
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     SCREAMING_SNAKE_CASE__      :

							"""simple docstring"""






							def __init__(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=13       ,		SCREAMING_SNAKE_CASE_=32       ,		SCREAMING_SNAKE_CASE_=2       ,		SCREAMING_SNAKE_CASE_=3       ,		SCREAMING_SNAKE_CASE_=16       ,		SCREAMING_SNAKE_CASE_=[1, 2, 1]       ,		SCREAMING_SNAKE_CASE_=[2, 2, 4]       ,		SCREAMING_SNAKE_CASE_=2       ,		SCREAMING_SNAKE_CASE_=2.0       ,		SCREAMING_SNAKE_CASE_=True       ,		SCREAMING_SNAKE_CASE_=0.0       ,		SCREAMING_SNAKE_CASE_=0.0       ,		SCREAMING_SNAKE_CASE_=0.1       ,		SCREAMING_SNAKE_CASE_="gelu"       ,		SCREAMING_SNAKE_CASE_=False       ,		SCREAMING_SNAKE_CASE_=True       ,		SCREAMING_SNAKE_CASE_=0.0_2       ,		SCREAMING_SNAKE_CASE_=1E-5       ,		SCREAMING_SNAKE_CASE_=True       ,		SCREAMING_SNAKE_CASE_=None       ,		SCREAMING_SNAKE_CASE_=True       ,		SCREAMING_SNAKE_CASE_=10       ,		SCREAMING_SNAKE_CASE_=8       ,		SCREAMING_SNAKE_CASE_=["stage1", "stage2"]       ,		SCREAMING_SNAKE_CASE_=[1, 2]       ,		)->				str:





													'''simple docstring'''






													__UpperCamelCase       					=     parent
													__UpperCamelCase       					=     batch_size
													__UpperCamelCase       					=     image_size
													__UpperCamelCase       					=     patch_size
													__UpperCamelCase       					=     num_channels
													__UpperCamelCase       					=     embed_dim
													__UpperCamelCase       					=     depths
													__UpperCamelCase       					=     num_heads
													__UpperCamelCase       					=     window_size
													__UpperCamelCase       					=     mlp_ratio
													__UpperCamelCase       					=     qkv_bias
													__UpperCamelCase       					=     hidden_dropout_prob
													__UpperCamelCase       					=     attention_probs_dropout_prob
													__UpperCamelCase       					=     drop_path_rate
													__UpperCamelCase       					=     hidden_act
													__UpperCamelCase       					=     use_absolute_embeddings
													__UpperCamelCase       					=     patch_norm
													__UpperCamelCase       					=     layer_norm_eps
													__UpperCamelCase       					=     initializer_range
													__UpperCamelCase       					=     is_training
													__UpperCamelCase       					=     scope
													__UpperCamelCase       					=     use_labels
													__UpperCamelCase       					=     type_sequence_label_size
													__UpperCamelCase       					=     encoder_stride
													__UpperCamelCase       					=     out_features
													__UpperCamelCase       					=     out_indices



							def       A__						(					self     )->				List[Any]:





													'''simple docstring'''






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

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

													__UpperCamelCase       					=     self.get_config()

													return config, pixel_values, labels



							def       A__						(					self     )->				List[Any]:





													'''simple docstring'''






													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       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )->				int:





													'''simple docstring'''






													__UpperCamelCase       					=     SwinModel(config=snake_case_     )
													model.to(snake_case_     )
													model.eval()
													__UpperCamelCase       					=     model(snake_case_     )

													__UpperCamelCase       					=     ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths     ) - 1))
													__UpperCamelCase       					=     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       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )->				List[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     SwinBackbone(config=snake_case_     )
													model.to(snake_case_     )
													model.eval()
													__UpperCamelCase       					=     model(snake_case_     )

													# 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
													__UpperCamelCase       					=     None
													__UpperCamelCase       					=     SwinBackbone(config=snake_case_     )
													model.to(snake_case_     )
													model.eval()
													__UpperCamelCase       					=     model(snake_case_     )

													# 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       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )->				Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       					=     SwinForMaskedImageModeling(config=snake_case_     )
													model.to(snake_case_     )
													model.eval()
													__UpperCamelCase       					=     model(snake_case_     )
													self.parent.assertEqual(
													    result.logits.shape       ,		(self.batch_size, self.num_channels, self.image_size, self.image_size)     )

													# test greyscale images
													__UpperCamelCase       					=     1
													__UpperCamelCase       					=     SwinForMaskedImageModeling(snake_case_     )
													model.to(snake_case_     )
													model.eval()

