


"""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 CLIPImageProcessor, CLIPProcessor


@require_vision
class       SCREAMING_SNAKE_CASE__				(		unittest.TestCase				):
       """simple docstring"""


       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[Any]								=							tempfile.mkdtemp()

         # fmt: off
         lowerCAmelCase     :     Any								=							["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     :     List[Any]								=							dict(zip(snake_case__		,     range(len(snake_case__						)						)						)						)
         lowerCAmelCase     :     Tuple								=							["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
         lowerCAmelCase     :     Union[str, Any]								=							{"unk_token": "<unk>"}

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

         lowerCAmelCase     :     Union[str, Any]								=							{
             "do_resize": True,
             "size": 20,
             "do_center_crop": True,
             "crop_size": 18,
             "do_normalize": True,
             "image_mean": [0.48145466, 0.4578275, 0.40821073],
             "image_std": [0.26862954, 0.26130258, 0.27577711],
         }
         lowerCAmelCase     :     Optional[Any]								=							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		,     **snake_case__						):






         """simple docstring"""





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

       def        lowercase__							(						self		,     **snake_case__						):






         """simple docstring"""





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

       def        lowercase__							(						self		,     **snake_case__						):






         """simple docstring"""





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

       def        lowercase__							(						self						):






         """simple docstring"""





         shutil.rmtree(self.tmpdirname						)

       def        lowercase__							(						self						):






         """simple docstring"""





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

         lowerCAmelCase     :     List[str]								=							[Image.fromarray(np.moveaxis(snake_case__		,     0		,     -1						)						) for x in image_inputs]

         return image_inputs

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Tuple								=							self.get_tokenizer()
         lowerCAmelCase     :     Dict								=							self.get_rust_tokenizer()
         lowerCAmelCase     :     Dict								=							self.get_image_processor()

         lowerCAmelCase     :     Any								=							CLIPProcessor(tokenizer=snake_case__		,     image_processor=snake_case__						)
         processor_slow.save_pretrained(self.tmpdirname						)
         lowerCAmelCase     :     Optional[Any]								=							CLIPProcessor.from_pretrained(self.tmpdirname		,     use_fast=snake_case__						)

         lowerCAmelCase     :     Dict								=							CLIPProcessor(tokenizer=snake_case__		,     image_processor=snake_case__						)
         processor_fast.save_pretrained(self.tmpdirname						)
         lowerCAmelCase     :     Dict								=							CLIPProcessor.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						):






         """simple docstring"""





         lowerCAmelCase     :     str								=							CLIPProcessor(tokenizer=self.get_tokenizer()		,     image_processor=self.get_image_processor()						)
         processor.save_pretrained(self.tmpdirname						)

         lowerCAmelCase     :     Any								=							self.get_tokenizer(bos_token="(BOS)"		,     eos_token="(EOS)"						)
         lowerCAmelCase     :     Any								=							self.get_image_processor(do_normalize=snake_case__		,     padding_value=1.0						)

         lowerCAmelCase     :     List[Any]								=							CLIPProcessor.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						):






         """simple docstring"""





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

         lowerCAmelCase     :     List[str]								=							CLIPProcessor(tokenizer=snake_case__		,     image_processor=snake_case__						)

         lowerCAmelCase     :     Optional[Any]								=							self.prepare_image_inputs()

         lowerCAmelCase     :     Optional[Any]								=							image_processor(snake_case__		,     return_tensors="np"						)
         lowerCAmelCase     :     Tuple								=							processor(images=snake_case__		,     return_tensors="np"						)

         for key in input_image_proc.keys():
           self.assertAlmostEqual(input_image_proc[key].sum()		,     input_processor[key].sum()		,     delta=1e-2						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							self.get_image_processor()
         lowerCAmelCase     :     Optional[int]								=							self.get_tokenizer()

         lowerCAmelCase     :     Tuple								=							CLIPProcessor(tokenizer=snake_case__		,     image_processor=snake_case__						)

         lowerCAmelCase     :     int								=							"lower newer"

         lowerCAmelCase     :     List[str]								=							processor(text=snake_case__						)

         lowerCAmelCase     :     List[str]								=							tokenizer(snake_case__						)

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

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							self.get_image_processor()
         lowerCAmelCase     :     Union[str, Any]								=							self.get_tokenizer()

         lowerCAmelCase     :     Optional[Any]								=							CLIPProcessor(tokenizer=snake_case__		,     image_processor=snake_case__						)

         lowerCAmelCase     :     List[str]								=							"lower newer"
         lowerCAmelCase     :     int								=							self.prepare_image_inputs()

         lowerCAmelCase     :     List[Any]								=							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						):






         """simple docstring"""





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

         lowerCAmelCase     :     str								=							CLIPProcessor(tokenizer=snake_case__		,     image_processor=snake_case__						)

         lowerCAmelCase     :     int								=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         lowerCAmelCase     :     List[Any]								=							processor.batch_decode(snake_case__						)
         lowerCAmelCase     :     List[Any]								=							tokenizer.batch_decode(snake_case__						)

         self.assertListEqual(snake_case__		,     snake_case__						)







       def        lowercase__							(						self						):






