
"""simple docstring"""
import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_=0 )     ->			int:


					'''simple docstring'''
					# Format the message.
					if name is None:
										__a:						Tuple		    =					None
					else:
										__a:						int		    =					""".""" * max(0	,				spaces - 2 ) + """# {:""" + str(50 - spaces ) + """s}"""
										__a:						str		    =					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     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			int:


					'''simple docstring'''
					# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
					# for compatibility with later versions of NVIDIA Megatron-LM.
					# The inverse operation is performed inside Megatron-LM to read checkpoints:
					# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
					# If param is the weight tensor of the self-attention block, the returned tensor
					# will have to be transposed one more time to be read by HuggingFace GPT2.
					__a:						List[Any]		    =					param.size()
					# other versions store [num_heads * num_splits * hidden_size, :]
					__a:						str		    =					(num_heads, num_splits, hidden_size) + input_shape[1:]
					__a:						Dict		    =					param.view(*lowerCamelCase_ )
					__a:						Optional[int]		    =					param.transpose(0	,				1 ).contiguous()
					__a:						Dict		    =					param.view(*lowerCamelCase_ )
					return param


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			List[Any]:


					'''simple docstring'''
					__a:						Optional[int]		    =					config.n_positions
					__a:						int		    =					config.n_layer
					__a:						Dict		    =					config.vocab_size
					__a:						int		    =					config.n_head
					__a:						Dict		    =					config.n_embd // config.n_head

					__a:						Optional[int]		    =					sd_megatron["""model.language_model.embedding.word_embeddings.weight"""][:vocab_size, :]
					__a:						Any		    =					{
					    """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"""],
					}

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

										__a:						Any		    =					sd_megatron[f"""{pf}{i}.input_layernorm.weight"""]
										__a:						Union[str, Any]		    =					sd_megatron[f"""{pf}{i}.input_layernorm.bias"""]

										__a:						Dict		    =					sd_megatron[f"""{pf}{i}.self_attention.query_key_value.weight"""]
										__a:						Union[str, Any]		    =					fix_query_key_value_ordering(lowerCamelCase_	,				3	,				lowerCamelCase_	,				lowerCamelCase_ )
										__a:						Any		    =					vala.transpose(0	,				1 ).contiguous()

										__a:						List[str]		    =					sd_megatron[f"""{pf}{i}.self_attention.query_key_value.bias"""]
										__a:						Dict		    =					fix_query_key_value_ordering(lowerCamelCase_	,				3	,				lowerCamelCase_	,				lowerCamelCase_ )
										__a:						List[Any]		    =					vala

										__a:						Dict		    =					sd_megatron[f"""{pf}{i}.self_attention.dense.weight"""].transpose(
										    0	,				1 )
										__a:						List[str]		    =					sd_megatron[f"""{pf}{i}.self_attention.dense.bias"""]
										__a:						Optional[int]		    =					sd_megatron[f"""{pf}{i}.post_attention_layernorm.weight"""]
										__a:						List[Any]		    =					sd_megatron[f"""{pf}{i}.post_attention_layernorm.bias"""]
										__a:						Dict		    =					sd_megatron[f"""{pf}{i}.mlp.dense_h_to_4h.weight"""].transpose(0	,				1 )
										__a:						Dict		    =					sd_megatron[f"""{pf}{i}.mlp.dense_h_to_4h.bias"""]
										__a:						Optional[Any]		    =					sd_megatron[f"""{pf}{i}.mlp.dense_4h_to_h.weight"""].transpose(
										    0	,				1 )
										__a:						Tuple		    =					sd_megatron[f"""{pf}{i}.mlp.dense_4h_to_h.bias"""]

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

					return sd_hf


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			int:


