



"""simple docstring"""
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 CLIPImageProcessor, CLIPProcessor




@require_vision
class 			snake_case_						(						unittest.TestCase				):






							def snake_case_  (   self      ):
									a_     :      int 						=     tempfile.mkdtemp()

									# fmt: off
									a_     :      int 						=     ["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
									a_     :      List[str] 						=     dict(zip(a_			,     range(len(a_      )      )      )      )
									a_     :      Optional[int] 						=     ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
									a_     :      List[str] 						=     {"unk_token": "<unk>"}

									a_     :      Optional[int] 						=     os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES["vocab_file"]      )
									a_     :      str 						=     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_      )      )

									a_     :      Dict 						=     {
									    "do_resize": True,
									    "size": 2_0,
									    "do_center_crop": True,
									    "crop_size": 1_8,
									    "do_normalize": True,
									    "image_mean": [0.48_145_466, 0.4_578_275, 0.40_821_073],
									    "image_std": [0.26_862_954, 0.26_130_258, 0.27_577_711],
									}
									a_     :      Any 						=     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_      ):
									return CLIPTokenizer.from_pretrained(self.tmpdirname			,     **a_      )






							def snake_case_  (   self			,     **a_      ):
									return CLIPTokenizerFast.from_pretrained(self.tmpdirname			,     **a_      )






							def snake_case_  (   self			,     **a_      ):
									return CLIPImageProcessor.from_pretrained(self.tmpdirname			,     **a_      )






							def snake_case_  (   self      ):
									shutil.rmtree(self.tmpdirname      )






							def snake_case_  (   self      ):
									a_     :      Any 						=     [np.random.randint(2_5_5			,     size=(3, 3_0, 4_0_0)			,     dtype=np.uinta      )]

									a_     :      List[str] 						=     [Image.fromarray(np.moveaxis(a_			,     0			,     -1      )      ) for x in image_inputs]

									return image_inputs






							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     self.get_tokenizer()
									a_     :      Optional[Any] 						=     self.get_rust_tokenizer()
									a_     :      Tuple 						=     self.get_image_processor()

									a_     :      Any 						=     CLIPProcessor(tokenizer=a_			,     image_processor=a_      )
									processor_slow.save_pretrained(self.tmpdirname      )
									a_     :      Tuple 						=     CLIPProcessor.from_pretrained(self.tmpdirname			,     use_fast=a_      )

									a_     :      Optional[int] 						=     CLIPProcessor(tokenizer=a_			,     image_processor=a_      )
									processor_fast.save_pretrained(self.tmpdirname      )
									a_     :      List[str] 						=     CLIPProcessor.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      ):
									a_     :      Optional[Any] 						=     CLIPProcessor(tokenizer=self.get_tokenizer()			,     image_processor=self.get_image_processor()      )
									processor.save_pretrained(self.tmpdirname      )

									a_     :      Union[str, Any] 						=     self.get_tokenizer(bos_token="(BOS)"			,     eos_token="(EOS)"      )
									a_     :      str 						=     self.get_image_processor(do_normalize=a_			,     padding_value=1.0      )

									a_     :      int 						=     CLIPProcessor.from_pretrained(
									    self.tmpdirname			,     bos_token="(BOS)"			,     eos_token="(EOS)"			,     do_normalize=a_			,     padding_value=1.0      )

									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      ):
									a_     :      int 						=     self.get_image_processor()
									a_     :      Dict 						=     self.get_tokenizer()

									a_     :      Dict 						=     CLIPProcessor(tokenizer=a_			,     image_processor=a_      )

									a_     :      List[Any] 						=     self.prepare_image_inputs()

									a_     :      Dict 						=     image_processor(a_			,     return_tensors="np"      )
									a_     :      Optional[int] 						=     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      ):
									a_     :      List[Any] 						=     self.get_image_processor()
									a_     :      List[str] 						=     self.get_tokenizer()

									a_     :      List[str] 						=     CLIPProcessor(tokenizer=a_			,     image_processor=a_      )

									a_     :      Union[str, Any] 						=     "lower newer"

									a_     :      Optional[Any] 						=     processor(text=a_      )

									a_     :      List[Any] 						=     tokenizer(a_      )

