



'''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 	a__     (				_lowercase	):


	def __init__(self : List[Any],					*__UpperCAmelCase : List[Any],					__UpperCAmelCase : Tuple=None,					__UpperCAmelCase : Optional[Any]=None,					**__UpperCAmelCase : List[Any]      )     ->			Tuple:

					"""simple docstring"""

					super().__init__(*__UpperCAmelCase,					**__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						int      					= eval_examples
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= post_process_function


	def 				lowercase__  (self : Any,					__UpperCAmelCase : Optional[Dataset] = None,					__UpperCAmelCase : Union[str, Any]=None,					__UpperCAmelCase : Optional[List[str]] = None,					__UpperCAmelCase : str = "eval",					**__UpperCAmelCase : Tuple,					)     ->			Dict[str, float]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= gen_kwargs.copy()
					SCREAMING_SNAKE_CASE  :						List[str]      					= (
					    gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length'''      ) is not None else self.args.generation_max_length
					)
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= (
					    gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''      ) is not None else self.args.generation_num_beams
					)
					SCREAMING_SNAKE_CASE  :						List[str]      					= gen_kwargs

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.eval_dataset if eval_dataset is None else eval_dataset
					SCREAMING_SNAKE_CASE  :						int      					= self.get_eval_dataloader(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= self.eval_examples if eval_examples is None else eval_examples

					# Temporarily disable metric computation, we will do it in the loop here.
					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.compute_metrics
					SCREAMING_SNAKE_CASE  :						List[str]      					= None
					SCREAMING_SNAKE_CASE  :						Any      					= time.time()
					SCREAMING_SNAKE_CASE  :						List[str]      					= self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
					try:
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= eval_loop(
									    __UpperCAmelCase,					description='''Evaluation''',					prediction_loss_only=True if compute_metrics is None else None,					ignore_keys=__UpperCAmelCase,					metric_key_prefix=__UpperCAmelCase,					)
					finally:
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= compute_metrics
					SCREAMING_SNAKE_CASE  :						str      					= 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(
					        __UpperCAmelCase,					__UpperCAmelCase,					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
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.post_process_function(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase      )
									SCREAMING_SNAKE_CASE  :						int      					= self.compute_metrics(__UpperCAmelCase      )

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

									metrics.update(output.metrics      )
					else:
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= output.metrics

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

					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()      )

					SCREAMING_SNAKE_CASE  :						str      					= self.callback_handler.on_evaluate(self.args,					self.state,					self.control,					__UpperCAmelCase      )
					return metrics






	def 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : int,					__UpperCAmelCase : Union[str, Any]=None,					__UpperCAmelCase : str = "test",					**__UpperCAmelCase : Union[str, Any]      )     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= gen_kwargs.copy()

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.get_test_dataloader(__UpperCAmelCase      )

					# Temporarily disable metric computation, we will do it in the loop here.
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.compute_metrics
					SCREAMING_SNAKE_CASE  :						List[str]      					= None
					SCREAMING_SNAKE_CASE  :						Dict      					= time.time()
					SCREAMING_SNAKE_CASE  :						Any      					= self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
					try:
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= eval_loop(
									    __UpperCAmelCase,					description='''Prediction''',					prediction_loss_only=True if compute_metrics is None else None,					ignore_keys=__UpperCAmelCase,					metric_key_prefix=__UpperCAmelCase,					)
					finally:
									SCREAMING_SNAKE_CASE  :						Tuple      					= compute_metrics

					SCREAMING_SNAKE_CASE  :						int      					= 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(
					        __UpperCAmelCase,					__UpperCAmelCase,					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

					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.post_process_function(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					'''predict'''      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.compute_metrics(__UpperCAmelCase      )

					# Prefix all keys with metric_key_prefix + '_'
					for key in list(metrics.keys()      ):
									if not key.startswith(F'''{metric_key_prefix}_'''      ):
													SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= metrics.pop(__UpperCAmelCase      )
					metrics.update(output.metrics      )
					return PredictionOutput(predictions=predictions.predictions,					label_ids=predictions.label_ids,					metrics=__UpperCAmelCase      )









'''simple docstring'''

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
	from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline




