
'''simple docstring'''

import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


snake_case									=    logging.get_logger(__name__)
snake_case									=    {"""vocab_file""": """spiece.model"""}

snake_case									=    {
    """vocab_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
    }
}

snake_case									=    {
    """albert-base-v1""": 5_12,
    """albert-large-v1""": 5_12,
    """albert-xlarge-v1""": 5_12,
    """albert-xxlarge-v1""": 5_12,
    """albert-base-v2""": 5_12,
    """albert-large-v2""": 5_12,
    """albert-xlarge-v2""": 5_12,
    """albert-xxlarge-v2""": 5_12,
}

snake_case									=    """▁"""




class    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				List[str]	=			VOCAB_FILES_NAMES
 A_				:				int	=			PRETRAINED_VOCAB_FILES_MAP
 A_				:				int	=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


 def __init__(    self  : List[Any]			,							a__  : List[Any]			,							a__  : List[Any]=True			,							a__  : Union[str, Any]=True			,							a__  : Dict=False			,							a__  : Union[str, Any]="[CLS]"			,							a__  : Any="[SEP]"			,							a__  : List[str]="<unk>"			,							a__  : Dict="[SEP]"			,							a__  : Optional[Any]="<pad>"			,							a__  : int="[CLS]"			,							a__  : Optional[Any]="[MASK]"			,							a__  : Optional[Dict[str, Any]] = None			,							**a__  : Dict			,							):
  '''simple docstring'''

  lowerCAmelCase__     :   Tuple     		=	(
      AddedToken(a__			,							lstrip=a__			,							rstrip=a__			,							normalized=a__    )
      if isinstance(a__			,							a__    )
      else mask_token
  )

  lowerCAmelCase__     :   List[Any]     		=	{} if sp_model_kwargs is None else sp_model_kwargs

  super().__init__(
      do_lower_case=a__			,							remove_space=a__			,							keep_accents=a__			,							bos_token=a__			,							eos_token=a__			,							unk_token=a__			,							sep_token=a__			,							pad_token=a__			,							cls_token=a__			,							mask_token=a__			,							sp_model_kwargs=self.sp_model_kwargs			,							**a__			,							)

  lowerCAmelCase__     :   List[str]     		=	do_lower_case
  lowerCAmelCase__     :   Tuple     		=	remove_space
  lowerCAmelCase__     :   Any     		=	keep_accents
  lowerCAmelCase__     :   Tuple     		=	vocab_file

  lowerCAmelCase__     :   List[Any]     		=	spm.SentencePieceProcessor(**self.sp_model_kwargs    )
  self.sp_model.Load(a__    )


 @property
 def    _A	(    self  : List[Any]    ):
  '''simple docstring'''

  return len(self.sp_model    )


 def    _A	(    self  : List[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Tuple     		=	{self.convert_ids_to_tokens(a__    ): i for i in range(self.vocab_size    )}
  vocab.update(self.added_tokens_encoder    )
  return vocab


 def __getstate__(    self  : List[str]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[Any]     		=	self.__dict__.copy()
  lowerCAmelCase__     :   Union[str, Any]     		=	None
  return state


 def __setstate__(    self  : str			,							a__  : Tuple    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	d

  # for backward compatibility
  if not hasattr(self			,							"sp_model_kwargs"    ):
   lowerCAmelCase__     :   Tuple     		=	{}

  lowerCAmelCase__     :   Optional[Any]     		=	spm.SentencePieceProcessor(**self.sp_model_kwargs    )
  self.sp_model.Load(self.vocab_file    )


 def    _A	(    self  : List[Any]			,							a__  : List[Any]    ):
  '''simple docstring'''

  if self.remove_space:
   lowerCAmelCase__     :   List[Any]     		=	" ".join(inputs.strip().split()    )
  else:
   lowerCAmelCase__     :   int     		=	inputs
  lowerCAmelCase__     :   Union[str, Any]     		=	outputs.replace("``"			,							"\""    ).replace("''"			,							"\""    )

  if not self.keep_accents:
   lowerCAmelCase__     :   Any     		=	unicodedata.normalize("NFKD"			,							a__    )
   lowerCAmelCase__     :   Union[str, Any]     		=	"".join([c for c in outputs if not unicodedata.combining(a__    )]    )
  if self.do_lower_case:
   lowerCAmelCase__     :   Union[str, Any]     		=	outputs.lower()

  return outputs


 def    _A	(    self  : Optional[Any]			,							a__  : str    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[Any]     		=	self.preprocess_text(a__    )
  lowerCAmelCase__     :   Optional[Any]     		=	self.sp_model.encode(a__			,							out_type=a__    )
  lowerCAmelCase__     :   List[str]     		=	[]
  for piece in pieces:
   if len(a__    ) > 1 and piece[-1] == str(","    ) and piece[-2].isdigit():
    lowerCAmelCase__     :   List[Any]     		=	self.sp_model.EncodeAsPieces(piece[:-1].replace(a__			,							""    )    )
    if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
     if len(cur_pieces[0]    ) == 1:
      lowerCAmelCase__     :   Dict     		=	cur_pieces[1:]
     else:
      lowerCAmelCase__     :   Dict     		=	cur_pieces[0][1:]
    cur_pieces.append(piece[-1]    )
    new_pieces.extend(a__    )
   else:
    new_pieces.append(a__    )

