




import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor



@require_tokenizers
@require_vision
class    lowercase_		(unittest.TestCase ):



						def 	__a					(			self	:   List[str]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							tempfile.mkdtemp()

								# fmt: off
								SCREAMING_SNAKE_CASE_					      =							['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
								# fmt: on
								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': {'height': 18, 'width': 18},
								    'do_normalize': True,
								    'image_mean': [0.5, 0.5, 0.5],
								    'image_std': [0.5, 0.5, 0.5],
								}
								SCREAMING_SNAKE_CASE_					      =							os.path.join(self.tmpdirname					,							snake_case__  )
								with open(self.image_processor_file					,							'w'					,							encoding='utf-8'  ) as fp:
										json.dump(snake_case__					,							snake_case__  )



						def 	__a					(			self	:   Dict					,							**snake_case__	:   str  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   Dict					,							**snake_case__	:   str  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								shutil.rmtree(self.tmpdirname  )



						def 	__a					(			self	:   int  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							[np.random.randint(2_55					,							size=(3, 30, 4_00)					,							dtype=np.uinta  )]

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

								return image_inputs



						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.get_tokenizer()
								SCREAMING_SNAKE_CASE_					      =							self.get_image_processor()

								SCREAMING_SNAKE_CASE_					      =							VisionTextDualEncoderProcessor(tokenizer=snake_case__					,							image_processor=snake_case__  )

								processor.save_pretrained(self.tmpdirname  )
								SCREAMING_SNAKE_CASE_					      =							VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname  )

								self.assertEqual(processor.tokenizer.get_vocab()					,							tokenizer.get_vocab()  )
								self.assertIsInstance(processor.tokenizer					,							(BertTokenizer, BertTokenizerFast)  )

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



						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							VisionTextDualEncoderProcessor(
								    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=snake_case__					,							padding_value=1.0  )

								SCREAMING_SNAKE_CASE_					      =							VisionTextDualEncoderProcessor.from_pretrained(
								    self.tmpdirname					,							bos_token='(BOS)'					,							eos_token='(EOS)'					,							do_normalize=snake_case__					,							padding_value=1.0  )

								self.assertEqual(processor.tokenizer.get_vocab()					,							tokenizer_add_kwargs.get_vocab()  )
								self.assertIsInstance(processor.tokenizer					,							(BertTokenizer, BertTokenizerFast)  )

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



						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.get_image_processor()
								SCREAMING_SNAKE_CASE_					      =							self.get_tokenizer()

								SCREAMING_SNAKE_CASE_					      =							VisionTextDualEncoderProcessor(tokenizer=snake_case__					,							image_processor=snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							self.prepare_image_inputs()

								SCREAMING_SNAKE_CASE_					      =							image_processor(snake_case__					,							return_tensors='np'  )
								SCREAMING_SNAKE_CASE_					      =							processor(images=snake_case__					,							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 	__a					(			self	:   List[str]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.get_image_processor()
								SCREAMING_SNAKE_CASE_					      =							self.get_tokenizer()

								SCREAMING_SNAKE_CASE_					      =							VisionTextDualEncoderProcessor(tokenizer=snake_case__					,							image_processor=snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							'lower newer'

								SCREAMING_SNAKE_CASE_					      =							processor(text=snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							tokenizer(snake_case__  )

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



						def 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.get_image_processor()
								SCREAMING_SNAKE_CASE_					      =							self.get_tokenizer()

								SCREAMING_SNAKE_CASE_					      =							VisionTextDualEncoderProcessor(tokenizer=snake_case__					,							image_processor=snake_case__  )

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

								SCREAMING_SNAKE_CASE_					      =							processor(text=snake_case__					,							images=snake_case__  )

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

								# test if it raises when no input is passed
								with self.assertRaises(snake_case__  ):
										processor()



						def 	__a					(			self	:   Dict  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.get_image_processor()
								SCREAMING_SNAKE_CASE_					      =							self.get_tokenizer()

								SCREAMING_SNAKE_CASE_					      =							VisionTextDualEncoderProcessor(tokenizer=snake_case__					,							image_processor=snake_case__  )

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

								SCREAMING_SNAKE_CASE_					      =							processor.batch_decode(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							tokenizer.batch_decode(snake_case__  )

								self.assertListEqual(snake_case__					,							snake_case__  )






						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.get_image_processor()
								SCREAMING_SNAKE_CASE_					      =							self.get_tokenizer()

								SCREAMING_SNAKE_CASE_					      =							VisionTextDualEncoderProcessor(tokenizer=snake_case__					,							image_processor=snake_case__  )

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

								SCREAMING_SNAKE_CASE_					      =							processor(text=snake_case__					,							images=snake_case__  )

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





from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch



@dataclass
class    lowercase_		:
						lowerCAmelCase__			      =42  # [batch_size x 3]
						lowerCAmelCase__			      =42  # [batch_size x 3]
						lowerCAmelCase__			      =42  # [batch_size x 3]
						lowerCAmelCase__			      =42  # [batch_size x 3]
						lowerCAmelCase__			      =42
						lowerCAmelCase__			      =42
						lowerCAmelCase__			      =42
						lowerCAmelCase__			      =42
						lowerCAmelCase__			      =42



