



'''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 OwlViTImageProcessor, OwlViTProcessor







@require_vision
class 	_lowerCAmelCase							(			unittest.TestCase ):


	def     _a	(self							):
				A_	:						Union[str, Any]	=     tempfile.mkdtemp()

				# fmt: off
				A_	:						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
				A_	:						Dict	=     dict(zip(lowercase       ,      range(len(lowercase							)							)							)							)
				A_	:						Optional[int]	=     ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
				A_	:						List[str]	=     {"""unk_token""": """<unk>"""}

				A_	:						Any	=     os.path.join(self.tmpdirname       ,      VOCAB_FILES_NAMES["""vocab_file"""]							)
				A_	:						Tuple	=     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							)							)

				A_	:						Optional[Any]	=     {
				    """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],
				}
				A_	:						str	=     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       ,      **lowercase							):
				return CLIPTokenizer.from_pretrained(self.tmpdirname       ,      pad_token="""!"""       ,      **lowercase							)


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


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


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


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

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

				return image_inputs


	def     _a	(self							):
				A_	:						Optional[int]	=     self.get_tokenizer()
				A_	:						int	=     self.get_rust_tokenizer()
				A_	:						Tuple	=     self.get_image_processor()

				A_	:						Tuple	=     OwlViTProcessor(tokenizer=lowercase       ,      image_processor=lowercase							)
				processor_slow.save_pretrained(self.tmpdirname							)
				A_	:						Dict	=     OwlViTProcessor.from_pretrained(self.tmpdirname       ,      use_fast=lowercase							)

				A_	:						Dict	=     OwlViTProcessor(tokenizer=lowercase       ,      image_processor=lowercase							)
				processor_fast.save_pretrained(self.tmpdirname							)
				A_	:						Optional[int]	=     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       ,      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							):
				A_	:						Dict	=     OwlViTProcessor(tokenizer=self.get_tokenizer()       ,      image_processor=self.get_image_processor()							)
				processor.save_pretrained(self.tmpdirname							)

				A_	:						Dict	=     self.get_tokenizer(bos_token="""(BOS)"""       ,      eos_token="""(EOS)"""							)
				A_	:						Optional[Any]	=     self.get_image_processor(do_normalize=lowercase							)

				A_	:						Tuple	=     OwlViTProcessor.from_pretrained(
				    self.tmpdirname       ,      bos_token="""(BOS)"""       ,      eos_token="""(EOS)"""       ,      do_normalize=lowercase							)

				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							):
				A_	:						List[Any]	=     self.get_image_processor()
				A_	:						int	=     self.get_tokenizer()

				A_	:						Optional[int]	=     OwlViTProcessor(tokenizer=lowercase       ,      image_processor=lowercase							)

				A_	:						Dict	=     self.prepare_image_inputs()

				A_	:						str	=     image_processor(lowercase       ,      return_tensors="""np"""							)
				A_	:						List[str]	=     processor(images=lowercase       ,      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_	:						List[str]	=     self.get_image_processor()
				A_	:						Optional[Any]	=     self.get_tokenizer()

				A_	:						str	=     OwlViTProcessor(tokenizer=lowercase       ,      image_processor=lowercase							)

				A_	:						int	=     """lower newer"""

				A_	:						Optional[Any]	=     processor(text=lowercase       ,      return_tensors="""np"""							)

				A_	:						Dict	=     tokenizer(lowercase       ,      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_	:						str	=     self.get_image_processor()
				A_	:						Optional[int]	=     self.get_tokenizer()

				A_	:						List[Any]	=     OwlViTProcessor(tokenizer=lowercase       ,      image_processor=lowercase							)

				A_	:						Optional[Any]	=     """lower newer"""
				A_	:						Tuple	=     self.prepare_image_inputs()

				A_	:						List[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							):
				A_	:						Dict	=     """google/owlvit-base-patch32"""
				A_	:						int	=     OwlViTProcessor.from_pretrained(lowercase							)

				A_	:						Any	=     ["""cat""", """nasa badge"""]
				A_	:						List[Any]	=     processor(text=lowercase							)

				A_	:						int	=     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(lowercase							):
							processor()


	def     _a	(self							):
				A_	:						Optional[int]	=     """google/owlvit-base-patch32"""
				A_	:						Optional[Any]	=     OwlViTProcessor.from_pretrained(lowercase							)

				A_	:						Dict	=     [["""cat""", """nasa badge"""], ["""person"""]]
				A_	:						Any	=     processor(text=lowercase							)

				A_	:						Dict	=     16
				A_	:						Union[str, Any]	=     len(lowercase							)
				A_	:						Union[str, Any]	=     max([len(lowercase							) 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(lowercase							):
							processor()


	def     _a	(self							):
				A_	:						Any	=     """google/owlvit-base-patch32"""
				A_	:						int	=     OwlViTProcessor.from_pretrained(lowercase							)

				A_	:						List[str]	=     ["""cat""", """nasa badge"""]
				A_	:						Optional[Any]	=     processor(text=lowercase							)

				A_	:						Any	=     16
				A_	:						Tuple	=     inputs["""input_ids"""]
				A_	:						Union[str, Any]	=     [
				    [49406, 2368, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
				    [49406, 6841, 11301, 49407, 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_	:						int	=     self.get_image_processor()
				A_	:						str	=     self.get_tokenizer()

				A_	:						Optional[Any]	=     OwlViTProcessor(tokenizer=lowercase       ,      image_processor=lowercase							)

				A_	:						Tuple	=     self.prepare_image_inputs()
				A_	:						int	=     self.prepare_image_inputs()

				A_	:						Any	=     processor(images=lowercase       ,      query_images=lowercase							)

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

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


	def     _a	(self							):
				A_	:						Any	=     self.get_image_processor()
				A_	:						List[str]	=     self.get_tokenizer()

				A_	:						Any	=     OwlViTProcessor(tokenizer=lowercase       ,      image_processor=lowercase							)

				A_	:						Union[str, Any]	=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

				A_	:						Dict	=     processor.batch_decode(lowercase							)
				A_	:						List[str]	=     tokenizer.batch_decode(lowercase							)

				self.assertListEqual(lowercase       ,      lowercase							)




'''simple docstring'''




import torch

from diffusers import StableDiffusionPipeline


lowerCamelCase							:Tuple				    =					'''path-to-your-trained-model'''
lowerCamelCase							:Optional[int]				    =					StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')

lowerCamelCase							:Optional[int]				    =					'''A photo of sks dog in a bucket'''
lowerCamelCase							:List[Any]				    =					pipe(prompt, num_inference_steps=5_0, guidance_scale=7.5).images[0]

image.save('''dog-bucket.png''')