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






							def snake_case_  (   self      ):
									a_     :      Optional[int] 						=     self.get_image_processor()
									a_     :      Optional[Any] 						=     self.get_tokenizer()

									a_     :      Union[str, Any] 						=     CLIPProcessor(tokenizer=a_			,     image_processor=a_      )

									a_     :      Union[str, Any] 						=     "lower newer"
									a_     :      Any 						=     self.prepare_image_inputs()

									a_     :      int 						=     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      ):
									a_     :      Dict 						=     self.get_image_processor()
									a_     :      int 						=     self.get_tokenizer()

									a_     :      Optional[int] 						=     CLIPProcessor(tokenizer=a_			,     image_processor=a_      )

									a_     :      List[Any] 						=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									a_     :      Optional[int] 						=     processor.batch_decode(a_      )
									a_     :      Any 						=     tokenizer.batch_decode(a_      )

									self.assertListEqual(a_			,     a_      )






							def snake_case_  (   self      ):
									a_     :      Dict 						=     self.get_image_processor()
									a_     :      List[str] 						=     self.get_tokenizer()

									a_     :      Union[str, Any] 						=     CLIPProcessor(tokenizer=a_			,     image_processor=a_      )

									a_     :      List[Any] 						=     "lower newer"
									a_     :      Optional[int] 						=     self.prepare_image_inputs()

									a_     :      List[str] 						=     processor(text=a_			,     images=a_      )

									self.assertListEqual(list(inputs.keys()      )			,     processor.model_input_names      )





"""simple docstring"""
import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					["image_processor", "tokenizer"]
							__lowerCAmelCase    					=					"ViltImageProcessor"
							__lowerCAmelCase    					=					("BertTokenizer", "BertTokenizerFast")






							def __init__(   self			,     a_=None			,     a_=None			,     **a_      ):
									a_     :      int 						=     None
									if "feature_extractor" in kwargs:
											warnings.warn(
											    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
											    " instead."			,     a_			,     )
											a_     :      List[Any] 						=     kwargs.pop("feature_extractor"      )

									a_     :      List[str] 						=     image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
											raise ValueError("You need to specify an `image_processor`."      )
									if tokenizer is None:
											raise ValueError("You need to specify a `tokenizer`."      )

									super().__init__(a_			,     a_      )
									a_     :      Dict 						=     self.image_processor






							def __call__(   self			,     a_			,     a_ = None			,     a_ = True			,     a_ = False			,     a_ = None			,     a_ = None			,     a_ = 0			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = False			,     a_ = False			,     a_ = False			,     a_ = False			,     a_ = True			,     a_ = None			,     **a_			,     ):
									a_     :      Union[str, Any] 						=     self.tokenizer(
									    text=a_			,     add_special_tokens=a_			,     padding=a_			,     truncation=a_			,     max_length=a_			,     stride=a_			,     pad_to_multiple_of=a_			,     return_token_type_ids=a_			,     return_attention_mask=a_			,     return_overflowing_tokens=a_			,     return_special_tokens_mask=a_			,     return_offsets_mapping=a_			,     return_length=a_			,     verbose=a_			,     return_tensors=a_			,     **a_			,     )
									# add pixel_values + pixel_mask
									a_     :      List[Any] 						=     self.image_processor(a_			,     return_tensors=a_      )
									encoding.update(a_      )

									return encoding






							def snake_case_  (   self			,     *a_			,     **a_      ):
									return self.tokenizer.batch_decode(*a_			,     **a_      )






							def snake_case_  (   self			,     *a_			,     **a_      ):
									return self.tokenizer.decode(*a_			,     **a_      )






							@property
							def snake_case_  (   self      ):
									a_     :      Union[str, Any] 						=     self.tokenizer.model_input_names
									a_     :      Dict 						=     self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )






							@property
							def snake_case_  (   self      ):
									warnings.warn(
									    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."			,     a_			,     )
									return self.image_processor_class






							@property
							def snake_case_  (   self      ):
									warnings.warn(
									    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,     a_			,     )
									return self.image_processor