
'''simple docstring'''







import string
from math import logaa



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str	,			_SCREAMING_SNAKE_CASE		:   str  ):
						_SCREAMING_SNAKE_CASE				= document.translate(
						    str.maketrans(""	,			""	,			string.punctuation  )  ).replace("\n"	,			""  )
						_SCREAMING_SNAKE_CASE				= document_without_punctuation.split(" "  )  # word tokenization
						return len([word for word in tokenize_document if word.lower() == term.lower()]  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str	,			_SCREAMING_SNAKE_CASE		:   str  ):
						_SCREAMING_SNAKE_CASE				= corpus.lower().translate(
						    str.maketrans(""	,			""	,			string.punctuation  )  )  # strip all punctuation and replace it with ''
						_SCREAMING_SNAKE_CASE				= corpus_without_punctuation.split("\n"  )
						_SCREAMING_SNAKE_CASE				= term.lower()
						return (len([doc for doc in docs if term in doc]  ), len(_SCREAMING_SNAKE_CASE  ))



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int	,			_SCREAMING_SNAKE_CASE		:   int	,			_SCREAMING_SNAKE_CASE		:   Optional[int]=False  ):
						if smoothing:
												if n == 0:
																		raise ValueError("log10(0) is undefined."  )
												return round(1 + logaa(n / (1 + df)  )	,			3  )

						if df == 0:
												raise ZeroDivisionError("df must be > 0"  )
						elif n == 0:
												raise ValueError("log10(0) is undefined."  )
						return round(logaa(n / df  )	,			3  )




def 			_a		(	_SCREAMING_SNAKE_CASE		:   int	,			_SCREAMING_SNAKE_CASE		:   int  ):
						return round(tf * idf	,			3  )

'''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 			lowerCAmelCase :




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

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

								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= 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      )
								_SCREAMING_SNAKE_CASE				= DDPMScheduler(
								    num_train_timesteps=1_000					,						beta_schedule="squaredcos_cap_v2"					,						beta_start=0.00_01					,						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      )
								_SCREAMING_SNAKE_CASE				= IFWatermarker()

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




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

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

								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= 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_14					,						time_embedding_act_fn="gelu"					,						time_embedding_dim=32					,						)
								unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= DDPMScheduler(
								    num_train_timesteps=1_000					,						beta_schedule="squaredcos_cap_v2"					,						beta_start=0.00_01					,						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      )
								_SCREAMING_SNAKE_CASE				= DDPMScheduler(
								    num_train_timesteps=1_000					,						beta_schedule="squaredcos_cap_v2"					,						beta_start=0.00_01					,						beta_end=0.02					,						)

								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= 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     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.get_dummy_components()
								_SCREAMING_SNAKE_CASE				= self.pipeline_class(**UpperCamelCase      )
								pipe.to(UpperCamelCase      )
								pipe.set_progress_bar_config(disable=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= self.get_dummy_inputs(UpperCamelCase      )

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

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

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

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

								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= pipe.encode_prompt(UpperCamelCase      )

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

								if mask_image is not None:
														_SCREAMING_SNAKE_CASE				= mask_image

								if original_image is not None:
														_SCREAMING_SNAKE_CASE				= original_image

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

								_SCREAMING_SNAKE_CASE				= pipe(**UpperCamelCase      )[0]

								with tempfile.TemporaryDirectory() as tmpdir:
														pipe.save_pretrained(UpperCamelCase      )
														_SCREAMING_SNAKE_CASE				= 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.'					,						)

								_SCREAMING_SNAKE_CASE				= self.get_dummy_inputs(UpperCamelCase      )

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

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

								if mask_image is not None:
														_SCREAMING_SNAKE_CASE				= mask_image

								if original_image is not None:
														_SCREAMING_SNAKE_CASE				= original_image

								_SCREAMING_SNAKE_CASE				= pipe_loaded(**UpperCamelCase      )[0]

								_SCREAMING_SNAKE_CASE				= np.abs(to_np(UpperCamelCase      ) - to_np(UpperCamelCase      )      ).max()
								self.assertLess(UpperCamelCase					,						1e-4      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.get_dummy_components()
								_SCREAMING_SNAKE_CASE				= self.pipeline_class(**UpperCamelCase      )
								pipe.to(UpperCamelCase      )
								pipe.set_progress_bar_config(disable=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= self.get_dummy_inputs(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= pipe(**UpperCamelCase      )[0]

								with tempfile.TemporaryDirectory() as tmpdir:
														pipe.save_pretrained(UpperCamelCase      )
														_SCREAMING_SNAKE_CASE				= 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

								_SCREAMING_SNAKE_CASE				= self.get_dummy_inputs(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= pipe_loaded(**UpperCamelCase      )[0]

								_SCREAMING_SNAKE_CASE				= np.abs(to_np(UpperCamelCase      ) - to_np(UpperCamelCase      )      ).max()
								self.assertLess(UpperCamelCase					,						1e-4      )