




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    snake_case__							(unittest.TestCase       ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					Optional[Any]:
							a   =      tempfile.mkdtemp()

							# fmt: off
							a   =      ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
							# fmt: on
							a   =      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]							)							)

							a   =      {
							    "image_mean": FLAVA_IMAGE_MEAN,
							    "image_std": FLAVA_IMAGE_STD,
							    "do_normalize": True,
							    "do_resize": True,
							    "size": 2_24,
							    "do_center_crop": True,
							    "crop_size": 2_24,
							    "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": 1_12,
							    "codebook_do_center_crop": True,
							    "codebook_crop_size": 1_12,
							    "codebook_do_map_pixels": True,
							    "codebook_do_normalize": True,
							    "codebook_image_mean": FLAVA_CODEBOOK_MEAN,
							    "codebook_image_std": FLAVA_CODEBOOK_STD,
							}

							a   =      os.path.join(self.tmpdirname ,				__lowerCamelCase							)
							with open(self.image_processor_file ,				"w" ,				encoding="utf-8"							) as fp:
										json.dump(__lowerCamelCase ,				__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					str ,				**__lowerCamelCase    :					str							) ->					Dict:
							return BertTokenizer.from_pretrained(self.tmpdirname ,				**__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					int ,				**__lowerCamelCase    :					Tuple							) ->					Optional[int]:
							return BertTokenizerFast.from_pretrained(self.tmpdirname ,				**__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					List[Any] ,				**__lowerCamelCase    :					Tuple							) ->					str:
							return FlavaImageProcessor.from_pretrained(self.tmpdirname ,				**__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					List[str]							) ->					Dict:
							shutil.rmtree(self.tmpdirname							)





				def 							__UpperCAmelCase							(				self    :					Dict							) ->					Any:
							a   =      [np.random.randint(2_55 ,				size=(3, 30, 4_00) ,				dtype=np.uinta							)]

							a   =      [Image.fromarray(np.moveaxis(__lowerCamelCase ,				0 ,				-1							)							) for x in image_inputs]

							return image_inputs





				def 							__UpperCAmelCase							(				self    :					List[str]							) ->					Any:
							a   =      self.get_tokenizer()
							a   =      self.get_rust_tokenizer()
							a   =      self.get_image_processor()

							a   =      FlavaProcessor(tokenizer=__lowerCamelCase ,				image_processor=__lowerCamelCase							)
							processor_slow.save_pretrained(self.tmpdirname							)
							a   =      FlavaProcessor.from_pretrained(self.tmpdirname ,				use_fast=__lowerCamelCase							)

							a   =      FlavaProcessor(tokenizer=__lowerCamelCase ,				image_processor=__lowerCamelCase							)
							processor_fast.save_pretrained(self.tmpdirname							)
							a   =      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 ,				__lowerCamelCase							)
							self.assertIsInstance(processor_fast.tokenizer ,				__lowerCamelCase							)

							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 ,				__lowerCamelCase							)
							self.assertIsInstance(processor_fast.image_processor ,				__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					Any:
							a   =      FlavaProcessor(tokenizer=self.get_tokenizer() ,				image_processor=self.get_image_processor()							)
							processor.save_pretrained(self.tmpdirname							)

							a   =      self.get_tokenizer(bos_token="(BOS)" ,				eos_token="(EOS)"							)
							a   =      self.get_image_processor(do_normalize=__lowerCamelCase ,				padding_value=1.0							)

							a   =      FlavaProcessor.from_pretrained(
							    self.tmpdirname ,				bos_token="(BOS)" ,				eos_token="(EOS)" ,				do_normalize=__lowerCamelCase ,				padding_value=1.0							)

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

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





				def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					Union[str, Any]:
							a   =      self.get_image_processor()
							a   =      self.get_tokenizer()

							a   =      FlavaProcessor(tokenizer=__lowerCamelCase ,				image_processor=__lowerCamelCase							)

							a   =      self.prepare_image_inputs()

							a   =      image_processor(__lowerCamelCase ,				return_tensors="np"							)
							a   =      processor(images=__lowerCamelCase ,				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(12_34							)
							a   =      image_processor(
							    __lowerCamelCase ,				return_image_mask=__lowerCamelCase ,				return_codebook_pixels=__lowerCamelCase ,				return_tensors="np"							)
							random.seed(12_34							)
							a   =      processor(
							    images=__lowerCamelCase ,				return_image_mask=__lowerCamelCase ,				return_codebook_pixels=__lowerCamelCase ,				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 							__UpperCAmelCase							(				self    :					Dict							) ->					List[Any]:
							a   =      self.get_image_processor()
							a   =      self.get_tokenizer()

							a   =      FlavaProcessor(tokenizer=__lowerCamelCase ,				image_processor=__lowerCamelCase							)

							a   =      "lower newer"

							a   =      processor(text=__lowerCamelCase							)

							a   =      tokenizer(__lowerCamelCase							)

