




import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import (
		    TFEfficientFormerForImageClassification,
		    TFEfficientFormerForImageClassificationWithTeacher,
		    TFEfficientFormerModel,
		)
		from transformers.models.efficientformer.modeling_tf_efficientformer import (
		    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		)


if is_vision_available():
		from PIL import Image

		from transformers import EfficientFormerImageProcessor
class 		UpperCAmelCase   :







						'''simple docstring'''





						def __init__(						self							:     Any       ,	__lowercase							:     int       ,	__lowercase							:     int = 13       ,	__lowercase							:     int = 64       ,	__lowercase							:     int = 2       ,	__lowercase							:     int = 3       ,	__lowercase							:     int = 3       ,	__lowercase							:     bool = True       ,	__lowercase							:     bool = True       ,	__lowercase							:     int = 1_28       ,	__lowercase							:     str=[16, 32, 64, 1_28]       ,	__lowercase							:     int = 7       ,	__lowercase							:     int = 4       ,	__lowercase							:     int = 37       ,	__lowercase							:     str = "gelu"       ,	__lowercase							:     float = 0.1       ,	__lowercase							:     float = 0.1       ,	__lowercase							:     int = 10       ,	__lowercase							:     float = 0.02       ,	__lowercase							:     int = 2       ,	__lowercase							:     int = 1       ,	__lowercase							:     int = 1_28       ,	__lowercase							:     List[int] = [2, 2, 2, 2]       ,	__lowercase							:     int = 2       ,	__lowercase							:     int = 2       ,	):






							"""simple docstring"""






							snake_case_		    =     parent
							snake_case_		    =     batch_size
							snake_case_		    =     image_size
							snake_case_		    =     patch_size
							snake_case_		    =     num_channels
							snake_case_		    =     is_training
							snake_case_		    =     use_labels
							snake_case_		    =     hidden_size
							snake_case_		    =     num_hidden_layers
							snake_case_		    =     num_attention_heads
							snake_case_		    =     intermediate_size
							snake_case_		    =     hidden_act
							snake_case_		    =     hidden_dropout_prob
							snake_case_		    =     attention_probs_dropout_prob
							snake_case_		    =     type_sequence_label_size
							snake_case_		    =     initializer_range
							snake_case_		    =     encoder_stride
							snake_case_		    =     num_attention_outputs
							snake_case_		    =     embed_dim
							snake_case_		    =     embed_dim + 1
							snake_case_		    =     resolution
							snake_case_		    =     depths
							snake_case_		    =     hidden_sizes
							snake_case_		    =     dim
							snake_case_		    =     mlp_expansion_ratio






						def        snake_case__						(						self							:     Optional[int]						):






							"""simple docstring"""






							snake_case_		    =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

							snake_case_		    =     None
							if self.use_labels:
								snake_case_		    =     ids_tensor([self.batch_size]       ,	self.type_sequence_label_size						)

							snake_case_		    =     self.get_config()

							return config, pixel_values, labels






						def        snake_case__						(						self							:     Optional[Any]						):






							"""simple docstring"""






							return EfficientFormerConfig(
							    image_size=self.image_size       ,	patch_size=self.patch_size       ,	num_channels=self.num_channels       ,	hidden_size=self.hidden_size       ,	num_hidden_layers=self.num_hidden_layers       ,	num_attention_heads=self.num_attention_heads       ,	intermediate_size=self.intermediate_size       ,	hidden_act=self.hidden_act       ,	hidden_dropout_prob=self.hidden_dropout_prob       ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,	is_decoder=UpperCAmelCase_       ,	initializer_range=self.initializer_range       ,	encoder_stride=self.encoder_stride       ,	resolution=self.resolution       ,	depths=self.depths       ,	hidden_sizes=self.hidden_sizes       ,	dim=self.dim       ,	mlp_expansion_ratio=self.mlp_expansion_ratio       ,	)






						def        snake_case__						(						self							:     Optional[Any]       ,	__lowercase							:     List[str]       ,	__lowercase							:     Optional[Any]       ,	__lowercase							:     Optional[int]						):






