



import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 		lowercase						(			snake_case__):






 """simple docstring"""


 a__				: Optional[int]		       =							(EulerDiscreteScheduler,)
 a__				: str		       =							10




 def   _SCREAMING_SNAKE_CASE		(     self     :						int						,  **__UpperCAmelCase     :						List[Any]					)      ->			str:
       UpperCAmelCase_=       {
           """num_train_timesteps""": 1_100,
           """beta_start""": 0.0_001,
           """beta_end""": 0.02,
           """beta_schedule""": """linear""",
       }

       config.update(**lowercase_					)
       return config




 def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			str:







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




 def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Optional[Any]:







       for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]						,  [0.0_002, 0.002, 0.02]					):
             self.check_over_configs(beta_start=lowercase_						,  beta_end=lowercase_					)




 def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Optional[int]:







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




 def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			Optional[int]:







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




 def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			str:
       UpperCAmelCase_=       self.scheduler_classes[0]
       UpperCAmelCase_=       self.get_scheduler_config()
       UpperCAmelCase_=       scheduler_class(**lowercase_					)

       scheduler.set_timesteps(self.num_inference_steps					)

       UpperCAmelCase_=       torch.manual_seed(0					)

       UpperCAmelCase_=       self.dummy_model()
       UpperCAmelCase_=       self.dummy_sample_deter * scheduler.init_noise_sigma
       UpperCAmelCase_=       sample.to(lowercase_					)

       for i, t in enumerate(scheduler.timesteps					):
             UpperCAmelCase_=       scheduler.scale_model_input(lowercase_						,  lowercase_					)

             UpperCAmelCase_=       model(lowercase_						,  lowercase_					)

             UpperCAmelCase_=       scheduler.step(lowercase_						,  lowercase_						,  lowercase_						,  generator=lowercase_					)
             UpperCAmelCase_=       output.prev_sample

       UpperCAmelCase_=       torch.sum(torch.abs(lowercase_					)					)
       UpperCAmelCase_=       torch.mean(torch.abs(lowercase_					)					)

       assert abs(result_sum.item() - 10.0_807					) < 1E-2
       assert abs(result_mean.item() - 0.0_131					) < 1E-3




 def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			Dict:
       UpperCAmelCase_=       self.scheduler_classes[0]
       UpperCAmelCase_=       self.get_scheduler_config(prediction_type="""v_prediction"""					)
       UpperCAmelCase_=       scheduler_class(**lowercase_					)

       scheduler.set_timesteps(self.num_inference_steps					)

       UpperCAmelCase_=       torch.manual_seed(0					)

       UpperCAmelCase_=       self.dummy_model()
       UpperCAmelCase_=       self.dummy_sample_deter * scheduler.init_noise_sigma
       UpperCAmelCase_=       sample.to(lowercase_					)

       for i, t in enumerate(scheduler.timesteps					):
             UpperCAmelCase_=       scheduler.scale_model_input(lowercase_						,  lowercase_					)

             UpperCAmelCase_=       model(lowercase_						,  lowercase_					)

             UpperCAmelCase_=       scheduler.step(lowercase_						,  lowercase_						,  lowercase_						,  generator=lowercase_					)
             UpperCAmelCase_=       output.prev_sample

       UpperCAmelCase_=       torch.sum(torch.abs(lowercase_					)					)
       UpperCAmelCase_=       torch.mean(torch.abs(lowercase_					)					)

       assert abs(result_sum.item() - 0.0_002					) < 1E-2
       assert abs(result_mean.item() - 2.2_676E-06					) < 1E-3




 def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			Dict:
       UpperCAmelCase_=       self.scheduler_classes[0]
       UpperCAmelCase_=       self.get_scheduler_config()
       UpperCAmelCase_=       scheduler_class(**lowercase_					)

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

       UpperCAmelCase_=       torch.manual_seed(0					)

       UpperCAmelCase_=       self.dummy_model()
       UpperCAmelCase_=       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       UpperCAmelCase_=       sample.to(lowercase_					)

       for t in scheduler.timesteps:
             UpperCAmelCase_=       scheduler.scale_model_input(lowercase_						,  lowercase_					)

             UpperCAmelCase_=       model(lowercase_						,  lowercase_					)

             UpperCAmelCase_=       scheduler.step(lowercase_						,  lowercase_						,  lowercase_						,  generator=lowercase_					)
             UpperCAmelCase_=       output.prev_sample