					'''simple docstring'''
					__a:						Optional[Any]		    =					6_40_00
					__a:						Optional[Any]		    =					config_megatron["""encoder_seq_length"""]
					__a:						Union[str, Any]		    =					config_megatron["""hidden_size"""]
					__a:						List[str]		    =					config_megatron["""num_layers"""]
					__a:						Union[str, Any]		    =					config_megatron["""num_attention_heads"""]
					__a:						Optional[int]		    =					config_megatron["""ffn_hidden_size"""]
					__a:						Any		    =					"""gelu"""
					__a:						List[Any]		    =					0.1
					__a:						Optional[Any]		    =					0.1
					__a:						Optional[Any]		    =					0.1
					__a:						List[Any]		    =					config_megatron["""layernorm_epsilon"""]  # 1e-5
					__a:						Any		    =					config_megatron["""init_method_std"""]  # 0.02
					__a:						Union[str, Any]		    =					config_megatron["""apply_query_key_layer_scaling"""]  # True
					__a:						str		    =					True
					__a:						Tuple		    =					True

					# This identifies the 6.7B (7B) model which uses a different tokenizer
					if config_megatron["hidden_size"] == 40_96:
										__a:						Union[str, Any]		    =					1  # <|endoftext|>
										__a:						Tuple		    =					1  # <|endoftext|>
										__a:						Optional[Any]		    =					0  # <unk>
					else:
										__a:						List[Any]		    =					2  # <s>
										__a:						Dict		    =					3  # <|endoftext|>
										__a:						Any		    =					0  # <pad>

					return config_hf


def     lowerCamelCase_    (	lowerCamelCase_ )     ->			List[str]:


					'''simple docstring'''
					print(lowerCamelCase_ )

					__a:						Dict		    =					args.checkpoint_path
					__a:						Any		    =					args.save_path
					if isfile(lowerCamelCase_ ):
										raise FileNotFoundError(f"""ERROR! could not find file {checkpoint_path}""" )

					# Load the model.
					__a:						str		    =					torch.load(lowerCamelCase_	,				map_location="""cpu""" )

					# Load the config.
					__a:						List[str]		    =					checkpoint["""hyper_parameters"""]["""cfg"""]
					__a:						Tuple		    =					GPTaConfig()
					__a:						Optional[int]		    =					copy_config(config_hf=lowerCamelCase_	,				config_megatron=lowerCamelCase_ )
					__a:						List[str]		    =					["""GPT2LMHeadModel"""]

					__a:						Union[str, Any]		    =					checkpoint["""state_dict"""]

					# Convert.
					print("""Converting""" )
					__a:						List[Any]		    =					convert_megatron_checkpoint(lowerCamelCase_	,				lowerCamelCase_ )

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

					__a:						Optional[Any]		    =					"""GPTSw3Tokenizer"""

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

					# Store the state_dict to file.
					__a:						Optional[int]		    =					os.path.join(lowerCamelCase_	,				"""pytorch_model.bin""" )
					print(f"""Saving checkpoint to \"{output_checkpoint_file}\"""" )
					torch.save(lowerCamelCase_	,				lowerCamelCase_ )


if __name__ == "__main__":
			lowercase		:						str =					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')
			lowercase		:						str =					parser.parse_args()
			main(_args)


"""simple docstring"""
import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
			from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
			lowercase		:						str =					None

if is_ray_available():
			import ray  # noqa: E402 # isort:skip
			from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
			lowercase		:						Optional[int] =					None
			lowercase		:						Union[str, Any] =					None
			lowercase		:						List[str] =					None


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


					'''simple docstring'''
					if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
										__a:						int		    =					unittest.skip("""test requires Datasets, Faiss, psutil""" )(lowerCamelCase_ )
					return test_case




@require_distributed_retrieval
class    _snake_case  (						_lowercase       ):






							def 							__lowercase  (					self						:			Any    ):






												"""simple docstring"""



