





"""simple docstring"""





import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest




@require_torchsde
class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      _A     :       str         =       (DPMSolverSDEScheduler,)
      _A     :       Any         =       10

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


        '''simple docstring'''




        __UpperCAmelCase    :						Union[str, Any]       =						{
            '''num_train_timesteps''': 1_100,
            '''beta_start''': 0.0_0_0_1,
            '''beta_end''': 0.0_2,
            '''beta_schedule''': '''linear''',
            '''noise_sampler_seed''': 0,
        }

        config.update(**__lowercase )
        return config

      def       A_					(							self       :      str ):


        '''simple docstring'''




        for timesteps in [10, 50, 100, 1_000]:
          self.check_over_configs(num_train_timesteps=__lowercase )

      def       A_					(							self       :      str ):


        '''simple docstring'''




        for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]						,				[0.0_0_0_2, 0.0_0_2, 0.0_2] ):
          self.check_over_configs(beta_start=__lowercase						,				beta_end=__lowercase )

      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        for schedule in ["linear", "scaled_linear"]:
          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       :      Tuple ):


        '''simple docstring'''




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

        scheduler.set_timesteps(self.num_inference_steps )

        __UpperCAmelCase    :						Dict       =						self.dummy_model()
        __UpperCAmelCase    :						List[Any]       =						self.dummy_sample_deter * scheduler.init_noise_sigma
        __UpperCAmelCase    :						int       =						sample.to(__lowercase )

        for i, t in enumerate(scheduler.timesteps ):
          __UpperCAmelCase    :						List[str]       =						scheduler.scale_model_input(__lowercase						,				__lowercase )

          __UpperCAmelCase    :						List[Any]       =						model(__lowercase						,				__lowercase )

          __UpperCAmelCase    :						Optional[int]       =						scheduler.step(__lowercase						,				__lowercase						,				__lowercase )
          __UpperCAmelCase    :						Union[str, Any]       =						output.prev_sample

        __UpperCAmelCase    :						List[Any]       =						torch.sum(torch.abs(__lowercase ) )
        __UpperCAmelCase    :						Dict       =						torch.mean(torch.abs(__lowercase ) )

        if torch_device in ["mps"]:
          assert abs(result_sum.item() - 1_6_7.4_7_8_2_1_0_4_4_9_2_1_8_7_5 ) < 1e-2
          assert abs(result_mean.item() - 0.2_1_7_8_7_0_5_9_6_4_5_6_5_2_7_7 ) < 1e-3
        elif torch_device in ["cuda"]:
          assert abs(result_sum.item() - 1_7_1.5_9_3_5_2_1_1_1_8_1_6_4_0_6 ) < 1e-2
          assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_6_8_9_2_2_9_9_6_5_2 ) < 1e-3
        else:
          assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2 ) < 1e-2
          assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6 ) < 1e-3

      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Any       =						self.scheduler_classes[0]
        __UpperCAmelCase    :						str       =						self.get_scheduler_config(prediction_type='''v_prediction''' )
        __UpperCAmelCase    :						str       =						scheduler_class(**__lowercase )

        scheduler.set_timesteps(self.num_inference_steps )

        __UpperCAmelCase    :						Dict       =						self.dummy_model()
        __UpperCAmelCase    :						Optional[Any]       =						self.dummy_sample_deter * scheduler.init_noise_sigma
        __UpperCAmelCase    :						Tuple       =						sample.to(__lowercase )

        for i, t in enumerate(scheduler.timesteps ):
          __UpperCAmelCase    :						List[Any]       =						scheduler.scale_model_input(__lowercase						,				__lowercase )

          __UpperCAmelCase    :						List[str]       =						model(__lowercase						,				__lowercase )

          __UpperCAmelCase    :						List[str]       =						scheduler.step(__lowercase						,				__lowercase						,				__lowercase )
          __UpperCAmelCase    :						int       =						output.prev_sample

