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     ):


   def 			lowercase_      (self     :						int		)						->				Dict:
         """simple docstring"""



         UpperCAmelCase__								=  tempfile.mkdtemp()

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

         UpperCAmelCase__								=  os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES["vocab_file"]		)
         UpperCAmelCase__								=  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		)		)

         UpperCAmelCase__								=  {
             'do_resize': True,
             'size': 2_0,
             'do_center_crop': True,
             'crop_size': 1_8,
             'do_normalize': True,
             'image_mean': [0.48145466, 0.4578275, 0.40821073],
             'image_std': [0.26862954, 0.26130258, 0.27577711],
         }
         UpperCAmelCase__								=  os.path.join(self.tmpdirname       ,  _A		)
         with open(self.image_processor_file       ,  "w"       ,  encoding="utf-8"		) as fp:
               json.dump(_A       ,  _A		)


   def 			lowercase_      (self     :						str       ,  **__UpperCAmelCase     :						Optional[Any]		)						->				Any:
         """simple docstring"""



         return CLIPTokenizer.from_pretrained(self.tmpdirname       ,  pad_token="!"       ,  **_A		)


   def 			lowercase_      (self     :						Dict       ,  **__UpperCAmelCase     :						str		)						->				Optional[int]:
         """simple docstring"""



         return CLIPTokenizerFast.from_pretrained(self.tmpdirname       ,  pad_token="!"       ,  **_A		)


   def 			lowercase_      (self     :						int       ,  **__UpperCAmelCase     :						str		)						->				Any:
         """simple docstring"""



         return OwlViTImageProcessor.from_pretrained(self.tmpdirname       ,  **_A		)


   def 			lowercase_      (self     :						Union[str, Any]		)						->				str:
         """simple docstring"""



         shutil.rmtree(self.tmpdirname		)


   def 			lowercase_      (self     :						str		)						->				Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  [np.random.randint(2_5_5       ,  size=(3, 3_0, 4_0_0)       ,  dtype=np.uinta		)]

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

         return image_inputs


   def 			lowercase_      (self     :						Tuple		)						->				int:
         """simple docstring"""



         UpperCAmelCase__								=  self.get_tokenizer()
         UpperCAmelCase__								=  self.get_rust_tokenizer()
         UpperCAmelCase__								=  self.get_image_processor()

         UpperCAmelCase__								=  OwlViTProcessor(tokenizer=_A       ,  image_processor=_A		)
         processor_slow.save_pretrained(self.tmpdirname		)
         UpperCAmelCase__								=  OwlViTProcessor.from_pretrained(self.tmpdirname       ,  use_fast=_A		)

         UpperCAmelCase__								=  OwlViTProcessor(tokenizer=_A       ,  image_processor=_A		)
         processor_fast.save_pretrained(self.tmpdirname		)
         UpperCAmelCase__								=  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 			lowercase_      (self     :						Union[str, Any]		)						->				Dict:
         """simple docstring"""



         UpperCAmelCase__								=  OwlViTProcessor(tokenizer=self.get_tokenizer()       ,  image_processor=self.get_image_processor()		)
         processor.save_pretrained(self.tmpdirname		)

         UpperCAmelCase__								=  self.get_tokenizer(bos_token="(BOS)"       ,  eos_token="(EOS)"		)
         UpperCAmelCase__								=  self.get_image_processor(do_normalize=_A		)

         UpperCAmelCase__								=  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 			lowercase_      (self     :						List[str]		)						->				Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.get_image_processor()
         UpperCAmelCase__								=  self.get_tokenizer()

         UpperCAmelCase__								=  OwlViTProcessor(tokenizer=_A       ,  image_processor=_A		)

         UpperCAmelCase__								=  self.prepare_image_inputs()

         UpperCAmelCase__								=  image_processor(_A       ,  return_tensors="np"		)
         UpperCAmelCase__								=  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 			lowercase_      (self     :						Optional[Any]		)						->				Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.get_image_processor()
         UpperCAmelCase__								=  self.get_tokenizer()

         UpperCAmelCase__								=  OwlViTProcessor(tokenizer=_A       ,  image_processor=_A		)

         UpperCAmelCase__								=  'lower newer'

         UpperCAmelCase__								=  processor(text=_A       ,  return_tensors="np"		)

         UpperCAmelCase__								=  tokenizer(_A       ,  return_tensors="np"		)

