



"""simple docstring"""

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available




@dataclass
class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Union[List[PIL.Image.Image], np.ndarray]
 _lowerCAmelCase     :    Optional[List[bool]]


if is_transformers_available() and is_torch_available():
     from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline










"""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  _UpperCAmelCase (    lowerCAmelCase__):

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

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

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

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

         metrics.update(output.metrics       )
     else:
         snake_case_				:  Tuple       =			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()       )

     snake_case_				:  str       =			self.callback_handler.on_evaluate(self.args						,  self.state						,  self.control						,  lowercase_       )
     return metrics






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

     snake_case_				:  Dict       =			self.get_test_dataloader(lowercase_       )

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

     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(
             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

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





