




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

__SCREAMING_SNAKE_CASE 						=logging.getLogger(__name__)

__SCREAMING_SNAKE_CASE 						=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""",
)

__SCREAMING_SNAKE_CASE 						=parser.parse_args()

if args.tokenizer_name:
			__SCREAMING_SNAKE_CASE 						=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 						=args.per_device_eval_batch_size

__SCREAMING_SNAKE_CASE 						=(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
__SCREAMING_SNAKE_CASE 						=True

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

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

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

			with builder.create_builder_config() as config:
						__SCREAMING_SNAKE_CASE 						=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)
						__SCREAMING_SNAKE_CASE 						=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 						=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 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    np.asarray(inputs['''input_ids''']  ,    dtype=np.intaa     )
							SCREAMING_SNAKE_CASE_   	=    np.asarray(inputs['''attention_mask''']  ,    dtype=np.intaa     )
							SCREAMING_SNAKE_CASE_   	=    np.asarray(inputs['''token_type_ids''']  ,    dtype=np.intaa     )

							# Copy inputs
							cuda.memcpy_htod_async(d_inputs[0]  ,    input_ids.ravel()  ,    _lowerCAmelCase     )
							cuda.memcpy_htod_async(d_inputs[1]  ,    attention_mask.ravel()  ,    _lowerCAmelCase     )
							cuda.memcpy_htod_async(d_inputs[2]  ,    token_type_ids.ravel()  ,    _lowerCAmelCase     )
							# start time
							SCREAMING_SNAKE_CASE_   	=    time.time()
							# Run inference
							context.execute_async(
							    bindings=[int(_lowerCAmelCase     ) for d_inp in d_inputs] + [int(_lowerCAmelCase     ), int(_lowerCAmelCase     )]  ,    stream_handle=stream.handle     )
							# Transfer predictions back from GPU
							cuda.memcpy_dtoh_async(_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     )
							cuda.memcpy_dtoh_async(_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     )
							# Synchronize the stream and take time
							stream.synchronize()
							# end time
							SCREAMING_SNAKE_CASE_   	=    time.time()
							SCREAMING_SNAKE_CASE_   	=    end_time - start_time
							SCREAMING_SNAKE_CASE_   	=    (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 						=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 						=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 						=raw_datasets["""validation"""].column_names

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

# Padding side determines if we do (question|context) or (context|question).
__SCREAMING_SNAKE_CASE 						=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 						=min(args.max_seq_length, tokenizer.model_max_length)

def 			a   (_lowerCAmelCase     ):
							# Some of the questions have lots of whitespace on the left, which is not useful and will make the
							# truncation of the context fail (the tokenized question will take a lots of space). So we remove that
							# left whitespace
							SCREAMING_SNAKE_CASE_   	=    [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.
							SCREAMING_SNAKE_CASE_   	=    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=_lowerCAmelCase  ,    stride=args.doc_stride  ,    return_overflowing_tokens=_lowerCAmelCase  ,    return_offsets_mapping=_lowerCAmelCase  ,    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.
							SCREAMING_SNAKE_CASE_   	=    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.
							SCREAMING_SNAKE_CASE_   	=    []

							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).
														SCREAMING_SNAKE_CASE_   	=    tokenized_examples.sequence_ids(_lowerCAmelCase     )
														SCREAMING_SNAKE_CASE_   	=    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.
														SCREAMING_SNAKE_CASE_   	=    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.
														SCREAMING_SNAKE_CASE_   	=    [
														    (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 						=raw_datasets["""validation"""]
# Validation Feature Creation
__SCREAMING_SNAKE_CASE 						=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 						=default_data_collator

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

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase="eval"     ):
							# Post-processing: we match the start logits and end logits to answers in the original context.
							SCREAMING_SNAKE_CASE_   	=    postprocess_qa_predictions(
							    examples=_lowerCAmelCase  ,    features=_lowerCAmelCase  ,    predictions=_lowerCAmelCase  ,    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=_lowerCAmelCase  ,    )
							# Format the result to the format the metric expects.
							if args.version_2_with_negative:
														SCREAMING_SNAKE_CASE_   	=    [
														    {'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
														]
							else:
														SCREAMING_SNAKE_CASE_   	=    [{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]

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


__SCREAMING_SNAKE_CASE 						=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 			a   (_lowerCAmelCase     ):
										return trt.volume(engine.get_binding_shape(_lowerCAmelCase     )     ) * engine.get_binding_dtype(_lowerCAmelCase     ).itemsize

