




import collections
import inspect
import unittest

from transformers import SwinConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
		from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor




class        snake_case__				:
				"""simple docstring"""



				def __init__( self							:	List[str]   , __lowerCamelCase							:	Union[str, Any]   , __lowerCamelCase							:	List[Any]=13   , __lowerCamelCase							:	int=32   , __lowerCamelCase							:	Any=2   , __lowerCamelCase							:	Any=3   , __lowerCamelCase							:	Tuple=16   , __lowerCamelCase							:	Optional[Any]=[1, 2, 1]   , __lowerCamelCase							:	Dict=[2, 2, 4]   , __lowerCamelCase							:	str=2   , __lowerCamelCase							:	Optional[Any]=2.0   , __lowerCamelCase							:	Any=True   , __lowerCamelCase							:	Optional[int]=0.0   , __lowerCamelCase							:	Optional[Any]=0.0   , __lowerCamelCase							:	Tuple=0.1   , __lowerCamelCase							:	Union[str, Any]="gelu"   , __lowerCamelCase							:	int=False   , __lowerCamelCase							:	Union[str, Any]=True   , __lowerCamelCase							:	Any=0.02   , __lowerCamelCase							:	List[str]=1e-5   , __lowerCamelCase							:	List[str]=True   , __lowerCamelCase							:	List[Any]=None   , __lowerCamelCase							:	List[Any]=True   , __lowerCamelCase							:	Any=10   , __lowerCamelCase							:	List[Any]=8   , __lowerCamelCase							:	int=["stage1", "stage2"]   , __lowerCamelCase							:	Optional[Any]=[1, 2]   , )   ->	Dict:
							a       =     parent
							a       =     batch_size
							a       =     image_size
							a       =     patch_size
							a       =     num_channels
							a       =     embed_dim
							a       =     depths
							a       =     num_heads
							a       =     window_size
							a       =     mlp_ratio
							a       =     qkv_bias
							a       =     hidden_dropout_prob
							a       =     attention_probs_dropout_prob
							a       =     drop_path_rate
							a       =     hidden_act
							a       =     use_absolute_embeddings
							a       =     patch_norm
							a       =     layer_norm_eps
							a       =     initializer_range
							a       =     is_training
							a       =     scope
							a       =     use_labels
							a       =     type_sequence_label_size
							a       =     encoder_stride
							a       =     out_features
							a       =     out_indices





				def 				__UpperCAmelCase				( self							:	Dict				)   ->	Union[str, Any]:
							a       =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

							a       =     self.get_config()

							return config, pixel_values, labels





				def 				__UpperCAmelCase				( self							:	List[str]				)   ->	Union[str, Any]:
							return SwinConfig(
							    image_size=self.image_size   , patch_size=self.patch_size   , num_channels=self.num_channels   , embed_dim=self.embed_dim   , depths=self.depths   , num_heads=self.num_heads   , window_size=self.window_size   , mlp_ratio=self.mlp_ratio   , qkv_bias=self.qkv_bias   , hidden_dropout_prob=self.hidden_dropout_prob   , attention_probs_dropout_prob=self.attention_probs_dropout_prob   , drop_path_rate=self.drop_path_rate   , hidden_act=self.hidden_act   , use_absolute_embeddings=self.use_absolute_embeddings   , path_norm=self.patch_norm   , layer_norm_eps=self.layer_norm_eps   , initializer_range=self.initializer_range   , encoder_stride=self.encoder_stride   , out_features=self.out_features   , out_indices=self.out_indices   , )





				def 				__UpperCAmelCase				( self							:	Tuple   , __lowerCamelCase							:	Optional[int]   , __lowerCamelCase							:	Optional[int]   , __lowerCamelCase							:	List[str]				)   ->	Any:
							a       =     SwinModel(config=__lowerCamelCase				)
							model.to(__lowerCamelCase				)
							model.eval()
							a       =     model(__lowerCamelCase				)

							a       =     ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths				) - 1))
							a       =     int(config.embed_dim * 2 ** (len(config.depths				) - 1)				)

							self.parent.assertEqual(result.last_hidden_state.shape   , (self.batch_size, expected_seq_len, expected_dim)				)





				def 				__UpperCAmelCase				( self							:	int   , __lowerCamelCase							:	Dict   , __lowerCamelCase							:	List[Any]   , __lowerCamelCase							:	int				)   ->	Optional[int]:
							a       =     SwinBackbone(config=__lowerCamelCase				)
							model.to(__lowerCamelCase				)
							model.eval()
							a       =     model(__lowerCamelCase				)

