

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase							:  Dict   	=						logging.get_logger(__name__)







def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       SwinConfig(
						    embed_dim=1_92				,	depths=(2, 2, 18, 2)				,	num_heads=(6, 12, 24, 48)				,	window_size=12				,	out_features=["stage2", "stage3", "stage4"]				,	)

						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       DetaConfig(
						    backbone_config=_UpperCAmelCase				,	num_queries=9_00				,	encoder_ffn_dim=20_48				,	decoder_ffn_dim=20_48				,	num_feature_levels=5				,	assign_first_stage=_UpperCAmelCase				,	with_box_refine=_UpperCAmelCase				,	two_stage=_UpperCAmelCase				,	)

						# set labels
						SCREAMING_SNAKE_CASE_: Dict  		=       "huggingface/label-files"
						if "o365" in model_name:
												SCREAMING_SNAKE_CASE_: Dict  		=       3_66
												SCREAMING_SNAKE_CASE_: str  		=       "object365-id2label.json"
						else:
												SCREAMING_SNAKE_CASE_: Tuple  		=       91
												SCREAMING_SNAKE_CASE_: List[str]  		=       "coco-detection-id2label.json"

						SCREAMING_SNAKE_CASE_: Tuple  		=       num_labels
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       json.load(open(cached_download(hf_hub_url(_UpperCAmelCase				,	_UpperCAmelCase				,	repo_type="dataset"    )    )				,	"r"    )    )
						SCREAMING_SNAKE_CASE_: int  		=       {int(_UpperCAmelCase    ): v for k, v in idalabel.items()}
						SCREAMING_SNAKE_CASE_: Any  		=       idalabel
						SCREAMING_SNAKE_CASE_: Any  		=       {v: k for k, v in idalabel.items()}

						return config







def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Any  		=       []

						# stem
						# fmt: off
						rename_keys.append(("backbone.0.body.patch_embed.proj.weight", "model.backbone.model.embeddings.patch_embeddings.projection.weight")    )
						rename_keys.append(("backbone.0.body.patch_embed.proj.bias", "model.backbone.model.embeddings.patch_embeddings.projection.bias")    )
						rename_keys.append(("backbone.0.body.patch_embed.norm.weight", "model.backbone.model.embeddings.norm.weight")    )
						rename_keys.append(("backbone.0.body.patch_embed.norm.bias", "model.backbone.model.embeddings.norm.bias")    )
						# stages
						for i in range(len(config.backbone_config.depths    )    ):
												for j in range(config.backbone_config.depths[i]    ):
																		rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm1.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")    )
																		rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm1.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")    )
																		rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table")    )
																		rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index")    )
																		rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")    )
																		rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")    )
																		rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm2.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")    )
																		rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm2.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")    )
																		rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")    )
																		rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")    )
																		rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight")    )
																		rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias")    )

												if i < 3:
																		rename_keys.append((f"backbone.0.body.layers.{i}.downsample.reduction.weight", f"model.backbone.model.encoder.layers.{i}.downsample.reduction.weight")    )
																		rename_keys.append((f"backbone.0.body.layers.{i}.downsample.norm.weight", f"model.backbone.model.encoder.layers.{i}.downsample.norm.weight")    )
																		rename_keys.append((f"backbone.0.body.layers.{i}.downsample.norm.bias", f"model.backbone.model.encoder.layers.{i}.downsample.norm.bias")    )

						rename_keys.append(("backbone.0.body.norm1.weight", "model.backbone.model.hidden_states_norms.stage2.weight")    )
						rename_keys.append(("backbone.0.body.norm1.bias", "model.backbone.model.hidden_states_norms.stage2.bias")    )
						rename_keys.append(("backbone.0.body.norm2.weight", "model.backbone.model.hidden_states_norms.stage3.weight")    )
						rename_keys.append(("backbone.0.body.norm2.bias", "model.backbone.model.hidden_states_norms.stage3.bias")    )
						rename_keys.append(("backbone.0.body.norm3.weight", "model.backbone.model.hidden_states_norms.stage4.weight")    )
						rename_keys.append(("backbone.0.body.norm3.bias", "model.backbone.model.hidden_states_norms.stage4.bias")    )

