





"""simple docstring"""





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class 							snake_case							(			_UpperCAmelCase ):

						'''simple docstring'''



						_A     :       int         =       (DDIMParallelScheduler,)
						_A     :       Any         =       (('eta', 0.0), ('num_inference_steps', 50))

						def       A_					(							self       :      Any						,				**__lowercase       :      str ):


								'''simple docstring'''




								__UpperCAmelCase    :						Tuple       =						{
								    """num_train_timesteps""": 1_000,
								    """beta_start""": 0.0_0_0_1,
								    """beta_end""": 0.0_2,
								    """beta_schedule""": """linear""",
								    """clip_sample""": True,
								}

								config.update(**lowercase_ )
								return config

						def       A_					(							self       :      Dict						,				**__lowercase       :      List[str] ):


								'''simple docstring'''




								__UpperCAmelCase    :						Optional[int]       =						self.scheduler_classes[0]
								__UpperCAmelCase    :						List[Any]       =						self.get_scheduler_config(**lowercase_ )
								__UpperCAmelCase    :						Any       =						scheduler_class(**lowercase_ )

								__UpperCAmelCase    :						Optional[int]       =						10, 0.0

								__UpperCAmelCase    :						Tuple       =						self.dummy_model()
								__UpperCAmelCase    :						Optional[int]       =						self.dummy_sample_deter

								scheduler.set_timesteps(lowercase_ )

								for t in scheduler.timesteps:
										__UpperCAmelCase    :						Any       =						model(lowercase_						,				lowercase_ )
										__UpperCAmelCase    :						Optional[int]       =						scheduler.step(lowercase_						,				lowercase_						,				lowercase_						,				lowercase_ ).prev_sample

								return sample

						def       A_					(							self       :      Tuple ):


								'''simple docstring'''




								for timesteps in [100, 500, 1_000]:
										self.check_over_configs(num_train_timesteps=lowercase_ )

						def       A_					(							self       :      Any ):


								'''simple docstring'''




								for steps_offset in [0, 1]:
										self.check_over_configs(steps_offset=lowercase_ )

								__UpperCAmelCase    :						Dict       =						self.scheduler_classes[0]
								__UpperCAmelCase    :						Union[str, Any]       =						self.get_scheduler_config(steps_offset=1 )
								__UpperCAmelCase    :						int       =						scheduler_class(**lowercase_ )
								scheduler.set_timesteps(5 )
								assert torch.equal(scheduler.timesteps						,				torch.LongTensor([801, 601, 401, 201, 1] ) )

						def       A_					(							self       :      Any ):


								'''simple docstring'''




								for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]						,				[0.0_0_2, 0.0_2, 0.2, 2] ):
										self.check_over_configs(beta_start=lowercase_						,				beta_end=lowercase_ )

						def       A_					(							self       :      Tuple ):


								'''simple docstring'''




								for schedule in ["linear", "squaredcos_cap_v2"]:
										self.check_over_configs(beta_schedule=lowercase_ )

						def       A_					(							self       :      Optional[int] ):


								'''simple docstring'''




								for prediction_type in ["epsilon", "v_prediction"]:
										self.check_over_configs(prediction_type=lowercase_ )

						def       A_					(							self       :      Optional[int] ):


								'''simple docstring'''




								for clip_sample in [True, False]:
										self.check_over_configs(clip_sample=lowercase_ )

						def       A_					(							self       :      Optional[Any] ):


								'''simple docstring'''




								for timestep_spacing in ["trailing", "leading"]:
										self.check_over_configs(timestep_spacing=lowercase_ )

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


								'''simple docstring'''




								for rescale_betas_zero_snr in [True, False]:
										self.check_over_configs(rescale_betas_zero_snr=lowercase_ )

						def       A_					(							self       :      Dict ):


								'''simple docstring'''




								self.check_over_configs(thresholding=lowercase_ )
								for threshold in [0.5, 1.0, 2.0]:
										for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(
												    thresholding=lowercase_						,				prediction_type=lowercase_						,				sample_max_value=lowercase_						,				)

						def       A_					(							self       :      List[Any] ):


								'''simple docstring'''




								for t in [1, 10, 49]:
										self.check_over_forward(time_step=lowercase_ )

						def       A_					(							self       :      Tuple ):


								'''simple docstring'''




								for t, num_inference_steps in zip([1, 10, 50]						,				[10, 50, 500] ):
										self.check_over_forward(time_step=lowercase_						,				num_inference_steps=lowercase_ )

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


								'''simple docstring'''




								for t, eta in zip([1, 10, 49]						,				[0.0, 0.5, 1.0] ):
										self.check_over_forward(time_step=lowercase_						,				eta=lowercase_ )

						def       A_					(							self       :      Dict ):


								'''simple docstring'''




