



"""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


__SCREAMING_SNAKE_CASE  :						Any    							=      trt.Logger(trt.Logger.WARNING)
__SCREAMING_SNAKE_CASE  :						Dict    							=      absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      logging.getLogger(__name__)

__SCREAMING_SNAKE_CASE  :						int    							=      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=3_8_4,
    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=1_2_8,
    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=2_0,
    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=3_0,
    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=4_2, 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''',
)

__SCREAMING_SNAKE_CASE  :						Tuple    							=      parser.parse_args()

if args.tokenizer_name:
			__SCREAMING_SNAKE_CASE  :						str    							=      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)

__SCREAMING_SNAKE_CASE  :						Any    							=      args.per_device_eval_batch_size

__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
__SCREAMING_SNAKE_CASE  :						int    							=      True

__SCREAMING_SNAKE_CASE  :						List[str]    							=      '''temp_engine/bert-fp32.engine'''
if args.fpaa:
			__SCREAMING_SNAKE_CASE  :						List[Any]    							=      '''temp_engine/bert-fp16.engine'''
if args.inta:
			__SCREAMING_SNAKE_CASE  :						int    							=      '''temp_engine/bert-int8.engine'''

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

__SCREAMING_SNAKE_CASE  :						str    							=      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
			__SCREAMING_SNAKE_CASE  :						Dict    							=      [network.get_input(i) for i in range(network.num_inputs)]
			__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      [_input.name for _input in network_inputs]  # ex: ["actual_input1"]

			with builder.create_builder_config() as config:
						__SCREAMING_SNAKE_CASE  :						str    							=      1 << 5_0
						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)
						__SCREAMING_SNAKE_CASE  :						int    							=      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)
						__SCREAMING_SNAKE_CASE  :						Any    							=      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        lowerCAmelCase_(   lowercase_     :							int						,   lowercase_     :							Tuple						,   lowercase_     :							Optional[int]						,   lowercase_     :							Tuple						,   lowercase_     :							int						,   lowercase_     :							int						,   lowercase_     :							Any						,   lowercase_     :							Union[str, Any]							)	->       Tuple:

				'''simple docstring'''





				_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()						,   lowercase_							)
				cuda.memcpy_htod_async(d_inputs[1]						,   attention_mask.ravel()						,   lowercase_							)
				cuda.memcpy_htod_async(d_inputs[2]						,   token_type_ids.ravel()						,   lowercase_							)
				# start time
				_lowerCamelCase        =				time.time()
				# Run inference
				context.execute_async(
				    bindings=[int(lowercase_							) for d_inp in d_inputs] + [int(lowercase_							), int(lowercase_							)]						,   stream_handle=stream.handle							)
				# Transfer predictions back from GPU
				cuda.memcpy_dtoh_async(lowercase_						,   lowercase_						,   lowercase_							)
				cuda.memcpy_dtoh_async(lowercase_						,   lowercase_						,   lowercase_							)
				# 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.
__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      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.
			__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      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.

__SCREAMING_SNAKE_CASE  :						Tuple    							=      raw_datasets['''validation'''].column_names

__SCREAMING_SNAKE_CASE  :						int    							=      '''question''' if '''question''' in column_names else column_names[0]
__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      '''context''' if '''context''' in column_names else column_names[1]
__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      '''answers''' if '''answers''' in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
__SCREAMING_SNAKE_CASE  :						Dict    							=      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}."""
			)

__SCREAMING_SNAKE_CASE  :						Dict    							=      min(args.max_seq_length, tokenizer.model_max_length)



def        lowerCAmelCase_(   lowercase_     :							Dict							)	->       Optional[int]:

				'''simple docstring'''





				_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=lowercase_						,   stride=args.doc_stride						,   return_overflowing_tokens=lowercase_						,   return_offsets_mapping=lowercase_						,   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(lowercase_							)
								_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


__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      raw_datasets['''validation''']
# Validation Feature Creation
__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      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''',
)

__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      default_data_collator

__SCREAMING_SNAKE_CASE  :						Dict    							=      eval_dataset.remove_columns(['''example_id''', '''offset_mapping'''])
__SCREAMING_SNAKE_CASE  :						List[Any]    							=      DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)



def        lowerCAmelCase_(   lowercase_     :							Any						,   lowercase_     :							Optional[Any]						,   lowercase_     :							Optional[Any]						,   lowercase_     :							List[Any]="eval"							)	->       Any:

				'''simple docstring'''





				_lowerCamelCase        =				postprocess_qa_predictions(
				    examples=lowercase_						,   features=lowercase_						,   predictions=lowercase_						,   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=lowercase_						,   )
				# 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=lowercase_						,   label_ids=lowercase_							)


__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      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        lowerCAmelCase_(   lowercase_     :							Dict							)	->       Dict:

							'''simple docstring'''





							return trt.volume(engine.get_binding_shape(lowercase_							)							) * engine.get_binding_dtype(lowercase_							).itemsize

