

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 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[Any]					, lowerCAmelCase:					List[str]					, lowerCAmelCase:					Any					, lowerCAmelCase:					str					, lowerCAmelCase:					str					, lowerCAmelCase:					List[str]					, lowerCAmelCase:					Dict					, lowerCAmelCase:					Optional[int]	)				->    List[Any]:
				_UpperCAmelCase    :						Dict							=      np.asarray(inputs["input_ids"]					, dtype=np.intaa	)
				_UpperCAmelCase    :						List[str]							=      np.asarray(inputs["attention_mask"]					, dtype=np.intaa	)
				_UpperCAmelCase    :						Union[str, Any]							=      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
				_UpperCAmelCase    :						Dict							=      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
				_UpperCAmelCase    :						Any							=      time.time()
				_UpperCAmelCase    :						Any							=      end_time - start_time
				_UpperCAmelCase    :						Optional[int]							=      (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 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					str	)				->    Any:
				# 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
				_UpperCAmelCase    :						Optional[Any]							=      [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.
				_UpperCAmelCase    :						List[str]							=      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.
				_UpperCAmelCase    :						List[Any]							=      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.
				_UpperCAmelCase    :						Tuple							=      []

				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).
								_UpperCAmelCase    :						Tuple							=      tokenized_examples.sequence_ids(lowerCAmelCase	)
								_UpperCAmelCase    :						Optional[Any]							=      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.
								_UpperCAmelCase    :						List[str]							=      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.
								_UpperCAmelCase    :						Any							=      [
								    (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 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Any					, lowerCAmelCase:					Dict					, lowerCAmelCase:					str					, lowerCAmelCase:					Optional[Any]="eval"	)				->    Union[str, Any]:
				# Post-processing: we match the start logits and end logits to answers in the original context.
				_UpperCAmelCase    :						Tuple							=      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:
								_UpperCAmelCase    :						Optional[int]							=      [
								    {"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
								]
				else:
								_UpperCAmelCase    :						Optional[Any]							=      [{"id": k, "prediction_text": v} for k, v in predictions.items()]

				_UpperCAmelCase    :						Optional[int]							=      [{"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 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					str	)				->    List[str]:
									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 * 1000 / niter))
					logger.info('Total Inference Time =  {:.3f} ms'.format(total_time * 1000))





					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}''')





from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)  # pylint: disable=invalid-name

SCREAMING_SNAKE_CASE_            = '\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Any					, lowerCAmelCase:					List[str]					, lowerCAmelCase:					int=8	)				->    int:
				_UpperCAmelCase    :						int							=      height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				_UpperCAmelCase    :						List[str]							=      width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor





class 			a					(		UpperCAmelCase			):


						def __init__(    self  ,   A_  ,   A_  ,   A_  ,   ):







										'''simple docstring'''


										super().__init__()

										self.register_modules(
										    unet=A_  ,   scheduler=A_  ,   movq=A_  ,   )
										_UpperCAmelCase    :						Optional[int]							=      2 ** (len(self.movq.config.block_out_channels		) - 1)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										if latents is None:
														_UpperCAmelCase    :						Any							=      randn_tensor(A_  ,   generator=A_  ,   device=A_  ,   dtype=A_		)
										else:
														if latents.shape != shape:
																		raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'		)
														_UpperCAmelCase    :						Optional[int]							=      latents.to(A_		)

										_UpperCAmelCase    :						Tuple							=      latents * scheduler.init_noise_sigma
										return latents


						def     _UpperCAmelCase			(    self  ,   A_=0		):







										'''simple docstring'''


										if is_accelerate_available():
														from accelerate import cpu_offload
										else:
														raise ImportError("Please install accelerate via `pip install accelerate`"		)

										_UpperCAmelCase    :						Union[str, Any]							=      torch.device(f'cuda:{gpu_id}'		)

										_UpperCAmelCase    :						Any							=      [
										    self.unet,
										    self.movq,
										]
										for cpu_offloaded_model in models:
														if cpu_offloaded_model is not None:
																		cpu_offload(A_  ,   A_		)


						def     _UpperCAmelCase			(    self  ,   A_=0		):







										'''simple docstring'''


										if is_accelerate_available() and is_accelerate_version(">="  ,   "0.17.0.dev0"		):
														from accelerate import cpu_offload_with_hook
										else:
														raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."		)

										_UpperCAmelCase    :						str							=      torch.device(f'cuda:{gpu_id}'		)

										if self.device.type != "cpu":
														self.to("cpu"  ,   silence_dtype_warnings=A_		)
														torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

										_UpperCAmelCase    :						Optional[int]							=      None
										for cpu_offloaded_model in [self.unet, self.movq]:
														_UpperCAmelCase						,							_UpperCAmelCase    :						Dict							=      cpu_offload_with_hook(A_  ,   A_  ,   prev_module_hook=A_		)

										# We'll offload the last model manually.
										_UpperCAmelCase    :						Optional[Any]							=      hook


						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										if not hasattr(self.unet  ,   "_hf_hook"		):
														return self.device
										for module in self.unet.modules():
														if (
														    hasattr(A_  ,   "_hf_hook"		)
														    and hasattr(module._hf_hook  ,   "execution_device"		)
														    and module._hf_hook.execution_device is not None
														):
																		return torch.device(module._hf_hook.execution_device		)
										return self.device