							# verify hidden states
							self.parent.assertEqual(len(result.feature_maps				)   , len(config.out_features				)				)
							self.parent.assertListEqual(list(result.feature_maps[0].shape				)   , [self.batch_size, model.channels[0], 16, 16]				)

							# verify channels
							self.parent.assertEqual(len(model.channels				)   , len(config.out_features				)				)

							# verify backbone works with out_features=None
							a       =     None
							a       =     SwinBackbone(config=__lowerCamelCase				)
							model.to(__lowerCamelCase				)
							model.eval()
							a       =     model(__lowerCamelCase				)

							# verify feature maps
							self.parent.assertEqual(len(result.feature_maps				)   , 1				)
							self.parent.assertListEqual(list(result.feature_maps[0].shape				)   , [self.batch_size, model.channels[-1], 4, 4]				)

							# verify channels
							self.parent.assertEqual(len(model.channels				)   , 1				)





				def 				__UpperCAmelCase				( self							:	Optional[int]   , __lowerCamelCase							:	int   , __lowerCamelCase							:	int   , __lowerCamelCase							:	List[Any]				)   ->	List[Any]:
							a       =     SwinForMaskedImageModeling(config=__lowerCamelCase				)
							model.to(__lowerCamelCase				)
							model.eval()
							a       =     model(__lowerCamelCase				)
							self.parent.assertEqual(
							    result.logits.shape   , (self.batch_size, self.num_channels, self.image_size, self.image_size)				)

							# test greyscale images
							a       =     1
							a       =     SwinForMaskedImageModeling(__lowerCamelCase				)
							model.to(__lowerCamelCase				)
							model.eval()

							a       =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
							a       =     model(__lowerCamelCase				)
							self.parent.assertEqual(result.logits.shape   , (self.batch_size, 1, self.image_size, self.image_size)				)





				def 				__UpperCAmelCase				( self							:	Dict   , __lowerCamelCase							:	int   , __lowerCamelCase							:	Tuple   , __lowerCamelCase							:	Union[str, Any]				)   ->	Optional[Any]:
							a       =     self.type_sequence_label_size
							a       =     SwinForImageClassification(__lowerCamelCase				)
							model.to(__lowerCamelCase				)
							model.eval()
							a       =     model(__lowerCamelCase   , labels=__lowerCamelCase				)
							self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.type_sequence_label_size)				)

							# test greyscale images
							a       =     1
							a       =     SwinForImageClassification(__lowerCamelCase				)
							model.to(__lowerCamelCase				)
							model.eval()

							a       =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
							a       =     model(__lowerCamelCase				)
							self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.type_sequence_label_size)				)





				def 				__UpperCAmelCase				( self							:	str				)   ->	List[str]:
							a       =     self.prepare_config_and_inputs()
							(
							    (
							    a
							)       ,						(
							    a
							)       ,						(
							    a
							)       ,						
							)       =     config_and_inputs
							a       =     {"pixel_values": pixel_values}
							return config, inputs_dict




@require_torch
class        snake_case__				(_UpperCamelCase       ,			_UpperCamelCase       ,			unittest.TestCase  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Tuple									=   (
				    (
				        SwinModel,
				        SwinBackbone,
				        SwinForImageClassification,
				        SwinForMaskedImageModeling,
				    )
				    if is_torch_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE_			:				int									=   (
				    {"""feature-extraction""": SwinModel, """image-classification""": SwinForImageClassification}
				    if is_torch_available()
				    else {}
				)
				SCREAMING_SNAKE_CASE_			:				str									=   True

				SCREAMING_SNAKE_CASE_			:				str									=   False
				SCREAMING_SNAKE_CASE_			:				Tuple									=   False
				SCREAMING_SNAKE_CASE_			:				Optional[int]									=   False





				def 				__UpperCAmelCase				( self							:	Dict				)   ->	Union[str, Any]:
							a       =     SwinModelTester(self				)
							a       =     ConfigTester(self   , config_class=__lowerCamelCase   , embed_dim=37				)





				def 				__UpperCAmelCase				( self							:	Union[str, Any]				)   ->	Any:
							self.create_and_test_config_common_properties()
							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()





				def 				__UpperCAmelCase				( self							:	Tuple				)   ->	List[Any]:
							return





				def 				__UpperCAmelCase				( self							:	Any				)   ->	Tuple:
							a       =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowerCamelCase				)





