





'''simple docstring'''






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


A__    :       List[str]              =logging.get_logger(__name__)


if is_vision_available():
     import PIL




class 			UpperCAmelCase					(      snake_case_       ):
      _lowercase:      Any											=       ['''pixel_values''']




      def __init__(      self      :			Any      ,      __snake_case      :			bool = True      ,      __snake_case      :			Dict[str, int] = None      ,      __snake_case      :			PILImageResampling = PILImageResampling.BICUBIC      ,      __snake_case      :			bool = True      ,      __snake_case      :			Dict[str, int] = None      ,      __snake_case      :			bool = True      ,      __snake_case      :			Union[int, float] = 1 / 2_55      ,      __snake_case      :			bool = True      ,      __snake_case      :			Optional[Union[float, List[float]]] = None      ,      __snake_case      :			Optional[Union[float, List[float]]] = None      ,      __snake_case      :			bool = True      ,      **__snake_case      :			int      ,      )    ->				None:
            super().__init__(**__snake_case  )
            _lowerCAmelCase												=							size if size is not None else {"""shortest_edge""": 2_24}
            _lowerCAmelCase												=							get_size_dict(__snake_case      ,      default_to_square=__snake_case  )
            _lowerCAmelCase												=							crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
            _lowerCAmelCase												=							get_size_dict(__snake_case      ,      default_to_square=__snake_case      ,      param_name="""crop_size"""  )

            _lowerCAmelCase												=							do_resize
            _lowerCAmelCase												=							size
            _lowerCAmelCase												=							resample
            _lowerCAmelCase												=							do_center_crop
            _lowerCAmelCase												=							crop_size
            _lowerCAmelCase												=							do_rescale
            _lowerCAmelCase												=							rescale_factor
            _lowerCAmelCase												=							do_normalize
            _lowerCAmelCase												=							image_mean if image_mean is not None else OPENAI_CLIP_MEAN
            _lowerCAmelCase												=							image_std if image_std is not None else OPENAI_CLIP_STD
            _lowerCAmelCase												=							do_convert_rgb




      def     lowercase__   (      self      :			Optional[Any]      ,      __snake_case      :			np.ndarray      ,      __snake_case      :			Dict[str, int]      ,      __snake_case      :			PILImageResampling = PILImageResampling.BICUBIC      ,      __snake_case      :			Optional[Union[str, ChannelDimension]] = None      ,      **__snake_case      :			Tuple      ,      )    ->				np.ndarray:
            _lowerCAmelCase												=							get_size_dict(__snake_case      ,      default_to_square=__snake_case  )
            if "shortest_edge" not in size:
                  raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"  )
            _lowerCAmelCase												=							get_resize_output_image_size(__snake_case      ,      size=size["""shortest_edge"""]      ,      default_to_square=__snake_case  )
            return resize(__snake_case      ,      size=__snake_case      ,      resample=__snake_case      ,      data_format=__snake_case      ,      **__snake_case  )




      def     lowercase__   (      self      :			Dict      ,      __snake_case      :			np.ndarray      ,      __snake_case      :			Dict[str, int]      ,      __snake_case      :			Optional[Union[str, ChannelDimension]] = None      ,      **__snake_case      :			Any      ,      )    ->				np.ndarray:
            _lowerCAmelCase												=							get_size_dict(__snake_case  )
            if "height" not in size or "width" not in size:
                  raise ValueError(f"The `size` parameter must contain the keys (height, width). Got {size.keys()}"  )
            return center_crop(__snake_case      ,      size=(size["""height"""], size["""width"""])      ,      data_format=__snake_case      ,      **__snake_case  )




      def     lowercase__   (      self      :			List[str]      ,      __snake_case      :			np.ndarray      ,      __snake_case      :			Union[int, float]      ,      __snake_case      :			Optional[Union[str, ChannelDimension]] = None      ,      **__snake_case      :			str      ,      )    ->				List[str]:
            return rescale(__snake_case      ,      scale=__snake_case      ,      data_format=__snake_case      ,      **__snake_case  )




      def     lowercase__   (      self      :			Tuple      ,      __snake_case      :			np.ndarray      ,      __snake_case      :			Union[float, List[float]]      ,      __snake_case      :			Union[float, List[float]]      ,      __snake_case      :			Optional[Union[str, ChannelDimension]] = None      ,      **__snake_case      :			List[str]      ,      )    ->				np.ndarray:
            return normalize(__snake_case      ,      mean=__snake_case      ,      std=__snake_case      ,      data_format=__snake_case      ,      **__snake_case  )




