

'''simple docstring'''





def  _A  (			SCREAMING_SNAKE_CASE			):
	UpperCAmelCase__:  int		       =   word.upper()
	UpperCAmelCase__:  Tuple		       =   0
	UpperCAmelCase__:  Union[str, Any]		       =   {}
	UpperCAmelCase__:  Optional[Any]		       =   []

	for letter in word:
		if letter not in letter_nums:
			UpperCAmelCase__:  Dict		       =   str(SCREAMING_SNAKE_CASE__			)
			next_num += 1
		word_pattern.append(letter_nums[letter]			)
	return ".".join(SCREAMING_SNAKE_CASE__			)


if __name__ == "__main__":
					import pprint
					import time

					_lowerCAmelCase     :			Union[str, Any]    =time.time()
					with open("""dictionary.txt""") as in_file:
										_lowerCAmelCase     :			Dict    =in_file.read().splitlines()

					_lowerCAmelCase     :			Any    ={}
					for word in word_list:
										_lowerCAmelCase     :			str    =get_word_pattern(word)
										if pattern in all_patterns:
															all_patterns[pattern].append(word)
										else:
															_lowerCAmelCase     :			List[str]    =[word]

					with open("""word_patterns.txt""", """w""") as out_file:
										out_file.write(pprint.pformat(all_patterns))

					_lowerCAmelCase     :			str    =round(time.time() - start_time, 2)
					print(F"Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.")
					# Done!  9,581 word patterns found in 0.58 seconds.


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       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  Optional[int]		       =   tempfile.mkdtemp()

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

						UpperCAmelCase__:  Tuple		       =   {
						    "image_mean": FLAVA_IMAGE_MEAN,
						    "image_std": FLAVA_IMAGE_STD,
						    "do_normalize": True,
						    "do_resize": True,
						    "size": 2_2_4,
						    "do_center_crop": True,
						    "crop_size": 2_2_4,
						    "input_size_patches": 1_4,
						    "total_mask_patches": 7_5,
						    "mask_group_max_patches": None,
						    "mask_group_min_patches": 1_6,
						    "mask_group_min_aspect_ratio": 0.3,
						    "mask_group_max_aspect_ratio": None,
						    "codebook_do_resize": True,
						    "codebook_size": 1_1_2,
						    "codebook_do_center_crop": True,
						    "codebook_crop_size": 1_1_2,
						    "codebook_do_map_pixels": True,
						    "codebook_do_normalize": True,
						    "codebook_image_mean": FLAVA_CODEBOOK_MEAN,
						    "codebook_image_std": FLAVA_CODEBOOK_STD,
						}

						UpperCAmelCase__:  Tuple		       =   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							,			**lowerCamelCase__	):
						return BertTokenizer.from_pretrained(self.tmpdirname							,			**lowerCamelCase__	)





					def       _UpperCAmelCase						(							self							,			**lowerCamelCase__	):
						return BertTokenizerFast.from_pretrained(self.tmpdirname							,			**lowerCamelCase__	)





					def       _UpperCAmelCase						(							self							,			**lowerCamelCase__	):
						return FlavaImageProcessor.from_pretrained(self.tmpdirname							,			**lowerCamelCase__	)





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





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  Dict		       =   [np.random.randint(2_5_5							,			size=(3, 3_0, 4_0_0)							,			dtype=np.uinta	)]

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

						return image_inputs





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  int		       =   self.get_tokenizer()
						UpperCAmelCase__:  Any		       =   self.get_rust_tokenizer()
						UpperCAmelCase__:  List[Any]		       =   self.get_image_processor()

						UpperCAmelCase__:  Tuple		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)
						processor_slow.save_pretrained(self.tmpdirname	)
						UpperCAmelCase__:  Dict		       =   FlavaProcessor.from_pretrained(self.tmpdirname							,			use_fast=lowerCamelCase__	)

						UpperCAmelCase__:  Union[str, Any]		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)
						processor_fast.save_pretrained(self.tmpdirname	)
						UpperCAmelCase__:  int		       =   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	):
						UpperCAmelCase__:  Any		       =   FlavaProcessor(tokenizer=self.get_tokenizer()							,			image_processor=self.get_image_processor()	)
						processor.save_pretrained(self.tmpdirname	)

						UpperCAmelCase__:  Any		       =   self.get_tokenizer(bos_token="(BOS)"							,			eos_token="(EOS)"	)
						UpperCAmelCase__:  str		       =   self.get_image_processor(do_normalize=lowerCamelCase__							,			padding_value=1.0	)

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

						UpperCAmelCase__:  Optional[Any]		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)

						UpperCAmelCase__:  Optional[int]		       =   self.prepare_image_inputs()

						UpperCAmelCase__:  Tuple		       =   image_processor(lowerCamelCase__							,			return_tensors="np"	)
						UpperCAmelCase__:  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(1_2_3_4	)
						UpperCAmelCase__:  Optional[int]		       =   image_processor(
						    lowerCamelCase__							,			return_image_mask=lowerCamelCase__							,			return_codebook_pixels=lowerCamelCase__							,			return_tensors="np"	)
						random.seed(1_2_3_4	)
						UpperCAmelCase__:  Tuple		       =   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	):
						UpperCAmelCase__:  Dict		       =   self.get_image_processor()
						UpperCAmelCase__:  str		       =   self.get_tokenizer()

						UpperCAmelCase__:  List[str]		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)

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

						UpperCAmelCase__:  Optional[Any]		       =   processor(text=lowerCamelCase__	)

						UpperCAmelCase__:  Optional[Any]		       =   tokenizer(lowerCamelCase__	)

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





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  List[Any]		       =   self.get_image_processor()
						UpperCAmelCase__:  int		       =   self.get_tokenizer()

						UpperCAmelCase__:  str		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)

						UpperCAmelCase__:  Any		       =   "lower newer"
						UpperCAmelCase__:  Any		       =   self.prepare_image_inputs()

						UpperCAmelCase__:  Optional[int]		       =   processor(text=lowerCamelCase__							,			images=lowerCamelCase__	)

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

						# add extra args
						UpperCAmelCase__:  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       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  Optional[Any]		       =   self.get_image_processor()
						UpperCAmelCase__:  List[Any]		       =   self.get_tokenizer()

						UpperCAmelCase__:  Any		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)

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

						UpperCAmelCase__:  str		       =   processor.batch_decode(lowerCamelCase__	)
						UpperCAmelCase__:  Dict		       =   tokenizer.batch_decode(lowerCamelCase__	)

						self.assertListEqual(lowerCamelCase__							,			lowerCamelCase__	)





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  List[str]		       =   self.get_image_processor()
						UpperCAmelCase__:  Dict		       =   self.get_tokenizer()

						UpperCAmelCase__:  Tuple		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)

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

						UpperCAmelCase__:  int		       =   processor(text=lowerCamelCase__							,			images=lowerCamelCase__	)

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