"""simple docstring"""



import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					Optional[int]							=					(EulerDiscreteScheduler,)
    UpperCamelCase_	:					str							=					1_0
    def  lowercase					(		self							:     Tuple	,      **A_							:     Tuple    )    ->       Union[str, Any]:
         __snake_case				=							{
             '''num_train_timesteps''': 1_100,
             '''beta_start''': 0.00_01,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
         }

         config.update(**A_    )
         return config
    def  lowercase					(		self							:     List[Any]    )    ->       Dict:
         for timesteps in [10, 50, 100, 1_000]:
              self.check_over_configs(num_train_timesteps=A_    )
    def  lowercase					(		self							:     Union[str, Any]    )    ->       Optional[Any]:
         for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]	,      [0.00_02, 0.0_02, 0.02]    ):
              self.check_over_configs(beta_start=A_	,      beta_end=A_    )
    def  lowercase					(		self							:     List[Any]    )    ->       Tuple:
         for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=A_    )
    def  lowercase					(		self							:     int    )    ->       Tuple:
         for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=A_    )
    def  lowercase					(		self							:     List[str]    )    ->       Any:
         __snake_case				=							self.scheduler_classes[0]
         __snake_case				=							self.get_scheduler_config()
         __snake_case				=							scheduler_class(**A_    )

         scheduler.set_timesteps(self.num_inference_steps    )

         __snake_case				=							torch.manual_seed(0    )

         __snake_case				=							self.dummy_model()
         __snake_case				=							self.dummy_sample_deter * scheduler.init_noise_sigma
         __snake_case				=							sample.to(A_    )

         for i, t in enumerate(scheduler.timesteps    ):
              __snake_case				=							scheduler.scale_model_input(A_	,      A_    )

              __snake_case				=							model(A_	,      A_    )

              __snake_case				=							scheduler.step(A_	,      A_	,      A_	,      generator=A_    )
              __snake_case				=							output.prev_sample

         __snake_case				=							torch.sum(torch.abs(A_    )    )
         __snake_case				=							torch.mean(torch.abs(A_    )    )

         assert abs(result_sum.item() - 10.08_07    ) < 1E-2
         assert abs(result_mean.item() - 0.01_31    ) < 1E-3
    def  lowercase					(		self							:     List[str]    )    ->       Dict:
         __snake_case				=							self.scheduler_classes[0]
         __snake_case				=							self.get_scheduler_config(prediction_type='''v_prediction'''    )
         __snake_case				=							scheduler_class(**A_    )

         scheduler.set_timesteps(self.num_inference_steps    )

         __snake_case				=							torch.manual_seed(0    )

         __snake_case				=							self.dummy_model()
         __snake_case				=							self.dummy_sample_deter * scheduler.init_noise_sigma
         __snake_case				=							sample.to(A_    )

         for i, t in enumerate(scheduler.timesteps    ):
              __snake_case				=							scheduler.scale_model_input(A_	,      A_    )

              __snake_case				=							model(A_	,      A_    )

              __snake_case				=							scheduler.step(A_	,      A_	,      A_	,      generator=A_    )
              __snake_case				=							output.prev_sample

         __snake_case				=							torch.sum(torch.abs(A_    )    )
         __snake_case				=							torch.mean(torch.abs(A_    )    )

         assert abs(result_sum.item() - 0.00_02    ) < 1E-2
         assert abs(result_mean.item() - 2.2_6_7_6E-0_6    ) < 1E-3
    def  lowercase					(		self							:     str    )    ->       int:
         __snake_case				=							self.scheduler_classes[0]
         __snake_case				=							self.get_scheduler_config()
         __snake_case				=							scheduler_class(**A_    )

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

         __snake_case				=							torch.manual_seed(0    )

         __snake_case				=							self.dummy_model()
         __snake_case				=							self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         __snake_case				=							sample.to(A_    )

         for t in scheduler.timesteps:
              __snake_case				=							scheduler.scale_model_input(A_	,      A_    )

              __snake_case				=							model(A_	,      A_    )

              __snake_case				=							scheduler.step(A_	,      A_	,      A_	,      generator=A_    )
              __snake_case				=							output.prev_sample

         __snake_case				=							torch.sum(torch.abs(A_    )    )
         __snake_case				=							torch.mean(torch.abs(A_    )    )

         assert abs(result_sum.item() - 10.08_07    ) < 1E-2
         assert abs(result_mean.item() - 0.01_31    ) < 1E-3







    def  lowercase					(		self							:     Optional[int]    )    ->       Any:
         __snake_case				=							self.scheduler_classes[0]
         __snake_case				=							self.get_scheduler_config()
         __snake_case				=							scheduler_class(**A_	,      use_karras_sigmas=A_    )

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

         __snake_case				=							torch.manual_seed(0    )

         __snake_case				=							self.dummy_model()
         __snake_case				=							self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         __snake_case				=							sample.to(A_    )

         for t in scheduler.timesteps:
              __snake_case				=							scheduler.scale_model_input(A_	,      A_    )

              __snake_case				=							model(A_	,      A_    )

              __snake_case				=							scheduler.step(A_	,      A_	,      A_	,      generator=A_    )
              __snake_case				=							output.prev_sample

