





'''simple docstring'''

import json
import os
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 AlignProcessor, EfficientNetImageProcessor


@require_vision
class 					a_     (				unittest.TestCase       ):

    '''simple docstring'''


    def 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			tempfile.mkdtemp()

        _SCREAMING_SNAKE_CASE									=			[
            """[UNK]""",
            """[CLS]""",
            """[SEP]""",
            """[PAD]""",
            """[MASK]""",
            """want""",
            """##want""",
            """##ed""",
            """wa""",
            """un""",
            """runn""",
            """##ing""",
            """,""",
            """low""",
            """lowest""",
        ]
        _SCREAMING_SNAKE_CASE									=			os.path.join(self.tmpdirname ,		VOCAB_FILES_NAMES["""vocab_file"""]       )
        with open(self.vocab_file ,		"""w""" ,		encoding="""utf-8"""       ) as vocab_writer:
            vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]       )       )

        _SCREAMING_SNAKE_CASE									=			{
            """do_resize""": True,
            """size""": 20,
            """do_center_crop""": True,
            """crop_size""": 18,
            """do_normalize""": True,
            """image_mean""": [0.4814_5466, 0.457_8275, 0.4082_1073],
            """image_std""": [0.2686_2954, 0.2613_0258, 0.2757_7711],
        }
        _SCREAMING_SNAKE_CASE									=			os.path.join(self.tmpdirname ,		A       )
        with open(self.image_processor_file ,		"""w""" ,		encoding="""utf-8"""       ) as fp:
            json.dump(A ,		A       )


    def 			snake_case_(		self ,		**A       )		->   Union[str, Any]:
        return BertTokenizer.from_pretrained(self.tmpdirname ,		**A       )


    def 			snake_case_(		self ,		**A       )		->   Optional[int]:
        return BertTokenizerFast.from_pretrained(self.tmpdirname ,		**A       )


    def 			snake_case_(		self ,		**A       )		->   Optional[Any]:
        return EfficientNetImageProcessor.from_pretrained(self.tmpdirname ,		**A       )


    def 			snake_case_(		self       )		->   Tuple:
        shutil.rmtree(self.tmpdirname       )


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			[np.random.randint(255 ,		size=(3, 30, 400) ,		dtype=np.uinta       )]
        _SCREAMING_SNAKE_CASE									=			[Image.fromarray(np.moveaxis(A ,		0 ,		-1       )       ) for x in image_inputs]
        return image_inputs


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			self.get_tokenizer()
        _SCREAMING_SNAKE_CASE									=			self.get_rust_tokenizer()
        _SCREAMING_SNAKE_CASE									=			self.get_image_processor()

        _SCREAMING_SNAKE_CASE									=			AlignProcessor(tokenizer=A ,		image_processor=A       )
        processor_slow.save_pretrained(self.tmpdirname       )
        _SCREAMING_SNAKE_CASE									=			AlignProcessor.from_pretrained(self.tmpdirname ,		use_fast=A       )

        _SCREAMING_SNAKE_CASE									=			AlignProcessor(tokenizer=A ,		image_processor=A       )
        processor_fast.save_pretrained(self.tmpdirname       )
        _SCREAMING_SNAKE_CASE									=			AlignProcessor.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 ,		A       )
        self.assertIsInstance(processor_fast.tokenizer ,		A       )

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


    def 			snake_case_(		self       )		->   Dict:
        _SCREAMING_SNAKE_CASE									=			AlignProcessor(tokenizer=self.get_tokenizer() ,		image_processor=self.get_image_processor()       )
        processor.save_pretrained(self.tmpdirname       )

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

        _SCREAMING_SNAKE_CASE									=			AlignProcessor.from_pretrained(
            self.tmpdirname ,		bos_token="""(BOS)""" ,		eos_token="""(EOS)""" ,		do_normalize=A ,		padding_value=1.0       )

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

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


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			self.get_image_processor()
        _SCREAMING_SNAKE_CASE									=			self.get_tokenizer()

        _SCREAMING_SNAKE_CASE									=			AlignProcessor(tokenizer=A ,		image_processor=A       )

        _SCREAMING_SNAKE_CASE									=			self.prepare_image_inputs()

        _SCREAMING_SNAKE_CASE									=			image_processor(A ,		return_tensors="""np"""       )
        _SCREAMING_SNAKE_CASE									=			processor(images=A ,		return_tensors="""np"""       )

        for key in input_image_proc.keys():
            self.assertAlmostEqual(input_image_proc[key].sum() ,		input_processor[key].sum() ,		delta=1e-2       )


    def 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			self.get_image_processor()
        _SCREAMING_SNAKE_CASE									=			self.get_tokenizer()

        _SCREAMING_SNAKE_CASE									=			AlignProcessor(tokenizer=A ,		image_processor=A       )

        _SCREAMING_SNAKE_CASE									=			"""lower newer"""

        _SCREAMING_SNAKE_CASE									=			processor(text=A       )

        _SCREAMING_SNAKE_CASE									=			tokenizer(A ,		padding="""max_length""" ,		max_length=64       )

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


    def 			snake_case_(		self       )		->   Tuple:
        _SCREAMING_SNAKE_CASE									=			self.get_image_processor()
        _SCREAMING_SNAKE_CASE									=			self.get_tokenizer()

        _SCREAMING_SNAKE_CASE									=			AlignProcessor(tokenizer=A ,		image_processor=A       )

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

        _SCREAMING_SNAKE_CASE									=			processor(text=A ,		images=A       )

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

        # test if it raises when no input is passed
        with pytest.raises(A       ):
            processor()


    def 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			self.get_image_processor()
        _SCREAMING_SNAKE_CASE									=			self.get_tokenizer()

        _SCREAMING_SNAKE_CASE									=			AlignProcessor(tokenizer=A ,		image_processor=A       )

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

        _SCREAMING_SNAKE_CASE									=			processor.batch_decode(A       )
        _SCREAMING_SNAKE_CASE									=			tokenizer.batch_decode(A       )

        self.assertListEqual(A ,		A       )





    def 			snake_case_(		self       )		->   Any:
        _SCREAMING_SNAKE_CASE									=			self.get_image_processor()
        _SCREAMING_SNAKE_CASE									=			self.get_tokenizer()

        _SCREAMING_SNAKE_CASE									=			AlignProcessor(tokenizer=A ,		image_processor=A       )

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

        _SCREAMING_SNAKE_CASE									=			processor(text=A ,		images=A       )

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










'''simple docstring'''

import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowercase_					= logging.getLogger(__name__)

if __name__ == "__main__":
      lowercase_					= argparse.ArgumentParser(
          description="""Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"""
      )
      parser.add_argument(
          """--data_file""", type=str, default="""data/dump.bert-base-uncased.pickle""", help="""The binarized dataset."""
      )
      parser.add_argument(
          """--token_counts_dump""", type=str, default="""data/token_counts.bert-base-uncased.pickle""", help="""The dump file."""
      )
      parser.add_argument("""--vocab_size""", default=30_522, type=int)
      lowercase_					= parser.parse_args()

      logger.info(f"""Loading data from {args.data_file}""")
      with open(args.data_file, """rb""") as fp:
            lowercase_					= pickle.load(fp)

      logger.info("""Counting occurrences for MLM.""")
      lowercase_					= Counter()
      for tk_ids in data:
            counter.update(tk_ids)
      lowercase_					= [0] * args.vocab_size
      for k, v in counter.items():
            lowercase_					= v

      logger.info(f"""Dump to {args.token_counts_dump}""")
      with open(args.token_counts_dump, """wb""") as handle:
            pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)



