




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 				UpperCAmelCase(					unittest.TestCase					):



	"""simple docstring"""






	def    __a     (					self    ) ->      Optional[int]:





								"""simple docstring"""


								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	,				lowerCamelCase    )
								with open(self.image_processor_file	,				"w"	,				encoding="utf-8"    ) as fp:
															json.dump(lowerCamelCase	,				lowerCamelCase    )

	def    __a     (					self	,				**lowerCamelCase    ) ->      List[str]:





								"""simple docstring"""


								return BertTokenizer.from_pretrained(self.tmpdirname	,				**lowerCamelCase    )

	def    __a     (					self	,				**lowerCamelCase    ) ->      Optional[int]:





								"""simple docstring"""


								return BertTokenizerFast.from_pretrained(self.tmpdirname	,				**lowerCamelCase    )

	def    __a     (					self	,				**lowerCamelCase    ) ->      Tuple:





								"""simple docstring"""


								return FlavaImageProcessor.from_pretrained(self.tmpdirname	,				**lowerCamelCase    )

	def    __a     (					self    ) ->      Tuple:





								"""simple docstring"""


								shutil.rmtree(self.tmpdirname    )

	def    __a     (					self    ) ->      List[Any]:





								"""simple docstring"""


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

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

								return image_inputs

	def    __a     (					self    ) ->      Any:





								"""simple docstring"""


								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=lowerCamelCase	,				image_processor=lowerCamelCase    )
								processor_slow.save_pretrained(self.tmpdirname    )
								lowercase__  :	int        =							FlavaProcessor.from_pretrained(self.tmpdirname	,				use_fast=lowerCamelCase    )

								lowercase__  :	Optional[int]        =							FlavaProcessor(tokenizer=lowerCamelCase	,				image_processor=lowerCamelCase    )
								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	,				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    __a     (					self    ) ->      List[str]:





								"""simple docstring"""


								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=lowerCamelCase	,				padding_value=1.0    )

								lowercase__  :	Dict        =							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    __a     (					self    ) ->      Dict:





								"""simple docstring"""


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

								lowercase__  :	Dict        =							FlavaProcessor(tokenizer=lowerCamelCase	,				image_processor=lowerCamelCase    )

								lowercase__  :	str        =							self.prepare_image_inputs()

								lowercase__  :	Tuple        =							image_processor(lowerCamelCase	,				return_tensors="np"    )
								lowercase__  :	List[str]        =							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(1234    )
								lowercase__  :	Union[str, Any]        =							image_processor(
								    lowerCamelCase	,				return_image_mask=lowerCamelCase	,				return_codebook_pixels=lowerCamelCase	,				return_tensors="np"    )
								random.seed(1234    )
								lowercase__  :	Optional[int]        =							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    __a     (					self    ) ->      Dict:





								"""simple docstring"""


								lowercase__  :	List[str]        =							self.get_image_processor()
								lowercase__  :	Union[str, Any]        =							self.get_tokenizer()

								lowercase__  :	Optional[Any]        =							FlavaProcessor(tokenizer=lowerCamelCase	,				image_processor=lowerCamelCase    )

								lowercase__  :	List[Any]        =							"lower newer"

								lowercase__  :	int        =							processor(text=lowerCamelCase    )

								lowercase__  :	Dict        =							tokenizer(lowerCamelCase    )

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

	def    __a     (					self    ) ->      Any:





								"""simple docstring"""


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

								lowercase__  :	Tuple        =							FlavaProcessor(tokenizer=lowerCamelCase	,				image_processor=lowerCamelCase    )

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

								lowercase__  :	Optional[Any]        =							processor(text=lowerCamelCase	,				images=lowerCamelCase    )

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

								# add extra args
								lowercase__  :	str        =							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    __a     (					self    ) ->      Union[str, Any]:





								"""simple docstring"""


								lowercase__  :	Dict        =							self.get_image_processor()
								lowercase__  :	List[str]        =							self.get_tokenizer()

								lowercase__  :	List[str]        =							FlavaProcessor(tokenizer=lowerCamelCase	,				image_processor=lowerCamelCase    )

