

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    _a						(unittest.TestCase    ):


					'''simple docstring'''






					def   __A      (			self      ):
						A__							:   Union[str, Any]					       =		tempfile.mkdtemp()

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

						A__							:   List[str]					       =		os.path.join(self.tmpdirname   ,	VOCAB_FILES_NAMES["""vocab_file"""]      )
						A__							:   Optional[int]					       =		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__							:   Any					       =		{
						    """do_resize""": True,
						    """size""": 20,
						    """do_center_crop""": True,
						    """crop_size""": 18,
						    """do_normalize""": True,
						    """image_mean""": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
						    """image_std""": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
						}
						A__							:   List[str]					       =		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__							:   Dict					       =		[np.random.randint(255   ,	size=(3, 30, 400)   ,	dtype=np.uinta      )]

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

						return image_inputs






					def   __A      (			self      ):
						A__							:   Tuple					       =		self.get_tokenizer()
						A__							:   Dict					       =		self.get_rust_tokenizer()
						A__							:   Union[str, Any]					       =		self.get_image_processor()

						A__							:   str					       =		OwlViTProcessor(tokenizer=A__   ,	image_processor=A__      )
						processor_slow.save_pretrained(self.tmpdirname      )
						A__							:   int					       =		OwlViTProcessor.from_pretrained(self.tmpdirname   ,	use_fast=A__      )

						A__							:   Dict					       =		OwlViTProcessor(tokenizer=A__   ,	image_processor=A__      )
						processor_fast.save_pretrained(self.tmpdirname      )
						A__							:   Union[str, Any]					       =		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__							:   Optional[int]					       =		OwlViTProcessor(tokenizer=self.get_tokenizer()   ,	image_processor=self.get_image_processor()      )
						processor.save_pretrained(self.tmpdirname      )

						A__							:   Tuple					       =		self.get_tokenizer(bos_token="""(BOS)"""   ,	eos_token="""(EOS)"""      )
						A__							:   Any					       =		self.get_image_processor(do_normalize=A__      )

						A__							:   List[str]					       =		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__							:   Optional[int]					       =		self.get_image_processor()
						A__							:   Tuple					       =		self.get_tokenizer()

						A__							:   Union[str, Any]					       =		OwlViTProcessor(tokenizer=A__   ,	image_processor=A__      )

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

						A__							:   Tuple					       =		image_processor(A__   ,	return_tensors="""np"""      )
						A__							:   Dict					       =		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__							:   int					       =		self.get_image_processor()
						A__							:   Union[str, Any]					       =		self.get_tokenizer()

						A__							:   str					       =		OwlViTProcessor(tokenizer=A__   ,	image_processor=A__      )

						A__							:   Optional[int]					       =		"""lower newer"""

						A__							:   Tuple					       =		processor(text=A__   ,	return_tensors="""np"""      )

						A__							:   Optional[Any]					       =		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__							:   List[str]					       =		self.get_image_processor()
						A__							:   Optional[Any]					       =		self.get_tokenizer()

						A__							:   Tuple					       =		OwlViTProcessor(tokenizer=A__   ,	image_processor=A__      )

						A__							:   Any					       =		"""lower newer"""
						A__							:   int					       =		self.prepare_image_inputs()

						A__							:   Tuple					       =		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__							:   Any					       =		"""google/owlvit-base-patch32"""
						A__							:   Dict					       =		OwlViTProcessor.from_pretrained(A__      )

						A__							:   Tuple					       =		["""cat""", """nasa badge"""]
						A__							:   Union[str, Any]					       =		processor(text=A__      )

						A__							:   Any					       =		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__							:   List[Any]					       =		"""google/owlvit-base-patch32"""
						A__							:   str					       =		OwlViTProcessor.from_pretrained(A__      )

						A__							:   str					       =		[["""cat""", """nasa badge"""], ["""person"""]]
						A__							:   int					       =		processor(text=A__      )

						A__							:   Any					       =		16
						A__							:   Union[str, Any]					       =		len(A__      )
						A__							:   str					       =		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__							:   Optional[int]					       =		"""google/owlvit-base-patch32"""
						A__							:   Union[str, Any]					       =		OwlViTProcessor.from_pretrained(A__      )

						A__							:   Union[str, Any]					       =		["""cat""", """nasa badge"""]
						A__							:   List[str]					       =		processor(text=A__      )

						A__							:   int					       =		16
						A__							:   Union[str, Any]					       =		inputs["""input_ids"""]
						A__							:   Optional[int]					       =		[
						    [4_9406, 2368, 4_9407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
						    [4_9406, 6841, 1_1301, 4_9407, 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__							:   str					       =		self.get_image_processor()
						A__							:   Any					       =		self.get_tokenizer()

						A__							:   List[str]					       =		OwlViTProcessor(tokenizer=A__   ,	image_processor=A__      )

						A__							:   Tuple					       =		self.prepare_image_inputs()
						A__							:   Tuple					       =		self.prepare_image_inputs()

