
"""simple docstring"""


import argparse
import logging
import os
import time
import timeit

import datasets
import numpy as np
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate


A_    							=					trt.Logger(trt.Logger.WARNING)
A_    							=					absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

A_    							=					logging.getLogger(__name__)

A_    							=					argparse.ArgumentParser()

# Required parameters
parser.add_argument(
    """--onnx_model_path""",
    default=None,
    type=str,
    required=True,
    help="""Path to ONNX model: """,
)

parser.add_argument(
    """--output_dir""",
    default=None,
    type=str,
    required=True,
    help="""The output directory where the model checkpoints and predictions will be written.""",
)

# Other parameters

parser.add_argument(
    """--tokenizer_name""",
    default="""""",
    type=str,
    required=True,
    help="""Pretrained tokenizer name or path if not the same as model_name""",
)

parser.add_argument(
    """--version_2_with_negative""",
    action="""store_true""",
    help="""If true, the SQuAD examples contain some that do not have an answer.""",
)
parser.add_argument(
    """--null_score_diff_threshold""",
    type=float,
    default=0.0,
    help="""If null_score - best_non_null is greater than the threshold predict null.""",
)

parser.add_argument(
    """--max_seq_length""",
    default=384,
    type=int,
    help=(
        """The maximum total input sequence length after WordPiece tokenization. Sequences """
        """longer than this will be truncated, and sequences shorter than this will be padded."""
    ),
)
parser.add_argument(
    """--doc_stride""",
    default=128,
    type=int,
    help="""When splitting up a long document into chunks, how much stride to take between chunks.""",
)

parser.add_argument("""--per_device_eval_batch_size""", default=8, type=int, help="""Batch size per GPU/CPU for evaluation.""")

parser.add_argument(
    """--n_best_size""",
    default=20,
    type=int,
    help="""The total number of n-best predictions to generate in the nbest_predictions.json output file.""",
)
parser.add_argument(
    """--max_answer_length""",
    default=30,
    type=int,
    help=(
        """The maximum length of an answer that can be generated. This is needed because the start """
        """and end predictions are not conditioned on one another."""
    ),
)

parser.add_argument("""--seed""", type=int, default=42, help="""random seed for initialization""")

parser.add_argument(
    """--dataset_name""",
    type=str,
    default=None,
    required=True,
    help="""The name of the dataset to use (via the datasets library).""",
)
parser.add_argument(
    """--dataset_config_name""",
    type=str,
    default=None,
    help="""The configuration name of the dataset to use (via the datasets library).""",
)
parser.add_argument(
    """--preprocessing_num_workers""", type=int, default=4, help="""A csv or a json file containing the training data."""
)
parser.add_argument("""--overwrite_cache""", action="""store_true""", help="""Overwrite the cached training and evaluation sets""")
parser.add_argument(
    """--fp16""",
    action="""store_true""",
    help="""Whether to use 16-bit (mixed) precision instead of 32-bit""",
)
parser.add_argument(
    """--int8""",
    action="""store_true""",
    help="""Whether to use INT8""",
)

A_    							=					parser.parse_args()

if args.tokenizer_name:
						A_    							=					AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
						raise ValueError(
						    """You are instantiating a new tokenizer from scratch. This is not supported by this script."""
						    """You can do it from another script, save it, and load it from here, using --tokenizer_name."""
						)

logger.info("""Training/evaluation parameters %s""", args)

A_    							=					args.per_device_eval_batch_size

A_    							=					(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
A_    							=					True

A_    							=					"""temp_engine/bert-fp32.engine"""
if args.fpaa:
						A_    							=					"""temp_engine/bert-fp16.engine"""
if args.inta:
						A_    							=					"""temp_engine/bert-int8.engine"""

# import ONNX file
if not os.path.exists("""temp_engine"""):
						os.makedirs("""temp_engine""")

A_    							=					1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
    network, TRT_LOGGER
) as parser:
						with open(args.onnx_model_path, """rb""") as model:
												if not parser.parse(model.read()):
																		for error in range(parser.num_errors):
																								print(parser.get_error(error))

