



'''simple docstring'''






import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       (PNDMScheduler,)
	lowerCamelCase__			 =       (("""num_inference_steps""", 50),)







	def      A	(						self			:     str		,			**__snake_case			:     Tuple		)			->		Any:
								UpperCAmelCase :       Optional[int] 							=		{
								    '''num_train_timesteps''': 1000,
								    '''beta_start''': 0.00_01,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								}

								config.update(**__snake_case		)
								return config







	def      A	(						self			:     Optional[Any]		,			__snake_case			:     Optional[Any]=0		,			**__snake_case			:     Tuple		)			->		Union[str, Any]:
								UpperCAmelCase :       List[str] 							=		dict(self.forward_default_kwargs		)
								UpperCAmelCase :       Tuple 							=		kwargs.pop('''num_inference_steps'''		,			__snake_case		)
								UpperCAmelCase :       Tuple 							=		self.dummy_sample
								UpperCAmelCase :       Union[str, Any] 							=		0.1 * sample
								UpperCAmelCase :       Any 							=		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
															UpperCAmelCase :       Union[str, Any] 							=		self.get_scheduler_config(**__snake_case		)
															UpperCAmelCase :       Dict 							=		scheduler_class(**__snake_case		)
															scheduler.set_timesteps(__snake_case		)
															# copy over dummy past residuals
															UpperCAmelCase :       str 							=		dummy_past_residuals[:]

															with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(__snake_case		)
																						UpperCAmelCase :       List[str] 							=		scheduler_class.from_pretrained(__snake_case		)
																						new_scheduler.set_timesteps(__snake_case		)
																						# copy over dummy past residuals
																						UpperCAmelCase :       Optional[Any] 							=		dummy_past_residuals[:]

															UpperCAmelCase :       int 							=		scheduler.step_prk(__snake_case		,			__snake_case		,			__snake_case		,			**__snake_case		).prev_sample
															UpperCAmelCase :       Dict 							=		new_scheduler.step_prk(__snake_case		,			__snake_case		,			__snake_case		,			**__snake_case		).prev_sample

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

															UpperCAmelCase :       Optional[Any] 							=		scheduler.step_plms(__snake_case		,			__snake_case		,			__snake_case		,			**__snake_case		).prev_sample
															UpperCAmelCase :       List[str] 							=		new_scheduler.step_plms(__snake_case		,			__snake_case		,			__snake_case		,			**__snake_case		).prev_sample

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







	def      A	(						self			:     Tuple		)			->		Tuple:
								pass







	def      A	(						self			:     int		,			__snake_case			:     List[Any]=0		,			**__snake_case			:     List[Any]		)			->		Union[str, Any]:
								UpperCAmelCase :       List[Any] 							=		dict(self.forward_default_kwargs		)
								UpperCAmelCase :       Tuple 							=		kwargs.pop('''num_inference_steps'''		,			__snake_case		)
								UpperCAmelCase :       Dict 							=		self.dummy_sample
								UpperCAmelCase :       List[Any] 							=		0.1 * sample
								UpperCAmelCase :       int 							=		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
															UpperCAmelCase :       Any 							=		self.get_scheduler_config()
															UpperCAmelCase :       Tuple 							=		scheduler_class(**__snake_case		)
															scheduler.set_timesteps(__snake_case		)

															# copy over dummy past residuals (must be after setting timesteps)
															UpperCAmelCase :       Union[str, Any] 							=		dummy_past_residuals[:]

															with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(__snake_case		)
																						UpperCAmelCase :       List[str] 							=		scheduler_class.from_pretrained(__snake_case		)
																						# copy over dummy past residuals
																						new_scheduler.set_timesteps(__snake_case		)

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

															UpperCAmelCase :       Optional[int] 							=		scheduler.step_prk(__snake_case		,			__snake_case		,			__snake_case		,			**__snake_case		).prev_sample
															UpperCAmelCase :       List[Any] 							=		new_scheduler.step_prk(__snake_case		,			__snake_case		,			__snake_case		,			**__snake_case		).prev_sample