         """simple docstring"""





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

         lowerCAmelCase     :     List[str]								=							CLIPProcessor(tokenizer=snake_case__		,     image_processor=snake_case__						)

         lowerCAmelCase     :     Optional[Any]								=							"lower newer"
         lowerCAmelCase     :     List[Any]								=							self.prepare_image_inputs()

         lowerCAmelCase     :     Tuple								=							processor(text=snake_case__		,     images=snake_case__						)

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













from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
 import PIL

if is_torch_available():
 import torch


lowerCamelCase	:							Any				=logging.get_logger(__name__)





class 			__a  (    A__       ):
 _lowerCAmelCase       :       List[str]        = ['''pixel_values''']







 def __init__(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   bool = True							,   SCREAMING_SNAKE_CASE					:   Dict[str, int] = None							,   SCREAMING_SNAKE_CASE					:   PILImageResampling = PILImageResampling.BILINEAR							,   SCREAMING_SNAKE_CASE					:   bool = True							,   SCREAMING_SNAKE_CASE					:   Union[int, float] = 1 / 2_55							,   SCREAMING_SNAKE_CASE					:   bool = True							,   SCREAMING_SNAKE_CASE					:   Dict[str, int] = None							,   SCREAMING_SNAKE_CASE					:   bool = True							,   **SCREAMING_SNAKE_CASE					:   Tuple							,   ):


      '''simple docstring'''






      super().__init__(**SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[str]       				=  size if size is not None else {"shortest_edge": 2_24}
      UpperCamelCase__			:   Any       				=  get_size_dict(SCREAMING_SNAKE_CASE							,   default_to_square=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[Any]       				=  crop_size if crop_size is not None else {"height": 2_56, "width": 2_56}
      UpperCamelCase__			:   int       				=  get_size_dict(SCREAMING_SNAKE_CASE							,   param_name="crop_size"						)

      UpperCamelCase__			:   Dict       				=  do_resize
      UpperCamelCase__			:   List[str]       				=  size
      UpperCamelCase__			:   int       				=  resample
      UpperCamelCase__			:   Optional[int]       				=  do_rescale
      UpperCamelCase__			:   List[Any]       				=  rescale_factor
      UpperCamelCase__			:   Union[str, Any]       				=  do_center_crop
      UpperCamelCase__			:   int       				=  crop_size
      UpperCamelCase__			:   Optional[int]       				=  do_flip_channel_order







 def 					__lowercase					(		self					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   np.ndarray							,   SCREAMING_SNAKE_CASE					:   Dict[str, int]							,   SCREAMING_SNAKE_CASE					:   PILImageResampling = PIL.Image.BILINEAR							,   SCREAMING_SNAKE_CASE					:   Optional[Union[str, ChannelDimension]] = None							,   **SCREAMING_SNAKE_CASE					:   Optional[Any]							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   Union[str, Any]       				=  get_size_dict(SCREAMING_SNAKE_CASE							,   default_to_square=SCREAMING_SNAKE_CASE						)
      if "shortest_edge" not in size:
           raise ValueError(F'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'						)
      UpperCamelCase__			:   Any       				=  get_resize_output_image_size(SCREAMING_SNAKE_CASE							,   size=size["shortest_edge"]							,   default_to_square=SCREAMING_SNAKE_CASE						)
      return resize(SCREAMING_SNAKE_CASE							,   size=SCREAMING_SNAKE_CASE							,   resample=SCREAMING_SNAKE_CASE							,   data_format=SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   np.ndarray							,   SCREAMING_SNAKE_CASE					:   Dict[str, int]							,   SCREAMING_SNAKE_CASE					:   Optional[Union[str, ChannelDimension]] = None							,   **SCREAMING_SNAKE_CASE					:   Optional[Any]							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  get_size_dict(SCREAMING_SNAKE_CASE						)
      if "height" not in size or "width" not in size:
           raise ValueError(F'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'						)
      return center_crop(SCREAMING_SNAKE_CASE							,   size=(size["height"], size["width"])							,   data_format=SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   int							,   SCREAMING_SNAKE_CASE					:   np.ndarray							,   SCREAMING_SNAKE_CASE					:   Union[int, float]							,   SCREAMING_SNAKE_CASE					:   Optional[Union[str, ChannelDimension]] = None							,   **SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   ):


      '''simple docstring'''






      return rescale(SCREAMING_SNAKE_CASE							,   scale=SCREAMING_SNAKE_CASE							,   data_format=SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   np.ndarray							,   SCREAMING_SNAKE_CASE					:   Optional[Union[str, ChannelDimension]] = None						):