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

								lowercase__  :	List[Any]        =							processor.batch_decode(lowerCamelCase    )
								lowercase__  :	str        =							tokenizer.batch_decode(lowerCamelCase    )

								self.assertListEqual(lowerCamelCase	,				lowerCamelCase    )







	def    __a     (					self    ) ->      List[Any]:





								"""simple docstring"""


								lowercase__  :	Optional[int]        =							self.get_image_processor()
								lowercase__  :	List[str]        =							self.get_tokenizer()

								lowercase__  :	Any        =							FlavaProcessor(tokenizer=lowerCamelCase	,				image_processor=lowerCamelCase    )

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

								lowercase__  :	Optional[Any]        =							processor(text=lowerCamelCase	,				images=lowerCamelCase    )

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





import numpy as np
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			int:
							lowercase__  :	str        =							n_draws * class_counts / class_counts.sum()
							# floored means we don't overshoot n_samples, but probably undershoot
							lowercase__  :	Union[str, Any]        =							np.floor(SCREAMING_SNAKE_CASE_    )
							# we add samples according to how much "left over" probability
							# they had, until we arrive at n_samples
							lowercase__  :	Tuple        =							int(n_draws - floored.sum()    )
							if need_to_add > 0:
														lowercase__  :	Union[str, Any]        =							continuous - floored
														lowercase__  :	Optional[Any]        =							np.sort(np.unique(SCREAMING_SNAKE_CASE_    )    )[::-1]
														# add according to remainder, but break ties
														# randomly to avoid biases
														for value in values:
																					((lowercase__) ,       )  :	Optional[Any]        =							np.where(remainder == value    )
																					# if we need_to_add less than what's in inds
																					# we draw randomly from them.
																					# if we need to add more, we add them all and
																					# go to the next value
																					lowercase__  :	Dict        =							min(len(SCREAMING_SNAKE_CASE_    )    ,SCREAMING_SNAKE_CASE_    )
																					lowercase__  :	Any        =							rng.choice(SCREAMING_SNAKE_CASE_    ,size=SCREAMING_SNAKE_CASE_    ,replace=SCREAMING_SNAKE_CASE_    )
																					floored[inds] += 1
																					need_to_add -= add_now
																					if need_to_add == 0:
																												break
							return floored.astype(np.intaa    )




def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_=10    )      ->			List[str]:
							lowercase__ ,       lowercase__  :	int        =							np.unique(SCREAMING_SNAKE_CASE_    ,return_inverse=SCREAMING_SNAKE_CASE_    )
							lowercase__  :	int        =							classes.shape[0]
							lowercase__  :	Union[str, Any]        =							np.bincount(SCREAMING_SNAKE_CASE_    )
							if np.min(SCREAMING_SNAKE_CASE_    ) < 2:
														raise ValueError("Minimum class count error"    )
							if n_train < n_classes:
														raise ValueError(
														    "The train_size = %d should be greater or " "equal to the number of classes = %d" % (n_train, n_classes)    )
							if n_test < n_classes:
														raise ValueError(
														    "The test_size = %d should be greater or " "equal to the number of classes = %d" % (n_test, n_classes)    )
							lowercase__  :	List[Any]        =							np.split(np.argsort(SCREAMING_SNAKE_CASE_    ,kind="mergesort"    )    ,np.cumsum(SCREAMING_SNAKE_CASE_    )[:-1]    )
							for _ in range(SCREAMING_SNAKE_CASE_    ):
														lowercase__  :	str        =							approximate_mode(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )
														lowercase__  :	int        =							class_counts - n_i
														lowercase__  :	int        =							approximate_mode(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )

														lowercase__  :	Tuple        =							[]
														lowercase__  :	int        =							[]

														for i in range(SCREAMING_SNAKE_CASE_    ):
																					lowercase__  :	Union[str, Any]        =							rng.permutation(class_counts[i]    )
																					lowercase__  :	List[Any]        =							class_indices[i].take(SCREAMING_SNAKE_CASE_    ,mode="clip"    )
																					train.extend(perm_indices_class_i[: n_i[i]]    )
																					test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]    )
														lowercase__  :	Tuple        =							rng.permutation(SCREAMING_SNAKE_CASE_    )
														lowercase__  :	Dict        =							rng.permutation(SCREAMING_SNAKE_CASE_    )

														yield train, test