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

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

			# Create a stream in which to copy inputs/outputs and run inference.
			__SCREAMING_SNAKE_CASE 						=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 						=0.0
			__SCREAMING_SNAKE_CASE 						=0
			__SCREAMING_SNAKE_CASE 						=timeit.default_timer()

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

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

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

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

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

			__SCREAMING_SNAKE_CASE 						=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_000 / niter))
			logger.info("""Total Inference Time =  {:.3f} ms""".format(total_time * 1_000))






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

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






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








import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference






class  __magic_name__						(  __UpperCAmelCase    ,  unittest.TestCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							List[str]	      =					ShapEImgaImgPipeline
				SCREAMING_SNAKE_CASE__					:							Dict	      =					["image"]
				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					["image"]
				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					[
				    "num_images_per_prompt",
				    "num_inference_steps",
				    "generator",
				    "latents",
				    "guidance_scale",
				    "frame_size",
				    "output_type",
				    "return_dict",
				]
				SCREAMING_SNAKE_CASE__					:							Optional[int]	      =					False


				@property
				def     _A							(							self:   Optional[Any]		):
											return 32


				@property
				def     _A							(							self:   Optional[int]		):
											return 32


				@property
				def     _A							(							self:   List[Any]		):
											return self.time_input_dim * 4


				@property
				def     _A							(							self:   Any		):
											return 8


				@property
				def     _A							(							self:   int		):
											torch.manual_seed(0		)
											SCREAMING_SNAKE_CASE_   	=    CLIPVisionConfig(
											    hidden_size=self.text_embedder_hidden_size		,					image_size=64		,					projection_dim=self.text_embedder_hidden_size		,					intermediate_size=37		,					num_attention_heads=4		,					num_channels=3		,					num_hidden_layers=5		,					patch_size=1		,					)

											SCREAMING_SNAKE_CASE_   	=    CLIPVisionModel(_lowerCamelCase		)
											return model


				@property
				def     _A							(							self:   List[Any]		):
											SCREAMING_SNAKE_CASE_   	=    CLIPImageProcessor(
											    crop_size=2_24		,					do_center_crop=_lowerCamelCase		,					do_normalize=_lowerCamelCase		,					do_resize=_lowerCamelCase		,					image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]		,					image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]		,					resample=3		,					size=2_24		,					)

											return image_processor


				@property
				def     _A							(							self:   Dict		):
											torch.manual_seed(0		)

											SCREAMING_SNAKE_CASE_   	=    {
											    '''num_attention_heads''': 2,
											    '''attention_head_dim''': 16,
											    '''embedding_dim''': self.time_input_dim,
											    '''num_embeddings''': 32,
											    '''embedding_proj_dim''': self.text_embedder_hidden_size,
											    '''time_embed_dim''': self.time_embed_dim,
											    '''num_layers''': 1,
											    '''clip_embed_dim''': self.time_input_dim * 2,
											    '''additional_embeddings''': 0,
											    '''time_embed_act_fn''': '''gelu''',
											    '''norm_in_type''': '''layer''',
											    '''embedding_proj_norm_type''': '''layer''',
											    '''encoder_hid_proj_type''': None,
											    '''added_emb_type''': None,
											}

											SCREAMING_SNAKE_CASE_   	=    PriorTransformer(**_lowerCamelCase		)
											return model


				@property
				def     _A							(							self:   List[Any]		):
											torch.manual_seed(0		)

											SCREAMING_SNAKE_CASE_   	=    {
											    '''param_shapes''': (
											        (self.renderer_dim, 93),
											        (self.renderer_dim, 8),
											        (self.renderer_dim, 8),
											        (self.renderer_dim, 8),
											    ),
											    '''d_latent''': self.time_input_dim,
											    '''d_hidden''': self.renderer_dim,
											    '''n_output''': 12,
											    '''background''': (
											        0.1,
											        0.1,
											        0.1,
											    ),
											}
											SCREAMING_SNAKE_CASE_   	=    ShapERenderer(**_lowerCamelCase		)
											return model


				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    self.dummy_prior
											SCREAMING_SNAKE_CASE_   	=    self.dummy_image_encoder
											SCREAMING_SNAKE_CASE_   	=    self.dummy_image_processor
											SCREAMING_SNAKE_CASE_   	=    self.dummy_renderer

