


import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig




def      __snake_case       (       lowerCamelCase_ :				List[Any]	,  lowerCamelCase_ :				List[Any]	,  lowerCamelCase_ :				int=0						):
				'''simple docstring'''




				if name is None:
								__magic_name__	  = None
				else:
								__magic_name__	  = "." * max(0	,  spaces - 2						) + "# {:" + str(50 - spaces						) + "s}"
								__magic_name__	  = fmt.format(lowerCamelCase_						)

				# Print and recurse (if needed).
				if isinstance(lowerCamelCase_	,  lowerCamelCase_						):
								if msg is not None:
												print(lowerCamelCase_						)
								for k in val.keys():
												recursive_print(lowerCamelCase_	,  val[k]	,  spaces + 2						)
				elif isinstance(lowerCamelCase_	,  torch.Tensor						):
								print(lowerCamelCase_	,  ":"	,  val.size()						)
				else:
								print(lowerCamelCase_	,  ":"	,  lowerCamelCase_						)




def      __snake_case       (       lowerCamelCase_ :				Optional[int]	,  lowerCamelCase_ :				Dict	,  lowerCamelCase_ :				int	,  lowerCamelCase_ :				Optional[Any]						):
				'''simple docstring'''




				__magic_name__	  = param.size()
				# other versions store [num_heads * num_splits * hidden_size, :]
				__magic_name__	  = (num_heads, num_splits, hidden_size) + input_shape[1:]
				__magic_name__	  = param.view(*lowerCamelCase_						)
				__magic_name__	  = param.transpose(0	,  1						).contiguous()
				__magic_name__	  = param.view(*lowerCamelCase_						)
				return param




def      __snake_case       (       lowerCamelCase_ :				Optional[Any]	,  lowerCamelCase_ :				List[str]						):
				'''simple docstring'''




				__magic_name__	  = config.n_positions
				__magic_name__	  = config.n_layer
				__magic_name__	  = config.vocab_size
				__magic_name__	  = config.n_head
				__magic_name__	  = config.n_embd // config.n_head

				__magic_name__	  = sd_megatron["model.language_model.embedding.word_embeddings.weight"][:vocab_size, :]
				__magic_name__	  = {
				    "transformer.wte.weight": word_embeddings,
				    "transformer.wpe.weight": sd_megatron["model.language_model.embedding.position_embeddings.weight"],
				    "transformer.ln_f.weight": sd_megatron["model.language_model.encoder.final_layernorm.weight"],
				    "transformer.ln_f.bias": sd_megatron["model.language_model.encoder.final_layernorm.bias"],
				}

				__magic_name__	  = "model.language_model.encoder.layers."
				for i in range(lowerCamelCase_						):
								__magic_name__	  = torch.tril(torch.ones((n_positions, n_positions)	,  dtype=torch.bool						)						)
								__magic_name__	  = causal_mask.view(1	,  1	,  lowerCamelCase_	,  lowerCamelCase_						)
								__magic_name__	  = causal_mask
								__magic_name__	  = torch.tensor(-1e4	,  dtype=torch.bfloataa						)

								__magic_name__	  = sd_megatron[F'{pf}{i}.input_layernorm.weight']
								__magic_name__	  = sd_megatron[F'{pf}{i}.input_layernorm.bias']

								__magic_name__	  = sd_megatron[F'{pf}{i}.self_attention.query_key_value.weight']
								__magic_name__	  = fix_query_key_value_ordering(lowerCamelCase_	,  3	,  lowerCamelCase_	,  lowerCamelCase_						)
								__magic_name__	  = vala.transpose(0	,  1						).contiguous()

								__magic_name__	  = sd_megatron[F'{pf}{i}.self_attention.query_key_value.bias']
								__magic_name__	  = fix_query_key_value_ordering(lowerCamelCase_	,  3	,  lowerCamelCase_	,  lowerCamelCase_						)
								__magic_name__	  = vala

								__magic_name__	  = sd_megatron[F'{pf}{i}.self_attention.dense.weight'].transpose(
								    0	,  1						)
								__magic_name__	  = sd_megatron[F'{pf}{i}.self_attention.dense.bias']
								__magic_name__	  = sd_megatron[F'{pf}{i}.post_attention_layernorm.weight']
								__magic_name__	  = sd_megatron[F'{pf}{i}.post_attention_layernorm.bias']
								__magic_name__	  = sd_megatron[F'{pf}{i}.mlp.dense_h_to_4h.weight'].transpose(0	,  1						)
								__magic_name__	  = sd_megatron[F'{pf}{i}.mlp.dense_h_to_4h.bias']
								__magic_name__	  = sd_megatron[F'{pf}{i}.mlp.dense_4h_to_h.weight'].transpose(
								    0	,  1						)
								__magic_name__	  = sd_megatron[F'{pf}{i}.mlp.dense_4h_to_h.bias']