				@unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."				)
				def 				__UpperCAmelCase				( self							:	str				)   ->	str:
							pass





				def 				__UpperCAmelCase				( self							:	Dict				)   ->	List[Any]:
							super().test_training_gradient_checkpointing()





				def 				__UpperCAmelCase				( self							:	Optional[int]				)   ->	Dict:
							a       =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*__lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	Dict				)   ->	Optional[Any]:
							a       =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*__lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	Union[str, Any]				)   ->	Dict:
							a       =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase				)





				@unittest.skip(reason="Swin does not use inputs_embeds"				)
				def 				__UpperCAmelCase				( self							:	int				)   ->	Union[str, Any]:
							pass





				@unittest.skip(reason="Swin Transformer does not use feedforward chunking"				)
				def 				__UpperCAmelCase				( self							:	Optional[int]				)   ->	Dict:
							pass





				def 				__UpperCAmelCase				( self							:	List[Any]				)   ->	Union[str, Any]:
							a       ,						a       =     self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										a       =     model_class(__lowerCamelCase				)
										self.assertIsInstance(model.get_input_embeddings()   , (nn.Module)				)
										a       =     model.get_output_embeddings()
										self.assertTrue(x is None or isinstance(__lowerCamelCase   , nn.Linear				)				)





				def 				__UpperCAmelCase				( self							:	List[Any]				)   ->	List[Any]:
							a       ,						a       =     self.model_tester.prepare_config_and_inputs_for_common()

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

										a       =     ["pixel_values"]
										self.assertListEqual(arg_names[:1]   , __lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	Union[str, Any]:
							a       ,						a       =     self.model_tester.prepare_config_and_inputs_for_common()
							a       =     True

							for model_class in self.all_model_classes:
										a       =     True
										a       =     False
										a       =     True
										a       =     model_class(__lowerCamelCase				)
										model.to(__lowerCamelCase				)
										model.eval()
										with torch.no_grad():
													a       =     model(**self._prepare_for_class(__lowerCamelCase   , __lowerCamelCase				)				)
										a       =     outputs.attentions
										a       =     len(self.model_tester.depths				)
										self.assertEqual(len(__lowerCamelCase				)   , __lowerCamelCase				)

										# check that output_attentions also work using config
										del inputs_dict["output_attentions"]
										a       =     True
										a       =     config.window_size**2
										a       =     model_class(__lowerCamelCase				)
										model.to(__lowerCamelCase				)
										model.eval()
										with torch.no_grad():
													a       =     model(**self._prepare_for_class(__lowerCamelCase   , __lowerCamelCase				)				)
										a       =     outputs.attentions
										self.assertEqual(len(__lowerCamelCase				)   , __lowerCamelCase				)

										self.assertListEqual(
										    list(attentions[0].shape[-3:]				)   , [self.model_tester.num_heads[0], window_size_squared, window_size_squared]   , )
										a       =     len(__lowerCamelCase				)

										# Check attention is always last and order is fine
										a       =     True
										a       =     True
										a       =     model_class(__lowerCamelCase				)
										model.to(__lowerCamelCase				)
										model.eval()
										with torch.no_grad():
													a       =     model(**self._prepare_for_class(__lowerCamelCase   , __lowerCamelCase				)				)

										# also another +1 for reshaped_hidden_states
										a       =     1 if model_class.__name__ == "SwinBackbone" else 2
										self.assertEqual(out_len + added_hidden_states   , len(__lowerCamelCase				)				)

										a       =     outputs.attentions

										self.assertEqual(len(__lowerCamelCase				)   , __lowerCamelCase				)

										self.assertListEqual(
										    list(self_attentions[0].shape[-3:]				)   , [self.model_tester.num_heads[0], window_size_squared, window_size_squared]   , )





				def 				__UpperCAmelCase				( self							:	List[Any]   , __lowerCamelCase							:	Any   , __lowerCamelCase							:	Tuple   , __lowerCamelCase							:	int   , __lowerCamelCase							:	Union[str, Any]				)   ->	Any:
							a       =     model_class(__lowerCamelCase				)
							model.to(__lowerCamelCase				)
							model.eval()

							with torch.no_grad():
										a       =     model(**self._prepare_for_class(__lowerCamelCase   , __lowerCamelCase				)				)

							a       =     outputs.hidden_states

							a       =     getattr(
							    self.model_tester   , "expected_num_hidden_layers"   , len(self.model_tester.depths				) + 1				)
							self.assertEqual(len(__lowerCamelCase				)   , __lowerCamelCase				)