											SCREAMING_SNAKE_CASE_   	=    HeunDiscreteScheduler(
											    beta_schedule='''exp'''		,					num_train_timesteps=10_24		,					prediction_type='''sample'''		,					use_karras_sigmas=_lowerCamelCase		,					clip_sample=_lowerCamelCase		,					clip_sample_range=1.0		,					)
											SCREAMING_SNAKE_CASE_   	=    {
											    '''prior''': prior,
											    '''image_encoder''': image_encoder,
											    '''image_processor''': image_processor,
											    '''renderer''': renderer,
											    '''scheduler''': scheduler,
											}

											return components


				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   Optional[Any]=0		):
											SCREAMING_SNAKE_CASE_   	=    floats_tensor((1, 3, 64, 64)		,					rng=random.Random(_lowerCamelCase		)		).to(_lowerCamelCase		)

											if str(_lowerCamelCase		).startswith('''mps'''		):
																		SCREAMING_SNAKE_CASE_   	=    torch.manual_seed(_lowerCamelCase		)
											else:
																		SCREAMING_SNAKE_CASE_   	=    torch.Generator(device=_lowerCamelCase		).manual_seed(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    {
											    '''image''': input_image,
											    '''generator''': generator,
											    '''num_inference_steps''': 1,
											    '''frame_size''': 32,
											    '''output_type''': '''np''',
											}
											return inputs


				def     _A							(							self:   Any		):
											SCREAMING_SNAKE_CASE_   	=    '''cpu'''

											SCREAMING_SNAKE_CASE_   	=    self.get_dummy_components()

											SCREAMING_SNAKE_CASE_   	=    self.pipeline_class(**_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    pipe.to(_lowerCamelCase		)

											pipe.set_progress_bar_config(disable=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    pipe(**self.get_dummy_inputs(_lowerCamelCase		)		)
											SCREAMING_SNAKE_CASE_   	=    output.images[0]
											SCREAMING_SNAKE_CASE_   	=    image[0, -3:, -3:, -1]

											assert image.shape == (20, 32, 32, 3)

											SCREAMING_SNAKE_CASE_   	=    np.array(
											    [
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											    ]		)

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


				def     _A							(							self:   str		):
											# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
											self._test_inference_batch_consistent(batch_sizes=[1, 2]		)


				def     _A							(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    torch_device == '''cpu'''
											SCREAMING_SNAKE_CASE_   	=    True
											self._test_inference_batch_single_identical(
											    batch_size=2		,					test_max_difference=_lowerCamelCase		,					relax_max_difference=_lowerCamelCase		,					)





				def     _A							(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    self.get_dummy_components()
											SCREAMING_SNAKE_CASE_   	=    self.pipeline_class(**_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    pipe.to(_lowerCamelCase		)
											pipe.set_progress_bar_config(disable=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    1
											SCREAMING_SNAKE_CASE_   	=    2

											SCREAMING_SNAKE_CASE_   	=    self.get_dummy_inputs(_lowerCamelCase		)

											for key in inputs.keys():
																		if key in self.batch_params:
																									SCREAMING_SNAKE_CASE_   	=    batch_size * [inputs[key]]

											SCREAMING_SNAKE_CASE_   	=    pipe(**_lowerCamelCase		,					num_images_per_prompt=_lowerCamelCase		)[0]

											assert images.shape[0] == batch_size * num_images_per_prompt






@slow
@require_torch_gpu
class  __magic_name__						(  unittest.TestCase):
				'''simple docstring'''







				def     _A							(							self:   Any		):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()





				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png'''		)
											SCREAMING_SNAKE_CASE_   	=    load_numpy(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/shap_e/test_shap_e_img2img_out.npy'''		)
											SCREAMING_SNAKE_CASE_   	=    ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img'''		)
											SCREAMING_SNAKE_CASE_   	=    pipe.to(_lowerCamelCase		)
											pipe.set_progress_bar_config(disable=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    torch.Generator(device=_lowerCamelCase		).manual_seed(0		)

											SCREAMING_SNAKE_CASE_   	=    pipe(
											    _lowerCamelCase		,					generator=_lowerCamelCase		,					guidance_scale=3.0		,					num_inference_steps=64		,					frame_size=64		,					output_type='''np'''		,					).images[0]

											assert images.shape == (20, 64, 64, 3)

											assert_mean_pixel_difference(_lowerCamelCase		,					_lowerCamelCase		)


