



"""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


A:   str								=		logging.get_logger(__name__)

A:   str								=		"▁"

A:   Union[str, Any]								=		{"vocab_file": "sentencepiece.bpe.model"}

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

A:   Dict								=		{
    "facebook/nllb-200-distilled-600M": 1_0_2_4,
}

# fmt: off
A:   List[Any]								=		["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       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   str						=						VOCAB_FILES_NAMES
   __lowerCAmelCase  :   List[Any]						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase  :   Union[str, Any]						=						PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase  :   int						=						['input_ids', 'attention_mask']

   __lowerCAmelCase  :   List[int]						=						[]
   __lowerCAmelCase  :   List[int]						=						[]

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE="<s>"      ,     _SCREAMING_SNAKE_CASE="</s>"      ,     _SCREAMING_SNAKE_CASE="</s>"      ,     _SCREAMING_SNAKE_CASE="<s>"      ,     _SCREAMING_SNAKE_CASE="<unk>"      ,     _SCREAMING_SNAKE_CASE="<pad>"      ,     _SCREAMING_SNAKE_CASE="<mask>"      ,     _SCREAMING_SNAKE_CASE=None      ,     _SCREAMING_SNAKE_CASE=None      ,     _SCREAMING_SNAKE_CASE=None      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE=None      ,     _SCREAMING_SNAKE_CASE=False      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	AddedToken(_SCREAMING_SNAKE_CASE      ,     lstrip=_SCREAMING_SNAKE_CASE      ,     rstrip=_SCREAMING_SNAKE_CASE						) if isinstance(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						) else mask_token

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

    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      ,     tokenizer_file=_SCREAMING_SNAKE_CASE      ,     src_lang=_SCREAMING_SNAKE_CASE      ,     tgt_lang=_SCREAMING_SNAKE_CASE      ,     additional_special_tokens=_SCREAMING_SNAKE_CASE      ,     sp_model_kwargs=self.sp_model_kwargs      ,     legacy_behaviour=_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE      ,     )

    UpperCAmelCase	:					Tuple	=	spm.SentencePieceProcessor(**self.sp_model_kwargs						)
    self.sp_model.Load(str(_SCREAMING_SNAKE_CASE						)						)
    UpperCAmelCase	:					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
    UpperCAmelCase	:					Dict	=	{"""<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
    UpperCAmelCase	:					str	=	1

    UpperCAmelCase	:					Optional[Any]	=	len(self.sp_model						)
    UpperCAmelCase	:					Dict	=	{
        code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_SCREAMING_SNAKE_CASE						)
    }
    UpperCAmelCase	:					Any	=	{v: k for k, v in self.lang_code_to_id.items()}
    UpperCAmelCase	:					Any	=	len(self.sp_model						) + len(self.lang_code_to_id						) + self.fairseq_offset

    self.fairseq_tokens_to_ids.update(self.lang_code_to_id						)
    UpperCAmelCase	:					Any	=	{v: k for k, v in self.fairseq_tokens_to_ids.items()}
    UpperCAmelCase	:					Optional[int]	=	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]						)

    UpperCAmelCase	:					List[str]	=	src_lang if src_lang is not None else """eng_Latn"""
    UpperCAmelCase	:					str	=	self.lang_code_to_id[self._src_lang]
    UpperCAmelCase	:					Optional[int]	=	tgt_lang
    self.set_src_lang_special_tokens(self._src_lang						)

   def __getstate__(   self						)  ->      Optional[int]:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	self.__dict__.copy()
    UpperCAmelCase	:					Optional[Any]	=	None
    UpperCAmelCase	:					Tuple	=	self.sp_model.serialized_model_proto()
    return state

   def __setstate__(   self      ,     _SCREAMING_SNAKE_CASE						)  ->      List[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Dict	=	d

    # for backward compatibility
    if not hasattr(self      ,     """sp_model_kwargs"""						):
     UpperCAmelCase	:					Union[str, Any]	=	{}