						# transformer encoder
						for i in range(config.encoder_layers    ):
												rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight", f"model.encoder.layers.{i}.self_attn.sampling_offsets.weight")    )
												rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias", f"model.encoder.layers.{i}.self_attn.sampling_offsets.bias")    )
												rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.attention_weights.weight", f"model.encoder.layers.{i}.self_attn.attention_weights.weight")    )
												rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.attention_weights.bias", f"model.encoder.layers.{i}.self_attn.attention_weights.bias")    )
												rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.value_proj.weight", f"model.encoder.layers.{i}.self_attn.value_proj.weight")    )
												rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.value_proj.bias", f"model.encoder.layers.{i}.self_attn.value_proj.bias")    )
												rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.output_proj.weight", f"model.encoder.layers.{i}.self_attn.output_proj.weight")    )
												rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.output_proj.bias", f"model.encoder.layers.{i}.self_attn.output_proj.bias")    )
												rename_keys.append((f"transformer.encoder.layers.{i}.norm1.weight", f"model.encoder.layers.{i}.self_attn_layer_norm.weight")    )
												rename_keys.append((f"transformer.encoder.layers.{i}.norm1.bias", f"model.encoder.layers.{i}.self_attn_layer_norm.bias")    )
												rename_keys.append((f"transformer.encoder.layers.{i}.linear1.weight", f"model.encoder.layers.{i}.fc1.weight")    )
												rename_keys.append((f"transformer.encoder.layers.{i}.linear1.bias", f"model.encoder.layers.{i}.fc1.bias")    )
												rename_keys.append((f"transformer.encoder.layers.{i}.linear2.weight", f"model.encoder.layers.{i}.fc2.weight")    )
												rename_keys.append((f"transformer.encoder.layers.{i}.linear2.bias", f"model.encoder.layers.{i}.fc2.bias")    )
												rename_keys.append((f"transformer.encoder.layers.{i}.norm2.weight", f"model.encoder.layers.{i}.final_layer_norm.weight")    )
												rename_keys.append((f"transformer.encoder.layers.{i}.norm2.bias", f"model.encoder.layers.{i}.final_layer_norm.bias")    )

						# transformer decoder
						for i in range(config.decoder_layers    ):
												rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight", f"model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight")    )
												rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias", f"model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias")    )
												rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.attention_weights.weight", f"model.decoder.layers.{i}.encoder_attn.attention_weights.weight")    )
												rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.attention_weights.bias", f"model.decoder.layers.{i}.encoder_attn.attention_weights.bias")    )
												rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.value_proj.weight", f"model.decoder.layers.{i}.encoder_attn.value_proj.weight")    )
												rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.value_proj.bias", f"model.decoder.layers.{i}.encoder_attn.value_proj.bias")    )
												rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.output_proj.weight", f"model.decoder.layers.{i}.encoder_attn.output_proj.weight")    )
												rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.output_proj.bias", f"model.decoder.layers.{i}.encoder_attn.output_proj.bias")    )
												rename_keys.append((f"transformer.decoder.layers.{i}.norm1.weight", f"model.decoder.layers.{i}.encoder_attn_layer_norm.weight")    )
												rename_keys.append((f"transformer.decoder.layers.{i}.norm1.bias", f"model.decoder.layers.{i}.encoder_attn_layer_norm.bias")    )
												rename_keys.append((f"transformer.decoder.layers.{i}.self_attn.out_proj.weight", f"model.decoder.layers.{i}.self_attn.out_proj.weight")    )
												rename_keys.append((f"transformer.decoder.layers.{i}.self_attn.out_proj.bias", f"model.decoder.layers.{i}.self_attn.out_proj.bias")    )
												rename_keys.append((f"transformer.decoder.layers.{i}.norm2.weight", f"model.decoder.layers.{i}.self_attn_layer_norm.weight")    )
												rename_keys.append((f"transformer.decoder.layers.{i}.norm2.bias", f"model.decoder.layers.{i}.self_attn_layer_norm.bias")    )
												rename_keys.append((f"transformer.decoder.layers.{i}.linear1.weight", f"model.decoder.layers.{i}.fc1.weight")    )
												rename_keys.append((f"transformer.decoder.layers.{i}.linear1.bias", f"model.decoder.layers.{i}.fc1.bias")    )
												rename_keys.append((f"transformer.decoder.layers.{i}.linear2.weight", f"model.decoder.layers.{i}.fc2.weight")    )
												rename_keys.append((f"transformer.decoder.layers.{i}.linear2.bias", f"model.decoder.layers.{i}.fc2.bias")    )
												rename_keys.append((f"transformer.decoder.layers.{i}.norm3.weight", f"model.decoder.layers.{i}.final_layer_norm.weight")    )
												rename_keys.append((f"transformer.decoder.layers.{i}.norm3.bias", f"model.decoder.layers.{i}.final_layer_norm.bias")    )

