





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     __magic_name__     :





     """simple docstring"""



     def   SCREAMING_SNAKE_CASE						(							self				:str     ):





        '''simple docstring'''




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

        torch.manual_seed(0     )
        A_    : Union[str, Any]								=	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"     )

        torch.manual_seed(0     )
        A_    : Optional[int]								=	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     )
        A_    : str								=	DDPMScheduler(
            num_train_timesteps=1_000	,					beta_schedule="squaredcos_cap_v2"	,					beta_start=0.0001	,					beta_end=0.02	,					thresholding=_a	,					dynamic_thresholding_ratio=0.95	,					sample_max_value=1.0	,					prediction_type="epsilon"	,					variance_type="learned_range"	,					)

        torch.manual_seed(0     )
        A_    : List[Any]								=	IFWatermarker()

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



     def   SCREAMING_SNAKE_CASE						(							self				:Dict     ):





        '''simple docstring'''




        torch.manual_seed(0     )
        A_    : Optional[int]								=	TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"     )

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

        torch.manual_seed(0     )
        A_    : Any								=	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     )
        A_    : Any								=	DDPMScheduler(
            num_train_timesteps=1_000	,					beta_schedule="squaredcos_cap_v2"	,					beta_start=0.0001	,					beta_end=0.02	,					thresholding=_a	,					dynamic_thresholding_ratio=0.95	,					sample_max_value=1.0	,					prediction_type="epsilon"	,					variance_type="learned_range"	,					)

        torch.manual_seed(0     )
        A_    : Any								=	DDPMScheduler(
            num_train_timesteps=1_000	,					beta_schedule="squaredcos_cap_v2"	,					beta_start=0.0001	,					beta_end=0.02	,					)

        torch.manual_seed(0     )
        A_    : Optional[Any]								=	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   SCREAMING_SNAKE_CASE						(							self				:int     ):





        '''simple docstring'''




        A_    : int								=	self.get_dummy_components()
        A_    : int								=	self.pipeline_class(**_a     )
        pipe.to(_a     )
        pipe.set_progress_bar_config(disable=_a     )

        A_    : Union[str, Any]								=	self.get_dummy_inputs(_a     )

        A_    : Optional[Any]								=	inputs["prompt"]
        A_    : Any								=	inputs["generator"]
        A_    : str								=	inputs["num_inference_steps"]
        A_    : Optional[Any]								=	inputs["output_type"]

        if "image" in inputs:
           A_    : str								=	inputs["image"]
        else:
           A_    : int								=	None

        if "mask_image" in inputs:
           A_    : Optional[Any]								=	inputs["mask_image"]
        else:
           A_    : Union[str, Any]								=	None

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

        A_					,  A_    : List[str]								=	pipe.encode_prompt(_a     )

        # inputs with prompt converted to embeddings
        A_    : List[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:
           A_    : List[str]								=	image

        if mask_image is not None:
           A_    : int								=	mask_image

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

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

        A_    : Optional[Any]								=	pipe(**_a     )[0]

        with tempfile.TemporaryDirectory() as tmpdir:
           pipe.save_pretrained(_a     )
           A_    : List[str]								=	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."	,					)

        A_    : Dict								=	self.get_dummy_inputs(_a     )

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

        # inputs with prompt converted to embeddings
        A_    : Optional[int]								=	{
            "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:
           A_    : List[Any]								=	image

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

        if original_image is not None:
           A_    : str								=	original_image

        A_    : Union[str, Any]								=	pipe_loaded(**_a     )[0]

        A_    : Tuple								=	np.abs(to_np(_a     ) - to_np(_a     )     ).max()
        self.assertLess(_a	,					1e-4     )





     def   SCREAMING_SNAKE_CASE						(							self				:List[str]     ):





        '''simple docstring'''




        A_    : Optional[int]								=	self.get_dummy_components()
        A_    : List[str]								=	self.pipeline_class(**_a     )
        pipe.to(_a     )
        pipe.set_progress_bar_config(disable=_a     )

        A_    : List[Any]								=	self.get_dummy_inputs(_a     )
        A_    : Optional[int]								=	pipe(**_a     )[0]

        with tempfile.TemporaryDirectory() as tmpdir:
           pipe.save_pretrained(_a     )
           A_    : int								=	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

        A_    : Any								=	self.get_dummy_inputs(_a     )
        A_    : List[str]								=	pipe_loaded(**_a     )[0]

        A_    : Dict								=	np.abs(to_np(_a     ) - to_np(_a     )     ).max()
        self.assertLess(_a	,					1e-4     )




"""simple docstring"""




lowercase_			     =  [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
lowercase_			     =  [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
lowercase_			     =  {
    0: "Sunday",
    1: "Monday",
    2: "Tuesday",
    3: "Wednesday",
    4: "Thursday",
    5: "Friday",
    6: "Saturday",
}

def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     int			) ->      str:
		assert len(str(lowerCAmelCase__			)			) > 2, "year should be in YYYY format"
		assert 1 <= month <= 12, "month should be between 1 to 12"
		assert 1 <= day <= 31, "day should be between 1 to 31"

		# Doomsday algorithm:
		__a           =			year // 100
		__a           =			(5 * (century % 4) + 2) % 7
		__a           =			year % 100
		__a           =			centurian % 12
		__a           =			(
		    (centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
		) % 7
		__a           =			(
		    DOOMSDAY_NOT_LEAP[month - 1]
		    if (year % 4 != 0) or (centurian == 0 and (year % 400) == 0)
		    else DOOMSDAY_LEAP[month - 1]
		)
		__a           =			(dooms_day + day - day_anchor) % 7
		return WEEK_DAY_NAMES[week_day]


if __name__ == "__main__":
				import doctest

				doctest.testmod()