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

															UpperCAmelCase :       str 							=		scheduler.step_plms(__snake_case		,			__snake_case		,			__snake_case		,			**__snake_case		).prev_sample
															UpperCAmelCase :       Optional[int] 							=		new_scheduler.step_plms(__snake_case		,			__snake_case		,			__snake_case		,			**__snake_case		).prev_sample

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







	def      A	(						self			:     Tuple		,			**__snake_case			:     Tuple		)			->		Any:
								UpperCAmelCase :       Dict 							=		self.scheduler_classes[0]
								UpperCAmelCase :       Tuple 							=		self.get_scheduler_config(**__snake_case		)
								UpperCAmelCase :       Tuple 							=		scheduler_class(**__snake_case		)

								UpperCAmelCase :       Any 							=		10
								UpperCAmelCase :       Optional[Any] 							=		self.dummy_model()
								UpperCAmelCase :       int 							=		self.dummy_sample_deter
								scheduler.set_timesteps(__snake_case		)

								for i, t in enumerate(scheduler.prk_timesteps		):
															UpperCAmelCase :       Tuple 							=		model(__snake_case		,			__snake_case		)
															UpperCAmelCase :       int 							=		scheduler.step_prk(__snake_case		,			__snake_case		,			__snake_case		).prev_sample

								for i, t in enumerate(scheduler.plms_timesteps		):
															UpperCAmelCase :       Any 							=		model(__snake_case		,			__snake_case		)
															UpperCAmelCase :       Any 							=		scheduler.step_plms(__snake_case		,			__snake_case		,			__snake_case		).prev_sample

								return sample







	def      A	(						self			:     Optional[int]		)			->		Optional[int]:
								UpperCAmelCase :       Union[str, Any] 							=		dict(self.forward_default_kwargs		)

								UpperCAmelCase :       Dict 							=		kwargs.pop('''num_inference_steps'''		,			__snake_case		)

								for scheduler_class in self.scheduler_classes:
															UpperCAmelCase :       List[Any] 							=		self.get_scheduler_config()
															UpperCAmelCase :       int 							=		scheduler_class(**__snake_case		)

															UpperCAmelCase :       List[str] 							=		self.dummy_sample
															UpperCAmelCase :       Any 							=		0.1 * sample

															if num_inference_steps is not None and hasattr(__snake_case		,			'''set_timesteps'''		):
																						scheduler.set_timesteps(__snake_case		)
															elif num_inference_steps is not None and not hasattr(__snake_case		,			'''set_timesteps'''		):
																						UpperCAmelCase :       List[Any] 							=		num_inference_steps

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

															UpperCAmelCase :       List[Any] 							=		scheduler.step_prk(__snake_case		,			0		,			__snake_case		,			**__snake_case		).prev_sample
															UpperCAmelCase :       Optional[Any] 							=		scheduler.step_prk(__snake_case		,			1		,			__snake_case		,			**__snake_case		).prev_sample

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

															UpperCAmelCase :       str 							=		scheduler.step_plms(__snake_case		,			0		,			__snake_case		,			**__snake_case		).prev_sample
															UpperCAmelCase :       List[str] 							=		scheduler.step_plms(__snake_case		,			1		,			__snake_case		,			**__snake_case		).prev_sample

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







	def      A	(						self			:     List[str]		)			->		List[Any]:
								for timesteps in [100, 1000]:
															self.check_over_configs(num_train_timesteps=__snake_case		)







	def      A	(						self			:     Any		)			->		Dict:
								for steps_offset in [0, 1]:
															self.check_over_configs(steps_offset=__snake_case		)