    UpperCAmelCase	:					Union[str, Any]	=	spm.SentencePieceProcessor(**self.sp_model_kwargs						)
    self.sp_model.LoadFromSerializedProto(self.sp_model_proto						)

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    return len(self.sp_model						) + len(self.lang_code_to_id						) + self.fairseq_offset + 1  # Plus 1 for the mask token

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




    return self._src_lang

   @src_lang.setter
   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      None:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	new_src_lang
    self.set_src_lang_special_tokens(self._src_lang						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = False						)  ->      List[int]:





    '''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						)

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

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None						)  ->      List[int]:





    '''simple docstring'''




    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 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None						)  ->      List[int]:





    '''simple docstring'''




    UpperCAmelCase	:					Union[str, Any]	=	[self.sep_token_id]
    UpperCAmelCase	:					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 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)  ->      Optional[Any]:





    '''simple docstring'''




    if src_lang is None or tgt_lang is None:
     raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""						)
    UpperCAmelCase	:					Any	=	src_lang
    UpperCAmelCase	:					Dict	=	self(_SCREAMING_SNAKE_CASE      ,     add_special_tokens=_SCREAMING_SNAKE_CASE      ,     return_tensors=_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					str	=	self.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					List[Any]	=	tgt_lang_id
    return inputs

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    UpperCAmelCase	:					Any	=	{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 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      List[str]:





    '''simple docstring'''




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

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      Tuple:





    '''simple docstring'''




    if token in self.fairseq_tokens_to_ids:
     return self.fairseq_tokens_to_ids[token]
    UpperCAmelCase	:					Optional[int]	=	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 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      List[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 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	"""""".join(_SCREAMING_SNAKE_CASE						).replace(_SCREAMING_SNAKE_CASE      ,     """ """						).strip()
    return out_string

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None						)  ->      Tuple[str]:





    '''simple docstring'''




    if not os.path.isdir(_SCREAMING_SNAKE_CASE						):
     logger.error(F"Vocabulary path ({save_directory}) should be a directory"						)
     return
    UpperCAmelCase	:					int	=	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:
      UpperCAmelCase	:					Any	=	self.sp_model.serialized_model_proto()
      fi.write(_SCREAMING_SNAKE_CASE						)

    return (out_vocab_file,)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = "eng_Latn"      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = "fra_Latn"      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      BatchEncoding:





    '''simple docstring'''




    UpperCAmelCase	:					List[str]	=	src_lang
    UpperCAmelCase	:					Tuple	=	tgt_lang
    return super().prepare_seqaseq_batch(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




    return self.set_src_lang_special_tokens(self.src_lang						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[Any]:





    '''simple docstring'''




    return self.set_tgt_lang_special_tokens(self.tgt_lang						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      None:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	self.lang_code_to_id[src_lang]
    if self.legacy_behaviour:
     UpperCAmelCase	:					Optional[int]	=	[]
     UpperCAmelCase	:					List[Any]	=	[self.eos_token_id, self.cur_lang_code]
    else:
     UpperCAmelCase	:					List[Any]	=	[self.cur_lang_code]
     UpperCAmelCase	:					Optional[Any]	=	[self.eos_token_id]




   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      None:





    '''simple docstring'''




    UpperCAmelCase	:					List[str]	=	self.lang_code_to_id[lang]
    if self.legacy_behaviour:
     UpperCAmelCase	:					Any	=	[]
     UpperCAmelCase	:					List[Any]	=	[self.eos_token_id, self.cur_lang_code]
    else:
     UpperCAmelCase	:					Tuple	=	[self.cur_lang_code]
     UpperCAmelCase	:					Optional[int]	=	[self.eos_token_id]







import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import YolosImageProcessor





class 						_A(					unittest.TestCase   ):


