




"""simple docstring"""





import random
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							import PIL

							from transformers import FlavaImageProcessor
							from transformers.image_utils import PILImageResampling
							from transformers.models.flava.image_processing_flava import (
							    FLAVA_CODEBOOK_MEAN,
							    FLAVA_CODEBOOK_STD,
							    FLAVA_IMAGE_MEAN,
							    FLAVA_IMAGE_STD,
							)
else:
							_lowercase	      =    _lowercase	      =    _lowercase	      =    _lowercase	      =    None

class       __lowercase       (			unittest.TestCase	):


				'''simple docstring'''


				def __init__(   self   :     Tuple      ,SCREAMING_SNAKE_CASE_   :     Any      ,SCREAMING_SNAKE_CASE_   :     Dict=7      ,SCREAMING_SNAKE_CASE_   :     Any=3      ,SCREAMING_SNAKE_CASE_   :     Any=3_0      ,SCREAMING_SNAKE_CASE_   :     Dict=4_0_0      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any]=True      ,SCREAMING_SNAKE_CASE_   :     List[Any]=None      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any]=True      ,SCREAMING_SNAKE_CASE_   :     List[Any]=None      ,SCREAMING_SNAKE_CASE_   :     Optional[int]=None      ,SCREAMING_SNAKE_CASE_   :     Any=True      ,SCREAMING_SNAKE_CASE_   :     Dict=1 / 2_5_5      ,SCREAMING_SNAKE_CASE_   :     str=True      ,SCREAMING_SNAKE_CASE_   :     Dict=FLAVA_IMAGE_MEAN      ,SCREAMING_SNAKE_CASE_   :     Optional[int]=FLAVA_IMAGE_STD      ,SCREAMING_SNAKE_CASE_   :     Optional[Any]=1_4      ,SCREAMING_SNAKE_CASE_   :     str=7_5      ,SCREAMING_SNAKE_CASE_   :     List[Any]=None      ,SCREAMING_SNAKE_CASE_   :     Tuple=1_6      ,SCREAMING_SNAKE_CASE_   :     str=0.3      ,SCREAMING_SNAKE_CASE_   :     List[Any]=None      ,SCREAMING_SNAKE_CASE_   :     List[str]=True      ,SCREAMING_SNAKE_CASE_   :     Optional[int]=None      ,SCREAMING_SNAKE_CASE_   :     Optional[int]=None      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any]=True      ,SCREAMING_SNAKE_CASE_   :     Dict=None      ,SCREAMING_SNAKE_CASE_   :     Optional[int]=True      ,SCREAMING_SNAKE_CASE_   :     List[Any]=True      ,SCREAMING_SNAKE_CASE_   :     List[Any]=FLAVA_CODEBOOK_MEAN      ,SCREAMING_SNAKE_CASE_   :     List[str]=FLAVA_CODEBOOK_STD      ,):
										snake_case__       :					List[Any]										=					size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
										snake_case__       :					List[str]										=					crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
										snake_case__       :					int										=					codebook_size if codebook_size is not None else {'''height''': 1_1_2, '''width''': 1_1_2}
										snake_case__       :					Tuple										=					codebook_crop_size if codebook_crop_size is not None else {'''height''': 1_1_2, '''width''': 1_1_2}

										snake_case__       :					int										=					parent
										snake_case__       :					Union[str, Any]										=					batch_size
										snake_case__       :					List[str]										=					num_channels
										snake_case__       :					Tuple										=					do_resize
										snake_case__       :					List[Any]										=					do_rescale
										snake_case__       :					Optional[Any]										=					rescale_factor
										snake_case__       :					List[str]										=					min_resolution
										snake_case__       :					Tuple										=					max_resolution
										snake_case__       :					List[str]										=					size
										snake_case__       :					Dict										=					resample if resample is not None else PILImageResampling.BICUBIC
										snake_case__       :					Optional[Any]										=					do_normalize
										snake_case__       :					int										=					image_mean
										snake_case__       :					Optional[int]										=					image_std
										snake_case__       :					str										=					do_center_crop
										snake_case__       :					Dict										=					crop_size