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


   def 			lowercase_      (self     :						int		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  self.get_image_processor()
         UpperCAmelCase__								=  self.get_tokenizer()

         UpperCAmelCase__								=  OwlViTProcessor(tokenizer=_A       ,  image_processor=_A		)

         UpperCAmelCase__								=  'lower newer'
         UpperCAmelCase__								=  self.prepare_image_inputs()

         UpperCAmelCase__								=  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 			lowercase_      (self     :						int		)						->				int:
         """simple docstring"""



         UpperCAmelCase__								=  'google/owlvit-base-patch32'
         UpperCAmelCase__								=  OwlViTProcessor.from_pretrained(_A		)

         UpperCAmelCase__								=  ['cat', 'nasa badge']
         UpperCAmelCase__								=  processor(text=_A		)

         UpperCAmelCase__								=  1_6
         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 			lowercase_      (self     :						Optional[Any]		)						->				List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  'google/owlvit-base-patch32'
         UpperCAmelCase__								=  OwlViTProcessor.from_pretrained(_A		)

         UpperCAmelCase__								=  [['cat', 'nasa badge'], ['person']]
         UpperCAmelCase__								=  processor(text=_A		)

         UpperCAmelCase__								=  1_6
         UpperCAmelCase__								=  len(_A		)
         UpperCAmelCase__								=  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 			lowercase_      (self     :						Optional[Any]		)						->				Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  'google/owlvit-base-patch32'
         UpperCAmelCase__								=  OwlViTProcessor.from_pretrained(_A		)

         UpperCAmelCase__								=  ['cat', 'nasa badge']
         UpperCAmelCase__								=  processor(text=_A		)

         UpperCAmelCase__								=  1_6
         UpperCAmelCase__								=  inputs['input_ids']
         UpperCAmelCase__								=  [
             [4_9_4_0_6, 2_3_6_8, 4_9_4_0_7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
             [4_9_4_0_6, 6_8_4_1, 1_1_3_0_1, 4_9_4_0_7, 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 			lowercase_      (self     :						Union[str, Any]		)						->				List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.get_image_processor()
         UpperCAmelCase__								=  self.get_tokenizer()

         UpperCAmelCase__								=  OwlViTProcessor(tokenizer=_A       ,  image_processor=_A		)

         UpperCAmelCase__								=  self.prepare_image_inputs()
         UpperCAmelCase__								=  self.prepare_image_inputs()

         UpperCAmelCase__								=  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 			lowercase_      (self     :						str		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  self.get_image_processor()
         UpperCAmelCase__								=  self.get_tokenizer()

         UpperCAmelCase__								=  OwlViTProcessor(tokenizer=_A       ,  image_processor=_A		)

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

         UpperCAmelCase__								=  processor.batch_decode(_A		)
         UpperCAmelCase__								=  tokenizer.batch_decode(_A		)

         self.assertListEqual(_A       ,  _A		)










import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
				from PIL import Image
else:





				class 						_A:


								"""simple docstring"""


								@staticmethod
								def 					UpperCAmelCase_		(			*_A     ,					**_A     ):
													pass







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      str:
					__A	:						str       =				hashlib.mda(image.tobytes()  )
					return m.hexdigest()[:10]







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      Dict:
					__A	:						Dict       =				np.array(a  )
					__A	:						List[Any]       =				npimg.shape
					return {"hash": hashimage(a  ), "shape": shape}





@is_pipeline_test
@require_vision
@require_torch
class 						_A(					unittest.TestCase   ):


				"""simple docstring"""


				UpperCamelCase    :  str					    =	dict(
				    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()   ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])   )
				UpperCamelCase    :  int					    =	dict(
				    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()   ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])   )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ):
									__A	:						Dict       =				MaskGenerationPipeline(model=_A     ,					image_processor=_A     )
									return image_segmenter, [
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									]



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									pass



				@require_tf
				@unittest.skip('Image segmentation not implemented in TF'     )
				def 					UpperCAmelCase_		(			self     ):
									pass



				@slow
				@require_torch
				def 					UpperCAmelCase_		(			self     ):
									__A	:						Union[str, Any]       =				pipeline('mask-generation'     ,					model='facebook/sam-vit-huge'     )

									__A	:						List[str]       =				image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg'     ,					points_per_batch=256     )

									# Shortening by hashing
									__A	:						List[Any]       =				[]
									for i, o in enumerate(outputs['masks']     ):
														new_outupt += [{"mask": mask_to_test_readable(_A     ), "scores": outputs["scores"][i]}]

