





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
     import torch


if is_vision_available():
     from PIL import Image

     from transformers import MgpstrProcessor, ViTImageProcessor



@require_torch
@require_vision
class        _SCREAMING_SNAKE_CASE						(     unittest.TestCase   ):







 '''simple docstring'''
 lowercase_		=      ViTImageProcessor if is_vision_available() else None

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       str)	->Any:



   '''simple docstring'''



   return self.image_processor_tester.prepare_image_processor_dict()

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any])	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =(3, 32, 128)
   lowerCamelCase__:				Optional[Any]        =tempfile.mkdtemp()

   # fmt: off
   lowerCamelCase__:				str        =["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
   # fmt: on
   lowerCamelCase__:				Optional[Any]        =dict(zip(UpperCAmelCase_	,			range(len(UpperCAmelCase_))))

   lowerCamelCase__:				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(json.dumps(UpperCAmelCase_) + "\n")

   lowerCamelCase__:				List[Any]        ={
       "do_normalize": False,
       "do_resize": True,
       "image_processor_type": "ViTImageProcessor",
       "resample": 3,
       "size": {"height": 32, "width": 128},
   }
   lowerCamelCase__:				int        =os.path.join(self.tmpdirname	,			UpperCAmelCase_)
   with open(self.image_processor_file	,			"w"	,			encoding="utf-8") as fp:
     json.dump(UpperCAmelCase_	,			UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			**UpperCAmelCase_    :       Any)	->List[str]:



   '''simple docstring'''



   return MgpstrTokenizer.from_pretrained(self.tmpdirname	,			**UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       str	,			**UpperCAmelCase_    :       Optional[int])	->List[Any]:



   '''simple docstring'''



   return ViTImageProcessor.from_pretrained(self.tmpdirname	,			**UpperCAmelCase_)

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



   '''simple docstring'''



   shutil.rmtree(self.tmpdirname)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:				Tuple        =np.random.randint(255	,			size=(3, 30, 400)	,			dtype=np.uinta)

   lowerCamelCase__:				List[Any]        =Image.fromarray(np.moveaxis(UpperCAmelCase_	,			0	,			-1))

   return image_input

 def 							SCREAMING_SNAKE_CASE_ (self    :       Any)	->Dict:



   '''simple docstring'''



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

   lowerCamelCase__:				Optional[Any]        =MgpstrProcessor(tokenizer=UpperCAmelCase_	,			image_processor=UpperCAmelCase_)
   processor.save_pretrained(self.tmpdirname)
   lowerCamelCase__:				List[str]        =MgpstrProcessor.from_pretrained(self.tmpdirname	,			use_fast=UpperCAmelCase_)

   self.assertEqual(processor.char_tokenizer.get_vocab()	,			tokenizer.get_vocab())
   self.assertIsInstance(processor.char_tokenizer	,			UpperCAmelCase_)

   self.assertEqual(processor.image_processor.to_json_string()	,			image_processor.to_json_string())
   self.assertIsInstance(processor.image_processor	,			UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->List[Any]:



   '''simple docstring'''



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

   lowerCamelCase__:				Optional[int]        =MgpstrProcessor(tokenizer=UpperCAmelCase_	,			image_processor=UpperCAmelCase_)
   processor.save_pretrained(self.tmpdirname)

   lowerCamelCase__:				Union[str, Any]        =self.get_tokenizer(bos_token="(BOS)"	,			eos_token="(EOS)")
   lowerCamelCase__:				Optional[int]        =self.get_image_processor(do_normalize=UpperCAmelCase_	,			padding_value=1.0)

   lowerCamelCase__:				Optional[Any]        =MgpstrProcessor.from_pretrained(
       self.tmpdirname	,			bos_token="(BOS)"	,			eos_token="(EOS)"	,			do_normalize=UpperCAmelCase_	,			padding_value=1.0)

   self.assertEqual(processor.char_tokenizer.get_vocab()	,			tokenizer_add_kwargs.get_vocab())
   self.assertIsInstance(processor.char_tokenizer	,			UpperCAmelCase_)

