'''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			):




    '''simple docstring'''







    def    _UpperCamelCase     (							self			)				->			Tuple:
         SCREAMING_SNAKE_CASE       :      Any     						=							tempfile.mkdtemp()

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

         SCREAMING_SNAKE_CASE       :      List[str]     						=							os.path.join(self.tmpdirname     ,				VOCAB_FILES_NAMES['vocab_file']			)
         SCREAMING_SNAKE_CASE       :      Any     						=							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__			)			)

         SCREAMING_SNAKE_CASE       :      Optional[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],
         }
         SCREAMING_SNAKE_CASE       :      Optional[int]     						=							os.path.join(self.tmpdirname     ,				lowercase__			)
         with open(self.image_processor_file     ,				'w'     ,				encoding='utf-8'			) as fp:
              json.dump(lowercase__     ,				lowercase__			)






    def    _UpperCamelCase     (							self     ,				**lowercase__			)				->			List[str]:
         return CLIPTokenizer.from_pretrained(self.tmpdirname     ,				**lowercase__			)






    def    _UpperCamelCase     (							self     ,				**lowercase__			)				->			int:
         return CLIPTokenizerFast.from_pretrained(self.tmpdirname     ,				**lowercase__			)






    def    _UpperCamelCase     (							self     ,				**lowercase__			)				->			int:
         return ViTImageProcessor.from_pretrained(self.tmpdirname     ,				**lowercase__			)






    def    _UpperCamelCase     (							self			)				->			str:
         shutil.rmtree(self.tmpdirname			)






    def    _UpperCamelCase     (							self			)				->			Tuple:
         SCREAMING_SNAKE_CASE       :      Tuple     						=							[np.random.randint(255     ,				size=(3, 30, 400)     ,				dtype=np.uinta			)]

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

         return image_inputs






    def    _UpperCamelCase     (							self			)				->			int:
         SCREAMING_SNAKE_CASE       :      List[str]     						=							self.get_tokenizer()
         SCREAMING_SNAKE_CASE       :      List[str]     						=							self.get_rust_tokenizer()
         SCREAMING_SNAKE_CASE       :      List[Any]     						=							self.get_image_processor()

         SCREAMING_SNAKE_CASE       :      str     						=							CLIPSegProcessor(tokenizer=lowercase__     ,				image_processor=lowercase__			)
         processor_slow.save_pretrained(self.tmpdirname			)
         SCREAMING_SNAKE_CASE       :      Optional[Any]     						=							CLIPSegProcessor.from_pretrained(self.tmpdirname     ,				use_fast=lowercase__			)

         SCREAMING_SNAKE_CASE       :      Optional[int]     						=							CLIPSegProcessor(tokenizer=lowercase__     ,				image_processor=lowercase__			)
         processor_fast.save_pretrained(self.tmpdirname			)
         SCREAMING_SNAKE_CASE       :      Optional[int]     						=							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     ,				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    _UpperCamelCase     (							self			)				->			int:
         SCREAMING_SNAKE_CASE       :      str     						=							CLIPSegProcessor(tokenizer=self.get_tokenizer()     ,				image_processor=self.get_image_processor()			)
         processor.save_pretrained(self.tmpdirname			)

         SCREAMING_SNAKE_CASE       :      List[str]     						=							self.get_tokenizer(bos_token='(BOS)'     ,				eos_token='(EOS)'			)
         SCREAMING_SNAKE_CASE       :      Union[str, Any]     						=							self.get_image_processor(do_normalize=lowercase__     ,				padding_value=1.0			)

         SCREAMING_SNAKE_CASE       :      List[Any]     						=							CLIPSegProcessor.from_pretrained(
             self.tmpdirname     ,				bos_token='(BOS)'     ,				eos_token='(EOS)'     ,				do_normalize=lowercase__     ,				padding_value=1.0			)

         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    _UpperCamelCase     (							self			)				->			Any:
         SCREAMING_SNAKE_CASE       :      Dict     						=							self.get_image_processor()
         SCREAMING_SNAKE_CASE       :      Dict     						=							self.get_tokenizer()

         SCREAMING_SNAKE_CASE       :      int     						=							CLIPSegProcessor(tokenizer=lowercase__     ,				image_processor=lowercase__			)

         SCREAMING_SNAKE_CASE       :      Dict     						=							self.prepare_image_inputs()

