


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


_snake_case             =     logging.get_logger(__name__)

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

_snake_case             =     {
    '''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''',
    },
}

_snake_case             =     {
    '''abeja/gpt-neox-japanese-2.7b''': 20_48,
}






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  with open(SCREAMING_SNAKE_CASE_      ,						"r"      ,						encoding="utf-8"	) as f:
    lowerCamelCase :					Dict    =				json.loads(f.read()	)

  lowerCamelCase :					List[Any]    =				collections.OrderedDict()
  lowerCamelCase :					Tuple    =				collections.OrderedDict()
  lowerCamelCase :					Tuple    =				collections.OrderedDict()
  with open(SCREAMING_SNAKE_CASE_      ,						"r"      ,						encoding="utf-8"	) as f:
    lowerCamelCase :					Union[str, Any]    =				f.readlines()
  lowerCamelCase :					Any    =				[[t.rstrip("\n"	)] if (t == "," or "," not in t) else t.rstrip("\n"	).split(","	) for t in token]
  for idx, b in enumerate(SCREAMING_SNAKE_CASE_	):
    lowerCamelCase :					Union[str, Any]    =				b
    lowerCamelCase :					Optional[int]    =				idx
    for wd in b:
      lowerCamelCase :					List[str]    =				idx

  return vocab, raw_vocab, ids_to_tokens, emoji
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      List[Any]     		=		VOCAB_FILES_NAMES
   __A :      Dict     		=		PRETRAINED_VOCAB_FILES_MAP
   __A :      List[str]     		=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __A :      Tuple     		=		["input_ids", "attention_mask"]






   def __init__(		self						,				__A						,				__A						,				__A="<|endoftext|>"						,				__A="<|endoftext|>"						,				__A="<|startoftext|>"						,				__A="<|endoftext|>"						,				__A=False						,				**__A						,				):






     """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)`" )
     lowerCamelCase :					List[Any]    =				do_clean_text
     lowerCamelCase    ,	lowerCamelCase    ,	lowerCamelCase    ,	lowerCamelCase :					Dict    =				load_vocab_and_emoji(__A						,				__A )
     lowerCamelCase :					List[Any]    =				SubWordJapaneseTokenizer(
         vocab=self.vocab						,				ids_to_tokens=self.ids_to_tokens						,				emoji=self.emoji )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     return len(self.raw_vocab )






   def _snake_case    (		self ):






     """simple docstring"""





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






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





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






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





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






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     return self.subword_tokenizer.convert_id_to_token(__A )






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Union[str, Any]    =				"".join(__A ).strip()
     return out_string






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Optional[Any]    =				[]
     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:
       lowerCamelCase :					List[str]    =				input_ids[-self.model_max_length :]
     return input_ids






   def _snake_case    (		self						,				__A						,				__A = None ):






     """simple docstring"""





     lowerCamelCase :					Optional[Any]    =				0
     if os.path.isdir(__A ):
       lowerCamelCase :					Union[str, Any]    =				os.path.join(
           __A						,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
       lowerCamelCase :					Optional[int]    =				os.path.join(
           __A						,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["emoji_file"] )
     else:
       lowerCamelCase :					Optional[Any]    =				(
           (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["vocab_file"]
       )
       lowerCamelCase :					Tuple    =				(
           (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!" )
           lowerCamelCase :					Union[str, Any]    =				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 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   def __init__(		self						,				__A						,				__A						,				__A ):






     """simple docstring"""





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






   def __len__(		self ):






     """simple docstring"""





     return len(self.ids_to_tokens )






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Union[str, Any]    =				self.content_repattera.sub("<URL>"						,				__A )
     lowerCamelCase :					Dict    =				self.content_repattera.sub("<EMAIL>"						,				__A )
     lowerCamelCase :					int    =				self.content_repattera.sub("<TEL>"						,				__A )
     lowerCamelCase :					Union[str, Any]    =				self.content_repattera.sub("<DATE>"						,				__A )
     lowerCamelCase :					Union[str, Any]    =				self.content_repattera.sub("<DATE>"						,				__A )
     lowerCamelCase :					int    =				self.content_repattera.sub("<PRICE>"						,				__A )
     lowerCamelCase :					Any    =				content.translate(self.content_transa )
     while "<BLOCK><BLOCK>" in content:
       lowerCamelCase :					Any    =				content.replace("<BLOCK><BLOCK>"						,				"<BLOCK>" )
     return content






