





import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType

class 	A_       :
	def __init__(    self							,					_A=2							,					_A=3							,					_A=6_4							,					_A=None		):






				'''simple docstring'''







				UpperCAmelCase   		=				np.random.default_rng(_A		)
				UpperCAmelCase   		=				length
				UpperCAmelCase   		=				rng.normal(size=(length,)		).astype(np.floataa		)
				UpperCAmelCase   		=				a * self.x + b + rng.normal(scale=0.1							,					size=(length,)		).astype(np.floataa		)
	def __len__(    self		):






				'''simple docstring'''







				return self.length







	def __getitem__(    self							,					_A		):






				'''simple docstring'''







				return {"x": self.x[i], "y": self.y[i]}

class 	A_       (torch.nn.Module			):
	def __init__(    self							,					_A=0							,					_A=0							,					_A=False		):






				'''simple docstring'''







				super().__init__()
				UpperCAmelCase   		=				torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
				UpperCAmelCase   		=				torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
				UpperCAmelCase   		=				True







	def      _lowercase				(    self							,					_A=None		):






				'''simple docstring'''







				if self.first_batch:
							print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""		)
							UpperCAmelCase   		=				False
				return x * self.a[0] + self.b[0]

class 	A_       (torch.nn.Module			):
	def __init__(    self							,					_A=0							,					_A=0							,					_A=False		):






				'''simple docstring'''







				super().__init__()
				UpperCAmelCase   		=				torch.nn.Parameter(torch.tensor(_A		).float()		)
				UpperCAmelCase   		=				torch.nn.Parameter(torch.tensor(_A		).float()		)
				UpperCAmelCase   		=				True







	def      _lowercase				(    self							,					_A=None		):






				'''simple docstring'''







				if self.first_batch:
							print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""		)
							UpperCAmelCase   		=				False
				return x * self.a + self.b





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ = 16 )  ->				Union[str, Any]:







			'''simple docstring'''

			from datasets import load_dataset
			from transformers import AutoTokenizer

			UpperCAmelCase   		=				AutoTokenizer.from_pretrained('''bert-base-cased''' )
			UpperCAmelCase   		=				{'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
			UpperCAmelCase   		=				load_dataset('''csv'''			,			data_files=UpperCamelCase__ )
			UpperCAmelCase   		=				datasets['''train'''].unique('''label''' )

			UpperCAmelCase   		=				{v: i for i, v in enumerate(UpperCamelCase__ )}

			def tokenize_function(UpperCamelCase__ ):
						# max_length=None => use the model max length (it's actually the default)
						UpperCAmelCase   		=				tokenizer(
						    examples['''sentence1''']			,			examples['''sentence2''']			,			truncation=UpperCamelCase__			,			max_length=UpperCamelCase__			,			padding='''max_length''' )
						if "label" in examples:
									UpperCAmelCase   		=				[label_to_id[l] for l in examples['''label''']]
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			UpperCAmelCase   		=				datasets.map(
			    UpperCamelCase__			,			batched=UpperCamelCase__			,			remove_columns=['''sentence1''', '''sentence2''', '''label''']			,			)

			def collate_fn(UpperCamelCase__ ):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(UpperCamelCase__			,			padding='''max_length'''			,			max_length=128			,			return_tensors='''pt''' )
						return tokenizer.pad(UpperCamelCase__			,			padding='''longest'''			,			return_tensors='''pt''' )

			# Instantiate dataloaders.
			UpperCAmelCase   		=				DataLoader(tokenized_datasets['''train''']			,			shuffle=UpperCamelCase__			,			collate_fn=UpperCamelCase__			,			batch_size=2 )
			UpperCAmelCase   		=				DataLoader(tokenized_datasets['''validation''']			,			shuffle=UpperCamelCase__			,			collate_fn=UpperCamelCase__			,			batch_size=1 )

			return train_dataloader, eval_dataloader










import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				Union[str, Any]:







			'''simple docstring'''

			if "model" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''model.'''			,			'''''' )
			if "norm1" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''norm1'''			,			'''attention.output.LayerNorm''' )
			if "norm2" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''norm2'''			,			'''output.LayerNorm''' )
			if "norm" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''norm'''			,			'''LayerNorm''' )
			if "transformer" in orig_key:
						UpperCAmelCase   		=				orig_key.split('''.''' )[0].split('''_''' )[-1]
						UpperCAmelCase   		=				orig_key.replace(F"""transformer_{layer_num}"""			,			F"""encoder.layer.{layer_num}""" )
			if "mha.attn" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''mha.attn'''			,			'''attention.self''' )
			if "mha" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''mha'''			,			'''attention''' )
			if "W_q" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''W_q'''			,			'''self.query''' )
			if "W_k" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''W_k'''			,			'''self.key''' )
			if "W_v" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''W_v'''			,			'''self.value''' )
			if "ff1" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''ff1'''			,			'''intermediate.dense''' )
			if "ff2" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''ff2'''			,			'''output.dense''' )
			if "ff" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''ff'''			,			'''output.dense''' )
			if "mlm_class" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''mlm.mlm_class'''			,			'''cls.predictions.decoder''' )
			if "mlm" in orig_key:
						UpperCAmelCase   		=				orig_key.replace('''mlm'''			,			'''cls.predictions.transform''' )
			if "cls" not in orig_key:
						UpperCAmelCase   		=				'''yoso.''' + orig_key

			return orig_key





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				Dict:







			'''simple docstring'''

			for key in orig_state_dict.copy().keys():
						UpperCAmelCase   		=				orig_state_dict.pop(UpperCamelCase__ )

						if ("pooler" in key) or ("sen_class" in key):
									continue
						else:
									UpperCAmelCase   		=				val

			UpperCAmelCase   		=				orig_state_dict['''cls.predictions.decoder.bias''']
			UpperCAmelCase   		=				torch.arange(UpperCamelCase__ ).expand((1, -1) ) + 2

			return orig_state_dict





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )  ->				int:







			'''simple docstring'''

			UpperCAmelCase   		=				torch.load(UpperCamelCase__			,			map_location='''cpu''' )['''model_state_dict''']
			UpperCAmelCase   		=				YosoConfig.from_json_file(UpperCamelCase__ )
			UpperCAmelCase   		=				YosoForMaskedLM(UpperCamelCase__ )

			UpperCAmelCase   		=				convert_checkpoint_helper(config.max_position_embeddings			,			UpperCamelCase__ )

			print(model.load_state_dict(UpperCamelCase__ ) )
			model.eval()
			model.save_pretrained(UpperCamelCase__ )

			print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}""" )


if __name__ == "__main__":
				__A				:    Any       =   argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
				)
				parser.add_argument(
				    "--config_file",
				    default=None,
				    type=str,
				    required=True,
				    help="The json file for YOSO model config.",
				)
				parser.add_argument(
				    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				__A				:    List[str]       =   parser.parse_args()
				convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