							# Swin has a different seq_length
							a       =     (
							    config.patch_size
							    if isinstance(config.patch_size   , collections.abc.Iterable				)
							    else (config.patch_size, config.patch_size)
							)

							a       =     (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

							self.assertListEqual(
							    list(hidden_states[0].shape[-2:]				)   , [num_patches, self.model_tester.embed_dim]   , )

							if not model_class.__name__ == "SwinBackbone":
										a       =     outputs.reshaped_hidden_states
										self.assertEqual(len(__lowerCamelCase				)   , __lowerCamelCase				)

										a       ,						a       ,						a       ,						a       =     reshaped_hidden_states[0].shape
										a       =     (
										    reshaped_hidden_states[0].view(__lowerCamelCase   , __lowerCamelCase   , height * width				).permute(0   , 2   , 1				)
										)
										self.assertListEqual(
										    list(reshaped_hidden_states.shape[-2:]				)   , [num_patches, self.model_tester.embed_dim]   , )





				def 				__UpperCAmelCase				( self							:	Any				)   ->	int:
							a       ,						a       =     self.model_tester.prepare_config_and_inputs_for_common()

							a       =     (
							    self.model_tester.image_size
							    if isinstance(self.model_tester.image_size   , collections.abc.Iterable				)
							    else (self.model_tester.image_size, self.model_tester.image_size)
							)

							for model_class in self.all_model_classes:
										a       =     True
										self.check_hidden_states_output(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				)

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

										self.check_hidden_states_output(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	Union[str, Any]				)   ->	List[Any]:
							a       ,						a       =     self.model_tester.prepare_config_and_inputs_for_common()
							a       =     3

							a       =     (
							    self.model_tester.image_size
							    if isinstance(self.model_tester.image_size   , collections.abc.Iterable				)
							    else (self.model_tester.image_size, self.model_tester.image_size)
							)
							a       =     (
							    config.patch_size
							    if isinstance(config.patch_size   , collections.abc.Iterable				)
							    else (config.patch_size, config.patch_size)
							)

							a       =     image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
							a       =     image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

							for model_class in self.all_model_classes:
										a       =     True
										self.check_hidden_states_output(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , (padded_height, padded_width)				)

										# check that output_hidden_states also work using config
										del inputs_dict["output_hidden_states"]
										a       =     True
										self.check_hidden_states_output(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , (padded_height, padded_width)				)





				@slow
				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	str:
							for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										a       =     SwinModel.from_pretrained(__lowerCamelCase				)
										self.assertIsNotNone(__lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	Any				)   ->	List[str]:
							a       ,						a       =     self.model_tester.prepare_config_and_inputs_for_common()

							a       =     _config_zero_init(__lowerCamelCase				)
							for model_class in self.all_model_classes:
										a       =     model_class(config=__lowerCamelCase				)
										for name, param in model.named_parameters():
													if "embeddings" not in name and param.requires_grad:
																self.assertIn(
																    ((param.data.mean() * 1e9).round() / 1e9).item()   , [0.0, 1.0]   , msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""   , )




@require_vision
@require_torch
class        snake_case__				(unittest.TestCase  ):
				"""simple docstring"""



				@cached_property
				def 				__UpperCAmelCase				( self							:	Optional[int]				)   ->	Dict:
							return (
							    AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224"				)
							    if is_vision_available()
							    else None
							)





				@slow
				def 				__UpperCAmelCase				( self							:	Union[str, Any]				)   ->	Dict:
							a       =     SwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"				).to(__lowerCamelCase				)
							a       =     self.default_image_processor

							a       =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
							a       =     image_processor(images=__lowerCamelCase   , return_tensors="pt"				).to(__lowerCamelCase				)

							# forward pass
							with torch.no_grad():
										a       =     model(**__lowerCamelCase				)

							# verify the logits
							a       =     torch.Size((1, 10_00)				)
							self.assertEqual(outputs.logits.shape   , __lowerCamelCase				)
							a       =     torch.tensor([-0.0_948, -0.6_454, -0.0_921]				).to(__lowerCamelCase				)
							self.assertTrue(torch.allclose(outputs.logits[0, :3]   , __lowerCamelCase   , atol=1e-4				)				)