   def _snake_case    (		self						,				__A						,				__A=False ):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				text.replace(" "						,				"<SP>" )
     lowerCamelCase :					Dict    =				text.replace("　"						,				"<SP>" )
     lowerCamelCase :					Dict    =				text.replace("\r\n"						,				"<BR>" )
     lowerCamelCase :					Tuple    =				text.replace("\n"						,				"<BR>" )
     lowerCamelCase :					Dict    =				text.replace("\r"						,				"<BR>" )
     lowerCamelCase :					Dict    =				text.replace("\t"						,				"<TAB>" )
     lowerCamelCase :					str    =				text.replace("—"						,				"ー" )
     lowerCamelCase :					Any    =				text.replace("−"						,				"ー" )
     for k, v in self.emoji["emoji"].items():
       if k in text:
         lowerCamelCase :					Tuple    =				text.replace(__A						,				__A )
     if clean:
       lowerCamelCase :					str    =				self.clean_text(__A )

     def check_simbol(__A ):
       lowerCamelCase :					List[Any]    =				x.encode()
       if len(__A ) == 1 and len(__A ) == 2:
         lowerCamelCase :					List[Any]    =				(int(e[0] ) << 8) + int(e[1] )
         if (
             (c >= 0xC2_A1 and c <= 0xC2_BF)
             or (c >= 0xC7_80 and c <= 0xC7_83)
             or (c >= 0xCA_B9 and c <= 0xCB_BF)
             or (c >= 0xCC_80 and c <= 0xCD_A2)
         ):
           return True
       return False

     def checkuae(__A ):
       lowerCamelCase :					Optional[Any]    =				x.encode()
       if len(__A ) == 1 and len(__A ) == 3:
         lowerCamelCase :					Optional[Any]    =				(int(e[0] ) << 16) + (int(e[1] ) << 8) + int(e[2] )
         if c >= 0xE2_80_80 and c <= 0xE2_B0_7F:
           return True
       return False

     lowerCamelCase :					Optional[Any]    =				0
     lowerCamelCase :					Optional[Any]    =				[]
     while pos < len(__A ):
       lowerCamelCase :					Any    =				min(len(__A )						,				pos + self.maxlen + 1 ) if text[pos] == "<" else pos + 3
       lowerCamelCase :					Optional[Any]    =				[]  # (token_id, token, pos)
       for e in range(__A						,				__A						,				-1 ):
         lowerCamelCase :					str    =				text[pos:e]
         if wd in self.vocab:
           if wd[0] == "<" and len(__A ) > 2:
             lowerCamelCase :					Dict    =				[(self.vocab[wd], wd, e)]
             break
           else:
             candidates.append((self.vocab[wd], wd, e) )
       if len(__A ) > 0:
         # the smallest token_id is adopted
         lowerCamelCase    ,	lowerCamelCase    ,	lowerCamelCase :					Union[str, Any]    =				sorted(__A						,				key=lambda __A : x[0] )[0]
         result.append(__A )
         lowerCamelCase :					Optional[Any]    =				e
       else:
         lowerCamelCase :					Union[str, Any]    =				pos + 1
         lowerCamelCase :					Union[str, Any]    =				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 )
         lowerCamelCase :					int    =				end
     return result






   def _snake_case    (		self						,				__A						,				__A="\n" ):






     """simple docstring"""





     lowerCamelCase :					List[Any]    =				[]
     lowerCamelCase :					Optional[Any]    =				[]
     lowerCamelCase :					Optional[int]    =				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" ) )
         lowerCamelCase :					Any    =				[]
       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" ) )
     lowerCamelCase :					Optional[int]    =				"".join(__A )
     return text







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import OwlViTImageProcessor, OwlViTProcessor
@require_vision
class 	UpperCAmelCase_					(					unittest.TestCase    ):

   '''simple docstring'''




   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					str    =				tempfile.mkdtemp()

     # fmt: off
     lowerCamelCase :					Any    =				["", "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
     # fmt: on
     lowerCamelCase :					List[Any]    =				dict(zip(__A						,				range(len(__A ) ) ) )
     lowerCamelCase :					List[Any]    =				["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
     lowerCamelCase :					Optional[Any]    =				{"unk_token": "<unk>"}

     lowerCamelCase :					int    =				os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES["vocab_file"] )
     lowerCamelCase :					List[Any]    =				os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES["merges_file"] )
     with open(self.vocab_file						,				"w"						,				encoding="utf-8" ) as fp:
       fp.write(json.dumps(__A ) + "\n" )
     with open(self.merges_file						,				"w"						,				encoding="utf-8" ) as fp:
       fp.write("\n".join(__A ) )

     lowerCamelCase :					str    =				{
         "do_resize": True,
         "size": 20,
         "do_center_crop": True,
         "crop_size": 18,
         "do_normalize": True,
         "image_mean": [0.48145466, 0.4578275, 0.40821073],
         "image_std": [0.26862954, 0.26130258, 0.27577711],
     }
     lowerCamelCase :					str    =				os.path.join(self.tmpdirname						,				__A )
     with open(self.image_processor_file						,				"w"						,				encoding="utf-8" ) as fp:
       json.dump(__A						,				__A )






   def _snake_case    (		self						,				**__A ):






