

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						(_snake_case				):


					'''simple docstring'''






					def __init__( self    ,       *A__    ,       A__=None    ,       A__=None    ,       **A__			):
						super().__init__(*__snake_case    ,       **__snake_case			)
						A__							:    List[str]        =		eval_examples
						A__							:    str        =		post_process_function






					def   __A			( self    ,       A__ = None    ,       A__=None    ,       A__ = None    ,       A__ = "eval"    ,       **A__    ,       ):
						A__							:    List[Any]        =		gen_kwargs.copy()
						A__							:    List[Any]        =		(
						    gen_kwargs["""max_length"""] if gen_kwargs.get("""max_length"""			) is not None else self.args.generation_max_length
						)
						A__							:    Any        =		(
						    gen_kwargs["""num_beams"""] if gen_kwargs.get("""num_beams"""			) is not None else self.args.generation_num_beams
						)
						A__							:    Union[str, Any]        =		gen_kwargs

						A__							:    Dict        =		self.eval_dataset if eval_dataset is None else eval_dataset
						A__							:    Dict        =		self.get_eval_dataloader(__snake_case			)
						A__							:    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.
						A__							:    List[Any]        =		self.compute_metrics
						A__							:    Tuple        =		None
						A__							:    Dict        =		time.time()
						A__							:    Dict        =		self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
						try:
							A__							:    List[Any]        =		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:
							A__							:    Union[str, Any]        =		compute_metrics
						A__							:    Any        =		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
							A__							:    int        =		self.post_process_function(__snake_case    ,       __snake_case    ,       __snake_case			)
							A__							:    List[Any]        =		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}_"""			):
									A__							:    Optional[int]        =		metrics.pop(__snake_case			)

							metrics.update(output.metrics			)
						else:
							A__							:    List[Any]        =		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()			)

						A__							:    Tuple        =		self.callback_handler.on_evaluate(self.args    ,       self.state    ,       self.control    ,       __snake_case			)
						return metrics






					def   __A			( self    ,       A__    ,       A__    ,       A__=None    ,       A__ = "test"    ,       **A__			):
						A__							:    str        =		gen_kwargs.copy()

						A__							:    int        =		self.get_test_dataloader(__snake_case			)

						# Temporarily disable metric computation, we will do it in the loop here.
						A__							:    int        =		self.compute_metrics
						A__							:    Dict        =		None
						A__							:    str        =		time.time()
						A__							:    List[Any]        =		self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
						try:
							A__							:    Dict        =		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:
							A__							:    List[Any]        =		compute_metrics

						A__							:    Dict        =		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

						A__							:    int        =		self.post_process_function(__snake_case    ,       __snake_case    ,       __snake_case    ,       """predict"""			)
						A__							:    Optional[int]        =		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}_"""			):
								A__							:    str        =		metrics.pop(__snake_case			)
						metrics.update(output.metrics			)
						return PredictionOutput(predictions=predictions.predictions    ,       label_ids=predictions.label_ids    ,       metrics=__snake_case			)







'''simple docstring'''


UpperCAmelCase_		:   Union[str, Any]							   =		'\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

UpperCAmelCase_		:   Dict							   =		[{'type': 'code', 'content': INSTALL_CONTENT}]
UpperCAmelCase_		:   Any							   =		{
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}