      '''simple docstring'''






      return flip_channel_order(SCREAMING_SNAKE_CASE							,   data_format=SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   ImageInput							,   SCREAMING_SNAKE_CASE					:   bool = None							,   SCREAMING_SNAKE_CASE					:   Dict[str, int] = None							,   SCREAMING_SNAKE_CASE					:   PILImageResampling = None							,   SCREAMING_SNAKE_CASE					:   bool = None							,   SCREAMING_SNAKE_CASE					:   float = None							,   SCREAMING_SNAKE_CASE					:   bool = None							,   SCREAMING_SNAKE_CASE					:   Dict[str, int] = None							,   SCREAMING_SNAKE_CASE					:   bool = None							,   SCREAMING_SNAKE_CASE					:   Optional[Union[str, TensorType]] = None							,   SCREAMING_SNAKE_CASE					:   ChannelDimension = ChannelDimension.FIRST							,   **SCREAMING_SNAKE_CASE					:   Dict							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   List[str]       				=  do_resize if do_resize is not None else self.do_resize
      UpperCamelCase__			:   List[str]       				=  resample if resample is not None else self.resample
      UpperCamelCase__			:   Any       				=  do_rescale if do_rescale is not None else self.do_rescale
      UpperCamelCase__			:   Tuple       				=  rescale_factor if rescale_factor is not None else self.rescale_factor
      UpperCamelCase__			:   Dict       				=  do_center_crop if do_center_crop is not None else self.do_center_crop
      UpperCamelCase__			:   Any       				=  (
          do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
      )

      UpperCamelCase__			:   Optional[int]       				=  size if size is not None else self.size
      UpperCamelCase__			:   List[str]       				=  get_size_dict(SCREAMING_SNAKE_CASE							,   default_to_square=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Any       				=  crop_size if crop_size is not None else self.crop_size
      UpperCamelCase__			:   Union[str, Any]       				=  get_size_dict(SCREAMING_SNAKE_CASE							,   param_name="crop_size"						)

      UpperCamelCase__			:   int       				=  make_list_of_images(SCREAMING_SNAKE_CASE						)

      if not valid_images(SCREAMING_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_rescale and rescale_factor is None:
           raise ValueError("Rescale factor must be specified if do_rescale is True."						)

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

      # All transformations expect numpy arrays.
      UpperCamelCase__			:   Tuple       				=  [to_numpy_array(SCREAMING_SNAKE_CASE						) for image in images]

      if do_resize:
           UpperCamelCase__			:   Optional[int]       				=  [self.resize(image=SCREAMING_SNAKE_CASE							,   size=SCREAMING_SNAKE_CASE							,   resample=SCREAMING_SNAKE_CASE						) for image in images]

      if do_center_crop:
           UpperCamelCase__			:   Any       				=  [self.center_crop(image=SCREAMING_SNAKE_CASE							,   size=SCREAMING_SNAKE_CASE						) for image in images]

      if do_rescale:
           UpperCamelCase__			:   str       				=  [self.rescale(image=SCREAMING_SNAKE_CASE							,   scale=SCREAMING_SNAKE_CASE						) for image in images]

      # the pretrained checkpoints assume images are BGR, not RGB
      if do_flip_channel_order:
           UpperCamelCase__			:   Any       				=  [self.flip_channel_order(image=SCREAMING_SNAKE_CASE						) for image in images]

      UpperCamelCase__			:   Optional[int]       				=  [to_channel_dimension_format(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						) for image in images]

      UpperCamelCase__			:   Optional[int]       				=  {"pixel_values": images}
      return BatchFeature(data=SCREAMING_SNAKE_CASE							,   tensor_type=SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Dict							,   SCREAMING_SNAKE_CASE					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   List[Tuple] = None						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[int]       				=  outputs.logits

      # Resize logits and compute semantic segmentation maps
      if target_sizes is not None:
           if len(SCREAMING_SNAKE_CASE						) != len(SCREAMING_SNAKE_CASE						):
                raise ValueError(
                    "Make sure that you pass in as many target sizes as the batch dimension of the logits"						)

           if is_torch_tensor(SCREAMING_SNAKE_CASE						):
                UpperCamelCase__			:   Optional[Any]       				=  target_sizes.numpy()

           UpperCamelCase__			:   Any       				=  []

           for idx in range(len(SCREAMING_SNAKE_CASE						)						):
                UpperCamelCase__			:   Optional[Any]       				=  torch.nn.functional.interpolate(
                    logits[idx].unsqueeze(dim=0						)							,   size=target_sizes[idx]							,   mode="bilinear"							,   align_corners=SCREAMING_SNAKE_CASE						)
                UpperCamelCase__			:   List[str]       				=  resized_logits[0].argmax(dim=0						)
                semantic_segmentation.append(SCREAMING_SNAKE_CASE						)
      else:
           UpperCamelCase__			:   List[str]       				=  logits.argmax(dim=1						)
           UpperCamelCase__			:   Dict       				=  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]						)]

      return semantic_segmentation