





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 				__lowerCAmelCase       (    unittest.TestCase):







      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




        a__	:					List[Any]										=tempfile.mkdtemp()

        a__	:					List[Any]										=BlipImageProcessor()
        a__	:					int										=BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel"							)

        a__	:					Optional[Any]										=BlipProcessor(lowerCAmelCase__		,      lowerCAmelCase__							)

        processor.save_pretrained(self.tmpdirname							)







      def    _lowercase  (    self		,      **lowerCAmelCase__							)   ->						List[Any]:



        '''simple docstring'''




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







      def    _lowercase  (    self		,      **lowerCAmelCase__							)   ->						List[Any]:



        '''simple docstring'''




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







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        shutil.rmtree(self.tmpdirname							)







      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




        a__	:					Union[str, Any]										=[np.random.randint(2_5_5		,      size=(3, 3_0, 4_0_0)		,      dtype=np.uinta							)]

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

        return image_inputs







      def    _lowercase  (    self							)   ->						Union[str, Any]:



        '''simple docstring'''




        a__	:					List[str]										=BlipProcessor(tokenizer=self.get_tokenizer()		,      image_processor=self.get_image_processor()							)
        processor.save_pretrained(self.tmpdirname							)

        a__	:					List[str]										=self.get_tokenizer(bos_token="(BOS)"		,      eos_token="(EOS)"							)
        a__	:					Union[str, Any]										=self.get_image_processor(do_normalize=lowerCAmelCase__		,      padding_value=1.0							)

        a__	:					Optional[Any]										=BlipProcessor.from_pretrained(
            self.tmpdirname		,      bos_token="(BOS)"		,      eos_token="(EOS)"		,      do_normalize=lowerCAmelCase__		,      padding_value=1.0							)

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

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







      def    _lowercase  (    self							)   ->						Union[str, Any]:



        '''simple docstring'''




        a__	:					str										=self.get_image_processor()
        a__	:					int										=self.get_tokenizer()

        a__	:					Optional[Any]										=BlipProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					Dict										=self.prepare_image_inputs()

        a__	:					List[str]										=image_processor(lowerCAmelCase__		,      return_tensors="np"							)
        a__	:					Union[str, Any]										=processor(images=lowerCAmelCase__		,      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[Any]:



        '''simple docstring'''




        a__	:					int										=self.get_image_processor()
        a__	:					int										=self.get_tokenizer()

        a__	:					Dict										=BlipProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					Dict										="lower newer"

        a__	:					Any										=processor(text=lowerCAmelCase__							)

        a__	:					Optional[int]										=tokenizer(lowerCAmelCase__		,      return_token_type_ids=lowerCAmelCase__							)

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







      def    _lowercase  (    self							)   ->						List[Any]:



        '''simple docstring'''




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

        a__	:					Dict										=BlipProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					List[str]										="lower newer"
        a__	:					List[str]										=self.prepare_image_inputs()

        a__	:					Union[str, Any]										=processor(text=lowerCAmelCase__		,      images=lowerCAmelCase__							)

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

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







      def    _lowercase  (    self							)   ->						int:



        '''simple docstring'''




        a__	:					Optional[int]										=self.get_image_processor()
        a__	:					Tuple										=self.get_tokenizer()

        a__	:					Optional[Any]										=BlipProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					Optional[Any]										=[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        a__	:					List[str]										=processor.batch_decode(lowerCAmelCase__							)
        a__	:					Dict										=tokenizer.batch_decode(lowerCAmelCase__							)

        self.assertListEqual(lowerCAmelCase__		,      lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        a__	:					int										=self.get_image_processor()
        a__	:					str										=self.get_tokenizer()

        a__	:					Union[str, Any]										=BlipProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					Optional[Any]										="lower newer"
        a__	:					int										=self.prepare_image_inputs()

        a__	:					Optional[int]										=processor(text=lowerCAmelCase__		,      images=lowerCAmelCase__							)

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







"""simple docstring"""






import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np





class  _A						:

      def 				A__       (						self						):







