
"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import AutoTokenizer, GPTJConfig, is_tf_available
from transformers.testing_utils import require_tf, slow, tooslow

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


if is_tf_available():
			import tensorflow as tf

			from transformers.models.gptj.modeling_tf_gptj import (
			    TFGPTJForCausalLM,
			    TFGPTJForQuestionAnswering,
			    TFGPTJForSequenceClassification,
			    TFGPTJModel,
			    shape_list,
			)


class    _lowerCAmelCase						:



							def __init__(      self ,						a_   )							->							str:
								_UpperCAmelCase =						parent
								_UpperCAmelCase =						13
								_UpperCAmelCase =						7
								_UpperCAmelCase =						True
								_UpperCAmelCase =						True
								_UpperCAmelCase =						True
								_UpperCAmelCase =						True
								_UpperCAmelCase =						True
								_UpperCAmelCase =						99
								_UpperCAmelCase =						32
								_UpperCAmelCase =						4
								_UpperCAmelCase =						2
								_UpperCAmelCase =						4
								_UpperCAmelCase =						37
								_UpperCAmelCase =						"gelu"
								_UpperCAmelCase =						0.1
								_UpperCAmelCase =						0.1
								_UpperCAmelCase =						512
								_UpperCAmelCase =						16
								_UpperCAmelCase =						2
								_UpperCAmelCase =						0.02
								_UpperCAmelCase =						3
								_UpperCAmelCase =						4
								_UpperCAmelCase =						None
								_UpperCAmelCase =						self.vocab_size - 1
								_UpperCAmelCase =						self.vocab_size - 1
								_UpperCAmelCase =						self.vocab_size - 1



							def       _a   (      self   )							->							Optional[Any]:
								_UpperCAmelCase =						ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size   )

								_UpperCAmelCase =						None
								if self.use_input_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 =						None
								if self.use_mc_token_ids:
									_UpperCAmelCase =						ids_tensor([self.batch_size, self.num_choices] ,						self.seq_length   )

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

								_UpperCAmelCase =						GPTJConfig(
								    vocab_size=self.vocab_size ,						n_embd=self.hidden_size ,						n_layer=self.num_hidden_layers ,						n_head=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 ,						n_positions=self.max_position_embeddings ,						type_vocab_size=self.type_vocab_size ,						initializer_range=self.initializer_range ,						bos_token_id=self.bos_token_id ,						eos_token_id=self.eos_token_id ,						pad_token_id=self.pad_token_id ,						rotary_dim=self.rotary_dim ,						return_dict=a_ ,						)

								_UpperCAmelCase =						ids_tensor([self.num_hidden_layers, self.num_attention_heads] ,						2   )

								return (
								    config,
								    input_ids,
								    input_mask,
								    head_mask,
								    token_type_ids,
								    mc_token_ids,
								    sequence_labels,
								    token_labels,
								    choice_labels,
								)



							def       _a   (      self ,						a_ ,						a_ ,						a_ ,						a_ ,						a_ ,						*a_   )							->							List[Any]:
								_UpperCAmelCase =						TFGPTJModel(config=a_   )
								_UpperCAmelCase =						{
								    "input_ids": input_ids,
								    "attention_mask": input_mask,
								    "token_type_ids": token_type_ids,
								}
								_UpperCAmelCase =						model(a_   )

								_UpperCAmelCase =						[input_ids, None, input_mask]  # None is the input for 'past'
								_UpperCAmelCase =						model(a_   )

								_UpperCAmelCase =						model(a_   )

								self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, self.seq_length, self.hidden_size)   )



							def       _a   (      self ,						a_ ,						a_ ,						a_ ,						a_ ,						a_ ,						*a_   )							->							List[str]:
								_UpperCAmelCase =						TFGPTJModel(config=a_   )

								# first forward pass
								_UpperCAmelCase =						model(a_ ,						token_type_ids=a_ ,						use_cache=a_   )
								_UpperCAmelCase =						model(a_ ,						token_type_ids=a_   )
								_UpperCAmelCase =						model(a_ ,						token_type_ids=a_ ,						use_cache=a_   )

								self.parent.assertTrue(len(a_   ) == len(a_   )   )
								self.parent.assertTrue(len(a_   ) == len(a_   ) + 1   )