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





				def 							__UpperCAmelCase							(				self    :					Any							) ->					Optional[Any]:
							a   =      self.get_image_processor()
							a   =      self.get_tokenizer()

							a   =      FlavaProcessor(tokenizer=__lowerCamelCase ,				image_processor=__lowerCamelCase							)

							a   =      "lower newer"
							a   =      self.prepare_image_inputs()

							a   =      processor(text=__lowerCamelCase ,				images=__lowerCamelCase							)

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

							# add extra args
							a   =      processor(text=__lowerCamelCase ,				images=__lowerCamelCase ,				return_codebook_pixels=__lowerCamelCase ,				return_image_mask=__lowerCamelCase							)

							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(__lowerCamelCase							):
										processor()





				def 							__UpperCAmelCase							(				self    :					List[str]							) ->					str:
							a   =      self.get_image_processor()
							a   =      self.get_tokenizer()

							a   =      FlavaProcessor(tokenizer=__lowerCamelCase ,				image_processor=__lowerCamelCase							)

							a   =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							a   =      processor.batch_decode(__lowerCamelCase							)
							a   =      tokenizer.batch_decode(__lowerCamelCase							)

							self.assertListEqual(__lowerCamelCase ,				__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Any							) ->					Optional[int]:
							a   =      self.get_image_processor()
							a   =      self.get_tokenizer()

							a   =      FlavaProcessor(tokenizer=__lowerCamelCase ,				image_processor=__lowerCamelCase							)

							a   =      "lower newer"
							a   =      self.prepare_image_inputs()

							a   =      processor(text=__lowerCamelCase ,				images=__lowerCamelCase							)

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












import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class    snake_case__							(_UpperCamelCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							List[Any]										= ["""image_processor""", """tokenizer"""]
				SCREAMING_SNAKE_CASE_	:							List[Any]										= """AutoImageProcessor"""
				SCREAMING_SNAKE_CASE_	:							Dict										= """AutoTokenizer"""





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

							a   =      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__(__lowerCamelCase ,				__lowerCamelCase							)
							a   =      self.image_processor
							a   =      False





				def __call__(				self    :					Any ,				*__lowerCamelCase    :					int ,				**__lowerCamelCase    :					Optional[int]							) ->					int:
							# For backward compatibility
							if self._in_target_context_manager:
										return self.current_processor(*__lowerCamelCase ,				**__lowerCamelCase							)

							a   =      kwargs.pop("images" ,				__lowerCamelCase							)
							a   =      kwargs.pop("text" ,				__lowerCamelCase							)
							if len(__lowerCamelCase							) > 0:
										a   =      args[0]
										a   =      args[1:]

							if images is None and text is None:
										raise ValueError("You need to specify either an `images` or `text` input to process."							)

							if images is not None:
										a   =      self.image_processor(__lowerCamelCase ,				*__lowerCamelCase ,				**__lowerCamelCase							)
							if text is not None:
										a   =      self.tokenizer(__lowerCamelCase ,				**__lowerCamelCase							)

							if text is None:
										return inputs
							elif images is None:
										return encodings
							else:
										a   =      encodings["input_ids"]
										return inputs





				def 							__UpperCAmelCase							(				self    :					Union[str, Any] ,				*__lowerCamelCase    :					str ,				**__lowerCamelCase    :					str							) ->					Optional[Any]:
							return self.tokenizer.batch_decode(*__lowerCamelCase ,				**__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Optional[Any] ,				*__lowerCamelCase    :					Union[str, Any] ,				**__lowerCamelCase    :					Any							) ->					str:
							return self.tokenizer.decode(*__lowerCamelCase ,				**__lowerCamelCase							)





				@contextmanager
				def 							__UpperCAmelCase							(				self    :					Any							) ->					Optional[int]:
							warnings.warn(
							    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
							    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
							    "your images inputs, or in a separate call."							)
							a   =      True
							a   =      self.tokenizer
							yield
							a   =      self.image_processor
							a   =      False





				@property
				def 							__UpperCAmelCase							(				self    :					Dict							) ->					Optional[Any]:
							warnings.warn(
							    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." ,				__lowerCamelCase ,				)
							return self.image_processor_class





				@property
				def 							__UpperCAmelCase							(				self    :					int							) ->					Union[str, Any]:
							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." ,				__lowerCamelCase ,				)
							return self.image_processor






