





import os
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_           =      """▁"""

snake_case_           =      {"""vocab_file""": """sentencepiece.bpe.model"""}

snake_case_           =      {
    """vocab_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

snake_case_           =      {
    """xlm-roberta-base""": 512,
    """xlm-roberta-large""": 512,
    """xlm-roberta-large-finetuned-conll02-dutch""": 512,
    """xlm-roberta-large-finetuned-conll02-spanish""": 512,
    """xlm-roberta-large-finetuned-conll03-english""": 512,
    """xlm-roberta-large-finetuned-conll03-german""": 512,
}

class 		SCREAMING_SNAKE_CASE__    (	lowerCAmelCase_  ):
						A_						:    List[Any]							=   VOCAB_FILES_NAMES
						A_						:    str							=   PRETRAINED_VOCAB_FILES_MAP
						A_						:    Any							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						A_						:    Optional[Any]							=   ["""input_ids""", """attention_mask"""]


						def __init__(self	:				Any    ,      a__	:				Optional[int]    ,      a__	:				List[Any]="<s>"    ,      a__	:				List[str]="</s>"    ,      a__	:				List[Any]="</s>"    ,      a__	:				List[str]="<s>"    ,      a__	:				List[Any]="<unk>"    ,      a__	:				Tuple="<pad>"    ,      a__	:				List[str]="<mask>"    ,      a__	:				List[Any] = None    ,      **a__	:				str    ,      ):





									"""simple docstring"""



									__snake_case								=       AddedToken(__SCREAMING_SNAKE_CASE    ,      lstrip=__SCREAMING_SNAKE_CASE    ,      rstrip=__SCREAMING_SNAKE_CASE				) if isinstance(__SCREAMING_SNAKE_CASE    ,      __SCREAMING_SNAKE_CASE				) else mask_token

									__snake_case								=       {} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    bos_token=__SCREAMING_SNAKE_CASE    ,      eos_token=__SCREAMING_SNAKE_CASE    ,      unk_token=__SCREAMING_SNAKE_CASE    ,      sep_token=__SCREAMING_SNAKE_CASE    ,      cls_token=__SCREAMING_SNAKE_CASE    ,      pad_token=__SCREAMING_SNAKE_CASE    ,      mask_token=__SCREAMING_SNAKE_CASE    ,      sp_model_kwargs=self.sp_model_kwargs    ,      **__SCREAMING_SNAKE_CASE    ,      )

									__snake_case								=       spm.SentencePieceProcessor(**self.sp_model_kwargs				)
									self.sp_model.Load(str(__SCREAMING_SNAKE_CASE				)				)
									__snake_case								=       vocab_file

									# Original fairseq vocab and spm vocab must be "aligned":
									# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
									# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
									# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
									# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

									# Mimic fairseq token-to-id alignment for the first 4 token
									__snake_case								=       {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

									# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
									__snake_case								=       1

									__snake_case								=       len(self.sp_model				) + self.fairseq_offset
									__snake_case								=       {v: k for k, v in self.fairseq_tokens_to_ids.items()}


						def __getstate__(self	:				int				):





									"""simple docstring"""



									__snake_case								=       self.__dict__.copy()
									__snake_case								=       None
									__snake_case								=       self.sp_model.serialized_model_proto()
									return state


						def __setstate__(self	:				Optional[Any]    ,      a__	:				Any				):





									"""simple docstring"""



									__snake_case								=       d

									# for backward compatibility
									if not hasattr(self    ,      '''sp_model_kwargs'''				):
												__snake_case								=       {}

									__snake_case								=       spm.SentencePieceProcessor(**self.sp_model_kwargs				)
									self.sp_model.LoadFromSerializedProto(self.sp_model_proto				)


						def        a			(self	:				Tuple    ,      a__	:				Dict    ,      a__	:				Dict = None				):





									"""simple docstring"""



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


						def        a			(self	:				List[str]    ,      a__	:				Optional[Any]    ,      a__	:				Tuple = None    ,      a__	:				str = False				):





									"""simple docstring"""



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

									if token_ids_a is None:
												return [1] + ([0] * len(__SCREAMING_SNAKE_CASE				)) + [1]
									return [1] + ([0] * len(__SCREAMING_SNAKE_CASE				)) + [1, 1] + ([0] * len(__SCREAMING_SNAKE_CASE				)) + [1]


						def        a			(self	:				List[Any]    ,      a__	:				Any    ,      a__	:				Union[str, Any] = None				):





