
# 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 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__=0						):
					'''simple docstring'''
					# Format the message.
					if name is None:
										lowercase		      =		None
					else:
										lowercase		      =		'''.''' * max(0					,     spaces - 2						) + '''# {:''' + str(50 - spaces						) + '''s}'''
										lowercase		      =		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 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__						):
					'''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 BERT.
					lowercase		      =		param.size()
					if checkpoint_version == 1.0:
										# version 1.0 stores [num_heads * hidden_size * num_splits, :]
										lowercase		      =		(num_heads, hidden_size, num_splits) + input_shape[1:]
										lowercase		      =		param.view(*lowerCAmelCase__						)
										lowercase		      =		param.transpose(0					,     2						)
										lowercase		      =		param.transpose(1					,     2						).contiguous()
					elif checkpoint_version >= 2.0:
										# other versions store [num_heads * num_splits * hidden_size, :]
										lowercase		      =		(num_heads, num_splits, hidden_size) + input_shape[1:]
										lowercase		      =		param.view(*lowerCAmelCase__						)
										lowercase		      =		param.transpose(0					,     1						).contiguous()
					lowercase		      =		param.view(*lowerCAmelCase__						)
					return param



def 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__						):
					'''simple docstring'''
					# The converted output model.
					lowercase		      =		{}

					# old versions did not store training args
					lowercase		      =		input_state_dict.get('''args'''					,     lowerCAmelCase__						)
					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))

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

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

					# The model.
					lowercase		      =		input_state_dict['''model''']
					# The language model.
					lowercase		      =		model['''language_model''']
					# The embeddings.
					lowercase		      =		lm['''embedding''']

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

					# The position embeddings.
					lowercase		      =		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.
					lowercase		      =		pos_embeddings

					# The token-type embeddings.
					lowercase		      =		embeddings['''tokentype_embeddings''']['''weight''']
					# Store the position embeddings.
					lowercase		      =		tokentype_embeddings

					# The transformer.
					lowercase		      =		lm['''transformer'''] if '''transformer''' in lm.keys() else lm['''encoder''']

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

					# The simple map of names for "automated" rules.
					lowercase		      =		{
					    '''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.
					lowercase		      =		None

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

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

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

										# The name of the layer.
										lowercase		      =		f'bert.encoder.layer.{layer_idx}'

										# For layernorm(s), simply store the layer norm.
										if op_name.endswith('''layernorm'''						):
															lowercase		      =		'''attention.ln''' if op_name.startswith('''input'''						) else '''ln'''
															lowercase		      =		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, ""

															lowercase		      =		fix_query_key_value_ordering(lowerCAmelCase__					,     lowerCAmelCase__					,     3					,     lowerCAmelCase__					,     lowerCAmelCase__						)
															# Store the tensor as we need the bias as well to interleave QKV and biases.
															lowercase		      =		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.
															lowercase		      =		attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
															lowercase		      =		attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
															lowercase		      =		attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

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

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

															# Clear the stored tensor.
															lowercase		      =		None

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

    # The final layernorm.
					lowercase		      =		transformer['''final_layernorm.weight''']
					lowercase		      =		transformer['''final_layernorm.bias''']

					# The pooler.
					lowercase		      =		lm['''pooler''']

					# Store the matrix and the bias.
					lowercase		      =		pooler['''dense.weight''']
					lowercase		      =		pooler['''dense.bias''']

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

					# The transform matrix.
					lowercase		      =		lm_head['''dense.weight''']
					lowercase		      =		lm_head['''dense.bias''']

					# The transform LN.
					lowercase		      =		lm_head['''layernorm.weight''']
					lowercase		      =		lm_head['''layernorm.bias''']

					# For the decoder, we replicate the weights.
					lowercase		      =		word_embeddings
					lowercase		      =		lm_head['''bias''']

					# The classifier from Megatron (for MLNI).
					lowercase		      =		model['''binary_head''']

					# Store the classifier.
					lowercase		      =		binary_head['''weight''']
					lowercase		      =		binary_head['''bias''']

					# It should be done!
					return output_state_dict



def 			UpperCamelCase       (				):
					'''simple docstring'''
					# Create the argument parser.
					lowercase		      =		argparse.ArgumentParser()
					parser.add_argument('''--print-checkpoint-structure'''					,     action='''store_true'''						)
					parser.add_argument('''path_to_checkpoint'''					,     type=lowerCAmelCase__					,     help='''Path to the ZIP file containing the checkpoint'''						)
					parser.add_argument(
					    '''--config_file'''					,     default=''''''					,     type=lowerCAmelCase__					,     help='''An optional config json file describing the pre-trained model.'''					,     )
					lowercase		      =		parser.parse_args()

					# Extract the basename.
					lowercase		      =		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:
																				lowercase		      =		torch.load(lowerCAmelCase__					,     map_location='''cpu'''						)
					else:
										lowercase		      =		torch.load(args.path_to_checkpoint					,     map_location='''cpu'''						)

					if args.config_file == "":
										# Default config of megatron-bert 345m
										lowercase		      =		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
										lowercase		      =		input_state_dict['''model''']['''lm_head''']['''bias'''].numel()
					else:
										lowercase		      =		MegatronBertConfig.from_json_file(args.config_file						)

					# Convert.
					print('''Converting'''						)
					lowercase		      =		convert_megatron_checkpoint(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__						)

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

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

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


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

if __name__ == "__main__":
							main()

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






import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference







@slow
@require_torch_gpu
class 							lowercase						(   unittest.TestCase						):




							def 					A__ (							self):
												lowercase		      =		'''runwayml/stable-diffusion-v1-5'''
												lowercase		      =		TextToVideoZeroPipeline.from_pretrained(A__  ,torch_dtype=torch.floataa).to('''cuda''')
												lowercase		      =		DDIMScheduler.from_config(pipe.scheduler.config)
												lowercase		      =		torch.Generator(device='''cuda''').manual_seed(0)

												lowercase		      =		'''A bear is playing a guitar on Times Square'''
												lowercase		      =		pipe(prompt=A__  ,generator=A__).images

												lowercase		      =		load_pt(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt''')

												assert_mean_pixel_difference(A__  ,A__)