												__a:						Optional[Any]		    =					tempfile.mkdtemp()
												__a:						int		    =					8

												# DPR tok
												__a:						Any		    =					[
												    """[UNK]""",
												    """[CLS]""",
												    """[SEP]""",
												    """[PAD]""",
												    """[MASK]""",
												    """want""",
												    """##want""",
												    """##ed""",
												    """wa""",
												    """un""",
												    """runn""",
												    """##ing""",
												    """,""",
												    """low""",
												    """lowest""",
												]
												__a:						str		    =					os.path.join(self.tmpdirname						, """dpr_tokenizer"""    )
												os.makedirs(snake_case__						, exist_ok=snake_case__    )
												__a:						Tuple		    =					os.path.join(snake_case__						, DPR_VOCAB_FILES_NAMES["""vocab_file"""]    )
												with open(self.vocab_file						, """w"""						, encoding="""utf-8"""    ) as vocab_writer:
																	vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]    )    )

												# BART tok
												__a:						Any		    =					[
												    """l""",
												    """o""",
												    """w""",
												    """e""",
												    """r""",
												    """s""",
												    """t""",
												    """i""",
												    """d""",
												    """n""",
												    """\u0120""",
												    """\u0120l""",
												    """\u0120n""",
												    """\u0120lo""",
												    """\u0120low""",
												    """er""",
												    """\u0120lowest""",
												    """\u0120newer""",
												    """\u0120wider""",
												    """<unk>""",
												]
												__a:						List[str]		    =					dict(zip(snake_case__						, range(len(snake_case__    )    )    )    )
												__a:						List[str]		    =					["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
												__a:						List[str]		    =					{"""unk_token""": """<unk>"""}

												__a:						Optional[int]		    =					os.path.join(self.tmpdirname						, """bart_tokenizer"""    )
												os.makedirs(snake_case__						, exist_ok=snake_case__    )
												__a:						Tuple		    =					os.path.join(snake_case__						, BART_VOCAB_FILES_NAMES["""vocab_file"""]    )
												__a:						str		    =					os.path.join(snake_case__						, BART_VOCAB_FILES_NAMES["""merges_file"""]    )
												with open(self.vocab_file						, """w"""						, encoding="""utf-8"""    ) as fp:
																	fp.write(json.dumps(snake_case__    ) + """\n"""    )
												with open(self.merges_file						, """w"""						, encoding="""utf-8"""    ) as fp:
																	fp.write("""\n""".join(snake_case__    )    )






							def 							__lowercase  (					self						:			Any    ):






												"""simple docstring"""



												return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname						, """dpr_tokenizer"""    )    )






							def 							__lowercase  (					self						:			Any    ):






												"""simple docstring"""



												return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname						, """bart_tokenizer"""    )    )






							def 							__lowercase  (					self						:			Tuple    ):






												"""simple docstring"""



												shutil.rmtree(self.tmpdirname    )






							def 							__lowercase  (					self						:			int    ):






												"""simple docstring"""



												__a:						int		    =					Dataset.from_dict(
												    {
												        """id""": ["""0""", """1"""],
												        """text""": ["""foo""", """bar"""],
												        """title""": ["""Foo""", """Bar"""],
												        """embeddings""": [np.ones(self.retrieval_vector_size    ), 2 * np.ones(self.retrieval_vector_size    )],
												    }    )
												dataset.add_faiss_index("""embeddings"""						, string_factory="""Flat"""						, metric_type=faiss.METRIC_INNER_PRODUCT    )
												return dataset






							def 							__lowercase  (					self						:			Any						, snake_case__						:			bool						, snake_case__						:			Dict=1_2_3_4_5    ):






												"""simple docstring"""



												__a:						str		    =					self.get_dummy_dataset()
												__a:						int		    =					RagConfig(
												    retrieval_vector_size=self.retrieval_vector_size						, question_encoder=DPRConfig().to_dict()						, generator=BartConfig().to_dict()						, )
												with patch("""transformers.models.rag.retrieval_rag.load_dataset"""    ) as mock_load_dataset:
																	__a:						List[str]		    =					dataset
																	__a:						List[Any]		    =					RagPyTorchDistributedRetriever(
																	    snake_case__						, question_encoder_tokenizer=self.get_dpr_tokenizer()						, generator_tokenizer=self.get_bart_tokenizer()						, )
																	if init_retrieval:
																						retriever.init_retrieval(snake_case__    )
												return retriever






							def 							__lowercase  (					self						:			Dict						, snake_case__						:			bool    ):