										snake_case__       :					Dict										=					input_size_patches
										snake_case__       :					Optional[int]										=					total_mask_patches
										snake_case__       :					Any										=					mask_group_max_patches
										snake_case__       :					Tuple										=					mask_group_min_patches
										snake_case__       :					List[Any]										=					mask_group_min_aspect_ratio
										snake_case__       :					int										=					mask_group_max_aspect_ratio

										snake_case__       :					Tuple										=					codebook_do_resize
										snake_case__       :					Union[str, Any]										=					codebook_size
										snake_case__       :					Union[str, Any]										=					codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
										snake_case__       :					Optional[int]										=					codebook_do_center_crop
										snake_case__       :					List[str]										=					codebook_crop_size
										snake_case__       :					Optional[Any]										=					codebook_do_map_pixels
										snake_case__       :					Tuple										=					codebook_do_normalize
										snake_case__       :					Any										=					codebook_image_mean
										snake_case__       :					Tuple										=					codebook_image_std

				def       _lowerCamelCase					(   self   :     int      ):
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "resample": self.resample,
										    "do_rescale": self.do_rescale,
										    "rescale_factor": self.rescale_factor,
										    "do_center_crop": self.do_center_crop,
										    "crop_size": self.crop_size,
										    "input_size_patches": self.input_size_patches,
										    "total_mask_patches": self.total_mask_patches,
										    "mask_group_max_patches": self.mask_group_max_patches,
										    "mask_group_min_patches": self.mask_group_min_patches,
										    "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
										    "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
										    "codebook_do_resize": self.codebook_do_resize,
										    "codebook_size": self.codebook_size,
										    "codebook_resample": self.codebook_resample,
										    "codebook_do_center_crop": self.codebook_do_center_crop,
										    "codebook_crop_size": self.codebook_crop_size,
										    "codebook_do_map_pixels": self.codebook_do_map_pixels,
										    "codebook_do_normalize": self.codebook_do_normalize,
										    "codebook_image_mean": self.codebook_image_mean,
										    "codebook_image_std": self.codebook_image_std,
										}

				def       _lowerCamelCase					(   self   :     Union[str, Any]      ):
										return (self.size["height"], self.size["width"])

				def       _lowerCamelCase					(   self   :     Tuple      ):
										return (
										    (self.input_size_patches, self.input_size_patches)
										    if not isinstance(self.input_size_patches      ,SCREAMING_SNAKE_CASE_      )
										    else self.input_size_patches
										)





				def       _lowerCamelCase					(   self   :     str      ):
										return (self.codebook_size["height"], self.codebook_size["width"])







@require_torch
@require_vision
class       __lowercase       (			snake_case_						,   unittest.TestCase	):


				'''simple docstring'''


				_lowerCAmelCase				=FlavaImageProcessor if is_vision_available() else None
				_lowerCAmelCase				=None

				def       _lowerCamelCase					(   self   :     int      ):
										snake_case__       :					Tuple										=					FlavaImageProcessingTester(self      )

				@property
				def       _lowerCamelCase					(   self   :     List[Any]      ):
										return self.image_processor_tester.prepare_image_processor_dict()

				def       _lowerCamelCase					(   self   :     List[Any]      ):
										snake_case__       :					List[Any]										=					self.image_processing_class(**self.image_processor_dict      )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,'''image_mean'''      )      )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,'''image_std'''      )      )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,'''do_normalize'''      )      )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,'''do_resize'''      )      )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,'''resample'''      )      )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,'''crop_size'''      )      )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,'''do_center_crop'''      )      )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,'''do_rescale'''      )      )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,'''rescale_factor'''      )      )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,'''masking_generator'''      )      )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,'''codebook_do_resize'''      )      )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,'''codebook_size'''      )      )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,'''codebook_resample'''      )      )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,'''codebook_do_center_crop'''      )      )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,'''codebook_crop_size'''      )      )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,'''codebook_do_map_pixels'''      )      )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,'''codebook_do_normalize'''      )      )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,'''codebook_image_mean'''      )      )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,'''codebook_image_std'''      )      )

