
"""simple docstring"""




import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert 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 FlavaImageProcessor, FlavaProcessor
			from transformers.models.flava.image_processing_flava import (
			    FLAVA_CODEBOOK_MEAN,
			    FLAVA_CODEBOOK_STD,
			    FLAVA_IMAGE_MEAN,
			    FLAVA_IMAGE_STD,
			)

@require_vision
class 		a__						(				unittest.TestCase						):
			def       __magic_name__      (      self    ):
										lowercase   :    Tuple			=							tempfile.mkdtemp()

										# fmt: off
										lowercase   :    List[str]			=							["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
										# fmt: on
										lowercase   :    Optional[Any]			=							os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES["vocab_file"]    )

										with open(self.vocab_file     ,   "w"     ,   encoding="utf-8"    ) as fp:
																	fp.write("".join([x + "\n" for x in vocab_tokens]    )    )

										lowercase   :    Union[str, Any]			=							{
										    "image_mean": FLAVA_IMAGE_MEAN,
										    "image_std": FLAVA_IMAGE_STD,
										    "do_normalize": True,
										    "do_resize": True,
										    "size": 224,
										    "do_center_crop": True,
										    "crop_size": 224,
										    "input_size_patches": 14,
										    "total_mask_patches": 75,
										    "mask_group_max_patches": None,
										    "mask_group_min_patches": 16,
										    "mask_group_min_aspect_ratio": 0.3,
										    "mask_group_max_aspect_ratio": None,
										    "codebook_do_resize": True,
										    "codebook_size": 112,
										    "codebook_do_center_crop": True,
										    "codebook_crop_size": 112,
										    "codebook_do_map_pixels": True,
										    "codebook_do_normalize": True,
										    "codebook_image_mean": FLAVA_CODEBOOK_MEAN,
										    "codebook_image_std": FLAVA_CODEBOOK_STD,
										}

										lowercase   :    Tuple			=							os.path.join(self.tmpdirname     ,   _a    )
										with open(self.image_processor_file     ,   "w"     ,   encoding="utf-8"    ) as fp:
																	json.dump(_a     ,   _a    )
			def       __magic_name__      (      self     ,   **_a    ):
										return BertTokenizer.from_pretrained(self.tmpdirname     ,   **_a    )
			def       __magic_name__      (      self     ,   **_a    ):
										return BertTokenizerFast.from_pretrained(self.tmpdirname     ,   **_a    )
			def       __magic_name__      (      self     ,   **_a    ):
										return FlavaImageProcessor.from_pretrained(self.tmpdirname     ,   **_a    )
			def       __magic_name__      (      self    ):
										shutil.rmtree(self.tmpdirname    )
			def       __magic_name__      (      self    ):
										lowercase   :    int			=							[np.random.randint(255     ,   size=(3, 30, 400)     ,   dtype=np.uinta    )]

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

										return image_inputs
			def       __magic_name__      (      self    ):
										lowercase   :    Optional[Any]			=							self.get_tokenizer()
										lowercase   :    List[Any]			=							self.get_rust_tokenizer()
										lowercase   :    List[str]			=							self.get_image_processor()

										lowercase   :    Optional[int]			=							FlavaProcessor(tokenizer=_a     ,   image_processor=_a    )
										processor_slow.save_pretrained(self.tmpdirname    )
										lowercase   :    int			=							FlavaProcessor.from_pretrained(self.tmpdirname     ,   use_fast=_a    )

										lowercase   :    Optional[int]			=							FlavaProcessor(tokenizer=_a     ,   image_processor=_a    )
										processor_fast.save_pretrained(self.tmpdirname    )
										lowercase   :    Optional[Any]			=							FlavaProcessor.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       __magic_name__      (      self    ):
										lowercase   :    Union[str, Any]			=							FlavaProcessor(tokenizer=self.get_tokenizer()     ,   image_processor=self.get_image_processor()    )
										processor.save_pretrained(self.tmpdirname    )

										lowercase   :    Optional[Any]			=							self.get_tokenizer(bos_token="(BOS)"     ,   eos_token="(EOS)"    )
										lowercase   :    Tuple			=							self.get_image_processor(do_normalize=_a     ,   padding_value=1.0    )