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

			# Allocate output buffer
			__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
			__SCREAMING_SNAKE_CASE  :						Dict    							=      cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
			__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      cuda.mem_alloc(h_outputa.nbytes)
			__SCREAMING_SNAKE_CASE  :						List[str]    							=      cuda.mem_alloc(h_outputa.nbytes)

			# Create a stream in which to copy inputs/outputs and run inference.
			__SCREAMING_SNAKE_CASE  :						List[str]    							=      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}""")

			__SCREAMING_SNAKE_CASE  :						List[str]    							=      0.0
			__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      0
			__SCREAMING_SNAKE_CASE  :						Any    							=      timeit.default_timer()

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

						__SCREAMING_SNAKE_CASE  :						int    							=      outputs
						__SCREAMING_SNAKE_CASE  :						List[str]    							=      torch.tensor(start_logits)
						__SCREAMING_SNAKE_CASE  :						Any    							=      torch.tensor(end_logits)

						# necessary to pad predictions and labels for being gathered
						__SCREAMING_SNAKE_CASE  :						List[Any]    							=      accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_0_0)
						__SCREAMING_SNAKE_CASE  :						int    							=      accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_0_0)

						__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
						__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_0_0)

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

			__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      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 * 1_0_0_0 / niter))
			logger.info('''Total Inference Time =  {:.3f} ms'''.format(total_time * 1_0_0_0))




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

__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      post_processing_function(eval_examples, eval_dataset, all_preds)
__SCREAMING_SNAKE_CASE  :						Dict    							=      metric.compute(predictions=prediction.predictions, references=prediction.label_ids)




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





"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 							lowerCamelCase_(   A__,				A__,				unittest.TestCase    ):





					'''simple docstring'''



					lowercase__   :       Tuple										= StableDiffusionXLImgaImgPipeline
					lowercase__   :       List[str]										= TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
					lowercase__   :       int										= PipelineTesterMixin.required_optional_params - {'latents'}
					lowercase__   :       Union[str, Any]										= TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
					lowercase__   :       Dict										= IMAGE_TO_IMAGE_IMAGE_PARAMS
					lowercase__   :       List[str]										= IMAGE_TO_IMAGE_IMAGE_PARAMS

					def 			snake_case__      (    self   ):
									torch.manual_seed(0   )
									_lowerCamelCase        =				UNetaDConditionModel(
									    block_out_channels=(3_2, 6_4)				,       layers_per_block=2				,       sample_size=3_2				,       in_channels=4				,       out_channels=4				,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,       up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				,       attention_head_dim=(2, 4)				,       use_linear_projection=lowerCamelCase__				,       addition_embed_type='''text_time'''				,       addition_time_embed_dim=8				,       transformer_layers_per_block=(1, 2)				,       projection_class_embeddings_input_dim=8_0				,       cross_attention_dim=6_4				,       )
									_lowerCamelCase        =				EulerDiscreteScheduler(
									    beta_start=0.0_0_0_8_5				,       beta_end=0.0_1_2				,       steps_offset=1				,       beta_schedule='''scaled_linear'''				,       timestep_spacing='''leading'''				,       )
									torch.manual_seed(0   )
									_lowerCamelCase        =				AutoencoderKL(
									    block_out_channels=[3_2, 6_4]				,       in_channels=3				,       out_channels=3				,       down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				,       up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				,       latent_channels=4				,       sample_size=1_2_8				,       )
									torch.manual_seed(0   )
									_lowerCamelCase        =				CLIPTextConfig(
									    bos_token_id=0				,       eos_token_id=2				,       hidden_size=3_2				,       intermediate_size=3_7				,       layer_norm_eps=1e-05				,       num_attention_heads=4				,       num_hidden_layers=5				,       pad_token_id=1				,       vocab_size=1_0_0_0				,       hidden_act='''gelu'''				,       projection_dim=3_2				,       )
									_lowerCamelCase        =				CLIPTextModel(lowerCamelCase__   )
									_lowerCamelCase        =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				,       local_files_only=lowerCamelCase__   )

									_lowerCamelCase        =				CLIPTextModelWithProjection(lowerCamelCase__   )
									_lowerCamelCase        =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				,       local_files_only=lowerCamelCase__   )

									_lowerCamelCase        =				{
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''vae''': vae,
									    '''text_encoder''': text_encoder,
									    '''tokenizer''': tokenizer,
									    '''text_encoder_2''': text_encoder_a,
									    '''tokenizer_2''': tokenizer_a,
									    # "safety_checker": None,
									    # "feature_extractor": None,
									}
									return components

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__=0   ):
									_lowerCamelCase        =				floats_tensor((1, 3, 3_2, 3_2)				,       rng=random.Random(lowerCamelCase__   )   ).to(lowerCamelCase__   )
									_lowerCamelCase        =				image / 2 + 0.5
									if str(lowerCamelCase__   ).startswith('''mps'''   ):
													_lowerCamelCase        =				torch.manual_seed(lowerCamelCase__   )
									else:
													_lowerCamelCase        =				torch.Generator(device=lowerCamelCase__   ).manual_seed(lowerCamelCase__   )
									_lowerCamelCase        =				{
									    '''prompt''': '''A painting of a squirrel eating a burger''',
									    '''image''': image,
									    '''generator''': generator,
									    '''num_inference_steps''': 2,
									    '''guidance_scale''': 5.0,
									    '''output_type''': '''numpy''',
									    '''strength''': 0.7_5,
									}
									return inputs