							"""simple docstring"""






							snake_case_		    =     TFEfficientFormerModel(config=UpperCAmelCase_						)
							snake_case_		    =     model(UpperCAmelCase_       ,	training=UpperCAmelCase_						)
							self.parent.assertEqual(result.last_hidden_state.shape       ,	(self.batch_size, self.seq_length, self.hidden_size)						)






						def        snake_case__						(						self							:     Dict       ,	__lowercase							:     str       ,	__lowercase							:     Tuple       ,	__lowercase							:     Optional[Any]						):






							"""simple docstring"""






							snake_case_		    =     self.type_sequence_label_size
							snake_case_		    =     TFEfficientFormerForImageClassification(UpperCAmelCase_						)
							snake_case_		    =     model(UpperCAmelCase_       ,	labels=UpperCAmelCase_       ,	training=UpperCAmelCase_						)
							self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.type_sequence_label_size)						)

							# test greyscale images
							snake_case_		    =     1
							snake_case_		    =     TFEfficientFormerForImageClassification(UpperCAmelCase_						)

							snake_case_		    =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
							snake_case_		    =     model(UpperCAmelCase_       ,	labels=UpperCAmelCase_						)
							self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.type_sequence_label_size)						)






						def        snake_case__						(						self							:     Dict						):






							"""simple docstring"""






							snake_case_		    =     self.prepare_config_and_inputs()
							snake_case_    ,      snake_case_    ,      snake_case_		    =     config_and_inputs
							snake_case_		    =     {"pixel_values": pixel_values}
							return config, inputs_dict







@require_tf
class 		UpperCAmelCase   (				_UpperCAmelCase       ,   _UpperCAmelCase       ,   unittest.TestCase			):







						'''simple docstring'''





						lowerCAmelCase_     		=   (
						    (
						        TFEfficientFormerModel,
						        TFEfficientFormerForImageClassificationWithTeacher,
						        TFEfficientFormerForImageClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						lowerCAmelCase_     		=   (
						    {
						        """feature-extraction""": TFEfficientFormerModel,
						        """image-classification""": (
						            TFEfficientFormerForImageClassification,
						            TFEfficientFormerForImageClassificationWithTeacher,
						        ),
						    }
						    if is_tf_available()
						    else {}
						)

						lowerCAmelCase_     		=   False

						lowerCAmelCase_     		=   False
						lowerCAmelCase_     		=   False
						lowerCAmelCase_     		=   False
						lowerCAmelCase_     		=   False






						def        snake_case__						(						self							:     int						):






							"""simple docstring"""






							snake_case_		    =     TFEfficientFormerModelTester(self						)
							snake_case_		    =     ConfigTester(
							    self       ,	config_class=UpperCAmelCase_       ,	has_text_modality=UpperCAmelCase_       ,	hidden_size=37						)






						def        snake_case__						(						self							:     str						):






							"""simple docstring"""






							self.config_tester.run_common_tests()






						@unittest.skip(reason="EfficientFormer does not use inputs_embeds"						)
						def        snake_case__						(						self							:     Union[str, Any]						):






							"""simple docstring"""






							pass






						@unittest.skip(reason="EfficientFormer does not support input and output embeddings"						)
						def        snake_case__						(						self							:     List[str]						):






							"""simple docstring"""






							pass






						def        snake_case__						(						self							:     Dict						):






							"""simple docstring"""






							snake_case_    ,      snake_case_		    =     self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								snake_case_		    =     model_class(UpperCAmelCase_						)
								snake_case_		    =     inspect.signature(model.call						)
								# signature.parameters is an OrderedDict => so arg_names order is deterministic
								snake_case_		    =     [*signature.parameters.keys()]

								snake_case_		    =     ["pixel_values"]
								self.assertListEqual(arg_names[:1]       ,	UpperCAmelCase_						)






						def        snake_case__						(						self							:     Optional[Any]						):