						# fmt: on

						return rename_keys







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       dct.pop(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: Any  		=       val







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       [int(backbone_config.embed_dim * 2**i    ) for i in range(len(backbone_config.depths    )    )]
						for i in range(len(backbone_config.depths    )    ):
												SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       num_features[i]
												for j in range(backbone_config.depths[i]    ):
																		# fmt: off
																		# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       state_dict.pop(f"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"    )
																		SCREAMING_SNAKE_CASE_: List[Any]  		=       state_dict.pop(f"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias"    )
																		# next, add query, keys and values (in that order) to the state dict
																		SCREAMING_SNAKE_CASE_: List[str]  		=       in_proj_weight[:dim, :]
																		SCREAMING_SNAKE_CASE_: List[str]  		=       in_proj_bias[: dim]
																		SCREAMING_SNAKE_CASE_: List[Any]  		=       in_proj_weight[
																		    dim : dim * 2, :
																		]
																		SCREAMING_SNAKE_CASE_: List[Any]  		=       in_proj_bias[
																		    dim : dim * 2
																		]
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       in_proj_weight[
																		    -dim :, :
																		]
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       in_proj_bias[-dim :]
																		# fmt: on







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						# transformer decoder self-attention layers
						SCREAMING_SNAKE_CASE_: int  		=       config.d_model
						for i in range(config.decoder_layers    ):
												# read in weights + bias of input projection layer of self-attention
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_weight"    )
												SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_bias"    )
												# next, add query, keys and values (in that order) to the state dict
												SCREAMING_SNAKE_CASE_: int  		=       in_proj_weight[:hidden_size, :]
												SCREAMING_SNAKE_CASE_: Tuple  		=       in_proj_bias[:hidden_size]
												SCREAMING_SNAKE_CASE_: List[Any]  		=       in_proj_weight[
												    hidden_size : hidden_size * 2, :
												]
												SCREAMING_SNAKE_CASE_: Optional[int]  		=       in_proj_bias[hidden_size : hidden_size * 2]
												SCREAMING_SNAKE_CASE_: List[str]  		=       in_proj_weight[-hidden_size:, :]
												SCREAMING_SNAKE_CASE_: int  		=       in_proj_bias[-hidden_size:]







def 			A_      (    ):
						SCREAMING_SNAKE_CASE_: Dict  		=       "http://images.cocodataset.org/val2017/000000039769.jpg"
						SCREAMING_SNAKE_CASE_: List[str]  		=       Image.open(requests.get(_UpperCAmelCase				,	stream=_UpperCAmelCase    ).raw    )

						return im







@torch.no_grad()
def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: int  		=       get_deta_config(_UpperCAmelCase    )