				# For LM head, transformers' wants the matrix to weight embeddings.
				__magic_name__	  = word_embeddings

				return sd_hf




def      __snake_case       (       lowerCamelCase_ :				List[str]	,  lowerCamelCase_ :				List[str]						):
				'''simple docstring'''




				__magic_name__	  = 6_4000
				__magic_name__	  = config_megatron["encoder_seq_length"]
				__magic_name__	  = config_megatron["hidden_size"]
				__magic_name__	  = config_megatron["num_layers"]
				__magic_name__	  = config_megatron["num_attention_heads"]
				__magic_name__	  = config_megatron["ffn_hidden_size"]
				__magic_name__	  = "gelu"
				__magic_name__	  = 0.1
				__magic_name__	  = 0.1
				__magic_name__	  = 0.1
				__magic_name__	  = config_megatron["layernorm_epsilon"]  # 1e-5
				__magic_name__	  = config_megatron["init_method_std"]  # 0.02
				__magic_name__	  = config_megatron["apply_query_key_layer_scaling"]  # True
				__magic_name__	  = True
				__magic_name__	  = True

				# This identifies the 6.7B (7B) model which uses a different tokenizer
				if config_megatron["hidden_size"] == 4096:
								__magic_name__	  = 1  # <|endoftext|>
								__magic_name__	  = 1  # <|endoftext|>
								__magic_name__	  = 0  # <unk>
				else:
								__magic_name__	  = 2  # <s>
								__magic_name__	  = 3  # <|endoftext|>
								__magic_name__	  = 0  # <pad>

				return config_hf




def      __snake_case       (       lowerCamelCase_ :				Tuple						):
				'''simple docstring'''




				print(lowerCamelCase_						)

				__magic_name__	  = args.checkpoint_path
				__magic_name__	  = args.save_path
				if isfile(lowerCamelCase_						):
								raise FileNotFoundError(F'ERROR! could not find file {checkpoint_path}'						)

				# Load the model.
				__magic_name__	  = torch.load(lowerCamelCase_	,  map_location="cpu"						)

				# Load the config.
				__magic_name__	  = checkpoint["hyper_parameters"]["cfg"]
				__magic_name__	  = GPTaConfig()
				__magic_name__	  = copy_config(config_hf=lowerCamelCase_	,  config_megatron=lowerCamelCase_						)
				__magic_name__	  = ["GPT2LMHeadModel"]

				__magic_name__	  = checkpoint["state_dict"]

				# Convert.
				print("Converting"						)
				__magic_name__	  = convert_megatron_checkpoint(lowerCamelCase_	,  lowerCamelCase_						)

				# Print the structure of converted state dict.
				if args.print_checkpoint_structure:
								recursive_print(lowerCamelCase_	,  lowerCamelCase_						)

				__magic_name__	  = "GPTSw3Tokenizer"

				# Store the config to file.
				print("Saving config"						)
				config_hf.save_pretrained(lowerCamelCase_						)

				# Store the state_dict to file.
				__magic_name__	  = os.path.join(lowerCamelCase_	,  "pytorch_model.bin"						)
				print(F'Saving checkpoint to "{output_checkpoint_file}"'						)
				torch.save(lowerCamelCase_	,  lowerCamelCase_						)


if __name__ == "__main__":
						__magic_name__   :   Optional[int]  							=argparse.ArgumentParser()
						parser.add_argument(
						    '--checkpoint_path',
						    type=str,
						    required=True,
						    help='e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000',
						)
						parser.add_argument('--save_path', type=str, required=True, help='e.g. /home/user/gpt-sw3/hf')
						parser.add_argument('--print-checkpoint-structure', action='store_true')
						__magic_name__   :   Optional[Any]  							=parser.parse_args()
						main(_args)







'''simple docstring'''
from __future__ import annotations

import unittest

from transformers import DebertaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    TFDebertaForMaskedLM,
						    TFDebertaForQuestionAnswering,
						    TFDebertaForSequenceClassification,
						    TFDebertaForTokenClassification,
						    TFDebertaModel,
						)






class 		UpperCamelCase_		:





