





"""simple docstring"""





import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize




class 							snake_case							:

						'''simple docstring'''



						def __init__(							self       :      str						,				__lowercase       :      int						,				__lowercase       :      Union[str, Any]=sys.maxsize ):


								'''simple docstring'''




								__UpperCAmelCase    :						Optional[int]       =						'''bilinear'''
								__UpperCAmelCase    :						Optional[Any]       =						max_size
								__UpperCAmelCase    :						List[Any]       =						short_edge_length







						def __call__(							self       :      int						,				__lowercase       :      Dict ):


								'''simple docstring'''




								__UpperCAmelCase    :						Any       =						[]
								for img in imgs:
										__UpperCAmelCase    :						Optional[Any]       =						img.shape[:2]
										# later: provide list and randomly choose index for resize
										__UpperCAmelCase    :						str       =						np.random.randint(self.short_edge_length[0]						,				self.short_edge_length[1] + 1 )
										if size == 0:
												return img
										__UpperCAmelCase    :						int       =						size * 1.0 / min(__lowercase						,				__lowercase )
										if h < w:
												__UpperCAmelCase    :						Dict       =						size, scale * w
										else:
												__UpperCAmelCase    :						str       =						scale * h, size
										if max(__lowercase						,				__lowercase ) > self.max_size:
												__UpperCAmelCase    :						Any       =						self.max_size * 1.0 / max(__lowercase						,				__lowercase )
												__UpperCAmelCase    :						Tuple       =						newh * scale
												__UpperCAmelCase    :						Any       =						neww * scale
										__UpperCAmelCase    :						List[str]       =						int(neww + 0.5 )
										__UpperCAmelCase    :						List[str]       =						int(newh + 0.5 )

										if img.dtype == np.uinta:
												__UpperCAmelCase    :						Optional[int]       =						Image.fromarray(__lowercase )
												__UpperCAmelCase    :						List[str]       =						pil_image.resize((neww, newh)						,				PILImageResampling.BILINEAR )
												__UpperCAmelCase    :						Optional[Any]       =						np.asarray(__lowercase )
										else:
												__UpperCAmelCase    :						Any       =						img.permute(2						,				0						,				1 ).unsqueeze(0 )  # 3, 0, 1)  # hw(c) -> nchw
												__UpperCAmelCase    :						Union[str, Any]       =						nn.functional.interpolate(
												    __lowercase						,				(newh, neww)						,				mode=self.interp_method						,				align_corners=__lowercase ).squeeze(0 )
										img_augs.append(__lowercase )

								return img_augs




class 							snake_case							:

						'''simple docstring'''



						def __init__(							self       :      Tuple						,				__lowercase       :      Dict ):


								'''simple docstring'''




								__UpperCAmelCase    :						Optional[Any]       =						ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]						,				cfg.INPUT.MAX_SIZE_TEST )
								__UpperCAmelCase    :						Any       =						cfg.INPUT.FORMAT
								__UpperCAmelCase    :						List[Any]       =						cfg.SIZE_DIVISIBILITY
								__UpperCAmelCase    :						List[str]       =						cfg.PAD_VALUE
								__UpperCAmelCase    :						Dict       =						cfg.INPUT.MAX_SIZE_TEST
								__UpperCAmelCase    :						Optional[Any]       =						cfg.MODEL.DEVICE
								__UpperCAmelCase    :						int       =						torch.tensor(cfg.MODEL.PIXEL_STD ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD )						,				1						,				1 )
								__UpperCAmelCase    :						Tuple       =						torch.tensor(cfg.MODEL.PIXEL_MEAN ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD )						,				1						,				1 )
								__UpperCAmelCase    :						int       =						lambda __lowercase : (x - self.pixel_mean) / self.pixel_std

						def       A_					(							self       :      List[Any]						,				__lowercase       :      List[Any] ):


								'''simple docstring'''




								__UpperCAmelCase    :						Optional[Any]       =						tuple(max(__lowercase ) for s in zip(*[img.shape for img in images] ) )
								__UpperCAmelCase    :						Union[str, Any]       =						[im.shape[-2:] for im in images]
								__UpperCAmelCase    :						Tuple       =						[
								    nn.functional.pad(
								        __lowercase						,				[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]						,				value=self.pad_value						,				)
								    for size, im in zip(__lowercase						,				__lowercase )
								]

								return torch.stack(__lowercase ), torch.tensor(__lowercase )







						def __call__(							self       :      List[Any]						,				__lowercase       :      str						,				__lowercase       :      Union[str, Any]=False ):