@require_torch
class        snake_case__				(unittest.TestCase       ,			_UpperCamelCase  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Union[str, Any]									=   (SwinBackbone,) if is_torch_available() else ()
				SCREAMING_SNAKE_CASE_			:				str									=   SwinConfig





				def 				__UpperCAmelCase				( self							:	Tuple				)   ->	Any:
							a       =     SwinModelTester(self				)












import logging
import os
import time
from argparse import Namespace
from pathlib import Path

import datasets
import torch
from accelerate import Accelerator, DistributedType
from accelerate.utils import ProjectConfiguration
from arguments import TrainingArguments
from datasets import load_dataset
from huggingface_hub import Repository
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed




class        snake_case__				(_UpperCamelCase  ):
				"""simple docstring"""



				def __init__( self							:	Union[str, Any]   , __lowerCamelCase							:	Dict   , __lowerCamelCase							:	Optional[Any]   , __lowerCamelCase							:	Optional[int]=False   , __lowerCamelCase							:	Optional[Any]=10_24   , __lowerCamelCase							:	str=10_24   , __lowerCamelCase							:	Any=3.6   , __lowerCamelCase							:	List[Any]=False   , )   ->	Optional[Any]:
							a       =     tokenizer
							a       =     tokenizer.bos_token_id
							a       =     dataset
							a       =     seq_length
							a       =     0
							a       =     infinite
							a       =     0
							a       =     tokenized

							if self.tokenized:
										a       =     seq_length * num_of_sequences
										a       =     "input_ids"
							else:
										a       =     seq_length * chars_per_token * num_of_sequences
										a       =     "content"





				def __iter__( self							:	Union[str, Any]				)   ->	List[str]:
							a       =     iter(self.dataset				)
							a       =     True
							while more_examples:
										a       ,						a       =     [], 0
										while True:
													if buffer_len >= self.max_buffer_size:
																break
													try:
																buffer.append(next(__lowerCamelCase				)[self.content_field]				)
																buffer_len += len(buffer[-1]				)
													except StopIteration:
																if self.infinite:
																			a       =     iter(self.dataset				)
																			self.epoch += 1
																			logger.info(f"""Dataset epoch: {self.epoch}"""				)
																else:
																			a       =     False
																			break
										if self.tokenized:
													a       =     buffer
										else:
													a       =     self.tokenizer(__lowerCamelCase   , truncation=__lowerCamelCase				)["input_ids"]
										a       =     []
										for tokenized_input in tokenized_inputs:
													all_token_ids.extend(tokenized_input + [self.concat_token_id]				)
										for i in range(0   , len(__lowerCamelCase				)   , self.seq_length				):
													a       =     all_token_ids[i : i + self.seq_length]
													if len(__lowerCamelCase				) == self.seq_length:
																self.current_size += 1
																yield torch.tensor(__lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	List[str]   , __lowerCamelCase							:	Optional[int]=10_00				)   ->	Union[str, Any]:
							return ShufflerIterDataPipe(self   , buffer_size=__lowerCamelCase				)




def      __magic_name__							(					A						:   Any  ):






			'''simple docstring'''







			a       =     args.model_ckpt.split("/"  )[-1]
			a       =     logging.getLogger(__name__  )
			a       =     Path(args.save_dir  ) / "log/"
			log_dir.mkdir(exist_ok=A  )
			a       =     F"""debug_{accelerator.process_index}.log"""
			logging.basicConfig(
			    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",    datefmt="%m/%d/%Y %H:%M:%S",    level=logging.INFO,    handlers=[logging.FileHandler(log_dir / filename  ), logging.StreamHandler()],    )
			if accelerator.is_main_process:  # we only want to setup logging once
						accelerator.init_trackers(A,    vars(A  )  )
						a       =     accelerator.trackers[0].run.name
						logger.setLevel(logging.INFO  )
						datasets.utils.logging.set_verbosity_info()
						transformers.utils.logging.set_verbosity_info()
			else:
						a       =     ""
						logger.setLevel(logging.ERROR  )
						datasets.utils.logging.set_verbosity_error()
						transformers.utils.logging.set_verbosity_error()
			return logger, run_name




def      __magic_name__							(					A						:   Any  ):






