import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation


UpperCamelCase__											=					logging.get_logger(__name__)

UpperCamelCase__											=					{'''vocab_file''': '''vocab.txt''', '''emoji_file''': '''emoji.json'''}

UpperCamelCase__											=					{
    '''vocab_file''': {
        '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt''',
    },
    '''emoji_file''': {
        '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json''',
    },
}

UpperCamelCase__											=					{
    '''abeja/gpt-neox-japanese-2.7b''': 2_0_4_8,
}




def    lowerCAmelCase_ (     __A,  __A							)     ->				List[str]:







      '''simple docstring'''


      with open(__A,  "r",  encoding="utf-8"							) as f:
            UpperCAmelCase__								=  json.loads(f.read()							)

      UpperCAmelCase__								=  collections.OrderedDict()
      UpperCAmelCase__								=  collections.OrderedDict()
      UpperCAmelCase__								=  collections.OrderedDict()
      with open(__A,  "r",  encoding="utf-8"							) as f:
            UpperCAmelCase__								=  f.readlines()
      UpperCAmelCase__								=  [[t.rstrip("\n"							)] if (t == ',' or ',' not in t) else t.rstrip("\n"							).split(","							) for t in token]
      for idx, b in enumerate(__A							):
            UpperCAmelCase__								=  b
            UpperCAmelCase__								=  idx
            for wd in b:
                  UpperCAmelCase__								=  idx

      return vocab, raw_vocab, ids_to_tokens, emoji





class      A							(				snake_case__     ):
   __UpperCAmelCase : Tuple      =			VOCAB_FILES_NAMES
   __UpperCAmelCase : str      =			PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase : List[Any]      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase : int      =			['''input_ids''', '''attention_mask''']


   def __init__(self     :						Tuple       ,  __UpperCAmelCase     :						List[str]       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						List[Any]="<|endoftext|>"       ,  __UpperCAmelCase     :						Union[str, Any]="<|endoftext|>"       ,  __UpperCAmelCase     :						Union[str, Any]="<|startoftext|>"       ,  __UpperCAmelCase     :						Dict="<|endoftext|>"       ,  __UpperCAmelCase     :						Union[str, Any]=False       ,  **__UpperCAmelCase     :						Dict       ,  )						->				Dict:
         """simple docstring"""



         super().__init__(
             unk_token=_A       ,  pad_token=_A       ,  bos_token=_A       ,  eos_token=_A       ,  do_clean_text=_A       ,  **_A       ,  )
         if not os.path.isfile(_A		):
               raise ValueError(
                   f"""Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"""
                   " model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"		)
         if not os.path.isfile(_A		):
               raise ValueError(
                   f"""Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"""
                   " pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"		)
         UpperCAmelCase__								=  do_clean_text
         UpperCAmelCase__								=  load_vocab_and_emoji(_A       ,  _A		)
         UpperCAmelCase__								=  SubWordJapaneseTokenizer(
             vocab=self.vocab       ,  ids_to_tokens=self.ids_to_tokens       ,  emoji=self.emoji		)


   @property
   def 			lowercase_      (self     :						Optional[Any]		)						->				Union[str, Any]:
         """simple docstring"""



         return len(self.raw_vocab		)


   def 			lowercase_      (self     :						str		)						->				str:
         """simple docstring"""



         return dict(self.raw_vocab       ,  **self.added_tokens_encoder		)


   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						List[str]		)						->				Optional[Any]:
         """simple docstring"""



         return self.subword_tokenizer.tokenize(_A       ,  clean=self.do_clean_text		)


   def 			lowercase_      (self     :						Optional[Any]       ,  __UpperCAmelCase     :						Dict		)						->				Tuple:
         """simple docstring"""



         return self.vocab.get(_A       ,  self.vocab.get(self.unk_token		)		)


   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						Optional[Any]		)						->				Optional[int]:
         """simple docstring"""



         return self.subword_tokenizer.convert_id_to_token(_A		)


   def 			lowercase_      (self     :						Optional[Any]       ,  __UpperCAmelCase     :						List[Any]		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  ''.join(_A		).strip()
         return out_string


   def 			lowercase_      (self     :						str       ,  __UpperCAmelCase     :						Optional[int]		)						->				Any:
         """simple docstring"""



