




'''simple docstring'''





import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
		import torch_xla.core.xla_model as xm
		import torch_xla.debug.metrics as met




class 			_snake_case		(     lowercase_							):




			def __init__( self    ,							*a__    ,							a__=None    ,							a__=None    ,							**a__							)    ->    Any:

										'''simple docstring'''




										super().__init__(*a__    ,							**a__							)
										snake_case_      =				eval_examples
										snake_case_      =				post_process_function




			def        lowerCAmelCase__    ( self    ,							a__ = None    ,							a__=None    ,							a__ = None    ,							a__ = "eval"    ,							**a__    ,							)    ->    Dict[str, float]:

										'''simple docstring'''




										snake_case_      =				gen_kwargs.copy()
										snake_case_      =				(
										    gen_kwargs["max_length"] if gen_kwargs.get("max_length"							) is not None else self.args.generation_max_length
										)
										snake_case_      =				(
										    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"							) is not None else self.args.generation_num_beams
										)
										snake_case_      =				gen_kwargs

										snake_case_      =				self.eval_dataset if eval_dataset is None else eval_dataset
										snake_case_      =				self.get_eval_dataloader(a__							)
										snake_case_      =				self.eval_examples if eval_examples is None else eval_examples

										# Temporarily disable metric computation, we will do it in the loop here.
										snake_case_      =				self.compute_metrics
										snake_case_      =				None
										snake_case_      =				time.time()
										snake_case_      =				self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
										try:
																	snake_case_      =				eval_loop(
																	    a__    ,							description="Evaluation"    ,							prediction_loss_only=True if compute_metrics is None else None    ,							ignore_keys=a__    ,							metric_key_prefix=a__    ,							)
										finally:
																	snake_case_      =				compute_metrics
										snake_case_      =				self.args.eval_batch_size * self.args.world_size
										if F'{metric_key_prefix}_jit_compilation_time' in output.metrics:
																	start_time += output.metrics[F'{metric_key_prefix}_jit_compilation_time']
										output.metrics.update(
										    speed_metrics(
										        a__    ,							a__    ,							num_samples=output.num_samples    ,							num_steps=math.ceil(output.num_samples / total_batch_size							)    ,							)							)

										if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
																	# Only the main node write the results by default
																	snake_case_      =				self.post_process_function(a__    ,							a__    ,							a__							)
																	snake_case_      =				self.compute_metrics(a__							)

																	# Prefix all keys with metric_key_prefix + '_'
																	for key in list(metrics.keys()							):
																								if not key.startswith(F'{metric_key_prefix}_'							):
																															snake_case_      =				metrics.pop(a__							)

																	metrics.update(output.metrics							)
										else:
																	snake_case_      =				output.metrics

										if self.args.should_log:
																	# Only the main node log the results by default
																	self.log(a__							)

										if self.args.tpu_metrics_debug or self.args.debug:
																	# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
																	xm.master_print(met.metrics_report()							)

										snake_case_      =				self.callback_handler.on_evaluate(self.args    ,							self.state    ,							self.control    ,							a__							)
										return metrics




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__    ,							a__=None    ,							a__ = "test"    ,							**a__							)    ->    List[str]:

										'''simple docstring'''




										snake_case_      =				gen_kwargs.copy()

										snake_case_      =				self.get_test_dataloader(a__							)

										# Temporarily disable metric computation, we will do it in the loop here.
										snake_case_      =				self.compute_metrics
										snake_case_      =				None
										snake_case_      =				time.time()
										snake_case_      =				self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
										try:
																	snake_case_      =				eval_loop(
																	    a__    ,							description="Prediction"    ,							prediction_loss_only=True if compute_metrics is None else None    ,							ignore_keys=a__    ,							metric_key_prefix=a__    ,							)
										finally:
																	snake_case_      =				compute_metrics

										snake_case_      =				self.args.eval_batch_size * self.args.world_size
										if F'{metric_key_prefix}_jit_compilation_time' in output.metrics:
																	start_time += output.metrics[F'{metric_key_prefix}_jit_compilation_time']
										output.metrics.update(
										    speed_metrics(
										        a__    ,							a__    ,							num_samples=output.num_samples    ,							num_steps=math.ceil(output.num_samples / total_batch_size							)    ,							)							)
										if self.post_process_function is None or self.compute_metrics is None:
																	return output

										snake_case_      =				self.post_process_function(a__    ,							a__    ,							a__    ,							"predict"							)
										snake_case_      =				self.compute_metrics(a__							)

										# Prefix all keys with metric_key_prefix + '_'
										for key in list(metrics.keys()							):
																	if not key.startswith(F'{metric_key_prefix}_'							):
																								snake_case_      =				metrics.pop(a__							)
										metrics.update(output.metrics							)
										return PredictionOutput(predictions=predictions.predictions    ,							label_ids=predictions.label_ids    ,							metrics=a__							)










'''simple docstring'''





import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


_SCREAMING_SNAKE_CASE							:       Any						=			False




class 			_snake_case		(     unittest.TestCase							):
			pass




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




			def        lowerCAmelCase__    ( self							)    ->    List[Any]:

										'''simple docstring'''




										snake_case_      =				VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"							)
										pipe.to(a__							)
										pipe.set_progress_bar_config(disable=a__							)

										snake_case_      =				load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"							)
										snake_case_      =				torch.manual_seed(0							)
										snake_case_      =				pipe(
										    image=a__    ,							generator=a__    ,							guidance_scale=7.5    ,							num_inference_steps=50    ,							output_type="numpy"    ,							).images

										snake_case_      =				image[0, 253:256, 253:256, -1]

										assert image.shape == (1, 512, 512, 3)
										snake_case_      =				np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5]							)

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