								__UpperCAmelCase    :						Dict       =						self.scheduler_classes[0]
								__UpperCAmelCase    :						Dict       =						self.get_scheduler_config()
								__UpperCAmelCase    :						Optional[Any]       =						scheduler_class(**lowercase_ )

								assert torch.sum(torch.abs(scheduler._get_variance(0						,				0 ) - 0.0 ) ) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(420						,				400 ) - 0.1_4_7_7_1 ) ) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(980						,				960 ) - 0.3_2_4_6_0 ) ) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(0						,				0 ) - 0.0 ) ) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(487						,				486 ) - 0.0_0_9_7_9 ) ) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(999						,				998 ) - 0.0_2 ) ) < 1e-5

						def       A_					(							self       :      int ):


								'''simple docstring'''




								__UpperCAmelCase    :						List[Any]       =						self.scheduler_classes[0]
								__UpperCAmelCase    :						List[Any]       =						self.get_scheduler_config()
								__UpperCAmelCase    :						str       =						scheduler_class(**lowercase_ )

								__UpperCAmelCase    :						Dict       =						10, 0.0
								scheduler.set_timesteps(lowercase_ )

								__UpperCAmelCase    :						Union[str, Any]       =						self.dummy_model()
								__UpperCAmelCase    :						int       =						self.dummy_sample_deter
								__UpperCAmelCase    :						List[Any]       =						self.dummy_sample_deter + 0.1
								__UpperCAmelCase    :						Optional[Any]       =						self.dummy_sample_deter - 0.1

								__UpperCAmelCase    :						int       =						samplea.shape[0]
								__UpperCAmelCase    :						Optional[int]       =						torch.stack([samplea, samplea, samplea]						,				dim=0 )
								__UpperCAmelCase    :						Dict       =						torch.arange(lowercase_ )[0:3, None].repeat(1						,				lowercase_ )

								__UpperCAmelCase    :						Any       =						model(samples.flatten(0						,				1 )						,				timesteps.flatten(0						,				1 ) )
								__UpperCAmelCase    :						Any       =						scheduler.batch_step_no_noise(lowercase_						,				timesteps.flatten(0						,				1 )						,				samples.flatten(0						,				1 )						,				lowercase_ )

								__UpperCAmelCase    :						Optional[int]       =						torch.sum(torch.abs(lowercase_ ) )
								__UpperCAmelCase    :						Tuple       =						torch.mean(torch.abs(lowercase_ ) )

								assert abs(result_sum.item() - 1_147.7_904 ) < 1e-2
								assert abs(result_mean.item() - 0.4_9_8_2 ) < 1e-3

						def       A_					(							self       :      Optional[Any] ):


								'''simple docstring'''




								__UpperCAmelCase    :						str       =						self.full_loop()

								__UpperCAmelCase    :						List[Any]       =						torch.sum(torch.abs(lowercase_ ) )
								__UpperCAmelCase    :						Union[str, Any]       =						torch.mean(torch.abs(lowercase_ ) )

								assert abs(result_sum.item() - 172.0_067 ) < 1e-2
								assert abs(result_mean.item() - 0.2_2_3_9_6_7 ) < 1e-3

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


								'''simple docstring'''




								__UpperCAmelCase    :						Dict       =						self.full_loop(prediction_type='''v_prediction''' )

								__UpperCAmelCase    :						Union[str, Any]       =						torch.sum(torch.abs(lowercase_ ) )
								__UpperCAmelCase    :						List[str]       =						torch.mean(torch.abs(lowercase_ ) )

								assert abs(result_sum.item() - 5_2.5_3_0_2 ) < 1e-2
								assert abs(result_mean.item() - 0.0_6_8_4 ) < 1e-3

						def       A_					(							self       :      int ):


								'''simple docstring'''




								__UpperCAmelCase    :						Tuple       =						self.full_loop(set_alpha_to_one=lowercase_						,				beta_start=0.0_1 )
								__UpperCAmelCase    :						Tuple       =						torch.sum(torch.abs(lowercase_ ) )
								__UpperCAmelCase    :						Optional[Any]       =						torch.mean(torch.abs(lowercase_ ) )

								assert abs(result_sum.item() - 149.8_295 ) < 1e-2
								assert abs(result_mean.item() - 0.1_9_5_1 ) < 1e-3







						def       A_					(							self       :      Optional[int] ):


								'''simple docstring'''




								__UpperCAmelCase    :						Optional[Any]       =						self.full_loop(set_alpha_to_one=lowercase_						,				beta_start=0.0_1 )
								__UpperCAmelCase    :						Dict       =						torch.sum(torch.abs(lowercase_ ) )
								__UpperCAmelCase    :						Dict       =						torch.mean(torch.abs(lowercase_ ) )

								assert abs(result_sum.item() - 149.0_784 ) < 1e-2
								assert abs(result_mean.item() - 0.1_9_4_1 ) < 1e-3






