


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







def   lowerCamelCase(				a__		,a__		,a__):
				# The converted output model.
				_SCREAMING_SNAKE_CASE     					={}

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

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

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

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

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

				# The position embeddings.
				_SCREAMING_SNAKE_CASE     					=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.
				_SCREAMING_SNAKE_CASE     					=pos_embeddings

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

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

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

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

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

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

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

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

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

												_SCREAMING_SNAKE_CASE     					=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.
												_SCREAMING_SNAKE_CASE     					=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.
												_SCREAMING_SNAKE_CASE     					=attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
												_SCREAMING_SNAKE_CASE     					=attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
												_SCREAMING_SNAKE_CASE     					=attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

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

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

												# Clear the stored tensor.
												_SCREAMING_SNAKE_CASE     					=None

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

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

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

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

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

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

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

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

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

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

				# It should be done!
				return output_state_dict







def   lowerCamelCase(				):
				# Create the argument parser.
				_SCREAMING_SNAKE_CASE     					=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.'''		,)
				_SCREAMING_SNAKE_CASE     					=parser.parse_args()

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

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

				# Convert.
				print('''Converting''')
				_SCREAMING_SNAKE_CASE     					=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.
				_SCREAMING_SNAKE_CASE     					=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
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class   A__							(						UpperCamelCase__				,       unittest.TestCase					):
						UpperCAmelCase     =  KandinskyPriorPipeline
						UpperCAmelCase     =  ["prompt"]
						UpperCAmelCase     =  ["prompt", "negative_prompt"]
						UpperCAmelCase     =  [
						    "num_images_per_prompt",
						    "generator",
						    "num_inference_steps",
						    "latents",
						    "negative_prompt",
						    "guidance_scale",
						    "output_type",
						    "return_dict",
						]
						UpperCAmelCase     =  False




						@property
						def 							__UpperCamelCase			(    self   : Tuple					)							->       Tuple:


										"""simple docstring"""





										return 32




						@property
						def 							__UpperCamelCase			(    self   : Optional[int]					)							->       Tuple:


										"""simple docstring"""





										return 32




						@property
						def 							__UpperCamelCase			(    self   : Tuple					)							->       int:


										"""simple docstring"""





										return self.time_input_dim




						@property
						def 							__UpperCamelCase			(    self   : Optional[Any]					)							->       Any:


										"""simple docstring"""





										return self.time_input_dim * 4




						@property
						def 							__UpperCamelCase			(    self   : List[str]					)							->       str:


										"""simple docstring"""





										return 100




						@property
						def 							__UpperCamelCase			(    self   : List[Any]					)							->       List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)
										return tokenizer




						@property
						def 							__UpperCamelCase			(    self   : Any					)							->       Dict:


										"""simple docstring"""





										torch.manual_seed(0					)
										_SCREAMING_SNAKE_CASE     					=CLIPTextConfig(
										    bos_token_id=0			,      eos_token_id=2			,      hidden_size=self.text_embedder_hidden_size			,      projection_dim=self.text_embedder_hidden_size			,      intermediate_size=37			,      layer_norm_eps=1E-05			,      num_attention_heads=4			,      num_hidden_layers=5			,      pad_token_id=1			,      vocab_size=1000			,      )
										return CLIPTextModelWithProjection(_a					)




						@property
						def 							__UpperCamelCase			(    self   : int					)							->       Any:


										"""simple docstring"""





										torch.manual_seed(0					)

										_SCREAMING_SNAKE_CASE     					={
										    '''num_attention_heads''': 2,
										    '''attention_head_dim''': 12,
										    '''embedding_dim''': self.text_embedder_hidden_size,
										    '''num_layers''': 1,
										}

										_SCREAMING_SNAKE_CASE     					=PriorTransformer(**_a					)
										# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
										_SCREAMING_SNAKE_CASE     					=nn.Parameter(torch.ones(model.clip_std.shape					)					)
										return model




						@property
						def 							__UpperCamelCase			(    self   : List[str]					)							->       Optional[Any]:


