



import argparse
import json
from pathlib import Path

import requests
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__A				    =						logging.get_logger(__name__)



def        __a     (    lowerCAmelCase_   :					List[Any]			,lowerCAmelCase_   :					Dict=False			,lowerCAmelCase_   :					List[str]=False				)      -> Optional[int]:






						'''simple docstring'''



						UpperCAmelCase_=   """backbone.""" if is_semantic else """"""

						UpperCAmelCase_=   []
						for i in range(config.num_hidden_layers				):
												# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
												rename_keys.append((F"""{prefix}blocks.{i}.norm1.weight""", F"""beit.encoder.layer.{i}.layernorm_before.weight""")				)
												rename_keys.append((F"""{prefix}blocks.{i}.norm1.bias""", F"""beit.encoder.layer.{i}.layernorm_before.bias""")				)
												rename_keys.append(
												    (F"""{prefix}blocks.{i}.attn.proj.weight""", F"""beit.encoder.layer.{i}.attention.output.dense.weight""")				)
												rename_keys.append(
												    (F"""{prefix}blocks.{i}.attn.proj.bias""", F"""beit.encoder.layer.{i}.attention.output.dense.bias""")				)
												rename_keys.append((F"""{prefix}blocks.{i}.norm2.weight""", F"""beit.encoder.layer.{i}.layernorm_after.weight""")				)
												rename_keys.append((F"""{prefix}blocks.{i}.norm2.bias""", F"""beit.encoder.layer.{i}.layernorm_after.bias""")				)
												rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc1.weight""", F"""beit.encoder.layer.{i}.intermediate.dense.weight""")				)
												rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc1.bias""", F"""beit.encoder.layer.{i}.intermediate.dense.bias""")				)
												rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc2.weight""", F"""beit.encoder.layer.{i}.output.dense.weight""")				)
												rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc2.bias""", F"""beit.encoder.layer.{i}.output.dense.bias""")				)

						# projection layer + position embeddings
						rename_keys.extend(
						    [
						        (F"""{prefix}cls_token""", """beit.embeddings.cls_token"""),
						        (F"""{prefix}patch_embed.proj.weight""", """beit.embeddings.patch_embeddings.projection.weight"""),
						        (F"""{prefix}patch_embed.proj.bias""", """beit.embeddings.patch_embeddings.projection.bias"""),
						    ]				)

						if has_lm_head:
												# mask token + shared relative position bias + layernorm
												rename_keys.extend(
												    [
												        ("""mask_token""", """beit.embeddings.mask_token"""),
												        (
												            """rel_pos_bias.relative_position_bias_table""",
												            """beit.encoder.relative_position_bias.relative_position_bias_table""",
												        ),
												        (
												            """rel_pos_bias.relative_position_index""",
												            """beit.encoder.relative_position_bias.relative_position_index""",
												        ),
												        ("""norm.weight""", """layernorm.weight"""),
												        ("""norm.bias""", """layernorm.bias"""),
												    ]				)
						elif is_semantic:
												# semantic segmentation classification heads
												rename_keys.extend(
												    [
												        ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
												        ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
												        ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
												        ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
												    ]				)
						else:
												# layernorm + classification head
												rename_keys.extend(
												    [
												        ("""fc_norm.weight""", """beit.pooler.layernorm.weight"""),
												        ("""fc_norm.bias""", """beit.pooler.layernorm.bias"""),
												        ("""head.weight""", """classifier.weight"""),
												        ("""head.bias""", """classifier.bias"""),
												    ]				)

						return rename_keys



def        __a     (    lowerCAmelCase_   :					Optional[int]			,lowerCAmelCase_   :					Any			,lowerCAmelCase_   :					Optional[Any]=False			,lowerCAmelCase_   :					Union[str, Any]=False				)      -> Dict:






						'''simple docstring'''



						for i in range(config.num_hidden_layers				):
												UpperCAmelCase_=   """backbone.""" if is_semantic else """"""
												# queries, keys and values
												UpperCAmelCase_=   state_dict.pop(F"""{prefix}blocks.{i}.attn.qkv.weight"""				)
												UpperCAmelCase_=   state_dict.pop(F"""{prefix}blocks.{i}.attn.q_bias"""				)
												UpperCAmelCase_=   state_dict.pop(F"""{prefix}blocks.{i}.attn.v_bias"""				)