				def       _lowerCamelCase					(   self   :     List[Any]      ):
										snake_case__       :					Union[str, Any]										=					self.image_processing_class.from_dict(self.image_processor_dict      )
										self.assertEqual(image_processor.size      ,{'''height''': 2_2_4, '''width''': 2_2_4}      )
										self.assertEqual(image_processor.crop_size      ,{'''height''': 2_2_4, '''width''': 2_2_4}      )
										self.assertEqual(image_processor.codebook_size      ,{'''height''': 1_1_2, '''width''': 1_1_2}      )
										self.assertEqual(image_processor.codebook_crop_size      ,{'''height''': 1_1_2, '''width''': 1_1_2}      )

										snake_case__       :					Tuple										=					self.image_processing_class.from_dict(
										    self.image_processor_dict      ,size=4_2      ,crop_size=8_4      ,codebook_size=3_3      ,codebook_crop_size=6_6      )
										self.assertEqual(image_processor.size      ,{'''height''': 4_2, '''width''': 4_2}      )
										self.assertEqual(image_processor.crop_size      ,{'''height''': 8_4, '''width''': 8_4}      )
										self.assertEqual(image_processor.codebook_size      ,{'''height''': 3_3, '''width''': 3_3}      )
										self.assertEqual(image_processor.codebook_crop_size      ,{'''height''': 6_6, '''width''': 6_6}      )

				def       _lowerCamelCase					(   self   :     Dict      ):
										pass

				def       _lowerCamelCase					(   self   :     Tuple      ):
										# Initialize image_processing
										snake_case__       :					Optional[int]										=					self.image_processing_class(**self.image_processor_dict      )
										# create random PIL images
										snake_case__       :					str										=					prepare_image_inputs(self.image_processor_tester      ,equal_resolution=SCREAMING_SNAKE_CASE_      )
										for image in image_inputs:
																self.assertIsInstance(SCREAMING_SNAKE_CASE_      ,PIL.Image.Image      )

										# Test not batched input
										snake_case__       :					Any										=					image_processing(image_inputs[0]      ,return_tensors='''pt'''      )

										# Test no bool masked pos
										self.assertFalse('''bool_masked_pos''' in encoded_images      )

										snake_case__       :					str										=					self.image_processor_tester.get_expected_image_size()

										self.assertEqual(
										    encoded_images.pixel_values.shape      ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,)

										# Test batched
										snake_case__       :					Tuple										=					image_processing(SCREAMING_SNAKE_CASE_      ,return_tensors='''pt'''      )
										snake_case__       :					Optional[Any]										=					self.image_processor_tester.get_expected_image_size()

										# Test no bool masked pos
										self.assertFalse('''bool_masked_pos''' in encoded_images      )

										self.assertEqual(
										    encoded_images.pixel_values.shape      ,(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )      ,)

				def       _lowerCamelCase					(   self   :     Tuple      ,SCREAMING_SNAKE_CASE_   :     Optional[int]      ,SCREAMING_SNAKE_CASE_   :     Optional[int]      ):
										# Initialize image_processing
										snake_case__       :					List[str]										=					self.image_processing_class(**self.image_processor_dict      )
										# create random tensors
										snake_case__       :					List[Any]										=					prepare_image_inputs(self.image_processor_tester      ,equal_resolution=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_      )
										for image in image_inputs:
																self.assertIsInstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      )

