






# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig


def 				_a     (   UpperCamelCase_    : str			,      UpperCamelCase_    : str			,      UpperCamelCase_    : Union[str, Any]=0      )       ->      str:
						"""simple docstring"""







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

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


def 				_a     (   UpperCamelCase_    : Optional[int]			,      UpperCamelCase_    : Tuple			,      UpperCamelCase_    : Optional[Any]			,      UpperCamelCase_    : Tuple			,      UpperCamelCase_    : Optional[int]      )       ->      List[Any]:
						"""simple docstring"""







						lowerCAmelCase__       					=  param.size()
						if checkpoint_version == 1.0:
												# version 1.0 stores [num_heads * hidden_size * num_splits, :]
												lowerCAmelCase__       					=  (num_heads, hidden_size, num_splits) + input_shape[1:]
												lowerCAmelCase__       					=  param.view(*__a      )
												lowerCAmelCase__       					=  param.transpose(0			,      2      )
												lowerCAmelCase__       					=  param.transpose(1			,      2      ).contiguous()
						elif checkpoint_version >= 2.0:
												# other versions store [num_heads * num_splits * hidden_size, :]
												lowerCAmelCase__       					=  (num_heads, num_splits, hidden_size) + input_shape[1:]
												lowerCAmelCase__       					=  param.view(*__a      )
												lowerCAmelCase__       					=  param.transpose(0			,      1      ).contiguous()
						lowerCAmelCase__       					=  param.view(*__a      )
						return param


def 				_a     (   UpperCamelCase_    : Tuple			,      UpperCamelCase_    : str			,      UpperCamelCase_    : Tuple      )       ->      Union[str, Any]:
						"""simple docstring"""







						lowerCAmelCase__       					=  {}

						# old versions did not store training args
						lowerCAmelCase__       					=  input_state_dict.get("args"			,      __a      )
						if ds_args is not None:
												# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
												# from pprint import pprint
												# pprint(vars(ds_args))

												lowerCAmelCase__       					=  ds_args.padded_vocab_size
												lowerCAmelCase__       					=  ds_args.max_position_embeddings
												lowerCAmelCase__       					=  ds_args.hidden_size
												lowerCAmelCase__       					=  ds_args.num_layers
												lowerCAmelCase__       					=  ds_args.num_attention_heads
												lowerCAmelCase__       					=  ds_args.ffn_hidden_size
												# pprint(config)

						# The number of heads.
						lowerCAmelCase__       					=  config.n_head
						# The hidden_size per head.
						lowerCAmelCase__       					=  config.n_embd // config.n_head
						# Megatron-LM checkpoint version
						if "checkpoint_version" in input_state_dict.keys():
												lowerCAmelCase__       					=  input_state_dict["checkpoint_version"]
						else:
												lowerCAmelCase__       					=  0.0

						# The model.
						lowerCAmelCase__       					=  input_state_dict["model"]
						# The language model.
						lowerCAmelCase__       					=  model["language_model"]
						# The embeddings.
						lowerCAmelCase__       					=  lm["embedding"]

						# The word embeddings.
						lowerCAmelCase__       					=  embeddings["word_embeddings"]["weight"]
						# Truncate the embedding table to vocab_size rows.
						lowerCAmelCase__       					=  word_embeddings[: config.vocab_size, :]
						lowerCAmelCase__       					=  word_embeddings

						# The position embeddings.
						lowerCAmelCase__       					=  embeddings["position_embeddings"]["weight"]
						# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
						lowerCAmelCase__       					=  pos_embeddings.size(0      )
						if n_positions != config.n_positions:
												raise ValueError(
												    F"pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match"      )
						# Store the position embeddings.
						lowerCAmelCase__       					=  pos_embeddings

						# The transformer.
						lowerCAmelCase__       					=  lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

						# The regex to extract layer names.
						lowerCAmelCase__       					=  re.compile(R"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"      )

						# The simple map of names for "automated" rules.
						lowerCAmelCase__       					=  {
						    "attention.dense": ".attn.c_proj.",
						    "self_attention.dense": ".attn.c_proj.",
						    "mlp.dense_h_to_4h": ".mlp.c_fc.",
						    "mlp.dense_4h_to_h": ".mlp.c_proj.",
						}

						# Extract the layers.
						for key, val in transformer.items():
												# Match the name.
												lowerCAmelCase__       					=  layer_re.match(__a      )

												# Stop if that's not a layer
												if m is None:
																		break

												# The index of the layer.
												lowerCAmelCase__       					=  int(m.group(1      )      )
												# The name of the operation.
												lowerCAmelCase__       					=  m.group(2      )
												# Is it a weight or a bias?
												lowerCAmelCase__       					=  m.group(3      )

												# The name of the layer.
												lowerCAmelCase__       					=  F"transformer.h.{layer_idx}"

												# For layernorm(s), simply store the layer norm.
												if op_name.endswith("layernorm"      ):
																		lowerCAmelCase__       					=  "ln_1" if op_name.startswith("input"      ) else "ln_2"
																		lowerCAmelCase__       					=  val

