

"""simple docstring"""






import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class        _lowercase					(     __UpperCAmelCase     ):
     _lowerCamelCase     =       (EulerDiscreteScheduler,)
     _lowerCamelCase     =       10







     def 	lowerCAmelCase__    (      self ,						**UpperCamelCase_       ):
      __magic_name__     =      {
          '''num_train_timesteps''': 1100,
          '''beta_start''': 0.0_0_0_1,
          '''beta_end''': 0.0_2,
          '''beta_schedule''': '''linear''',
      }

      config.update(**UpperCamelCase_       )
      return config







     def 	lowerCAmelCase__    (      self       ):
      for timesteps in [10, 50, 100, 1000]:
       self.check_over_configs(num_train_timesteps=UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      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=UpperCamelCase_ ,						beta_end=UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      for schedule in ["linear", "scaled_linear"]:
       self.check_over_configs(beta_schedule=UpperCamelCase_       )







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







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.scheduler_classes[0]
      __magic_name__     =      self.get_scheduler_config()
      __magic_name__     =      scheduler_class(**UpperCamelCase_       )

      scheduler.set_timesteps(self.num_inference_steps       )

      __magic_name__     =      torch.manual_seed(0       )

      __magic_name__     =      self.dummy_model()
      __magic_name__     =      self.dummy_sample_deter * scheduler.init_noise_sigma
      __magic_name__     =      sample.to(UpperCamelCase_       )

      for i, t in enumerate(scheduler.timesteps       ):
       __magic_name__     =      scheduler.scale_model_input(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      model(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      scheduler.step(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						generator=UpperCamelCase_       )
       __magic_name__     =      output.prev_sample

      __magic_name__     =      torch.sum(torch.abs(UpperCamelCase_       )       )
      __magic_name__     =      torch.mean(torch.abs(UpperCamelCase_       )       )

      assert abs(result_sum.item() - 10.0807       ) < 1E-2
      assert abs(result_mean.item() - 0.0_1_3_1       ) < 1E-3







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.scheduler_classes[0]
      __magic_name__     =      self.get_scheduler_config(prediction_type='''v_prediction'''       )
      __magic_name__     =      scheduler_class(**UpperCamelCase_       )

      scheduler.set_timesteps(self.num_inference_steps       )

      __magic_name__     =      torch.manual_seed(0       )

      __magic_name__     =      self.dummy_model()
      __magic_name__     =      self.dummy_sample_deter * scheduler.init_noise_sigma
      __magic_name__     =      sample.to(UpperCamelCase_       )

      for i, t in enumerate(scheduler.timesteps       ):
       __magic_name__     =      scheduler.scale_model_input(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      model(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      scheduler.step(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						generator=UpperCamelCase_       )
       __magic_name__     =      output.prev_sample

      __magic_name__     =      torch.sum(torch.abs(UpperCamelCase_       )       )
      __magic_name__     =      torch.mean(torch.abs(UpperCamelCase_       )       )

      assert abs(result_sum.item() - 0.0_0_0_2       ) < 1E-2
      assert abs(result_mean.item() - 2.2_6_7_6E-0_6       ) < 1E-3







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.scheduler_classes[0]
      __magic_name__     =      self.get_scheduler_config()
      __magic_name__     =      scheduler_class(**UpperCamelCase_       )

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

      __magic_name__     =      torch.manual_seed(0       )

      __magic_name__     =      self.dummy_model()
      __magic_name__     =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      __magic_name__     =      sample.to(UpperCamelCase_       )

      for t in scheduler.timesteps:
       __magic_name__     =      scheduler.scale_model_input(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      model(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      scheduler.step(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						generator=UpperCamelCase_       )
       __magic_name__     =      output.prev_sample

      __magic_name__     =      torch.sum(torch.abs(UpperCamelCase_       )       )
      __magic_name__     =      torch.mean(torch.abs(UpperCamelCase_       )       )

      assert abs(result_sum.item() - 10.0807       ) < 1E-2
      assert abs(result_mean.item() - 0.0_1_3_1       ) < 1E-3







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.scheduler_classes[0]
      __magic_name__     =      self.get_scheduler_config()
      __magic_name__     =      scheduler_class(**UpperCamelCase_ ,						use_karras_sigmas=UpperCamelCase_       )

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

      __magic_name__     =      torch.manual_seed(0       )

      __magic_name__     =      self.dummy_model()
      __magic_name__     =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      __magic_name__     =      sample.to(UpperCamelCase_       )

      for t in scheduler.timesteps:
       __magic_name__     =      scheduler.scale_model_input(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      model(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      scheduler.step(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						generator=UpperCamelCase_       )
       __magic_name__     =      output.prev_sample

      __magic_name__     =      torch.sum(torch.abs(UpperCamelCase_       )       )
      __magic_name__     =      torch.mean(torch.abs(UpperCamelCase_       )       )

      assert abs(result_sum.item() - 124.52_2994_9951_1719       ) < 1E-2
      assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3       ) < 1E-3