							"""simple docstring"""






							def check_hidden_states_output(__lowercase							:     Any       ,	__lowercase							:     str       ,	__lowercase							:     Union[str, Any]						):
								snake_case_		    =     model_class(UpperCAmelCase_						)

								snake_case_		    =     model(**self._prepare_for_class(UpperCAmelCase_       ,	UpperCAmelCase_						)       ,	training=UpperCAmelCase_						)
								snake_case_		    =     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

								snake_case_		    =     getattr(
								    self.model_tester       ,	"expected_num_hidden_layers"       ,	self.model_tester.num_hidden_layers + 1						)
								self.assertEqual(len(UpperCAmelCase_						)       ,	UpperCAmelCase_						)

								if hasattr(self.model_tester       ,	"encoder_seq_length"						):
									snake_case_		    =     self.model_tester.encoder_seq_length
									if hasattr(self.model_tester       ,	"chunk_length"						) and self.model_tester.chunk_length > 1:
										snake_case_		    =     seq_length * self.model_tester.chunk_length
								else:
									snake_case_		    =     self.model_tester.seq_length

								self.assertListEqual(
								    list(hidden_states[-1].shape[-2:]						)       ,	[seq_length, self.model_tester.hidden_size]       ,	)

								if config.is_encoder_decoder:
									snake_case_		    =     outputs.decoder_hidden_states

									self.asseretIsInstance(UpperCAmelCase_       ,	(list, tuple)						)
									self.assertEqual(len(UpperCAmelCase_						)       ,	UpperCAmelCase_						)
									snake_case_		    =     getattr(self.model_tester       ,	"seq_length"       ,	UpperCAmelCase_						)
									snake_case_		    =     getattr(self.model_tester       ,	"decoder_seq_length"       ,	UpperCAmelCase_						)

									self.assertListEqual(
									    list(hidden_states[-1].shape[-2:]						)       ,	[decoder_seq_length, self.model_tester.hidden_size]       ,	)

							snake_case_    ,      snake_case_		    =     self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								snake_case_		    =     True
								check_hidden_states_output(UpperCAmelCase_       ,	UpperCAmelCase_       ,	UpperCAmelCase_						)

								# check that output_hidden_states also work using config
								del inputs_dict["output_hidden_states"]
								snake_case_		    =     True

								check_hidden_states_output(UpperCAmelCase_       ,	UpperCAmelCase_       ,	UpperCAmelCase_						)






						def        snake_case__						(						self							:     Dict       ,	__lowercase							:     List[Any]       ,	__lowercase							:     Any       ,	__lowercase							:     Union[str, Any]=False						):






							"""simple docstring"""






							snake_case_		    =     super()._prepare_for_class(UpperCAmelCase_       ,	UpperCAmelCase_       ,	return_labels=UpperCAmelCase_						)

							if return_labels:
								if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
									del inputs_dict["labels"]

							return inputs_dict






						def        snake_case__						(						self							:     Dict						):






							"""simple docstring"""






							snake_case_		    =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCAmelCase_						)






						@unittest.skip(reason="EfficientFormer does not implement masked image modeling yet"						)
						def        snake_case__						(						self							:     Optional[int]						):






							"""simple docstring"""






							snake_case_		    =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase_						)






						def        snake_case__						(						self							:     Optional[int]						):






							"""simple docstring"""






							snake_case_		    =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_						)






						@slow
						def        snake_case__						(						self							:     Union[str, Any]						):






							"""simple docstring"""






							for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								snake_case_		    =     TFEfficientFormerModel.from_pretrained(UpperCAmelCase_						)
								self.assertIsNotNone(UpperCAmelCase_						)






						def        snake_case__						(						self							:     Optional[int]						):






							"""simple docstring"""






							snake_case_    ,      snake_case_		    =     self.model_tester.prepare_config_and_inputs_for_common()
							snake_case_		    =     True

							snake_case_		    =     getattr(self.model_tester       ,	"seq_length"       ,	UpperCAmelCase_						)
							snake_case_		    =     getattr(self.model_tester       ,	"encoder_seq_length"       ,	UpperCAmelCase_						)
							snake_case_		    =     getattr(self.model_tester       ,	"key_length"       ,	UpperCAmelCase_						)
							snake_case_		    =     getattr(self.model_tester       ,	"chunk_length"       ,	UpperCAmelCase_						)

							if chunk_length is not None and hasattr(self.model_tester       ,	"num_hashes"						):
								snake_case_		    =     encoder_seq_length * self.model_tester.num_hashes

							for model_class in self.all_model_classes:
								snake_case_		    =     True
								snake_case_		    =     False
								snake_case_		    =     True
								snake_case_		    =     model_class(UpperCAmelCase_						)

								snake_case_		    =     model(**self._prepare_for_class(UpperCAmelCase_       ,	UpperCAmelCase_						)       ,	training=UpperCAmelCase_						)
								snake_case_		    =     outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
								self.assertEqual(len(UpperCAmelCase_						)       ,	self.model_tester.num_attention_outputs						)