										"""simple docstring"""





										torch.manual_seed(0					)
										_SCREAMING_SNAKE_CASE     					=CLIPVisionConfig(
										    hidden_size=self.text_embedder_hidden_size			,      image_size=224			,      projection_dim=self.text_embedder_hidden_size			,      intermediate_size=37			,      num_attention_heads=4			,      num_channels=3			,      num_hidden_layers=5			,      patch_size=14			,      )

										_SCREAMING_SNAKE_CASE     					=CLIPVisionModelWithProjection(_a					)
										return model




						@property
						def 							__UpperCamelCase			(    self   : Any					)							->       int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=CLIPImageProcessor(
										    crop_size=224			,      do_center_crop=_a			,      do_normalize=_a			,      do_resize=_a			,      image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]			,      image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]			,      resample=3			,      size=224			,      )

										return image_processor




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=self.dummy_prior
										_SCREAMING_SNAKE_CASE     					=self.dummy_image_encoder
										_SCREAMING_SNAKE_CASE     					=self.dummy_text_encoder
										_SCREAMING_SNAKE_CASE     					=self.dummy_tokenizer
										_SCREAMING_SNAKE_CASE     					=self.dummy_image_processor

										_SCREAMING_SNAKE_CASE     					=UnCLIPScheduler(
										    variance_type='''fixed_small_log'''			,      prediction_type='''sample'''			,      num_train_timesteps=1000			,      clip_sample=_a			,      clip_sample_range=10.0			,      )

										_SCREAMING_SNAKE_CASE     					={
										    '''prior''': prior,
										    '''image_encoder''': image_encoder,
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''scheduler''': scheduler,
										    '''image_processor''': image_processor,
										}

										return components




						def 							__UpperCamelCase			(    self   : Optional[Any]			,      _a   : Tuple			,      _a   : str=0					)							->       Any:


										"""simple docstring"""





										if str(_a					).startswith('''mps'''					):
														_SCREAMING_SNAKE_CASE     					=torch.manual_seed(_a					)
										else:
														_SCREAMING_SNAKE_CASE     					=torch.Generator(device=_a					).manual_seed(_a					)
										_SCREAMING_SNAKE_CASE     					={
										    '''prompt''': '''horse''',
										    '''generator''': generator,
										    '''guidance_scale''': 4.0,
										    '''num_inference_steps''': 2,
										    '''output_type''': '''np''',
										}
										return inputs




						def 							__UpperCamelCase			(    self   : Optional[int]					)							->       Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					='''cpu'''

										_SCREAMING_SNAKE_CASE     					=self.get_dummy_components()

										_SCREAMING_SNAKE_CASE     					=self.pipeline_class(**_a					)
										_SCREAMING_SNAKE_CASE     					=pipe.to(_a					)

										pipe.set_progress_bar_config(disable=_a					)

										_SCREAMING_SNAKE_CASE     					=pipe(**self.get_dummy_inputs(_a					)					)
										_SCREAMING_SNAKE_CASE     					=output.image_embeds

										_SCREAMING_SNAKE_CASE     					=pipe(
										    **self.get_dummy_inputs(_a					)			,      return_dict=_a			,      )[0]

										_SCREAMING_SNAKE_CASE     					=image[0, -10:]
										_SCREAMING_SNAKE_CASE     					=image_from_tuple[0, -10:]

										assert image.shape == (1, 32)

										_SCREAMING_SNAKE_CASE     					=np.array(
										    [-0.05_32, 1.71_20, 0.36_56, -1.08_52, -0.89_46, -1.17_56, 0.43_48, 0.24_82, 0.51_46, -0.11_56]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1E-2




						@skip_mps
						def 							__UpperCamelCase			(    self   : List[str]					)							->       List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=torch_device == '''cpu'''
										_SCREAMING_SNAKE_CASE     					=True
										_SCREAMING_SNAKE_CASE     					=False

										self._test_inference_batch_single_identical(
										    test_max_difference=_a			,      relax_max_difference=_a			,      test_mean_pixel_difference=_a			,      )




						@skip_mps
						def 							__UpperCamelCase			(    self   : List[Any]					)							->       str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=torch_device == '''cpu'''
										_SCREAMING_SNAKE_CASE     					=False

										self._test_attention_slicing_forward_pass(
										    test_max_difference=_a			,      test_mean_pixel_difference=_a			,      )