





'''simple docstring'''




import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class   A_	(  lowerCAmelCase_							,						unittest.TestCase			):
 _lowerCamelCase  : Tuple           =							DDIMPipeline
 _lowerCamelCase  : int           =							UNCONDITIONAL_IMAGE_GENERATION_PARAMS
 _lowerCamelCase  : Optional[Any]           =							PipelineTesterMixin.required_optional_params - {
     """num_images_per_prompt""",
     """latents""",
     """callback""",
     """callback_steps""",
 }
 _lowerCamelCase  : Dict           =							UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
 _lowerCamelCase  : Any           =							False




 def      lowercase	(					self       :    List[Any]    ):
      torch.manual_seed(0    )
      _UpperCAmelCase     							=   UNetaDModel(
          block_out_channels=(3_2, 6_4)	,			layers_per_block=2	,			sample_size=3_2	,			in_channels=3	,			out_channels=3	,			down_block_types=("DownBlock2D", "AttnDownBlock2D")	,			up_block_types=("AttnUpBlock2D", "UpBlock2D")	,			)
      _UpperCAmelCase     							=   DDIMScheduler()
      _UpperCAmelCase     							=   {"unet": unet, "scheduler": scheduler}
      return components




 def      lowercase	(					self       :    List[str]	,			snake_case_       :    int	,			snake_case_       :    Tuple=0    ):
      if str(snake_case_    ).startswith("mps"    ):
           _UpperCAmelCase     							=   torch.manual_seed(snake_case_    )
      else:
           _UpperCAmelCase     							=   torch.Generator(device=snake_case_    ).manual_seed(snake_case_    )
      _UpperCAmelCase     							=   {
          "batch_size": 1,
          "generator": generator,
          "num_inference_steps": 2,
          "output_type": "numpy",
      }
      return inputs




 def      lowercase	(					self       :    Tuple    ):
      _UpperCAmelCase     							=   "cpu"

      _UpperCAmelCase     							=   self.get_dummy_components()
      _UpperCAmelCase     							=   self.pipeline_class(**snake_case_    )
      pipe.to(snake_case_    )
      pipe.set_progress_bar_config(disable=snake_case_    )

      _UpperCAmelCase     							=   self.get_dummy_inputs(snake_case_    )
      _UpperCAmelCase     							=   pipe(**snake_case_    ).images
      _UpperCAmelCase     							=   image[0, -3:, -3:, -1]

      self.assertEqual(image.shape	,			(1, 3_2, 3_2, 3)    )
      _UpperCAmelCase     							=   np.array(
          [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]    )
      _UpperCAmelCase     							=   np.abs(image_slice.flatten() - expected_slice    ).max()
      self.assertLessEqual(snake_case_	,			1e-3    )




 def      lowercase	(					self       :    List[Any]    ):
      super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3    )




 def      lowercase	(					self       :    str    ):
      super().test_save_load_local(expected_max_difference=3e-3    )




 def      lowercase	(					self       :    Dict    ):
      super().test_save_load_optional_components(expected_max_difference=3e-3    )




 def      lowercase	(					self       :    int    ):
      super().test_inference_batch_single_identical(expected_max_diff=3e-3    )







@slow
@require_torch_gpu
class   A_	(  unittest.TestCase			):




 def      lowercase	(					self       :    Optional[int]    ):
      _UpperCAmelCase     							=   "google/ddpm-cifar10-32"

      _UpperCAmelCase     							=   UNetaDModel.from_pretrained(snake_case_    )
      _UpperCAmelCase     							=   DDIMScheduler()

      _UpperCAmelCase     							=   DDIMPipeline(unet=snake_case_	,			scheduler=snake_case_    )
      ddim.to(snake_case_    )
      ddim.set_progress_bar_config(disable=snake_case_    )

      _UpperCAmelCase     							=   torch.manual_seed(0    )
      _UpperCAmelCase     							=   ddim(generator=snake_case_	,			eta=0.0	,			output_type="numpy"    ).images

      _UpperCAmelCase     							=   image[0, -3:, -3:, -1]

      assert image.shape == (1, 3_2, 3_2, 3)
      _UpperCAmelCase     							=   np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3]    )

      assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2




 def      lowercase	(					self       :    List[str]    ):
      _UpperCAmelCase     							=   "google/ddpm-ema-bedroom-256"

      _UpperCAmelCase     							=   UNetaDModel.from_pretrained(snake_case_    )
      _UpperCAmelCase     							=   DDIMScheduler.from_pretrained(snake_case_    )