     """simple docstring"""





     return CLIPTokenizer.from_pretrained(self.tmpdirname						,				pad_token="!"						,				**__A )






   def _snake_case    (		self						,				**__A ):






     """simple docstring"""





     return CLIPTokenizerFast.from_pretrained(self.tmpdirname						,				pad_token="!"						,				**__A )






   def _snake_case    (		self						,				**__A ):






     """simple docstring"""





     return OwlViTImageProcessor.from_pretrained(self.tmpdirname						,				**__A )






   def _snake_case    (		self ):






     """simple docstring"""





     shutil.rmtree(self.tmpdirname )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Dict    =				[np.random.randint(255						,				size=(3, 30, 400)						,				dtype=np.uinta )]

     lowerCamelCase :					Tuple    =				[Image.fromarray(np.moveaxis(__A						,				0						,				-1 ) ) for x in image_inputs]

     return image_inputs






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[Any]    =				self.get_tokenizer()
     lowerCamelCase :					Optional[Any]    =				self.get_rust_tokenizer()
     lowerCamelCase :					Tuple    =				self.get_image_processor()

     lowerCamelCase :					List[Any]    =				OwlViTProcessor(tokenizer=__A						,				image_processor=__A )
     processor_slow.save_pretrained(self.tmpdirname )
     lowerCamelCase :					Optional[Any]    =				OwlViTProcessor.from_pretrained(self.tmpdirname						,				use_fast=__A )

     lowerCamelCase :					Optional[int]    =				OwlViTProcessor(tokenizer=__A						,				image_processor=__A )
     processor_fast.save_pretrained(self.tmpdirname )
     lowerCamelCase :					Tuple    =				OwlViTProcessor.from_pretrained(self.tmpdirname )

     self.assertEqual(processor_slow.tokenizer.get_vocab()						,				tokenizer_slow.get_vocab() )
     self.assertEqual(processor_fast.tokenizer.get_vocab()						,				tokenizer_fast.get_vocab() )
     self.assertEqual(tokenizer_slow.get_vocab()						,				tokenizer_fast.get_vocab() )
     self.assertIsInstance(processor_slow.tokenizer						,				__A )
     self.assertIsInstance(processor_fast.tokenizer						,				__A )

     self.assertEqual(processor_slow.image_processor.to_json_string()						,				image_processor.to_json_string() )
     self.assertEqual(processor_fast.image_processor.to_json_string()						,				image_processor.to_json_string() )
     self.assertIsInstance(processor_slow.image_processor						,				__A )
     self.assertIsInstance(processor_fast.image_processor						,				__A )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Optional[Any]    =				OwlViTProcessor(tokenizer=self.get_tokenizer()						,				image_processor=self.get_image_processor() )
     processor.save_pretrained(self.tmpdirname )

     lowerCamelCase :					int    =				self.get_tokenizer(bos_token="(BOS)"						,				eos_token="(EOS)" )
     lowerCamelCase :					List[str]    =				self.get_image_processor(do_normalize=__A )

     lowerCamelCase :					Optional[int]    =				OwlViTProcessor.from_pretrained(
         self.tmpdirname						,				bos_token="(BOS)"						,				eos_token="(EOS)"						,				do_normalize=__A )

     self.assertEqual(processor.tokenizer.get_vocab()						,				tokenizer_add_kwargs.get_vocab() )
     self.assertIsInstance(processor.tokenizer						,				__A )

     self.assertEqual(processor.image_processor.to_json_string()						,				image_processor_add_kwargs.to_json_string() )
     self.assertIsInstance(processor.image_processor						,				__A )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[Any]    =				self.get_image_processor()
     lowerCamelCase :					Optional[int]    =				self.get_tokenizer()

     lowerCamelCase :					Dict    =				OwlViTProcessor(tokenizer=__A						,				image_processor=__A )

     lowerCamelCase :					Tuple    =				self.prepare_image_inputs()

     lowerCamelCase :					int    =				image_processor(__A						,				return_tensors="np" )
     lowerCamelCase :					Union[str, Any]    =				processor(images=__A						,				return_tensors="np" )

     for key in input_image_proc.keys():
       self.assertAlmostEqual(input_image_proc[key].sum()						,				input_processor[key].sum()						,				delta=1e-2 )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Union[str, Any]    =				self.get_image_processor()
     lowerCamelCase :					Dict    =				self.get_tokenizer()