						"""simple docstring"""




						def __init__( self				:					Dict	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					Dict=13	,				_lowerCamelCase				:					Union[str, Any]=7	,				_lowerCamelCase				:					Union[str, Any]=True	,				_lowerCamelCase				:					Optional[Any]=True	,				_lowerCamelCase				:					Tuple=True	,				_lowerCamelCase				:					Any=True	,				_lowerCamelCase				:					int=99	,				_lowerCamelCase				:					Any=32	,				_lowerCamelCase				:					Any=2	,				_lowerCamelCase				:					str=4	,				_lowerCamelCase				:					str=37	,				_lowerCamelCase				:					Optional[int]="gelu"	,				_lowerCamelCase				:					Optional[Any]=0.1	,				_lowerCamelCase				:					Dict=0.1	,				_lowerCamelCase				:					int=5_12	,				_lowerCamelCase				:					Union[str, Any]=16	,				_lowerCamelCase				:					int=2	,				_lowerCamelCase				:					List[Any]=0.02	,				_lowerCamelCase				:					str=3	,				_lowerCamelCase				:					Tuple=4	,				_lowerCamelCase				:					int=None	,				)							->					Optional[int]:
										__magic_name__	  = parent
										__magic_name__	  = 13
										__magic_name__	  = 7
										__magic_name__	  = True
										__magic_name__	  = True
										__magic_name__	  = True
										__magic_name__	  = True
										__magic_name__	  = 99
										__magic_name__	  = 32
										__magic_name__	  = 2
										__magic_name__	  = 4
										__magic_name__	  = 37
										__magic_name__	  = "gelu"
										__magic_name__	  = 0.1
										__magic_name__	  = 0.1
										__magic_name__	  = 5_12
										__magic_name__	  = 16
										__magic_name__	  = False
										__magic_name__	  = -1
										__magic_name__	  = True
										__magic_name__	  = 2
										__magic_name__	  = 0.02
										__magic_name__	  = 3
										__magic_name__	  = 4
										__magic_name__	  = None
						def 					__A					( self				:					Any  )							->					Union[str, Any]:
										__magic_name__	  = ids_tensor([self.batch_size, self.seq_length]	,				self.vocab_size  )

										__magic_name__	  = None
										if self.use_input_mask:
														__magic_name__	  = random_attention_mask([self.batch_size, self.seq_length]  )

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

										__magic_name__	  = None
										__magic_name__	  = None
										__magic_name__	  = None
										if self.use_labels:
														__magic_name__	  = ids_tensor([self.batch_size]	,				self.type_sequence_label_size  )
														__magic_name__	  = ids_tensor([self.batch_size, self.seq_length]	,				self.num_labels  )

										__magic_name__	  = DebertaConfig(
										    vocab_size=self.vocab_size	,				hidden_size=self.hidden_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	,				relative_attention=self.relative_attention	,				max_relative_positions=self.max_relative_positions	,				position_biased_input=self.position_biased_input	,				initializer_range=self.initializer_range	,				return_dict=_lowerCamelCase	,				)

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
						def 					__A					( self				:					Optional[Any]	,				_lowerCamelCase				:					Optional[Any]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					List[Any]	,				_lowerCamelCase				:					Tuple	,				_lowerCamelCase				:					Union[str, Any]  )							->					Optional[int]:
										__magic_name__	  = TFDebertaModel(config=_lowerCamelCase  )
										__magic_name__	  = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

										__magic_name__	  = [input_ids, input_mask]
										__magic_name__	  = model(_lowerCamelCase  )

										__magic_name__	  = model(_lowerCamelCase  )

										self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, self.seq_length, self.hidden_size)  )
						def 					__A					( self				:					Optional[int]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					Tuple  )							->					Dict:
										__magic_name__	  = TFDebertaForMaskedLM(config=_lowerCamelCase  )
										__magic_name__	  = {
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}
										__magic_name__	  = model(_lowerCamelCase  )
										self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.seq_length, self.vocab_size)  )
						def 					__A					( self				:					List[str]	,				_lowerCamelCase				:					Tuple	,				_lowerCamelCase				:					Dict	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					List[Any]	,				_lowerCamelCase				:					List[Any]	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					Tuple  )							->					str:
										__magic_name__	  = self.num_labels
										__magic_name__	  = TFDebertaForSequenceClassification(config=_lowerCamelCase  )
										__magic_name__	  = {
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}