									# fmt: off
									self.assertEqual(
									    nested_simplify(_A     ,					decimals=4     )     ,					[
									        {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0_4_4_4},
									        {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_2_1},
									        {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0_1_6_7},
									        {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0_1_3_2},
									        {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0_0_5_3},
									        {'mask': {'hash': 'e2d0b7a0b7', 'shape': (480, 640)}, 'scores': 0.9_9_6_7},
									        {'mask': {'hash': '453c7844bd', 'shape': (480, 640)}, 'scores': 0.9_9_3},
									        {'mask': {'hash': '3d44f2926d', 'shape': (480, 640)}, 'scores': 0.9_9_0_9},
									        {'mask': {'hash': '64033ddc3f', 'shape': (480, 640)}, 'scores': 0.9_8_7_9},
									        {'mask': {'hash': '801064ff79', 'shape': (480, 640)}, 'scores': 0.9_8_3_4},
									        {'mask': {'hash': '6172f276ef', 'shape': (480, 640)}, 'scores': 0.9_7_1_6},
									        {'mask': {'hash': 'b49e60e084', 'shape': (480, 640)}, 'scores': 0.9_6_1_2},
									        {'mask': {'hash': 'a811e775fd', 'shape': (480, 640)}, 'scores': 0.9_5_9_9},
									        {'mask': {'hash': 'a6a8ebcf4b', 'shape': (480, 640)}, 'scores': 0.9_5_5_2},
									        {'mask': {'hash': '9d8257e080', 'shape': (480, 640)}, 'scores': 0.9_5_3_2},
									        {'mask': {'hash': '32de6454a8', 'shape': (480, 640)}, 'scores': 0.9_5_1_6},
									        {'mask': {'hash': 'af3d4af2c8', 'shape': (480, 640)}, 'scores': 0.9_4_9_9},
									        {'mask': {'hash': '3c6db475fb', 'shape': (480, 640)}, 'scores': 0.9_4_8_3},
									        {'mask': {'hash': 'c290813fb9', 'shape': (480, 640)}, 'scores': 0.9_4_6_4},
									        {'mask': {'hash': 'b6f0b8f606', 'shape': (480, 640)}, 'scores': 0.9_4_3},
									        {'mask': {'hash': '92ce16bfdf', 'shape': (480, 640)}, 'scores': 0.9_4_3},
									        {'mask': {'hash': 'c749b25868', 'shape': (480, 640)}, 'scores': 0.9_4_0_8},
									        {'mask': {'hash': 'efb6cab859', 'shape': (480, 640)}, 'scores': 0.9_3_3_5},
									        {'mask': {'hash': '1ff2eafb30', 'shape': (480, 640)}, 'scores': 0.9_3_2_6},
									        {'mask': {'hash': '788b798e24', 'shape': (480, 640)}, 'scores': 0.9_2_6_2},
									        {'mask': {'hash': 'abea804f0e', 'shape': (480, 640)}, 'scores': 0.8_9_9_9},
									        {'mask': {'hash': '7b9e8ddb73', 'shape': (480, 640)}, 'scores': 0.8_9_8_6},
									        {'mask': {'hash': 'cd24047c8a', 'shape': (480, 640)}, 'scores': 0.8_9_8_4},
									        {'mask': {'hash': '6943e6bcbd', 'shape': (480, 640)}, 'scores': 0.8_8_7_3},
									        {'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.8_8_7_1}
									    ]     ,					)
									# fmt: on





				@require_torch
				@slow
				def 					UpperCAmelCase_		(			self     ):
									__A	:						Optional[Any]       =				'facebook/sam-vit-huge'
									__A	:						List[str]       =				pipeline('mask-generation'     ,					model=_A     )

									__A	:						Tuple       =				image_segmenter(
									    'http://images.cocodataset.org/val2017/000000039769.jpg'     ,					pred_iou_thresh=1     ,					points_per_batch=256     )

									# Shortening by hashing
									__A	:						List[str]       =				[]
									for i, o in enumerate(outputs['masks']     ):
														new_outupt += [{"mask": mask_to_test_readable(_A     ), "scores": outputs["scores"][i]}]

									self.assertEqual(
									    nested_simplify(_A     ,					decimals=4     )     ,					[
									        {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0_4_4_4},
									        {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_2_1_0},
									        {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0_1_6_7},
									        {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0_1_3_2},
									        {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0_0_5_3},
									    ]     ,					)