				"""simple docstring"""


				def __init__(			self     ,					_A     ,					_A=7     ,					_A=3     ,					_A=30     ,					_A=400     ,					_A=True     ,					_A=None     ,					_A=True     ,					_A=[0.5, 0.5, 0.5]     ,					_A=[0.5, 0.5, 0.5]     ,					_A=True     ,					_A=1 / 255     ,					_A=True     ,					):
									# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
									__A	:						List[Any]       =				size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
									__A	:						Union[str, Any]       =				parent
									__A	:						Optional[int]       =				batch_size
									__A	:						int       =				num_channels
									__A	:						int       =				min_resolution
									__A	:						Any       =				max_resolution
									__A	:						List[Any]       =				do_resize
									__A	:						List[Any]       =				size
									__A	:						Union[str, Any]       =				do_normalize
									__A	:						Optional[int]       =				image_mean
									__A	:						Optional[int]       =				image_std
									__A	:						int       =				do_rescale
									__A	:						str       =				rescale_factor
									__A	:						Tuple       =				do_pad



				def 					UpperCAmelCase_		(			self     ):
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_rescale": self.do_rescale,
									    "rescale_factor": self.rescale_factor,
									    "do_pad": self.do_pad,
									}





				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A=False     ):
									if not batched:
														__A	:						List[str]       =				image_inputs[0]
														if isinstance(_A     ,					Image.Image     ):
																			__A    ,				__A	:						int       =				image.size
														else:
																			__A    ,				__A	:						Any       =				image.shape[1], image.shape[2]
														if w < h:
																			__A	:						List[Any]       =				int(self.size['shortest_edge'] * h / w     )
																			__A	:						List[Any]       =				self.size['shortest_edge']
														elif w > h:
																			__A	:						Union[str, Any]       =				self.size['shortest_edge']
																			__A	:						str       =				int(self.size['shortest_edge'] * w / h     )
														else:
																			__A	:						Dict       =				self.size['shortest_edge']
																			__A	:						str       =				self.size['shortest_edge']

									else:
														__A	:						int       =				[]
														for image in image_inputs:
																			__A    ,				__A	:						Optional[Any]       =				self.get_expected_values([image]     )
																			expected_values.append((expected_height, expected_width)     )
														__A	:						List[str]       =				max(_A     ,					key=lambda _A     : item[0]     )[0]
														__A	:						str       =				max(_A     ,					key=lambda _A     : item[1]     )[1]

									return expected_height, expected_width





@require_torch
@require_vision
class 						_A(					snake_case__				, unittest.TestCase   ):


				"""simple docstring"""


				UpperCamelCase    :  List[str]					    =	YolosImageProcessor if is_vision_available() else None



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Dict       =				YolosImageProcessingTester(self     )



				@property
				def 					UpperCAmelCase_		(			self     ):
									return self.image_processor_tester.prepare_image_processor_dict()



				def 					UpperCAmelCase_		(			self     ):
									__A	:						str       =				self.image_processing_class(**self.image_processor_dict     )
									self.assertTrue(hasattr(_A     ,					'image_mean'     )     )
									self.assertTrue(hasattr(_A     ,					'image_std'     )     )
									self.assertTrue(hasattr(_A     ,					'do_normalize'     )     )
									self.assertTrue(hasattr(_A     ,					'do_resize'     )     )
									self.assertTrue(hasattr(_A     ,					'size'     )     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Tuple       =				self.image_processing_class.from_dict(self.image_processor_dict     )
									self.assertEqual(image_processor.size     ,					{'shortest_edge': 18, 'longest_edge': 1333}     )
									self.assertEqual(image_processor.do_pad     ,					_A     )

									__A	:						Dict       =				self.image_processing_class.from_dict(
									    self.image_processor_dict     ,					size=42     ,					max_size=84     ,					pad_and_return_pixel_mask=_A     )
									self.assertEqual(image_processor.size     ,					{'shortest_edge': 42, 'longest_edge': 84}     )
									self.assertEqual(image_processor.do_pad     ,					_A     )



				def 					UpperCAmelCase_		(			self     ):
									pass



				def 					UpperCAmelCase_		(			self     ):
									# Initialize image_processing
									__A	:						List[str]       =				self.image_processing_class(**self.image_processor_dict     )
									# create random PIL images
									__A	:						List[Any]       =				prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=_A     )
									for image in image_inputs:
														self.assertIsInstance(_A     ,					Image.Image     )