    # Query input names and shapes from parsed TensorRT network
						A_    							=					[network.get_input(i) for i in range(network.num_inputs)]
						A_    							=					[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

						with builder.create_builder_config() as config:
												A_    							=					1 << 50
												if STRICT_TYPES:
																		config.set_flag(trt.BuilderFlag.STRICT_TYPES)
												if args.fpaa:
																		config.set_flag(trt.BuilderFlag.FPaa)
												if args.inta:
																		config.set_flag(trt.BuilderFlag.INTa)
												A_    							=					builder.create_optimization_profile()
												config.add_optimization_profile(profile)
												for i in range(len(input_names)):
																		profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
												A_    							=					builder.build_engine(network, config)

												# serialize_engine and store in file (can be directly loaded and deserialized):
												with open(engine_name, """wb""") as f:
																		f.write(engine.serialize())






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						):
			lowerCamelCase_					=  np.asarray(inputs['''input_ids''']	,dtype=np.intaa						)
			lowerCamelCase_					=  np.asarray(inputs['''attention_mask''']	,dtype=np.intaa						)
			lowerCamelCase_					=  np.asarray(inputs['''token_type_ids''']	,dtype=np.intaa						)

			# Copy inputs
			cuda.memcpy_htod_async(d_inputs[0]	,input_ids.ravel()	,__UpperCamelCase						)
			cuda.memcpy_htod_async(d_inputs[1]	,attention_mask.ravel()	,__UpperCamelCase						)
			cuda.memcpy_htod_async(d_inputs[2]	,token_type_ids.ravel()	,__UpperCamelCase						)
			# start time
			lowerCamelCase_					=  time.time()
			# Run inference
			context.execute_async(
			    bindings=[int(__UpperCamelCase						) for d_inp in d_inputs] + [int(__UpperCamelCase						), int(__UpperCamelCase						)]	,stream_handle=stream.handle						)
			# Transfer predictions back from GPU
			cuda.memcpy_dtoh_async(__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase						)
			cuda.memcpy_dtoh_async(__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase						)
			# Synchronize the stream and take time
			stream.synchronize()
			# end time
			lowerCamelCase_					=  time.time()
			lowerCamelCase_					=  end_time - start_time
			lowerCamelCase_					=  (h_outputa, h_outputa)
			# print(outputs)
			return outputs, infer_time


# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
A_    							=					Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""",
    datefmt="""%m/%d/%Y %H:%M:%S""",
    level=logging.INFO,
)

# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
						datasets.utils.logging.set_verbosity_warning()
						transformers.utils.logging.set_verbosity_info()
else:
						datasets.utils.logging.set_verbosity_error()
						transformers.utils.logging.set_verbosity_error()

# If passed along, set the training seed now.
if args.seed is not None:
						set_seed(args.seed)

# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
						# Downloading and loading a dataset from the hub.
						A_    							=					load_dataset(args.dataset_name, args.dataset_config_name)
else:
						raise ValueError("""Evaluation requires a dataset name""")
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.

A_    							=					raw_datasets["""validation"""].column_names

A_    							=					"""question""" if """question""" in column_names else column_names[0]
A_    							=					"""context""" if """context""" in column_names else column_names[1]
A_    							=					"""answers""" if """answers""" in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
A_    							=					tokenizer.padding_side == """right"""

if args.max_seq_length > tokenizer.model_max_length:
						logger.warning(
						    f"The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the"
						    f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
						)

A_    							=					min(args.max_seq_length, tokenizer.model_max_length)






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  [q.lstrip() for q in examples[question_column_name]]

			# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
			# in one example possible giving several features when a context is long, each of those features having a
			# context that overlaps a bit the context of the previous feature.
			lowerCamelCase_					=  tokenizer(
			    examples[question_column_name if pad_on_right else context_column_name]	,examples[context_column_name if pad_on_right else question_column_name]	,truncation='''only_second''' if pad_on_right else '''only_first'''	,max_length=__UpperCamelCase	,stride=args.doc_stride	,return_overflowing_tokens=__UpperCamelCase	,return_offsets_mapping=__UpperCamelCase	,padding='''max_length'''	,)