						# load original state dict
						if model_name == "deta-swin-large":
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       hf_hub_download(repo_id="nielsr/deta-checkpoints"				,	filename="adet_swin_ft.pth"    )
						elif model_name == "deta-swin-large-o365":
												SCREAMING_SNAKE_CASE_: Tuple  		=       hf_hub_download(repo_id="jozhang97/deta-swin-l-o365"				,	filename="deta_swin_pt_o365.pth"    )
						else:
												raise ValueError(f"Model name {model_name} not supported"    )

						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       torch.load(_UpperCAmelCase				,	map_location="cpu"    )["model"]

						# original state dict
						for name, param in state_dict.items():
												print(_UpperCAmelCase				,	param.shape    )

						# rename keys
						SCREAMING_SNAKE_CASE_: str  		=       create_rename_keys(_UpperCAmelCase    )
						for src, dest in rename_keys:
												rename_key(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )
						read_in_swin_q_k_v(_UpperCAmelCase				,	config.backbone_config    )
						read_in_decoder_q_k_v(_UpperCAmelCase				,	_UpperCAmelCase    )

						# fix some prefixes
						for key in state_dict.copy().keys():
												if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
																		SCREAMING_SNAKE_CASE_: Any  		=       state_dict.pop(_UpperCAmelCase    )
																		SCREAMING_SNAKE_CASE_: Dict  		=       val
												if "input_proj" in key:
																		SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       state_dict.pop(_UpperCAmelCase    )
																		SCREAMING_SNAKE_CASE_: Tuple  		=       val
												if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
																		SCREAMING_SNAKE_CASE_: str  		=       state_dict.pop(_UpperCAmelCase    )
																		SCREAMING_SNAKE_CASE_: Optional[Any]  		=       val

    # finally, create HuggingFace model and load state dict
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       DetaForObjectDetection(_UpperCAmelCase    )
						model.load_state_dict(_UpperCAmelCase    )
						model.eval()

						SCREAMING_SNAKE_CASE_: Optional[int]  		=       "cuda" if torch.cuda.is_available() else "cpu"
						model.to(_UpperCAmelCase    )

						# load image processor
						SCREAMING_SNAKE_CASE_: str  		=       DetaImageProcessor(format="coco_detection"    )

						# verify our conversion on image
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       prepare_img()
						SCREAMING_SNAKE_CASE_: Any  		=       processor(images=_UpperCAmelCase				,	return_tensors="pt"    )
						SCREAMING_SNAKE_CASE_: str  		=       encoding["pixel_values"]
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       model(pixel_values.to(_UpperCAmelCase    )    )

						# verify logits
						print("Logits:"				,	outputs.logits[0, :3, :3]    )
						print("Boxes:"				,	outputs.pred_boxes[0, :3, :3]    )
						if model_name == "deta-swin-large":
												SCREAMING_SNAKE_CASE_: int  		=       torch.tensor(
												    [[-7.6_3_0_8, -2.8_4_8_5, -5.3_7_3_7], [-7.2_0_3_7, -4.5_5_0_5, -4.8_0_2_7], [-7.2_9_4_3, -4.2_6_1_1, -4.6_6_1_7]]    )
												SCREAMING_SNAKE_CASE_: List[Any]  		=       torch.tensor([[0.4_9_8_7, 0.4_9_6_9, 0.9_9_9_9], [0.2_5_4_9, 0.5_4_9_8, 0.4_8_0_5], [0.5_4_9_8, 0.2_7_5_7, 0.0_5_6_9]]    )
						elif model_name == "deta-swin-large-o365":
												SCREAMING_SNAKE_CASE_: int  		=       torch.tensor(
												    [[-8.0_1_2_2, -3.5_7_2_0, -4.9_7_1_7], [-8.1_5_4_7, -3.6_8_8_6, -4.6_3_8_9], [-7.6_6_1_0, -3.6_1_9_4, -5.0_1_3_4]]    )
												SCREAMING_SNAKE_CASE_: List[str]  		=       torch.tensor([[0.2_5_2_3, 0.5_5_4_9, 0.4_8_8_1], [0.7_7_1_5, 0.4_1_4_9, 0.4_6_0_1], [0.5_5_0_3, 0.2_7_5_3, 0.0_5_7_5]]    )
						assert torch.allclose(outputs.logits[0, :3, :3]				,	expected_logits.to(_UpperCAmelCase    )				,	atol=1e-4    )
						assert torch.allclose(outputs.pred_boxes[0, :3, :3]				,	expected_boxes.to(_UpperCAmelCase    )				,	atol=1e-4    )
						print("Everything ok!"    )

