


'''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, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast





@require_vision
class      UpperCAmelCase_						(unittest.TestCase						):





    """simple docstring"""






    def    lowercase_       (							self			)       ->  int:
         __lowerCamelCase     :			str              =	tempfile.mkdtemp()

         __lowerCamelCase     :			Tuple              =	CLIPImageProcessor()
         __lowerCamelCase     :			Tuple              =	BertTokenizer.from_pretrained(
             'hf-internal-testing/tiny-random-BertModel'    ,  model_input_names=['input_ids', 'attention_mask']			)

         __lowerCamelCase     :			List[Any]              =	GitProcessor(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)

         processor.save_pretrained(self.tmpdirname			)







    def    lowercase_       (							self    ,  **SCREAMING_SNAKE_CASE_			)       ->  Dict:
         return AutoProcessor.from_pretrained(self.tmpdirname    ,  **SCREAMING_SNAKE_CASE_			).tokenizer







    def    lowercase_       (							self    ,  **SCREAMING_SNAKE_CASE_			)       ->  List[str]:
         return AutoProcessor.from_pretrained(self.tmpdirname    ,  **SCREAMING_SNAKE_CASE_			).image_processor







    def    lowercase_       (							self			)       ->  str:
         shutil.rmtree(self.tmpdirname			)







    def    lowercase_       (							self			)       ->  Tuple:
         __lowerCamelCase     :			List[str]              =	[np.random.randint(2_55    ,  size=(3, 30, 4_00)    ,  dtype=np.uinta			)]

         __lowerCamelCase     :			Optional[Any]              =	[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_    ,  0    ,  -1			)			) for x in image_inputs]

         return image_inputs







    def    lowercase_       (							self			)       ->  List[Any]:
         __lowerCamelCase     :			Dict              =	GitProcessor(tokenizer=self.get_tokenizer()    ,  image_processor=self.get_image_processor()			)
         processor.save_pretrained(self.tmpdirname			)

         __lowerCamelCase     :			Optional[int]              =	self.get_tokenizer(bos_token='(BOS)'    ,  eos_token='(EOS)'			)
         __lowerCamelCase     :			Dict              =	self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_    ,  padding_value=1.0			)

         __lowerCamelCase     :			List[str]              =	GitProcessor.from_pretrained(
             self.tmpdirname    ,  bos_token='(BOS)'    ,  eos_token='(EOS)'    ,  do_normalize=SCREAMING_SNAKE_CASE_    ,  padding_value=1.0			)

         self.assertEqual(processor.tokenizer.get_vocab()    ,  tokenizer_add_kwargs.get_vocab()			)
         self.assertIsInstance(processor.tokenizer    ,  SCREAMING_SNAKE_CASE_			)

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







    def    lowercase_       (							self			)       ->  Any:
         __lowerCamelCase     :			Tuple              =	self.get_image_processor()
         __lowerCamelCase     :			Tuple              =	self.get_tokenizer()

         __lowerCamelCase     :			Union[str, Any]              =	GitProcessor(tokenizer=SCREAMING_SNAKE_CASE_    ,  image_processor=SCREAMING_SNAKE_CASE_			)

         __lowerCamelCase     :			Optional[Any]              =	self.prepare_image_inputs()

         __lowerCamelCase     :			Any              =	image_processor(SCREAMING_SNAKE_CASE_    ,  return_tensors='np'			)
         __lowerCamelCase     :			List[str]              =	processor(images=SCREAMING_SNAKE_CASE_    ,  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    lowercase_       (							self			)       ->  Union[str, Any]:
         __lowerCamelCase     :			List[Any]              =	self.get_image_processor()
         __lowerCamelCase     :			int              =	self.get_tokenizer()

         __lowerCamelCase     :			List[Any]              =	GitProcessor(tokenizer=SCREAMING_SNAKE_CASE_    ,  image_processor=SCREAMING_SNAKE_CASE_			)