												UpperCAmelCase_=   in_proj_weight[
												    : config.hidden_size, :
												]
												UpperCAmelCase_=   q_bias
												UpperCAmelCase_=   in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												UpperCAmelCase_=   in_proj_weight[
												    -config.hidden_size :, :
												]
												UpperCAmelCase_=   v_bias

												# gamma_1 and gamma_2
												# we call them lambda because otherwise they are renamed when using .from_pretrained
												UpperCAmelCase_=   state_dict.pop(F"""{prefix}blocks.{i}.gamma_1"""				)
												UpperCAmelCase_=   state_dict.pop(F"""{prefix}blocks.{i}.gamma_2"""				)

												UpperCAmelCase_=   gamma_a
												UpperCAmelCase_=   gamma_a

												# relative_position bias table + index
												if not has_lm_head:
																		# each layer has its own relative position bias
																		UpperCAmelCase_=   state_dict.pop(F"""{prefix}blocks.{i}.attn.relative_position_bias_table"""				)
																		UpperCAmelCase_=   state_dict.pop(F"""{prefix}blocks.{i}.attn.relative_position_index"""				)

																		UpperCAmelCase_=   table
																		UpperCAmelCase_=   index



def        __a     (    lowerCAmelCase_   :					Optional[Any]			,lowerCAmelCase_   :					Optional[Any]			,lowerCAmelCase_   :					Optional[Any]				)      -> Union[str, Any]:






						'''simple docstring'''



						UpperCAmelCase_=   dct.pop(lowerCAmelCase_				)
						UpperCAmelCase_=   val



def        __a     (    )      -> Optional[Any]:






						'''simple docstring'''



						UpperCAmelCase_=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
						UpperCAmelCase_=   Image.open(requests.get(lowerCAmelCase_			,stream=lowerCAmelCase_				).raw				)
						return im



@torch.no_grad()
def        __a     (    lowerCAmelCase_   :					Tuple			,lowerCAmelCase_   :					Dict				)      -> List[str]:






						'''simple docstring'''



						UpperCAmelCase_=   BeitConfig()
						UpperCAmelCase_=   False
						UpperCAmelCase_=   False
						UpperCAmelCase_=   """huggingface/label-files"""
						# set config parameters based on URL
						if checkpoint_url[-9:-4] == "pt22k":
												# masked image modeling
												UpperCAmelCase_=   True
												UpperCAmelCase_=   True
												UpperCAmelCase_=   True
						elif checkpoint_url[-9:-4] == "ft22k":
												# intermediate fine-tuning on ImageNet-22k
												UpperCAmelCase_=   True
												UpperCAmelCase_=   2_18_41
												UpperCAmelCase_=   """imagenet-22k-id2label.json"""
												UpperCAmelCase_=   json.load(open(hf_hub_download(lowerCAmelCase_			,lowerCAmelCase_			,repo_type="""dataset"""				)			,"""r"""				)				)
												UpperCAmelCase_=   {int(lowerCAmelCase_				): v for k, v in idalabel.items()}
												# this dataset contains 21843 labels but the model only has 21841
												# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
												del idalabel[92_05]
												del idalabel[1_50_27]
												UpperCAmelCase_=   idalabel
												UpperCAmelCase_=   {v: k for k, v in idalabel.items()}
						elif checkpoint_url[-8:-4] == "to1k":
												# fine-tuning on ImageNet-1k
												UpperCAmelCase_=   True
												UpperCAmelCase_=   10_00
												UpperCAmelCase_=   """imagenet-1k-id2label.json"""
												UpperCAmelCase_=   json.load(open(hf_hub_download(lowerCAmelCase_			,lowerCAmelCase_			,repo_type="""dataset"""				)			,"""r"""				)				)
												UpperCAmelCase_=   {int(lowerCAmelCase_				): v for k, v in idalabel.items()}
												UpperCAmelCase_=   idalabel
												UpperCAmelCase_=   {v: k for k, v in idalabel.items()}
												if "384" in checkpoint_url:
																		UpperCAmelCase_=   3_84
												if "512" in checkpoint_url:
																		UpperCAmelCase_=   5_12
						elif "ade20k" in checkpoint_url:
												# fine-tuning
												UpperCAmelCase_=   True
												UpperCAmelCase_=   1_50
												UpperCAmelCase_=   """ade20k-id2label.json"""
												UpperCAmelCase_=   json.load(open(hf_hub_download(lowerCAmelCase_			,lowerCAmelCase_			,repo_type="""dataset"""				)			,"""r"""				)				)
												UpperCAmelCase_=   {int(lowerCAmelCase_				): v for k, v in idalabel.items()}
												UpperCAmelCase_=   idalabel
												UpperCAmelCase_=   {v: k for k, v in idalabel.items()}
												UpperCAmelCase_=   6_40
												UpperCAmelCase_=   True
						else:
												raise ValueError("""Checkpoint not supported, URL should either end with 'pt22k', 'ft22k', 'to1k' or 'ade20k'"""				)