			# Since one example might give us several features if it has a long context, we need a map from a feature to
			# its corresponding example. This key gives us just that.
			lowerCamelCase_					=  tokenized_examples.pop('''overflow_to_sample_mapping'''						)

			# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
			# corresponding example_id and we will store the offset mappings.
			lowerCamelCase_					=  []

			for i in range(len(tokenized_examples['''input_ids''']						)						):
						# Grab the sequence corresponding to that example (to know what is the context and what is the question).
						lowerCamelCase_					=  tokenized_examples.sequence_ids(__UpperCamelCase						)
						lowerCamelCase_					=  1 if pad_on_right else 0

						# One example can give several spans, this is the index of the example containing this span of text.
						lowerCamelCase_					=  sample_mapping[i]
						tokenized_examples["example_id"].append(examples['''id'''][sample_index]						)

						# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
						# position is part of the context or not.
						lowerCamelCase_					=  [
						    (o if sequence_ids[k] == context_index else None)
						    for k, o in enumerate(tokenized_examples['''offset_mapping'''][i]						)
						]

			return tokenized_examples


A_    							=					raw_datasets["""validation"""]
# Validation Feature Creation
A_    							=					eval_examples.map(
    prepare_validation_features,
    batched=True,
    num_proc=args.preprocessing_num_workers,
    remove_columns=column_names,
    load_from_cache_file=not args.overwrite_cache,
    desc="""Running tokenizer on validation dataset""",
)

A_    							=					default_data_collator

A_    							=					eval_dataset.remove_columns(["""example_id""", """offset_mapping"""])
A_    							=					DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__="eval"						):
			lowerCamelCase_					=  postprocess_qa_predictions(
			    examples=__UpperCamelCase	,features=__UpperCamelCase	,predictions=__UpperCamelCase	,version_2_with_negative=args.version_2_with_negative	,n_best_size=args.n_best_size	,max_answer_length=args.max_answer_length	,null_score_diff_threshold=args.null_score_diff_threshold	,output_dir=args.output_dir	,prefix=__UpperCamelCase	,)
			# Format the result to the format the metric expects.
			if args.version_2_with_negative:
						lowerCamelCase_					=  [
						    {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
						]
			else:
						lowerCamelCase_					=  [{'id': k, 'prediction_text': v} for k, v in predictions.items()]

			lowerCamelCase_					=  [{'id': ex['id'], 'answers': ex[answer_column_name]} for ex in examples]
			return EvalPrediction(predictions=__UpperCamelCase	,label_ids=__UpperCamelCase						)


A_    							=					load_metric("""squad_v2""" if args.version_2_with_negative else """squad""")

# Evaluation!
logger.info("""Loading ONNX model %s for evaluation""", args.onnx_model_path)
with open(engine_name, """rb""") as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
    f.read()
) as engine, engine.create_execution_context() as context:
						# setup for TRT inferrence
						for i in range(len(input_names)):
												context.set_binding_shape(i, INPUT_SHAPE)
						assert context.all_binding_shapes_specified






						def  lowercase						(	lowerCAmelCase__						):
									return trt.volume(engine.get_binding_shape(__UpperCamelCase						)						) * engine.get_binding_dtype(__UpperCamelCase						).itemsize

						# Allocate device memory for inputs and outputs.
						A_    							=					[cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

						# Allocate output buffer
						A_    							=					cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
						A_    							=					cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
						A_    							=					cuda.mem_alloc(h_outputa.nbytes)
						A_    							=					cuda.mem_alloc(h_outputa.nbytes)

						# Create a stream in which to copy inputs/outputs and run inference.
						A_    							=					cuda.Stream()

						# Evaluation
						logger.info("""***** Running Evaluation *****""")
						logger.info(f"  Num examples = {len(eval_dataset)}")
						logger.info(f"  Batch size = {args.per_device_eval_batch_size}")

						A_    							=					0.0
						A_    							=					0
						A_    							=					timeit.default_timer()

						A_    							=					None
						for step, batch in enumerate(eval_dataloader):
												A_							,   A_    							=					model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
												total_time += infer_time
												niter += 1