									# Test not batched input
									__A	:						Any       =				image_processing(image_inputs[0]     ,					return_tensors='pt'     ).pixel_values

									__A    ,				__A	:						Optional[int]       =				self.image_processor_tester.get_expected_values(_A     )

									self.assertEqual(
									    encoded_images.shape     ,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,					)

									# Test batched
									__A    ,				__A	:						Optional[Any]       =				self.image_processor_tester.get_expected_values(_A     ,					batched=_A     )

									__A	:						str       =				image_processing(_A     ,					return_tensors='pt'     ).pixel_values
									self.assertEqual(
									    encoded_images.shape     ,					(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )     ,					)



				def 					UpperCAmelCase_		(			self     ):
									# Initialize image_processing
									__A	:						Optional[Any]       =				self.image_processing_class(**self.image_processor_dict     )
									# create random numpy tensors
									__A	:						List[Any]       =				prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=_A     ,					numpify=_A     )
									for image in image_inputs:
														self.assertIsInstance(_A     ,					np.ndarray     )

									# Test not batched input
									__A	:						str       =				image_processing(image_inputs[0]     ,					return_tensors='pt'     ).pixel_values

									__A    ,				__A	:						List[Any]       =				self.image_processor_tester.get_expected_values(_A     )

									self.assertEqual(
									    encoded_images.shape     ,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,					)

									# Test batched
									__A	:						Tuple       =				image_processing(_A     ,					return_tensors='pt'     ).pixel_values

									__A    ,				__A	:						Optional[int]       =				self.image_processor_tester.get_expected_values(_A     ,					batched=_A     )

									self.assertEqual(
									    encoded_images.shape     ,					(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )     ,					)



				def 					UpperCAmelCase_		(			self     ):
									# Initialize image_processing
									__A	:						Union[str, Any]       =				self.image_processing_class(**self.image_processor_dict     )
									# create random PyTorch tensors
									__A	:						Dict       =				prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=_A     ,					torchify=_A     )
									for image in image_inputs:
														self.assertIsInstance(_A     ,					torch.Tensor     )

									# Test not batched input
									__A	:						Union[str, Any]       =				image_processing(image_inputs[0]     ,					return_tensors='pt'     ).pixel_values

									__A    ,				__A	:						Union[str, Any]       =				self.image_processor_tester.get_expected_values(_A     )

									self.assertEqual(
									    encoded_images.shape     ,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,					)

									# Test batched
									__A	:						Optional[int]       =				image_processing(_A     ,					return_tensors='pt'     ).pixel_values

									__A    ,				__A	:						Optional[int]       =				self.image_processor_tester.get_expected_values(_A     ,					batched=_A     )

									self.assertEqual(
									    encoded_images.shape     ,					(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )     ,					)



				def 					UpperCAmelCase_		(			self     ):
									# Initialize image_processings
									__A	:						Tuple       =				self.image_processing_class(**self.image_processor_dict     )
									__A	:						Any       =				self.image_processing_class(do_resize=_A     ,					do_normalize=_A     ,					do_rescale=_A     )
									# create random PyTorch tensors
									__A	:						Union[str, Any]       =				prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=_A     ,					torchify=_A     )
									for image in image_inputs:
														self.assertIsInstance(_A     ,					torch.Tensor     )

									# Test whether the method "pad" and calling the image processor return the same tensors
									__A	:						Optional[int]       =				image_processing_a.pad(_A     ,					return_tensors='pt'     )
									__A	:						Optional[int]       =				image_processing_a(_A     ,					return_tensors='pt'     )

									self.assertTrue(
									    torch.allclose(encoded_images_with_method['pixel_values']     ,					encoded_images['pixel_values']     ,					atol=1e-4     )     )