						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
								assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
								assert len(self.x.shape  ) == len(self.y.shape  ) == len(self.z.shape  ) == len(self.origin.shape  ) == 2



						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								return torch.from_numpy(np.array([self.width, self.height]					,							dtype=np.floataa  )  )



						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								return torch.from_numpy(np.array([self.x_fov, self.y_fov]					,							dtype=np.floataa  )  )



						def 	__a					(			self	:   int  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							torch.arange(self.height * self.width  )
								SCREAMING_SNAKE_CASE_					      =							torch.stack(
								    [
								        pixel_indices % self.width,
								        torch.div(snake_case__					,							self.width					,							rounding_mode='trunc'  ),
								    ]					,							axis=1					,							)
								return coords



						@property
						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_      ,				*SCREAMING_SNAKE_CASE_					      =							self.shape
								SCREAMING_SNAKE_CASE_					      =							int(np.prod(snake_case__  )  )

								SCREAMING_SNAKE_CASE_					      =							self.get_image_coords()
								SCREAMING_SNAKE_CASE_					      =							torch.broadcast_to(coords.unsqueeze(0  )					,							[batch_size * inner_batch_size, *coords.shape]  )
								SCREAMING_SNAKE_CASE_					      =							self.get_camera_rays(snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							rays.view(snake_case__					,							inner_batch_size * self.height * self.width					,							2					,							3  )

								return rays



						def 	__a					(			self	:   Optional[Any]					,							snake_case__	:   torch.Tensor  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_      ,				*SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							coords.shape
								assert n_coords == 2
								assert batch_size == self.origin.shape[0]

								SCREAMING_SNAKE_CASE_					      =							coords.view(snake_case__					,							-1					,							2  )

								SCREAMING_SNAKE_CASE_					      =							self.resolution()
								SCREAMING_SNAKE_CASE_					      =							self.fov()

								SCREAMING_SNAKE_CASE_					      =							(flat.float() / (res - 1)) * 2 - 1
								SCREAMING_SNAKE_CASE_					      =							fracs * torch.tan(fov / 2  )

								SCREAMING_SNAKE_CASE_					      =							fracs.view(snake_case__					,							-1					,							2  )
								SCREAMING_SNAKE_CASE_					      =							(
								    self.z.view(snake_case__					,							1					,							3  )
								    + self.x.view(snake_case__					,							1					,							3  ) * fracs[:, :, :1]
								    + self.y.view(snake_case__					,							1					,							3  ) * fracs[:, :, 1:]
								)
								SCREAMING_SNAKE_CASE_					      =							directions / directions.norm(dim=-1					,							keepdim=snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							torch.stack(
								    [
								        torch.broadcast_to(self.origin.view(snake_case__					,							1					,							3  )					,							[batch_size, directions.shape[1], 3]  ),
								        directions,
								    ]					,							dim=2					,							)
								return rays.view(snake_case__					,							*snake_case__					,							2					,							3  )






						def 	__a					(			self	:   Optional[int]					,							snake_case__	:   int					,							snake_case__	:   int  ):

								"""simple docstring"""

								assert width * self.height == height * self.width, "The aspect ratio should not change."
								return DifferentiableProjectiveCamera(
								    origin=self.origin					,							x=self.x					,							y=self.y					,							z=self.z					,							width=snake_case__					,							height=snake_case__					,							x_fov=self.x_fov					,							y_fov=self.y_fov					,							)


def 					_a							(					lowerCAmelCase )->	DifferentiableProjectiveCamera:
		SCREAMING_SNAKE_CASE_					      =							[]
		SCREAMING_SNAKE_CASE_					      =							[]
		SCREAMING_SNAKE_CASE_					      =							[]
		SCREAMING_SNAKE_CASE_					      =							[]
		for theta in np.linspace(0    ,						2 * np.pi    ,						num=20 ):
				SCREAMING_SNAKE_CASE_					      =							np.array([np.sin(lowerCAmelCase ), np.cos(lowerCAmelCase ), -0.5] )
				z /= np.sqrt(np.sum(z**2 ) )
				SCREAMING_SNAKE_CASE_					      =							-z * 4
				SCREAMING_SNAKE_CASE_					      =							np.array([np.cos(lowerCAmelCase ), -np.sin(lowerCAmelCase ), 0.0] )
				SCREAMING_SNAKE_CASE_					      =							np.cross(lowerCAmelCase    ,						lowerCAmelCase )
				origins.append(lowerCAmelCase )
				xs.append(lowerCAmelCase )
				ys.append(lowerCAmelCase )
				zs.append(lowerCAmelCase )
		return DifferentiableProjectiveCamera(
		    origin=torch.from_numpy(np.stack(lowerCAmelCase    ,						axis=0 ) ).float()    ,						x=torch.from_numpy(np.stack(lowerCAmelCase    ,						axis=0 ) ).float()    ,						y=torch.from_numpy(np.stack(lowerCAmelCase    ,						axis=0 ) ).float()    ,						z=torch.from_numpy(np.stack(lowerCAmelCase    ,						axis=0 ) ).float()    ,						width=lowerCAmelCase    ,						height=lowerCAmelCase    ,						x_fov=0.7    ,						y_fov=0.7    ,						shape=(1, len(lowerCAmelCase ))    ,						)