								'''simple docstring'''




								with torch.no_grad():
										if not isinstance(__lowercase						,				__lowercase ):
												__UpperCAmelCase    :						Any       =						[images]
										if single_image:
												assert len(__lowercase ) == 1
										for i in range(len(__lowercase ) ):
												if isinstance(images[i]						,				torch.Tensor ):
														images.insert(__lowercase						,				images.pop(__lowercase ).to(self.device ).float() )
												elif not isinstance(images[i]						,				torch.Tensor ):
														images.insert(
														    __lowercase						,				torch.as_tensor(img_tensorize(images.pop(__lowercase )						,				input_format=self.input_format ) )
														    .to(self.device )
														    .float()						,				)
														  # resize smallest edge
										__UpperCAmelCase    :						Union[str, Any]       =						torch.tensor([im.shape[:2] for im in images] )
										__UpperCAmelCase    :						Dict       =						self.aug(__lowercase )
										# transpose images and convert to torch tensors
										# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
										# now normalize before pad to avoid useless arithmetic
										__UpperCAmelCase    :						List[str]       =						[self.normalizer(__lowercase ) for x in images]
										# now pad them to do the following operations
										__UpperCAmelCase    :						Dict       =						self.pad(__lowercase )
										# Normalize

										if self.size_divisibility > 0:
												raise NotImplementedError()
										# pad
										__UpperCAmelCase    :						Union[str, Any]       =						torch.true_divide(__lowercase						,				__lowercase )
										if single_image:
												return images[0], sizes[0], scales_yx[0]
										else:
												return images, sizes, scales_yx


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->Optional[int]:





		"""simple docstring"""


		boxes[:, 0::2] *= scale_yx[:, 1]
		boxes[:, 1::2] *= scale_yx[:, 0]
		return boxes







def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->Union[str, Any]:





		"""simple docstring"""


		assert torch.isfinite(__A  ).all(), "Box tensor contains infinite or NaN!"
		__UpperCAmelCase    :						Optional[Any]       =						box_size
		tensor[:, 0].clamp_(min=0  ,			max=__A  )
		tensor[:, 1].clamp_(min=0  ,			max=__A  )
		tensor[:, 2].clamp_(min=0  ,			max=__A  )
		tensor[:, 3].clamp_(min=0  ,			max=__A  )






"""simple docstring"""





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip 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 CLIPSegProcessor, ViTImageProcessor




@require_vision
class 							snake_case							(			unittest.TestCase ):

      '''simple docstring'''



      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Any       =						tempfile.mkdtemp()

        # fmt: off
        __UpperCAmelCase    :						Optional[int]       =						['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
        # fmt: on
        __UpperCAmelCase    :						List[str]       =						dict(zip(__lowercase						,				range(len(__lowercase ) ) ) )
        __UpperCAmelCase    :						Any       =						['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
        __UpperCAmelCase    :						Dict       =						{'''unk_token''': '''<unk>'''}

        __UpperCAmelCase    :						Optional[int]       =						os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES['''vocab_file'''] )
        __UpperCAmelCase    :						Any       =						os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES['''merges_file'''] )
        with open(self.vocab_file						,				'''w'''						,				encoding='''utf-8''' ) as fp:
          fp.write(json.dumps(__lowercase ) + '''\n''' )
        with open(self.merges_file						,				'''w'''						,				encoding='''utf-8''' ) as fp:
          fp.write('''\n'''.join(__lowercase ) )

        __UpperCAmelCase    :						List[str]       =						{
            '''do_resize''': True,
            '''size''': 20,
            '''do_center_crop''': True,
            '''crop_size''': 18,
            '''do_normalize''': True,
            '''image_mean''': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
            '''image_std''': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
        }
        __UpperCAmelCase    :						Any       =						os.path.join(self.tmpdirname						,				__lowercase )
        with open(self.image_processor_file						,				'''w'''						,				encoding='''utf-8''' ) as fp:
          json.dump(__lowercase						,				__lowercase )

      def       A_					(							self       :      Optional[Any]						,				**__lowercase       :      Tuple ):


        '''simple docstring'''




        return CLIPTokenizer.from_pretrained(self.tmpdirname						,				**__lowercase )

      def       A_					(							self       :      Any						,				**__lowercase       :      Optional[Any] ):