  return new_pieces


 def    _A	(    self  : Union[str, Any]			,							a__  : Dict    ):
  '''simple docstring'''

  return self.sp_model.PieceToId(a__    )


 def    _A	(    self  : Optional[Any]			,							a__  : Dict    ):
  '''simple docstring'''

  return self.sp_model.IdToPiece(a__    )


 def    _A	(    self  : int			,							a__  : str    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Dict     		=	[]
  lowerCAmelCase__     :   Dict     		=	""
  lowerCAmelCase__     :   Optional[int]     		=	False
  for token in tokens:
   # make sure that special tokens are not decoded using sentencepiece model
   if token in self.all_special_tokens:
    if not prev_is_special:
     out_string += " "
    out_string += self.sp_model.decode(a__    ) + token
    lowerCAmelCase__     :   Optional[int]     		=	True
    lowerCAmelCase__     :   Any     		=	[]
   else:
    current_sub_tokens.append(a__    )
    lowerCAmelCase__     :   Any     		=	False
  out_string += self.sp_model.decode(a__    )
  return out_string.strip()


 def    _A	(    self  : Dict			,							a__  : List[int]			,							a__  : Optional[List[int]] = None    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Tuple     		=	[self.sep_token_id]
  lowerCAmelCase__     :   str     		=	[self.cls_token_id]
  if token_ids_a is None:
   return cls + token_ids_a + sep
  return cls + token_ids_a + sep + token_ids_a + sep


 def    _A	(    self  : Tuple			,							a__  : List[int]			,							a__  : Optional[List[int]] = None			,							a__  : bool = False    ):
  '''simple docstring'''

  if already_has_special_tokens:
   return super().get_special_tokens_mask(
       token_ids_a=a__			,							token_ids_a=a__			,							already_has_special_tokens=a__    )

  if token_ids_a is not None:
   return [1] + ([0] * len(a__    )) + [1] + ([0] * len(a__    )) + [1]
  return [1] + ([0] * len(a__    )) + [1]


 def    _A	(    self  : Dict			,							a__  : List[int]			,							a__  : Optional[List[int]] = None    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Any     		=	[self.sep_token_id]
  lowerCAmelCase__     :   Optional[int]     		=	[self.cls_token_id]

  if token_ids_a is None:
   return len(cls + token_ids_a + sep    ) * [0]
  return len(cls + token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]


 def    _A	(    self  : Dict			,							a__  : str			,							a__  : Optional[str] = None    ):
  '''simple docstring'''

  if not os.path.isdir(a__    ):
   logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''    )
   return
  lowerCAmelCase__     :   Dict     		=	os.path.join(
      a__			,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )

  if os.path.abspath(self.vocab_file    ) != os.path.abspath(a__    ) and os.path.isfile(self.vocab_file    ):
   copyfile(self.vocab_file			,							a__    )
  elif not os.path.isfile(self.vocab_file    ):
   with open(a__			,							"wb"    ) as fi:
    lowerCAmelCase__     :   List[Any]     		=	self.sp_model.serialized_model_proto()
    fi.write(a__    )

  return (out_vocab_file,)







'''simple docstring'''

import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image
else:




     class    lowerCAmelCase       :


      @staticmethod
      def    _A	(    *a__  : str			,							**a__  : List[str]    ):
       '''simple docstring'''

       pass


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   List[str]     		=	hashlib.mda(image.tobytes()			)
 return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class    lowerCAmelCase       (     unittest.TestCase  ):
 A_				:				List[Any]	=			MODEL_FOR_DEPTH_ESTIMATION_MAPPING


 def    _A	(    self  : List[Any]			,							a__  : int			,							a__  : Dict			,							a__  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[str]     		=	DepthEstimationPipeline(model=a__			,							image_processor=a__    )
  return depth_estimator, [
      "./tests/fixtures/tests_samples/COCO/000000039769.png",
      "./tests/fixtures/tests_samples/COCO/000000039769.png",
  ]


 def    _A	(    self  : int			,							a__  : Optional[int]			,							a__  : List[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Any     		=	depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
  self.assertEqual({"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )}			,							a__    )
  import datasets

  lowerCAmelCase__     :   List[str]     		=	datasets.load_dataset("hf-internal-testing/fixtures_image_utils"			,							"image"			,							split="test"    )
  lowerCAmelCase__     :   List[str]     		=	depth_estimator(
      [
          Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    ),
          "http://images.cocodataset.org/val2017/000000039769.jpg",
          # RGBA
          dataset[0]["file"],
          # LA
          dataset[1]["file"],
          # L
          dataset[2]["file"],
      ]    )
  self.assertEqual(
      [
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
      ]			,							a__			,							)


 @require_tf
 @unittest.skip("Depth estimation is not implemented in TF"    )
 def    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  pass


 @slow
 @require_torch
 def    _A	(    self  : Any    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	"Intel/dpt-large"
  lowerCAmelCase__     :   Dict     		=	pipeline("depth-estimation"			,							model=a__    )
  lowerCAmelCase__     :   Any     		=	depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"    )
  lowerCAmelCase__     :   Any     		=	hashimage(outputs["depth"]    )

  # This seems flaky.
  # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
  self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()    )			,							29.304    )
  self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()    )			,							2.662    )


 @require_torch
 def    _A	(    self  : Any    ):
  '''simple docstring'''

  self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"    )





