






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 __lowerCamelCase					( unittest.TestCase):





	"""simple docstring"""






	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     tempfile.mkdtemp()

							# fmt: off
							_UpperCAmelCase							   =     ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
							# fmt: on
							_UpperCAmelCase							   =     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							   =     {
							    '''image_mean''': FLAVA_IMAGE_MEAN,
							    '''image_std''': FLAVA_IMAGE_STD,
							    '''do_normalize''': True,
							    '''do_resize''': True,
							    '''size''': 224,
							    '''do_center_crop''': True,
							    '''crop_size''': 224,
							    '''input_size_patches''': 14,
							    '''total_mask_patches''': 75,
							    '''mask_group_max_patches''': None,
							    '''mask_group_min_patches''': 16,
							    '''mask_group_min_aspect_ratio''': 0.3,
							    '''mask_group_max_aspect_ratio''': None,
							    '''codebook_do_resize''': True,
							    '''codebook_size''': 112,
							    '''codebook_do_center_crop''': True,
							    '''codebook_crop_size''': 112,
							    '''codebook_do_map_pixels''': True,
							    '''codebook_do_normalize''': True,
							    '''codebook_image_mean''': FLAVA_CODEBOOK_MEAN,
							    '''codebook_image_std''': FLAVA_CODEBOOK_STD,
							}

							_UpperCAmelCase							   =     os.path.join(self.tmpdirname			,       __UpperCamelCase       )
							with open(self.image_processor_file			,       'w'			,       encoding='utf-8'       ) as fp:
													json.dump(__UpperCamelCase			,       __UpperCamelCase       )

	def 			UpperCamelCase       (				self			,       **UpperCAmelCase       ):
							"""simple docstring"""







							return BertTokenizer.from_pretrained(self.tmpdirname			,       **__UpperCamelCase       )

	def 			UpperCamelCase       (				self			,       **UpperCAmelCase       ):
							"""simple docstring"""







							return BertTokenizerFast.from_pretrained(self.tmpdirname			,       **__UpperCamelCase       )

	def 			UpperCamelCase       (				self			,       **UpperCAmelCase       ):
							"""simple docstring"""







							return FlavaImageProcessor.from_pretrained(self.tmpdirname			,       **__UpperCamelCase       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							shutil.rmtree(self.tmpdirname       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     [np.random.randint(255			,       size=(3, 30, 400)			,       dtype=np.uinta       )]

							_UpperCAmelCase							   =     [Image.fromarray(np.moveaxis(__UpperCamelCase			,       0			,       -1       )       ) for x in image_inputs]

							return image_inputs

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self.get_tokenizer()
							_UpperCAmelCase							   =     self.get_rust_tokenizer()
							_UpperCAmelCase							   =     self.get_image_processor()

							_UpperCAmelCase							   =     FlavaProcessor(tokenizer=__UpperCamelCase			,       image_processor=__UpperCamelCase       )
							processor_slow.save_pretrained(self.tmpdirname       )
							_UpperCAmelCase							   =     FlavaProcessor.from_pretrained(self.tmpdirname			,       use_fast=__UpperCamelCase       )

							_UpperCAmelCase							   =     FlavaProcessor(tokenizer=__UpperCamelCase			,       image_processor=__UpperCamelCase       )
							processor_fast.save_pretrained(self.tmpdirname       )
							_UpperCAmelCase							   =     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			,       __UpperCamelCase       )
							self.assertIsInstance(processor_fast.tokenizer			,       __UpperCamelCase       )

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

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     FlavaProcessor(tokenizer=self.get_tokenizer()			,       image_processor=self.get_image_processor()       )
							processor.save_pretrained(self.tmpdirname       )

							_UpperCAmelCase							   =     self.get_tokenizer(bos_token='(BOS)'			,       eos_token='(EOS)'       )
							_UpperCAmelCase							   =     self.get_image_processor(do_normalize=__UpperCamelCase			,       padding_value=1.0       )

							_UpperCAmelCase							   =     FlavaProcessor.from_pretrained(
							    self.tmpdirname			,       bos_token='(BOS)'			,       eos_token='(EOS)'			,       do_normalize=__UpperCamelCase			,       padding_value=1.0       )

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

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

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self.get_image_processor()
							_UpperCAmelCase							   =     self.get_tokenizer()

							_UpperCAmelCase							   =     FlavaProcessor(tokenizer=__UpperCamelCase			,       image_processor=__UpperCamelCase       )