            """simple docstring"""



            torch.manual_seed(0						)
            lowercase														=	TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""						)

            torch.manual_seed(0						)
            lowercase														=	AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""						)

            torch.manual_seed(0						)
            lowercase														=	UNetaDConditionModel(
                sample_size=32		,       layers_per_block=1		,       block_out_channels=[32, 64]		,       down_block_types=[
                    """ResnetDownsampleBlock2D""",
                    """SimpleCrossAttnDownBlock2D""",
                ]		,       mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""		,       up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]		,       in_channels=3		,       out_channels=6		,       cross_attention_dim=32		,       encoder_hid_dim=32		,       attention_head_dim=8		,       addition_embed_type="""text"""		,       addition_embed_type_num_heads=2		,       cross_attention_norm="""group_norm"""		,       resnet_time_scale_shift="""scale_shift"""		,       act_fn="""gelu"""		,       )
            unet.set_attn_processor(AttnAddedKVProcessor()						)  # For reproducibility tests

            torch.manual_seed(0						)
            lowercase														=	DDPMScheduler(
                num_train_timesteps=1000		,       beta_schedule="""squaredcos_cap_v2"""		,       beta_start=0.0_0_0_1		,       beta_end=0.0_2		,       thresholding=__lowerCAmelCase		,       dynamic_thresholding_ratio=0.9_5		,       sample_max_value=1.0		,       prediction_type="""epsilon"""		,       variance_type="""learned_range"""		,       )

            torch.manual_seed(0						)
            lowercase														=	IFWatermarker()

            return {
                "text_encoder": text_encoder,
                "tokenizer": tokenizer,
                "unet": unet,
                "scheduler": scheduler,
                "watermarker": watermarker,
                "safety_checker": None,
                "feature_extractor": None,
            }

      def 				A__       (						self						):







            """simple docstring"""



            torch.manual_seed(0						)
            lowercase														=	TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""						)

            torch.manual_seed(0						)
            lowercase														=	AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""						)

            torch.manual_seed(0						)
            lowercase														=	UNetaDConditionModel(
                sample_size=32		,       layers_per_block=[1, 2]		,       block_out_channels=[32, 64]		,       down_block_types=[
                    """ResnetDownsampleBlock2D""",
                    """SimpleCrossAttnDownBlock2D""",
                ]		,       mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""		,       up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]		,       in_channels=6		,       out_channels=6		,       cross_attention_dim=32		,       encoder_hid_dim=32		,       attention_head_dim=8		,       addition_embed_type="""text"""		,       addition_embed_type_num_heads=2		,       cross_attention_norm="""group_norm"""		,       resnet_time_scale_shift="""scale_shift"""		,       act_fn="""gelu"""		,       class_embed_type="""timestep"""		,       mid_block_scale_factor=1.4_1_4		,       time_embedding_act_fn="""gelu"""		,       time_embedding_dim=32		,       )
            unet.set_attn_processor(AttnAddedKVProcessor()						)  # For reproducibility tests

            torch.manual_seed(0						)
            lowercase														=	DDPMScheduler(
                num_train_timesteps=1000		,       beta_schedule="""squaredcos_cap_v2"""		,       beta_start=0.0_0_0_1		,       beta_end=0.0_2		,       thresholding=__lowerCAmelCase		,       dynamic_thresholding_ratio=0.9_5		,       sample_max_value=1.0		,       prediction_type="""epsilon"""		,       variance_type="""learned_range"""		,       )

            torch.manual_seed(0						)
            lowercase														=	DDPMScheduler(
                num_train_timesteps=1000		,       beta_schedule="""squaredcos_cap_v2"""		,       beta_start=0.0_0_0_1		,       beta_end=0.0_2		,       )

            torch.manual_seed(0						)
            lowercase														=	IFWatermarker()

            return {
                "text_encoder": text_encoder,
                "tokenizer": tokenizer,
                "unet": unet,
                "scheduler": scheduler,
                "image_noising_scheduler": image_noising_scheduler,
                "watermarker": watermarker,
                "safety_checker": None,
                "feature_extractor": None,
            }

