





'''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 				__lowercase							:







	'''simple docstring'''

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







				'''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=__a  ,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     _UpperCAmelCase  (self			)				->				Tuple:







				'''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=__a  ,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     _UpperCAmelCase  (self			)				->				int:







				'''simple docstring'''





				__lowercase					    =				self.get_dummy_components()
				__lowercase					    =				self.pipeline_class(**__a			)
				pipe.to(__a			)
				pipe.set_progress_bar_config(disable=__a			)

				__lowercase					    =				self.get_dummy_inputs(__a			)

				__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					    =				pipe.encode_prompt(__a			)

				# 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(__a  ,__a  ,__a			)

				__lowercase					    =				pipe(**__a			)[0]

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

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

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

				__lowercase					    =				self.get_dummy_inputs(__a			)

				__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(**__a			)[0]

				__lowercase					    =				np.abs(to_np(__a			) - to_np(__a			)			).max()
				self.assertLess(__a  ,1E-4			)


	def     _UpperCAmelCase  (self			)				->				Tuple:







				'''simple docstring'''





				__lowercase					    =				self.get_dummy_components()
				__lowercase					    =				self.pipeline_class(**__a			)
				pipe.to(__a			)
				pipe.set_progress_bar_config(disable=__a			)

				__lowercase					    =				self.get_dummy_inputs(__a			)
				__lowercase					    =				pipe(**__a			)[0]

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

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

				__lowercase					    =				self.get_dummy_inputs(__a			)
				__lowercase					    =				pipe_loaded(**__a			)[0]

				__lowercase					    =				np.abs(to_np(__a			) - to_np(__a			)			).max()
				self.assertLess(__a  ,1E-4			)











'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase      :   Union[str, Any]      =       {
    """configuration_instructblip""": [
        """INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InstructBlipConfig""",
        """InstructBlipQFormerConfig""",
        """InstructBlipVisionConfig""",
    ],
    """processing_instructblip""": ["""InstructBlipProcessor"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase      :   Union[str, Any]      =       [
          """INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """InstructBlipQFormerModel""",
          """InstructBlipPreTrainedModel""",
          """InstructBlipForConditionalGeneration""",
          """InstructBlipVisionModel""",
      ]

if TYPE_CHECKING:
      from .configuration_instructblip import (
          INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
          InstructBlipConfig,
          InstructBlipQFormerConfig,
          InstructBlipVisionConfig,
      )
      from .processing_instructblip import InstructBlipProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_instructblip import (
                INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                InstructBlipForConditionalGeneration,
                InstructBlipPreTrainedModel,
                InstructBlipQFormerModel,
                InstructBlipVisionModel,
            )

else:
      import sys

      lowercase      :   List[Any]      =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