							_UpperCAmelCase							   =     self.prepare_image_inputs()

							_UpperCAmelCase							   =     image_processor(__UpperCamelCase			,       return_tensors='np'       )
							_UpperCAmelCase							   =     processor(images=__UpperCamelCase			,       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(1234       )
							_UpperCAmelCase							   =     image_processor(
							    __UpperCamelCase			,       return_image_mask=__UpperCamelCase			,       return_codebook_pixels=__UpperCamelCase			,       return_tensors='np'       )
							random.seed(1234       )
							_UpperCAmelCase							   =     processor(
							    images=__UpperCamelCase			,       return_image_mask=__UpperCamelCase			,       return_codebook_pixels=__UpperCamelCase			,       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       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self.get_image_processor()
							_UpperCAmelCase							   =     self.get_tokenizer()

							_UpperCAmelCase							   =     FlavaProcessor(tokenizer=__UpperCamelCase			,       image_processor=__UpperCamelCase       )

							_UpperCAmelCase							   =     '''lower newer'''

							_UpperCAmelCase							   =     processor(text=__UpperCamelCase       )

							_UpperCAmelCase							   =     tokenizer(__UpperCamelCase       )

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

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self.get_image_processor()
							_UpperCAmelCase							   =     self.get_tokenizer()

							_UpperCAmelCase							   =     FlavaProcessor(tokenizer=__UpperCamelCase			,       image_processor=__UpperCamelCase       )

							_UpperCAmelCase							   =     '''lower newer'''
							_UpperCAmelCase							   =     self.prepare_image_inputs()

							_UpperCAmelCase							   =     processor(text=__UpperCamelCase			,       images=__UpperCamelCase       )

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

							# add extra args
							_UpperCAmelCase							   =     processor(text=__UpperCamelCase			,       images=__UpperCamelCase			,       return_codebook_pixels=__UpperCamelCase			,       return_image_mask=__UpperCamelCase       )

							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(__UpperCamelCase       ):
													processor()

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self.get_image_processor()
							_UpperCAmelCase							   =     self.get_tokenizer()

							_UpperCAmelCase							   =     FlavaProcessor(tokenizer=__UpperCamelCase			,       image_processor=__UpperCamelCase       )

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

							_UpperCAmelCase							   =     processor.batch_decode(__UpperCamelCase       )
							_UpperCAmelCase							   =     tokenizer.batch_decode(__UpperCamelCase       )

							self.assertListEqual(__UpperCamelCase			,       __UpperCamelCase       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self.get_image_processor()
							_UpperCAmelCase							   =     self.get_tokenizer()

							_UpperCAmelCase							   =     FlavaProcessor(tokenizer=__UpperCamelCase			,       image_processor=__UpperCamelCase       )

							_UpperCAmelCase							   =     '''lower newer'''
							_UpperCAmelCase							   =     self.prepare_image_inputs()

							_UpperCAmelCase							   =     processor(text=__UpperCamelCase			,       images=__UpperCamelCase       )

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





'''simple docstring'''



from __future__ import annotations

# Define the Gravitational Constant G and the function
_lowercase       :						List[str]						=6.6743E-11  # unit of G : m^3 * kg^-1 * s^-2


def 							__UpperCAmelCase				(				UpperCamelCase__				:float   ,				UpperCamelCase__				:float   ,				UpperCamelCase__				:float   ,				UpperCamelCase__				:float			)		->						dict[str, float]:
				snake_case__      :   str      =    mass_a * mass_a

				if (force, mass_a, mass_a, distance).count(0			) != 1:
								raise ValueError('''One and only one argument must be 0'''			)
				if force < 0:
								raise ValueError('''Gravitational force can not be negative'''			)
				if distance < 0:
								raise ValueError('''Distance can not be negative'''			)
				if mass_a < 0 or mass_a < 0:
								raise ValueError('''Mass can not be negative'''			)
				if force == 0:
								snake_case__      :   Dict      =    GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
								return {"force": force}
				elif mass_a == 0:
								snake_case__      :   List[str]      =    (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
								return {"mass_1": mass_a}
				elif mass_a == 0:
								snake_case__      :   List[Any]      =    (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
								return {"mass_2": mass_a}
				elif distance == 0:
								snake_case__      :   str      =    (GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
								return {"distance": distance}
				raise ValueError('''One and only one argument must be 0'''			)


# Run doctest
if __name__ == "__main__":
				import doctest

				doctest.testmod()