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



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )->				Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     self.type_sequence_label_size
													__UpperCamelCase       					=     SwinForImageClassification(snake_case_     )
													model.to(snake_case_     )
													model.eval()
													__UpperCamelCase       					=     model(snake_case_       ,		labels=snake_case_     )
													self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.type_sequence_label_size)     )

													# test greyscale images
													__UpperCamelCase       					=     1
													__UpperCamelCase       					=     SwinForImageClassification(snake_case_     )
													model.to(snake_case_     )
													model.eval()

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



							def       A__						(					self     )->				Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       					=     self.prepare_config_and_inputs()
													(
													    __UpperCamelCase	
													)       					=     config_and_inputs
													__UpperCamelCase       					=     {'pixel_values': pixel_values}
													return config, inputs_dict





@require_torch
class     SCREAMING_SNAKE_CASE__      (							UpperCAmelCase__				,						UpperCAmelCase__				,						unittest.TestCase      ):

							"""simple docstring"""






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

							_snake_case       						=					False
							_snake_case       						=					False
							_snake_case       						=					False



							def       A__						(					self     )->				int:





													'''simple docstring'''






													__UpperCamelCase       					=     SwinModelTester(self     )
													__UpperCamelCase       					=     ConfigTester(self       ,		config_class=snake_case_       ,		embed_dim=37     )



							def       A__						(					self     )->				List[str]:





													'''simple docstring'''






													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       A__						(					self     )->				Tuple:





													'''simple docstring'''






													return



							def       A__						(					self     )->				Union[str, Any]:





													'''simple docstring'''






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



							@unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.'''     )
							def       A__						(					self     )->				Dict:





													'''simple docstring'''






													pass



							def       A__						(					self     )->				Optional[int]:





													'''simple docstring'''






													super().test_training_gradient_checkpointing()



							def       A__						(					self     )->				Tuple:





													'''simple docstring'''






													__UpperCamelCase       					=     self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_backbone(*snake_case_     )



							def       A__						(					self     )->				Union[str, Any]:





													'''simple docstring'''






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



							def       A__						(					self     )->				List[Any]:





													'''simple docstring'''






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



							@unittest.skip(reason='''Swin does not use inputs_embeds'''     )
							def       A__						(					self     )->				str:





													'''simple docstring'''






													pass



							@unittest.skip(reason='''Swin Transformer does not use feedforward chunking'''     )
							def       A__						(					self     )->				List[Any]:





													'''simple docstring'''






													pass



							def       A__						(					self     )->				Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     self.model_tester.prepare_config_and_inputs_for_common()

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



							def       A__						(					self     )->				Tuple:





													'''simple docstring'''






													__UpperCamelCase       					=     self.model_tester.prepare_config_and_inputs_for_common()

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

																			__UpperCamelCase       					=     ['pixel_values']
																			self.assertListEqual(arg_names[:1]       ,		snake_case_     )



							def       A__						(					self     )->				Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     self.model_tester.prepare_config_and_inputs_for_common()
													__UpperCamelCase       					=     True

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

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

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

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

																			# also another +1 for reshaped_hidden_states
																			__UpperCamelCase       					=     1 if model_class.__name__ == 'SwinBackbone' else 2
																			self.assertEqual(out_len + added_hidden_states       ,		len(snake_case_     )     )

																			__UpperCamelCase       					=     outputs.attentions

																			self.assertEqual(len(snake_case_     )       ,		snake_case_     )

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



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )->				Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     model_class(snake_case_     )
													model.to(snake_case_     )
													model.eval()

													with torch.no_grad():
																			__UpperCamelCase       					=     model(**self._prepare_for_class(snake_case_       ,		snake_case_     )     )

													__UpperCamelCase       					=     outputs.hidden_states

													__UpperCamelCase       					=     getattr(
													    self.model_tester       ,		'''expected_num_hidden_layers'''       ,		len(self.model_tester.depths     ) + 1     )
													self.assertEqual(len(snake_case_     )       ,		snake_case_     )

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

													__UpperCamelCase       					=     (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":
																			__UpperCamelCase       					=     outputs.reshaped_hidden_states
																			self.assertEqual(len(snake_case_     )       ,		snake_case_     )

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



							def       A__						(					self     )->				Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     self.model_tester.prepare_config_and_inputs_for_common()

