





'''simple docstring'''






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import PoolFormerImageProcessor




class 			UpperCAmelCase					(      unittest.TestCase       ):




      def __init__(      self      :			Dict      ,      __snake_case      :			Union[str, Any]      ,      __snake_case      :			Any=7      ,      __snake_case      :			Dict=3      ,      __snake_case      :			Tuple=30      ,      __snake_case      :			str=4_00      ,      __snake_case      :			List[Any]=True      ,      __snake_case      :			Dict=None      ,      __snake_case      :			str=0.9      ,      __snake_case      :			Union[str, Any]=None      ,      __snake_case      :			Tuple=True      ,      __snake_case      :			str=[0.5, 0.5, 0.5]      ,      __snake_case      :			Dict=[0.5, 0.5, 0.5]      ,      )    ->				Optional[Any]:
            _lowerCAmelCase												=							size if size is not None else {"""shortest_edge""": 30}
            _lowerCAmelCase												=							crop_size if crop_size is not None else {"""height""": 30, """width""": 30}
            _lowerCAmelCase												=							parent
            _lowerCAmelCase												=							batch_size
            _lowerCAmelCase												=							num_channels
            _lowerCAmelCase												=							min_resolution
            _lowerCAmelCase												=							max_resolution
            _lowerCAmelCase												=							do_resize_and_center_crop
            _lowerCAmelCase												=							size
            _lowerCAmelCase												=							crop_pct
            _lowerCAmelCase												=							crop_size
            _lowerCAmelCase												=							do_normalize
            _lowerCAmelCase												=							image_mean
            _lowerCAmelCase												=							image_std




      def     lowercase__   (      self      :			Any  )    ->				Optional[int]:
            return {
                "size": self.size,
                "do_resize_and_center_crop": self.do_resize_and_center_crop,
                "crop_pct": self.crop_pct,
                "crop_size": self.crop_size,
                "do_normalize": self.do_normalize,
                "image_mean": self.image_mean,
                "image_std": self.image_std,
            }




@require_torch
@require_vision
class 			UpperCAmelCase					(      snake_case_							,		unittest.TestCase       ):
      _lowercase:      Optional[Any]											=       PoolFormerImageProcessor if is_vision_available() else None




      def     lowercase__   (      self      :			int  )    ->				Optional[int]:
            _lowerCAmelCase												=							PoolFormerImageProcessingTester(self  )




      @property
      def     lowercase__   (      self      :			Optional[Any]  )    ->				Optional[Any]:
            return self.image_processor_tester.prepare_image_processor_dict()




      def     lowercase__   (      self      :			int  )    ->				int:
            _lowerCAmelCase												=							self.image_processing_class(**self.image_processor_dict  )
            self.assertTrue(hasattr(__snake_case      ,      """do_resize_and_center_crop"""  )  )
            self.assertTrue(hasattr(__snake_case      ,      """size"""  )  )
            self.assertTrue(hasattr(__snake_case      ,      """crop_pct"""  )  )
            self.assertTrue(hasattr(__snake_case      ,      """do_normalize"""  )  )
            self.assertTrue(hasattr(__snake_case      ,      """image_mean"""  )  )
            self.assertTrue(hasattr(__snake_case      ,      """image_std"""  )  )




      def     lowercase__   (      self      :			Any  )    ->				Dict:
            _lowerCAmelCase												=							self.image_processing_class.from_dict(self.image_processor_dict  )
            self.assertEqual(image_processor.size      ,      {"""shortest_edge""": 30}  )
            self.assertEqual(image_processor.crop_size      ,      {"""height""": 30, """width""": 30}  )