								_UpperCAmelCase ,      _UpperCAmelCase =						outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								_UpperCAmelCase =						ids_tensor((self.batch_size, 1) ,						config.vocab_size   )
								_UpperCAmelCase =						ids_tensor([self.batch_size, 1] ,						self.type_vocab_size   )

								# append to next input_ids and token_type_ids
								_UpperCAmelCase =						tf.concat([input_ids, next_tokens] ,						axis=-1   )
								_UpperCAmelCase =						tf.concat([token_type_ids, next_token_types] ,						axis=-1   )

								_UpperCAmelCase =						model(a_ ,						token_type_ids=a_   )["last_hidden_state"]
								_UpperCAmelCase =						model(a_ ,						token_type_ids=a_ ,						past_key_values=a_   )[
								    "last_hidden_state"
								]

								# select random slice
								_UpperCAmelCase =						int(ids_tensor((1,) ,						shape_list(a_   )[-1]   )   )
								_UpperCAmelCase =						output_from_no_past[:, -1, random_slice_idx]
								_UpperCAmelCase =						output_from_past[:, 0, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(a_ ,						a_ ,						rtol=1e-6   )



							def       _a   (      self ,						a_ ,						a_ ,						a_ ,						a_ ,						a_ ,						*a_   )							->							Union[str, Any]:
								_UpperCAmelCase =						TFGPTJModel(config=a_   )

								# create attention mask
								_UpperCAmelCase =						self.seq_length // 2
								_UpperCAmelCase =						tf.ones((self.batch_size, half_seq_length) ,						dtype=tf.intaa   )
								_UpperCAmelCase =						tf.zeros((self.batch_size, self.seq_length - half_seq_length) ,						dtype=tf.intaa   )
								_UpperCAmelCase =						tf.concat([attn_mask_begin, attn_mask_end] ,						axis=1   )

								# first forward pass
								_UpperCAmelCase ,      _UpperCAmelCase =						model(a_ ,						attention_mask=a_   ).to_tuple()

								# create hypothetical next token and extent to next_input_ids
								_UpperCAmelCase =						ids_tensor((self.batch_size, 1) ,						config.vocab_size   )

								# change a random masked slice from input_ids
								_UpperCAmelCase =						ids_tensor((1,) ,						a_   ).numpy() + 1
								_UpperCAmelCase =						ids_tensor((self.batch_size, self.seq_length) ,						config.vocab_size   )
								_UpperCAmelCase =						tf.range(self.seq_length   ) == (self.seq_length - random_seq_idx_to_change)
								_UpperCAmelCase =						tf.transpose(
								    tf.broadcast_to(tf.expand_dims(a_ ,						-1   ) ,						(self.seq_length, self.batch_size)   )   )
								_UpperCAmelCase =						tf.where(a_ ,						a_ ,						a_   )

								# append to next input_ids and attn_mask
								_UpperCAmelCase =						tf.concat([input_ids, next_tokens] ,						axis=-1   )
								_UpperCAmelCase =						tf.concat([attn_mask, tf.ones((shape_list(a_   )[0], 1) ,						dtype=tf.intaa   )] ,						axis=1   )

								# get two different outputs
								_UpperCAmelCase =						model(a_ ,						attention_mask=a_   )["last_hidden_state"]
								_UpperCAmelCase =						model(a_ ,						past_key_values=a_ ,						attention_mask=a_   )[
								    "last_hidden_state"
								]

								# select random slice
								_UpperCAmelCase =						int(ids_tensor((1,) ,						shape_list(a_   )[-1]   )   )
								_UpperCAmelCase =						output_from_no_past[:, -1, random_slice_idx]
								_UpperCAmelCase =						output_from_past[:, 0, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(a_ ,						a_ ,						rtol=1e-12   )



							def       _a   (      self ,						a_ ,						a_ ,						a_ ,						a_ ,						a_ ,						*a_   )							->							Optional[Any]:
								_UpperCAmelCase =						TFGPTJModel(config=a_   )

								_UpperCAmelCase =						input_ids[:1, :]
								_UpperCAmelCase =						input_mask[:1, :]
								_UpperCAmelCase =						token_type_ids[:1, :]
								_UpperCAmelCase =						1

								# first forward pass
								_UpperCAmelCase =						model(a_ ,						attention_mask=a_ ,						token_type_ids=a_ ,						use_cache=a_   )