						if pytorch_dump_folder_path:
												# Save model and processor
												logger.info(f"Saving PyTorch model and processor to {pytorch_dump_folder_path}..."    )
												Path(_UpperCAmelCase    ).mkdir(exist_ok=_UpperCAmelCase    )
												model.save_pretrained(_UpperCAmelCase    )
												processor.save_pretrained(_UpperCAmelCase    )

						# Push to hub
						if push_to_hub:
												print("Pushing model and processor to hub..."    )
												model.push_to_hub(f"jozhang97/{model_name}"    )
												processor.push_to_hub(f"jozhang97/{model_name}"    )


if __name__ == "__main__":
						lowerCAmelCase							:  str   	=						argparse.ArgumentParser()

						parser.add_argument(
						    """--model_name""",
						    type=str,
						    default="""deta-swin-large""",
						    choices=["""deta-swin-large""", """deta-swin-large-o365"""],
						    help="""Name of the model you'd like to convert.""",
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""",
						    default=None,
						    type=str,
						    help="""Path to the folder to output PyTorch model.""",
						)
						parser.add_argument(
						    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
						)
						lowerCAmelCase							:  Any   	=						parser.parse_args()
						convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









import argparse

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowerCAmelCase							:  Optional[Any]   	=						16
lowerCAmelCase							:  List[str]   	=						32







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase = 16    ):
						SCREAMING_SNAKE_CASE_: Tuple  		=       AutoTokenizer.from_pretrained("bert-base-cased"    )
						SCREAMING_SNAKE_CASE_: List[Any]  		=       load_dataset("glue"				,	"mrpc"    )

						def tokenize_function(_UpperCAmelCase    ):
												# max_length=None => use the model max length (it's actually the default)
												SCREAMING_SNAKE_CASE_: Any  		=       tokenizer(examples["sentence1"]				,	examples["sentence2"]				,	truncation=_UpperCAmelCase				,	max_length=_UpperCAmelCase    )
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						# starting with the main process first:
						with accelerator.main_process_first():
												SCREAMING_SNAKE_CASE_: Tuple  		=       datasets.map(
												    _UpperCAmelCase				,	batched=_UpperCAmelCase				,	remove_columns=["idx", "sentence1", "sentence2"]				,	)

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       tokenized_datasets.rename_column("label"				,	"labels"    )

						def collate_fn(_UpperCAmelCase    ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												SCREAMING_SNAKE_CASE_: List[str]  		=       1_28 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		SCREAMING_SNAKE_CASE_: List[Any]  		=       16
												elif accelerator.mixed_precision != "no":
																		SCREAMING_SNAKE_CASE_: Optional[Any]  		=       8
												else:
																		SCREAMING_SNAKE_CASE_: List[str]  		=       None

												return tokenizer.pad(
												    _UpperCAmelCase				,	padding="longest"				,	max_length=_UpperCAmelCase				,	pad_to_multiple_of=_UpperCAmelCase				,	return_tensors="pt"				,	)