        '''simple docstring'''




        return CLIPTokenizerFast.from_pretrained(self.tmpdirname						,				**__lowercase )

      def       A_					(							self       :      Dict						,				**__lowercase       :      str ):


        '''simple docstring'''




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

      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        shutil.rmtree(self.tmpdirname )

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


        '''simple docstring'''




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

        __UpperCAmelCase    :						int       =						[Image.fromarray(np.moveaxis(__lowercase						,				0						,				-1 ) ) for x in image_inputs]

        return image_inputs

      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						self.get_tokenizer()
        __UpperCAmelCase    :						Dict       =						self.get_rust_tokenizer()
        __UpperCAmelCase    :						Optional[Any]       =						self.get_image_processor()

        __UpperCAmelCase    :						Any       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )
        processor_slow.save_pretrained(self.tmpdirname )
        __UpperCAmelCase    :						List[Any]       =						CLIPSegProcessor.from_pretrained(self.tmpdirname						,				use_fast=__lowercase )

        __UpperCAmelCase    :						Tuple       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )
        processor_fast.save_pretrained(self.tmpdirname )
        __UpperCAmelCase    :						str       =						CLIPSegProcessor.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						,				__lowercase )
        self.assertIsInstance(processor_fast.tokenizer						,				__lowercase )

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

      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Any       =						CLIPSegProcessor(tokenizer=self.get_tokenizer()						,				image_processor=self.get_image_processor() )
        processor.save_pretrained(self.tmpdirname )

        __UpperCAmelCase    :						Any       =						self.get_tokenizer(bos_token='''(BOS)'''						,				eos_token='''(EOS)''' )
        __UpperCAmelCase    :						Tuple       =						self.get_image_processor(do_normalize=__lowercase						,				padding_value=1.0 )

        __UpperCAmelCase    :						List[str]       =						CLIPSegProcessor.from_pretrained(
            self.tmpdirname						,				bos_token='''(BOS)'''						,				eos_token='''(EOS)'''						,				do_normalize=__lowercase						,				padding_value=1.0 )

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

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

      def       A_					(							self       :      int ):


        '''simple docstring'''




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

        __UpperCAmelCase    :						Optional[Any]       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )

        __UpperCAmelCase    :						List[Any]       =						self.prepare_image_inputs()

        __UpperCAmelCase    :						Dict       =						image_processor(__lowercase						,				return_tensors='''np''' )
        __UpperCAmelCase    :						Optional[int]       =						processor(images=__lowercase						,				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       :      Dict ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						self.get_image_processor()
        __UpperCAmelCase    :						Any       =						self.get_tokenizer()

        __UpperCAmelCase    :						Any       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )

        __UpperCAmelCase    :						int       =						'''lower newer'''

        __UpperCAmelCase    :						int       =						processor(text=__lowercase )

        __UpperCAmelCase    :						Any       =						tokenizer(__lowercase )

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

      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						self.get_image_processor()
        __UpperCAmelCase    :						Union[str, Any]       =						self.get_tokenizer()

        __UpperCAmelCase    :						List[Any]       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )

        __UpperCAmelCase    :						str       =						'''lower newer'''
        __UpperCAmelCase    :						int       =						self.prepare_image_inputs()

        __UpperCAmelCase    :						str       =						processor(text=__lowercase						,				images=__lowercase )

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

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

      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Dict       =						self.get_image_processor()
        __UpperCAmelCase    :						str       =						self.get_tokenizer()

        __UpperCAmelCase    :						Any       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )

        __UpperCAmelCase    :						Union[str, Any]       =						self.prepare_image_inputs()
        __UpperCAmelCase    :						Optional[Any]       =						self.prepare_image_inputs()

        __UpperCAmelCase    :						Tuple       =						processor(images=__lowercase						,				visual_prompt=__lowercase )

        self.assertListEqual(list(inputs.keys() )						,				['''pixel_values''', '''conditional_pixel_values'''] )

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







      def       A_					(							self       :      str ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						self.get_image_processor()
        __UpperCAmelCase    :						Any       =						self.get_tokenizer()

        __UpperCAmelCase    :						Any       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )

        __UpperCAmelCase    :						Dict       =						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        __UpperCAmelCase    :						int       =						processor.batch_decode(__lowercase )
        __UpperCAmelCase    :						int       =						tokenizer.batch_decode(__lowercase )

        self.assertListEqual(__lowercase						,				__lowercase )