


"""simple docstring"""


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 			A__	(						unittest.TestCase):


     """simple docstring"""
     snake_case__	:					List[str]													=ViTImageProcessor if is_vision_available() else None

     @property
     def 		a__						(	self:						Dict	)->    Dict:
           return self.image_processor_tester.prepare_image_processor_dict()

     def 		a__						(	self:						Any	)->    Dict:
           lowerCamelCase					:					Optional[Any]        =	(3, 32, 128)
           lowerCamelCase					:					int        =	tempfile.mkdtemp()

           # fmt: off
           lowerCamelCase					:					Dict        =	["""[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					:					str        =	dict(zip(__a     ,		range(len(__a	)	)	)	)

           lowerCamelCase					:					List[str]        =	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(__a	) + """\n"""	)

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

     def 		a__						(	self:						List[Any]     ,		**__a:						List[str]	)->    List[Any]:
           return MgpstrTokenizer.from_pretrained(self.tmpdirname     ,		**__a	)

     def 		a__						(	self:						int     ,		**__a:						List[Any]	)->    Dict:
           return ViTImageProcessor.from_pretrained(self.tmpdirname     ,		**__a	)

     def 		a__						(	self:						List[str]	)->    Union[str, Any]:
           shutil.rmtree(self.tmpdirname	)

     def 		a__						(	self:						Dict	)->    Dict:
           lowerCamelCase					:					str        =	np.random.randint(255     ,		size=(3, 30, 400)     ,		dtype=np.uinta	)

           lowerCamelCase					:					Dict        =	Image.fromarray(np.moveaxis(__a     ,		0     ,		-1	)	)

           return image_input

     def 		a__						(	self:						Any	)->    List[Any]:
           lowerCamelCase					:					Dict        =	self.get_tokenizer()
           lowerCamelCase					:					Tuple        =	self.get_image_processor()

           lowerCamelCase					:					Dict        =	MgpstrProcessor(tokenizer=__a     ,		image_processor=__a	)
           processor.save_pretrained(self.tmpdirname	)
           lowerCamelCase					:					List[Any]        =	MgpstrProcessor.from_pretrained(self.tmpdirname     ,		use_fast=__a	)

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

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

     def 		a__						(	self:						Optional[int]	)->    Optional[int]:
           lowerCamelCase					:					Any        =	self.get_tokenizer()
           lowerCamelCase					:					List[str]        =	self.get_image_processor()

           lowerCamelCase					:					str        =	MgpstrProcessor(tokenizer=__a     ,		image_processor=__a	)
           processor.save_pretrained(self.tmpdirname	)

           lowerCamelCase					:					Any        =	self.get_tokenizer(bos_token="""(BOS)"""     ,		eos_token="""(EOS)"""	)
           lowerCamelCase					:					Optional[Any]        =	self.get_image_processor(do_normalize=__a     ,		padding_value=1.0	)

           lowerCamelCase					:					Tuple        =	MgpstrProcessor.from_pretrained(
               self.tmpdirname     ,		bos_token="""(BOS)"""     ,		eos_token="""(EOS)"""     ,		do_normalize=__a     ,		padding_value=1.0	)

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

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

     def 		a__						(	self:						Any	)->    List[Any]:
           lowerCamelCase					:					Dict        =	self.get_image_processor()
           lowerCamelCase					:					Optional[int]        =	self.get_tokenizer()

           lowerCamelCase					:					int        =	MgpstrProcessor(tokenizer=__a     ,		image_processor=__a	)

           lowerCamelCase					:					List[str]        =	self.prepare_image_inputs()

           lowerCamelCase					:					List[Any]        =	image_processor(__a     ,		return_tensors="""np"""	)
           lowerCamelCase					:					Any        =	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 		a__						(	self:						Optional[int]	)->    str:
           lowerCamelCase					:					Any        =	self.get_image_processor()
           lowerCamelCase					:					List[str]        =	self.get_tokenizer()

           lowerCamelCase					:					List[str]        =	MgpstrProcessor(tokenizer=__a     ,		image_processor=__a	)