								# check that output_attentions also work using config
								del inputs_dict["output_attentions"]
								snake_case_		    =     True
								snake_case_		    =     model_class(UpperCAmelCase_						)
								snake_case_		    =     model(**self._prepare_for_class(UpperCAmelCase_       ,	UpperCAmelCase_						)       ,	training=UpperCAmelCase_						)

								snake_case_		    =     outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
								self.assertEqual(len(UpperCAmelCase_						)       ,	self.model_tester.num_attention_outputs						)

								if chunk_length is not None:
									self.assertListEqual(
									    list(attentions[0].shape[-4:]						)       ,	[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]       ,	)
								else:
									self.assertListEqual(
									    list(attentions[0].shape[-3:]						)       ,	[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]       ,	)






						def        snake_case__						(						self							:     Dict						):






							"""simple docstring"""






							snake_case_    ,      snake_case_		    =     self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								# Prepare our model
								snake_case_		    =     model_class(UpperCAmelCase_						)
								# These are maximally general inputs for the model, with multiple None dimensions
								# Hopefully this will catch any conditionals that fail for flexible shapes
								snake_case_		    =     {
								    key: tf.keras.Input(shape=val.shape[1:]       ,	dtype=val.dtype       ,	name=UpperCAmelCase_						)
								    for key, val in model.input_signature.items()
								    if key in model.dummy_inputs
								}
								snake_case_		    =     model(UpperCAmelCase_						)
								self.assertTrue(outputs_dict is not None						)







def 						lowerCamelCase__    (		):







	'''simple docstring'''





	snake_case_		    =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
	return image






@require_tf
@require_vision
class 		UpperCAmelCase   (				unittest.TestCase			):







						'''simple docstring'''





						@cached_property
						def        snake_case__						(						self							:     List[Any]						):






							"""simple docstring"""






							return (
							    EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300"						)
							    if is_vision_available()
							    else None
							)






						@slow
						def        snake_case__						(						self							:     Optional[Any]						):






							"""simple docstring"""






							snake_case_		    =     TFEfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300"						)
							snake_case_		    =     self.default_image_processor
							snake_case_		    =     prepare_img()
							snake_case_		    =     image_processor(images=UpperCAmelCase_       ,	return_tensors="tf"						)
							# forward pass
							snake_case_		    =     model(**UpperCAmelCase_       ,	training=UpperCAmelCase_						)
							# verify the logits
							snake_case_		    =     tf.TensorShape((1, 10_00)						)
							self.assertEqual(outputs.logits.shape       ,	UpperCAmelCase_						)
							snake_case_		    =     tf.constant([-0.0555, 0.4825, -0.0852]						)
							self.assertTrue(np.allclose(outputs.logits[0, :3]       ,	UpperCAmelCase_       ,	atol=1E-4						)						)






						@slow
						def        snake_case__						(						self							:     Dict						):






							"""simple docstring"""






							snake_case_		    =     TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
							    "snap-research/efficientformer-l1-300"						)
							snake_case_		    =     self.default_image_processor
							snake_case_		    =     prepare_img()
							snake_case_		    =     image_processor(images=UpperCAmelCase_       ,	return_tensors="tf"						)
							# forward pass
							snake_case_		    =     model(**UpperCAmelCase_       ,	training=UpperCAmelCase_						)
							# verify the logits
							snake_case_		    =     tf.TensorShape((1, 10_00)						)
							self.assertEqual(outputs.logits.shape       ,	UpperCAmelCase_						)
							snake_case_		    =     tf.constant([-0.1312, 0.4353, -1.0499]						)
							self.assertTrue(np.allclose(outputs.logits[0, :3]       ,	UpperCAmelCase_       ,	atol=1E-4						)						)


from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
				from fairscale.optim import OSS