												# Transpose the QKV matrix.
												elif (
												    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
												) and weight_or_bias == "weight":
																		# Insert a tensor of 1x1xDxD bias.
																		lowerCAmelCase__       					=  torch.tril(torch.ones((n_positions, n_positions)			,      dtype=torch.floataa      )      ).view(
																		    1			,      1			,      __a			,      __a      )
																		lowerCAmelCase__       					=  causal_mask

																		# Insert a "dummy" tensor for masked_bias.
																		lowerCAmelCase__       					=  torch.tensor(-1e4			,      dtype=torch.floataa      )
																		lowerCAmelCase__       					=  masked_bias

																		lowerCAmelCase__       					=  fix_query_key_value_ordering(__a			,      __a			,      3			,      __a			,      __a      )
																		# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
																		lowerCAmelCase__       					=  out_val.transpose(0			,      1      ).contiguous()
																		# Store.
																		lowerCAmelCase__       					=  out_val

												# Transpose the bias.
												elif (
												    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
												) and weight_or_bias == "bias":
																		lowerCAmelCase__       					=  fix_query_key_value_ordering(__a			,      __a			,      3			,      __a			,      __a      )
																		# Store. No change of shape.
																		lowerCAmelCase__       					=  out_val

												# Transpose the weights.
												elif weight_or_bias == "weight":
																		lowerCAmelCase__       					=  megatron_to_transformers[op_name]
																		lowerCAmelCase__       					=  val.transpose(0			,      1      )

												# Copy the bias.
												elif weight_or_bias == "bias":
																		lowerCAmelCase__       					=  megatron_to_transformers[op_name]
																		lowerCAmelCase__       					=  val

    # DEBUG.
						assert config.n_layer == layer_idx + 1

						# The final layernorm.
						lowerCAmelCase__       					=  transformer["final_layernorm.weight"]
						lowerCAmelCase__       					=  transformer["final_layernorm.bias"]

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

						# It should be done!
						return output_state_dict


def 				_a     (   )       ->      Dict:
						"""simple docstring"""







						lowerCAmelCase__       					=  argparse.ArgumentParser()
						parser.add_argument("--print-checkpoint-structure"			,      action="store_true"      )
						parser.add_argument(
						    "path_to_checkpoint"			,      type=__a			,      help="Path to the checkpoint file (.zip archive or direct .pt file)"			,      )
						parser.add_argument(
						    "--config_file"			,      default=""			,      type=__a			,      help="An optional config json file describing the pre-trained model."			,      )
						lowerCAmelCase__       					=  parser.parse_args()

						# Extract the basename.
						lowerCAmelCase__       					=  os.path.dirname(args.path_to_checkpoint      )

						# Load the model.
						# the .zip is very optional, let's keep it for backward compatibility
						print(F"Extracting PyTorch state dictionary from {args.path_to_checkpoint}"      )
						if args.path_to_checkpoint.endswith(".zip"      ):
												with zipfile.ZipFile(args.path_to_checkpoint			,      "r"      ) as checkpoint:
																		with checkpoint.open("release/mp_rank_00/model_optim_rng.pt"      ) as pytorch_dict:
																								lowerCAmelCase__       					=  torch.load(__a			,      map_location="cpu"      )
						else:
												lowerCAmelCase__       					=  torch.load(args.path_to_checkpoint			,      map_location="cpu"      )

						lowerCAmelCase__       					=  input_state_dict.get("args"			,      __a      )

						# Read the config, or default to the model released by NVIDIA.
						if args.config_file == "":
												if ds_args is not None:
																		if ds_args.bias_gelu_fusion:
																								lowerCAmelCase__       					=  "gelu_fast"
																		elif ds_args.openai_gelu:
																								lowerCAmelCase__       					=  "gelu_new"
																		else:
																								lowerCAmelCase__       					=  "gelu"
												else:
																		# in the very early days this used to be "gelu_new"
																		lowerCAmelCase__       					=  "gelu_new"

												# Spell out all parameters in case the defaults change.
												lowerCAmelCase__       					=  GPTaConfig(
												    vocab_size=50_257			,      n_positions=1_024			,      n_embd=1_024			,      n_layer=24			,      n_head=16			,      n_inner=4_096			,      activation_function=__a			,      resid_pdrop=0.1			,      embd_pdrop=0.1			,      attn_pdrop=0.1			,      layer_norm_epsilon=1e-5			,      initializer_range=0.02			,      summary_type="cls_index"			,      summary_use_proj=__a			,      summary_activation=__a			,      summary_proj_to_labels=__a			,      summary_first_dropout=0.1			,      scale_attn_weights=__a			,      use_cache=__a			,      bos_token_id=50_256			,      eos_token_id=50_256			,      )
						else:
												lowerCAmelCase__       					=  GPTaConfig.from_json_file(args.config_file      )

						lowerCAmelCase__       					=  ["GPT2LMHeadModel"]

						# Convert.
						print("Converting"      )
						lowerCAmelCase__       					=  convert_megatron_checkpoint(__a			,      __a			,      __a      )