										# Test not batched input
										snake_case__       :					int										=					image_processing(image_inputs[0]      ,return_tensors='''pt'''      )

										snake_case__       :					Dict										=					self.image_processor_tester.get_expected_image_size()
										self.assertEqual(
										    encoded_images.pixel_values.shape      ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,)

										snake_case__       :					Tuple										=					image_processing(SCREAMING_SNAKE_CASE_      ,return_image_mask=SCREAMING_SNAKE_CASE_      ,return_tensors='''pt'''      )

										snake_case__       :					Dict										=					self.image_processor_tester.get_expected_image_size()
										self.assertEqual(
										    encoded_images.pixel_values.shape      ,(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )      ,)

										snake_case__       :					List[Any]										=					self.image_processor_tester.get_expected_mask_size()
										self.assertEqual(
										    encoded_images.bool_masked_pos.shape      ,(
										        self.image_processor_tester.batch_size,
										        expected_height,
										        expected_width,
										    )      ,)

										# Test batched
										snake_case__       :					str										=					image_processing(SCREAMING_SNAKE_CASE_      ,return_tensors='''pt'''      ).pixel_values

										snake_case__       :					Optional[Any]										=					self.image_processor_tester.get_expected_image_size()
										self.assertEqual(
										    encoded_images.shape      ,(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )      ,)

										# Test masking
										snake_case__       :					int										=					image_processing(SCREAMING_SNAKE_CASE_      ,return_image_mask=SCREAMING_SNAKE_CASE_      ,return_tensors='''pt'''      )

										snake_case__       :					Any										=					self.image_processor_tester.get_expected_image_size()
										self.assertEqual(
										    encoded_images.pixel_values.shape      ,(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )      ,)

										snake_case__       :					Any										=					self.image_processor_tester.get_expected_mask_size()
										self.assertEqual(
										    encoded_images.bool_masked_pos.shape      ,(
										        self.image_processor_tester.batch_size,
										        expected_height,
										        expected_width,
										    )      ,)

				def       _lowerCamelCase					(   self   :     Any      ):
										self._test_call_framework(np.ndarray      ,prepare_kwargs={'''numpify''': True}      )

				def       _lowerCamelCase					(   self   :     Any      ):
										self._test_call_framework(torch.Tensor      ,prepare_kwargs={'''torchify''': True}      )

				def       _lowerCamelCase					(   self   :     Union[str, Any]      ):
										# Initialize image_processing
										random.seed(1_2_3_4      )
										snake_case__       :					List[Any]										=					self.image_processing_class(**self.image_processor_dict      )
										snake_case__       :					Any										=					prepare_image_inputs(self.image_processor_tester      ,equal_resolution=SCREAMING_SNAKE_CASE_      ,torchify=SCREAMING_SNAKE_CASE_      )

										# Test not batched input
										snake_case__       :					Union[str, Any]										=					image_processing(image_inputs[0]      ,return_image_mask=SCREAMING_SNAKE_CASE_      ,return_tensors='''pt'''      )
										self.assertEqual(encoded_images.bool_masked_pos.sum().item()      ,7_5      )





				def       _lowerCamelCase					(   self   :     str      ):
										# Initialize image_processing
										snake_case__       :					str										=					self.image_processing_class(**self.image_processor_dict      )
										# create random PIL images
										snake_case__       :					List[str]										=					prepare_image_inputs(self.image_processor_tester      ,equal_resolution=SCREAMING_SNAKE_CASE_      )
										for image in image_inputs:
																self.assertIsInstance(SCREAMING_SNAKE_CASE_      ,PIL.Image.Image      )

										# Test not batched input
										snake_case__       :					List[str]										=					image_processing(image_inputs[0]      ,return_codebook_pixels=SCREAMING_SNAKE_CASE_      ,return_tensors='''pt'''      )
										snake_case__       :					Union[str, Any]										=					self.image_processor_tester.get_expected_codebook_image_size()
										self.assertEqual(
										    encoded_images.codebook_pixel_values.shape      ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,)