__snake_case								=  logging.get_logger(__name__)

__snake_case								=  {
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    """constant""": get_constant_schedule,
    """constant_w_warmup""": get_constant_schedule_with_warmup,
}




class 						lowercase__  (						_UpperCAmelCase  ):
	def __init__(			self							:   str       ,   UpperCAmelCase_							:   int=None       ,   UpperCAmelCase_							:   List[str]=None       ,   *UpperCAmelCase_							:   Union[str, Any]       ,   **UpperCAmelCase_							:   Optional[Any]							):
						super().__init__(*UpperCAmelCase_       ,   **UpperCAmelCase_							)

						if config is None:
											assert isinstance(self.model       ,   UpperCAmelCase_							), (
											    "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
											    F' {self.model.__class__}'
											)
											SCREAMING_SNAKE_CASE__													= self.model.config
						else:
											SCREAMING_SNAKE_CASE__													= config

						SCREAMING_SNAKE_CASE__													= data_args
						SCREAMING_SNAKE_CASE__													= self.config.tgt_vocab_size if isinstance(self.config       ,   UpperCAmelCase_							) else self.config.vocab_size

						if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
											assert self.config.pad_token_id is not None, (
											    "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
											    " calculation or doing label smoothing."
											)

						if self.config.pad_token_id is None and self.config.eos_token_id is not None:
											logger.warning(
											    F'The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'
											    ' padding..'							)

						if self.args.label_smoothing == 0:
											SCREAMING_SNAKE_CASE__													= torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id							)
						else:
											# dynamically import label_smoothed_nll_loss
											from utils import label_smoothed_nll_loss

											SCREAMING_SNAKE_CASE__													= label_smoothed_nll_loss
	def      A_				(			self							:   Tuple       ,   UpperCAmelCase_							:   int							):
						if self.optimizer is None:
											SCREAMING_SNAKE_CASE__													= ['bias', 'LayerNorm.weight']
											SCREAMING_SNAKE_CASE__													= [
											    {
											        'params': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay							)],
											        'weight_decay': self.args.weight_decay,
											    },
											    {
											        'params': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay							)],
											        'weight_decay': 0.0,
											    },
											]
											SCREAMING_SNAKE_CASE__													= Adafactor if self.args.adafactor else AdamW
											if self.args.adafactor:
																SCREAMING_SNAKE_CASE__													= Adafactor
																SCREAMING_SNAKE_CASE__													= {'scale_parameter': False, 'relative_step': False}
											else:
																SCREAMING_SNAKE_CASE__													= AdamW
																SCREAMING_SNAKE_CASE__													= {
																    'betas': (self.args.adam_betaa, self.args.adam_betaa),
																    'eps': self.args.adam_epsilon,
																}
											SCREAMING_SNAKE_CASE__													= self.args.learning_rate
											if self.sharded_ddp:
																SCREAMING_SNAKE_CASE__													= OSS(
																    params=UpperCAmelCase_       ,   optim=UpperCAmelCase_       ,   **UpperCAmelCase_       ,   )
											else:
																SCREAMING_SNAKE_CASE__													= optimizer_cls(UpperCAmelCase_       ,   **UpperCAmelCase_							)

						if self.lr_scheduler is None:
											SCREAMING_SNAKE_CASE__													= self._get_lr_scheduler(UpperCAmelCase_							)
						else:  # ignoring --lr_scheduler
											logger.warning('scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'							)
	def      A_				(			self							:   str       ,   UpperCAmelCase_							:   List[Any]							):
						SCREAMING_SNAKE_CASE__													= arg_to_scheduler[self.args.lr_scheduler]
						if self.args.lr_scheduler == "constant":
											SCREAMING_SNAKE_CASE__													= schedule_func(self.optimizer							)
						elif self.args.lr_scheduler == "constant_w_warmup":
											SCREAMING_SNAKE_CASE__													= schedule_func(self.optimizer       ,   num_warmup_steps=self.args.warmup_steps							)
						else:
											SCREAMING_SNAKE_CASE__													= schedule_func(
											    self.optimizer       ,   num_warmup_steps=self.args.warmup_steps       ,   num_training_steps=UpperCAmelCase_							)
						return scheduler
	def      A_				(			self							:   List[str]							):
						if isinstance(self.train_dataset       ,   torch.utils.data.IterableDataset							):
											return None
						elif is_torch_tpu_available():
											return get_tpu_sampler(self.train_dataset							)
						else:
											if self.args.sortish_sampler:
																self.train_dataset.make_sortish_sampler(
																    self.args.per_device_train_batch_size       ,   distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)       ,   )