			'''simple docstring'''







			a       =     {"streaming": True}
			a       =     load_dataset(args.dataset_name_train,    split="train",    **A  )
			a       =     train_data.shuffle(buffer_size=args.shuffle_buffer,    seed=args.seed  )
			a       =     load_dataset(args.dataset_name_valid,    split="train",    **A  )
			a       =     ConstantLengthDataset(
			    A,    A,    infinite=A,    seq_length=args.seq_length,    tokenized=args.tokenized  )
			a       =     ConstantLengthDataset(
			    A,    A,    infinite=A,    seq_length=args.seq_length,    tokenized=args.tokenized  )
			a       =     train_dataset.shuffle(buffer_size=args.shuffle_buffer  )
			a       =     DataLoader(A,    batch_size=args.train_batch_size,    shuffle=A  )
			a       =     DataLoader(A,    batch_size=args.valid_batch_size  )
			return train_dataloader, eval_dataloader




def      __magic_name__							(					A						:   Union[str, Any],    A						:   Optional[Any],    A						:   Tuple=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]  ):






			'''simple docstring'''







			a       ,						a       =     [], []
			for n, p in model.named_parameters():
						if any(nd in n for nd in no_decay  ):
									params_without_wd.append(A  )
						else:
									params_with_wd.append(A  )
			return [
			    {"params": params_with_wd, "weight_decay": args.weight_decay},
			    {"params": params_without_wd, "weight_decay": 0.0},
			]




def      __magic_name__							(					A						:   List[str],    A						:   List[str]  ):






			'''simple docstring'''







			logger.info(F"""Step {step}: {metrics}"""  )
			if accelerator.is_main_process:
						accelerator.log(A,    A  )




def      __magic_name__							(					A						:   Union[str, Any],    A						:   Optional[int],    A						:   List[str]  ):






			'''simple docstring'''







			a       =     accelerator.unwrap_model(A  ).config
			a       =     4 if args.gradient_checkpointing else 3
			a       =     args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
			a       =     24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
			a       =     factor * (
			    1.0
			    + (args.seq_length / (6.0 * config_model.n_embd))
			    + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
			)
			a       =     flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
			return tflops




def      __magic_name__							(					A						:   List[Any]  ):






			'''simple docstring'''







			model.eval()
			a       =     []
			for step, batch in enumerate(A  ):
						with torch.no_grad():
									a       =     model(A,    labels=A  )
						a       =     outputs.loss.repeat(args.valid_batch_size  )
						losses.append(accelerator.gather(A  )  )
						if args.max_eval_steps > 0 and step >= args.max_eval_steps:
									break
			a       =     torch.cat(A  )
			a       =     losses[: eval_dataloader.dataset.current_size].mean()
			try:
						a       =     torch.exp(A  )
			except OverflowError:
						a       =     float("inf"  )
			return loss.item(), perplexity.item()


# Settings
__lowerCAmelCase						:							Optional[int]			     =						HfArgumentParser(TrainingArguments)
__lowerCAmelCase						:							Optional[Any]			     =						parser.parse_args()

# Accelerator
__lowerCAmelCase						:							int			     =						ProjectConfiguration(project_dir=args.save_dir, logging_dir='log')
__lowerCAmelCase						:							Union[str, Any]			     =						Accelerator(log_with=['wandb', 'tensorboard'], project_config=config)
__lowerCAmelCase						:							Tuple			     =						{str(k): str(v) for k, v in accelerator.state.__dict__.items()}

__lowerCAmelCase						:							Dict			     =						Namespace(**vars(args), **acc_state)
__lowerCAmelCase						:							Optional[int]			     =						accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
		__lowerCAmelCase						:							List[Any]			     =						Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
__lowerCAmelCase     ,	__lowerCAmelCase						:							Dict			     =						setup_logging(args)
logger.info(accelerator.state)

# Checkout new branch on repo
if accelerator.is_main_process:
		hf_repo.git_checkout(run_name, create_branch_ok=True)

# Load model and tokenizer
__lowerCAmelCase						:							Union[str, Any]			     =						AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
		model.gradient_checkpointing_enable()
__lowerCAmelCase						:							str			     =						AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
__lowerCAmelCase     ,	__lowerCAmelCase						:							Tuple			     =						create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
__lowerCAmelCase						:							Any			     =						AdamW(get_grouped_params(model, args), lr=args.learning_rate)
__lowerCAmelCase						:							Optional[Any]			     =						get_scheduler(
    name=args.lr_scheduler_type,
    optimizer=optimizer,
    num_warmup_steps=args.num_warmup_steps,
    num_training_steps=args.max_train_steps,
)
accelerator.register_for_checkpointing(lr_scheduler)




def      __magic_name__							(					):