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

 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->str:



   '''simple docstring'''



   lowerCamelCase__:				Any        =self.get_image_processor()
   lowerCamelCase__:				Dict        =self.get_tokenizer()

   lowerCamelCase__:				Any        =MgpstrProcessor(tokenizer=UpperCAmelCase_	,			image_processor=UpperCAmelCase_)

   lowerCamelCase__:				str        =self.prepare_image_inputs()

   lowerCamelCase__:				int        =image_processor(UpperCAmelCase_	,			return_tensors="np")
   lowerCamelCase__:				Any        =processor(images=UpperCAmelCase_	,			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 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->Dict:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =self.get_image_processor()
   lowerCamelCase__:				Union[str, Any]        =self.get_tokenizer()

   lowerCamelCase__:				str        =MgpstrProcessor(tokenizer=UpperCAmelCase_	,			image_processor=UpperCAmelCase_)

   lowerCamelCase__:				Tuple        ="test"

   lowerCamelCase__:				str        =processor(text=UpperCAmelCase_)

   lowerCamelCase__:				Union[str, Any]        =tokenizer(UpperCAmelCase_)
   for key in encoded_tok.keys():
     self.assertListEqual(encoded_tok[key]	,			encoded_processor[key])

 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:				int        =self.get_image_processor()
   lowerCamelCase__:				int        =self.get_tokenizer()

   lowerCamelCase__:				Optional[int]        =MgpstrProcessor(tokenizer=UpperCAmelCase_	,			image_processor=UpperCAmelCase_)

   lowerCamelCase__:				str        ="test"
   lowerCamelCase__:				Optional[Any]        =self.prepare_image_inputs()

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

   self.assertListEqual(list(inputs.keys())	,			["pixel_values", "labels"])

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

 def 							SCREAMING_SNAKE_CASE_ (self    :       Any)	->Tuple:



   '''simple docstring'''



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

   lowerCamelCase__:				Optional[Any]        =MgpstrProcessor(tokenizer=UpperCAmelCase_	,			image_processor=UpperCAmelCase_)

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

   lowerCamelCase__:				Any        =processor.char_decode(UpperCAmelCase_)
   lowerCamelCase__:				Optional[int]        =tokenizer.batch_decode(UpperCAmelCase_)
   lowerCamelCase__:				List[Any]        =[seq.replace(" "	,			"") for seq in decoded_tok]

   self.assertListEqual(UpperCAmelCase_	,			UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->Dict:



   '''simple docstring'''



   lowerCamelCase__:				Dict        =self.get_image_processor()
   lowerCamelCase__:				Optional[Any]        =self.get_tokenizer()

   lowerCamelCase__:				Optional[int]        =MgpstrProcessor(tokenizer=UpperCAmelCase_	,			image_processor=UpperCAmelCase_)

   lowerCamelCase__:				List[str]        =None
   lowerCamelCase__:				Union[str, Any]        =self.prepare_image_inputs()

   lowerCamelCase__:				Union[str, Any]        =processor(text=UpperCAmelCase_	,			images=UpperCAmelCase_)

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







 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->Dict:



   '''simple docstring'''



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

   lowerCamelCase__:				Optional[Any]        =MgpstrProcessor(tokenizer=UpperCAmelCase_	,			image_processor=UpperCAmelCase_)

   lowerCamelCase__:				Union[str, Any]        =torch.randn(1	,			27	,			38)
   lowerCamelCase__:				str        =torch.randn(1	,			27	,			50_257)
   lowerCamelCase__:				Union[str, Any]        =torch.randn(1	,			27	,			30_522)

   lowerCamelCase__:				List[Any]        =processor.batch_decode([char_input, bpe_input, wp_input])

   self.assertListEqual(list(results.keys())	,			["generated_text", "scores", "char_preds", "bpe_preds", "wp_preds"])








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
)
__A     =    logging.getLogger(__name__)

if __name__ == "__main__":
     __A     =    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=3_0522, type=int)
     __A     =    parser.parse_args()

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

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

