

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  lowercase				(       lowercase_			):




				def __init__(					self      ,   *snake_case      ,   snake_case=None      ,   snake_case=None      ,   **snake_case       ):
											super().__init__(*snake_case      ,   **snake_case       )
											snake_case_							=    eval_examples
											snake_case_							=    post_process_function




				def     a							(					self      ,   snake_case = None      ,   snake_case=None      ,   snake_case = None      ,   snake_case = "eval"      ,   **snake_case      ,   ):
											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(snake_case       )
											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(
																		    snake_case      ,   description='Evaluation'      ,   prediction_loss_only=True if compute_metrics is None else None      ,   ignore_keys=snake_case      ,   metric_key_prefix=snake_case      ,   )
											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(
											        snake_case      ,   snake_case      ,   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(snake_case      ,   snake_case      ,   snake_case       )
																		snake_case_							=    self.compute_metrics(snake_case       )

																		# 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(snake_case       )

																		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(snake_case       )

											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      ,   snake_case       )
											return metrics




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case=None      ,   snake_case = "test"      ,   **snake_case       ):
											snake_case_							=    gen_kwargs.copy()

											snake_case_							=    self.get_test_dataloader(snake_case       )

											# 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(
																		    snake_case      ,   description='Prediction'      ,   prediction_loss_only=True if compute_metrics is None else None      ,   ignore_keys=snake_case      ,   metric_key_prefix=snake_case      ,   )
											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(
											        snake_case      ,   snake_case      ,   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(snake_case      ,   snake_case      ,   snake_case      ,   'predict'       )
											snake_case_							=    self.compute_metrics(snake_case       )

											# 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(snake_case       )
											metrics.update(output.metrics       )
											return PredictionOutput(predictions=predictions.predictions      ,   label_ids=predictions.label_ids      ,   metrics=snake_case       )







import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
					import jax
					import jax.numpy as jnp
					from flax.jax_utils import replicate
					from flax.training.common_utils import shard







@slow
@require_flax
class  lowercase				(       unittest.TestCase			):




				def     a							(					self       ):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()




				def     a							(					self       ):
											snake_case_							=    load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/sd2-inpaint/init_image.png'       )
											snake_case_							=    load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'       )

											snake_case_							=    'xvjiarui/stable-diffusion-2-inpainting'
											snake_case_				,    snake_case_							=    FlaxStableDiffusionInpaintPipeline.from_pretrained(snake_case      ,   safety_checker=snake_case       )

											snake_case_							=    'Face of a yellow cat, high resolution, sitting on a park bench'

											snake_case_							=    jax.random.PRNGKey(0       )
											snake_case_							=    50

											snake_case_							=    jax.device_count()
											snake_case_							=    num_samples * [prompt]
											snake_case_							=    num_samples * [init_image]
											snake_case_							=    num_samples * [mask_image]
											snake_case_				,    snake_case_				,    snake_case_							=    pipeline.prepare_inputs(snake_case      ,   snake_case      ,   snake_case       )

											# shard inputs and rng
											snake_case_							=    replicate(snake_case       )
											snake_case_							=    jax.random.split(snake_case      ,   jax.device_count()       )
											snake_case_							=    shard(snake_case       )
											snake_case_							=    shard(snake_case       )
											snake_case_							=    shard(snake_case       )

											snake_case_							=    pipeline(
											    snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   jit=snake_case       )

											snake_case_							=    output.images.reshape(snake_case      ,   512      ,   512      ,   3       )

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

											snake_case_							=    jnp.asarray(jax.device_get(image_slice.flatten()       )       )
											snake_case_							=    jnp.array(
											    [0.3_61_13_07, 0.37_64_97_36, 0.3_75_74_08, 0.38_21_39_53, 0.39_29_51_67, 0.3_84_16_31, 0.41_55_49_78, 0.4_13_74_75, 0.4_21_70_84]       )
											print(F'''output_slice: {output_slice}'''       )
											assert jnp.abs(output_slice - expected_slice       ).max() < 1e-2