       UpperCAmelCase_=       torch.sum(torch.abs(lowercase_					)					)
       UpperCAmelCase_=       torch.mean(torch.abs(lowercase_					)					)

       assert abs(result_sum.item() - 10.0_807					) < 1E-2
       assert abs(result_mean.item() - 0.0_131					) < 1E-3




 def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			str:
       UpperCAmelCase_=       self.scheduler_classes[0]
       UpperCAmelCase_=       self.get_scheduler_config()
       UpperCAmelCase_=       scheduler_class(**lowercase_						,  use_karras_sigmas=lowercase_					)

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

       UpperCAmelCase_=       torch.manual_seed(0					)

       UpperCAmelCase_=       self.dummy_model()
       UpperCAmelCase_=       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       UpperCAmelCase_=       sample.to(lowercase_					)

       for t in scheduler.timesteps:
             UpperCAmelCase_=       scheduler.scale_model_input(lowercase_						,  lowercase_					)

             UpperCAmelCase_=       model(lowercase_						,  lowercase_					)

             UpperCAmelCase_=       scheduler.step(lowercase_						,  lowercase_						,  lowercase_						,  generator=lowercase_					)
             UpperCAmelCase_=       output.prev_sample

       UpperCAmelCase_=       torch.sum(torch.abs(lowercase_					)					)
       UpperCAmelCase_=       torch.mean(torch.abs(lowercase_					)					)

       assert abs(result_sum.item() - 124.52_299_499_511_719					) < 1E-2
       assert abs(result_mean.item() - 0.16_213_932_633_399_963					) < 1E-3









import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast






@require_vision
class 		lowercase						(			unittest.TestCase):






	"""simple docstring"""


	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			List[Any]:
							UpperCAmelCase_=       tempfile.mkdtemp()

							UpperCAmelCase_=       BlipImageProcessor()
							UpperCAmelCase_=       GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""					)

							UpperCAmelCase_=       BlipaProcessor(__UpperCAmelCase						,  __UpperCAmelCase					)

							processor.save_pretrained(self.tmpdirname					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]						,  **__UpperCAmelCase     :						Union[str, Any]					)      ->			int:
							return AutoProcessor.from_pretrained(self.tmpdirname						,  **__UpperCAmelCase					).tokenizer




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]						,  **__UpperCAmelCase     :						str					)      ->			Optional[int]:
							return AutoProcessor.from_pretrained(self.tmpdirname						,  **__UpperCAmelCase					).image_processor




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			List[Any]:
							shutil.rmtree(self.tmpdirname					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			Any:
							UpperCAmelCase_=       [np.random.randint(255						,  size=(3, 30, 400)						,  dtype=np.uinta					)]

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

							return image_inputs




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			Dict:
							UpperCAmelCase_=       BlipaProcessor(tokenizer=self.get_tokenizer()						,  image_processor=self.get_image_processor()					)
							processor.save_pretrained(self.tmpdirname					)

							UpperCAmelCase_=       self.get_tokenizer(bos_token="""(BOS)"""						,  eos_token="""(EOS)"""					)
							UpperCAmelCase_=       self.get_image_processor(do_normalize=__UpperCAmelCase						,  padding_value=1.0					)

							UpperCAmelCase_=       BlipaProcessor.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						,  __UpperCAmelCase					)

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Optional[int]:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

							UpperCAmelCase_=       self.prepare_image_inputs()

							UpperCAmelCase_=       image_processor(__UpperCAmelCase						,  return_tensors="""np"""					)
							UpperCAmelCase_=       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   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			Any:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

							UpperCAmelCase_=       """lower newer"""

							UpperCAmelCase_=       processor(text=__UpperCAmelCase					)

							UpperCAmelCase_=       tokenizer(__UpperCAmelCase						,  return_token_type_ids=__UpperCAmelCase					)

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			Dict:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

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

							UpperCAmelCase_=       processor(text=__UpperCAmelCase						,  images=__UpperCAmelCase					)

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

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Any:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

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

							UpperCAmelCase_=       processor.batch_decode(__UpperCAmelCase					)
							UpperCAmelCase_=       tokenizer.batch_decode(__UpperCAmelCase					)

							self.assertListEqual(__UpperCAmelCase						,  __UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			int:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

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

							UpperCAmelCase_=       processor(text=__UpperCAmelCase						,  images=__UpperCAmelCase					)

							# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
							self.assertListEqual(list(inputs.keys()					)						,  ["""pixel_values""", """input_ids""", """attention_mask"""]					)