         SCREAMING_SNAKE_CASE       :      List[Any]     						=							image_processor(lowercase__     ,				return_tensors='np'			)
         SCREAMING_SNAKE_CASE       :      Tuple     						=							processor(images=lowercase__     ,				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    _UpperCamelCase     (							self			)				->			List[Any]:
         SCREAMING_SNAKE_CASE       :      Tuple     						=							self.get_image_processor()
         SCREAMING_SNAKE_CASE       :      Union[str, Any]     						=							self.get_tokenizer()

         SCREAMING_SNAKE_CASE       :      Union[str, Any]     						=							CLIPSegProcessor(tokenizer=lowercase__     ,				image_processor=lowercase__			)

         SCREAMING_SNAKE_CASE       :      Optional[int]     						=							'lower newer'

         SCREAMING_SNAKE_CASE       :      Tuple     						=							processor(text=lowercase__			)

         SCREAMING_SNAKE_CASE       :      Dict     						=							tokenizer(lowercase__			)

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






    def    _UpperCamelCase     (							self			)				->			List[Any]:
         SCREAMING_SNAKE_CASE       :      int     						=							self.get_image_processor()
         SCREAMING_SNAKE_CASE       :      Optional[int]     						=							self.get_tokenizer()

         SCREAMING_SNAKE_CASE       :      List[str]     						=							CLIPSegProcessor(tokenizer=lowercase__     ,				image_processor=lowercase__			)

         SCREAMING_SNAKE_CASE       :      Dict     						=							'lower newer'
         SCREAMING_SNAKE_CASE       :      List[Any]     						=							self.prepare_image_inputs()

         SCREAMING_SNAKE_CASE       :      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    _UpperCamelCase     (							self			)				->			List[Any]:
         SCREAMING_SNAKE_CASE       :      Union[str, Any]     						=							self.get_image_processor()
         SCREAMING_SNAKE_CASE       :      int     						=							self.get_tokenizer()

         SCREAMING_SNAKE_CASE       :      Optional[Any]     						=							CLIPSegProcessor(tokenizer=lowercase__     ,				image_processor=lowercase__			)

         SCREAMING_SNAKE_CASE       :      Optional[Any]     						=							self.prepare_image_inputs()
         SCREAMING_SNAKE_CASE       :      List[Any]     						=							self.prepare_image_inputs()

         SCREAMING_SNAKE_CASE       :      str     						=							processor(images=lowercase__     ,				visual_prompt=lowercase__			)

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

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






    def    _UpperCamelCase     (							self			)				->			Optional[int]:
         SCREAMING_SNAKE_CASE       :      Union[str, Any]     						=							self.get_image_processor()
         SCREAMING_SNAKE_CASE       :      Tuple     						=							self.get_tokenizer()

         SCREAMING_SNAKE_CASE       :      Optional[Any]     						=							CLIPSegProcessor(tokenizer=lowercase__     ,				image_processor=lowercase__			)

         SCREAMING_SNAKE_CASE       :      Optional[Any]     						=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         SCREAMING_SNAKE_CASE       :      Dict     						=							processor.batch_decode(lowercase__			)
         SCREAMING_SNAKE_CASE       :      Optional[int]     						=							tokenizer.batch_decode(lowercase__			)

         self.assertListEqual(lowercase__     ,				lowercase__			)











import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				lowercase__											=			(IPNDMScheduler,)
				lowercase__											=			(("""num_inference_steps""", 50),)




				def 							lowercase__  (	self		:       Union[str, Any],    **lowerCamelCase		:       Tuple    ):

								'''simple docstring'''
								lowercase__  					=     {'''num_train_timesteps''': 1_000}
								config.update(**lowerCamelCase    )
								return config




				def 							lowercase__  (	self		:       Any,    lowerCamelCase		:       Any=0,    **lowerCamelCase		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     dict(self.forward_default_kwargs    )
								lowercase__  					=     kwargs.pop('''num_inference_steps''',    lowerCamelCase    )
								lowercase__  					=     self.dummy_sample
								lowercase__  					=     0.1 * sample
								lowercase__  					=     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
												lowercase__  					=     self.get_scheduler_config(**lowerCamelCase    )
												lowercase__  					=     scheduler_class(**lowerCamelCase    )
												scheduler.set_timesteps(lowerCamelCase    )
												# copy over dummy past residuals
												lowercase__  					=     dummy_past_residuals[:]

												if time_step is None:
																lowercase__  					=     scheduler.timesteps[len(scheduler.timesteps    ) // 2]

												with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(lowerCamelCase    )
																lowercase__  					=     scheduler_class.from_pretrained(lowerCamelCase    )
																new_scheduler.set_timesteps(lowerCamelCase    )
																# copy over dummy past residuals
																lowercase__  					=     dummy_past_residuals[:]

