
"""simple docstring"""


import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






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

			return orig_key






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__						):
			for key in orig_state_dict.copy().keys():
						lowerCamelCase_					=  orig_state_dict.pop(lowerCAmelCase__						)

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

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

			return orig_state_dict






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						):
			lowerCamelCase_					=  torch.load(lowerCAmelCase__	,map_location='''cpu'''						)['''model_state_dict''']
			lowerCamelCase_					=  YosoConfig.from_json_file(lowerCAmelCase__						)
			lowerCamelCase_					=  YosoForMaskedLM(lowerCAmelCase__						)

			lowerCamelCase_					=  convert_checkpoint_helper(config.max_position_embeddings	,lowerCAmelCase__						)

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

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


if __name__ == "__main__":
						A_    							=					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_    							=					parser.parse_args()
						convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





"""simple docstring"""


import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD






def  lowercase						(	lowerCAmelCase__						):
			def wrapper(*lowerCAmelCase__	,**lowerCAmelCase__						):
						lowerCamelCase_					=  timeit.default_timer()
						lowerCamelCase_					=  func(*lowerCAmelCase__	,**lowerCAmelCase__						)
						lowerCamelCase_					=  timeit.default_timer() - starttime
						return delta

			lowerCamelCase_					=  func.__name__

			return wrapper






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__=100	,lowerCAmelCase__=None						):
			lowerCamelCase_					=  []
			lowerCamelCase_					=  seq_shapes or {}
			for i in range(lowerCAmelCase__						):
						lowerCamelCase_					=  {}
						for col_id, (k, v) in enumerate(features.items()						):
									if isinstance(lowerCAmelCase__	,_ArrayXD						):
												lowerCamelCase_					=  np.random.rand(*v.shape						).astype(v.dtype						)
									elif isinstance(lowerCAmelCase__	,datasets.Value						):
												if v.dtype == "string":
															lowerCamelCase_					=  '''The small grey turtle was surprisingly fast when challenged.'''
												else:
															lowerCamelCase_					=  np.random.randint(10	,size=1						).astype(v.dtype						).item()
									elif isinstance(lowerCAmelCase__	,datasets.Sequence						):
												while isinstance(lowerCAmelCase__	,datasets.Sequence						):
															lowerCamelCase_					=  v.feature
												lowerCamelCase_					=  seq_shapes[k]
												lowerCamelCase_					=  np.random.rand(*lowerCAmelCase__						).astype(v.dtype						)
									lowerCamelCase_					=  data

						dummy_data.append((i, example)						)

			return dummy_data






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__=100	,lowerCAmelCase__=None						):
			lowerCamelCase_					=  generate_examples(lowerCAmelCase__	,num_examples=lowerCAmelCase__	,seq_shapes=lowerCAmelCase__						)

			with ArrowWriter(features=lowerCAmelCase__	,path=lowerCAmelCase__						) as writer:
						for key, record in dummy_data:
									lowerCamelCase_					=  features.encode_example(lowerCAmelCase__						)
									writer.write(lowerCAmelCase__						)

						lowerCamelCase_		, lowerCamelCase_					=  writer.finalize()

			if not num_final_examples == num_examples:
						raise ValueError(
						    f"Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."						)

			lowerCamelCase_					=  datasets.Dataset.from_file(filename=lowerCAmelCase__	,info=datasets.DatasetInfo(features=lowerCAmelCase__						)						)

			return dataset