"""simple docstring"""






import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class        _lowercase					(     unittest.TestCase     ):







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      tempfile.mkdtemp()

      # fmt: off
      __magic_name__     =      ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
      # fmt: on
      __magic_name__     =      os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES['''vocab_file''']       )
      with open(self.vocab_file ,						'''w''' ,						encoding='''utf-8'''       ) as vocab_writer:
       vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]       )       )

      __magic_name__     =      {
          '''do_resize''': True,
          '''size''': {'''height''': 18, '''width''': 18},
          '''do_normalize''': True,
          '''image_mean''': [0.5, 0.5, 0.5],
          '''image_std''': [0.5, 0.5, 0.5],
      }
      __magic_name__     =      os.path.join(self.tmpdirname ,						UpperCamelCase_       )
      with open(self.image_processor_file ,						'''w''' ,						encoding='''utf-8'''       ) as fp:
       json.dump(UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						**UpperCamelCase_       ):
      return BertTokenizer.from_pretrained(self.tmpdirname ,						**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						**UpperCamelCase_       ):
      return ViTImageProcessor.from_pretrained(self.tmpdirname ,						**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      shutil.rmtree(self.tmpdirname       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      [np.random.randint(255 ,						size=(3, 30, 400) ,						dtype=np.uinta       )]

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

      return image_inputs







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_tokenizer()
      __magic_name__     =      self.get_image_processor()

      __magic_name__     =      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_ ,						image_processor=UpperCamelCase_       )

      processor.save_pretrained(self.tmpdirname       )
      __magic_name__     =      VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname       )

      self.assertEqual(processor.tokenizer.get_vocab() ,						tokenizer.get_vocab()       )
      self.assertIsInstance(processor.tokenizer ,						(BertTokenizer, BertTokenizerFast)       )

      self.assertEqual(processor.image_processor.to_json_string() ,						image_processor.to_json_string()       )
      self.assertIsInstance(processor.image_processor ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      VisionTextDualEncoderProcessor(
          tokenizer=self.get_tokenizer() ,						image_processor=self.get_image_processor()       )
      processor.save_pretrained(self.tmpdirname       )

      __magic_name__     =      self.get_tokenizer(bos_token='''(BOS)''' ,						eos_token='''(EOS)'''       )
      __magic_name__     =      self.get_image_processor(do_normalize=UpperCamelCase_ ,						padding_value=1.0       )

      __magic_name__     =      VisionTextDualEncoderProcessor.from_pretrained(
          self.tmpdirname ,						bos_token='''(BOS)''' ,						eos_token='''(EOS)''' ,						do_normalize=UpperCamelCase_ ,						padding_value=1.0       )

      self.assertEqual(processor.tokenizer.get_vocab() ,						tokenizer_add_kwargs.get_vocab()       )
      self.assertIsInstance(processor.tokenizer ,						(BertTokenizer, BertTokenizerFast)       )

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







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_image_processor()
      __magic_name__     =      self.get_tokenizer()

      __magic_name__     =      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_ ,						image_processor=UpperCamelCase_       )

      __magic_name__     =      self.prepare_image_inputs()

      __magic_name__     =      image_processor(UpperCamelCase_ ,						return_tensors='''np'''       )
      __magic_name__     =      processor(images=UpperCamelCase_ ,						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 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_image_processor()
      __magic_name__     =      self.get_tokenizer()

      __magic_name__     =      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_ ,						image_processor=UpperCamelCase_       )

      __magic_name__     =      '''lower newer'''

      __magic_name__     =      processor(text=UpperCamelCase_       )

      __magic_name__     =      tokenizer(UpperCamelCase_       )

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







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_image_processor()
      __magic_name__     =      self.get_tokenizer()

      __magic_name__     =      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_ ,						image_processor=UpperCamelCase_       )

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

      __magic_name__     =      processor(text=UpperCamelCase_ ,						images=UpperCamelCase_       )

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

      # test if it raises when no input is passed
      with self.assertRaises(UpperCamelCase_       ):
       processor()







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_image_processor()
      __magic_name__     =      self.get_tokenizer()

      __magic_name__     =      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_ ,						image_processor=UpperCamelCase_       )

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

      __magic_name__     =      processor.batch_decode(UpperCamelCase_       )
      __magic_name__     =      tokenizer.batch_decode(UpperCamelCase_       )

      self.assertListEqual(UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_image_processor()
      __magic_name__     =      self.get_tokenizer()

      __magic_name__     =      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_ ,						image_processor=UpperCamelCase_       )

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

      __magic_name__     =      processor(text=UpperCamelCase_ ,						images=UpperCamelCase_       )

      self.assertListEqual(list(inputs.keys()       ) ,						processor.model_input_names       )