						@torch.no_grad()
						@replace_example_docstring(A_		)
						def __call__(    self  ,   A_  ,   A_  ,   A_  ,   A_ = 512  ,   A_ = 512  ,   A_ = 100  ,   A_ = 4.0  ,   A_ = 1  ,   A_ = None  ,   A_ = None  ,   A_ = "pil"  ,   A_ = True  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      self._execution_device

										_UpperCAmelCase    :						Tuple							=      guidance_scale > 1.0

										if isinstance(A_  ,   A_		):
														_UpperCAmelCase    :						Union[str, Any]							=      torch.cat(A_  ,   dim=0		)
										if isinstance(A_  ,   A_		):
														_UpperCAmelCase    :						Dict							=      torch.cat(A_  ,   dim=0		)
										if isinstance(A_  ,   A_		):
														_UpperCAmelCase    :						Any							=      torch.cat(A_  ,   dim=0		)

										_UpperCAmelCase    :						Optional[Any]							=      image_embeds.shape[0] * num_images_per_prompt

										if do_classifier_free_guidance:
														_UpperCAmelCase    :						Optional[int]							=      image_embeds.repeat_interleave(A_  ,   dim=0		)
														_UpperCAmelCase    :						Union[str, Any]							=      negative_image_embeds.repeat_interleave(A_  ,   dim=0		)
														_UpperCAmelCase    :						Tuple							=      hint.repeat_interleave(A_  ,   dim=0		)

										_UpperCAmelCase    :						List[Any]							=      torch.cat([negative_image_embeds, image_embeds]  ,   dim=0		).to(dtype=self.unet.dtype  ,   device=A_		)
										_UpperCAmelCase    :						List[Any]							=      torch.cat([hint, hint]  ,   dim=0		).to(dtype=self.unet.dtype  ,   device=A_		)
										self.scheduler.set_timesteps(A_  ,   device=A_		)
										_UpperCAmelCase    :						Dict							=      self.scheduler.timesteps

										_UpperCAmelCase    :						Union[str, Any]							=      self.movq.config.latent_channels

										_UpperCAmelCase						,							_UpperCAmelCase    :						Optional[int]							=      downscale_height_and_width(A_  ,   A_  ,   self.movq_scale_factor		)

										# create initial latent
										_UpperCAmelCase    :						Tuple							=      self.prepare_latents(
										    (batch_size, num_channels_latents, height, width)  ,   image_embeds.dtype  ,   A_  ,   A_  ,   A_  ,   self.scheduler  ,   )

										for i, t in enumerate(self.progress_bar(A_		)		):
														# expand the latents if we are doing classifier free guidance
														_UpperCAmelCase    :						List[str]							=      torch.cat([latents] * 2		) if do_classifier_free_guidance else latents

														_UpperCAmelCase    :						Optional[Any]							=      {"image_embeds": image_embeds, "hint": hint}
														_UpperCAmelCase    :						Optional[int]							=      self.unet(
														    sample=A_  ,   timestep=A_  ,   encoder_hidden_states=A_  ,   added_cond_kwargs=A_  ,   return_dict=A_  ,   )[0]

														if do_classifier_free_guidance:
																		_UpperCAmelCase						,							_UpperCAmelCase    :						Any							=      noise_pred.split(latents.shape[1]  ,   dim=1		)
																		_UpperCAmelCase						,							_UpperCAmelCase    :						Tuple							=      noise_pred.chunk(2		)
																		_UpperCAmelCase						,							_UpperCAmelCase    :						List[Any]							=      variance_pred.chunk(2		)
																		_UpperCAmelCase    :						Optional[Any]							=      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																		_UpperCAmelCase    :						int							=      torch.cat([noise_pred, variance_pred_text]  ,   dim=1		)

														if not (
														    hasattr(self.scheduler.config  ,   "variance_type"		)
														    and self.scheduler.config.variance_type in ["learned", "learned_range"]
														):
																		_UpperCAmelCase						,							_UpperCAmelCase    :						Optional[Any]							=      noise_pred.split(latents.shape[1]  ,   dim=1		)

														# compute the previous noisy sample x_t -> x_t-1
														_UpperCAmelCase    :						int							=      self.scheduler.step(
														    A_  ,   A_  ,   A_  ,   generator=A_  ,   )[0]
										# post-processing
										_UpperCAmelCase    :						Optional[Any]							=      self.movq.decode(A_  ,   force_not_quantize=A_		)["sample"]

										if output_type not in ["pt", "np", "pil"]:
														raise ValueError(f'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'		)

										if output_type in ["np", "pil"]:
														_UpperCAmelCase    :						Union[str, Any]							=      image * 0.5 + 0.5
														_UpperCAmelCase    :						Dict							=      image.clamp(0  ,   1		)
														_UpperCAmelCase    :						List[str]							=      image.cpu().permute(0  ,   2  ,   3  ,   1		).float().numpy()

										if output_type == "pil":
														_UpperCAmelCase    :						str							=      self.numpy_to_pil(A_		)

										if not return_dict:
														return (image,)

										return ImagePipelineOutput(images=A_		)


