






"""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        lowerCAmelCase__       (  unittest.TestCase					):
   def 	__a				(						self	:     List[str]				):


        '''simple docstring'''
        UpperCAmelCase__		:  str				=				tempfile.mkdtemp()

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

        UpperCAmelCase__		:  str				=				{
            "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__		:  str				=				os.path.join(self.tmpdirname				,       snake_case__				)
        with open(self.image_processor_file				,       "w"				,       encoding="utf-8"				) as fp:
             json.dump(snake_case__				,       snake_case__				)
   def 	__a				(						self	:     Union[str, Any]				,       **snake_case__	:     List[str]				):


        '''simple docstring'''
        return BertTokenizer.from_pretrained(self.tmpdirname				,       **snake_case__				)
   def 	__a				(						self	:     List[str]				,       **snake_case__	:     Union[str, Any]				):


        '''simple docstring'''
        return BertTokenizerFast.from_pretrained(self.tmpdirname				,       **snake_case__				)
   def 	__a				(						self	:     Optional[int]				,       **snake_case__	:     Optional[Any]				):


        '''simple docstring'''
        return FlavaImageProcessor.from_pretrained(self.tmpdirname				,       **snake_case__				)
   def 	__a				(						self	:     List[str]				):


        '''simple docstring'''
        shutil.rmtree(self.tmpdirname				)
   def 	__a				(						self	:     Optional[int]				):


        '''simple docstring'''
        UpperCAmelCase__		:  Optional[Any]				=				[np.random.randint(2_5_5				,       size=(3, 3_0, 4_0_0)				,       dtype=np.uinta				)]

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

        return image_inputs
   def 	__a				(						self	:     int				):


        '''simple docstring'''
        UpperCAmelCase__		:  Tuple				=				self.get_tokenizer()
        UpperCAmelCase__		:  List[Any]				=				self.get_rust_tokenizer()
        UpperCAmelCase__		:  Dict				=				self.get_image_processor()

        UpperCAmelCase__		:  List[str]				=				FlavaProcessor(tokenizer=snake_case__				,       image_processor=snake_case__				)
        processor_slow.save_pretrained(self.tmpdirname				)
        UpperCAmelCase__		:  Dict				=				FlavaProcessor.from_pretrained(self.tmpdirname				,       use_fast=snake_case__				)

        UpperCAmelCase__		:  Optional[Any]				=				FlavaProcessor(tokenizer=snake_case__				,       image_processor=snake_case__				)
        processor_fast.save_pretrained(self.tmpdirname				)
        UpperCAmelCase__		:  Dict				=				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				,       snake_case__				)
        self.assertIsInstance(processor_fast.tokenizer				,       snake_case__				)

        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				,       snake_case__				)
        self.assertIsInstance(processor_fast.image_processor				,       snake_case__				)
   def 	__a				(						self	:     Union[str, Any]				):


        '''simple docstring'''
        UpperCAmelCase__		:  Dict				=				FlavaProcessor(tokenizer=self.get_tokenizer()				,       image_processor=self.get_image_processor()				)
        processor.save_pretrained(self.tmpdirname				)

        UpperCAmelCase__		:  List[str]				=				self.get_tokenizer(bos_token="(BOS)"				,       eos_token="(EOS)"				)
        UpperCAmelCase__		:  Optional[Any]				=				self.get_image_processor(do_normalize=snake_case__				,       padding_value=1.0				)

        UpperCAmelCase__		:  Any				=				FlavaProcessor.from_pretrained(
            self.tmpdirname				,       bos_token="(BOS)"				,       eos_token="(EOS)"				,       do_normalize=snake_case__				,       padding_value=1.0				)

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

        self.assertEqual(processor.image_processor.to_json_string()				,       image_processor_add_kwargs.to_json_string()				)
        self.assertIsInstance(processor.image_processor				,       snake_case__				)
   def 	__a				(						self	:     List[Any]				):