            _lowerCAmelCase												=							self.image_processing_class.from_dict(self.image_processor_dict      ,      size=42      ,      crop_size=84  )
            self.assertEqual(image_processor.size      ,      {"""shortest_edge""": 42}  )
            self.assertEqual(image_processor.crop_size      ,      {"""height""": 84, """width""": 84}  )




      def     lowercase__   (      self      :			Any  )    ->				int:
            pass




      def     lowercase__   (      self      :			Optional[int]  )    ->				Optional[int]:
            # Initialize image_processing
            _lowerCAmelCase												=							self.image_processing_class(**self.image_processor_dict  )
            # create random PIL images
            _lowerCAmelCase												=							prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=__snake_case  )
            for image in image_inputs:
                  self.assertIsInstance(__snake_case      ,      Image.Image  )

            # Test not batched input
            _lowerCAmelCase												=							image_processing(image_inputs[0]      ,      return_tensors="""pt"""  ).pixel_values

            self.assertEqual(
                encoded_images.shape      ,      (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )      ,      )

            # Test batched
            _lowerCAmelCase												=							image_processing(__snake_case      ,      return_tensors="""pt"""  ).pixel_values
            self.assertEqual(
                encoded_images.shape      ,      (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )      ,      )




      def     lowercase__   (      self      :			int  )    ->				Any:
            # Initialize image_processing
            _lowerCAmelCase												=							self.image_processing_class(**self.image_processor_dict  )
            # create random numpy tensors
            _lowerCAmelCase												=							prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=__snake_case      ,      numpify=__snake_case  )
            for image in image_inputs:
                  self.assertIsInstance(__snake_case      ,      np.ndarray  )

            # Test not batched input
            _lowerCAmelCase												=							image_processing(image_inputs[0]      ,      return_tensors="""pt"""  ).pixel_values
            self.assertEqual(
                encoded_images.shape      ,      (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )      ,      )

            # Test batched
            _lowerCAmelCase												=							image_processing(__snake_case      ,      return_tensors="""pt"""  ).pixel_values
            self.assertEqual(
                encoded_images.shape      ,      (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )      ,      )




      def     lowercase__   (      self      :			Optional[Any]  )    ->				Optional[Any]:
            # Initialize image_processing
            _lowerCAmelCase												=							self.image_processing_class(**self.image_processor_dict  )
            # create random PyTorch tensors
            _lowerCAmelCase												=							prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=__snake_case      ,      torchify=__snake_case  )
            for image in image_inputs:
                  self.assertIsInstance(__snake_case      ,      torch.Tensor  )

            # Test not batched input
            _lowerCAmelCase												=							image_processing(image_inputs[0]      ,      return_tensors="""pt"""  ).pixel_values
            self.assertEqual(
                encoded_images.shape      ,      (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )      ,      )

            # Test batched
            _lowerCAmelCase												=							image_processing(__snake_case      ,      return_tensors="""pt"""  ).pixel_values
            self.assertEqual(
                encoded_images.shape      ,      (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )      ,      )













'''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 			UpperCAmelCase					(      unittest.TestCase       ):




      def     lowercase__   (      self      :			List[str]  )    ->				Dict:
            _lowerCAmelCase												=							tempfile.mkdtemp()

            # fmt: off
            _lowerCAmelCase												=							["""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
            _lowerCAmelCase												=							dict(zip(__snake_case      ,      range(len(__snake_case  )  )  )  )
            _lowerCAmelCase												=							["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
            _lowerCAmelCase												=							{"""unk_token""": """<unk>"""}

            _lowerCAmelCase												=							os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES["""vocab_file"""]  )
            _lowerCAmelCase												=							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(__snake_case  ) + """\n"""  )
            with open(self.merges_file      ,      """w"""      ,      encoding="""utf-8"""  ) as fp:
                  fp.write("""\n""".join(__snake_case  )  )