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.get_dummy_components()
            lowercase														=	self.pipeline_class(**__lowerCAmelCase						)
            pipe.to(__lowerCAmelCase						)
            pipe.set_progress_bar_config(disable=__lowerCAmelCase						)

            lowercase														=	self.get_dummy_inputs(__lowerCAmelCase						)

            lowercase														=	inputs["""prompt"""]
            lowercase														=	inputs["""generator"""]
            lowercase														=	inputs["""num_inference_steps"""]
            lowercase														=	inputs["""output_type"""]

            if "image" in inputs:
                  lowercase														=	inputs["""image"""]
            else:
                  lowercase														=	None

            if "mask_image" in inputs:
                  lowercase														=	inputs["""mask_image"""]
            else:
                  lowercase														=	None

            if "original_image" in inputs:
                  lowercase														=	inputs["""original_image"""]
            else:
                  lowercase														=	None

            lowercase							,							lowercase														=	pipe.encode_prompt(__lowerCAmelCase						)

            # inputs with prompt converted to embeddings
            lowercase														=	{
                """prompt_embeds""": prompt_embeds,
                """negative_prompt_embeds""": negative_prompt_embeds,
                """generator""": generator,
                """num_inference_steps""": num_inference_steps,
                """output_type""": output_type,
            }

            if image is not None:
                  lowercase														=	image

            if mask_image is not None:
                  lowercase														=	mask_image

            if original_image is not None:
                  lowercase														=	original_image

            # set all optional components to None
            for optional_component in pipe._optional_components:
                  setattr(__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						)

            lowercase														=	pipe(**__lowerCAmelCase						)[0]

            with tempfile.TemporaryDirectory() as tmpdir:
                  pipe.save_pretrained(__lowerCAmelCase						)
                  lowercase														=	self.pipeline_class.from_pretrained(__lowerCAmelCase						)
                  pipe_loaded.to(__lowerCAmelCase						)
                  pipe_loaded.set_progress_bar_config(disable=__lowerCAmelCase						)

            pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()						)  # For reproducibility tests

            for optional_component in pipe._optional_components:
                  self.assertTrue(
                      getattr(__lowerCAmelCase		,       __lowerCAmelCase						) is None		,       f'`{optional_component}` did not stay set to None after loading.'		,       )

            lowercase														=	self.get_dummy_inputs(__lowerCAmelCase						)

            lowercase														=	inputs["""generator"""]
            lowercase														=	inputs["""num_inference_steps"""]
            lowercase														=	inputs["""output_type"""]

            # inputs with prompt converted to embeddings
            lowercase														=	{
                """prompt_embeds""": prompt_embeds,
                """negative_prompt_embeds""": negative_prompt_embeds,
                """generator""": generator,
                """num_inference_steps""": num_inference_steps,
                """output_type""": output_type,
            }

            if image is not None:
                  lowercase														=	image

            if mask_image is not None:
                  lowercase														=	mask_image

            if original_image is not None:
                  lowercase														=	original_image

            lowercase														=	pipe_loaded(**__lowerCAmelCase						)[0]

            lowercase														=	np.abs(to_np(__lowerCAmelCase						) - to_np(__lowerCAmelCase						)						).max()
            self.assertLess(__lowerCAmelCase		,       1E-4						)







      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.get_dummy_components()
            lowercase														=	self.pipeline_class(**__lowerCAmelCase						)
            pipe.to(__lowerCAmelCase						)
            pipe.set_progress_bar_config(disable=__lowerCAmelCase						)

            lowercase														=	self.get_dummy_inputs(__lowerCAmelCase						)
            lowercase														=	pipe(**__lowerCAmelCase						)[0]

            with tempfile.TemporaryDirectory() as tmpdir:
                  pipe.save_pretrained(__lowerCAmelCase						)
                  lowercase														=	self.pipeline_class.from_pretrained(__lowerCAmelCase						)
                  pipe_loaded.to(__lowerCAmelCase						)
                  pipe_loaded.set_progress_bar_config(disable=__lowerCAmelCase						)

            pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()						)  # For reproducibility tests

            lowercase														=	self.get_dummy_inputs(__lowerCAmelCase						)
            lowercase														=	pipe_loaded(**__lowerCAmelCase						)[0]

            lowercase														=	np.abs(to_np(__lowerCAmelCase						) - to_np(__lowerCAmelCase						)						).max()
            self.assertLess(__lowerCAmelCase		,       1E-4						)