						A__							:   str					       =		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__							:   Optional[Any]					       =		self.get_image_processor()
						A__							:   Dict					       =		self.get_tokenizer()

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

						A__							:   List[Any]					       =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						A__							:   Any					       =		processor.batch_decode(A__      )
						A__							:   List[str]					       =		tokenizer.batch_decode(A__      )

						self.assertListEqual(A__   ,	A__      )






import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
	import onnxruntime as ort


A_	:			Union[str, Any]  	=      logging.get_logger(__name__)

A_	:			str  	=      {
    'tensor(bool)': np.bool_,
    'tensor(int8)': np.inta,
    'tensor(uint8)': np.uinta,
    'tensor(int16)': np.intaa,
    'tensor(uint16)': np.uintaa,
    'tensor(int32)': np.intaa,
    'tensor(uint32)': np.uintaa,
    'tensor(int64)': np.intaa,
    'tensor(uint64)': np.uintaa,
    'tensor(float16)': np.floataa,
    'tensor(float)': np.floataa,
    'tensor(double)': np.floataa,
}



class    _a						:


					'''simple docstring'''






					def __init__(			self   ,	A__=None   ,	**A__      ):
						logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future."""      )
						A__							:   Dict					       =		model
						A__							:   Any					       =		kwargs.get("""model_save_dir"""   ,	A__      )
						A__							:   Optional[int]					       =		kwargs.get("""latest_model_name"""   ,	A__      )






					def __call__(			self   ,	**A__      ):
						A__							:   int					       =		{k: np.array(A__      ) for k, v in kwargs.items()}
						return self.model.run(A__   ,	A__      )






					@staticmethod
					def   __A      (			A__   ,	A__=None   ,	A__=None      ):
						if provider is None:
							logger.info("""No onnxruntime provider specified, using CPUExecutionProvider"""      )
							A__							:   List[Any]					       =		"""CPUExecutionProvider"""

						return ort.InferenceSession(A__   ,	providers=[provider]   ,	sess_options=A__      )






					def   __A      (			self   ,	A__   ,	A__ = None   ,	**A__      ):
						A__							:   List[Any]					       =		file_name if file_name is not None else ONNX_WEIGHTS_NAME

						A__							:   List[Any]					       =		self.model_save_dir.joinpath(self.latest_model_name      )
						A__							:   Optional[int]					       =		Path(A__      ).joinpath(A__      )
						try:
							shutil.copyfile(A__   ,	A__      )
						except shutil.SameFileError:
							pass

						# copy external weights (for models >2GB)
						A__							:   str					       =		self.model_save_dir.joinpath(A__      )
						if src_path.exists():
							A__							:   List[str]					       =		Path(A__      ).joinpath(A__      )
							try:
								shutil.copyfile(A__   ,	A__      )
							except shutil.SameFileError:
								pass






					def   __A      (			self   ,	A__   ,	**A__   ,	):
						if os.path.isfile(A__      ):
							logger.error(F"""Provided path ({save_directory}) should be a directory, not a file"""      )
							return

						os.makedirs(A__   ,	exist_ok=A__      )

						# saving model weights/files
						self._save_pretrained(A__   ,	**A__      )






					@classmethod
					def   __A      (			cls   ,	A__   ,	A__ = None   ,	A__ = None   ,	A__ = False   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	**A__   ,	):
						A__							:   str					       =		file_name if file_name is not None else ONNX_WEIGHTS_NAME
						# load model from local directory
						if os.path.isdir(A__      ):
							A__							:   Dict					       =		OnnxRuntimeModel.load_model(
							    os.path.join(A__   ,	A__      )   ,	provider=A__   ,	sess_options=A__      )
							A__							:   Optional[Any]					       =		Path(A__      )
						# load model from hub
						else:
							# download model
							A__							:   Union[str, Any]					       =		hf_hub_download(
							    repo_id=A__   ,	filename=A__   ,	use_auth_token=A__   ,	revision=A__   ,	cache_dir=A__   ,	force_download=A__   ,	)
							A__							:   List[str]					       =		Path(A__      ).parent
							A__							:   str					       =		Path(A__      ).name
							A__							:   Optional[int]					       =		OnnxRuntimeModel.load_model(A__   ,	provider=A__   ,	sess_options=A__      )
						return cls(model=A__   ,	**A__      )






					@classmethod
					def   __A      (			cls   ,	A__   ,	A__ = True   ,	A__ = None   ,	A__ = None   ,	**A__   ,	):
						A__							:   Optional[Any]					       =		None
						if len(str(A__      ).split("""@"""      )      ) == 2:
							A__       ,       A__							:   Union[str, Any]					       =		model_id.split("""@"""      )

						return cls._from_pretrained(
						    model_id=A__   ,	revision=A__   ,	cache_dir=A__   ,	force_download=A__   ,	use_auth_token=A__   ,	**A__   ,	)