        '''simple docstring'''
        UpperCAmelCase__		:  str				=				self.get_image_processor()
        UpperCAmelCase__		:  int				=				self.get_tokenizer()

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

        UpperCAmelCase__		:  Dict				=				self.prepare_image_inputs()

        UpperCAmelCase__		:  Optional[int]				=				image_processor(snake_case__				,       return_tensors="np"				)
        UpperCAmelCase__		:  Any				=				processor(images=snake_case__				,       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__		:  Dict				=				image_processor(
            snake_case__				,       return_image_mask=snake_case__				,       return_codebook_pixels=snake_case__				,       return_tensors="np"				)
        random.seed(1_2_3_4				)
        UpperCAmelCase__		:  List[str]				=				processor(
            images=snake_case__				,       return_image_mask=snake_case__				,       return_codebook_pixels=snake_case__				,       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	:     List[Any]				):


        '''simple docstring'''
        UpperCAmelCase__		:  Tuple				=				self.get_image_processor()
        UpperCAmelCase__		:  Dict				=				self.get_tokenizer()

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

        UpperCAmelCase__		:  int				=				"lower newer"

        UpperCAmelCase__		:  List[str]				=				processor(text=snake_case__				)

        UpperCAmelCase__		:  Optional[int]				=				tokenizer(snake_case__				)

        for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]				,       encoded_processor[key]				)
   def 	__a				(						self	:     List[Any]				):


        '''simple docstring'''
        UpperCAmelCase__		:  Tuple				=				self.get_image_processor()
        UpperCAmelCase__		:  int				=				self.get_tokenizer()

        UpperCAmelCase__		:  Dict				=				FlavaProcessor(tokenizer=snake_case__				,       image_processor=snake_case__				)

        UpperCAmelCase__		:  Tuple				=				"lower newer"
        UpperCAmelCase__		:  Optional[int]				=				self.prepare_image_inputs()

        UpperCAmelCase__		:  Tuple				=				processor(text=snake_case__				,       images=snake_case__				)

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

        # add extra args
        UpperCAmelCase__		:  Dict				=				processor(text=snake_case__				,       images=snake_case__				,       return_codebook_pixels=snake_case__				,       return_image_mask=snake_case__				)

        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(snake_case__				):
             processor()
   def 	__a				(						self	:     int				):


        '''simple docstring'''
        UpperCAmelCase__		:  Any				=				self.get_image_processor()
        UpperCAmelCase__		:  Any				=				self.get_tokenizer()

        UpperCAmelCase__		:  List[Any]				=				FlavaProcessor(tokenizer=snake_case__				,       image_processor=snake_case__				)

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

        UpperCAmelCase__		:  Union[str, Any]				=				processor.batch_decode(snake_case__				)
        UpperCAmelCase__		:  Dict				=				tokenizer.batch_decode(snake_case__				)

        self.assertListEqual(snake_case__				,       snake_case__				)




   def 	__a				(						self	:     Any				):


        '''simple docstring'''
        UpperCAmelCase__		:  List[str]				=				self.get_image_processor()
        UpperCAmelCase__		:  Dict				=				self.get_tokenizer()

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

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

        UpperCAmelCase__		:  Any				=				processor(text=snake_case__				,       images=snake_case__				)

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










"""simple docstring"""



def      SCREAMING_SNAKE_CASE__    (   snake_case    :      str							)->						str:






     '''simple docstring'''



     UpperCAmelCase__		:  Tuple				=				word.upper()
     UpperCAmelCase__		:  Union[str, Any]				=				0
     UpperCAmelCase__		:  Optional[Any]				=				{}
     UpperCAmelCase__		:  List[Any]				=				[]

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


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

 _lowerCAmelCase     :     Optional[Any]            =	time.time()
 with open("""dictionary.txt""") as in_file:
  _lowerCAmelCase     :     Optional[int]            =	in_file.read().splitlines()

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

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

 _lowerCAmelCase     :     Union[str, Any]            =	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.