						# Instantiate dataloaders.
						SCREAMING_SNAKE_CASE_: Dict  		=       DataLoader(
						    tokenized_datasets["train"]				,	shuffle=_UpperCAmelCase				,	collate_fn=_UpperCAmelCase				,	batch_size=_UpperCAmelCase				,	drop_last=_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       DataLoader(
						    tokenized_datasets["validation"]				,	shuffle=_UpperCAmelCase				,	collate_fn=_UpperCAmelCase				,	batch_size=_UpperCAmelCase				,	drop_last=(accelerator.mixed_precision == "fp8")				,	)

						return train_dataloader, eval_dataloader







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						# Initialize accelerator
						SCREAMING_SNAKE_CASE_: str  		=       Accelerator(cpu=args.cpu				,	mixed_precision=args.mixed_precision    )
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						SCREAMING_SNAKE_CASE_: int  		=       config["lr"]
						SCREAMING_SNAKE_CASE_: Any  		=       int(config["num_epochs"]    )
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       int(config["seed"]    )
						SCREAMING_SNAKE_CASE_: List[Any]  		=       int(config["batch_size"]    )

						SCREAMING_SNAKE_CASE_: List[str]  		=       evaluate.load("glue"				,	"mrpc"    )

						# If the batch size is too big we use gradient accumulation
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												SCREAMING_SNAKE_CASE_: Tuple  		=       batch_size // MAX_GPU_BATCH_SIZE
												SCREAMING_SNAKE_CASE_: Dict  		=       MAX_GPU_BATCH_SIZE

						set_seed(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: str  		=       get_dataloaders(_UpperCAmelCase				,	_UpperCAmelCase    )
						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						SCREAMING_SNAKE_CASE_: List[Any]  		=       AutoModelForSequenceClassification.from_pretrained("bert-base-cased"				,	return_dict=_UpperCAmelCase    )

						# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
						# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
						# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
						SCREAMING_SNAKE_CASE_: Tuple  		=       model.to(accelerator.device    )
						# Instantiate optimizer
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       AdamW(params=model.parameters()				,	lr=_UpperCAmelCase    )

						# Instantiate scheduler
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       get_linear_schedule_with_warmup(
						    optimizer=_UpperCAmelCase				,	num_warmup_steps=1_00				,	num_training_steps=(len(_UpperCAmelCase    ) * num_epochs) // gradient_accumulation_steps				,	)

						# Prepare everything
						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
						# prepare method.

						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[int]  		=       accelerator.prepare(
						    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )

						# Now we train the model
						for epoch in range(_UpperCAmelCase    ):
												model.train()
												for step, batch in enumerate(_UpperCAmelCase    ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device    )
																		SCREAMING_SNAKE_CASE_: Tuple  		=       model(**_UpperCAmelCase    )
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       outputs.loss
																		SCREAMING_SNAKE_CASE_: Tuple  		=       loss / gradient_accumulation_steps
																		accelerator.backward(_UpperCAmelCase    )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

												model.eval()
												for step, batch in enumerate(_UpperCAmelCase    ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device    )
																		with torch.no_grad():
																								SCREAMING_SNAKE_CASE_: Optional[int]  		=       model(**_UpperCAmelCase    )
																		SCREAMING_SNAKE_CASE_: int  		=       outputs.logits.argmax(dim=-1    )
																		SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: int  		=       accelerator.gather_for_metrics((predictions, batch["labels"])    )
																		metric.add_batch(
																		    predictions=_UpperCAmelCase				,	references=_UpperCAmelCase				,	)

												SCREAMING_SNAKE_CASE_: List[Any]  		=       metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"epoch {epoch}:"				,	_UpperCAmelCase    )







def 			A_      (    ):
						SCREAMING_SNAKE_CASE_: Any  		=       argparse.ArgumentParser(description="Simple example of training script."    )
						parser.add_argument(
						    "--mixed_precision"				,	type=_UpperCAmelCase				,	default=_UpperCAmelCase				,	choices=["no", "fp16", "bf16", "fp8"]				,	help="Whether to use mixed precision. Choose"
						    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
						    "and an Nvidia Ampere GPU."				,	)
						parser.add_argument("--cpu"				,	action="store_true"				,	help="If passed, will train on the CPU."    )
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       parser.parse_args()
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
						training_function(_UpperCAmelCase				,	_UpperCAmelCase    )


if __name__ == "__main__":
						main()






