'''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__						(    _UpperCAmelCase):







			def __init__(	self				:  Union[str, Any]				,			*lowercase_				:  int				,			lowercase_				:  List[Any]=None				,			lowercase_				:  Any=None				,			**lowercase_				:  Union[str, Any]    ):
										super().__init__(*lowercase_				,			**lowercase_    )
										lowercase_					:			Tuple										=       eval_examples
										lowercase_					:			Optional[Any]										=       post_process_function







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]				,			lowercase_				:  Optional[Dataset] = None				,			lowercase_				:  Optional[int]=None				,			lowercase_				:  Optional[List[str]] = None				,			lowercase_				:  str = "eval"				,			**lowercase_				:  Any				,			):
										lowercase_					:			Dict										=       gen_kwargs.copy()
										lowercase_					:			Optional[int]										=       (
										    gen_kwargs["""max_length"""] if gen_kwargs.get("""max_length"""    ) is not None else self.args.generation_max_length
										)
										lowercase_					:			int										=       (
										    gen_kwargs["""num_beams"""] if gen_kwargs.get("""num_beams"""    ) is not None else self.args.generation_num_beams
										)
										lowercase_					:			str										=       gen_kwargs

										lowercase_					:			Optional[int]										=       self.eval_dataset if eval_dataset is None else eval_dataset
										lowercase_					:			Optional[Any]										=       self.get_eval_dataloader(lowercase_    )
										lowercase_					:			Any										=       self.eval_examples if eval_examples is None else eval_examples

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

																	# Prefix all keys with metric_key_prefix + '_'
																	for key in list(metrics.keys()    ):
																								if not key.startswith(f'''{metric_key_prefix}_'''    ):
																															lowercase_					:			Any										=       metrics.pop(lowercase_    )

																	metrics.update(output.metrics    )
										else:
																	lowercase_					:			Any										=       output.metrics

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

										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()    )

										lowercase_					:			List[Any]										=       self.callback_handler.on_evaluate(self.args				,			self.state				,			self.control				,			lowercase_    )
										return metrics







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]				,			lowercase_				:  Optional[Any]				,			lowercase_				:  Optional[int]				,			lowercase_				:  List[Any]=None				,			lowercase_				:  str = "test"				,			**lowercase_				:  Tuple    ):
										lowercase_					:			Dict										=       gen_kwargs.copy()

										lowercase_					:			str										=       self.get_test_dataloader(lowercase_    )

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

										lowercase_					:			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(
										        lowercase_				,			lowercase_				,			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

										lowercase_					:			Tuple										=       self.post_process_function(lowercase_				,			lowercase_				,			lowercase_				,			"""predict"""    )
										lowercase_					:			str										=       self.compute_metrics(lowercase_    )

										# Prefix all keys with metric_key_prefix + '_'
										for key in list(metrics.keys()    ):
																	if not key.startswith(f'''{metric_key_prefix}_'''    ):
																								lowercase_					:			int										=       metrics.pop(lowercase_    )
										metrics.update(output.metrics    )
										return PredictionOutput(predictions=predictions.predictions				,			label_ids=predictions.label_ids				,			metrics=lowercase_    )






'''simple docstring'''




import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
						import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin





@require_torch
class     __magic_name__						(    unittest.TestCase,     _UpperCAmelCase):







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										lowercase_					:			List[str]										=       load_tool("""text-to-speech"""    )
										self.tool.setup()







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										# SpeechT5 isn't deterministic
										torch.manual_seed(0    )
										lowercase_					:			List[str]										=       self.tool("""hey"""    )
										lowercase_					:			Optional[int]										=       result.to_raw()
										self.assertTrue(
										    torch.allclose(
										        resulting_tensor[:3]				,			torch.tensor([-0.0_00_59_66_66_88_32_11_58_29, -0.0_00_36_57_64_01_90_79_50_64, -0.00_01_34_39_50_27_99_88_34_85]    )				,			)    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										# SpeechT5 isn't deterministic
										torch.manual_seed(0    )
										lowercase_					:			Union[str, Any]										=       self.tool("""hey"""    )
										lowercase_					:			int										=       result.to_raw()
										self.assertTrue(
										    torch.allclose(
										        resulting_tensor[:3]				,			torch.tensor([-0.0_00_59_66_66_88_32_11_58_29, -0.0_00_36_57_64_01_90_79_50_64, -0.00_01_34_39_50_27_99_88_34_85]    )				,			)    )





