"""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, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


_UpperCamelCase     :		Any											=					logging.get_logger(__name__)

_UpperCamelCase     :		int											=					"▁"

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

_UpperCamelCase     :		str											=					{
    "vocab_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model"
        ),
    }
}

_UpperCamelCase     :		Optional[int]											=					{
    "facebook/nllb-200-distilled-600M": 10_24,
}

# fmt: off
_UpperCamelCase     :		Tuple											=					["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]


class   UpperCAmelCase_			(					UpperCAmelCase_):
      lowerCamelCase__	:					Optional[Any]										=						VOCAB_FILES_NAMES
      lowerCamelCase__	:					List[Any]										=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCamelCase__	:					Optional[int]										=						PRETRAINED_VOCAB_FILES_MAP
      lowerCamelCase__	:					Optional[Any]										=						["input_ids", "attention_mask"]

      lowerCamelCase__	:					List[int]										=						[]
      lowerCamelCase__	:					List[int]										=						[]


      def __init__(     self	,    a	,    a="<s>"	,    a="</s>"	,    a="</s>"	,    a="<s>"	,    a="<unk>"	,    a="<pad>"	,    a="<mask>"	,    a=None	,    a=None	,    a=None	,    a = None	,    a=None	,    a=False	,    **a	,    ) ->      Any:
             lowercase__    :	Dict     					=			AddedToken(_snake_case	,    lstrip=_snake_case	,    rstrip=_snake_case							) if isinstance(_snake_case	,    _snake_case							) else mask_token

             lowercase__    :	Any     					=			{} if sp_model_kwargs is None else sp_model_kwargs
             lowercase__    :	List[str]     					=			legacy_behaviour

             super().__init__(
                 bos_token=_snake_case	,    eos_token=_snake_case	,    unk_token=_snake_case	,    sep_token=_snake_case	,    cls_token=_snake_case	,    pad_token=_snake_case	,    mask_token=_snake_case	,    tokenizer_file=_snake_case	,    src_lang=_snake_case	,    tgt_lang=_snake_case	,    additional_special_tokens=_snake_case	,    sp_model_kwargs=self.sp_model_kwargs	,    legacy_behaviour=_snake_case	,    **_snake_case	,    )

             lowercase__    :	Tuple     					=			spm.SentencePieceProcessor(**self.sp_model_kwargs							)
             self.sp_model.Load(str(_snake_case							)							)
             lowercase__    :	List[str]     					=			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>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
             # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

             # Mimic fairseq token-to-id alignment for the first 4 token
             lowercase__    :	Optional[Any]     					=			{'<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__    :	int     					=			1

             lowercase__    :	List[str]     					=			len(self.sp_model							)
             lowercase__    :	List[Any]     					=			{
                 code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_snake_case							)
             }
             lowercase__    :	Union[str, Any]     					=			{v: k for k, v in self.lang_code_to_id.items()}
             lowercase__    :	int     					=			len(self.sp_model							) + len(self.lang_code_to_id							) + self.fairseq_offset

             self.fairseq_tokens_to_ids.update(self.lang_code_to_id							)
             lowercase__    :	List[Any]     					=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}
             lowercase__    :	Dict     					=			list(self.lang_code_to_id.keys()							)

             if additional_special_tokens is not None:
                    # Only add those special tokens if they are not already there.
                    self._additional_special_tokens.extend(
                        [t for t in additional_special_tokens if t not in self._additional_special_tokens]							)

             lowercase__    :	int     					=			src_lang if src_lang is not None else 'eng_Latn'
             lowercase__    :	Tuple     					=			self.lang_code_to_id[self._src_lang]
             lowercase__    :	Union[str, Any]     					=			tgt_lang
             self.set_src_lang_special_tokens(self._src_lang							)


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


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

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

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


      @property
      def 	_UpperCAmelCase (     self							) ->      int:
             return len(self.sp_model							) + len(self.lang_code_to_id							) + self.fairseq_offset + 1  # Plus 1 for the mask token


      @property
      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             return self._src_lang