         __lowerCamelCase     :			Any              =	'lower newer'

         __lowerCamelCase     :			Optional[Any]              =	processor(text=SCREAMING_SNAKE_CASE_			)

         __lowerCamelCase     :			Optional[Any]              =	tokenizer(SCREAMING_SNAKE_CASE_    ,  return_token_type_ids=SCREAMING_SNAKE_CASE_			)

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







    def    lowercase_       (							self			)       ->  Optional[Any]:
         __lowerCamelCase     :			Tuple              =	self.get_image_processor()
         __lowerCamelCase     :			Optional[Any]              =	self.get_tokenizer()

         __lowerCamelCase     :			int              =	GitProcessor(tokenizer=SCREAMING_SNAKE_CASE_    ,  image_processor=SCREAMING_SNAKE_CASE_			)

         __lowerCamelCase     :			Union[str, Any]              =	'lower newer'
         __lowerCamelCase     :			Optional[int]              =	self.prepare_image_inputs()

         __lowerCamelCase     :			Dict              =	processor(text=SCREAMING_SNAKE_CASE_    ,  images=SCREAMING_SNAKE_CASE_			)

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

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







    def    lowercase_       (							self			)       ->  List[Any]:
         __lowerCamelCase     :			List[Any]              =	self.get_image_processor()
         __lowerCamelCase     :			Optional[Any]              =	self.get_tokenizer()

         __lowerCamelCase     :			str              =	GitProcessor(tokenizer=SCREAMING_SNAKE_CASE_    ,  image_processor=SCREAMING_SNAKE_CASE_			)

         __lowerCamelCase     :			Optional[int]              =	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         __lowerCamelCase     :			str              =	processor.batch_decode(SCREAMING_SNAKE_CASE_			)
         __lowerCamelCase     :			Dict              =	tokenizer.batch_decode(SCREAMING_SNAKE_CASE_			)

         self.assertListEqual(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)







    def    lowercase_       (							self			)       ->  Optional[int]:
         __lowerCamelCase     :			List[str]              =	self.get_image_processor()
         __lowerCamelCase     :			Any              =	self.get_tokenizer()

         __lowerCamelCase     :			List[str]              =	GitProcessor(tokenizer=SCREAMING_SNAKE_CASE_    ,  image_processor=SCREAMING_SNAKE_CASE_			)

         __lowerCamelCase     :			Tuple              =	'lower newer'
         __lowerCamelCase     :			Optional[Any]              =	self.prepare_image_inputs()

         __lowerCamelCase     :			Dict              =	processor(text=SCREAMING_SNAKE_CASE_    ,  images=SCREAMING_SNAKE_CASE_			)

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



'''simple docstring'''



import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class      UpperCAmelCase_						(_UpperCAmelCase						):





    """simple docstring"""






    lowerCamelCase						:      Optional[Any]							  =    (KDPMaAncestralDiscreteScheduler,)
    lowerCamelCase						:      Tuple							  =    1_0







    def    lowercase_       (							self    ,  **SCREAMING_SNAKE_CASE_			)       ->  Tuple:
         __lowerCamelCase     :			Union[str, Any]              =	{
             'num_train_timesteps': 11_00,
             'beta_start': 0.0_0_0_1,
             'beta_end': 0.0_2,
             'beta_schedule': 'linear',
         }

         config.update(**SCREAMING_SNAKE_CASE_			)
         return config







    def    lowercase_       (							self			)       ->  Union[str, Any]:
         for timesteps in [10, 50, 1_00, 10_00]:
              self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_			)







    def    lowercase_       (							self			)       ->  Dict:
         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=SCREAMING_SNAKE_CASE_    ,  beta_end=SCREAMING_SNAKE_CASE_			)







    def    lowercase_       (							self			)       ->  List[str]:
         for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_			)







    def    lowercase_       (							self			)       ->  int:
         if torch_device == "mps":
              return
         __lowerCamelCase     :			Union[str, Any]              =	self.scheduler_classes[0]
         __lowerCamelCase     :			Optional[Any]              =	self.get_scheduler_config()
         __lowerCamelCase     :			Optional[int]              =	scheduler_class(**SCREAMING_SNAKE_CASE_			)

         scheduler.set_timesteps(self.num_inference_steps			)