         __snake_case				=							torch.sum(torch.abs(A_    )    )
         __snake_case				=							torch.mean(torch.abs(A_    )    )

         assert abs(result_sum.item() - 1_24.52_29_94_99_51_17_19    ) < 1E-2
         assert abs(result_mean.item() - 0.1_62_13_93_26_33_39_99_63    ) < 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,
           BertTokenizerFast,
           BlipImageProcessor,
           GPTaTokenizer,
           InstructBlipProcessor,
           PreTrainedTokenizerFast,
       )



@require_vision
class      _A	(						unittest.TestCase					):




    """simple docstring"""




    def  lowercase					(		self							:     str    )    ->       str:
         __snake_case				=							tempfile.mkdtemp()

         __snake_case				=							BlipImageProcessor()
         __snake_case				=							GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''    )
         __snake_case				=							BertTokenizerFast.from_pretrained('''hf-internal-testing/tiny-random-bert'''    )

         __snake_case				=							InstructBlipProcessor(A_	,      A_	,      A_    )

         processor.save_pretrained(self.tmpdirname    )
    def  lowercase					(		self							:     Tuple	,      **A_							:     str    )    ->       List[Any]:
         return AutoProcessor.from_pretrained(self.tmpdirname	,      **A_    ).tokenizer
    def  lowercase					(		self							:     Union[str, Any]	,      **A_							:     Tuple    )    ->       Union[str, Any]:
         return AutoProcessor.from_pretrained(self.tmpdirname	,      **A_    ).image_processor
    def  lowercase					(		self							:     Union[str, Any]	,      **A_							:     Tuple    )    ->       Dict:
         return AutoProcessor.from_pretrained(self.tmpdirname	,      **A_    ).qformer_tokenizer
    def  lowercase					(		self							:     Optional[int]    )    ->       Optional[Any]:
         shutil.rmtree(self.tmpdirname    )
    def  lowercase					(		self							:     Optional[int]    )    ->       Tuple:
         __snake_case				=							[np.random.randint(255	,      size=(3, 30, 400)	,      dtype=np.uinta    )]

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

         return image_inputs
    def  lowercase					(		self							:     int    )    ->       Dict:
         __snake_case				=							InstructBlipProcessor(
             tokenizer=self.get_tokenizer()	,      image_processor=self.get_image_processor()	,      qformer_tokenizer=self.get_qformer_tokenizer()	,      )
         processor.save_pretrained(self.tmpdirname    )

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

         __snake_case				=							InstructBlipProcessor.from_pretrained(
             self.tmpdirname	,      bos_token='''(BOS)'''	,      eos_token='''(EOS)'''	,      do_normalize=A_	,      padding_value=1.0    )

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

         self.assertEqual(processor.image_processor.to_json_string()	,      image_processor_add_kwargs.to_json_string()    )
         self.assertIsInstance(processor.image_processor	,      A_    )
         self.assertIsInstance(processor.qformer_tokenizer	,      A_    )
    def  lowercase					(		self							:     int    )    ->       str:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							self.get_qformer_tokenizer()

         __snake_case				=							InstructBlipProcessor(
             tokenizer=A_	,      image_processor=A_	,      qformer_tokenizer=A_    )

         __snake_case				=							self.prepare_image_inputs()

         __snake_case				=							image_processor(A_	,      return_tensors='''np'''    )
         __snake_case				=							processor(images=A_	,      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							:     List[str]    )    ->       Optional[int]:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							self.get_qformer_tokenizer()

         __snake_case				=							InstructBlipProcessor(
             tokenizer=A_	,      image_processor=A_	,      qformer_tokenizer=A_    )

         __snake_case				=							'''lower newer'''

         __snake_case				=							processor(text=A_    )

         __snake_case				=							tokenizer(A_	,      return_token_type_ids=A_    )
         __snake_case				=							qformer_tokenizer(A_	,      return_token_type_ids=A_    )

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

         for key in encoded_tokens_qformer.keys():
              self.assertListEqual(encoded_tokens_qformer[key]	,      encoded_processor['''qformer_''' + key]    )
    def  lowercase					(		self							:     List[str]    )    ->       int:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							self.get_qformer_tokenizer()

         __snake_case				=							InstructBlipProcessor(
             tokenizer=A_	,      image_processor=A_	,      qformer_tokenizer=A_    )

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

         __snake_case				=							processor(text=A_	,      images=A_    )

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

         # test if it raises when no input is passed
         with pytest.raises(A_    ):
              processor()
    def  lowercase					(		self							:     str    )    ->       Union[str, Any]:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							self.get_qformer_tokenizer()

         __snake_case				=							InstructBlipProcessor(
             tokenizer=A_	,      image_processor=A_	,      qformer_tokenizer=A_    )

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

         __snake_case				=							processor.batch_decode(A_    )
         __snake_case				=							tokenizer.batch_decode(A_    )

         self.assertListEqual(A_	,      A_    )







    def  lowercase					(		self							:     int    )    ->       List[str]:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							self.get_qformer_tokenizer()

         __snake_case				=							InstructBlipProcessor(
             tokenizer=A_	,      image_processor=A_	,      qformer_tokenizer=A_    )

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

         __snake_case				=							processor(text=A_	,      images=A_    )

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