"""simple docstring"""





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


_UpperCamelCase     :		str											=					logging.get_logger(__name__)

_UpperCamelCase     :		List[Any]											=					"▁"

_UpperCamelCase     :		List[str]											=					{"vocab_file": "sentencepiece.bpe.model"}

_UpperCamelCase     :		int											=					{
    "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"
        ),
    }
}

_UpperCamelCase     :		List[str]											=					{
    "xlm-roberta-base": 5_12,
    "xlm-roberta-large": 5_12,
    "xlm-roberta-large-finetuned-conll02-dutch": 5_12,
    "xlm-roberta-large-finetuned-conll02-spanish": 5_12,
    "xlm-roberta-large-finetuned-conll03-english": 5_12,
    "xlm-roberta-large-finetuned-conll03-german": 5_12,
}


class   UpperCAmelCase_			(					_a):
      lowerCamelCase__	:					Tuple										=						VOCAB_FILES_NAMES
      lowerCamelCase__	:					List[str]										=						PRETRAINED_VOCAB_FILES_MAP
      lowerCamelCase__	:					str										=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCamelCase__	:					Any										=						["input_ids", "attention_mask"]


      def __init__(     self	,    a	,    a="<s>"	,    a="</s>"	,    a="</s>"	,    a="<s>"	,    a="<unk>"	,    a="<pad>"	,    a="<mask>"	,    a = None	,    **a	,    ) ->      None:
             # Mask token behave like a normal word, i.e. include the space before it
             lowercase__    :	Optional[int]     					=			AddedToken(a	,    lstrip=a	,    rstrip=a							) if isinstance(a	,    a							) else mask_token

             lowercase__    :	Any     					=			{} if sp_model_kwargs is None else sp_model_kwargs

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

             lowercase__    :	int     					=			spm.SentencePieceProcessor(**self.sp_model_kwargs							)
             self.sp_model.Load(str(a							)							)
             lowercase__    :	int     					=			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
             lowercase__    :	int     					=			{'<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
             lowercase__    :	Optional[int]     					=			1

             lowercase__    :	List[str]     					=			len(self.sp_model							) + self.fairseq_offset
             lowercase__    :	Any     					=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}


      def __getstate__(     self							) ->      List[Any]:
             lowercase__    :	List[str]     					=			self.__dict__.copy()
             lowercase__    :	int     					=			None
             lowercase__    :	List[str]     					=			self.sp_model.serialized_model_proto()
             return state


      def __setstate__(     self	,    a							) ->      List[Any]:
             lowercase__    :	List[Any]     					=			d

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

             lowercase__    :	Tuple     					=			spm.SentencePieceProcessor(**self.sp_model_kwargs							)
             self.sp_model.LoadFromSerializedProto(self.sp_model_proto							)


      def 	_UpperCAmelCase (     self	,    a	,    a = None							) ->      List[int]:

             if token_ids_a is None:
                    return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
             lowercase__    :	Optional[int]     					=			[self.cls_token_id]
             lowercase__    :	str     					=			[self.sep_token_id]
             return cls + token_ids_a + sep + sep + token_ids_a + sep


      def 	_UpperCAmelCase (     self	,    a	,    a = None	,    a = False							) ->      List[int]:

             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 None:
                    return [1] + ([0] * len(a							)) + [1]
             return [1] + ([0] * len(a							)) + [1, 1] + ([0] * len(a							)) + [1]


      def 	_UpperCAmelCase (     self	,    a	,    a = None							) ->      List[int]:
             lowercase__    :	List[Any]     					=			[self.sep_token_id]
             lowercase__    :	List[Any]     					=			[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 	_UpperCAmelCase (     self							) ->      Optional[int]:
             return len(self.sp_model							) + self.fairseq_offset + 1  # Add the <mask> token


      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	List[str]     					=			{self.convert_ids_to_tokens(a							): i for i in range(self.vocab_size							)}
             vocab.update(self.added_tokens_encoder							)
             return vocab


      def 	_UpperCAmelCase (     self	,    a							) ->      List[str]:
             return self.sp_model.encode(a	,    out_type=a							)


      def 	_UpperCAmelCase (     self	,    a							) ->      Dict:
             if token in self.fairseq_tokens_to_ids:
                    return self.fairseq_tokens_to_ids[token]
             lowercase__    :	Union[str, Any]     					=			self.sp_model.PieceToId(a							)

             # 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 	_UpperCAmelCase (     self	,    a							) ->      int:
             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 	_UpperCAmelCase (     self	,    a							) ->      str:
             lowercase__    :	List[Any]     					=			''.join(a							).replace(a	,    ' '							).strip()
             return out_string


      def 	_UpperCAmelCase (     self	,    a	,    a = None							) ->      Tuple[str]:
             if not os.path.isdir(a							):
                    logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
                    return
             lowercase__    :	Optional[Any]     					=			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:
                           lowercase__    :	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   UpperCAmelCase_			:


             @staticmethod
             def 	_UpperCAmelCase (     *a	,    **a							) ->      int:
                    pass





def        a_  (   _lowerCAmelCase						:				Image  ):





       '''simple docstring'''

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






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class   UpperCAmelCase_			(					unittest.TestCase):
      lowerCamelCase__	:					Union[str, Any]										=						MODEL_FOR_DEPTH_ESTIMATION_MAPPING


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a							) ->      Dict:
             lowercase__    :	Union[str, Any]     					=			DepthEstimationPipeline(model=a	,    image_processor=a							)
             return depth_estimator, [
                 "./tests/fixtures/tests_samples/COCO/000000039769.png",
                 "./tests/fixtures/tests_samples/COCO/000000039769.png",
             ]


      def 	_UpperCAmelCase (     self	,    a	,    a							) ->      Optional[int]:
             lowercase__    :	Tuple     					=			depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
             self.assertEqual({'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)}	,    a							)
             import datasets

             lowercase__    :	Tuple     					=			datasets.load_dataset('hf-internal-testing/fixtures_image_utils'	,    'image'	,    split='test'							)
             lowercase__    :	List[Any]     					=			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 	_UpperCAmelCase (     self							) ->      Optional[int]:
             pass


      @slow
      @require_torch
      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	Tuple     					=			'Intel/dpt-large'
             lowercase__    :	Optional[int]     					=			pipeline('depth-estimation'	,    model=a							)
             lowercase__    :	List[Any]     					=			depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'							)
             lowercase__    :	Optional[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 	_UpperCAmelCase (     self							) ->      Optional[int]:
             # This is highly irregular to have no small tests.
             self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'							)

