




import gc
import os
import tempfile
import unittest

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNetaDConditionModel
from diffusers.loaders import AttnProcsLayers, LoraLoaderMixin
from diffusers.models.attention_processor import (
    Attention,
    AttnProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
    XFormersAttnProcessor,
)
from diffusers.utils import TEXT_ENCODER_ATTN_MODULE, floats_tensor, torch_device


def   A		(__A  :       nn.Module			)  -> Union[str, Any]:







     """simple docstring"""


     UpperCAmelCase_		=				{}
     for name in unet.attn_processors.keys():
          UpperCAmelCase_		=				None if name.endswith('''attn1.processor'''			) else unet.config.cross_attention_dim
          if name.startswith('''mid_block'''			):
               UpperCAmelCase_		=				unet.config.block_out_channels[-1]
          elif name.startswith('''up_blocks'''			):
               UpperCAmelCase_		=				int(name[len('''up_blocks.'''			)]			)
               UpperCAmelCase_		=				list(reversed(unet.config.block_out_channels			)			)[block_id]
          elif name.startswith('''down_blocks'''			):
               UpperCAmelCase_		=				int(name[len('''down_blocks.'''			)]			)
               UpperCAmelCase_		=				unet.config.block_out_channels[block_id]
          UpperCAmelCase_		=				(
              LoRAAttnProcessor2_0 if hasattr(__A  ,							'''scaled_dot_product_attention'''			) else LoRAAttnProcessor
          )
          UpperCAmelCase_		=				lora_attn_processor_class(
              hidden_size=__A  ,							cross_attention_dim=__A			)
     UpperCAmelCase_		=				AttnProcsLayers(__A			)
     return lora_attn_procs, unet_lora_layers


def   A		(__A  :       nn.Module			)  -> Union[str, Any]:







     """simple docstring"""


     UpperCAmelCase_		=				{}
     UpperCAmelCase_		=				(
         LoRAAttnProcessor2_0 if hasattr(__A  ,							'''scaled_dot_product_attention'''			) else LoRAAttnProcessor
     )
     for name, module in text_encoder.named_modules():
          if name.endswith(__A			):
               UpperCAmelCase_		=				lora_attn_processor_class(
                   hidden_size=module.out_proj.out_features  ,							cross_attention_dim=__A			)
     return text_lora_attn_procs


def   A		(__A  :       nn.Module			)  -> int:







     """simple docstring"""


     UpperCAmelCase_		=				create_text_encoder_lora_attn_procs(__A			)
     UpperCAmelCase_		=				AttnProcsLayers(__A			)
     return text_encoder_lora_layers


def   A		(__A  :       Optional[Any]  ,							__A  :       Any=False			)  -> Union[str, Any]:







     """simple docstring"""


     for _, attn_proc in text_lora_attn_procs.items():
          # set up.weights
          for layer_name, layer_module in attn_proc.named_modules():
               if layer_name.endswith('''_lora'''			):
                    UpperCAmelCase_		=				(
                        torch.randn_like(layer_module.up.weight			)
                        if randn_weight
                        else torch.zeros_like(layer_module.up.weight			)
                    )
                    UpperCAmelCase_		=				torch.nn.Parameter(__A			)







class __snake_case     (     unittest.TestCase						):







   def lowerCamelCase		( self						:		Tuple):

        """simple docstring"""


        torch.manual_seed(0)
        UpperCAmelCase_		=				UNetaDConditionModel(
            block_out_channels=(32, 64)   ,		layers_per_block=2   ,		sample_size=32   ,		in_channels=4   ,		out_channels=4   ,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')   ,		up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')   ,		cross_attention_dim=32   ,		)
        UpperCAmelCase_		=				DDIMScheduler(
            beta_start=0.0_0_0_8_5   ,		beta_end=0.0_1_2   ,		beta_schedule='''scaled_linear'''   ,		clip_sample=_snake_case   ,		set_alpha_to_one=_snake_case   ,		steps_offset=1   ,		)
        torch.manual_seed(0)
        UpperCAmelCase_		=				AutoencoderKL(
            block_out_channels=[32, 64]   ,		in_channels=3   ,		out_channels=3   ,		down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']   ,		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']   ,		latent_channels=4   ,		)
        UpperCAmelCase_		=				CLIPTextConfig(
            bos_token_id=0   ,		eos_token_id=2   ,		hidden_size=32   ,		intermediate_size=37   ,		layer_norm_eps=1e-05   ,		num_attention_heads=4   ,		num_hidden_layers=5   ,		pad_token_id=1   ,		vocab_size=1000   ,		)
        UpperCAmelCase_		=				CLIPTextModel(_snake_case)
        UpperCAmelCase_		=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