											return (
											    RandomSampler(self.train_dataset							)
											    if self.args.local_rank == -1
											    else DistributedSampler(self.train_dataset							)
											)
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   Tuple       ,   UpperCAmelCase_							:   Optional[Any]       ,   UpperCAmelCase_							:   Dict							):
						if self.args.label_smoothing == 0:
											if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
																# force training to ignore pad token
																SCREAMING_SNAKE_CASE__													= model(**UpperCAmelCase_       ,   use_cache=UpperCAmelCase_							)[0]
																SCREAMING_SNAKE_CASE__													= self.loss_fn(logits.view(-1       ,   logits.shape[-1]							)       ,   labels.view(-1							)							)
											else:
																# compute usual loss via models
																SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= model(**UpperCAmelCase_       ,   labels=UpperCAmelCase_       ,   use_cache=UpperCAmelCase_							)[:2]
						else:
											# compute label smoothed loss
											SCREAMING_SNAKE_CASE__													= model(**UpperCAmelCase_       ,   use_cache=UpperCAmelCase_							)[0]
											SCREAMING_SNAKE_CASE__													= torch.nn.functional.log_softmax(UpperCAmelCase_       ,   dim=-1							)
											SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= self.loss_fn(UpperCAmelCase_       ,   UpperCAmelCase_       ,   self.args.label_smoothing       ,   ignore_index=self.config.pad_token_id							)
						return loss, logits
	def      A_				(			self							:   Union[str, Any]       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   List[Any]							):
						SCREAMING_SNAKE_CASE__													= inputs.pop('labels'							)
						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= self._compute_loss(UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_							)
						return loss
	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   nn.Module       ,   UpperCAmelCase_							:   Dict[str, Union[torch.Tensor, Any]]       ,   UpperCAmelCase_							:   bool       ,   UpperCAmelCase_							:   Optional[List[str]] = None       ,   ):
						SCREAMING_SNAKE_CASE__													= self._prepare_inputs(UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= {
						    'max_length': self.data_args.val_max_target_length
						    if self.data_args is not None
						    else self.config.max_length,
						    'num_beams': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
						}

						if self.args.predict_with_generate and not self.args.prediction_loss_only:
											SCREAMING_SNAKE_CASE__													= self.model.generate(
											    inputs['input_ids']       ,   attention_mask=inputs['attention_mask']       ,   **UpperCAmelCase_       ,   )
											# in case the batch is shorter than max length, the output should be padded
											if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
																SCREAMING_SNAKE_CASE__													= self._pad_tensors_to_max_len(UpperCAmelCase_       ,   gen_kwargs['max_length']							)

						SCREAMING_SNAKE_CASE__													= inputs.pop('labels'							)
						with torch.no_grad():
											# compute loss on predict data
											SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= self._compute_loss(UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= loss.mean().detach()
						if self.args.prediction_loss_only:
											return (loss, None, None)

						SCREAMING_SNAKE_CASE__													= generated_tokens if self.args.predict_with_generate else logits

						if labels.shape[-1] < gen_kwargs["max_length"]:
											SCREAMING_SNAKE_CASE__													= self._pad_tensors_to_max_len(UpperCAmelCase_       ,   gen_kwargs['max_length']							)

						return (loss, logits, labels)





	def      A_				(			self							:   Union[str, Any]       ,   UpperCAmelCase_							:   Tuple       ,   UpperCAmelCase_							:   List[str]							):
						# If PAD token is not defined at least EOS token has to be defined
						SCREAMING_SNAKE_CASE__													= self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

						if pad_token_id is None:
											raise ValueError(
											    'Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'
											    F' padded to `max_length`={max_length}'							)

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