								_UpperCAmelCase ,      _UpperCAmelCase =						outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								_UpperCAmelCase =						ids_tensor((self.batch_size, 3) ,						config.vocab_size   )
								_UpperCAmelCase =						ids_tensor((self.batch_size, 3) ,						2   )
								_UpperCAmelCase =						ids_tensor((self.batch_size, 3) ,						self.type_vocab_size   )

								# append to next input_ids and token_type_ids
								_UpperCAmelCase =						tf.concat([input_ids, next_tokens] ,						axis=-1   )
								_UpperCAmelCase =						tf.concat([input_mask, next_attn_mask] ,						axis=-1   )
								_UpperCAmelCase =						tf.concat([token_type_ids, next_token_types] ,						axis=-1   )

								_UpperCAmelCase =						model(
								    a_ ,						token_type_ids=a_ ,						attention_mask=a_   )["last_hidden_state"]
								_UpperCAmelCase =						model(
								    a_ ,						token_type_ids=a_ ,						attention_mask=a_ ,						past_key_values=a_ ,						)["last_hidden_state"]
								self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]   )

								# select random slice
								_UpperCAmelCase =						int(ids_tensor((1,) ,						shape_list(a_   )[-1]   )   )
								_UpperCAmelCase =						output_from_no_past[:, -3:, random_slice_idx]
								_UpperCAmelCase =						output_from_past[:, :, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(a_ ,						a_ ,						rtol=1e-3   )



							def       _a   (      self ,						a_ ,						a_ ,						a_ ,						a_ ,						a_ ,						*a_   )							->							Optional[Any]:
								_UpperCAmelCase =						TFGPTJForCausalLM(config=a_   )
								_UpperCAmelCase =						{
								    "input_ids": input_ids,
								    "attention_mask": input_mask,
								    "token_type_ids": token_type_ids,
								}
								_UpperCAmelCase =						model(a_   )
								self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.seq_length, self.vocab_size)   )



							def       _a   (      self   )							->							int:
								_UpperCAmelCase =						self.prepare_config_and_inputs()

								(
								    (
								    _UpperCAmelCase
								) ,      (
								    _UpperCAmelCase
								) ,      (
								    _UpperCAmelCase
								) ,      (
								    _UpperCAmelCase
								) ,      (
								    _UpperCAmelCase
								) ,      (
								    _UpperCAmelCase
								) ,      (
								    _UpperCAmelCase
								) ,      (
								    _UpperCAmelCase
								) ,      (
								    _UpperCAmelCase
								) ,      
								) =						config_and_inputs

								_UpperCAmelCase =						{
								    "input_ids": input_ids,
								    "token_type_ids": token_type_ids,
								    "attention_mask": input_mask,
								}
								return config, inputs_dict





@require_tf
class    _lowerCAmelCase						(						lowerCamelCase    ,       lowerCamelCase    ,       lowerCamelCase    ,       unittest.TestCase						):
							lowercase_     :     List[Any]       =							(
							    (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
							    if is_tf_available()
							    else ()
							)

							lowercase_     :     int       =							(TFGPTJForCausalLM,) if is_tf_available() else ()
							lowercase_     :     Optional[Any]       =							(
							    {
							        '''feature-extraction''': TFGPTJModel,
							        '''question-answering''': TFGPTJForQuestionAnswering,
							        '''text-classification''': TFGPTJForSequenceClassification,
							        '''text-generation''': TFGPTJForCausalLM,
							        '''zero-shot''': TFGPTJForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							lowercase_     :     Dict       =							False
							lowercase_     :     Optional[int]       =							False
							lowercase_     :     List[Any]       =							False
							lowercase_     :     List[str]       =							False



							def       _a   (      self ,						a_ ,						a_ ,						a_ ,						a_ ,						a_   )							->							List[str]:
								if (
								    pipeline_test_casse_name == "QAPipelineTests"
								    and tokenizer_name is not None
								    and not tokenizer_name.endswith("Fast"   )
								):
									# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
									# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
									# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
									return True

								return False



							def       _a   (      self   )							->							str:
								_UpperCAmelCase =						TFGPTJModelTester(self   )
								_UpperCAmelCase =						ConfigTester(self ,						config_class=a_ ,						n_embd=37   )



							def       _a   (      self   )							->							Any:
								self.config_tester.run_common_tests()



							def       _a   (      self   )							->							Union[str, Any]:
								_UpperCAmelCase =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_model(*a_   )



							def       _a   (      self   )							->							List[Any]:
								_UpperCAmelCase =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_model_past(*a_   )



							def       _a   (      self   )							->							Optional[Any]:
								_UpperCAmelCase =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_model_attention_mask_past(*a_   )



							def       _a   (      self   )							->							List[str]:
								_UpperCAmelCase =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_model_past_large_inputs(*a_   )



							def       _a   (      self   )							->							int:
								_UpperCAmelCase =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_lm_head_model(*a_   )