        UpperCAmelCase_	,			UpperCAmelCase_		=				create_unet_lora_layers(_snake_case)
        UpperCAmelCase_		=				create_text_encoder_lora_layers(_snake_case)

        UpperCAmelCase_		=				{
            '''unet''': unet,
            '''scheduler''': scheduler,
            '''vae''': vae,
            '''text_encoder''': text_encoder,
            '''tokenizer''': tokenizer,
            '''safety_checker''': None,
            '''feature_extractor''': None,
        }
        UpperCAmelCase_		=				{
            '''unet_lora_layers''': unet_lora_layers,
            '''text_encoder_lora_layers''': text_encoder_lora_layers,
            '''unet_lora_attn_procs''': unet_lora_attn_procs,
        }
        return pipeline_components, lora_components







   def lowerCamelCase		( self						:		List[str]):

        """simple docstring"""


        UpperCAmelCase_		=				1
        UpperCAmelCase_		=				10
        UpperCAmelCase_		=				4
        UpperCAmelCase_		=				(32, 32)

        UpperCAmelCase_		=				torch.manual_seed(0)
        UpperCAmelCase_		=				floats_tensor((batch_size, num_channels) + sizes)
        UpperCAmelCase_		=				torch.randint(1   ,		_snake_case   ,		size=(batch_size, sequence_length)   ,		generator=_snake_case)

        UpperCAmelCase_		=				{
            '''prompt''': '''A painting of a squirrel eating a burger''',
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''guidance_scale''': 6.0,
            '''output_type''': '''numpy''',
        }

        return noise, input_ids, pipeline_inputs

        # copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb







   def lowerCamelCase		( self						:		Any):

        """simple docstring"""


        UpperCAmelCase_		=				77

        UpperCAmelCase_		=				torch.randint(2   ,		56   ,		size=(1, max_seq_length)   ,		generator=torch.manual_seed(0))

        UpperCAmelCase_		=				{}
        UpperCAmelCase_		=				inputs
        return prepared_inputs







   def lowerCamelCase		( self						:		int   ,		_snake_case						:		int):

        """simple docstring"""


        UpperCAmelCase_	,			UpperCAmelCase_		=				self.get_dummy_components()
        LoraLoaderMixin.save_lora_weights(
            save_directory=_snake_case   ,		unet_lora_layers=lora_components['''unet_lora_layers''']   ,		text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']   ,		)
        self.assertTrue(os.path.isfile(os.path.join(_snake_case   ,		'''pytorch_lora_weights.bin''')))







   def lowerCamelCase		( self						:		List[Any]):

        """simple docstring"""


        UpperCAmelCase_	,			UpperCAmelCase_		=				self.get_dummy_components()
        UpperCAmelCase_		=				StableDiffusionPipeline(**_snake_case)
        UpperCAmelCase_		=				sd_pipe.to(_snake_case)
        sd_pipe.set_progress_bar_config(disable=_snake_case)

        UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_		=				self.get_dummy_inputs()

        UpperCAmelCase_		=				sd_pipe(**_snake_case).images
        UpperCAmelCase_		=				original_images[0, -3:, -3:, -1]

        with tempfile.TemporaryDirectory() as tmpdirname:
             LoraLoaderMixin.save_lora_weights(
                 save_directory=_snake_case   ,		unet_lora_layers=lora_components['''unet_lora_layers''']   ,		text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']   ,		)
             self.assertTrue(os.path.isfile(os.path.join(_snake_case   ,		'''pytorch_lora_weights.bin''')))
             sd_pipe.load_lora_weights(_snake_case)

        UpperCAmelCase_		=				sd_pipe(**_snake_case).images
        UpperCAmelCase_		=				lora_images[0, -3:, -3:, -1]

        # Outputs shouldn't match.
        self.assertFalse(torch.allclose(torch.from_numpy(_snake_case)   ,		torch.from_numpy(_snake_case)))







   def lowerCamelCase		( self						:		Any):

        """simple docstring"""


        UpperCAmelCase_	,			UpperCAmelCase_		=				self.get_dummy_components()
        UpperCAmelCase_		=				StableDiffusionPipeline(**_snake_case)
        UpperCAmelCase_		=				sd_pipe.to(_snake_case)
        sd_pipe.set_progress_bar_config(disable=_snake_case)

        UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_		=				self.get_dummy_inputs()

        UpperCAmelCase_		=				sd_pipe(**_snake_case).images
        UpperCAmelCase_		=				original_images[0, -3:, -3:, -1]

        with tempfile.TemporaryDirectory() as tmpdirname:
             LoraLoaderMixin.save_lora_weights(
                 save_directory=_snake_case   ,		unet_lora_layers=lora_components['''unet_lora_layers''']   ,		text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']   ,		safe_serialization=_snake_case   ,		)
             self.assertTrue(os.path.isfile(os.path.join(_snake_case   ,		'''pytorch_lora_weights.safetensors''')))
             sd_pipe.load_lora_weights(_snake_case)

        UpperCAmelCase_		=				sd_pipe(**_snake_case).images
        UpperCAmelCase_		=				lora_images[0, -3:, -3:, -1]

        # Outputs shouldn't match.
        self.assertFalse(torch.allclose(torch.from_numpy(_snake_case)   ,		torch.from_numpy(_snake_case)))







   def lowerCamelCase		( self						:		Any):

        """simple docstring"""


        UpperCAmelCase_	,			UpperCAmelCase_		=				self.get_dummy_components()
        UpperCAmelCase_		=				lora_components['''unet_lora_attn_procs''']
        UpperCAmelCase_		=				StableDiffusionPipeline(**_snake_case)
        UpperCAmelCase_		=				sd_pipe.to(_snake_case)
        sd_pipe.set_progress_bar_config(disable=_snake_case)

        UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_		=				self.get_dummy_inputs()

        UpperCAmelCase_		=				sd_pipe(**_snake_case).images
        UpperCAmelCase_		=				original_images[0, -3:, -3:, -1]

        with tempfile.TemporaryDirectory() as tmpdirname:
             UpperCAmelCase_		=				sd_pipe.unet
             unet.set_attn_processor(_snake_case)
             unet.save_attn_procs(_snake_case)
             self.assertTrue(os.path.isfile(os.path.join(_snake_case   ,		'''pytorch_lora_weights.bin''')))
             sd_pipe.load_lora_weights(_snake_case)

        UpperCAmelCase_		=				sd_pipe(**_snake_case).images
        UpperCAmelCase_		=				lora_images[0, -3:, -3:, -1]

        # Outputs shouldn't match.
        self.assertFalse(torch.allclose(torch.from_numpy(_snake_case)   ,		torch.from_numpy(_snake_case)))







   def lowerCamelCase		( self						:		int):

        """simple docstring"""


        UpperCAmelCase_	,			UpperCAmelCase_		=				self.get_dummy_components()
        UpperCAmelCase_		=				StableDiffusionPipeline(**_snake_case)

        UpperCAmelCase_		=				self.get_dummy_tokens()

        # inference without lora
        UpperCAmelCase_		=				pipe.text_encoder(**_snake_case)[0]
        assert outputs_without_lora.shape == (1, 77, 32)

        # create lora_attn_procs with zeroed out up.weights
        UpperCAmelCase_		=				create_text_encoder_lora_attn_procs(pipe.text_encoder)
        set_lora_up_weights(_snake_case   ,		randn_weight=_snake_case)

        # monkey patch
        pipe._modify_text_encoder(_snake_case)

        # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
        del text_attn_procs
        gc.collect()

        # inference with lora
        UpperCAmelCase_		=				pipe.text_encoder(**_snake_case)[0]
        assert outputs_with_lora.shape == (1, 77, 32)

        assert torch.allclose(
            _snake_case   ,		_snake_case), "lora_up_weight are all zero, so the lora outputs should be the same to without lora outputs"

        # create lora_attn_procs with randn up.weights
        UpperCAmelCase_		=				create_text_encoder_lora_attn_procs(pipe.text_encoder)
        set_lora_up_weights(_snake_case   ,		randn_weight=_snake_case)

        # monkey patch
        pipe._modify_text_encoder(_snake_case)

        # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
        del text_attn_procs
        gc.collect()

        # inference with lora
        UpperCAmelCase_		=				pipe.text_encoder(**_snake_case)[0]
        assert outputs_with_lora.shape == (1, 77, 32)

        assert not torch.allclose(
            _snake_case   ,		_snake_case), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"







   def lowerCamelCase		( self						:		int):

        """simple docstring"""