         UpperCAmelCase__								=  []
         for is_user, text in conversation.iter_texts():
               input_ids.extend(self.encode(_A       ,  add_special_tokens=_A		) + [self.eos_token_id]		)

         if len(_A		) > self.model_max_length:
               UpperCAmelCase__								=  input_ids[-self.model_max_length :]
         return input_ids





   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						Any = None		)						->				Any:
         """simple docstring"""



         UpperCAmelCase__								=  0
         if os.path.isdir(_A		):
               UpperCAmelCase__								=  os.path.join(
                   _A       ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
               UpperCAmelCase__								=  os.path.join(
                   _A       ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["emoji_file"]		)
         else:
               UpperCAmelCase__								=  (
                   (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['vocab_file']
               )
               UpperCAmelCase__								=  (
                   (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['emoji_file']
               )
         with open(_A       ,  "w"       ,  encoding="utf-8"		) as writer:
               for token_index, token in self.ids_to_tokens.items():
                     if index != token_index:
                           logger.warning(
                               f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
                               " Please check that the vocabulary is not corrupted!"		)
                           UpperCAmelCase__								=  token_index
                     writer.write(",".join(_A		) + "\n"		)
                     index += 1
         with open(_A       ,  "w"       ,  encoding="utf-8"		) as writer:
               json.dump(self.emoji       ,  _A		)
         return vocab_file, emoji_file





class      A							(				snake_case__     ):


   def __init__(self     :						int       ,  __UpperCAmelCase     :						Optional[Any]       ,  __UpperCAmelCase     :						List[str]       ,  __UpperCAmelCase     :						List[str]		)						->				List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  vocab  # same as swe
         UpperCAmelCase__								=  ids_to_tokens  # same as bpe
         UpperCAmelCase__								=  emoji
         UpperCAmelCase__								=  np.max([len(_A		) for w in self.vocab.keys()]		)
         UpperCAmelCase__								=  re.compile(r"(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)"		)
         UpperCAmelCase__								=  re.compile(r"[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*"		)
         UpperCAmelCase__								=  re.compile(r"[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}"		)
         UpperCAmelCase__								=  re.compile(
             r"([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"		)
         UpperCAmelCase__								=  re.compile(
             r"(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"		)
         UpperCAmelCase__								=  re.compile(
             r"((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*"		)
         UpperCAmelCase__								=  '─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿'
         UpperCAmelCase__								=  '▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟'
         UpperCAmelCase__								=  str.maketrans({k: "<BLOCK>" for k in keisen + blocks}		)


   def __len__(self     :						Tuple		)						->				Optional[Any]:
         """simple docstring"""



         return len(self.ids_to_tokens		)


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						Union[str, Any]		)						->				Any:
         """simple docstring"""



         UpperCAmelCase__								=  self.content_repattera.sub("<URL>"       ,  _A		)
         UpperCAmelCase__								=  self.content_repattera.sub("<EMAIL>"       ,  _A		)
         UpperCAmelCase__								=  self.content_repattera.sub("<TEL>"       ,  _A		)
         UpperCAmelCase__								=  self.content_repattera.sub("<DATE>"       ,  _A		)
         UpperCAmelCase__								=  self.content_repattera.sub("<DATE>"       ,  _A		)
         UpperCAmelCase__								=  self.content_repattera.sub("<PRICE>"       ,  _A		)
         UpperCAmelCase__								=  content.translate(self.content_transa		)
         while "<BLOCK><BLOCK>" in content:
               UpperCAmelCase__								=  content.replace("<BLOCK><BLOCK>"       ,  "<BLOCK>"		)
         return content


   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						List[str]       ,  __UpperCAmelCase     :						Optional[Any]=False		)						->				Dict:
         """simple docstring"""