										lowercase   :    Dict			=							FlavaProcessor.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       __magic_name__      (      self    ):
										lowercase   :    Any			=							self.get_image_processor()
										lowercase   :    List[Any]			=							self.get_tokenizer()

										lowercase   :    Dict			=							FlavaProcessor(tokenizer=_a     ,   image_processor=_a    )

										lowercase   :    str			=							self.prepare_image_inputs()

										lowercase   :    Tuple			=							image_processor(_a     ,   return_tensors="np"    )
										lowercase   :    List[str]			=							processor(images=_a     ,   return_tensors="np"    )

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

										# With rest of the args
										random.seed(1_234    )
										lowercase   :    Union[str, Any]			=							image_processor(
										    _a     ,   return_image_mask=_a     ,   return_codebook_pixels=_a     ,   return_tensors="np"    )
										random.seed(1_234    )
										lowercase   :    Optional[int]			=							processor(
										    images=_a     ,   return_image_mask=_a     ,   return_codebook_pixels=_a     ,   return_tensors="np"    )

										for key in input_feat_extract.keys():
																	self.assertAlmostEqual(input_feat_extract[key].sum()     ,   input_processor[key].sum()     ,   delta=1E-2    )
			def       __magic_name__      (      self    ):
										lowercase   :    List[str]			=							self.get_image_processor()
										lowercase   :    Union[str, Any]			=							self.get_tokenizer()

										lowercase   :    Optional[Any]			=							FlavaProcessor(tokenizer=_a     ,   image_processor=_a    )

										lowercase   :    List[Any]			=							"lower newer"

										lowercase   :    int			=							processor(text=_a    )

										lowercase   :    Dict			=							tokenizer(_a    )

										for key in encoded_tok.keys():
																	self.assertListEqual(encoded_tok[key]     ,   encoded_processor[key]    )
			def       __magic_name__      (      self    ):
										lowercase   :    int			=							self.get_image_processor()
										lowercase   :    Optional[int]			=							self.get_tokenizer()

										lowercase   :    Tuple			=							FlavaProcessor(tokenizer=_a     ,   image_processor=_a    )

										lowercase   :    List[str]			=							"lower newer"
										lowercase   :    Tuple			=							self.prepare_image_inputs()

										lowercase   :    Optional[Any]			=							processor(text=_a     ,   images=_a    )

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

										# add extra args
										lowercase   :    str			=							processor(text=_a     ,   images=_a     ,   return_codebook_pixels=_a     ,   return_image_mask=_a    )

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

										# test if it raises when no input is passed
										with pytest.raises(_a    ):
																	processor()
			def       __magic_name__      (      self    ):
										lowercase   :    Dict			=							self.get_image_processor()
										lowercase   :    List[str]			=							self.get_tokenizer()

										lowercase   :    List[str]			=							FlavaProcessor(tokenizer=_a     ,   image_processor=_a    )

										lowercase   :    Dict			=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										lowercase   :    List[Any]			=							processor.batch_decode(_a    )
										lowercase   :    str			=							tokenizer.batch_decode(_a    )

										self.assertListEqual(_a     ,   _a    )

			def       __magic_name__      (      self    ):
										lowercase   :    Optional[int]			=							self.get_image_processor()
										lowercase   :    List[str]			=							self.get_tokenizer()

										lowercase   :    Any			=							FlavaProcessor(tokenizer=_a     ,   image_processor=_a    )

										lowercase   :    int			=							"lower newer"
										lowercase   :    Tuple			=							self.prepare_image_inputs()

										lowercase   :    Optional[Any]			=							processor(text=_a     ,   images=_a    )

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




"""simple docstring"""




import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


_A      :	List[str]       		=	logging.get_logger(__name__)

class 		a__						(				a_						):
   def __init__(      self     ,   *_a     ,   **_a    ):
          warnings.warn(
              "The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
              " Please use ConvNextImageProcessor instead."     ,   _a     ,   )
          super().__init__(*_a     ,   **_a    )


