






'''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    _SCREAMING_SNAKE_CASE							:


			'''simple docstring'''





			def 				A      (   self							:						Optional[Any]			)							->    List[Any]:

									'''simple docstring'''







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

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

									torch.manual_seed(0			)
									UpperCamelCase__         = UNetaDConditionModel(
									    sample_size=3_2 ,      layers_per_block=1 ,      block_out_channels=[3_2, 6_4] ,      down_block_types=[
									        """ResnetDownsampleBlock2D""",
									        """SimpleCrossAttnDownBlock2D""",
									    ] ,      mid_block_type="""UNetMidBlock2DSimpleCrossAttn""" ,      up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""] ,      in_channels=3 ,      out_channels=6 ,      cross_attention_dim=3_2 ,      encoder_hid_dim=3_2 ,      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			)
									UpperCamelCase__         = DDPMScheduler(
									    num_train_timesteps=1_0_0_0 ,      beta_schedule="""squaredcos_cap_v2""" ,      beta_start=0.0_0_0_1 ,      beta_end=0.0_2 ,      thresholding=lowercase ,      dynamic_thresholding_ratio=0.9_5 ,      sample_max_value=1.0 ,      prediction_type="""epsilon""" ,      variance_type="""learned_range""" ,      )

									torch.manual_seed(0			)
									UpperCamelCase__         = IFWatermarker()

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






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

									'''simple docstring'''







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

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

									torch.manual_seed(0			)
									UpperCamelCase__         = UNetaDConditionModel(
									    sample_size=3_2 ,      layers_per_block=[1, 2] ,      block_out_channels=[3_2, 6_4] ,      down_block_types=[
									        """ResnetDownsampleBlock2D""",
									        """SimpleCrossAttnDownBlock2D""",
									    ] ,      mid_block_type="""UNetMidBlock2DSimpleCrossAttn""" ,      up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""] ,      in_channels=6 ,      out_channels=6 ,      cross_attention_dim=3_2 ,      encoder_hid_dim=3_2 ,      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=3_2 ,      )
									unet.set_attn_processor(AttnAddedKVProcessor()			)  # For reproducibility tests

									torch.manual_seed(0			)
									UpperCamelCase__         = DDPMScheduler(
									    num_train_timesteps=1_0_0_0 ,      beta_schedule="""squaredcos_cap_v2""" ,      beta_start=0.0_0_0_1 ,      beta_end=0.0_2 ,      thresholding=lowercase ,      dynamic_thresholding_ratio=0.9_5 ,      sample_max_value=1.0 ,      prediction_type="""epsilon""" ,      variance_type="""learned_range""" ,      )

									torch.manual_seed(0			)
									UpperCamelCase__         = DDPMScheduler(
									    num_train_timesteps=1_0_0_0 ,      beta_schedule="""squaredcos_cap_v2""" ,      beta_start=0.0_0_0_1 ,      beta_end=0.0_2 ,      )

									torch.manual_seed(0			)
									UpperCamelCase__         = 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							:						Any			)							->    Optional[Any]:

									'''simple docstring'''







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

									UpperCamelCase__         = self.get_dummy_inputs(lowercase			)

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

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

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

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

									UpperCamelCase__    ,   UpperCamelCase__         = pipe.encode_prompt(lowercase			)

									# inputs with prompt converted to embeddings
									UpperCamelCase__         = {
									    """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:
															UpperCamelCase__         = image

									if mask_image is not None:
															UpperCamelCase__         = mask_image

									if original_image is not None:
															UpperCamelCase__         = original_image

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

									UpperCamelCase__         = pipe(**lowercase			)[0]

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

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

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

									UpperCamelCase__         = self.get_dummy_inputs(lowercase			)

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

									# inputs with prompt converted to embeddings
									UpperCamelCase__         = {
									    """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:
															UpperCamelCase__         = image

									if mask_image is not None:
															UpperCamelCase__         = mask_image

									if original_image is not None:
															UpperCamelCase__         = original_image

									UpperCamelCase__         = pipe_loaded(**lowercase			)[0]

									UpperCamelCase__         = np.abs(to_np(lowercase			) - to_np(lowercase			)			).max()
									self.assertLess(lowercase ,      1e-4			)







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

									'''simple docstring'''







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

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

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

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

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

									UpperCamelCase__         = np.abs(to_np(lowercase			) - to_np(lowercase			)			).max()
									self.assertLess(lowercase ,      1e-4			)








'''simple docstring'''


from typing import TYPE_CHECKING

from ..utils import _LazyModule


lowerCamelCase_				: List[Any]    	=							{
    '''config''': [
        '''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
        '''OnnxConfig''',
        '''OnnxConfigWithPast''',
        '''OnnxSeq2SeqConfigWithPast''',
        '''PatchingSpec''',
    ],
    '''convert''': ['''export''', '''validate_model_outputs'''],
    '''features''': ['''FeaturesManager'''],
    '''utils''': ['''ParameterFormat''', '''compute_serialized_parameters_size'''],
}


if TYPE_CHECKING:
					from .config import (
					    EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
					    OnnxConfig,
					    OnnxConfigWithPast,
					    OnnxSeqaSeqConfigWithPast,
					    PatchingSpec,
					)
					from .convert import export, validate_model_outputs
					from .features import FeaturesManager
					from .utils import ParameterFormat, compute_serialized_parameters_size

else:
					import sys

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