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
									_lowerCamelCase        =				self.get_dummy_components()
									_lowerCamelCase        =				StableDiffusionXLImgaImgPipeline(**lowerCamelCase__   )
									_lowerCamelCase        =				sd_pipe.to(lowerCamelCase__   )
									sd_pipe.set_progress_bar_config(disable=lowerCamelCase__   )

									_lowerCamelCase        =				self.get_dummy_inputs(lowerCamelCase__   )
									_lowerCamelCase        =				sd_pipe(**lowerCamelCase__   ).images
									_lowerCamelCase        =				image[0, -3:, -3:, -1]

									assert image.shape == (1, 3_2, 3_2, 3)

									_lowerCamelCase        =				np.array([0.4_6_5_6, 0.4_8_4_0, 0.4_4_3_9, 0.6_6_9_8, 0.5_5_7_4, 0.4_5_2_4, 0.5_7_9_9, 0.5_9_4_3, 0.5_1_6_5]   )

									assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2

					def 			snake_case__      (    self   ):
									super().test_attention_slicing_forward_pass(expected_max_diff=3e-3   )

					def 			snake_case__      (    self   ):
									super().test_inference_batch_single_identical(expected_max_diff=3e-3   )

					def 			snake_case__      (    self   ):
									pass





					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.get_dummy_components()
									_lowerCamelCase        =				StableDiffusionXLImgaImgPipeline(**lowerCamelCase__   )
									_lowerCamelCase        =				sd_pipe.to(lowerCamelCase__   )
									_lowerCamelCase        =				sd_pipe.to(lowerCamelCase__   )
									sd_pipe.set_progress_bar_config(disable=lowerCamelCase__   )

									# forward without prompt embeds
									_lowerCamelCase        =				self.get_dummy_inputs(lowerCamelCase__   )
									_lowerCamelCase        =				3 * ['''this is a negative prompt''']
									_lowerCamelCase        =				negative_prompt
									_lowerCamelCase        =				3 * [inputs['''prompt''']]

									_lowerCamelCase        =				sd_pipe(**lowerCamelCase__   )
									_lowerCamelCase        =				output.images[0, -3:, -3:, -1]

									# forward with prompt embeds
									_lowerCamelCase        =				self.get_dummy_inputs(lowerCamelCase__   )
									_lowerCamelCase        =				3 * ['''this is a negative prompt''']
									_lowerCamelCase        =				3 * [inputs.pop('''prompt'''   )]

									(
									    (
									    _lowerCamelCase
									)  ,     (
									    _lowerCamelCase
									)  ,     (
									    _lowerCamelCase
									)  ,     (
									    _lowerCamelCase
									)  ,     
									)        =				sd_pipe.encode_prompt(lowerCamelCase__				,       negative_prompt=lowerCamelCase__   )

									_lowerCamelCase        =				sd_pipe(
									    **lowerCamelCase__				,       prompt_embeds=lowerCamelCase__				,       negative_prompt_embeds=lowerCamelCase__				,       pooled_prompt_embeds=lowerCamelCase__				,       negative_pooled_prompt_embeds=lowerCamelCase__				,       )
									_lowerCamelCase        =				output.images[0, -3:, -3:, -1]

									# make sure that it's equal
									assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()   ).max() < 1e-4


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





					'''simple docstring'''



					def 			snake_case__      (    self   ):
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__="cpu"				,       lowerCamelCase__=torch.floataa				,       lowerCamelCase__=0   ):
									_lowerCamelCase        =				torch.Generator(device=lowerCamelCase__   ).manual_seed(lowerCamelCase__   )
									_lowerCamelCase        =				np.random.RandomState(lowerCamelCase__   ).standard_normal((1, 4, 6_4, 6_4)   )
									_lowerCamelCase        =				torch.from_numpy(lowerCamelCase__   ).to(device=lowerCamelCase__				,       dtype=lowerCamelCase__   )
									_lowerCamelCase        =				{
									    '''prompt''': '''a photograph of an astronaut riding a horse''',
									    '''latents''': latents,
									    '''generator''': generator,
									    '''num_inference_steps''': 3,
									    '''guidance_scale''': 7.5,
									    '''output_type''': '''numpy''',
									}
									return inputs





					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''   )
									pipe.to(lowerCamelCase__   )
									pipe.set_progress_bar_config(disable=lowerCamelCase__   )

									_lowerCamelCase        =				self.get_inputs(lowerCamelCase__   )
									_lowerCamelCase        =				pipe(**lowerCamelCase__   ).images
									_lowerCamelCase        =				image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									_lowerCamelCase        =				np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6]   )
									assert np.abs(image_slice - expected_slice   ).max() < 7e-3