     lowerCamelCase :					Union[str, Any]    =				OwlViTProcessor(tokenizer=__A						,				image_processor=__A )

     lowerCamelCase :					Tuple    =				"lower newer"

     lowerCamelCase :					Union[str, Any]    =				processor(text=__A						,				return_tensors="np" )

     lowerCamelCase :					List[Any]    =				tokenizer(__A						,				return_tensors="np" )

     for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key][0].tolist()						,				encoded_processor[key][0].tolist() )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Any    =				self.get_image_processor()
     lowerCamelCase :					Any    =				self.get_tokenizer()

     lowerCamelCase :					int    =				OwlViTProcessor(tokenizer=__A						,				image_processor=__A )

     lowerCamelCase :					Optional[Any]    =				"lower newer"
     lowerCamelCase :					Dict    =				self.prepare_image_inputs()

     lowerCamelCase :					Any    =				processor(text=__A						,				images=__A )

     self.assertListEqual(list(inputs.keys() )						,				["input_ids", "attention_mask", "pixel_values"] )

     # test if it raises when no input is passed
     with pytest.raises(__A ):
       processor()






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Any    =				"google/owlvit-base-patch32"
     lowerCamelCase :					List[Any]    =				OwlViTProcessor.from_pretrained(__A )

     lowerCamelCase :					Tuple    =				["cat", "nasa badge"]
     lowerCamelCase :					str    =				processor(text=__A )

     lowerCamelCase :					Union[str, Any]    =				16
     self.assertListEqual(list(inputs.keys() )						,				["input_ids", "attention_mask"] )
     self.assertEqual(inputs["input_ids"].shape						,				(2, seq_length) )

     # test if it raises when no input is passed
     with pytest.raises(__A ):
       processor()






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					str    =				"google/owlvit-base-patch32"
     lowerCamelCase :					Optional[int]    =				OwlViTProcessor.from_pretrained(__A )

     lowerCamelCase :					Dict    =				[["cat", "nasa badge"], ["person"]]
     lowerCamelCase :					int    =				processor(text=__A )

     lowerCamelCase :					Tuple    =				16
     lowerCamelCase :					Any    =				len(__A )
     lowerCamelCase :					Optional[Any]    =				max([len(__A ) for texts in input_texts] )

     self.assertListEqual(list(inputs.keys() )						,				["input_ids", "attention_mask"] )
     self.assertEqual(inputs["input_ids"].shape						,				(batch_size * num_max_text_queries, seq_length) )

     # test if it raises when no input is passed
     with pytest.raises(__A ):
       processor()






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Dict    =				"google/owlvit-base-patch32"
     lowerCamelCase :					Tuple    =				OwlViTProcessor.from_pretrained(__A )

     lowerCamelCase :					List[Any]    =				["cat", "nasa badge"]
     lowerCamelCase :					Optional[Any]    =				processor(text=__A )

     lowerCamelCase :					int    =				16
     lowerCamelCase :					List[str]    =				inputs["input_ids"]
     lowerCamelCase :					int    =				[
         [4_9406, 2368, 4_9407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
         [4_9406, 6841, 1_1301, 4_9407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
     ]

     self.assertListEqual(list(inputs.keys() )						,				["input_ids", "attention_mask"] )
     self.assertEqual(inputs["input_ids"].shape						,				(2, seq_length) )
     self.assertListEqual(list(input_ids[0] )						,				predicted_ids[0] )
     self.assertListEqual(list(input_ids[1] )						,				predicted_ids[1] )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Any    =				self.get_image_processor()
     lowerCamelCase :					List[str]    =				self.get_tokenizer()

     lowerCamelCase :					str    =				OwlViTProcessor(tokenizer=__A						,				image_processor=__A )

     lowerCamelCase :					Dict    =				self.prepare_image_inputs()
     lowerCamelCase :					Union[str, Any]    =				self.prepare_image_inputs()

     lowerCamelCase :					Any    =				processor(images=__A						,				query_images=__A )

     self.assertListEqual(list(inputs.keys() )						,				["query_pixel_values", "pixel_values"] )

     # test if it raises when no input is passed
     with pytest.raises(__A ):
       processor()






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Optional[Any]    =				self.get_image_processor()
     lowerCamelCase :					Optional[int]    =				self.get_tokenizer()

     lowerCamelCase :					Dict    =				OwlViTProcessor(tokenizer=__A						,				image_processor=__A )

     lowerCamelCase :					Optional[int]    =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     lowerCamelCase :					List[Any]    =				processor.batch_decode(__A )
     lowerCamelCase :					Union[str, Any]    =				tokenizer.batch_decode(__A )

     self.assertListEqual(__A						,				__A )