      def     lowercase__   (      self      :			Optional[int]      ,      __snake_case      :			ImageInput      ,      __snake_case      :			bool = None      ,      __snake_case      :			Dict[str, int] = None      ,      __snake_case      :			PILImageResampling = None      ,      __snake_case      :			bool = None      ,      __snake_case      :			int = None      ,      __snake_case      :			bool = None      ,      __snake_case      :			float = None      ,      __snake_case      :			bool = None      ,      __snake_case      :			Optional[Union[float, List[float]]] = None      ,      __snake_case      :			Optional[Union[float, List[float]]] = None      ,      __snake_case      :			bool = None      ,      __snake_case      :			Optional[Union[str, TensorType]] = None      ,      __snake_case      :			Optional[ChannelDimension] = ChannelDimension.FIRST      ,      **__snake_case      :			Tuple      ,      )    ->				PIL.Image.Image:
            _lowerCAmelCase												=							do_resize if do_resize is not None else self.do_resize
            _lowerCAmelCase												=							size if size is not None else self.size
            _lowerCAmelCase												=							get_size_dict(__snake_case      ,      param_name="""size"""      ,      default_to_square=__snake_case  )
            _lowerCAmelCase												=							resample if resample is not None else self.resample
            _lowerCAmelCase												=							do_center_crop if do_center_crop is not None else self.do_center_crop
            _lowerCAmelCase												=							crop_size if crop_size is not None else self.crop_size
            _lowerCAmelCase												=							get_size_dict(__snake_case      ,      param_name="""crop_size"""      ,      default_to_square=__snake_case  )
            _lowerCAmelCase												=							do_rescale if do_rescale is not None else self.do_rescale
            _lowerCAmelCase												=							rescale_factor if rescale_factor is not None else self.rescale_factor
            _lowerCAmelCase												=							do_normalize if do_normalize is not None else self.do_normalize
            _lowerCAmelCase												=							image_mean if image_mean is not None else self.image_mean
            _lowerCAmelCase												=							image_std if image_std is not None else self.image_std
            _lowerCAmelCase												=							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

            _lowerCAmelCase												=							make_list_of_images(__snake_case  )

            if not valid_images(__snake_case  ):
                  raise ValueError(
                      """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                      """torch.Tensor, tf.Tensor or jax.ndarray."""  )

            if do_resize and size is None:
                  raise ValueError("""Size must be specified if do_resize is True."""  )

            if do_center_crop and crop_size is None:
                  raise ValueError("""Crop size must be specified if do_center_crop is True."""  )

            if do_rescale and rescale_factor is None:
                  raise ValueError("""Rescale factor must be specified if do_rescale is True."""  )

            if do_normalize and (image_mean is None or image_std is None):
                  raise ValueError("""Image mean and std must be specified if do_normalize is True."""  )

            # PIL RGBA images are converted to RGB
            if do_convert_rgb:
                  _lowerCAmelCase												=							[convert_to_rgb(__snake_case  ) for image in images]

            # All transformations expect numpy arrays.
            _lowerCAmelCase												=							[to_numpy_array(__snake_case  ) for image in images]

            if do_resize:
                  _lowerCAmelCase												=							[self.resize(image=__snake_case      ,      size=__snake_case      ,      resample=__snake_case  ) for image in images]

            if do_center_crop:
                  _lowerCAmelCase												=							[self.center_crop(image=__snake_case      ,      size=__snake_case  ) for image in images]

            if do_rescale:
                  _lowerCAmelCase												=							[self.rescale(image=__snake_case      ,      scale=__snake_case  ) for image in images]

            if do_normalize:
                  _lowerCAmelCase												=							[self.normalize(image=__snake_case      ,      mean=__snake_case      ,      std=__snake_case  ) for image in images]

            _lowerCAmelCase												=							[to_channel_dimension_format(__snake_case      ,      __snake_case  ) for image in images]

            _lowerCAmelCase												=							{"""pixel_values""": images}
            return BatchFeature(data=__snake_case      ,      tensor_type=__snake_case  )













'''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  )






