
"""simple docstring"""

import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM
def 		UpperCAmelCase					(		a_			):





		'''simple docstring'''


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

		return orig_key
def 		UpperCAmelCase					(		a_,						a_			):





		'''simple docstring'''


		for key in orig_state_dict.copy().keys():
				lowerCamelCase     :			Union[str, Any]		=    orig_state_dict.pop(__lowerCamelCase			)

				if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
						continue
				else:
						lowerCamelCase     :			Dict		=    val

		lowerCamelCase     :			Dict		=    orig_state_dict["cls.predictions.decoder.bias"]
		lowerCamelCase     :			List[Any]		=    (
		    torch.arange(config.max_position_embeddings			).expand((1, -1)			) + 2
		)

		return orig_state_dict
def 		UpperCAmelCase					(		a_,						a_,						a_			):





		'''simple docstring'''


		lowerCamelCase     :			Dict		=    torch.load(__lowerCamelCase,						map_location='cpu'			)["model_state_dict"]
		lowerCamelCase     :			List[Any]		=    NystromformerConfig.from_json_file(__lowerCamelCase			)
		lowerCamelCase     :			List[str]		=    NystromformerForMaskedLM(__lowerCamelCase			)

		lowerCamelCase     :			int		=    convert_checkpoint_helper(__lowerCamelCase,						__lowerCamelCase			)

		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 Nystromformer pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for Nystromformer 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_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)













import warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


UpperCamelCase__          =logging.get_logger(__name__)

if is_tf_available():
       import tensorflow as tf





@dataclass
class 	lowerCAmelCase__(       __lowercase			):






       '''simple docstring'''


       __snake_case       	=			'tf'
       __snake_case       	=			field(
           default=__lowercase     ,   metadata={'help': 'Name of TPU'}     ,   )

       __snake_case       	=			field(
           default=__lowercase     ,   metadata={'help': 'Zone of TPU'}     ,   )

       __snake_case       	=			field(
           default=__lowercase     ,   metadata={'help': 'Name of Cloud TPU-enabled project'}     ,   )

       __snake_case       	=			field(
           default=1.0     ,   metadata={'help': 'Power for the Polynomial decay LR scheduler.'}     ,   )

       __snake_case       	=			field(default=__lowercase     ,   metadata={'help': 'Whether to activate the XLA compilation or not'}			)




       @cached_property
       def 			UpperCamelCase_	(					self							)	->   Tuple["tf.distribute.Strategy", int]:
            requires_backends(self			,  ["tf"]							)
            logger.info("Tensorflow: setting up strategy"							)

            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							tf.config.list_physical_devices("GPU"							)

            # Set to float16 at first
            if self.fpaa:
                 tf.keras.mixed_precision.set_global_policy("mixed_float16"							)

            if self.no_cuda:
                 _SCREAMING_SNAKE_CASE				:		List[Any]  			=							tf.distribute.OneDeviceStrategy(device="/cpu:0"							)
            else:
                 try:
                      if self.tpu_name:
                           _SCREAMING_SNAKE_CASE				:		Any  			=							tf.distribute.cluster_resolver.TPUClusterResolver(
                               self.tpu_name			,  zone=self.tpu_zone			,  project=self.gcp_project							)
                      else:
                           _SCREAMING_SNAKE_CASE				:		str  			=							tf.distribute.cluster_resolver.TPUClusterResolver()
                 except ValueError:
                      if self.tpu_name:
                           raise RuntimeError(F"""Couldn't connect to TPU {self.tpu_name}!"""							)
                      else:
                           _SCREAMING_SNAKE_CASE				:		Any  			=							None

                 if tpu:
                      # Set to bfloat16 in case of TPU
                      if self.fpaa:
                           tf.keras.mixed_precision.set_global_policy("mixed_bfloat16"							)

                      tf.config.experimental_connect_to_cluster(__lowerCamelCase							)
                      tf.tpu.experimental.initialize_tpu_system(__lowerCamelCase							)

                      _SCREAMING_SNAKE_CASE				:		List[str]  			=							tf.distribute.TPUStrategy(__lowerCamelCase							)

                 elif len(__lowerCamelCase							) == 0:
                      _SCREAMING_SNAKE_CASE				:		Dict  			=							tf.distribute.OneDeviceStrategy(device="/cpu:0"							)
                 elif len(__lowerCamelCase							) == 1:
                      _SCREAMING_SNAKE_CASE				:		Dict  			=							tf.distribute.OneDeviceStrategy(device="/gpu:0"							)
                 elif len(__lowerCamelCase							) > 1:
                      # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
                      _SCREAMING_SNAKE_CASE				:		Any  			=							tf.distribute.MirroredStrategy()
                 else:
                      raise ValueError("Cannot find the proper strategy, please check your environment properties."							)

            return strategy




       @property
       def 			UpperCamelCase_	(					self							)	->   "tf.distribute.Strategy":
            requires_backends(self			,  ["tf"]							)
            return self._setup_strategy




       @property
       def 			UpperCamelCase_	(					self							)	->   int:
            requires_backends(self			,  ["tf"]							)
            return self._setup_strategy.num_replicas_in_sync




       @property
       def 			UpperCamelCase_	(					self							)	->   List[Any]:
            return False  # TF Logging is handled by Keras not the Trainer




       @property
       def 			UpperCamelCase_	(					self							)	->   int:
            if self.per_gpu_train_batch_size:
                 logger.warning(
                     "Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future "
                     "version. Using `--per_device_train_batch_size` is preferred."							)
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							self.per_gpu_train_batch_size or self.per_device_train_batch_size
            return per_device_batch_size * self.n_replicas




       @property
       def 			UpperCamelCase_	(					self							)	->   int:
            if self.per_gpu_eval_batch_size:
                 logger.warning(
                     "Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future "
                     "version. Using `--per_device_eval_batch_size` is preferred."							)
            _SCREAMING_SNAKE_CASE				:		int  			=							self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
            return per_device_batch_size * self.n_replicas







       @property
       def 			UpperCamelCase_	(					self							)	->   int:
            requires_backends(self			,  ["tf"]							)
            warnings.warn(
                "The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead."			,  __lowerCamelCase			,  )
            return self._setup_strategy.num_replicas_in_sync