




import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
				from transformers.models.electra.modeling_flax_electra import (
				    FlaxElectraForCausalLM,
				    FlaxElectraForMaskedLM,
				    FlaxElectraForMultipleChoice,
				    FlaxElectraForPreTraining,
				    FlaxElectraForQuestionAnswering,
				    FlaxElectraForSequenceClassification,
				    FlaxElectraForTokenClassification,
				    FlaxElectraModel,
				)






class    __lowerCAmelCase			(	unittest.TestCase			):







			def __init__(	self   :      int   ,   snake_case__   :      Any   ,   snake_case__   :      Tuple=13   ,   snake_case__   :      List[str]=7   ,   snake_case__   :      Optional[Any]=True   ,   snake_case__   :      Optional[Any]=True   ,   snake_case__   :      Union[str, Any]=True   ,   snake_case__   :      Dict=True   ,   snake_case__   :      Any=99   ,   snake_case__   :      Tuple=24   ,   snake_case__   :      Optional[int]=32   ,   snake_case__   :      Union[str, Any]=5   ,   snake_case__   :      Tuple=4   ,   snake_case__   :      Optional[Any]=37   ,   snake_case__   :      Optional[Any]="gelu"   ,   snake_case__   :      Optional[Any]=0.1   ,   snake_case__   :      Optional[int]=0.1   ,   snake_case__   :      int=512   ,   snake_case__   :      int=16   ,   snake_case__   :      Any=2   ,   snake_case__   :      Union[str, Any]=0.02   ,   snake_case__   :      Dict=4   ,   ):




									"""simple docstring"""


									_UpperCAmelCase													=					parent
									_UpperCAmelCase													=					batch_size
									_UpperCAmelCase													=					seq_length
									_UpperCAmelCase													=					is_training
									_UpperCAmelCase													=					use_attention_mask
									_UpperCAmelCase													=					use_token_type_ids
									_UpperCAmelCase													=					use_labels
									_UpperCAmelCase													=					vocab_size
									_UpperCAmelCase													=					hidden_size
									_UpperCAmelCase													=					embedding_size
									_UpperCAmelCase													=					num_hidden_layers
									_UpperCAmelCase													=					num_attention_heads
									_UpperCAmelCase													=					intermediate_size
									_UpperCAmelCase													=					hidden_act
									_UpperCAmelCase													=					hidden_dropout_prob
									_UpperCAmelCase													=					attention_probs_dropout_prob
									_UpperCAmelCase													=					max_position_embeddings
									_UpperCAmelCase													=					type_vocab_size
									_UpperCAmelCase													=					type_sequence_label_size
									_UpperCAmelCase													=					initializer_range
									_UpperCAmelCase													=					num_choices







			def UpperCamelCase      (	self   :      Tuple				):




									"""simple docstring"""


									_UpperCAmelCase													=					ids_tensor([self.batch_size, self.seq_length]   ,   self.vocab_size				)

									_UpperCAmelCase													=					None
									if self.use_attention_mask:
															_UpperCAmelCase													=					random_attention_mask([self.batch_size, self.seq_length]				)

									_UpperCAmelCase													=					None
									if self.use_token_type_ids:
															_UpperCAmelCase													=					ids_tensor([self.batch_size, self.seq_length]   ,   self.type_vocab_size				)

									_UpperCAmelCase													=					ElectraConfig(
									    vocab_size=self.vocab_size   ,   hidden_size=self.hidden_size   ,   embedding_size=self.embedding_size   ,   num_hidden_layers=self.num_hidden_layers   ,   num_attention_heads=self.num_attention_heads   ,   intermediate_size=self.intermediate_size   ,   hidden_act=self.hidden_act   ,   hidden_dropout_prob=self.hidden_dropout_prob   ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,   max_position_embeddings=self.max_position_embeddings   ,   type_vocab_size=self.type_vocab_size   ,   initializer_range=self.initializer_range   ,   )

									return config, input_ids, token_type_ids, attention_mask







			def UpperCamelCase      (	self   :      Union[str, Any]				):




									"""simple docstring"""


									_UpperCAmelCase													=					self.prepare_config_and_inputs()
									_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase													=					config_and_inputs
									_UpperCAmelCase													=					{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
									return config, inputs_dict






@require_flax
class    __lowerCAmelCase			(	UpperCAmelCase__ ,		unittest.TestCase			):
			snake_case_				:			str								=					True

			snake_case_				:			int								=					(
			    (
			        FlaxElectraModel,
			        FlaxElectraForCausalLM,
			        FlaxElectraForMaskedLM,
			        FlaxElectraForPreTraining,
			        FlaxElectraForTokenClassification,
			        FlaxElectraForQuestionAnswering,
			        FlaxElectraForMultipleChoice,
			        FlaxElectraForSequenceClassification,
			    )
			    if is_flax_available()
			    else ()
			)







			def UpperCamelCase      (	self   :      Union[str, Any]				):




									"""simple docstring"""


									_UpperCAmelCase													=					FlaxElectraModelTester(self				)







			@slow
			def UpperCamelCase      (	self   :      int				):




									"""simple docstring"""


									for model_class_name in self.all_model_classes:
															if model_class_name == FlaxElectraForMaskedLM:
																					_UpperCAmelCase													=					model_class_name.from_pretrained("google/electra-small-generator"				)
															else:
																					_UpperCAmelCase													=					model_class_name.from_pretrained("google/electra-small-discriminator"				)
															_UpperCAmelCase													=					model(np.ones((1, 1)				)				)
															self.assertIsNotNone(snake_case__				)










import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM


def 							__SCREAMING_SNAKE_CASE  (      snake_case_						):

						'''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													=					"nystromformer." + orig_key

						return orig_key


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_						):

						'''simple docstring'''


						for key in orig_state_dict.copy().keys():
												_UpperCAmelCase													=					orig_state_dict.pop(snake_case_						)

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

						_UpperCAmelCase													=					orig_state_dict["cls.predictions.decoder.bias"]
						_UpperCAmelCase													=					(
						    torch.arange(config.max_position_embeddings						).expand((1, -1)						) + 2
						)

						return orig_state_dict


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_     ,   snake_case_						):

						'''simple docstring'''


						_UpperCAmelCase													=					torch.load(snake_case_     ,   map_location="cpu"						)["model_state_dict"]
						_UpperCAmelCase													=					NystromformerConfig.from_json_file(snake_case_						)
						_UpperCAmelCase													=					NystromformerForMaskedLM(snake_case_						)

						_UpperCAmelCase													=					convert_checkpoint_helper(snake_case_     ,   snake_case_						)

						model.load_state_dict(snake_case_						)
						model.eval()
						model.save_pretrained(snake_case_						)

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


if __name__ == "__main__":
				lowercase_     :    List[str]      =			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.'
				)
				lowercase_     :    int      =			parser.parse_args()
				convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)