         __lowerCamelCase     :			int              =	torch.manual_seed(0			)

         __lowerCamelCase     :			int              =	self.dummy_model()
         __lowerCamelCase     :			List[Any]              =	self.dummy_sample_deter * scheduler.init_noise_sigma
         __lowerCamelCase     :			Any              =	sample.to(SCREAMING_SNAKE_CASE_			)

         for i, t in enumerate(scheduler.timesteps			):
              __lowerCamelCase     :			Dict              =	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)

              __lowerCamelCase     :			Dict              =	model(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)

              __lowerCamelCase     :			Union[str, Any]              =	scheduler.step(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_			)
              __lowerCamelCase     :			Any              =	output.prev_sample

         __lowerCamelCase     :			Tuple              =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
         __lowerCamelCase     :			Optional[int]              =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

         assert abs(result_sum.item() - 1_3_8_4_9.3_8_7_7			) < 1E-2
         assert abs(result_mean.item() - 1_8.0_3_3_1			) < 5E-3







    def    lowercase_       (							self			)       ->  List[str]:
         for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_			)







    def    lowercase_       (							self			)       ->  List[str]:
         if torch_device == "mps":
              return
         __lowerCamelCase     :			Union[str, Any]              =	self.scheduler_classes[0]
         __lowerCamelCase     :			Optional[Any]              =	self.get_scheduler_config(prediction_type='v_prediction'			)
         __lowerCamelCase     :			str              =	scheduler_class(**SCREAMING_SNAKE_CASE_			)

         scheduler.set_timesteps(self.num_inference_steps			)

         __lowerCamelCase     :			int              =	self.dummy_model()
         __lowerCamelCase     :			int              =	self.dummy_sample_deter * scheduler.init_noise_sigma
         __lowerCamelCase     :			Dict              =	sample.to(SCREAMING_SNAKE_CASE_			)

         __lowerCamelCase     :			int              =	torch.manual_seed(0			)

         for i, t in enumerate(scheduler.timesteps			):
              __lowerCamelCase     :			Any              =	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)

              __lowerCamelCase     :			List[str]              =	model(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)

              __lowerCamelCase     :			str              =	scheduler.step(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_			)
              __lowerCamelCase     :			Tuple              =	output.prev_sample

         __lowerCamelCase     :			int              =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
         __lowerCamelCase     :			int              =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

         assert abs(result_sum.item() - 3_2_8.9_9_7_0			) < 1E-2
         assert abs(result_mean.item() - 0.4_2_8_4			) < 1E-3







    def    lowercase_       (							self			)       ->  Dict:
         if torch_device == "mps":
              return
         __lowerCamelCase     :			Optional[int]              =	self.scheduler_classes[0]
         __lowerCamelCase     :			str              =	self.get_scheduler_config()
         __lowerCamelCase     :			Union[str, Any]              =	scheduler_class(**SCREAMING_SNAKE_CASE_			)

         scheduler.set_timesteps(self.num_inference_steps    ,  device=SCREAMING_SNAKE_CASE_			)
         __lowerCamelCase     :			List[Any]              =	torch.manual_seed(0			)

         __lowerCamelCase     :			Optional[Any]              =	self.dummy_model()
         __lowerCamelCase     :			Dict              =	self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE_			) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
              __lowerCamelCase     :			Any              =	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)

              __lowerCamelCase     :			Optional[Any]              =	model(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)

              __lowerCamelCase     :			Dict              =	scheduler.step(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_			)
              __lowerCamelCase     :			Union[str, Any]              =	output.prev_sample

         __lowerCamelCase     :			int              =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
         __lowerCamelCase     :			Dict              =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

         assert abs(result_sum.item() - 1_3_8_4_9.3_8_1_8			) < 1E-1
         assert abs(result_mean.item() - 1_8.0_3_3_1			) < 1E-3