						# size of the architecture
						if "base" in checkpoint_url:
												pass
						elif "large" in checkpoint_url:
												UpperCAmelCase_=   10_24
												UpperCAmelCase_=   40_96
												UpperCAmelCase_=   24
												UpperCAmelCase_=   16
												if "ade20k" in checkpoint_url:
																		UpperCAmelCase_=   6_40
																		UpperCAmelCase_=   [7, 11, 15, 23]
						else:
												raise ValueError("""Should either find 'base' or 'large' in checkpoint URL"""				)

						# load state_dict of original model, remove and rename some keys
						UpperCAmelCase_=   torch.hub.load_state_dict_from_url(lowerCAmelCase_			,map_location="""cpu"""			,check_hash=lowerCAmelCase_				)
						UpperCAmelCase_=   state_dict["""model"""] if """ade20k""" not in checkpoint_url else state_dict["""state_dict"""]

						UpperCAmelCase_=   create_rename_keys(lowerCAmelCase_			,has_lm_head=lowerCAmelCase_			,is_semantic=lowerCAmelCase_				)
						for src, dest in rename_keys:
												rename_key(lowerCAmelCase_			,lowerCAmelCase_			,lowerCAmelCase_				)
						read_in_q_k_v(lowerCAmelCase_			,lowerCAmelCase_			,has_lm_head=lowerCAmelCase_			,is_semantic=lowerCAmelCase_				)
						if is_semantic:
												# add prefix to decoder keys
												for key, val in state_dict.copy().items():
																		UpperCAmelCase_=   state_dict.pop(lowerCAmelCase_				)
																		if key.startswith("""backbone.fpn"""				):
																								UpperCAmelCase_=   key.replace("""backbone.fpn"""			,"""fpn"""				)
																		UpperCAmelCase_=   val

    # load HuggingFace model
						if checkpoint_url[-9:-4] == "pt22k":
												UpperCAmelCase_=   BeitForMaskedImageModeling(lowerCAmelCase_				)
						elif "ade20k" in checkpoint_url:
												UpperCAmelCase_=   BeitForSemanticSegmentation(lowerCAmelCase_				)
						else:
												UpperCAmelCase_=   BeitForImageClassification(lowerCAmelCase_				)
						model.eval()
						model.load_state_dict(lowerCAmelCase_				)

						# Check outputs on an image
						if is_semantic:
												UpperCAmelCase_=   BeitImageProcessor(size=config.image_size			,do_center_crop=lowerCAmelCase_				)
												UpperCAmelCase_=   load_dataset("""hf-internal-testing/fixtures_ade20k"""			,split="""test"""				)
												UpperCAmelCase_=   Image.open(ds[0]["""file"""]				)
						else:
												UpperCAmelCase_=   BeitImageProcessor(
												    size=config.image_size			,resample=PILImageResampling.BILINEAR			,do_center_crop=lowerCAmelCase_				)
												UpperCAmelCase_=   prepare_img()

						UpperCAmelCase_=   image_processor(images=lowerCAmelCase_			,return_tensors="""pt"""				)
						UpperCAmelCase_=   encoding["""pixel_values"""]

						UpperCAmelCase_=   model(lowerCAmelCase_				)
						UpperCAmelCase_=   outputs.logits