										__magic_name__	  = model(_lowerCamelCase  )
										self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.num_labels)  )
						def 					__A					( self				:					List[str]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					Any	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					List[str]  )							->					Tuple:
										__magic_name__	  = self.num_labels
										__magic_name__	  = TFDebertaForTokenClassification(config=_lowerCamelCase  )
										__magic_name__	  = {
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}
										__magic_name__	  = model(_lowerCamelCase  )
										self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.seq_length, self.num_labels)  )
						def 					__A					( self				:					str	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					List[Any]	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					List[str]  )							->					Tuple:
										__magic_name__	  = TFDebertaForQuestionAnswering(config=_lowerCamelCase  )
										__magic_name__	  = {
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}

										__magic_name__	  = model(_lowerCamelCase  )
										self.parent.assertEqual(result.start_logits.shape	,				(self.batch_size, self.seq_length)  )
										self.parent.assertEqual(result.end_logits.shape	,				(self.batch_size, self.seq_length)  )

						def 					__A					( self				:					Dict  )							->					Dict:
										__magic_name__	  = self.prepare_config_and_inputs()
										(
										    (
										    __magic_name__
										)  ,      (
										    __magic_name__
										)  ,      (
										    __magic_name__
										)  ,      (
										    __magic_name__
										)  ,      (
										    __magic_name__
										)  ,      (
										    __magic_name__
										)  ,      (
										    __magic_name__
										)  ,      
										)	  = config_and_inputs
										__magic_name__	  = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
										return config, inputs_dict






@require_tf
class 		UpperCamelCase_		(					A   ,		A   ,		unittest.TestCase   ):





						"""simple docstring"""




						UpperCAmelCase__	:       Tuple         =     (
						    (
						        TFDebertaModel,
						        TFDebertaForMaskedLM,
						        TFDebertaForQuestionAnswering,
						        TFDebertaForSequenceClassification,
						        TFDebertaForTokenClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						UpperCAmelCase__	:       Optional[Any]         =     (
						    {
						        '''feature-extraction''': TFDebertaModel,
						        '''fill-mask''': TFDebertaForMaskedLM,
						        '''question-answering''': TFDebertaForQuestionAnswering,
						        '''text-classification''': TFDebertaForSequenceClassification,
						        '''token-classification''': TFDebertaForTokenClassification,
						        '''zero-shot''': TFDebertaForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)

						UpperCAmelCase__	:       List[str]         =     False
						UpperCAmelCase__	:       List[str]         =     False
						def 					__A					( self				:					Optional[Any]  )							->					str:
										__magic_name__	  = TFDebertaModelTester(self  )
										__magic_name__	  = ConfigTester(self	,				config_class=_lowerCamelCase	,				hidden_size=37  )
						def 					__A					( self				:					int  )							->					Optional[Any]:
										self.config_tester.run_common_tests()
						def 					__A					( self				:					List[str]  )							->					Optional[Any]:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_lowerCamelCase  )
						def 					__A					( self				:					List[str]  )							->					Dict:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*_lowerCamelCase  )
						def 					__A					( self				:					Tuple  )							->					Union[str, Any]:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*_lowerCamelCase  )
						def 					__A					( self				:					List[Any]  )							->					Union[str, Any]:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*_lowerCamelCase  )
						def 					__A					( self				:					Union[str, Any]  )							->					str:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase  )

						@slow
						def 					__A					( self				:					Optional[int]  )							->					Any:
										__magic_name__	  = TFDebertaModel.from_pretrained("kamalkraj/deberta-base"  )
										self.assertIsNotNone(_lowerCamelCase  )






@require_tf
class 		UpperCamelCase_		(					unittest.TestCase   ):





						"""simple docstring"""




						@unittest.skip(reason="Model not available yet"  )
						def 					__A					( self				:					str  )							->					str:
										pass

						@slow
						def 					__A					( self				:					str  )							->					Dict:
										__magic_name__	  = TFDebertaModel.from_pretrained("kamalkraj/deberta-base"  )
										__magic_name__	  = tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]  )
										__magic_name__	  = tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
										__magic_name__	  = model(_lowerCamelCase	,				attention_mask=_lowerCamelCase  )[0]

										__magic_name__	  = tf.constant(
										    [
										        [
										            [-0.59_855_896, -0.80_552_566, -0.8_462_135],
										            [1.4_484_025, -0.93_483_794, -0.80_593_085],
										            [0.3_122_741, 0.00_316_059, -1.4_131_377],
										        ]
										    ]  )
										tf.debugging.assert_near(output[:, 1:4, 1:4]	,				_lowerCamelCase	,				atol=1e-4  )



