





'''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 CLIPSegProcessor, ViTImageProcessor






@require_vision
class       UpperCamelCase__  (		unittest.TestCase     ):







	def 						__a   (				self			:   Optional[Any]		):




				'''simple docstring'''
				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(lowerCamelCase , range(len(lowerCamelCase		)		)		)		)
				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(lowerCamelCase		) + "\n"		)
				with open(self.merges_file , "w" , encoding="utf-8"		) as fp:
							fp.write("\n".join(lowerCamelCase		)		)

				a__     = {
				    "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],
				}
				a__     = os.path.join(self.tmpdirname , lowerCamelCase		)
				with open(self.image_processor_file , "w" , encoding="utf-8"		) as fp:
							json.dump(lowerCamelCase , lowerCamelCase		)







	def 						__a   (				self			:   Optional[Any] , **lowerCamelCase			:   int		):




				'''simple docstring'''
				return CLIPTokenizer.from_pretrained(self.tmpdirname , **lowerCamelCase		)







	def 						__a   (				self			:   Optional[int] , **lowerCamelCase			:   List[str]		):




				'''simple docstring'''
				return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **lowerCamelCase		)







	def 						__a   (				self			:   Union[str, Any] , **lowerCamelCase			:   int		):




				'''simple docstring'''
				return ViTImageProcessor.from_pretrained(self.tmpdirname , **lowerCamelCase		)







	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				shutil.rmtree(self.tmpdirname		)







	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				a__     = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta		)]

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

				return image_inputs







	def 						__a   (				self			:   str		):




				'''simple docstring'''
				a__     = self.get_tokenizer()
				a__     = self.get_rust_tokenizer()
				a__     = self.get_image_processor()

				a__     = CLIPSegProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase		)
				processor_slow.save_pretrained(self.tmpdirname		)
				a__     = CLIPSegProcessor.from_pretrained(self.tmpdirname , use_fast=lowerCamelCase		)

				a__     = CLIPSegProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase		)
				processor_fast.save_pretrained(self.tmpdirname		)
				a__     = CLIPSegProcessor.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 , lowerCamelCase		)
				self.assertIsInstance(processor_fast.tokenizer , lowerCamelCase		)

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







	def 						__a   (				self			:   Union[str, Any]		):




				'''simple docstring'''
				a__     = CLIPSegProcessor(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=lowerCamelCase , padding_value=1.0		)

				a__     = CLIPSegProcessor.from_pretrained(
				    self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=lowerCamelCase , padding_value=1.0		)

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

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







	def 						__a   (				self			:   Any		):




				'''simple docstring'''
				a__     = self.get_image_processor()
				a__     = self.get_tokenizer()

				a__     = CLIPSegProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase		)

				a__     = self.prepare_image_inputs()

				a__     = image_processor(lowerCamelCase , return_tensors="np"		)
				a__     = processor(images=lowerCamelCase , return_tensors="np"		)

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







	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				a__     = self.get_image_processor()
				a__     = self.get_tokenizer()

				a__     = CLIPSegProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase		)

				a__     = "lower newer"

				a__     = processor(text=lowerCamelCase		)

				a__     = tokenizer(lowerCamelCase		)

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







	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				a__     = self.get_image_processor()
				a__     = self.get_tokenizer()

				a__     = CLIPSegProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase		)

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

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

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

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







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				a__     = self.get_image_processor()
				a__     = self.get_tokenizer()

				a__     = CLIPSegProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase		)

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

				a__     = processor(images=lowerCamelCase , visual_prompt=lowerCamelCase		)

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

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







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				a__     = self.get_image_processor()
				a__     = self.get_tokenizer()

				a__     = CLIPSegProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase		)

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

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

				self.assertListEqual(lowerCamelCase , lowerCamelCase		)







'''simple docstring'''


import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(".")







def    _lowerCamelCase  (__lowerCamelCase  :     List[str]     )   ->    List[str]:
			a__     = test_file.split(os.path.sep     )
			if components[0:2] != ["tests", "models"]:
						raise ValueError(
						    "`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got "
						    f'''{test_file} instead.'''     )
			a__     = components[-1]
			if not test_fn.endswith("py"     ):
						raise ValueError(f'''`test_file` should be a python file. Got {test_fn} instead.'''     )
			if not test_fn.startswith("test_modeling_"     ):
						raise ValueError(
						    f'''`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead.'''     )

			a__     = components[:-1] + [test_fn.replace(".py"					,  ""     )]
			a__     = ".".join(__lowerCamelCase     )

			return test_module_path







def    _lowerCamelCase  (__lowerCamelCase  :     Union[str, Any]     )   ->    Optional[Any]:
			a__     = get_module_path(__lowerCamelCase     )
			a__     = importlib.import_module(__lowerCamelCase     )

			return test_module







def    _lowerCamelCase  (__lowerCamelCase  :     Optional[int]     )   ->    Union[str, Any]:
			a__     = []
			a__     = get_test_module(__lowerCamelCase     )
			for attr in dir(__lowerCamelCase     ):
						if attr.endswith("ModelTester"     ):
									tester_classes.append(getattr(__lowerCamelCase					,  __lowerCamelCase     )     )

    # sort with class names
			return sorted(__lowerCamelCase					,  key=lambda __lowerCamelCase     : x.__name__     )







def    _lowerCamelCase  (__lowerCamelCase  :     Tuple     )   ->    Tuple:
			a__     = []
			a__     = get_test_module(__lowerCamelCase     )
			for attr in dir(__lowerCamelCase     ):
						a__     = getattr(__lowerCamelCase					,  __lowerCamelCase     )
						# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
						# `all_model_classes` is not empty (which also excludes other special classes).
						a__     = getattr(__lowerCamelCase					,  "all_model_classes"					,  []     )
						if len(__lowerCamelCase     ) > 0:
									test_classes.append(__lowerCamelCase     )

    # sort with class names
			return sorted(__lowerCamelCase					,  key=lambda __lowerCamelCase     : x.__name__     )







def    _lowerCamelCase  (__lowerCamelCase  :     List[Any]     )   ->    List[Any]:
			a__     = get_test_classes(__lowerCamelCase     )
			a__     = set()
			for test_class in test_classes:
						model_classes.update(test_class.all_model_classes     )

			# sort with class names
			return sorted(__lowerCamelCase					,  key=lambda __lowerCamelCase     : x.__name__     )







def    _lowerCamelCase  (__lowerCamelCase  :     str     )   ->    Dict:
			a__     = test_class()
			if hasattr(__lowerCamelCase					,  "setUp"     ):
						test.setUp()

			a__     = None
			if hasattr(__lowerCamelCase					,  "model_tester"     ):
						# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
						if test.model_tester is not None:
									a__     = test.model_tester.__class__

			return model_tester







def    _lowerCamelCase  (__lowerCamelCase  :     Dict					,  __lowerCamelCase  :     Optional[int]     )   ->    List[Any]:
			a__     = get_test_classes(__lowerCamelCase     )

			a__     = []
			for test_class in test_classes:
						if model_class in test_class.all_model_classes:
									target_test_classes.append(__lowerCamelCase     )

    # sort with class names
			return sorted(__lowerCamelCase					,  key=lambda __lowerCamelCase     : x.__name__     )







def    _lowerCamelCase  (__lowerCamelCase  :     Any					,  __lowerCamelCase  :     Tuple     )   ->    Dict:
			a__     = get_test_classes_for_model(__lowerCamelCase					,  __lowerCamelCase     )

			a__     = []
			for test_class in test_classes:
						a__     = get_model_tester_from_test_class(__lowerCamelCase     )
						if tester_class is not None:
									tester_classes.append(__lowerCamelCase     )

    # sort with class names
			return sorted(__lowerCamelCase					,  key=lambda __lowerCamelCase     : x.__name__     )







def    _lowerCamelCase  (__lowerCamelCase  :     List[str]     )   ->    int:
			a__     = get_test_classes(__lowerCamelCase     )
			a__     = {test_class: get_model_tester_from_test_class(__lowerCamelCase     ) for test_class in test_classes}
			return test_tester_mapping







def    _lowerCamelCase  (__lowerCamelCase  :     Any     )   ->    List[Any]:
			a__     = get_model_classes(__lowerCamelCase     )
			a__     = {
			    model_class: get_test_classes_for_model(__lowerCamelCase					,  __lowerCamelCase     ) for model_class in model_classes
			}
			return model_test_mapping







def    _lowerCamelCase  (__lowerCamelCase  :     Tuple     )   ->    Tuple:
			a__     = get_model_classes(__lowerCamelCase     )
			a__     = {
			    model_class: get_tester_classes_for_model(__lowerCamelCase					,  __lowerCamelCase     ) for model_class in model_classes
			}
			return model_to_tester_mapping







def    _lowerCamelCase  (__lowerCamelCase  :     str     )   ->    Dict:
			if isinstance(__lowerCamelCase					,  __lowerCamelCase     ):
						return o
			elif isinstance(__lowerCamelCase					,  __lowerCamelCase     ):
						return o.__name__
			elif isinstance(__lowerCamelCase					,  (list, tuple)     ):
						return [to_json(__lowerCamelCase     ) for x in o]
			elif isinstance(__lowerCamelCase					,  __lowerCamelCase     ):
						return {to_json(__lowerCamelCase     ): to_json(__lowerCamelCase     ) for k, v in o.items()}
			else:
						return o