												A_							,   A_    							=					outputs
												A_    							=					torch.tensor(start_logits)
												A_    							=					torch.tensor(end_logits)

												# necessary to pad predictions and labels for being gathered
												A_    							=					accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
												A_    							=					accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

												A_    							=					(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
												A_    							=					logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

						if all_preds is not None:
												A_    							=					nested_truncate(all_preds, len(eval_dataset))

						A_    							=					timeit.default_timer() - start_time
						logger.info("""  Evaluation done in total %f secs (%f sec per example)""", evalTime, evalTime / len(eval_dataset))
						# Inference time from TRT
						logger.info("""Average Inference Time = {:.3f} ms""".format(total_time * 1000 / niter))
						logger.info("""Total Inference Time =  {:.3f} ms""".format(total_time * 1000))


						logger.info("""Total Number of Inference =  %d""", niter)

A_    							=					post_processing_function(eval_examples, eval_dataset, all_preds)
A_    							=					metric.compute(predictions=prediction.predictions, references=prediction.label_ids)


logger.info(f"Evaluation metrics: {eval_metric}")









'''simple docstring'''



import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 							_a				(			SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE ,      unittest.TestCase ):





       '''simple docstring'''






       A						:				int        =						StableDiffusionDiffEditPipeline
       A						:				str        =						TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
       A						:				int        =						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
       A						:				str        =						frozenset(
           [] )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       A						:				Union[str, Any]        =						frozenset([] )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             torch.manual_seed(0  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						UNetaDConditionModel(
                 block_out_channels=(32, 64),							layers_per_block=2,							sample_size=32,							in_channels=4,							out_channels=4,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D'),							up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D'),							cross_attention_dim=32,							attention_head_dim=(2, 4),							use_linear_projection=A,							)
             SCREAMING_SNAKE_CASE   :      int    					=						DDIMScheduler(
                 beta_start=0.0_00_85,							beta_end=0.0_12,							beta_schedule='scaled_linear',							clip_sample=A,							set_alpha_to_one=A,							)
             SCREAMING_SNAKE_CASE   :      str    					=						DDIMInverseScheduler(
                 beta_start=0.0_00_85,							beta_end=0.0_12,							beta_schedule='scaled_linear',							clip_sample=A,							set_alpha_to_zero=A,							)
             torch.manual_seed(0  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						AutoencoderKL(
                 block_out_channels=[32, 64],							in_channels=3,							out_channels=3,							down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'],							up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'],							latent_channels=4,							sample_size=128,							)
             torch.manual_seed(0  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						CLIPTextConfig(
                 bos_token_id=0,							eos_token_id=2,							hidden_size=32,							intermediate_size=37,							layer_norm_eps=1E-05,							num_attention_heads=4,							num_hidden_layers=5,							pad_token_id=1,							vocab_size=1_000,							hidden_act='gelu',							projection_dim=512,							)
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						CLIPTextModel(A  )
             SCREAMING_SNAKE_CASE   :      str    					=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

             SCREAMING_SNAKE_CASE   :      int    					=						{
                 'unet': unet,
                 'scheduler': scheduler,
                 'inverse_scheduler': inverse_scheduler,
                 'vae': vae,
                 'text_encoder': text_encoder,
                 'tokenizer': tokenizer,
                 'safety_checker': None,
                 'feature_extractor': None,
             }

             return components







       def        UpperCamelCase_							(      self,							A,							A=0  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						floats_tensor((1, 16, 16),							rng=random.Random(A  )  ).to(A  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						floats_tensor((1, 2, 4, 16, 16),							rng=random.Random(A  )  ).to(A  )
             if str(A  ).startswith('mps'  ):
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						torch.manual_seed(A  )
             else:
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						torch.Generator(device=A  ).manual_seed(A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						{
                 'prompt': 'a dog and a newt',
                 'mask_image': mask,
                 'image_latents': latents,
                 'generator': generator,
                 'num_inference_steps': 2,
                 'inpaint_strength': 1.0,
                 'guidance_scale': 6.0,
                 'output_type': 'numpy',
             }