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

						# Add tokenizer class info to config
						# see https://github.com/huggingface/transformers/issues/13906)
						if ds_args is not None:
												lowerCAmelCase__       					=  ds_args.tokenizer_type
												if tokenizer_type == "GPT2BPETokenizer":
																		lowerCAmelCase__       					=  "gpt2"
												elif tokenizer_type == "PretrainedFromHF":
																		lowerCAmelCase__       					=  ds_args.tokenizer_name_or_path
												else:
																		raise ValueError(F"Unrecognized tokenizer_type {tokenizer_type}"      )
						else:
												lowerCAmelCase__       					=  "gpt2"

						lowerCAmelCase__       					=  AutoTokenizer.from_pretrained(__a      )
						lowerCAmelCase__       					=  type(__a      ).__name__
						lowerCAmelCase__       					=  tokenizer_class

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

						# Save tokenizer based on args
						print(F"Adding {tokenizer_class} tokenizer files"      )
						tokenizer.save_pretrained(__a      )

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


####################################################################################################

if __name__ == "__main__":
	main()

####################################################################################################













import unittest

import numpy as np

from transformers import AlbertConfig, 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():
					import jax.numpy as jnp

					from transformers.models.albert.modeling_flax_albert import (
					    FlaxAlbertForMaskedLM,
					    FlaxAlbertForMultipleChoice,
					    FlaxAlbertForPreTraining,
					    FlaxAlbertForQuestionAnswering,
					    FlaxAlbertForSequenceClassification,
					    FlaxAlbertForTokenClassification,
					    FlaxAlbertModel,
					)


class   A			(unittest.TestCase    ):
			'''simple docstring'''




			def __init__(			self   :    List[str]							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    List[str]=13							,	__lowerCAmelCase   :    int=7							,	__lowerCAmelCase   :    Tuple=True							,	__lowerCAmelCase   :    int=True							,	__lowerCAmelCase   :    Dict=True							,	__lowerCAmelCase   :    Union[str, Any]=True							,	__lowerCAmelCase   :    List[Any]=99							,	__lowerCAmelCase   :    Optional[Any]=32							,	__lowerCAmelCase   :    Optional[Any]=5							,	__lowerCAmelCase   :    Tuple=4							,	__lowerCAmelCase   :    Any=37							,	__lowerCAmelCase   :    str="gelu"							,	__lowerCAmelCase   :    Optional[Any]=0.1							,	__lowerCAmelCase   :    Union[str, Any]=0.1							,	__lowerCAmelCase   :    List[str]=5_12							,	__lowerCAmelCase   :    int=16							,	__lowerCAmelCase   :    Optional[int]=2							,	__lowerCAmelCase   :    List[Any]=0.0_2							,	__lowerCAmelCase   :    Tuple=4							,	)	->	Dict:







					"""simple docstring"""






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






			def    a_       (			self   :    Any   )	->	str:







					"""simple docstring"""






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

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

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

					A__        =   AlbertConfig(
					    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							,	is_decoder=__lowerCAmelCase							,	initializer_range=self.initializer_range							,	)

					return config, input_ids, token_type_ids, attention_mask







			def    a_       (			self   :    Optional[int]   )	->	Optional[int]:







					"""simple docstring"""






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


@require_flax
class   A			(SCREAMING_SNAKE_CASE   ,     unittest.TestCase    ):
			'''simple docstring'''




			__lowerCamelCase	:       str							=    (
			    (
			        FlaxAlbertModel,
			        FlaxAlbertForPreTraining,
			        FlaxAlbertForMaskedLM,
			        FlaxAlbertForMultipleChoice,
			        FlaxAlbertForQuestionAnswering,
			        FlaxAlbertForSequenceClassification,
			        FlaxAlbertForTokenClassification,
			        FlaxAlbertForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)






			def    a_       (			self   :    str   )	->	Optional[int]:







					"""simple docstring"""






					A__        =   FlaxAlbertModelTester(self   )







			@slow
			def    a_       (			self   :    int   )	->	Tuple:







					"""simple docstring"""






					for model_class_name in self.all_model_classes:
							A__        =   model_class_name.from_pretrained("""albert-base-v2"""   )
							A__        =   model(np.ones((1, 1)   )   )
							self.assertIsNotNone(__lowerCAmelCase   )


@require_flax
class   A			(unittest.TestCase    ):
			'''simple docstring'''




			@slow
			def    a_       (			self   :    Dict   )	->	List[Any]:







					"""simple docstring"""






					A__        =   FlaxAlbertModel.from_pretrained("""albert-base-v2"""   )
					A__        =   np.array([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]   )
					A__        =   np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]   )
					A__        =   model(__lowerCAmelCase							,	attention_mask=__lowerCAmelCase   )[0]
					A__        =   (1, 11, 7_68)
					self.assertEqual(output.shape							,	__lowerCAmelCase   )
					A__        =   np.array(
					    [[[-0.6_5_1_3, 1.5_0_3_5, -0.2_7_6_6], [-0.6_5_1_5, 1.5_0_4_6, -0.2_7_8_0], [-0.6_5_1_2, 1.5_0_4_9, -0.2_7_8_4]]]   )

					self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]							,	__lowerCAmelCase							,	atol=1e-4   )   )