												"""simple docstring"""



												ray.init(local_mode=snake_case__    )
												__a:						int		    =					RagConfig(
												    retrieval_vector_size=self.retrieval_vector_size						, question_encoder=DPRConfig().to_dict()						, generator=BartConfig().to_dict()						, )
												__a:						List[str]		    =					ray.remote(snake_case__    )
												__a:						Tuple		    =					[remote_cls.remote() for _ in range(1    )]
												with patch("""transformers.models.rag.retrieval_rag.load_dataset"""    ) as mock_load_dataset:
																	__a:						int		    =					self.get_dummy_dataset()
																	__a:						List[Any]		    =					RagRayDistributedRetriever(
																	    snake_case__						, question_encoder_tokenizer=self.get_dpr_tokenizer()						, generator_tokenizer=self.get_bart_tokenizer()						, retrieval_workers=snake_case__						, )
																	if init_retrieval:
																						retriever.init_retrieval()
												return retriever






							def 							__lowercase  (					self						:			str						, snake_case__						:			bool						, snake_case__						:			bool						, snake_case__						:			Union[str, Any]=1_2_3_4_5    ):






												"""simple docstring"""



												__a:						List[Any]		    =					self.get_dummy_dataset()
												__a:						Optional[Any]		    =					RagConfig(
												    retrieval_vector_size=self.retrieval_vector_size						, question_encoder=DPRConfig().to_dict()						, generator=BartConfig().to_dict()						, index_name="""custom"""						, )
												if from_disk:
																	__a:						int		    =					os.path.join(self.tmpdirname						, """dataset"""    )
																	__a:						int		    =					os.path.join(self.tmpdirname						, """index.faiss"""    )
																	dataset.get_index("""embeddings"""    ).save(os.path.join(self.tmpdirname						, """index.faiss"""    )    )
																	dataset.drop_index("""embeddings"""    )
																	dataset.save_to_disk(os.path.join(self.tmpdirname						, """dataset"""    )    )
																	del dataset
																	__a:						str		    =					RagPyTorchDistributedRetriever(
																	    snake_case__						, question_encoder_tokenizer=self.get_dpr_tokenizer()						, generator_tokenizer=self.get_bart_tokenizer()						, )
												else:
																	__a:						List[Any]		    =					RagPyTorchDistributedRetriever(
																	    snake_case__						, question_encoder_tokenizer=self.get_dpr_tokenizer()						, generator_tokenizer=self.get_bart_tokenizer()						, index=CustomHFIndex(config.retrieval_vector_size						, snake_case__    )						, )
												if init_retrieval:
																	retriever.init_retrieval(snake_case__    )
												return retriever






							def 							__lowercase  (					self						:			str						, snake_case__						:			bool						, snake_case__						:			bool    ):






												"""simple docstring"""



												ray.init(local_mode=snake_case__    )
												__a:						Tuple		    =					self.get_dummy_dataset()
												__a:						Dict		    =					RagConfig(
												    retrieval_vector_size=self.retrieval_vector_size						, question_encoder=DPRConfig().to_dict()						, generator=BartConfig().to_dict()						, index_name="""custom"""						, )
												__a:						Optional[Any]		    =					ray.remote(snake_case__    )
												__a:						Any		    =					[remote_cls.remote() for _ in range(1    )]
												if from_disk:
																	__a:						List[Any]		    =					os.path.join(self.tmpdirname						, """dataset"""    )
																	__a:						Any		    =					os.path.join(self.tmpdirname						, """index.faiss"""    )
																	dataset.get_index("""embeddings"""    ).save(os.path.join(self.tmpdirname						, """index.faiss"""    )    )
																	dataset.drop_index("""embeddings"""    )
																	dataset.save_to_disk(os.path.join(self.tmpdirname						, """dataset"""    )    )
																	del dataset
																	__a:						Any		    =					RagRayDistributedRetriever(
																	    snake_case__						, question_encoder_tokenizer=self.get_dpr_tokenizer()						, generator_tokenizer=self.get_bart_tokenizer()						, retrieval_workers=snake_case__						, index=CustomHFIndex.load_from_disk(
																	        vector_size=config.retrieval_vector_size						, dataset_path=config.passages_path						, index_path=config.index_path						, )						, )
												else:
																	__a:						Any		    =					RagRayDistributedRetriever(
																	    snake_case__						, question_encoder_tokenizer=self.get_dpr_tokenizer()						, generator_tokenizer=self.get_bart_tokenizer()						, retrieval_workers=snake_case__						, index=CustomHFIndex(config.retrieval_vector_size						, snake_case__    )						, )
												if init_retrieval:
																	retriever.init_retrieval()
												return retriever