             return inputs







       def        UpperCamelCase_							(      self,							A,							A=0  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						floats_tensor((1, 3, 32, 32),							rng=random.Random(A  )  ).to(A  )
             SCREAMING_SNAKE_CASE   :      Any    					=						image.cpu().permute(0,							2,							3,							1  )[0]
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						Image.fromarray(np.uinta(A  )  ).convert('RGB'  )
             if str(A  ).startswith('mps'  ):
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						torch.manual_seed(A  )
             else:
                   SCREAMING_SNAKE_CASE   :      int    					=						torch.Generator(device=A  ).manual_seed(A  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						{
                 'image': image,
                 'source_prompt': 'a cat and a frog',
                 'target_prompt': 'a dog and a newt',
                 'generator': generator,
                 'num_inference_steps': 2,
                 'num_maps_per_mask': 2,
                 'mask_encode_strength': 1.0,
                 'guidance_scale': 6.0,
                 'output_type': 'numpy',
             }

             return inputs







       def        UpperCamelCase_							(      self,							A,							A=0  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						floats_tensor((1, 3, 32, 32),							rng=random.Random(A  )  ).to(A  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						image.cpu().permute(0,							2,							3,							1  )[0]
             SCREAMING_SNAKE_CASE   :      int    					=						Image.fromarray(np.uinta(A  )  ).convert('RGB'  )
             if str(A  ).startswith('mps'  ):
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						torch.manual_seed(A  )
             else:
                   SCREAMING_SNAKE_CASE   :      Any    					=						torch.Generator(device=A  ).manual_seed(A  )
             SCREAMING_SNAKE_CASE   :      Any    					=						{
                 'image': image,
                 'prompt': 'a cat and a frog',
                 'generator': generator,
                 'num_inference_steps': 2,
                 'inpaint_strength': 1.0,
                 'guidance_scale': 6.0,
                 'decode_latents': True,
                 'output_type': 'numpy',
             }
             return inputs







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             if not hasattr(self.pipeline_class,							'_optional_components'  ):
                   return

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

             # set all optional components to None and update pipeline config accordingly
             for optional_component in pipe._optional_components:
                   setattr(A,							A,							A  )
             pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}  )

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.get_dummy_inputs(A  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						pipe(**A  )[0]

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

             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.",							)

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.get_dummy_inputs(A  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						pipe_loaded(**A  )[0]

             SCREAMING_SNAKE_CASE   :      List[str]    					=						np.abs(output - output_loaded  ).max()
             self.assertLess(A,							1E-4  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						'cpu'

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.get_dummy_components()
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.pipeline_class(**A  )
             pipe.to(A  )
             pipe.set_progress_bar_config(disable=A  )

             SCREAMING_SNAKE_CASE   :      str    					=						self.get_dummy_mask_inputs(A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						pipe.generate_mask(**A  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						mask[0, -3:, -3:]

             self.assertEqual(mask.shape,							(1, 16, 16)  )
             SCREAMING_SNAKE_CASE   :      Any    					=						np.array([0] * 9  )
             SCREAMING_SNAKE_CASE   :      Any    					=						np.abs(mask_slice.flatten() - expected_slice  ).max()
             self.assertLessEqual(A,							1E-3  )
             self.assertEqual(mask[0, -3, -4],							0  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						'cpu'

             SCREAMING_SNAKE_CASE   :      Dict    					=						self.get_dummy_components()
             SCREAMING_SNAKE_CASE   :      Dict    					=						self.pipeline_class(**A  )
             pipe.to(A  )
             pipe.set_progress_bar_config(disable=A  )

             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.get_dummy_inversion_inputs(A  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						pipe.invert(**A  ).images
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						image[0, -1, -3:, -3:]

             self.assertEqual(image.shape,							(2, 32, 32, 3)  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						np.array(
                 [0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99],							)
             SCREAMING_SNAKE_CASE   :      Dict    					=						np.abs(image_slice.flatten() - expected_slice  ).max()
             self.assertLessEqual(A,							1E-3  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             super().test_inference_batch_single_identical(expected_max_diff=5E-3  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						'cpu'

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.get_dummy_components()

