



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

import numpy as np

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


UpperCAmelCase							 =					logging.get_logger(__name__)

class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      List[Any]         =					["""pixel_values"""]


							def __init__(							self  ,  a_ = True  ,  a_ = None  ,  a_ = PILImageResampling.BICUBIC  ,  a_ = True  ,  a_ = None  ,  a_ = True  ,  a_ = 1 / 255  ,  a_ = True  ,  a_ = IMAGENET_DEFAULT_MEAN  ,  a_ = IMAGENET_DEFAULT_STD  ,  **a_  ,  ):
											super().__init__(**a_		)
											a__				      =		size if size is not None else {"""shortest_edge""": 224}
											a__				      =		get_size_dict(a_  ,  default_to_square=a_		)
											a__				      =		crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
											a__				      =		get_size_dict(a_  ,  param_name="""crop_size"""		)

											a__				      =		do_resize
											a__				      =		size
											a__				      =		resample
											a__				      =		do_center_crop
											a__				      =		crop_size
											a__				      =		do_rescale
											a__				      =		rescale_factor
											a__				      =		do_normalize
											a__				      =		image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
											a__				      =		image_std if image_std is not None else IMAGENET_DEFAULT_STD


							def _a      (							self  ,  a_  ,  a_  ,  a_ = PILImageResampling.BICUBIC  ,  a_ = None  ,  **a_  ,  ):
											a__				      =		get_size_dict(a_  ,  default_to_square=a_		)
											# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
											if "shortest_edge" in size:
															a__				      =		int((256 / 224) * size["""shortest_edge"""]		)
															a__				      =		get_resize_output_image_size(a_  ,  size=a_  ,  default_to_square=a_		)
															a__				      =		{"""height""": output_size[0], """width""": output_size[1]}
											if "height" not in size_dict or "width" not in size_dict:
															raise ValueError(
															    F'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'''		)
											return resize(
											    a_  ,  size=(size_dict["""height"""], size_dict["""width"""])  ,  resample=a_  ,  data_format=a_  ,  **a_		)


							def _a      (							self  ,  a_  ,  a_  ,  a_ = None  ,  **a_  ,  ):
											a__				      =		get_size_dict(a_		)
											if "height" not in size or "width" not in size:
															raise ValueError(F'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'''		)
											return center_crop(a_  ,  size=(size["""height"""], size["""width"""])  ,  data_format=a_  ,  **a_		)


							def _a      (							self  ,  a_  ,  a_  ,  a_ = None  ,  **a_  ,  ):
											return rescale(a_  ,  scale=a_  ,  data_format=a_  ,  **a_		)


							def _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_ = None  ,  **a_  ,  ):
											return normalize(a_  ,  mean=a_  ,  std=a_  ,  data_format=a_  ,  **a_		)







							def _a      (							self  ,  a_  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = ChannelDimension.FIRST  ,  **a_  ,  ):
											a__				      =		do_resize if do_resize is not None else self.do_resize
											a__				      =		resample if resample is not None else self.resample
											a__				      =		do_center_crop if do_center_crop is not None else self.do_center_crop
											a__				      =		do_rescale if do_rescale is not None else self.do_rescale
											a__				      =		rescale_factor if rescale_factor is not None else self.rescale_factor
											a__				      =		do_normalize if do_normalize is not None else self.do_normalize
											a__				      =		image_mean if image_mean is not None else self.image_mean
											a__				      =		image_std if image_std is not None else self.image_std

											a__				      =		size if size is not None else self.size
											a__				      =		get_size_dict(a_  ,  default_to_square=a_		)
											a__				      =		crop_size if crop_size is not None else self.crop_size
											a__				      =		get_size_dict(a_  ,  param_name="""crop_size"""		)

											a__				      =		make_list_of_images(a_		)

											if not valid_images(a_		):
															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."""		)

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

											if do_resize:
															a__				      =		[self.resize(a_  ,  a_  ,  a_		) for image in images]

											if do_center_crop:
															a__				      =		[self.center_crop(a_  ,  a_		) for image in images]

											if do_rescale:
															a__				      =		[self.rescale(a_  ,  a_		) for image in images]

											if do_normalize:
															a__				      =		[self.normalize(a_  ,  a_  ,  a_		) for image in images]

											a__				      =		[to_channel_dimension_format(a_  ,  a_		) for image in images]

											a__				      =		{"""pixel_values""": images}
											return BatchFeature(data=a_  ,  tensor_type=a_		)






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 OwlViTImageProcessor, OwlViTProcessor

@require_vision
class  __snake_case      (   unittest.TestCase):







							'''simple docstring'''






							def _a      (							self		):
											a__				      =		tempfile.mkdtemp()

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

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

											a__				      =		{
											    """do_resize""": True,
											    """size""": 20,
											    """do_center_crop""": True,
											    """crop_size""": 18,
											    """do_normalize""": True,
											    """image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
											    """image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
											}
											a__				      =		os.path.join(self.tmpdirname  ,  a_		)
											with open(self.image_processor_file  ,  """w"""  ,  encoding="""utf-8"""		) as fp:
															json.dump(a_  ,  a_		)


							def _a      (							self  ,  **a_		):
											return CLIPTokenizer.from_pretrained(self.tmpdirname  ,  pad_token="""!"""  ,  **a_		)


							def _a      (							self  ,  **a_		):
											return CLIPTokenizerFast.from_pretrained(self.tmpdirname  ,  pad_token="""!"""  ,  **a_		)


							def _a      (							self  ,  **a_		):
											return OwlViTImageProcessor.from_pretrained(self.tmpdirname  ,  **a_		)


							def _a      (							self		):
											shutil.rmtree(self.tmpdirname		)


							def _a      (							self		):
											a__				      =		[np.random.randint(255  ,  size=(3, 30, 400)  ,  dtype=np.uinta		)]