      @src_lang.setter
      def 	_UpperCAmelCase (     self	,    a							) ->      Dict:
             lowercase__    :	Optional[int]     					=			new_src_lang
             self.set_src_lang_special_tokens(self._src_lang							)


      def 	_UpperCAmelCase (     self	,    a	,    a = None	,    a = False							) ->      Optional[Any]:


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

             lowercase__    :	List[str]     					=			[1] * len(self.prefix_tokens							)
             lowercase__    :	Dict     					=			[1] * len(self.suffix_tokens							)
             if token_ids_a is None:
                    return prefix_ones + ([0] * len(_snake_case							)) + suffix_ones
             return prefix_ones + ([0] * len(_snake_case							)) + ([0] * len(_snake_case							)) + suffix_ones


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


             if token_ids_a is None:
                    return self.prefix_tokens + token_ids_a + self.suffix_tokens
             # We don't expect to process pairs, but leave the pair logic for API consistency
             return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


      def 	_UpperCAmelCase (     self	,    a	,    a = None							) ->      Dict:
             lowercase__    :	List[Any]     					=			[self.sep_token_id]
             lowercase__    :	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 + sep + token_ids_a + sep							) * [0]


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a	,    **a							) ->      str:


             if src_lang is None or tgt_lang is None:
                    raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'							)
             lowercase__    :	Optional[Any]     					=			src_lang
             lowercase__    :	Union[str, Any]     					=			self(_snake_case	,    add_special_tokens=_snake_case	,    return_tensors=_snake_case	,    **_snake_case							)
             lowercase__    :	Any     					=			self.convert_tokens_to_ids(_snake_case							)
             lowercase__    :	str     					=			tgt_lang_id
             return inputs


      def 	_UpperCAmelCase (     self							) ->      Dict:
             lowercase__    :	Tuple     					=			{self.convert_ids_to_tokens(_snake_case							): i for i in range(self.vocab_size							)}
             vocab.update(self.added_tokens_encoder							)
             return vocab


      def 	_UpperCAmelCase (     self	,    a							) ->      Optional[int]:
             return self.sp_model.encode(_snake_case	,    out_type=_snake_case							)


      def 	_UpperCAmelCase (     self	,    a							) ->      Optional[int]:


             if token in self.fairseq_tokens_to_ids:
                    return self.fairseq_tokens_to_ids[token]
             lowercase__    :	str     					=			self.sp_model.PieceToId(_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 	_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							) ->      List[str]:
             lowercase__    :	str     					=			''.join(_snake_case							).replace(_snake_case	,    ' '							).strip()
             return out_string


      def 	_UpperCAmelCase (     self	,    a	,    a = None							) ->      Any:


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

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

             return (out_vocab_file,)


      def 	_UpperCAmelCase (     self	,    a	,    a = "eng_Latn"	,    a = None	,    a = "fra_Latn"	,    **a	,    ) ->      Optional[int]:
             lowercase__    :	str     					=			src_lang
             lowercase__    :	List[Any]     					=			tgt_lang
             return super().prepare_seqaseq_batch(_snake_case	,    _snake_case	,    **_snake_case							)


      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             return self.set_src_lang_special_tokens(self.src_lang							)


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             return self.set_tgt_lang_special_tokens(self.tgt_lang							)


      def 	_UpperCAmelCase (     self	,    a							) ->      Optional[Any]:
             lowercase__    :	int     					=			self.lang_code_to_id[src_lang]
             if self.legacy_behaviour:
                    lowercase__    :	List[str]     					=			[]
                    lowercase__    :	int     					=			[self.eos_token_id, self.cur_lang_code]
             else:
                    lowercase__    :	List[str]     					=			[self.cur_lang_code]
                    lowercase__    :	str     					=			[self.eos_token_id]


      def 	_UpperCAmelCase (     self	,    a							) ->      List[str]:
             lowercase__    :	Tuple     					=			self.lang_code_to_id[lang]
             if self.legacy_behaviour:
                    lowercase__    :	Dict     					=			[]
                    lowercase__    :	List[Any]     					=			[self.eos_token_id, self.cur_lang_code]
             else:
                    lowercase__    :	Dict     					=			[self.cur_lang_code]
                    lowercase__    :	Any     					=			[self.eos_token_id]


"""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'							)