													__UpperCamelCase       					=     (
													    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:
																			__UpperCamelCase       					=     True
																			self.check_hidden_states_output(snake_case_       ,		snake_case_       ,		snake_case_       ,		snake_case_     )

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

																			self.check_hidden_states_output(snake_case_       ,		snake_case_       ,		snake_case_       ,		snake_case_     )



							def       A__						(					self     )->				Any:





													'''simple docstring'''






													__UpperCamelCase       					=     self.model_tester.prepare_config_and_inputs_for_common()
													__UpperCamelCase       					=     3

													__UpperCamelCase       					=     (
													    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)
													)
													__UpperCamelCase       					=     (
													    config.patch_size
													    if isinstance(config.patch_size       ,		collections.abc.Iterable     )
													    else (config.patch_size, config.patch_size)
													)

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

													for model_class in self.all_model_classes:
																			__UpperCamelCase       					=     True
																			self.check_hidden_states_output(snake_case_       ,		snake_case_       ,		snake_case_       ,		(padded_height, padded_width)     )

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



							@slow
							def       A__						(					self     )->				List[Any]:





													'''simple docstring'''






													for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			__UpperCamelCase       					=     SwinModel.from_pretrained(snake_case_     )
																			self.assertIsNotNone(snake_case_     )



							def       A__						(					self     )->				Any:





													'''simple docstring'''






													__UpperCamelCase       					=     self.model_tester.prepare_config_and_inputs_for_common()

													__UpperCamelCase       					=     _config_zero_init(snake_case_     )
													for model_class in self.all_model_classes:
																			__UpperCamelCase       					=     model_class(config=snake_case_     )
																			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     SCREAMING_SNAKE_CASE__      (							unittest.TestCase      ):

							"""simple docstring"""






							@cached_property
							def       A__						(					self     )->				List[str]:





													'''simple docstring'''






													return (
													    AutoImageProcessor.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''     )
													    if is_vision_available()
													    else None
													)



							@slow
							def       A__						(					self     )->				Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       					=     SwinForImageClassification.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''     ).to(snake_case_     )
													__UpperCamelCase       					=     self.default_image_processor

													__UpperCamelCase       					=     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
													__UpperCamelCase       					=     image_processor(images=snake_case_       ,		return_tensors='''pt'''     ).to(snake_case_     )

													# forward pass
													with torch.no_grad():
																			__UpperCamelCase       					=     model(**snake_case_     )

													# verify the logits
													__UpperCamelCase       					=     torch.Size((1, 1000)     )
													self.assertEqual(outputs.logits.shape       ,		snake_case_     )
													__UpperCamelCase       					=     torch.tensor([-0.0_9_4_8, -0.6_4_5_4, -0.0_9_2_1]     ).to(snake_case_     )
													self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,		snake_case_       ,		atol=1E-4     )     )





@require_torch
class     SCREAMING_SNAKE_CASE__      (							unittest.TestCase				,						UpperCAmelCase__      ):

							"""simple docstring"""






							_snake_case       						=					(SwinBackbone,) if is_torch_available() else ()
							_snake_case       						=					SwinConfig



							def       A__						(					self     )->				Dict:





													'''simple docstring'''






													__UpperCamelCase       					=     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     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							def __init__(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=False       ,		SCREAMING_SNAKE_CASE_=1024       ,		SCREAMING_SNAKE_CASE_=1024       ,		SCREAMING_SNAKE_CASE_=3.6       ,		SCREAMING_SNAKE_CASE_=False       ,		)->				Dict:





													'''simple docstring'''






													__UpperCamelCase       					=     tokenizer
													__UpperCamelCase       					=     tokenizer.bos_token_id
													__UpperCamelCase       					=     dataset
													__UpperCamelCase       					=     seq_length
													__UpperCamelCase       					=     0
													__UpperCamelCase       					=     infinite
													__UpperCamelCase       					=     0
													__UpperCamelCase       					=     tokenized

													if self.tokenized:
																			__UpperCamelCase       					=     seq_length * num_of_sequences
																			__UpperCamelCase       					=     '''input_ids'''
													else:
																			__UpperCamelCase       					=     seq_length * chars_per_token * num_of_sequences
																			__UpperCamelCase       					=     '''content'''



							def __iter__(					self     )->				str:





													'''simple docstring'''






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



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_=1000     )->				int:





													'''simple docstring'''






													return ShufflerIterDataPipe(self       ,		buffer_size=SCREAMING_SNAKE_CASE_     )





def 		A_	(							snake_case							:				Any	) ->      Dict:



