
"""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 							_SCREAMING_SNAKE_CASE	(	_a				):






				'''simple docstring'''



				def __init__(       self						:      str					,  *UpperCAmelCase_						:      str					,  UpperCAmelCase_						:      int=None					,  UpperCAmelCase_						:      Tuple=None					,  **UpperCAmelCase_						:      str	)		->	List[Any]:







											"""simple docstring"""




											super().__init__(*UpperCAmelCase_					,  **UpperCAmelCase_	)
											_lowerCAmelCase							=      eval_examples
											_lowerCAmelCase							=      post_process_function


				def 					__lowerCamelCase						(       self						:      str					,  UpperCAmelCase_						:      str = None					,  UpperCAmelCase_						:      List[str]=None					,  UpperCAmelCase_						:      Optional[Any] = None					,  UpperCAmelCase_						:      Tuple = "eval"					,  **UpperCAmelCase_						:      List[Any]					,  )		->	Dict[str, float]:







											"""simple docstring"""




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

											_lowerCAmelCase							=      self.eval_dataset if eval_dataset is None else eval_dataset
											_lowerCAmelCase							=      self.get_eval_dataloader(UpperCAmelCase_	)
											_lowerCAmelCase							=      self.eval_examples if eval_examples is None else eval_examples

											# Temporarily disable metric computation, we will do it in the loop here.
											_lowerCAmelCase							=      self.compute_metrics
											_lowerCAmelCase							=      None
											_lowerCAmelCase							=      time.time()
											_lowerCAmelCase							=      self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
											try:
																		_lowerCAmelCase							=      eval_loop(
																		    UpperCAmelCase_					,  description='Evaluation'					,  prediction_loss_only=True if compute_metrics is None else None					,  ignore_keys=UpperCAmelCase_					,  metric_key_prefix=UpperCAmelCase_					,  )
											finally:
																		_lowerCAmelCase							=      compute_metrics
											_lowerCAmelCase							=      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
																		_lowerCAmelCase							=      self.post_process_function(UpperCAmelCase_					,  UpperCAmelCase_					,  UpperCAmelCase_	)
																		_lowerCAmelCase							=      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}_"""	):
																																_lowerCAmelCase							=      metrics.pop(UpperCAmelCase_	)

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

											_lowerCAmelCase							=      self.callback_handler.on_evaluate(self.args					,  self.state					,  self.control					,  UpperCAmelCase_	)
											return metrics







				def 					__lowerCamelCase						(       self						:      Dict					,  UpperCAmelCase_						:      Any					,  UpperCAmelCase_						:      Optional[Any]					,  UpperCAmelCase_						:      Any=None					,  UpperCAmelCase_						:      Optional[Any] = "test"					,  **UpperCAmelCase_						:      str	)		->	List[str]:







											"""simple docstring"""




											_lowerCAmelCase							=      gen_kwargs.copy()

											_lowerCAmelCase							=      self.get_test_dataloader(UpperCAmelCase_	)

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

											_lowerCAmelCase							=      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

											_lowerCAmelCase							=      self.post_process_function(UpperCAmelCase_					,  UpperCAmelCase_					,  UpperCAmelCase_					,  'predict'	)
											_lowerCAmelCase							=      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}_"""	):
																									_lowerCAmelCase							=      metrics.pop(UpperCAmelCase_	)
											metrics.update(output.metrics	)
											return PredictionOutput(predictions=predictions.predictions					,  label_ids=predictions.label_ids					,  metrics=UpperCAmelCase_	)










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE			: List[Any]				   =					{
    "configuration_poolformer": [
        "POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "PoolFormerConfig",
        "PoolFormerOnnxConfig",
    ]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE			: List[str]				   =					["PoolFormerFeatureExtractor"]
 SCREAMING_SNAKE_CASE			: List[Any]				   =					["PoolFormerImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE			: List[Any]				   =					[
     "POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "PoolFormerForImageClassification",
     "PoolFormerModel",
     "PoolFormerPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_poolformer import (
     POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     PoolFormerConfig,
     PoolFormerOnnxConfig,
 )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_poolformer import PoolFormerFeatureExtractor
  from .image_processing_poolformer import PoolFormerImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_poolformer import (
      POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      PoolFormerForImageClassification,
      PoolFormerModel,
      PoolFormerPreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE			: str				   =					_LazyModule(__name__, globals()["__file__"], _import_structure)