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     new_scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

												assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     new_scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

												assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								pass




				def 							lowercase__  (	self		:       Dict,    lowerCamelCase		:       Optional[int]=0,    **lowerCamelCase		:       Dict    ):

								'''simple docstring'''
								lowercase__  					=     dict(self.forward_default_kwargs    )
								lowercase__  					=     kwargs.pop('''num_inference_steps''',    lowerCamelCase    )
								lowercase__  					=     self.dummy_sample
								lowercase__  					=     0.1 * sample
								lowercase__  					=     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
												lowercase__  					=     self.get_scheduler_config()
												lowercase__  					=     scheduler_class(**lowerCamelCase    )
												scheduler.set_timesteps(lowerCamelCase    )

												# copy over dummy past residuals (must be after setting timesteps)
												lowercase__  					=     dummy_past_residuals[:]

												if time_step is None:
																lowercase__  					=     scheduler.timesteps[len(scheduler.timesteps    ) // 2]

												with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(lowerCamelCase    )
																lowercase__  					=     scheduler_class.from_pretrained(lowerCamelCase    )
																# copy over dummy past residuals
																new_scheduler.set_timesteps(lowerCamelCase    )

																# copy over dummy past residual (must be after setting timesteps)
																lowercase__  					=     dummy_past_residuals[:]

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     new_scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

												assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     new_scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

												assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"




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

								'''simple docstring'''
								lowercase__  					=     self.scheduler_classes[0]
								lowercase__  					=     self.get_scheduler_config(**lowerCamelCase    )
								lowercase__  					=     scheduler_class(**lowerCamelCase    )

								lowercase__  					=     10
								lowercase__  					=     self.dummy_model()
								lowercase__  					=     self.dummy_sample_deter
								scheduler.set_timesteps(lowerCamelCase    )

								for i, t in enumerate(scheduler.timesteps    ):
												lowercase__  					=     model(lowerCamelCase,    lowerCamelCase    )
												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase    ).prev_sample

								for i, t in enumerate(scheduler.timesteps    ):
												lowercase__  					=     model(lowerCamelCase,    lowerCamelCase    )
												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase    ).prev_sample

								return sample




				def 							lowercase__  (	self		:       List[Any]    ):

								'''simple docstring'''
								lowercase__  					=     dict(self.forward_default_kwargs    )

								lowercase__  					=     kwargs.pop('''num_inference_steps''',    lowerCamelCase    )

								for scheduler_class in self.scheduler_classes:
												lowercase__  					=     self.get_scheduler_config()
												lowercase__  					=     scheduler_class(**lowerCamelCase    )

												lowercase__  					=     self.dummy_sample
												lowercase__  					=     0.1 * sample

												if num_inference_steps is not None and hasattr(lowerCamelCase,    '''set_timesteps'''    ):
																scheduler.set_timesteps(lowerCamelCase    )
												elif num_inference_steps is not None and not hasattr(lowerCamelCase,    '''set_timesteps'''    ):
																lowercase__  					=     num_inference_steps

												# copy over dummy past residuals (must be done after set_timesteps)
												lowercase__  					=     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
												lowercase__  					=     dummy_past_residuals[:]

												lowercase__  					=     scheduler.timesteps[5]
												lowercase__  					=     scheduler.timesteps[6]

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

												self.assertEqual(output_a.shape,    sample.shape    )
												self.assertEqual(output_a.shape,    output_a.shape    )

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

												self.assertEqual(output_a.shape,    sample.shape    )
												self.assertEqual(output_a.shape,    output_a.shape    )




				def 							lowercase__  (	self		:       Dict    ):

								'''simple docstring'''
								for timesteps in [100, 1_000]:
												self.check_over_configs(num_train_timesteps=lowerCamelCase,    time_step=lowerCamelCase    )




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								for t, num_inference_steps in zip([1, 5, 10],    [10, 50, 100]    ):
												self.check_over_forward(num_inference_steps=lowerCamelCase,    time_step=lowerCamelCase    )




				def 							lowercase__  (	self		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     self.full_loop()
								lowercase__  					=     torch.mean(torch.abs(lowerCamelCase    )    )

								assert abs(result_mean.item() - 2_540_529    ) < 10