             SCREAMING_SNAKE_CASE   :      Dict    					=						{'beta_start': 0.0_00_85, 'beta_end': 0.0_12, 'beta_schedule': 'scaled_linear'}
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						DPMSolverMultistepScheduler(**A  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						DPMSolverMultistepInverseScheduler(**A  )

             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.pipeline_class(**A  )
             pipe.to(A  )
             pipe.set_progress_bar_config(disable=A  )

             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.get_dummy_inversion_inputs(A  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						pipe.invert(**A  ).images
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						image[0, -1, -3:, -3:]

             self.assertEqual(image.shape,							(2, 32, 32, 3)  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						np.array(
                 [0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99],							)
             SCREAMING_SNAKE_CASE   :      Any    					=						np.abs(image_slice.flatten() - expected_slice  ).max()
             self.assertLessEqual(A,							1E-3  )






@require_torch_gpu
@slow
class 							_a				(			unittest.TestCase ):





       '''simple docstring'''






       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()







       @classmethod
       def        UpperCamelCase_							(      cls  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'  )

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						raw_image.convert('RGB'  ).resize((768, 768)  )

             SCREAMING_SNAKE_CASE   :      List[str]    					=						raw_image







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						torch.manual_seed(0  )

             SCREAMING_SNAKE_CASE   :      Dict    					=						StableDiffusionDiffEditPipeline.from_pretrained(
                 'stabilityai/stable-diffusion-2-1',							safety_checker=A,							torch_dtype=torch.floataa  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						DDIMScheduler.from_config(pipe.scheduler.config  )
             SCREAMING_SNAKE_CASE   :      int    					=						DDIMInverseScheduler.from_config(pipe.scheduler.config  )
             pipe.enable_model_cpu_offload()
             pipe.set_progress_bar_config(disable=A  )

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						'a bowl of fruit'
             SCREAMING_SNAKE_CASE   :      List[str]    					=						'a bowl of pears'

             SCREAMING_SNAKE_CASE   :      Dict    					=						pipe.generate_mask(
                 image=self.raw_image,							source_prompt=A,							target_prompt=A,							generator=A,							)

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						pipe.invert(
                 prompt=A,							image=self.raw_image,							inpaint_strength=0.7,							generator=A  ).latents

             SCREAMING_SNAKE_CASE   :      List[str]    					=						pipe(
                 prompt=A,							mask_image=A,							image_latents=A,							generator=A,							negative_prompt=A,							inpaint_strength=0.7,							output_type='numpy',							).images[0]

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						(
                 np.array(
                     load_image(
                         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                         '/diffedit/pears.png'  ).resize((768, 768)  )  )
                 / 255
             )
             assert np.abs((expected_image - image).max()  ) < 5E-1







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						torch.manual_seed(0  )

             SCREAMING_SNAKE_CASE   :      int    					=						StableDiffusionDiffEditPipeline.from_pretrained(
                 'stabilityai/stable-diffusion-2-1',							safety_checker=A,							torch_dtype=torch.floataa  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config  )
             pipe.enable_model_cpu_offload()
             pipe.set_progress_bar_config(disable=A  )

             SCREAMING_SNAKE_CASE   :      str    					=						'a bowl of fruit'
             SCREAMING_SNAKE_CASE   :      Tuple    					=						'a bowl of pears'

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						pipe.generate_mask(
                 image=self.raw_image,							source_prompt=A,							target_prompt=A,							generator=A,							)

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						pipe.invert(
                 prompt=A,							image=self.raw_image,							inpaint_strength=0.7,							generator=A,							num_inference_steps=25,							).latents

             SCREAMING_SNAKE_CASE   :      str    					=						pipe(
                 prompt=A,							mask_image=A,							image_latents=A,							generator=A,							negative_prompt=A,							inpaint_strength=0.7,							num_inference_steps=25,							output_type='numpy',							).images[0]

             SCREAMING_SNAKE_CASE   :      Tuple    					=						(
                 np.array(
                     load_image(
                         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                         '/diffedit/pears.png'  ).resize((768, 768)  )  )
                 / 255
             )
             assert np.abs((expected_image - image).max()  ) < 5E-1