            _lowerCAmelCase												=							{
                """do_resize""": True,
                """size""": 20,
                """do_center_crop""": True,
                """crop_size""": 18,
                """do_normalize""": True,
                """image_mean""": [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
                """image_std""": [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
            }
            _lowerCAmelCase												=							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     lowercase__   (      self      :			Optional[int]      ,      **__snake_case      :			Dict  )    ->				List[str]:
            return CLIPTokenizer.from_pretrained(self.tmpdirname      ,      **__snake_case  )




      def     lowercase__   (      self      :			List[str]      ,      **__snake_case      :			Any  )    ->				List[Any]:
            return CLIPTokenizerFast.from_pretrained(self.tmpdirname      ,      **__snake_case  )




      def     lowercase__   (      self      :			Tuple      ,      **__snake_case      :			List[str]  )    ->				int:
            return ViTImageProcessor.from_pretrained(self.tmpdirname      ,      **__snake_case  )




      def     lowercase__   (      self      :			str  )    ->				Optional[Any]:
            shutil.rmtree(self.tmpdirname  )




      def     lowercase__   (      self      :			str  )    ->				List[Any]:
            _lowerCAmelCase												=							[np.random.randint(2_55      ,      size=(3, 30, 4_00)      ,      dtype=np.uinta  )]

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

            return image_inputs




      def     lowercase__   (      self      :			Optional[Any]  )    ->				List[str]:
            _lowerCAmelCase												=							self.get_tokenizer()
            _lowerCAmelCase												=							self.get_rust_tokenizer()
            _lowerCAmelCase												=							self.get_image_processor()

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )
            processor_slow.save_pretrained(self.tmpdirname  )
            _lowerCAmelCase												=							CLIPSegProcessor.from_pretrained(self.tmpdirname      ,      use_fast=__snake_case  )

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )
            processor_fast.save_pretrained(self.tmpdirname  )
            _lowerCAmelCase												=							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      ,      __snake_case  )
            self.assertIsInstance(processor_fast.tokenizer      ,      __snake_case  )

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




      def     lowercase__   (      self      :			Optional[int]  )    ->				Dict:
            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=self.get_tokenizer()      ,      image_processor=self.get_image_processor()  )
            processor.save_pretrained(self.tmpdirname  )

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

            _lowerCAmelCase												=							CLIPSegProcessor.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      ,      __snake_case  )

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




      def     lowercase__   (      self      :			Tuple  )    ->				List[Any]:
            _lowerCAmelCase												=							self.get_image_processor()
            _lowerCAmelCase												=							self.get_tokenizer()

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )

            _lowerCAmelCase												=							self.prepare_image_inputs()

            _lowerCAmelCase												=							image_processor(__snake_case      ,      return_tensors="""np"""  )
            _lowerCAmelCase												=							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     lowercase__   (      self      :			Any  )    ->				str:
            _lowerCAmelCase												=							self.get_image_processor()
            _lowerCAmelCase												=							self.get_tokenizer()

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )

            _lowerCAmelCase												=							"""lower newer"""

            _lowerCAmelCase												=							processor(text=__snake_case  )

            _lowerCAmelCase												=							tokenizer(__snake_case  )

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




      def     lowercase__   (      self      :			str  )    ->				Union[str, Any]:
            _lowerCAmelCase												=							self.get_image_processor()
            _lowerCAmelCase												=							self.get_tokenizer()

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )

            _lowerCAmelCase												=							"""lower newer"""
            _lowerCAmelCase												=							self.prepare_image_inputs()

            _lowerCAmelCase												=							processor(text=__snake_case      ,      images=__snake_case  )

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

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




      def     lowercase__   (      self      :			Union[str, Any]  )    ->				Dict:
            _lowerCAmelCase												=							self.get_image_processor()
            _lowerCAmelCase												=							self.get_tokenizer()

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )

            _lowerCAmelCase												=							self.prepare_image_inputs()
            _lowerCAmelCase												=							self.prepare_image_inputs()

            _lowerCAmelCase												=							processor(images=__snake_case      ,      visual_prompt=__snake_case  )

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

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




      def     lowercase__   (      self      :			int  )    ->				Tuple:
            _lowerCAmelCase												=							self.get_image_processor()
            _lowerCAmelCase												=							self.get_tokenizer()

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )

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

            _lowerCAmelCase												=							processor.batch_decode(__snake_case  )
            _lowerCAmelCase												=							tokenizer.batch_decode(__snake_case  )

            self.assertListEqual(__snake_case      ,      __snake_case  )