										# Test batched
										snake_case__       :					Optional[int]										=					image_processing(SCREAMING_SNAKE_CASE_      ,return_codebook_pixels=SCREAMING_SNAKE_CASE_      ,return_tensors='''pt'''      )
										snake_case__       :					Dict										=					self.image_processor_tester.get_expected_codebook_image_size()
										self.assertEqual(
										    encoded_images.codebook_pixel_values.shape      ,(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )      ,)








"""simple docstring"""





from copy import deepcopy
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import Dataset

from .deepspeed import is_deepspeed_zeroa_enabled
from .generation.configuration_utils import GenerationConfig
from .trainer import Trainer
from .utils import logging


if TYPE_CHECKING:
							from .data.data_collator import DataCollator
							from .modeling_utils import PreTrainedModel
							from .tokenization_utils_base import PreTrainedTokenizerBase
							from .trainer_callback import TrainerCallback
							from .trainer_utils import EvalPrediction, PredictionOutput
							from .training_args import TrainingArguments


_lowercase	      =    logging.get_logger(__name__)

class       __lowercase       (			snake_case_	):


				'''simple docstring'''


				def __init__(   self   :     Union[str, Any]      ,SCREAMING_SNAKE_CASE_   :     Union["PreTrainedModel", nn.Module] = None      ,SCREAMING_SNAKE_CASE_   :     "TrainingArguments" = None      ,SCREAMING_SNAKE_CASE_   :     Optional["DataCollator"] = None      ,SCREAMING_SNAKE_CASE_   :     Optional[Dataset] = None      ,SCREAMING_SNAKE_CASE_   :     Optional[Union[Dataset, Dict[str, Dataset]]] = None      ,SCREAMING_SNAKE_CASE_   :     Optional["PreTrainedTokenizerBase"] = None      ,SCREAMING_SNAKE_CASE_   :     Optional[Callable[[], "PreTrainedModel"]] = None      ,SCREAMING_SNAKE_CASE_   :     Optional[Callable[["EvalPrediction"], Dict]] = None      ,SCREAMING_SNAKE_CASE_   :     Optional[List["TrainerCallback"]] = None      ,SCREAMING_SNAKE_CASE_   :     Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None)      ,SCREAMING_SNAKE_CASE_   :     Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None      ,):
										super().__init__(
										    model=SCREAMING_SNAKE_CASE_      ,args=SCREAMING_SNAKE_CASE_      ,data_collator=SCREAMING_SNAKE_CASE_      ,train_dataset=SCREAMING_SNAKE_CASE_      ,eval_dataset=SCREAMING_SNAKE_CASE_      ,tokenizer=SCREAMING_SNAKE_CASE_      ,model_init=SCREAMING_SNAKE_CASE_      ,compute_metrics=SCREAMING_SNAKE_CASE_      ,callbacks=SCREAMING_SNAKE_CASE_      ,optimizers=SCREAMING_SNAKE_CASE_      ,preprocess_logits_for_metrics=SCREAMING_SNAKE_CASE_      ,)

										# Override self.model.generation_config if a GenerationConfig is specified in args.
										# Priority: args.generation_config > model.generation_config > default GenerationConfig.
										if self.args.generation_config is not None:
																snake_case__       :					List[Any]										=					self.load_generation_config(self.args.generation_config      )
																snake_case__       :					Optional[int]										=					gen_config

				@staticmethod
				def       _lowerCamelCase					(   SCREAMING_SNAKE_CASE_   :     Union[str, GenerationConfig]      ):

										# GenerationConfig provided, nothing to do
										if isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ):
																return deepcopy(SCREAMING_SNAKE_CASE_      )

										# str or Path
										snake_case__       :					Optional[int]										=					Path(SCREAMING_SNAKE_CASE_      ) if isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ) else gen_config_arg
										snake_case__       :					Optional[Any]										=					None