      _UpperCAmelCase     							=   DDIMPipeline(unet=snake_case_	,			scheduler=snake_case_    )
      ddpm.to(snake_case_    )
      ddpm.set_progress_bar_config(disable=snake_case_    )

      _UpperCAmelCase     							=   torch.manual_seed(0    )
      _UpperCAmelCase     							=   ddpm(generator=snake_case_	,			output_type="numpy"    ).images

      _UpperCAmelCase     							=   image[0, -3:, -3:, -1]

      assert image.shape == (1, 2_5_6, 2_5_6, 3)
      _UpperCAmelCase     							=   np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9]    )

      assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2











'''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   A_	(  unittest.TestCase			):




 def      lowercase	(					self       :    int    ):
      _UpperCAmelCase     							=   tempfile.mkdtemp()

      _UpperCAmelCase     							=   BlipImageProcessor()
      _UpperCAmelCase     							=   BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel"    )

      _UpperCAmelCase     							=   BlipProcessor(snake_case_	,			snake_case_    )

      processor.save_pretrained(self.tmpdirname    )




 def      lowercase	(					self       :    Tuple	,			**snake_case_       :    int    ):
      return AutoProcessor.from_pretrained(self.tmpdirname	,			**snake_case_    ).tokenizer




 def      lowercase	(					self       :    Dict	,			**snake_case_       :    Any    ):
      return AutoProcessor.from_pretrained(self.tmpdirname	,			**snake_case_    ).image_processor




 def      lowercase	(					self       :    int    ):
      shutil.rmtree(self.tmpdirname    )




 def      lowercase	(					self       :    Optional[Any]    ):
      _UpperCAmelCase     							=   [np.random.randint(2_5_5	,			size=(3, 3_0, 4_0_0)	,			dtype=np.uinta    )]

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

      return image_inputs




 def      lowercase	(					self       :    int    ):
      _UpperCAmelCase     							=   BlipProcessor(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=snake_case_	,			padding_value=1.0    )

      _UpperCAmelCase     							=   BlipProcessor.from_pretrained(
          self.tmpdirname	,			bos_token="(BOS)"	,			eos_token="(EOS)"	,			do_normalize=snake_case_	,			padding_value=1.0    )

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

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




 def      lowercase	(					self       :    Any    ):
      _UpperCAmelCase     							=   self.get_image_processor()
      _UpperCAmelCase     							=   self.get_tokenizer()

      _UpperCAmelCase     							=   BlipProcessor(tokenizer=snake_case_	,			image_processor=snake_case_    )

      _UpperCAmelCase     							=   self.prepare_image_inputs()

      _UpperCAmelCase     							=   image_processor(snake_case_	,			return_tensors="np"    )
      _UpperCAmelCase     							=   processor(images=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       :    Optional[int]    ):
      _UpperCAmelCase     							=   self.get_image_processor()
      _UpperCAmelCase     							=   self.get_tokenizer()

      _UpperCAmelCase     							=   BlipProcessor(tokenizer=snake_case_	,			image_processor=snake_case_    )

      _UpperCAmelCase     							=   "lower newer"

      _UpperCAmelCase     							=   processor(text=snake_case_    )

      _UpperCAmelCase     							=   tokenizer(snake_case_	,			return_token_type_ids=snake_case_    )

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




 def      lowercase	(					self       :    Optional[Any]    ):
      _UpperCAmelCase     							=   self.get_image_processor()
      _UpperCAmelCase     							=   self.get_tokenizer()

      _UpperCAmelCase     							=   BlipProcessor(tokenizer=snake_case_	,			image_processor=snake_case_    )

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

      _UpperCAmelCase     							=   processor(text=snake_case_	,			images=snake_case_    )

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

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




 def      lowercase	(					self       :    Union[str, Any]    ):
      _UpperCAmelCase     							=   self.get_image_processor()
      _UpperCAmelCase     							=   self.get_tokenizer()

      _UpperCAmelCase     							=   BlipProcessor(tokenizer=snake_case_	,			image_processor=snake_case_    )

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

      _UpperCAmelCase     							=   processor.batch_decode(snake_case_    )
      _UpperCAmelCase     							=   tokenizer.batch_decode(snake_case_    )

      self.assertListEqual(snake_case_	,			snake_case_    )




 def      lowercase	(					self       :    str    ):
      _UpperCAmelCase     							=   self.get_image_processor()
      _UpperCAmelCase     							=   self.get_tokenizer()

      _UpperCAmelCase     							=   BlipProcessor(tokenizer=snake_case_	,			image_processor=snake_case_    )

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

      _UpperCAmelCase     							=   processor(text=snake_case_	,			images=snake_case_    )

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




