





"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_snake_case     :		List[Any]       						= logging.get_logger(__name__)

_snake_case     :		Union[str, Any]       						= {'vocab_file': 'spiece.model'}

_snake_case     :		Dict       						= {
    'vocab_file': {
        'bert_for_seq_generation': (
            'https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'
        ),
    }
}

_snake_case     :		Optional[int]       						= {'bert_for_seq_generation': 512}






class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							VOCAB_FILES_NAMES
					UpperCamelCase      =							PRETRAINED_VOCAB_FILES_MAP
					UpperCamelCase      =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCamelCase      =							[]
					UpperCamelCase      =							['''input_ids''', '''attention_mask''']


					def __init__(					self			:List[str]     ,       __UpperCamelCase			:Optional[int]     ,       __UpperCamelCase			:int="<s>"     ,       __UpperCamelCase			:int="</s>"     ,       __UpperCamelCase			:Dict="<unk>"     ,       __UpperCamelCase			:Optional[int]="<pad>"     ,       __UpperCamelCase			:Tuple="<::::>"     ,       __UpperCamelCase			:Optional[Dict[str, Any]] = None     ,       **__UpperCamelCase			:Optional[Any]     ,       ):
												A		=  {} if sp_model_kwargs is None else sp_model_kwargs

												# Add extra_ids to the special token list
												super().__init__(
												    bos_token=__UpperCamelCase     ,       eos_token=__UpperCamelCase     ,       unk_token=__UpperCamelCase     ,       pad_token=__UpperCamelCase     ,       sep_token=__UpperCamelCase     ,       sp_model_kwargs=self.sp_model_kwargs     ,       **__UpperCamelCase     ,       )

												A		=  vocab_file

												A		=  spm.SentencePieceProcessor(**self.sp_model_kwargs		)
												self.sp_model.Load(__UpperCamelCase		)


					@property
					def 	lowerCamelCase       (					self			:Tuple		):
												return self.sp_model.get_piece_size()


					def 	lowerCamelCase       (					self			:Union[str, Any]		):
												A		=  {self.convert_ids_to_tokens(__UpperCamelCase		): i for i in range(self.vocab_size		)}
												vocab.update(self.added_tokens_encoder		)
												return vocab


					def __getstate__(					self			:Optional[int]		):
												A		=  self.__dict__.copy()
												A		=  None
												return state


					def __setstate__(					self			:Optional[int]     ,       __UpperCamelCase			:Dict		):
												A		=  d

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

												A		=  spm.SentencePieceProcessor(**self.sp_model_kwargs		)
												self.sp_model.Load(self.vocab_file		)


					def 	lowerCamelCase       (					self			:Optional[int]     ,       __UpperCamelCase			:str		):
												return self.sp_model.encode(__UpperCamelCase     ,       out_type=__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:List[str]     ,       __UpperCamelCase			:Union[str, Any]		):
												return self.sp_model.piece_to_id(__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Any     ,       __UpperCamelCase			:Optional[Any]		):
												A		=  self.sp_model.IdToPiece(__UpperCamelCase		)
												return token


					def 	lowerCamelCase       (					self			:str     ,       __UpperCamelCase			:int		):
												A		=  []
												A		=  ""
												for token in tokens:
																			# make sure that special tokens are not decoded using sentencepiece model
																			if token in self.all_special_tokens:
																										out_string += self.sp_model.decode(__UpperCamelCase		) + token
																										A		=  []
																			else:
																										current_sub_tokens.append(__UpperCamelCase		)
												out_string += self.sp_model.decode(__UpperCamelCase		)
												return out_string.strip()


					def 	lowerCamelCase       (					self			:Tuple     ,       __UpperCamelCase			:str     ,       __UpperCamelCase			:Optional[str] = None		):
												if not os.path.isdir(__UpperCamelCase		):
																			logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
																			return
												A		=  os.path.join(
												    __UpperCamelCase     ,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

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

												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 	lowerCamelCase       (					*__UpperCamelCase			:List[Any]     ,       **__UpperCamelCase			:List[Any]		):
														pass






def 					A__				(			UpperCamelCase       ):
							A		=  hashlib.mda(image.tobytes()       )
							return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 					_UpperCAmelCase   (	unittest.TestCase ):
					UpperCamelCase      =							MODEL_FOR_DEPTH_ESTIMATION_MAPPING


					def 	lowerCamelCase       (					self			:Optional[Any]     ,       __UpperCamelCase			:Union[str, Any]     ,       __UpperCamelCase			:List[str]     ,       __UpperCamelCase			:Optional[int]		):
												A		=  DepthEstimationPipeline(model=__UpperCamelCase     ,       image_processor=__UpperCamelCase		)
												return depth_estimator, [
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												]


					def 	lowerCamelCase       (					self			:Dict     ,       __UpperCamelCase			:Optional[int]     ,       __UpperCamelCase			:Optional[Any]		):
												A		=  depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
												self.assertEqual({"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)}     ,       __UpperCamelCase		)
												import datasets

												A		=  datasets.load_dataset("hf-internal-testing/fixtures_image_utils"     ,       "image"     ,       split="test"		)
												A		=  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		)},
												    ]     ,       __UpperCamelCase     ,       )


					@require_tf
					@unittest.skip("Depth estimation is not implemented in TF"		)
					def 	lowerCamelCase       (					self			:Optional[Any]		):
												pass


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