										# Figuring if it is path pointing to a file, pointing to a directory or else a model id or URL
										# This step is required in order to determine config_file_name
										if pretrained_model_name.is_file():
																snake_case__       :					List[Any]										=					pretrained_model_name.name
																snake_case__       :					Any										=					pretrained_model_name.parent
										# dir path
										elif pretrained_model_name.is_dir():
																pass
										# model id or URL
										else:
																snake_case__       :					Optional[Any]										=					gen_config_arg

										snake_case__       :					Optional[Any]										=					GenerationConfig.from_pretrained(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      )
										return gen_config

				def       _lowerCamelCase					(   self   :     List[str]      ,SCREAMING_SNAKE_CASE_   :     Optional[Dataset] = None      ,SCREAMING_SNAKE_CASE_   :     Optional[List[str]] = None      ,SCREAMING_SNAKE_CASE_   :     str = "eval"      ,**SCREAMING_SNAKE_CASE_   :     int      ,):
										snake_case__       :					List[str]										=					gen_kwargs.copy()
										if gen_kwargs.get('''max_length'''      ) is None and gen_kwargs.get('''max_new_tokens'''      ) is None:
																snake_case__       :					Optional[int]										=					self.args.generation_max_length
										snake_case__       :					Optional[Any]										=					(
										    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

										return super().evaluate(SCREAMING_SNAKE_CASE_      ,ignore_keys=SCREAMING_SNAKE_CASE_      ,metric_key_prefix=SCREAMING_SNAKE_CASE_      )

				def       _lowerCamelCase					(   self   :     List[Any]      ,SCREAMING_SNAKE_CASE_   :     Dataset      ,SCREAMING_SNAKE_CASE_   :     Optional[List[str]] = None      ,SCREAMING_SNAKE_CASE_   :     str = "test"      ,**SCREAMING_SNAKE_CASE_   :     int      ,):
										snake_case__       :					Union[str, Any]										=					gen_kwargs.copy()
										if gen_kwargs.get('''max_length'''      ) is None and gen_kwargs.get('''max_new_tokens'''      ) is None:
																snake_case__       :					Any										=					self.args.generation_max_length
										snake_case__       :					str										=					(
										    gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''      ) is not None else self.args.generation_num_beams
										)
										snake_case__       :					List[str]										=					gen_kwargs

										return super().predict(SCREAMING_SNAKE_CASE_      ,ignore_keys=SCREAMING_SNAKE_CASE_      ,metric_key_prefix=SCREAMING_SNAKE_CASE_      )

				def       _lowerCamelCase					(   self   :     Optional[int]      ,SCREAMING_SNAKE_CASE_   :     nn.Module      ,SCREAMING_SNAKE_CASE_   :     Dict[str, Union[torch.Tensor, Any]]      ,SCREAMING_SNAKE_CASE_   :     bool      ,SCREAMING_SNAKE_CASE_   :     Optional[List[str]] = None      ,**SCREAMING_SNAKE_CASE_   :     List[Any]      ,):

										if not self.args.predict_with_generate or prediction_loss_only:
																return super().prediction_step(
																    SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,prediction_loss_only=SCREAMING_SNAKE_CASE_      ,ignore_keys=SCREAMING_SNAKE_CASE_      )

										snake_case__       :					List[Any]										=					'''labels''' in inputs
										snake_case__       :					Tuple										=					self._prepare_inputs(SCREAMING_SNAKE_CASE_      )