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

											return image_inputs


							def _a      (							self		):
											a__				      =		self.get_tokenizer()
											a__				      =		self.get_rust_tokenizer()
											a__				      =		self.get_image_processor()

											a__				      =		OwlViTProcessor(tokenizer=a_  ,  image_processor=a_		)
											processor_slow.save_pretrained(self.tmpdirname		)
											a__				      =		OwlViTProcessor.from_pretrained(self.tmpdirname  ,  use_fast=a_		)

											a__				      =		OwlViTProcessor(tokenizer=a_  ,  image_processor=a_		)
											processor_fast.save_pretrained(self.tmpdirname		)
											a__				      =		OwlViTProcessor.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  ,  a_		)
											self.assertIsInstance(processor_fast.tokenizer  ,  a_		)

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


							def _a      (							self		):
											a__				      =		OwlViTProcessor(tokenizer=self.get_tokenizer()  ,  image_processor=self.get_image_processor()		)
											processor.save_pretrained(self.tmpdirname		)

											a__				      =		self.get_tokenizer(bos_token="""(BOS)"""  ,  eos_token="""(EOS)"""		)
											a__				      =		self.get_image_processor(do_normalize=a_		)

											a__				      =		OwlViTProcessor.from_pretrained(
											    self.tmpdirname  ,  bos_token="""(BOS)"""  ,  eos_token="""(EOS)"""  ,  do_normalize=a_		)

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

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


							def _a      (							self		):
											a__				      =		self.get_image_processor()
											a__				      =		self.get_tokenizer()

											a__				      =		OwlViTProcessor(tokenizer=a_  ,  image_processor=a_		)

											a__				      =		self.prepare_image_inputs()

											a__				      =		image_processor(a_  ,  return_tensors="""np"""		)
											a__				      =		processor(images=a_  ,  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 _a      (							self		):
											a__				      =		self.get_image_processor()
											a__				      =		self.get_tokenizer()

											a__				      =		OwlViTProcessor(tokenizer=a_  ,  image_processor=a_		)

											a__				      =		"""lower newer"""

											a__				      =		processor(text=a_  ,  return_tensors="""np"""		)

											a__				      =		tokenizer(a_  ,  return_tensors="""np"""		)

											for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key][0].tolist()  ,  encoded_processor[key][0].tolist()		)


							def _a      (							self		):
											a__				      =		self.get_image_processor()
											a__				      =		self.get_tokenizer()

											a__				      =		OwlViTProcessor(tokenizer=a_  ,  image_processor=a_		)

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

											a__				      =		processor(text=a_  ,  images=a_		)

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

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


							def _a      (							self		):
											a__				      =		"""google/owlvit-base-patch32"""
											a__				      =		OwlViTProcessor.from_pretrained(a_		)

											a__				      =		["""cat""", """nasa badge"""]
											a__				      =		processor(text=a_		)

											a__				      =		16
											self.assertListEqual(list(inputs.keys()		)  ,  ["""input_ids""", """attention_mask"""]		)
											self.assertEqual(inputs["""input_ids"""].shape  ,  (2, seq_length)		)

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


							def _a      (							self		):
											a__				      =		"""google/owlvit-base-patch32"""
											a__				      =		OwlViTProcessor.from_pretrained(a_		)

											a__				      =		[["""cat""", """nasa badge"""], ["""person"""]]
											a__				      =		processor(text=a_		)

											a__				      =		16
											a__				      =		len(a_		)
											a__				      =		max([len(a_		) for texts in input_texts]		)

											self.assertListEqual(list(inputs.keys()		)  ,  ["""input_ids""", """attention_mask"""]		)
											self.assertEqual(inputs["""input_ids"""].shape  ,  (batch_size * num_max_text_queries, seq_length)		)

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


							def _a      (							self		):
											a__				      =		"""google/owlvit-base-patch32"""
											a__				      =		OwlViTProcessor.from_pretrained(a_		)

											a__				      =		["""cat""", """nasa badge"""]
											a__				      =		processor(text=a_		)

											a__				      =		16
											a__				      =		inputs["""input_ids"""]
											a__				      =		[
											    [49_406, 2_368, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
											    [49_406, 6_841, 11_301, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
											]

											self.assertListEqual(list(inputs.keys()		)  ,  ["""input_ids""", """attention_mask"""]		)
											self.assertEqual(inputs["""input_ids"""].shape  ,  (2, seq_length)		)
											self.assertListEqual(list(input_ids[0]		)  ,  predicted_ids[0]		)
											self.assertListEqual(list(input_ids[1]		)  ,  predicted_ids[1]		)


							def _a      (							self		):
											a__				      =		self.get_image_processor()
											a__				      =		self.get_tokenizer()

											a__				      =		OwlViTProcessor(tokenizer=a_  ,  image_processor=a_		)

											a__				      =		self.prepare_image_inputs()
											a__				      =		self.prepare_image_inputs()

											a__				      =		processor(images=a_  ,  query_images=a_		)

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

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







							def _a      (							self		):
											a__				      =		self.get_image_processor()
											a__				      =		self.get_tokenizer()

											a__				      =		OwlViTProcessor(tokenizer=a_  ,  image_processor=a_		)

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

											a__				      =		processor.batch_decode(a_		)
											a__				      =		tokenizer.batch_decode(a_		)

											self.assertListEqual(a_  ,  a_		)