						# verify logits
						UpperCAmelCase_=   torch.Size([1, 10_00]				)
						if checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k"""				):
												UpperCAmelCase_=   torch.Size([1, 1_96, 81_92]				)
						elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k"""				):
												UpperCAmelCase_=   torch.Size([1, 1_96, 81_92]				)
						elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22k"""				):
												UpperCAmelCase_=   torch.Size([1, 2_18_41]				)
												UpperCAmelCase_=   torch.tensor([2.2_288, 2.4_671, 0.7_395]				)
												UpperCAmelCase_=   23_97
						elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22k"""				):
												UpperCAmelCase_=   torch.Size([1, 2_18_41]				)
												UpperCAmelCase_=   torch.tensor([1.6_881, -0.2_787, 0.5_901]				)
												UpperCAmelCase_=   23_96
						elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft1k"""				):
												UpperCAmelCase_=   torch.tensor([0.1_241, 0.0_798, -0.6_569]				)
												UpperCAmelCase_=   2_85
						elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22kto1k"""				):
												UpperCAmelCase_=   torch.tensor([-1.2_385, -1.0_987, -1.0_108]				)
												UpperCAmelCase_=   2_81
						elif checkpoint_url[:-4].endswith("""beit_base_patch16_384_pt22k_ft22kto1k"""				):
												UpperCAmelCase_=   torch.tensor([-1.5_303, -0.9_484, -0.3_147]				)
												UpperCAmelCase_=   7_61
						elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft1k"""				):
												UpperCAmelCase_=   torch.tensor([0.4_610, -0.0_928, 0.2_086]				)
												UpperCAmelCase_=   7_61
						elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22kto1k"""				):
												UpperCAmelCase_=   torch.tensor([-0.4_804, 0.6_257, -0.1_837]				)
												UpperCAmelCase_=   7_61
						elif checkpoint_url[:-4].endswith("""beit_large_patch16_384_pt22k_ft22kto1k"""				):
												UpperCAmelCase_=   torch.tensor([[-0.5_122, 0.5_117, -0.2_113]]				)
												UpperCAmelCase_=   7_61
						elif checkpoint_url[:-4].endswith("""beit_large_patch16_512_pt22k_ft22kto1k"""				):
												UpperCAmelCase_=   torch.tensor([-0.3_062, 0.7_261, 0.4_852]				)
												UpperCAmelCase_=   7_61
						elif checkpoint_url[:-4].endswith("""beit_base_patch16_640_pt22k_ft22ktoade20k"""				):
												UpperCAmelCase_=   (1, 1_50, 1_60, 1_60)
												UpperCAmelCase_=   torch.tensor(
												    [
												        [[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
												        [[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
												        [[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
												    ]				)
						elif checkpoint_url[:-4].endswith("""beit_large_patch16_640_pt22k_ft22ktoade20k"""				):
												UpperCAmelCase_=   (1, 1_50, 1_60, 1_60)
												UpperCAmelCase_=   torch.tensor(
												    [
												        [[-4.3_305, -2.3_049, -3.0_161], [-2.9_591, -1.5_305, -2.2_251], [-3.4_198, -1.8_004, -2.9_062]],
												        [[-5.8_922, -3.7_435, -4.3_978], [-4.2_063, -2.7_872, -3.4_755], [-4.2_791, -3.1_874, -4.1_681]],
												        [[0.9_895, 4.3_467, 4.7_663], [4.2_476, 5.6_830, 6.1_518], [4.5_550, 6.2_495, 6.5_154]],
												    ]				)
						else:
												raise ValueError("""Can't verify logits as model is not supported"""				)

						assert logits.shape == expected_shape, "Shape of logits not as expected"
						if not has_lm_head:
												if is_semantic:
																		assert torch.allclose(
																		    logits[0, :3, :3, :3]			,lowerCAmelCase_			,atol=1E-3				), "First elements of logits not as expected"
												else:
																		print("""Predicted class idx:"""			,logits.argmax(-1				).item()				)
																		assert torch.allclose(
																		    logits[0, :3]			,lowerCAmelCase_			,atol=1E-3				), "First elements of logits not as expected"
																		assert logits.argmax(-1				).item() == expected_class_idx, "Predicted class index not as expected"