										# XXX: adapt synced_gpus for fairscale as well
										# Priority (handled in generate):
										# gen_kwargs > model.generation_config > default GenerationConfig()

										if len(SCREAMING_SNAKE_CASE_      ) == 0 and hasattr(self      ,'''_gen_kwargs'''      ):
																snake_case__       :					Any										=					self._gen_kwargs.copy()

										if gen_kwargs.get('''max_length'''      ) is None and gen_kwargs.get('''max_new_tokens'''      ) is None:
																snake_case__       :					Any										=					self.model.config.max_length
										snake_case__       :					int										=					(
										    gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''      ) is not None else self.model.config.num_beams
										)
										snake_case__       :					Optional[int]										=					True if is_deepspeed_zeroa_enabled() else False
										snake_case__       :					Any										=					(
										    gen_kwargs['''synced_gpus'''] if gen_kwargs.get('''synced_gpus'''      ) is not None else default_synced_gpus
										)

										# If the `decoder_input_ids` was created from `labels`, evict the former, so that the model can freely generate
										# (otherwise, it would continue generating from the padded `decoder_input_ids`)
										if (
										    "labels" in inputs
										    and "decoder_input_ids" in inputs
										    and inputs["labels"].shape == inputs["decoder_input_ids"].shape
										):
																snake_case__       :					List[str]										=					{k: v for k, v in inputs.items() if k != '''decoder_input_ids'''}
										snake_case__       :					List[str]										=					self.model.generate(**SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_      )

										# Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
										# TODO: remove this hack when the legacy code that initializes generation_config from a model config is
										# removed in https://github.com/huggingface/transformers/blob/98d88b23f54e5a23e741833f1e973fdf600cc2c5/src/transformers/generation/utils.py#L1183
										if self.model.generation_config._from_model_config:
																snake_case__       :					int										=					False

										# Retrieves GenerationConfig from model.generation_config
										snake_case__       :					Dict										=					self.model.generation_config
										# in case the batch is shorter than max length, the output should be padded
										if generated_tokens.shape[-1] < gen_config.max_length:
																snake_case__       :					Tuple										=					self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_      ,gen_config.max_length      )
										elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
																snake_case__       :					Any										=					self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_      ,gen_config.max_new_tokens + 1      )

										with torch.no_grad():
																if has_labels:
																						with self.compute_loss_context_manager():
																												snake_case__       :					Union[str, Any]										=					model(**SCREAMING_SNAKE_CASE_      )
																						if self.label_smoother is not None:
																												snake_case__       :					str										=					self.label_smoother(SCREAMING_SNAKE_CASE_      ,inputs['''labels''']      ).mean().detach()
																						else:
																												snake_case__       :					Optional[Any]										=					(outputs['''loss'''] if isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ) else outputs[0]).mean().detach()
																else:
																						snake_case__       :					Optional[Any]										=					None

										if self.args.prediction_loss_only:
																return loss, None, None

										if has_labels:
																snake_case__       :					Tuple										=					inputs['''labels''']
																if labels.shape[-1] < gen_config.max_length:
																						snake_case__       :					List[str]										=					self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_      ,gen_config.max_length      )
																elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
																						snake_case__       :					Optional[int]										=					self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_      ,gen_config.max_new_tokens + 1      )
										else:
																snake_case__       :					List[str]										=					None

										return loss, generated_tokens, labels





				def       _lowerCamelCase					(   self   :     Dict      ,SCREAMING_SNAKE_CASE_   :     Dict      ,SCREAMING_SNAKE_CASE_   :     Dict      ):
										if self.tokenizer is not None and hasattr(self.tokenizer      ,'''pad_token_id'''      ):
																# If PAD token is not defined at least EOS token has to be defined
																snake_case__       :					List[str]										=					(
																    self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
																)
										else:
																if self.model.config.pad_token_id is not None:
																						snake_case__       :					Any										=					self.model.config.pad_token_id
																else:
																						raise ValueError('''Pad_token_id must be set in the configuration of the model, in order to pad tensors'''      )

										snake_case__       :					str										=					pad_token_id * torch.ones(
										    (tensor.shape[0], max_length)      ,dtype=tensor.dtype      ,device=tensor.device      )
										snake_case__       :					str										=					tensor
										return padded_tensor


