

'''simple docstring'''





import unittest

from cer import CER


_lowerCAmelCase     :			str    =CER()

class __UpperCamelCase    (							unittest.TestCase		):






					'''simple docstring'''



					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  Optional[int]		       =   ["White House"]
						UpperCAmelCase__:  Dict		       =   ["white house"]
						# S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
						UpperCAmelCase__:  List[str]		       =   cer.compute(predictions=lowerCamelCase__							,			references=lowerCamelCase__	)
						self.assertTrue(abs(char_error_rate - 0.1_818_181_818	) < 1e-6	)





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  int		       =   ["were wolf"]
						UpperCAmelCase__:  Union[str, Any]		       =   ["werewolf"]
						# S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
						UpperCAmelCase__:  str		       =   cer.compute(predictions=lowerCamelCase__							,			references=lowerCamelCase__	)
						self.assertTrue(abs(char_error_rate - 0.1_111_111	) < 1e-6	)

						UpperCAmelCase__:  Dict		       =   ["werewolf"]
						UpperCAmelCase__:  List[str]		       =   ["weae     wolf"]
						# S = 1, D = 1, I = 0, N = 8, CER = 0.25
						UpperCAmelCase__:  List[str]		       =   cer.compute(predictions=lowerCamelCase__							,			references=lowerCamelCase__	)
						self.assertTrue(abs(char_error_rate - 0.25	) < 1e-6	)

						# consecutive whitespaces case 1
						UpperCAmelCase__:  Optional[Any]		       =   ["were wolf"]
						UpperCAmelCase__:  Tuple		       =   ["were               wolf"]
						# S = 0, D = 0, I = 0, N = 9, CER = 0
						UpperCAmelCase__:  Optional[Any]		       =   cer.compute(predictions=lowerCamelCase__							,			references=lowerCamelCase__	)
						self.assertTrue(abs(char_error_rate - 0.0	) < 1e-6	)

						# consecutive whitespaces case 2
						UpperCAmelCase__:  List[Any]		       =   ["were   wolf"]
						UpperCAmelCase__:  int		       =   ["were               wolf"]
						# S = 0, D = 0, I = 0, N = 9, CER = 0
						UpperCAmelCase__:  Optional[Any]		       =   cer.compute(predictions=lowerCamelCase__							,			references=lowerCamelCase__	)
						self.assertTrue(abs(char_error_rate - 0.0	) < 1e-6	)





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  str		       =   ["werewolf"]
						UpperCAmelCase__:  List[Any]		       =   ["weaewolf"]
						# S = 1, D = 0, I = 0, N = 8, CER = 0.125
						UpperCAmelCase__:  Tuple		       =   cer.compute(predictions=lowerCamelCase__							,			references=lowerCamelCase__	)
						self.assertTrue(abs(char_error_rate - 0.125	) < 1e-6	)





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  Optional[int]		       =   ["werewolf"]
						UpperCAmelCase__:  List[str]		       =   ["wereawolf"]
						# S = 0, D = 1, I = 0, N = 8, CER = 0.125
						UpperCAmelCase__:  List[str]		       =   cer.compute(predictions=lowerCamelCase__							,			references=lowerCamelCase__	)
						self.assertTrue(abs(char_error_rate - 0.125	) < 1e-6	)





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  List[Any]		       =   ["werewolf"]
						UpperCAmelCase__:  Any		       =   ["wereolf"]
						# S = 0, D = 0, I = 1, N = 8, CER = 0.125
						UpperCAmelCase__:  List[str]		       =   cer.compute(predictions=lowerCamelCase__							,			references=lowerCamelCase__	)
						self.assertTrue(abs(char_error_rate - 0.125	) < 1e-6	)





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  Optional[Any]		       =   ["werewolf"]
						UpperCAmelCase__:  str		       =   cer.compute(predictions=lowerCamelCase__							,			references=lowerCamelCase__	)
						self.assertEqual(lowerCamelCase__							,			0.0	)





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  Dict		       =   ["werewolf", "I am your father"]
						UpperCAmelCase__:  Optional[Any]		       =   cer.compute(predictions=lowerCamelCase__							,			references=lowerCamelCase__	)
						self.assertEqual(lowerCamelCase__							,			0.0	)

						UpperCAmelCase__:  str		       =   ["werewolf", "I am your father", "doge"]
						UpperCAmelCase__:  Tuple		       =   ["werxwolf", "I       am your father", "doge"]
						# S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
						UpperCAmelCase__:  str		       =   cer.compute(predictions=lowerCamelCase__							,			references=lowerCamelCase__	)
						self.assertTrue(abs(char_error_rate - 0.03_571_428	) < 1e-6	)





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  List[str]		       =   ["My hovercraft", "is full of eels"]
						UpperCAmelCase__:  Tuple		       =   ["My hovercraft is full", " of eels"]
						# S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
						# whitespace at the front of " of eels" will be strip during preporcessing
						# so need to insert 2 whitespaces
						UpperCAmelCase__:  int		       =   cer.compute(predictions=lowerCamelCase__							,			references=lowerCamelCase__							,			concatenate_texts=lowerCamelCase__	)
						self.assertTrue(abs(char_error_rate - 0.071_428	) < 1e-6	)





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  Any		       =   ["我能吞下玻璃而不伤身体"]
						UpperCAmelCase__:  List[str]		       =   [" 能吞虾玻璃而 不霜身体啦"]
						# S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
						UpperCAmelCase__:  Optional[int]		       =   cer.compute(predictions=lowerCamelCase__							,			references=lowerCamelCase__	)
						self.assertTrue(abs(char_error_rate - 0.4_545_454_545	) < 1e-6	)

						UpperCAmelCase__:  Optional[int]		       =   ["我能吞下玻璃", "而不伤身体"]
						UpperCAmelCase__:  Dict		       =   ["我    能 吞 下 玻 璃", "而不伤身体"]
						# S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
						UpperCAmelCase__:  Tuple		       =   cer.compute(predictions=lowerCamelCase__							,			references=lowerCamelCase__	)
						self.assertTrue(abs(char_error_rate - 0.454_545_454_545	) < 1e-6	)

						UpperCAmelCase__:  Any		       =   ["我能吞下玻璃而不伤身体"]
						UpperCAmelCase__:  Optional[int]		       =   cer.compute(predictions=lowerCamelCase__							,			references=lowerCamelCase__	)
						self.assertFalse(lowerCamelCase__							,			0.0	)





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  Optional[Any]		       =   [""]
						UpperCAmelCase__:  str		       =   ["Hypothesis"]
						with self.assertRaises(lowerCamelCase__	):
							cer.compute(predictions=lowerCamelCase__							,			references=lowerCamelCase__	)


if __name__ == "__main__":
					unittest.main()


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	)