         UpperCAmelCase__								=  text.replace(" "       ,  "<SP>"		)
         UpperCAmelCase__								=  text.replace("　"       ,  "<SP>"		)
         UpperCAmelCase__								=  text.replace("\r\n"       ,  "<BR>"		)
         UpperCAmelCase__								=  text.replace("\n"       ,  "<BR>"		)
         UpperCAmelCase__								=  text.replace("\r"       ,  "<BR>"		)
         UpperCAmelCase__								=  text.replace("\t"       ,  "<TAB>"		)
         UpperCAmelCase__								=  text.replace("—"       ,  "ー"		)
         UpperCAmelCase__								=  text.replace("−"       ,  "ー"		)
         for k, v in self.emoji["emoji"].items():
               if k in text:
                     UpperCAmelCase__								=  text.replace(_A       ,  _A		)
         if clean:
               UpperCAmelCase__								=  self.clean_text(_A		)

         def check_simbol(__UpperCAmelCase     :						int		):
               UpperCAmelCase__								=  x.encode()
               if len(_A		) == 1 and len(_A		) == 2:
                     UpperCAmelCase__								=  (int(e[0]		) << 8) + int(e[1]		)
                     if (
                         (c >= 0XC_2A1 and c <= 0XC_2BF)
                         or (c >= 0XC_780 and c <= 0XC_783)
                         or (c >= 0XC_AB9 and c <= 0XC_BBF)
                         or (c >= 0XC_C80 and c <= 0XC_DA2)
                     ):
                           return True
               return False

         def checkuae(__UpperCAmelCase     :						str		):
               UpperCAmelCase__								=  x.encode()
               if len(_A		) == 1 and len(_A		) == 3:
                     UpperCAmelCase__								=  (int(e[0]		) << 1_6) + (int(e[1]		) << 8) + int(e[2]		)
                     if c >= 0XE28_080 and c <= 0XE2B_07F:
                           return True
               return False

         UpperCAmelCase__								=  0
         UpperCAmelCase__								=  []
         while pos < len(_A		):
               UpperCAmelCase__								=  min(len(_A		)       ,  pos + self.maxlen + 1		) if text[pos] == '<' else pos + 3
               UpperCAmelCase__								=  []  # (token_id, token, pos)
               for e in range(_A       ,  _A       ,  -1		):
                     UpperCAmelCase__								=  text[pos:e]
                     if wd in self.vocab:
                           if wd[0] == "<" and len(_A		) > 2:
                                 UpperCAmelCase__								=  [(self.vocab[wd], wd, e)]
                                 break
                           else:
                                 candidates.append((self.vocab[wd], wd, e)		)
               if len(_A		) > 0:
                     # the smallest token_id is adopted
                     UpperCAmelCase__								=  sorted(_A       ,  key=lambda __UpperCAmelCase		: x[0]		)[0]
                     result.append(_A		)
                     UpperCAmelCase__								=  e
               else:
                     UpperCAmelCase__								=  pos + 1
                     UpperCAmelCase__								=  text[pos:end]
                     if check_simbol(_A		):
                           result.append("<KIGOU>"		)
                     elif checkuae(_A		):
                           result.append("<U2000U2BFF>"		)
                     else:
                           for i in wd.encode("utf-8"		):
                                 result.append("<|byte%d|>" % i		)
                     UpperCAmelCase__								=  end
         return result





   def 			lowercase_      (self     :						Tuple       ,  __UpperCAmelCase     :						List[Any]       ,  __UpperCAmelCase     :						Union[str, Any]="\n"		)						->				int:
         """simple docstring"""



         UpperCAmelCase__								=  []
         UpperCAmelCase__								=  []
         UpperCAmelCase__								=  self.ids_to_tokens[index][0]
         if word[:6] == "<|byte" and word[-2:] == "|>":
               byte_tokens.append(int(word[6:-2]		)		)
         else:
               if len(_A		) > 0:
                     words.append(bytearray(_A		).decode("utf-8"       ,  errors="replace"		)		)
                     UpperCAmelCase__								=  []
               if word[:7] == "<|emoji" and word[-2:] == "|>":
                     words.append(self.emoji["emoji_inv"][word]		)
               elif word == "<SP>":
                     words.append(" "		)
               elif word == "<BR>":
                     words.append(_A		)
               elif word == "<TAB>":
                     words.append("\t"		)
               elif word == "<BLOCK>":
                     words.append("▀"		)
               elif word == "<KIGOU>":
                     words.append("ǀ"		)
               elif word == "<U2000U2BFF>":
                     words.append("‖"		)
               else:
                     words.append(_A		)
         if len(_A		) > 0:
               words.append(bytearray(_A		).decode("utf-8"       ,  errors="replace"		)		)
         UpperCAmelCase__								=  ''.join(_A		)
         return text










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     )     )




