


from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
		from .features import FeatureType







@dataclass
class        __lowercase       :



		"""simple docstring"""

		UpperCamelCase     :					Dict			       =		4_2
		UpperCamelCase     :					str			       =		None
		# Automatically constructed
		UpperCamelCase     :					int			       =		"dict"
		UpperCamelCase     :					str			       =		None
		UpperCamelCase     :					Optional[int]			       =		field(default="Translation"			,							init=a_			,							repr=a_  )







		def __call__( self	)  -> str:







					'''simple docstring'''







					return pa.struct({lang: pa.string() for lang in sorted(self.languages	)}	)







		def 					__A   ( self	)  -> Union["FeatureType", Dict[str, "FeatureType"]]:







					'''simple docstring'''







					from .features import Value

					return {k: Value("""string"""	) for k in sorted(self.languages	)}







@dataclass
class        __lowercase       :



		"""simple docstring"""

		UpperCamelCase     :					Dict			       =		None
		UpperCamelCase     :					Optional[Any]			       =		None
		UpperCamelCase     :					Union[str, Any]			       =		None
		# Automatically constructed
		UpperCamelCase     :					Dict			       =		"dict"
		UpperCamelCase     :					List[str]			       =		None
		UpperCamelCase     :					Union[str, Any]			       =		field(default="TranslationVariableLanguages"			,							init=a_			,							repr=a_  )







		def 					__A   ( self	)  -> Union[str, Any]:







					'''simple docstring'''







					lowerCamelCase    		=  sorted(set(self.languages	)	) if self.languages else None
					lowerCamelCase    		=  len(self.languages	) if self.languages else None







		def __call__( self	)  -> Optional[int]:







					'''simple docstring'''







					return pa.struct({"""language""": pa.list_(pa.string()	), """translation""": pa.list_(pa.string()	)}	)







		def 					__A   ( self		,      A	)  -> int:







					'''simple docstring'''







					lowerCamelCase    		=  set(self.languages	)
					if self.languages and set(A	) - lang_set:
								raise ValueError(
								    F'Some languages in example ({", ".join(sorted(set(A	) - lang_set	)	)}) are not in valid set ({", ".join(A	)}).'	)

					# Convert dictionary into tuples, splitting out cases where there are
					# multiple translations for a single language.
					lowerCamelCase    		=  []
					for lang, text in translation_dict.items():
								if isinstance(A		,      A	):
											translation_tuples.append((lang, text)	)
								else:
											translation_tuples.extend([(lang, el) for el in text]	)

        # Ensure translations are in ascending order by language code.
					lowerCamelCase    		=  zip(*sorted(A	)	)

					return {"language": languages, "translation": translations}







		def 					__A   ( self	)  -> Union["FeatureType", Dict[str, "FeatureType"]]:







					'''simple docstring'''







					from .features import Sequence, Value

					return {
					    "language": Sequence(Value("""string"""	)	),
					    "translation": Sequence(Value("""string"""	)	),
					}





'''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    __magic_name__				(						lowerCAmelCase							):






       def __init__(     self    ,				*snake_case    ,				snake_case=None    ,				snake_case=None    ,				**snake_case)		->    Tuple:




          '''simple docstring'''
          super().__init__(*snake_case    ,				**snake_case)
          _UpperCAmelCase  :  Any				       =eval_examples
          _UpperCAmelCase  :  Optional[int]				       =post_process_function






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




          '''simple docstring'''
          _UpperCAmelCase  :  Tuple				       =gen_kwargs.copy()
          _UpperCAmelCase  :  int				       =(
              gen_kwargs['max_length'] if gen_kwargs.get('max_length') is not None else self.args.generation_max_length
          )
          _UpperCAmelCase  :  Dict				       =(
              gen_kwargs['num_beams'] if gen_kwargs.get('num_beams') is not None else self.args.generation_num_beams
          )
          _UpperCAmelCase  :  Dict				       =gen_kwargs

          _UpperCAmelCase  :  Tuple				       =self.eval_dataset if eval_dataset is None else eval_dataset
          _UpperCAmelCase  :  Union[str, Any]				       =self.get_eval_dataloader(snake_case)
          _UpperCAmelCase  :  Tuple				       =self.eval_examples if eval_examples is None else eval_examples

          # Temporarily disable metric computation, we will do it in the loop here.
          _UpperCAmelCase  :  Dict				       =self.compute_metrics
          _UpperCAmelCase  :  Optional[Any]				       =None
          _UpperCAmelCase  :  str				       =time.time()
          _UpperCAmelCase  :  int				       =self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
          try:
             _UpperCAmelCase  :  str				       =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:
             _UpperCAmelCase  :  str				       =compute_metrics
          _UpperCAmelCase  :  Optional[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(
                  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
             _UpperCAmelCase  :  Tuple				       =self.post_process_function(snake_case    ,				snake_case    ,				snake_case)
             _UpperCAmelCase  :  Union[str, 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}_"):
                   _UpperCAmelCase  :  Any				       =metrics.pop(snake_case)

             metrics.update(output.metrics)
          else:
             _UpperCAmelCase  :  str				       =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())

          _UpperCAmelCase  :  Any				       =self.callback_handler.on_evaluate(self.args    ,				self.state    ,				self.control    ,				snake_case)
          return metrics






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case    ,				snake_case=None    ,				snake_case = "test"    ,				**snake_case)		->    Dict:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[int]				       =gen_kwargs.copy()

          _UpperCAmelCase  :  int				       =self.get_test_dataloader(snake_case)

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

          _UpperCAmelCase  :  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

          _UpperCAmelCase  :  Tuple				       =self.post_process_function(snake_case    ,				snake_case    ,				snake_case    ,				'predict')
          _UpperCAmelCase  :  Union[str, 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}_"):
                _UpperCAmelCase  :  Any				       =metrics.pop(snake_case)
          metrics.update(output.metrics)
          return PredictionOutput(predictions=predictions.predictions    ,				label_ids=predictions.label_ids    ,				metrics=snake_case)