						Path(lowerCAmelCase_				).mkdir(exist_ok=lowerCAmelCase_				)
						print(F"""Saving model to {pytorch_dump_folder_path}"""				)
						model.save_pretrained(lowerCAmelCase_				)
						print(F"""Saving image processor to {pytorch_dump_folder_path}"""				)
						image_processor.save_pretrained(lowerCAmelCase_				)


if __name__ == "__main__":
	__A				    =						argparse.ArgumentParser()

	parser.add_argument(
	    '''--checkpoint_url''',
	    default='''https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth''',
	    type=str,
	    help='''URL to the original PyTorch checkpoint (.pth file).''',
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
	)
	__A				    =						parser.parse_args()
	convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









import argparse
import logging
import os
import sys
import time

import tensorflow as tf
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers.utils import is_sagemaker_dp_enabled


if os.environ.get('''SDP_ENABLED''') or is_sagemaker_dp_enabled():
	__A				    =						True
	__A				    =						'''p3dn.24xlarge'''
	import smdistributed.dataparallel.tensorflow as sdp
else:
	__A				    =						False



def        __a     (    lowerCAmelCase_   :					Optional[Any]			,lowerCAmelCase_   :					Union[str, Any]			,lowerCAmelCase_   :					int			,lowerCAmelCase_   :					int			,lowerCAmelCase_   :					Optional[int]			,lowerCAmelCase_   :					Any			,lowerCAmelCase_   :					int=None				)      -> Optional[int]:






						'''simple docstring'''



						UpperCAmelCase_=   tqdm(lowerCAmelCase_				)
						for i, batch in enumerate(lowerCAmelCase_				):
												with tf.GradientTape() as tape:
																		UpperCAmelCase_,   UpperCAmelCase_=   batch
																		UpperCAmelCase_=   model(lowerCAmelCase_				)
																		UpperCAmelCase_=   loss(lowerCAmelCase_			,outputs.logits				)

												if SDP_ENABLED:
																		UpperCAmelCase_=   sdp.DistributedGradientTape(lowerCAmelCase_			,sparse_as_dense=lowerCAmelCase_				)

												UpperCAmelCase_=   tape.gradient(lowerCAmelCase_			,model.trainable_variables				)
												opt.apply_gradients(zip(lowerCAmelCase_			,model.trainable_variables				)				)

												pbar.set_description(F"""Loss: {loss_value:.4f}"""				)

												if SDP_ENABLED and i == 0:
																		sdp.broadcast_variables(model.variables			,root_rank=0				)
																		sdp.broadcast_variables(opt.variables()			,root_rank=0				)

												if max_steps and i >= max_steps:
																		break

						UpperCAmelCase_=   {"""loss""": loss_value.numpy()}
						return train_results



def        __a     (    lowerCAmelCase_   :					Optional[int]			,lowerCAmelCase_   :					Dict			,lowerCAmelCase_   :					str				)      -> str:






						'''simple docstring'''



						UpperCAmelCase_,   UpperCAmelCase_=   load_dataset("""imdb"""			,split=["""train""", """test"""]				)

						# Preprocess train dataset
						UpperCAmelCase_=   train_dataset.map(
						    lambda lowerCAmelCase_				: tokenizer(e["""text"""]			,truncation=lowerCAmelCase_			,padding="""max_length"""				)			,batched=lowerCAmelCase_				)
						train_dataset.set_format(type="""tensorflow"""			,columns=["""input_ids""", """attention_mask""", """label"""]				)

						UpperCAmelCase_=   {
						    x: train_dataset[x].to_tensor(default_value=0			,shape=[None, tokenizer.model_max_length]				)
						    for x in ["""input_ids""", """attention_mask"""]
						}
						UpperCAmelCase_=   tf.data.Dataset.from_tensor_slices((train_features, train_dataset["""label"""])				)

						# Preprocess test dataset
						UpperCAmelCase_=   test_dataset.map(
						    lambda lowerCAmelCase_				: tokenizer(e["""text"""]			,truncation=lowerCAmelCase_			,padding="""max_length"""				)			,batched=lowerCAmelCase_				)
						test_dataset.set_format(type="""tensorflow"""			,columns=["""input_ids""", """attention_mask""", """label"""]				)