           lowerCamelCase					:					Optional[Any]        =	"""test"""

           lowerCamelCase					:					Union[str, Any]        =	processor(text=__a	)

           lowerCamelCase					:					Dict        =	tokenizer(__a	)
           for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]     ,		encoded_processor[key]	)

     def 		a__						(	self:						str	)->    Dict:
           lowerCamelCase					:					int        =	self.get_image_processor()
           lowerCamelCase					:					str        =	self.get_tokenizer()

           lowerCamelCase					:					str        =	MgpstrProcessor(tokenizer=__a     ,		image_processor=__a	)

           lowerCamelCase					:					Dict        =	"""test"""
           lowerCamelCase					:					Dict        =	self.prepare_image_inputs()

           lowerCamelCase					:					Optional[Any]        =	processor(text=__a     ,		images=__a	)

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

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

     def 		a__						(	self:						Optional[Any]	)->    int:
           lowerCamelCase					:					List[str]        =	self.get_image_processor()
           lowerCamelCase					:					Optional[int]        =	self.get_tokenizer()

           lowerCamelCase					:					Optional[int]        =	MgpstrProcessor(tokenizer=__a     ,		image_processor=__a	)

           lowerCamelCase					:					Optional[int]        =	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

           lowerCamelCase					:					Tuple        =	processor.char_decode(__a	)
           lowerCamelCase					:					Optional[Any]        =	tokenizer.batch_decode(__a	)
           lowerCamelCase					:					Optional[Any]        =	[seq.replace(""" """     ,		""""""	) for seq in decoded_tok]

           self.assertListEqual(__a     ,		__a	)

     def 		a__						(	self:						Any	)->    List[Any]:
           lowerCamelCase					:					int        =	self.get_image_processor()
           lowerCamelCase					:					str        =	self.get_tokenizer()

           lowerCamelCase					:					Dict        =	MgpstrProcessor(tokenizer=__a     ,		image_processor=__a	)

           lowerCamelCase					:					Tuple        =	None
           lowerCamelCase					:					Tuple        =	self.prepare_image_inputs()

           lowerCamelCase					:					Tuple        =	processor(text=__a     ,		images=__a	)

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







     def 		a__						(	self:						Union[str, Any]	)->    str:
           lowerCamelCase					:					Dict        =	self.get_image_processor()
           lowerCamelCase					:					Optional[int]        =	self.get_tokenizer()

           lowerCamelCase					:					str        =	MgpstrProcessor(tokenizer=__a     ,		image_processor=__a	)

           lowerCamelCase					:					Dict        =	torch.randn(1     ,		27     ,		38	)
           lowerCamelCase					:					str        =	torch.randn(1     ,		27     ,		50_257	)
           lowerCamelCase					:					List[Any]        =	torch.randn(1     ,		27     ,		30_522	)

           lowerCamelCase					:					Optional[Any]        =	processor.batch_decode([char_input, bpe_input, wp_input]	)

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








"""simple docstring"""


from __future__ import annotations

__lowerCamelCase      :int	    =		10




def   snake_case		(     UpperCamelCase__       :						list[int]    )   ->      list[int]:
      lowerCamelCase					:					int        =	1
      lowerCamelCase					:					Union[str, Any]        =	max(UpperCamelCase__    )
      while placement <= max_digit:
            # declare and initialize empty buckets
            lowerCamelCase					:					list[list]        =	[[] for _ in range(UpperCamelCase__    )]
            # split list_of_ints between the buckets
            for i in list_of_ints:
                  lowerCamelCase					:					Any        =	int((i / placement) % RADIX    )
                  buckets[tmp].append(UpperCamelCase__    )
            # put each buckets' contents into list_of_ints
            lowerCamelCase					:					Dict        =	0
            for b in range(UpperCamelCase__    ):
                  for i in buckets[b]:
                        lowerCamelCase					:					List[str]        =	i
                        a += 1
        # move to next
            placement *= RADIX
      return list_of_ints


if __name__ == "__main__":
    import doctest

    doctest.testmod()