			'''simple docstring'''







			return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
__lowerCAmelCase     ,	__lowerCAmelCase     ,	__lowerCAmelCase     ,	__lowerCAmelCase						:							List[str]			     =						accelerator.prepare(
    model, optimizer, train_dataloader, eval_dataloader
)

# load in the weights and states from a previous save
if args.resume_from_checkpoint:
		if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
				accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}''')
				accelerator.load_state(args.resume_from_checkpoint)
				__lowerCAmelCase						:							Dict			     =						os.path.basename(args.resume_from_checkpoint)
		else:
				# Get the most recent checkpoint
				__lowerCAmelCase						:							Optional[Any]			     =						[f.name for f in os.scandir(args.save_dir) if f.is_dir() and 'step' in str(f)]
				dirs.sort(key=os.path.getctime)
				__lowerCAmelCase						:							List[Any]			     =						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
		# Extract the step of the checkpoint to continue from there
		__lowerCAmelCase						:							Dict			     =						os.path.splitext(path)[0]
		__lowerCAmelCase						:							List[Any]			     =						int(training_difference.replace('step_', ''))

# Train model
model.train()
__lowerCAmelCase						:							int			     =						0
__lowerCAmelCase						:							Union[str, Any]			     =						time.time()
__lowerCAmelCase						:							List[Any]			     =						0
for step, batch in enumerate(train_dataloader, start=1):
		if args.resume_from_checkpoint and step < resume_step:
				continue  # we need to skip steps until we reach the resumed step
		__lowerCAmelCase						:							List[str]			     =						model(batch, labels=batch, use_cache=False).loss
		__lowerCAmelCase						:							Any			     =						accelerator.gather(loss.repeat(args.train_batch_size)).mean()
		loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
		log_metrics(step, {'samples': step * samples_per_step, 'loss_per_step/train': loss.item()})
		__lowerCAmelCase						:							Tuple			     =						loss / args.gradient_accumulation_steps
		if step % args.gradient_accumulation_steps != 0:
				# Prevent backward from doing gradient all_reduce in every step
				if accelerator.distributed_type == DistributedType.MULTI_GPU:
						with model.no_sync():
								accelerator.backward(loss)
				else:
						accelerator.backward(loss)
		else:
				__lowerCAmelCase						:							Optional[Any]			     =						get_lr()
				accelerator.backward(loss)
				accelerator.clip_grad_norm_(model.parameters(), 1.0)
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()
				__lowerCAmelCase						:							str			     =						time.time() - t_start
				__lowerCAmelCase						:							Optional[Any]			     =						compute_tflops(elapsed_time, accelerator, args)
				log_metrics(
				    step,
				    {
				        'steps': completed_steps,
				        'loss/train': loss_tracking,
				        'lr': lr,
				        'tflops': tflops,
				        'time_per_iteration': elapsed_time,
				    },
				)
				__lowerCAmelCase						:							Dict			     =						time.time()
				__lowerCAmelCase						:							str			     =						0
				completed_steps += 1
		if step % args.save_checkpoint_steps == 0:
				logger.info('Evaluating and saving model checkpoint')
				__lowerCAmelCase     ,	__lowerCAmelCase						:							Any			     =						evaluate(args)
				log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
				accelerator.wait_for_everyone()
				__lowerCAmelCase						:							Tuple			     =						os.path.join(args.save_dir, F'''step_{step}''')
				accelerator.save_state(save_dir)
				if accelerator.is_main_process:
						hf_repo.push_to_hub(commit_message=F'''step {step}''')
				model.train()
		if completed_steps >= args.max_train_steps:
				break

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
__lowerCAmelCase     ,	__lowerCAmelCase						:							str			     =						evaluate(args)
log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
accelerator.wait_for_everyone()
__lowerCAmelCase						:							Optional[Any]			     =						accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
__lowerCAmelCase						:							Any			     =						os.path.join(args.save_dir, F'''step_{step}''')
accelerator.save_state(save_dir)
if accelerator.is_main_process:
		hf_repo.push_to_hub(commit_message='final model')






