple docstring"""




									self.assertTrue(_is_whitespace(""" """  )  )
									self.assertTrue(_is_whitespace("""\t"""  )  )
									self.assertTrue(_is_whitespace("""\r"""  )  )
									self.assertTrue(_is_whitespace("""\n"""  )  )
									self.assertTrue(_is_whitespace("""\u00A0"""  )  )

									self.assertFalse(_is_whitespace("""A"""  )  )
									self.assertFalse(_is_whitespace("""-"""  )  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									self.assertTrue(_is_control("""\u0005"""  )  )

									self.assertFalse(_is_control("""A"""  )  )
									self.assertFalse(_is_control(""" """  )  )
									self.assertFalse(_is_control("""\t"""  )  )
									self.assertFalse(_is_control("""\r"""  )  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									self.assertTrue(_is_punctuation("""-"""  )  )
									self.assertTrue(_is_punctuation("""$"""  )  )
									self.assertTrue(_is_punctuation("""`"""  )  )
									self.assertTrue(_is_punctuation("""."""  )  )

									self.assertFalse(_is_punctuation("""A"""  )  )
									self.assertFalse(_is_punctuation(""" """  )  )

						@slow
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




									a__       :						Optional[Any]							=  self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased"""  )

									a__       :						Dict							=  tokenizer.encode("""sequence builders""" ,       add_special_tokens=__lowercase  )
									a__       :						str							=  tokenizer.encode("""multi-sequence build""" ,       add_special_tokens=__lowercase  )

									a__       :						Optional[Any]							=  tokenizer.build_inputs_with_special_tokens(__lowercase  )
									a__       :						Optional[int]							=  tokenizer.build_inputs_with_special_tokens(__lowercase ,       __lowercase  )

									assert encoded_sentence == text + [1_0_2]
									assert encoded_pair == text + [1_0_2] + text_a + [1_0_2]