									"""simple docstring"""



									__snake_case								=       [self.sep_token_id]
									__snake_case								=       [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 + sep + token_ids_a + sep				) * [0]


						@property
						def        a			(self	:				List[str]				):





									"""simple docstring"""



									return len(self.sp_model				) + self.fairseq_offset + 1  # Add the <mask> token


						def        a			(self	:				List[str]				):





									"""simple docstring"""



									__snake_case								=       {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE				): i for i in range(self.vocab_size				)}
									vocab.update(self.added_tokens_encoder				)
									return vocab


						def        a			(self	:				int    ,      a__	:				List[Any]				):





									"""simple docstring"""



									return self.sp_model.encode(__SCREAMING_SNAKE_CASE    ,      out_type=__SCREAMING_SNAKE_CASE				)


						def        a			(self	:				Optional[int]    ,      a__	:				Optional[Any]				):





									"""simple docstring"""



									if token in self.fairseq_tokens_to_ids:
												return self.fairseq_tokens_to_ids[token]
									__snake_case								=       self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE				)

									# Need to return unknown token if the SP model returned 0
									return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


						def        a			(self	:				Optional[int]    ,      a__	:				Any				):





									"""simple docstring"""



									if index in self.fairseq_ids_to_tokens:
												return self.fairseq_ids_to_tokens[index]
									return self.sp_model.IdToPiece(index - self.fairseq_offset				)


						def        a			(self	:				str    ,      a__	:				List[Any]				):





									"""simple docstring"""



									__snake_case								=       ''''''.join(__SCREAMING_SNAKE_CASE				).replace(__SCREAMING_SNAKE_CASE    ,      ''' '''				).strip()
									return out_string


						def        a			(self	:				Optional[Any]    ,      a__	:				str    ,      a__	:				Any = None				):





									"""simple docstring"""



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

									if os.path.abspath(self.vocab_file				) != os.path.abspath(__SCREAMING_SNAKE_CASE				) and os.path.isfile(self.vocab_file				):
												copyfile(self.vocab_file    ,      __SCREAMING_SNAKE_CASE				)
									elif not os.path.isfile(self.vocab_file				):
												with open(__SCREAMING_SNAKE_CASE    ,      '''wb'''				) as fi:
															__snake_case								=       self.sp_model.serialized_model_proto()
															fi.write(__SCREAMING_SNAKE_CASE				)

									return (out_vocab_file,)








from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
    from PIL import Image

    from ..image_utils import load_image

if is_torch_available():
    import torch

    from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

snake_case_           =      logging.get_logger(__name__)

@add_end_docstrings(_UpperCAmelCase  )
class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):


      def __init__(self	:				int    ,      *a__	:				List[Any]    ,      **a__	:				Dict				):





         """simple docstring"""



         super().__init__(*a__    ,      **a__				)
         requires_backends(self    ,      '''vision'''				)
         self.check_model_type(a__				)


      def __call__(self	:				Optional[Any]    ,      a__	:				Union[str, List[str], "Image.Image", List["Image.Image"]]    ,      **a__	:				List[str]				):





         """simple docstring"""



         return super().__call__(a__    ,      **a__				)


      def        a			(self	:				int    ,      **a__	:				int				):





         """simple docstring"""



         return {}, {}, {}


      def        a			(self	:				Optional[int]    ,      a__	:				Optional[int]				):





         """simple docstring"""



         __snake_case								=       load_image(a__				)
         __snake_case								=       image.size
         __snake_case								=       self.image_processor(images=a__    ,      return_tensors=self.framework				)
         return model_inputs


      def        a			(self	:				List[Any]    ,      a__	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       self.model(**a__				)
         return model_outputs


      def        a			(self	:				int    ,      a__	:				str				):





         """simple docstring"""



         __snake_case								=       model_outputs.predicted_depth
         __snake_case								=       torch.nn.functional.interpolate(
             predicted_depth.unsqueeze(1				)    ,      size=self.image_size[::-1]    ,      mode='''bicubic'''    ,      align_corners=a__				)
         __snake_case								=       prediction.squeeze().cpu().numpy()
         __snake_case								=       (output * 255 / np.max(a__				)).astype('''uint8'''				)
         __snake_case								=       Image.fromarray(a__				)
         __snake_case								=       {}
         __snake_case								=       predicted_depth
         __snake_case								=       depth
         return output_dict