        __UpperCAmelCase    :						Any       =						torch.sum(torch.abs(__lowercase ) )
        __UpperCAmelCase    :						Optional[Any]       =						torch.mean(torch.abs(__lowercase ) )

        if torch_device in ["mps"]:
          assert abs(result_sum.item() - 1_2_4.7_7_1_4_9_2_0_0_4_3_9_4_5_3 ) < 1e-2
          assert abs(result_mean.item() - 0.1_6_2_2_6_2_8_9_0_1_4_8_1_6_2_8_4 ) < 1e-3
        elif torch_device in ["cuda"]:
          assert abs(result_sum.item() - 1_2_8.1_6_6_3_3_6_0_5_9_5_7_0_3 ) < 1e-2
          assert abs(result_mean.item() - 0.1_6_6_8_8_3_2_6_0_0_1_1_6_7_2_9_7 ) < 1e-3
        else:
          assert abs(result_sum.item() - 1_1_9.8_4_8_7_5_4_8_8_2_8_1_2_5 ) < 1e-2
          assert abs(result_mean.item() - 0.1_5_6_0_5_3_0_6_6_2_5_3_6_6_2_1 ) < 1e-3

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


        '''simple docstring'''




        __UpperCAmelCase    :						Union[str, Any]       =						self.scheduler_classes[0]
        __UpperCAmelCase    :						int       =						self.get_scheduler_config()
        __UpperCAmelCase    :						Optional[Any]       =						scheduler_class(**__lowercase )

        scheduler.set_timesteps(self.num_inference_steps						,				device=__lowercase )

        __UpperCAmelCase    :						Any       =						self.dummy_model()
        __UpperCAmelCase    :						Optional[int]       =						self.dummy_sample_deter.to(__lowercase ) * scheduler.init_noise_sigma

        for t in scheduler.timesteps:
          __UpperCAmelCase    :						Union[str, Any]       =						scheduler.scale_model_input(__lowercase						,				__lowercase )

          __UpperCAmelCase    :						Dict       =						model(__lowercase						,				__lowercase )

          __UpperCAmelCase    :						Optional[int]       =						scheduler.step(__lowercase						,				__lowercase						,				__lowercase )
          __UpperCAmelCase    :						Tuple       =						output.prev_sample

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

        if torch_device in ["mps"]:
          assert abs(result_sum.item() - 1_6_7.4_6_9_5_7_3_9_7_4_6_0_9_3_8 ) < 1e-2
          assert abs(result_mean.item() - 0.2_1_8_0_5_9_3_4_6_0_7_9_8_2_6_3_5 ) < 1e-3
        elif torch_device in ["cuda"]:
          assert abs(result_sum.item() - 1_7_1.5_9_3_5_3_6_3_7_6_9_5_3_1_2 ) < 1e-2
          assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_8_3_8_2_4_1_5_7_7_1 ) < 1e-3
        else:
          assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2 ) < 1e-2
          assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6 ) < 1e-3







      def       A_					(							self       :      Tuple ):


        '''simple docstring'''




        __UpperCAmelCase    :						Union[str, Any]       =						self.scheduler_classes[0]
        __UpperCAmelCase    :						Optional[int]       =						self.get_scheduler_config()
        __UpperCAmelCase    :						Optional[int]       =						scheduler_class(**__lowercase						,				use_karras_sigmas=__lowercase )

        scheduler.set_timesteps(self.num_inference_steps						,				device=__lowercase )

        __UpperCAmelCase    :						Any       =						self.dummy_model()
        __UpperCAmelCase    :						int       =						self.dummy_sample_deter.to(__lowercase ) * scheduler.init_noise_sigma
        __UpperCAmelCase    :						Tuple       =						sample.to(__lowercase )

        for t in scheduler.timesteps:
          __UpperCAmelCase    :						int       =						scheduler.scale_model_input(__lowercase						,				__lowercase )

          __UpperCAmelCase    :						List[Any]       =						model(__lowercase						,				__lowercase )

          __UpperCAmelCase    :						List[str]       =						scheduler.step(__lowercase						,				__lowercase						,				__lowercase )
          __UpperCAmelCase    :						int       =						output.prev_sample

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

        if torch_device in ["mps"]:
          assert abs(result_sum.item() - 1_7_6.6_6_9_7_4_1_3_5_7_4_2_1_8_8 ) < 1e-2
          assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1 ) < 1e-2
        elif torch_device in ["cuda"]:
          assert abs(result_sum.item() - 1_7_7.6_3_6_5_3_5_6_4_4_5_3_1_2_5 ) < 1e-2
          assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1 ) < 1e-2
        else:
          assert abs(result_sum.item() - 1_7_0.3_1_3_5_2_2_3_3_8_8_6_7_2 ) < 1e-2
          assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1 ) < 1e-2






"""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 )