						'''simple docstring'''

						__UpperCamelCase       					=     args.model_ckpt.split('''/'''	)[-1]
						__UpperCamelCase       					=     logging.getLogger(__name__	)
						__UpperCamelCase       					=     Path(args.save_dir	) / '''log/'''
						log_dir.mkdir(exist_ok=snake_case	)
						__UpperCamelCase       					=     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(snake_case	, vars(snake_case	)	)
												__UpperCamelCase       					=     accelerator.trackers[0].run.name
												logger.setLevel(logging.INFO	)
												datasets.utils.logging.set_verbosity_info()
												transformers.utils.logging.set_verbosity_info()
						else:
												__UpperCamelCase       					=     ''''''
												logger.setLevel(logging.ERROR	)
												datasets.utils.logging.set_verbosity_error()
												transformers.utils.logging.set_verbosity_error()
						return logger, run_name




def 		A_	(							snake_case							:				Any	) ->      Tuple:



						'''simple docstring'''

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




def 		A_	(							snake_case							:				Tuple	, snake_case							:				List[str]	, snake_case							:				Union[str, Any]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]	) ->      Union[str, Any]:



						'''simple docstring'''

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




def 		A_	(							snake_case							:				int	, snake_case							:				str	) ->      List[str]:



						'''simple docstring'''

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




def 		A_	(							snake_case							:				Any	, snake_case							:				Dict	, snake_case							:				Union[str, Any]	) ->      Optional[int]:



						'''simple docstring'''

						__UpperCamelCase       					=     accelerator.unwrap_model(snake_case	).config
						__UpperCamelCase       					=     4 if args.gradient_checkpointing else 3
						__UpperCamelCase       					=     args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
						__UpperCamelCase       					=     24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
						__UpperCamelCase       					=     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))
						)
						__UpperCamelCase       					=     flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
						return tflops




def 		A_	(							snake_case							:				Tuple	) ->      Union[str, Any]:



						'''simple docstring'''

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


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

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

lowercase__							:							Union[str, Any]													=     Namespace(**vars(args), **acc_state)
lowercase__							:							List[Any]													=     accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
				lowercase__							:							Dict													=     Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
lowercase__						,					lowercase__							:							List[Any]													=     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
lowercase__							:							Union[str, Any]													=     AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
				model.gradient_checkpointing_enable()
lowercase__							:							int													=     AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
lowercase__						,					lowercase__							:							Union[str, Any]													=     create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
lowercase__							:							int													=     AdamW(get_grouped_params(model, args), lr=args.learning_rate)
lowercase__							:							int													=     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 		A_	(							) ->      Optional[int]:



						'''simple docstring'''

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


# Prepare everything with our `accelerator`.
lowercase__						,					lowercase__						,					lowercase__						,					lowercase__							:							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)
								lowercase__							:							Optional[int]													=     os.path.basename(args.resume_from_checkpoint)
				else:
								# Get the most recent checkpoint
								lowercase__							:							Union[str, 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)
								lowercase__							:							Dict													=     dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
				# Extract the step of the checkpoint to continue from there
				lowercase__							:							List[Any]													=     os.path.splitext(path)[0]
				lowercase__							:							Optional[Any]													=     int(training_difference.replace("step_", ""))

# Train model
model.train()
lowercase__							:							Optional[Any]													=     0
lowercase__							:							Any													=     time.time()
lowercase__							:							Optional[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
				lowercase__							:							Tuple													=     model(batch, labels=batch, use_cache=False).loss
				lowercase__							:							Union[str, 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()})
				lowercase__							:							str													=     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:
								lowercase__							:							Union[str, Any]													=     get_lr()
								accelerator.backward(loss)
								accelerator.clip_grad_norm_(model.parameters(), 1.0)
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()
								lowercase__							:							List[Any]													=     time.time() - t_start
								lowercase__							:							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,
								    },
								)
								lowercase__							:							Any													=     time.time()
								lowercase__							:							Optional[Any]													=     0
								completed_steps += 1
				if step % args.save_checkpoint_steps == 0:
								logger.info("Evaluating and saving model checkpoint")
								lowercase__						,					lowercase__							:							Dict													=     evaluate(args)
								log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
								accelerator.wait_for_everyone()
								lowercase__							:							List[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=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")
lowercase__						,					lowercase__							:							Any													=     evaluate(args)
log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
accelerator.wait_for_everyone()
lowercase__							:							int													=     accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
lowercase__							:							List[str]													=     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")