							def 							__lowercase  (					self						:			str						, snake_case__						:			RagRetriever						, snake_case__						:			np.array						, snake_case__						:			int    ):






												"""simple docstring"""



												__a    ,				__a    ,				__a:						Optional[Any]		    =					retriever.retrieve(snake_case__						, n_docs=snake_case__    )
												self.assertEqual(retrieved_doc_embeds.shape						, (2, n_docs, self.retrieval_vector_size)    )
												self.assertEqual(len(snake_case__    )						, 2    )
												self.assertEqual(sorted(doc_dicts[0]    )						, ["""embeddings""", """id""", """text""", """title"""]    )
												self.assertEqual(len(doc_dicts[0]["""id"""]    )						, snake_case__    )
												self.assertEqual(doc_dicts[0]["""id"""][0]						, """1"""    )  # max inner product is reached with second doc
												self.assertEqual(doc_dicts[1]["""id"""][0]						, """0"""    )  # max inner product is reached with first doc
												self.assertListEqual(doc_ids.tolist()						, [[1], [0]]    )






							def 							__lowercase  (					self						:			List[str]    ):






												"""simple docstring"""



												__a:						int		    =					1
												__a:						Tuple		    =					np.array(
												    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]						, dtype=np.floataa    )

												self.distributed_retriever_check(
												    self.get_dummy_pytorch_distributed_retriever(init_retrieval=snake_case__    )						, snake_case__						, snake_case__    )






							def 							__lowercase  (					self						:			str    ):






												"""simple docstring"""



												__a:						Tuple		    =					1
												__a:						Tuple		    =					np.array(
												    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]						, dtype=np.floataa    )

												self.distributed_retriever_check(
												    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=snake_case__						, from_disk=snake_case__    )						, snake_case__						, snake_case__						, )






							def 							__lowercase  (					self						:			List[Any]    ):






												"""simple docstring"""



												__a:						List[Any]		    =					1
												__a:						int		    =					np.array(
												    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]						, dtype=np.floataa    )

												self.distributed_retriever_check(
												    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=snake_case__						, from_disk=snake_case__    )						, snake_case__						, snake_case__						, )






							@require_ray
							def 							__lowercase  (					self						:			str    ):






												"""simple docstring"""



												__a:						int		    =					1
												__a:						str		    =					np.array(
												    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]						, dtype=np.floataa    )

												self.distributed_retriever_check(
												    self.get_dummy_ray_distributed_retriever(init_retrieval=snake_case__    )						, snake_case__						, snake_case__    )
												ray.shutdown()






							@require_ray
							def 							__lowercase  (					self						:			Dict    ):






												"""simple docstring"""



												__a:						List[Any]		    =					1
												__a:						List[str]		    =					np.array(
												    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]						, dtype=np.floataa    )
												with self.assertRaises(snake_case__    ):
																	self.distributed_retriever_check(
																	    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=snake_case__						, from_disk=snake_case__    )						, snake_case__						, snake_case__						, )
												ray.shutdown()






							@require_ray
							def 							__lowercase  (					self						:			List[str]    ):






												"""simple docstring"""



												__a:						Tuple		    =					1
												__a:						str		    =					np.array(
												    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]						, dtype=np.floataa    )

												self.distributed_retriever_check(
												    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=snake_case__						, from_disk=snake_case__    )						, snake_case__						, snake_case__    )
												ray.shutdown()