        UpperCAmelCase_	,			UpperCAmelCase_		=				self.get_dummy_components()
        UpperCAmelCase_		=				StableDiffusionPipeline(**_snake_case)

        UpperCAmelCase_		=				self.get_dummy_tokens()

        # inference without lora
        UpperCAmelCase_		=				pipe.text_encoder(**_snake_case)[0]
        assert outputs_without_lora.shape == (1, 77, 32)

        # create lora_attn_procs with randn up.weights
        UpperCAmelCase_		=				create_text_encoder_lora_attn_procs(pipe.text_encoder)
        set_lora_up_weights(_snake_case   ,		randn_weight=_snake_case)

        # monkey patch
        pipe._modify_text_encoder(_snake_case)

        # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
        del text_attn_procs
        gc.collect()

        # inference with lora
        UpperCAmelCase_		=				pipe.text_encoder(**_snake_case)[0]
        assert outputs_with_lora.shape == (1, 77, 32)

        assert not torch.allclose(
            _snake_case   ,		_snake_case), "lora outputs should be different to without lora outputs"

        # remove monkey patch
        pipe._remove_text_encoder_monkey_patch()

        # inference with removed lora
        UpperCAmelCase_		=				pipe.text_encoder(**_snake_case)[0]
        assert outputs_without_lora_removed.shape == (1, 77, 32)

        assert torch.allclose(
            _snake_case   ,		_snake_case), "remove lora monkey patch should restore the original outputs"







   def lowerCamelCase		( self						:		Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_	,			UpperCAmelCase_		=				self.get_dummy_components()
        UpperCAmelCase_		=				StableDiffusionPipeline(**_snake_case)
        UpperCAmelCase_		=				sd_pipe.to(_snake_case)
        sd_pipe.set_progress_bar_config(disable=_snake_case)

        UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_		=				self.get_dummy_inputs()

        with tempfile.TemporaryDirectory() as tmpdirname:
             LoraLoaderMixin.save_lora_weights(
                 save_directory=_snake_case   ,		unet_lora_layers=lora_components['''unet_lora_layers''']   ,		text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']   ,		)
             self.assertTrue(os.path.isfile(os.path.join(_snake_case   ,		'''pytorch_lora_weights.bin''')))
             sd_pipe.load_lora_weights(_snake_case)

        UpperCAmelCase_		=				sd_pipe(**_snake_case).images
        UpperCAmelCase_		=				lora_images[0, -3:, -3:, -1]

        UpperCAmelCase_		=				sd_pipe(**_snake_case   ,		cross_attention_kwargs={'''scale''': 0.5}).images
        UpperCAmelCase_		=				lora_images_with_scale[0, -3:, -3:, -1]

        # Outputs shouldn't match.
        self.assertFalse(
            torch.allclose(torch.from_numpy(_snake_case)   ,		torch.from_numpy(_snake_case)))







   def lowerCamelCase		( self						:		Optional[int]):

        """simple docstring"""


        with tempfile.TemporaryDirectory() as tmpdirname:
             self.create_lora_weight_file(_snake_case)

             UpperCAmelCase_	,			UpperCAmelCase_		=				self.get_dummy_components()
             UpperCAmelCase_		=				StableDiffusionPipeline(**_snake_case)
             UpperCAmelCase_		=				sd_pipe.to(_snake_case)
             sd_pipe.set_progress_bar_config(disable=_snake_case)

             # check if vanilla attention processors are used
             for _, module in sd_pipe.unet.named_modules():
                  if isinstance(_snake_case   ,		_snake_case):
                       self.assertIsInstance(module.processor   ,		(AttnProcessor, AttnProcessor2_0))

            # load LoRA weight file
             sd_pipe.load_lora_weights(_snake_case)

             # check if lora attention processors are used
             for _, module in sd_pipe.unet.named_modules():
                  if isinstance(_snake_case   ,		_snake_case):
                       UpperCAmelCase_		=				(
                           LoRAAttnProcessor2_0 if hasattr(_snake_case   ,		'''scaled_dot_product_attention''') else LoRAAttnProcessor
                       )
                       self.assertIsInstance(module.processor   ,		_snake_case)







   @unittest.skipIf(torch_device != '''cuda'''   ,		'''This test is supposed to run on GPU''')
   def lowerCamelCase		( self						:		Any):

        """simple docstring"""


        with tempfile.TemporaryDirectory() as tmpdirname:
             self.create_lora_weight_file(_snake_case)