"""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 							snake_case							(			unittest.TestCase ):

      '''simple docstring'''



      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Any       =						tempfile.mkdtemp()

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

        __UpperCAmelCase    :						Optional[int]       =						os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES['''vocab_file'''] )
        __UpperCAmelCase    :						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 ) )

        __UpperCAmelCase    :						List[str]       =						{
            '''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],
        }
        __UpperCAmelCase    :						Any       =						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       :      Optional[Any]						,				**__lowercase       :      Tuple ):


        '''simple docstring'''




        return CLIPTokenizer.from_pretrained(self.tmpdirname						,				**__lowercase )

      def       A_					(							self       :      Any						,				**__lowercase       :      Optional[Any] ):


        '''simple docstring'''




        return CLIPTokenizerFast.from_pretrained(self.tmpdirname						,				**__lowercase )

      def       A_					(							self       :      Dict						,				**__lowercase       :      str ):


        '''simple docstring'''




        return ViTImageProcessor.from_pretrained(self.tmpdirname						,				**__lowercase )

      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        shutil.rmtree(self.tmpdirname )

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


        '''simple docstring'''




        __UpperCAmelCase    :						str       =						[np.random.randint(255						,				size=(3, 30, 400)						,				dtype=np.uinta )]

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

        return image_inputs

      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						self.get_tokenizer()
        __UpperCAmelCase    :						Dict       =						self.get_rust_tokenizer()
        __UpperCAmelCase    :						Optional[Any]       =						self.get_image_processor()

        __UpperCAmelCase    :						Any       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )
        processor_slow.save_pretrained(self.tmpdirname )
        __UpperCAmelCase    :						List[Any]       =						CLIPSegProcessor.from_pretrained(self.tmpdirname						,				use_fast=__lowercase )

        __UpperCAmelCase    :						Tuple       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )
        processor_fast.save_pretrained(self.tmpdirname )
        __UpperCAmelCase    :						str       =						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       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Any       =						CLIPSegProcessor(tokenizer=self.get_tokenizer()						,				image_processor=self.get_image_processor() )
        processor.save_pretrained(self.tmpdirname )

        __UpperCAmelCase    :						Any       =						self.get_tokenizer(bos_token='''(BOS)'''						,				eos_token='''(EOS)''' )
        __UpperCAmelCase    :						Tuple       =						self.get_image_processor(do_normalize=__lowercase						,				padding_value=1.0 )

        __UpperCAmelCase    :						List[str]       =						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       A_					(							self       :      int ):


        '''simple docstring'''




        __UpperCAmelCase    :						int       =						self.get_image_processor()
        __UpperCAmelCase    :						List[str]       =						self.get_tokenizer()

        __UpperCAmelCase    :						Optional[Any]       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )

        __UpperCAmelCase    :						List[Any]       =						self.prepare_image_inputs()

        __UpperCAmelCase    :						Dict       =						image_processor(__lowercase						,				return_tensors='''np''' )
        __UpperCAmelCase    :						Optional[int]       =						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       A_					(							self       :      Dict ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						self.get_image_processor()
        __UpperCAmelCase    :						Any       =						self.get_tokenizer()

        __UpperCAmelCase    :						Any       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )

        __UpperCAmelCase    :						int       =						'''lower newer'''

        __UpperCAmelCase    :						int       =						processor(text=__lowercase )

        __UpperCAmelCase    :						Any       =						tokenizer(__lowercase )

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

      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						self.get_image_processor()
        __UpperCAmelCase    :						Union[str, Any]       =						self.get_tokenizer()

        __UpperCAmelCase    :						List[Any]       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )

        __UpperCAmelCase    :						str       =						'''lower newer'''
        __UpperCAmelCase    :						int       =						self.prepare_image_inputs()

        __UpperCAmelCase    :						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       :      List[str] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Dict       =						self.get_image_processor()
        __UpperCAmelCase    :						str       =						self.get_tokenizer()

        __UpperCAmelCase    :						Any       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )

        __UpperCAmelCase    :						Union[str, Any]       =						self.prepare_image_inputs()
        __UpperCAmelCase    :						Optional[Any]       =						self.prepare_image_inputs()

        __UpperCAmelCase    :						Tuple       =						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       A_					(							self       :      str ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						self.get_image_processor()
        __UpperCAmelCase    :						Any       =						self.get_tokenizer()

        __UpperCAmelCase    :						Any       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )

        __UpperCAmelCase    :						Dict       =						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        __UpperCAmelCase    :						int       =						processor.batch_decode(__lowercase )
        __UpperCAmelCase    :						int       =						tokenizer.batch_decode(__lowercase )

        self.assertListEqual(__lowercase						,				__lowercase )