							@slow
							@unittest.skipIf(
							    not is_tf_available() or len(tf.config.list_physical_devices("GPU"   )   ) > 0 ,						"skip testing on GPU for now to avoid GPU OOM." ,						)
							def       _a   (      self   )							->							Optional[Any]:
								_UpperCAmelCase =						TFGPTJModel.from_pretrained("EleutherAI/gpt-j-6B" ,						from_pt=a_   )
								self.assertIsNotNone(a_   )



							@unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor."   )
							def       _a   (      self   )							->							str:
								super().test_resize_token_embeddings()





@require_tf
@tooslow
# Marked as @tooslow due to GPU OOM -- but still useful to run locally. Requires ~39GB of RAM.
class    _lowerCAmelCase						(						unittest.TestCase						):



							def       _a   (      self   )							->							Tuple:
								_UpperCAmelCase =						TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B" ,						from_pt=a_   )
								_UpperCAmelCase =						tf.convert_to_tensor([[464, 3290]] ,						dtype=tf.intaa   )  # The dog
								# fmt: off
								# The dog is a man's best friend. It is a loyal companion, and it is a friend
								_UpperCAmelCase =						[464, 3290, 318, 257, 582, 338, 1266, 1545, 13, 632, 318, 257, 9112, 15185, 11, 290, 340, 318, 257, 1545]
								# fmt: on
								_UpperCAmelCase =						model.generate(a_ ,						do_sample=a_   )
								self.assertListEqual(output_ids[0].numpy().tolist() ,						a_   )



							def       _a   (      self   )							->							Dict:
								_UpperCAmelCase =						AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B" ,						revision="float16"   )
								_UpperCAmelCase =						TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B" ,						revision="float16" ,						from_pt=a_   )

								_UpperCAmelCase =						tokenizer("Today is a nice day and" ,						return_tensors="tf"   )
								# forces the generation to happen on CPU, to avoid GPU-related quirks
								with tf.device(":/CPU:0"   ):
									_UpperCAmelCase =						model.generate(**a_ ,						do_sample=a_ ,						seed=[42, 0]   )
								_UpperCAmelCase =						tokenizer.decode(output_ids[0] ,						skip_special_tokens=a_   )

								_UpperCAmelCase =						"Today is a nice day and I’m going to go for a walk. I’"
								self.assertEqual(a_ ,						a_   )



							def       _a   (      self   )							->							int:
								_UpperCAmelCase =						TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B" ,						revision="float16" ,						from_pt=a_   )
								_UpperCAmelCase =						AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B" ,						revision="float16"   )

								_UpperCAmelCase =						"left"

								# Define PAD Token = EOS Token = 50256
								_UpperCAmelCase =						tokenizer.eos_token
								_UpperCAmelCase =						model.config.eos_token_id

								# use different length sentences to test batching
								_UpperCAmelCase =						[
								    "Hello, my dog is a little",
								    "Today, I",
								]
								_UpperCAmelCase =						[
								    "Hello, my dog is a little over a year old and has been diagnosed with hip dysplasia",
								    "Today, I’m going to be talking about a topic that’",
								]
								return model, tokenizer, sentences, expected_output_sentences



							def       _a   (      self   )							->							Optional[Any]:
								# Confirms that we get the expected results with left-padded beam search
								_UpperCAmelCase ,      _UpperCAmelCase ,      _UpperCAmelCase ,      _UpperCAmelCase =						self._get_beam_search_test_objects()

								_UpperCAmelCase =						tokenizer(a_ ,						return_tensors="tf" ,						padding=a_   )
								_UpperCAmelCase =						model.generate(**a_ ,						do_sample=a_ ,						num_beams=2   )
								_UpperCAmelCase =						tokenizer.batch_decode(a_ ,						skip_special_tokens=a_   )
								self.assertListEqual(a_ ,						a_   )



							def       _a   (      self   )							->							List[str]:
								# Confirms that left-padding is working properly
								_UpperCAmelCase ,      _UpperCAmelCase ,      _UpperCAmelCase ,      _UpperCAmelCase =						self._get_beam_search_test_objects()