				@slow
				def 					UpperCAmelCase_		(			self     ):
									# prepare image and target
									__A	:						List[Any]       =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
									with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'     ,					'r'     ) as f:
														__A	:						Optional[Any]       =				json.loads(f.read()     )

									__A	:						Optional[Any]       =				{'image_id': 39769, 'annotations': target}

									# encode them
									__A	:						str       =				YolosImageProcessor.from_pretrained('hustvl/yolos-small'     )
									__A	:						List[Any]       =				image_processing(images=_A     ,					annotations=_A     ,					return_tensors='pt'     )

									# verify pixel values
									__A	:						List[Any]       =				torch.Size([1, 3, 800, 1066]     )
									self.assertEqual(encoding['pixel_values'].shape     ,					_A     )

									__A	:						Union[str, Any]       =				torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]     )
									self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]     ,					_A     ,					atol=1e-4     )     )

									# verify area
									__A	:						List[Any]       =				torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['area']     ,					_A     )     )
									# verify boxes
									__A	:						Any       =				torch.Size([6, 4]     )
									self.assertEqual(encoding['labels'][0]['boxes'].shape     ,					_A     )
									__A	:						Optional[Any]       =				torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]     ,					_A     ,					atol=1e-3     )     )
									# verify image_id
									__A	:						Optional[int]       =				torch.tensor([39769]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']     ,					_A     )     )
									# verify is_crowd
									__A	:						str       =				torch.tensor([0, 0, 0, 0, 0, 0]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']     ,					_A     )     )
									# verify class_labels
									__A	:						Any       =				torch.tensor([75, 75, 63, 65, 17, 17]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']     ,					_A     )     )
									# verify orig_size
									__A	:						int       =				torch.tensor([480, 640]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']     ,					_A     )     )
									# verify size
									__A	:						str       =				torch.tensor([800, 1066]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['size']     ,					_A     )     )





				@slow
				def 					UpperCAmelCase_		(			self     ):
									# prepare image, target and masks_path
									__A	:						Dict       =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
									with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'     ,					'r'     ) as f:
														__A	:						Tuple       =				json.loads(f.read()     )

									__A	:						Any       =				{'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

									__A	:						List[Any]       =				pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'     )

									# encode them
									__A	:						Any       =				YolosImageProcessor(format='coco_panoptic'     )
									__A	:						List[Any]       =				image_processing(images=_A     ,					annotations=_A     ,					masks_path=_A     ,					return_tensors='pt'     )

									# verify pixel values
									__A	:						Any       =				torch.Size([1, 3, 800, 1066]     )
									self.assertEqual(encoding['pixel_values'].shape     ,					_A     )

									__A	:						Union[str, Any]       =				torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]     )
									self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]     ,					_A     ,					atol=1e-4     )     )

									# verify area
									__A	:						int       =				torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['area']     ,					_A     )     )
									# verify boxes
									__A	:						Optional[int]       =				torch.Size([6, 4]     )
									self.assertEqual(encoding['labels'][0]['boxes'].shape     ,					_A     )
									__A	:						Optional[Any]       =				torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]     ,					_A     ,					atol=1e-3     )     )
									# verify image_id
									__A	:						Union[str, Any]       =				torch.tensor([39769]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']     ,					_A     )     )
									# verify is_crowd
									__A	:						Tuple       =				torch.tensor([0, 0, 0, 0, 0, 0]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']     ,					_A     )     )
									# verify class_labels
									__A	:						List[str]       =				torch.tensor([17, 17, 63, 75, 75, 93]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']     ,					_A     )     )
									# verify masks
									__A	:						Tuple       =				822873
									self.assertEqual(encoding['labels'][0]['masks'].sum().item()     ,					_A     )
									# verify orig_size
									__A	:						str       =				torch.tensor([480, 640]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']     ,					_A     )     )
									# verify size
									__A	:						int       =				torch.tensor([800, 1066]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['size']     ,					_A     )     )