             UpperCAmelCase_	,			UpperCAmelCase_		=				self.get_dummy_components()
             UpperCAmelCase_		=				StableDiffusionPipeline(**_snake_case)
             UpperCAmelCase_		=				sd_pipe.to(_snake_case)
             sd_pipe.set_progress_bar_config(disable=_snake_case)

             # enable XFormers
             sd_pipe.enable_xformers_memory_efficient_attention()

             # check if xFormers attention processors are used
             for _, module in sd_pipe.unet.named_modules():
                  if isinstance(_snake_case   ,		_snake_case):
                       self.assertIsInstance(module.processor   ,		_snake_case)

            # load LoRA weight file
             sd_pipe.load_lora_weights(_snake_case)

             # check if lora attention processors are used
             for _, module in sd_pipe.unet.named_modules():
                  if isinstance(_snake_case   ,		_snake_case):
                       self.assertIsInstance(module.processor   ,		_snake_case)







   @unittest.skipIf(torch_device != '''cuda'''   ,		'''This test is supposed to run on GPU''')
   def lowerCamelCase		( self						:		Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_	,			UpperCAmelCase_		=				self.get_dummy_components()
        UpperCAmelCase_		=				StableDiffusionPipeline(**_snake_case)
        UpperCAmelCase_		=				sd_pipe.to(_snake_case)
        sd_pipe.set_progress_bar_config(disable=_snake_case)

        UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_		=				self.get_dummy_inputs()

        # enable XFormers
        sd_pipe.enable_xformers_memory_efficient_attention()

        UpperCAmelCase_		=				sd_pipe(**_snake_case).images
        UpperCAmelCase_		=				original_images[0, -3:, -3:, -1]

        with tempfile.TemporaryDirectory() as tmpdirname:
             LoraLoaderMixin.save_lora_weights(
                 save_directory=_snake_case   ,		unet_lora_layers=lora_components['''unet_lora_layers''']   ,		text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']   ,		)
             self.assertTrue(os.path.isfile(os.path.join(_snake_case   ,		'''pytorch_lora_weights.bin''')))
             sd_pipe.load_lora_weights(_snake_case)

        UpperCAmelCase_		=				sd_pipe(**_snake_case).images
        UpperCAmelCase_		=				lora_images[0, -3:, -3:, -1]

        # Outputs shouldn't match.
        self.assertFalse(torch.allclose(torch.from_numpy(_snake_case)   ,		torch.from_numpy(_snake_case)))












# 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   A		(__A  :       List[Any]  ,							__A  :       str  ,							__A  :       Optional[int]=0			)  -> Dict:







     """simple docstring"""


     if name is None:
          UpperCAmelCase_		=				None
     else:
          UpperCAmelCase_		=				'''.''' * max(0  ,							spaces - 2			) + '''# {:''' + str(50 - spaces			) + '''s}'''
          UpperCAmelCase_		=				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		(__A  :       Any  ,							__A  :       str  ,							__A  :       Tuple  ,							__A  :       Optional[int]  ,							__A  :       Any			)  -> Union[str, Any]:







     """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(*__A			)
          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(*__A			)
          UpperCAmelCase_		=				param.transpose(0  ,							1			).contiguous()
     UpperCAmelCase_		=				param.view(*__A			)
     return param


def   A		(__A  :       Optional[Any]  ,							__A  :       Tuple  ,							__A  :       Optional[int]			)  -> Union[str, Any]:







     """simple docstring"""


     UpperCAmelCase_		=				{}

     # old versions did not store training args
     UpperCAmelCase_		=				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))

          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(__A			)

          # 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(__A  ,							__A  ,							3  ,							__A  ,							__A			)
               # 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(__A  ,							__A  ,							3  ,							__A  ,							__A			)
               # 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   A		()  -> Dict:







     """simple docstring"""


     UpperCAmelCase_		=				argparse.ArgumentParser()
     parser.add_argument('''--print-checkpoint-structure'''  ,							action='''store_true'''			)
     parser.add_argument('''path_to_checkpoint'''  ,							type=__A  ,							help='''Path to the ZIP file containing the checkpoint'''			)
     parser.add_argument(
         '''--config_file'''  ,							default=''''''  ,							type=__A  ,							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(__A  ,							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(__A  ,							__A  ,							__A			)

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

     # Store the config to file.
     print('''Saving config'''			)
     config.save_pretrained(__A			)

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


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

if __name__ == "__main__":
    main()

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