								_UpperCAmelCase =						tokenizer(a_ ,						return_tensors="tf" ,						padding=a_   )
								_UpperCAmelCase =						tokenizer(sentences[0] ,						return_tensors="tf"   )
								_UpperCAmelCase =						model.generate(**a_ ,						do_sample=a_ ,						num_beams=2   )
								_UpperCAmelCase =						(
								    shape_list(inputs_non_padded["input_ids"]   )[-1]
								    - tf.reduce_sum(tf.cast(inputs["attention_mask"][-1] ,						tf.intaa   )   ).numpy()
								)
								_UpperCAmelCase =						tokenizer(sentences[1] ,						return_tensors="tf"   )
								_UpperCAmelCase =						model.generate(
								    **a_ ,						do_sample=a_ ,						num_beams=2 ,						max_length=model.config.max_length - num_paddings   )
								_UpperCAmelCase =						tokenizer.decode(output_non_padded[0] ,						skip_special_tokens=a_   )
								_UpperCAmelCase =						tokenizer.decode(output_padded[0] ,						skip_special_tokens=a_   )
								self.assertListEqual(a_ ,						[non_padded_sentence, padded_sentence]   )



							def       _a   (      self   )							->							str:
								# Confirms that XLA is working properly
								_UpperCAmelCase ,      _UpperCAmelCase ,      _UpperCAmelCase ,      _UpperCAmelCase =						self._get_beam_search_test_objects()

								_UpperCAmelCase =						tokenizer(a_ ,						return_tensors="tf" ,						padding=a_   )
								_UpperCAmelCase =						tf.function(model.generate ,						jit_compile=a_   )
								_UpperCAmelCase =						xla_generate(**a_ ,						do_sample=a_ ,						num_beams=2   )
								_UpperCAmelCase =						tokenizer.batch_decode(a_ ,						skip_special_tokens=a_   )
								self.assertListEqual(a_ ,						a_   )






"""simple docstring"""
# 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_bert/convert_megatron_bert_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 MegatronBertConfig




def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__=0      ):


	"""simple docstring"""






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

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




def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__      ):


	"""simple docstring"""






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




def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__      ):


	"""simple docstring"""






	_UpperCAmelCase =						{}

	# old versions did not store training args
	_UpperCAmelCase =						input_state_dict.get("args"				,     UpperCamelCase__      )
	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))

		_UpperCAmelCase =						ds_args.tokenizer_type
		_UpperCAmelCase =						ds_args.padded_vocab_size
		_UpperCAmelCase =						ds_args.max_position_embeddings
		_UpperCAmelCase =						ds_args.hidden_size
		_UpperCAmelCase =						ds_args.num_layers
		_UpperCAmelCase =						ds_args.num_attention_heads
		_UpperCAmelCase =						ds_args.ffn_hidden_size if "ffn_hidden_size" in ds_args else 4 * ds_args.hidden_size
		# pprint(config)

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

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

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

	# The position embeddings.
	_UpperCAmelCase =						embeddings["position_embeddings"]["weight"]
	assert pos_embeddings.size(0      ) == config.max_position_embeddings and pos_embeddings.size(1      ) == config.hidden_size
	# Store the position embeddings.
	_UpperCAmelCase =						pos_embeddings

	# The token-type embeddings.
	_UpperCAmelCase =						embeddings["tokentype_embeddings"]["weight"]
	# Store the position embeddings.
	_UpperCAmelCase =						tokentype_embeddings

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

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

	# The simple map of names for "automated" rules.
	_UpperCAmelCase =						{
	    "attention.dense": ".attention.output.dense.",
	    "self_attention.dense": ".attention.output.dense.",
	    "mlp.dense_h_to_4h": ".intermediate.dense.",
	    "mlp.dense_4h_to_h": ".output.dense.",
	}

	# Keep track of the attention/query/value tensor.
	_UpperCAmelCase =						None

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

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

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

		# The name of the layer.
		_UpperCAmelCase =						f"bert.encoder.layer.{layer_idx}"

		# For layernorm(s), simply store the layer norm.
		if op_name.endswith("layernorm"      ):
			_UpperCAmelCase =						"attention.ln" if op_name.startswith("input"      ) else "ln"
			_UpperCAmelCase =						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":
			# Make sure the QKV pointer is nil.
			assert attention_qkv_weight is None, ""