						UpperCAmelCase_=   {
						    x: test_dataset[x].to_tensor(default_value=0			,shape=[None, tokenizer.model_max_length]				)
						    for x in ["""input_ids""", """attention_mask"""]
						}
						UpperCAmelCase_=   tf.data.Dataset.from_tensor_slices((test_features, test_dataset["""label"""])				)

						if SDP_ENABLED:
												UpperCAmelCase_=   tf_train_dataset.shard(sdp.size()			,sdp.rank()				)
												UpperCAmelCase_=   tf_test_dataset.shard(sdp.size()			,sdp.rank()				)
						UpperCAmelCase_=   tf_train_dataset.batch(lowerCAmelCase_			,drop_remainder=lowerCAmelCase_				)
						UpperCAmelCase_=   tf_test_dataset.batch(lowerCAmelCase_			,drop_remainder=lowerCAmelCase_				)

						return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
	__A				    =						argparse.ArgumentParser()

	# Hyperparameters sent by the client are passed as command-line arguments to the script.
	parser.add_argument('''--epochs''', type=int, default=3)
	parser.add_argument('''--per_device_train_batch_size''', type=int, default=16)
	parser.add_argument('''--per_device_eval_batch_size''', type=int, default=8)
	parser.add_argument('''--model_name_or_path''', type=str)
	parser.add_argument('''--learning_rate''', type=str, default=5E-5)
	parser.add_argument('''--do_train''', type=bool, default=True)
	parser.add_argument('''--do_eval''', type=bool, default=True)
	parser.add_argument('''--output_dir''', type=str)
	parser.add_argument('''--max_steps''', type=int, default=None)

	# Data, model, and output directories
	parser.add_argument('''--output_data_dir''', type=str, default=os.environ['''SM_OUTPUT_DATA_DIR'''])
	parser.add_argument('''--model_dir''', type=str, default=os.environ['''SM_MODEL_DIR'''])
	parser.add_argument('''--n_gpus''', type=str, default=os.environ['''SM_NUM_GPUS'''])

	__A    ,			__A				    =						parser.parse_known_args()

	# Set up logging
	__A				    =						logging.getLogger(__name__)

	logging.basicConfig(
	    level=logging.getLevelName('''INFO'''),
	    handlers=[logging.StreamHandler(sys.stdout)],
	    format='''%(asctime)s - %(name)s - %(levelname)s - %(message)s''',
	)

	if SDP_ENABLED:
		sdp.init()

		__A				    =						tf.config.experimental.list_physical_devices('''GPU''')
		for gpu in gpus:
			tf.config.experimental.set_memory_growth(gpu, True)
		if gpus:
			tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], '''GPU''')

    # Load model and tokenizer
	__A				    =						TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
	__A				    =						AutoTokenizer.from_pretrained(args.model_name_or_path)

	# get datasets
	__A    ,			__A				    =						get_datasets(
	    tokenizer=tokenizer,
	    train_batch_size=args.per_device_train_batch_size,
	    eval_batch_size=args.per_device_eval_batch_size,
	)

	# fine optimizer and loss
	__A				    =						tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
	__A				    =						tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
	__A				    =						[tf.keras.metrics.SparseCategoricalAccuracy()]
	model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

	# Training
	if args.do_train:
		# train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
		__A				    =						time.time()
		__A				    =						fit(
		    model,
		    loss,
		    optimizer,
		    tf_train_dataset,
		    args.epochs,
		    args.per_device_train_batch_size,
		    max_steps=args.max_steps,
		)
		__A				    =						time.time() - start_train_time
		logger.info('''*** Train ***''')
		logger.info(f'train_runtime = {end_train_time}')

		__A				    =						os.path.join(args.output_dir, '''train_results.txt''')

		if not SDP_ENABLED or sdp.rank() == 0:
			with open(output_eval_file, '''w''') as writer:
				logger.info('''***** Train results *****''')
				logger.info(train_results)
				for key, value in train_results.items():
					logger.info(f'  {key} = {value}')
					writer.write(f'{key} = {value}\n')

    # Evaluation
	if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
		__A				    =						model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
		logger.info('''*** Evaluate ***''')

		__A				    =						os.path.join(args.output_dir, '''eval_results.txt''')

		with open(output_eval_file, '''w''') as writer:
			logger.info('''***** Eval results *****''')
			logger.info(result)
			for key, value in result.items():
				logger.info(f'  {key} = {value}')
				writer.write(f'{key} = {value}\n')

    # Save result
	if SDP_ENABLED:
		if sdp.rank() == 0:
			model.save_pretrained(args.output_dir)
			tokenizer.save_pretrained(args.output_dir)
	else:
		model.save_pretrained(args.output_dir)
		tokenizer.save_pretrained(args.output_dir)