								UpperCAmelCase :       Union[str, Any] 							=		self.scheduler_classes[0]
								UpperCAmelCase :       str 							=		self.get_scheduler_config(steps_offset=1		)
								UpperCAmelCase :       Tuple 							=		scheduler_class(**__snake_case		)
								scheduler.set_timesteps(10		)
								assert torch.equal(
								    scheduler.timesteps		,			torch.LongTensor(
								        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]		)		,			)







	def      A	(						self			:     int		)			->		Optional[Any]:
								for beta_start, beta_end in zip([0.00_01, 0.0_01]		,			[0.0_02, 0.02]		):
															self.check_over_configs(beta_start=__snake_case		,			beta_end=__snake_case		)







	def      A	(						self			:     List[Any]		)			->		Optional[Any]:
								for schedule in ["linear", "squaredcos_cap_v2"]:
															self.check_over_configs(beta_schedule=__snake_case		)







	def      A	(						self			:     int		)			->		Optional[Any]:
								for prediction_type in ["epsilon", "v_prediction"]:
															self.check_over_configs(prediction_type=__snake_case		)







	def      A	(						self			:     Any		)			->		Any:
								for t in [1, 5, 10]:
															self.check_over_forward(time_step=__snake_case		)







	def      A	(						self			:     Dict		)			->		int:
								for t, num_inference_steps in zip([1, 5, 10]		,			[10, 50, 100]		):
															self.check_over_forward(num_inference_steps=__snake_case		)







	def      A	(						self			:     List[Any]		)			->		int:
								# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
								UpperCAmelCase :       str 							=		27

								for scheduler_class in self.scheduler_classes:
															UpperCAmelCase :       Tuple 							=		self.dummy_sample
															UpperCAmelCase :       List[Any] 							=		0.1 * sample

															UpperCAmelCase :       Optional[int] 							=		self.get_scheduler_config()
															UpperCAmelCase :       int 							=		scheduler_class(**__snake_case		)

															scheduler.set_timesteps(__snake_case		)

															# before power of 3 fix, would error on first step, so we only need to do two
															for i, t in enumerate(scheduler.prk_timesteps[:2]		):
																						UpperCAmelCase :       str 							=		scheduler.step_prk(__snake_case		,			__snake_case		,			__snake_case		).prev_sample







	def      A	(						self			:     Optional[int]		)			->		Optional[int]:
								with self.assertRaises(__snake_case		):
															UpperCAmelCase :       Optional[int] 							=		self.scheduler_classes[0]
															UpperCAmelCase :       List[str] 							=		self.get_scheduler_config()
															UpperCAmelCase :       List[str] 							=		scheduler_class(**__snake_case		)

															scheduler.step_plms(self.dummy_sample		,			1		,			self.dummy_sample		).prev_sample







	def      A	(						self			:     Tuple		)			->		List[str]:
								UpperCAmelCase :       Union[str, Any] 							=		self.full_loop()
								UpperCAmelCase :       str 							=		torch.sum(torch.abs(__snake_case		)		)
								UpperCAmelCase :       List[str] 							=		torch.mean(torch.abs(__snake_case		)		)

								assert abs(result_sum.item() - 1_98.13_18		) < 1E-2
								assert abs(result_mean.item() - 0.25_80		) < 1E-3







	def      A	(						self			:     Union[str, Any]		)			->		Dict:
								UpperCAmelCase :       int 							=		self.full_loop(prediction_type='''v_prediction'''		)
								UpperCAmelCase :       List[Any] 							=		torch.sum(torch.abs(__snake_case		)		)
								UpperCAmelCase :       Dict 							=		torch.mean(torch.abs(__snake_case		)		)

								assert abs(result_sum.item() - 67.39_86		) < 1E-2
								assert abs(result_mean.item() - 0.08_78		) < 1E-3







	def      A	(						self			:     Any		)			->		List[str]:
								# We specify different beta, so that the first alpha is 0.99
								UpperCAmelCase :       int 							=		self.full_loop(set_alpha_to_one=__snake_case		,			beta_start=0.01		)
								UpperCAmelCase :       Optional[int] 							=		torch.sum(torch.abs(__snake_case		)		)
								UpperCAmelCase :       str 							=		torch.mean(torch.abs(__snake_case		)		)