			_UpperCAmelCase =						fix_query_key_value_ordering(UpperCamelCase__				,     UpperCamelCase__				,     3				,     UpperCamelCase__				,     UpperCamelCase__      )
			# Store the tensor as we need the bias as well to interleave QKV and biases.
			_UpperCAmelCase =						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":
			# Make sure we read the weight tensor.
			assert attention_qkv_weight is not None, ""

			# Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
			_UpperCAmelCase =						attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
			_UpperCAmelCase =						attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
			_UpperCAmelCase =						attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

			_UpperCAmelCase =						fix_query_key_value_ordering(UpperCamelCase__				,     UpperCamelCase__				,     3				,     UpperCamelCase__				,     UpperCamelCase__      )
			# Split the bias.
			_UpperCAmelCase =						out_val[0 * config.hidden_size : 1 * config.hidden_size]
			_UpperCAmelCase =						out_val[1 * config.hidden_size : 2 * config.hidden_size]
			_UpperCAmelCase =						out_val[2 * config.hidden_size : 3 * config.hidden_size]

			# Store.
			_UpperCAmelCase =						q
			_UpperCAmelCase =						q_bias
			_UpperCAmelCase =						k
			_UpperCAmelCase =						k_bias
			_UpperCAmelCase =						v
			_UpperCAmelCase =						v_bias

			# Clear the stored tensor.
			_UpperCAmelCase =						None

		# Copy weights and biases as is.
		elif weight_or_bias in ["weight", "bias"]:
			_UpperCAmelCase =						megatron_to_transformers[op_name]
			_UpperCAmelCase =						val

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

	# The pooler.
	_UpperCAmelCase =						lm["pooler"]

	# Store the matrix and the bias.
	_UpperCAmelCase =						pooler["dense.weight"]
	_UpperCAmelCase =						pooler["dense.bias"]

	# The LM head from Megatron (for RACE).
	_UpperCAmelCase =						model["lm_head"]

	# The transform matrix.
	_UpperCAmelCase =						lm_head["dense.weight"]
	_UpperCAmelCase =						lm_head["dense.bias"]

	# The transform LN.
	_UpperCAmelCase =						lm_head["layernorm.weight"]
	_UpperCAmelCase =						lm_head["layernorm.bias"]

	# For the decoder, we replicate the weights.
	_UpperCAmelCase =						word_embeddings
	_UpperCAmelCase =						lm_head["bias"]

	# The classifier from Megatron (for MLNI).
	_UpperCAmelCase =						model["binary_head"]

	# Store the classifier.
	_UpperCAmelCase =						binary_head["weight"]
	_UpperCAmelCase =						binary_head["bias"]

	# It should be done!
	return output_state_dict




def     __lowerCamelCase  (		):


	"""simple docstring"""






	_UpperCAmelCase =						argparse.ArgumentParser()
	parser.add_argument("--print-checkpoint-structure"				,     action="store_true"      )
	parser.add_argument("path_to_checkpoint"				,     type=UpperCamelCase__				,     help="Path to the ZIP file containing the checkpoint"      )
	parser.add_argument(
	    "--config_file"				,     default=""				,     type=UpperCamelCase__				,     help="An optional config json file describing the pre-trained model."				,     )
	_UpperCAmelCase =						parser.parse_args()

	# Extract the basename.
	_UpperCAmelCase =						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:
				_UpperCAmelCase =						torch.load(UpperCamelCase__				,     map_location="cpu"      )
	else:
		_UpperCAmelCase =						torch.load(args.path_to_checkpoint				,     map_location="cpu"      )

	if args.config_file == "":
		# Default config of megatron-bert 345m
		_UpperCAmelCase =						MegatronBertConfig()

		# different megatron-bert-*-345m models have different vocab sizes, so override the default
		# config (which is for megatron-bert-cased-345m) with the actual vocab dimension
		_UpperCAmelCase =						input_state_dict["model"]["lm_head"]["bias"].numel()
	else:
		_UpperCAmelCase =						MegatronBertConfig.from_json_file(args.config_file      )

	# Convert.
	print("Converting"      )
	_UpperCAmelCase =						convert_megatron_checkpoint(UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__      )

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

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

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


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

if __name__ == "__main__":
			main()

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




