



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 		snake_case    :



   '''simple docstring'''






   def   UpperCamelCase_	(       self				:      Optional[int])					->					Optional[Any]:

    """simple docstring"""







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

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

    torch.manual_seed(0)
    _snake_case     :	List[str]											=       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)
    _snake_case     :	Union[str, Any]											=       DDPMScheduler(
        num_train_timesteps=1000					,		beta_schedule="""squaredcos_cap_v2"""					,		beta_start=0.0_001					,		beta_end=0.02					,		thresholding=__UpperCAmelCase					,		dynamic_thresholding_ratio=0.95					,		sample_max_value=1.0					,		prediction_type="""epsilon"""					,		variance_type="""learned_range"""					,		)

    torch.manual_seed(0)
    _snake_case     :	Optional[Any]											=       IFWatermarker()

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

   def   UpperCamelCase_	(       self				:      List[str])					->					Tuple:

    """simple docstring"""







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

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

    torch.manual_seed(0)
    _snake_case     :	int											=       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=1.414					,		time_embedding_act_fn="""gelu"""					,		time_embedding_dim=32					,		)
    unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

    torch.manual_seed(0)
    _snake_case     :	List[str]											=       DDPMScheduler(
        num_train_timesteps=1000					,		beta_schedule="""squaredcos_cap_v2"""					,		beta_start=0.0_001					,		beta_end=0.02					,		thresholding=__UpperCAmelCase					,		dynamic_thresholding_ratio=0.95					,		sample_max_value=1.0					,		prediction_type="""epsilon"""					,		variance_type="""learned_range"""					,		)

    torch.manual_seed(0)
    _snake_case     :	Any											=       DDPMScheduler(
        num_train_timesteps=1000					,		beta_schedule="""squaredcos_cap_v2"""					,		beta_start=0.0_001					,		beta_end=0.02					,		)

    torch.manual_seed(0)
    _snake_case     :	Optional[int]											=       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				:      str)					->					Any:

    """simple docstring"""







    _snake_case     :	str											=       self.get_dummy_components()
    _snake_case     :	Dict											=       self.pipeline_class(**__UpperCAmelCase)
    pipe.to(__UpperCAmelCase)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase)

    _snake_case     :	Any											=       self.get_dummy_inputs(__UpperCAmelCase)

    _snake_case     :	str											=       inputs["prompt"]
    _snake_case     :	Optional[int]											=       inputs["generator"]
    _snake_case     :	List[Any]											=       inputs["num_inference_steps"]
    _snake_case     :	Tuple											=       inputs["output_type"]

    if "image" in inputs:
     _snake_case     :	List[str]											=       inputs["image"]
    else:
     _snake_case     :	Optional[Any]											=       None

    if "mask_image" in inputs:
     _snake_case     :	int											=       inputs["mask_image"]
    else:
     _snake_case     :	Union[str, Any]											=       None

    if "original_image" in inputs:
     _snake_case     :	Optional[int]											=       inputs["original_image"]
    else:
     _snake_case     :	int											=       None

    _snake_case     :	List[Any]											=       pipe.encode_prompt(__UpperCAmelCase)

    # inputs with prompt converted to embeddings
    _snake_case     :	Union[str, Any]											=       {
        "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:
     _snake_case     :	Union[str, Any]											=       image

    if mask_image is not None:
     _snake_case     :	int											=       mask_image

    if original_image is not None:
     _snake_case     :	int											=       original_image

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

    _snake_case     :	Tuple											=       pipe(**__UpperCAmelCase)[0]

    with tempfile.TemporaryDirectory() as tmpdir:
     pipe.save_pretrained(__UpperCAmelCase)
     _snake_case     :	Tuple											=       self.pipeline_class.from_pretrained(__UpperCAmelCase)
     pipe_loaded.to(__UpperCAmelCase)
     pipe_loaded.set_progress_bar_config(disable=__UpperCAmelCase)

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

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

    _snake_case     :	Union[str, Any]											=       self.get_dummy_inputs(__UpperCAmelCase)

    _snake_case     :	Optional[Any]											=       inputs["generator"]
    _snake_case     :	Tuple											=       inputs["num_inference_steps"]
    _snake_case     :	Optional[int]											=       inputs["output_type"]

    # inputs with prompt converted to embeddings
    _snake_case     :	List[str]											=       {
        "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:
     _snake_case     :	List[Any]											=       image

    if mask_image is not None:
     _snake_case     :	Optional[Any]											=       mask_image

    if original_image is not None:
     _snake_case     :	Optional[int]											=       original_image

    _snake_case     :	Optional[int]											=       pipe_loaded(**__UpperCAmelCase)[0]

    _snake_case     :	Optional[Any]											=       np.abs(to_np(__UpperCAmelCase) - to_np(__UpperCAmelCase)).max()
    self.assertLess(__UpperCAmelCase					,		1E-4)


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

    """simple docstring"""







    _snake_case     :	Optional[int]											=       self.get_dummy_components()
    _snake_case     :	Tuple											=       self.pipeline_class(**__UpperCAmelCase)
    pipe.to(__UpperCAmelCase)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase)

    _snake_case     :	List[str]											=       self.get_dummy_inputs(__UpperCAmelCase)
    _snake_case     :	Dict											=       pipe(**__UpperCAmelCase)[0]

    with tempfile.TemporaryDirectory() as tmpdir:
     pipe.save_pretrained(__UpperCAmelCase)
     _snake_case     :	List[Any]											=       self.pipeline_class.from_pretrained(__UpperCAmelCase)
     pipe_loaded.to(__UpperCAmelCase)
     pipe_loaded.set_progress_bar_config(disable=__UpperCAmelCase)

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

    _snake_case     :	int											=       self.get_dummy_inputs(__UpperCAmelCase)
    _snake_case     :	Dict											=       pipe_loaded(**__UpperCAmelCase)[0]

    _snake_case     :	Dict											=       np.abs(to_np(__UpperCAmelCase) - to_np(__UpperCAmelCase)).max()
    self.assertLess(__UpperCAmelCase					,		1E-4)








from abc import ABC, abstractmethod
from argparse import ArgumentParser




class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						@staticmethod
						@abstractmethod
						def        UpperCAmelCase__					(__UpperCAmelCase:					ArgumentParser   )    ->     Tuple:




											'''simple docstring'''



											raise NotImplementedError()



						@abstractmethod
						def        UpperCAmelCase__					(self:					List[str]   )    ->     List[str]:




											'''simple docstring'''



											raise NotImplementedError()