								assert abs(result_sum.item() - 2_30.03_99		) < 1E-2
								assert abs(result_mean.item() - 0.29_95		) < 1E-3







	def      A	(						self			:     List[Any]		)			->		str:
								# We specify different beta, so that the first alpha is 0.99
								UpperCAmelCase :       List[str] 							=		self.full_loop(set_alpha_to_one=__snake_case		,			beta_start=0.01		)
								UpperCAmelCase :       Union[str, Any] 							=		torch.sum(torch.abs(__snake_case		)		)
								UpperCAmelCase :       List[str] 							=		torch.mean(torch.abs(__snake_case		)		)

								assert abs(result_sum.item() - 1_86.94_82		) < 1E-2
								assert abs(result_mean.item() - 0.24_34		) < 1E-3












'''simple docstring'''




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, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast


@require_vision
class  SCREAMING_SNAKE_CASE    (unittest.TestCase							):


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Dict      =       tempfile.mkdtemp()

          __A						: Optional[int]      =       BlipImageProcessor()
          __A						: List[str]      =       BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel')

          __A						: Any      =       BlipProcessor(_UpperCAmelCase						,	_UpperCAmelCase)

          processor.save_pretrained(self.tmpdirname)


     def 							SCREAMING_SNAKE_CASE  (						self						,	**_UpperCAmelCase):

          '''simple docstring'''






          return AutoProcessor.from_pretrained(self.tmpdirname						,	**_UpperCAmelCase).tokenizer


     def 							SCREAMING_SNAKE_CASE  (						self						,	**_UpperCAmelCase):

          '''simple docstring'''






          return AutoProcessor.from_pretrained(self.tmpdirname						,	**_UpperCAmelCase).image_processor


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          shutil.rmtree(self.tmpdirname)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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

          __A						: Union[str, Any]      =       [Image.fromarray(np.moveaxis(_UpperCAmelCase						,	0						,	-1)) for x in image_inputs]

          return image_inputs


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: int      =       BlipProcessor(tokenizer=self.get_tokenizer()						,	image_processor=self.get_image_processor())
          processor.save_pretrained(self.tmpdirname)

          __A						: int      =       self.get_tokenizer(bos_token='(BOS)'						,	eos_token='(EOS)')
          __A						: List[Any]      =       self.get_image_processor(do_normalize=_UpperCAmelCase						,	padding_value=1.0)

          __A						: Tuple      =       BlipProcessor.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):

          '''simple docstring'''






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

          __A						: int      =       BlipProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)

          __A						: List[str]      =       self.prepare_image_inputs()

          __A						: str      =       image_processor(_UpperCAmelCase						,	return_tensors='np')
          __A						: str      =       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):

          '''simple docstring'''






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

          __A						: Union[str, Any]      =       BlipProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)

          __A						: str      =       'lower newer'

          __A						: Dict      =       processor(text=_UpperCAmelCase)

          __A						: Tuple      =       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):

          '''simple docstring'''






          __A						: List[Any]      =       self.get_image_processor()
          __A						: Optional[Any]      =       self.get_tokenizer()

          __A						: Optional[Any]      =       BlipProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)

          __A						: Dict      =       'lower newer'
          __A						: int      =       self.prepare_image_inputs()

          __A						: List[Any]      =       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):

          '''simple docstring'''






          __A						: Union[str, Any]      =       self.get_image_processor()
          __A						: Optional[Any]      =       self.get_tokenizer()

          __A						: Union[str, Any]      =       BlipProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)

          __A						: Tuple      =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          __A						: List[str]      =       processor.batch_decode(_UpperCAmelCase)
          __A						: List[str]      =       tokenizer.batch_decode(_UpperCAmelCase)

          self.assertListEqual(_UpperCAmelCase						,	_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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

          __A						: Optional[Any]      =       BlipProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)

          __A						: str      =       'lower newer'
          __A						: str      =       self.prepare_image_inputs()

          __A						: Optional[int]      =       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'])