


"""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 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							__UpperCAmelCase		:					int          =ViTImageProcessor if is_vision_available() else None

							@property
							def     snake_case       (  self			):
										return self.image_processor_tester.prepare_image_processor_dict()

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    (3, 32, 1_28)
										__lowerCAmelCase       					=    tempfile.mkdtemp()

										# fmt: off
										__lowerCAmelCase       					=    ["[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       					=    dict(zip(__a					,     range(len(__a			)			)			)			)

										__lowerCAmelCase       					=    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       					=    {
										    "do_normalize": False,
										    "do_resize": True,
										    "image_processor_type": "ViTImageProcessor",
										    "resample": 3,
										    "size": {"height": 32, "width": 1_28},
										}
										__lowerCAmelCase       					=    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			):
										return MgpstrTokenizer.from_pretrained(self.tmpdirname					,     **__a			)

							def     snake_case       (  self					,     **__a			):
										return ViTImageProcessor.from_pretrained(self.tmpdirname					,     **__a			)

							def     snake_case       (  self			):
										shutil.rmtree(self.tmpdirname			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    np.random.randint(2_55					,     size=(3, 30, 4_00)					,     dtype=np.uinta			)

										__lowerCAmelCase       					=    Image.fromarray(np.moveaxis(__a					,     0					,     -1			)			)

										return image_input

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.get_tokenizer()
										__lowerCAmelCase       					=    self.get_image_processor()

										__lowerCAmelCase       					=    MgpstrProcessor(tokenizer=__a					,     image_processor=__a			)
										processor.save_pretrained(self.tmpdirname			)
										__lowerCAmelCase       					=    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     snake_case       (  self			):
										__lowerCAmelCase       					=    self.get_tokenizer()
										__lowerCAmelCase       					=    self.get_image_processor()

										__lowerCAmelCase       					=    MgpstrProcessor(tokenizer=__a					,     image_processor=__a			)
										processor.save_pretrained(self.tmpdirname			)

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

										__lowerCAmelCase       					=    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     snake_case       (  self			):
										__lowerCAmelCase       					=    self.get_image_processor()
										__lowerCAmelCase       					=    self.get_tokenizer()

										__lowerCAmelCase       					=    MgpstrProcessor(tokenizer=__a					,     image_processor=__a			)

										__lowerCAmelCase       					=    self.prepare_image_inputs()

										__lowerCAmelCase       					=    image_processor(__a					,     return_tensors="np"			)
										__lowerCAmelCase       					=    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			):
										__lowerCAmelCase       					=    self.get_image_processor()
										__lowerCAmelCase       					=    self.get_tokenizer()

										__lowerCAmelCase       					=    MgpstrProcessor(tokenizer=__a					,     image_processor=__a			)

										__lowerCAmelCase       					=    "test"

										__lowerCAmelCase       					=    processor(text=__a			)

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

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.get_image_processor()
										__lowerCAmelCase       					=    self.get_tokenizer()

										__lowerCAmelCase       					=    MgpstrProcessor(tokenizer=__a					,     image_processor=__a			)

										__lowerCAmelCase       					=    "test"
										__lowerCAmelCase       					=    self.prepare_image_inputs()

										__lowerCAmelCase       					=    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     snake_case       (  self			):
										__lowerCAmelCase       					=    self.get_image_processor()
										__lowerCAmelCase       					=    self.get_tokenizer()

										__lowerCAmelCase       					=    MgpstrProcessor(tokenizer=__a					,     image_processor=__a			)

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

										__lowerCAmelCase       					=    processor.char_decode(__a			)
										__lowerCAmelCase       					=    tokenizer.batch_decode(__a			)
										__lowerCAmelCase       					=    [seq.replace(" "					,     ""			) for seq in decoded_tok]

										self.assertListEqual(__a					,     __a			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.get_image_processor()
										__lowerCAmelCase       					=    self.get_tokenizer()

										__lowerCAmelCase       					=    MgpstrProcessor(tokenizer=__a					,     image_processor=__a			)

										__lowerCAmelCase       					=    None
										__lowerCAmelCase       					=    self.prepare_image_inputs()

										__lowerCAmelCase       					=    processor(text=__a					,     images=__a			)

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



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.get_image_processor()
										__lowerCAmelCase       					=    self.get_tokenizer()

										__lowerCAmelCase       					=    MgpstrProcessor(tokenizer=__a					,     image_processor=__a			)

										__lowerCAmelCase       					=    torch.randn(1					,     27					,     38			)
										__lowerCAmelCase       					=    torch.randn(1					,     27					,     5_02_57			)
										__lowerCAmelCase       					=    torch.randn(1					,     27					,     3_05_22			)

										__lowerCAmelCase       					=    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 itertools import product


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    sides_number
			__lowerCAmelCase       					=    max_face_number * dice_number
			__lowerCAmelCase       					=    [0] * (max_total + 1)

			__lowerCAmelCase       					=    1
			__lowerCAmelCase       					=    range(_UpperCamelCase					,				max_face_number + 1   )
			for dice_numbers in product(_UpperCamelCase					,				repeat=_UpperCamelCase   ):
						__lowerCAmelCase       					=    sum(_UpperCamelCase   )
						totals_frequencies[total] += 1

			return totals_frequencies


def _lowerCamelCase			(	):

			'''simple docstring'''




			__lowerCAmelCase       					=    total_frequency_distribution(
			    sides_number=4					,				dice_number=9   )
			__lowerCAmelCase       					=    total_frequency_distribution(
			    sides_number=6					,				dice_number=6   )

			__lowerCAmelCase       					=    0
			__lowerCAmelCase       					=    9
			__lowerCAmelCase       					=    4 * 9
			__lowerCAmelCase       					=    6
			for peter_total in range(_UpperCamelCase					,				max_peter_total + 1   ):
						peter_wins_count += peter_totals_frequencies[peter_total] * sum(
						    colin_totals_frequencies[min_colin_total:peter_total]   )

			__lowerCAmelCase       					=    (4**9) * (6**6)
			__lowerCAmelCase       					=    peter_wins_count / total_games_number

			__lowerCAmelCase       					=    round(_UpperCamelCase					,				ndigits=7   )

			return rounded_peter_win_probability


if __name__ == "__main__":
	print(f'''{solution() = }''')




