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

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

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

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Tuple						,  _lowerCamelCase :			Tuple						,  _lowerCamelCase :			List[Any]=0)					->    Union[str, Any]:






							'''simple docstring'''





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






							'''simple docstring'''





							__UpperCamelCase	:   Union[str, Any]			   =					param.size()
							if checkpoint_version == 1.0:
														# version 1.0 stores [num_heads * hidden_size * num_splits, :]
														__UpperCamelCase	:   Union[str, Any]			   =					(num_heads, hidden_size, num_splits) + input_shape[1:]
														__UpperCamelCase	:   Dict			   =					param.view(*_lowerCamelCase)
														__UpperCamelCase	:   str			   =					param.transpose(0						,  2)
														__UpperCamelCase	:   Any			   =					param.transpose(1						,  2).contiguous()
							elif checkpoint_version >= 2.0:
														# other versions store [num_heads * num_splits * hidden_size, :]
														__UpperCamelCase	:   Dict			   =					(num_heads, num_splits, hidden_size) + input_shape[1:]
														__UpperCamelCase	:   Optional[int]			   =					param.view(*_lowerCamelCase)
														__UpperCamelCase	:   List[str]			   =					param.transpose(0						,  1).contiguous()
							__UpperCamelCase	:   List[Any]			   =					param.view(*_lowerCamelCase)
							return param




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Dict						,  _lowerCamelCase :			Union[str, Any]						,  _lowerCamelCase :			Tuple)					->    Tuple:






							'''simple docstring'''





							__UpperCamelCase	:   Optional[Any]			   =					{}

							# old versions did not store training args
							__UpperCamelCase	:   Union[str, Any]			   =					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))

														__UpperCamelCase	:   Optional[Any]			   =					ds_args.padded_vocab_size
														__UpperCamelCase	:   List[Any]			   =					ds_args.max_position_embeddings
														__UpperCamelCase	:   Dict			   =					ds_args.hidden_size
														__UpperCamelCase	:   List[Any]			   =					ds_args.num_layers
														__UpperCamelCase	:   str			   =					ds_args.num_attention_heads
														__UpperCamelCase	:   str			   =					ds_args.ffn_hidden_size
														# pprint(config)

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

							# The model.
							__UpperCamelCase	:   Tuple			   =					input_state_dict['model']
							# The language model.
							__UpperCamelCase	:   List[Any]			   =					model['language_model']
							# The embeddings.
							__UpperCamelCase	:   Union[str, Any]			   =					lm['embedding']

							# The word embeddings.
							__UpperCamelCase	:   Optional[int]			   =					embeddings['word_embeddings']['weight']
							# Truncate the embedding table to vocab_size rows.
							__UpperCamelCase	:   Union[str, Any]			   =					word_embeddings[: config.vocab_size, :]
							__UpperCamelCase	:   Dict			   =					word_embeddings

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

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

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

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

							# Extract the layers.
							for key, val in transformer.items():
														# Match the name.
														__UpperCamelCase	:   Union[str, Any]			   =					layer_re.match(_lowerCamelCase)

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

														# The index of the layer.
														__UpperCamelCase	:   int			   =					int(m.group(1))
														# The name of the operation.
														__UpperCamelCase	:   List[Any]			   =					m.group(2)
														# Is it a weight or a bias?
														__UpperCamelCase	:   List[Any]			   =					m.group(3)

														# The name of the layer.
														__UpperCamelCase	:   Tuple			   =					F'transformer.h.{layer_idx}'

														# For layernorm(s), simply store the layer norm.
														if op_name.endswith("layernorm"):
																					__UpperCamelCase	:   Optional[Any]			   =					'ln_1' if op_name.startswith("input") else 'ln_2'
																					__UpperCamelCase	:   str			   =					val

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

																					# Insert a "dummy" tensor for masked_bias.
																					__UpperCamelCase	:   List[Any]			   =					torch.tensor(-1e4						,  dtype=torch.floataa)
																					__UpperCamelCase	:   Union[str, Any]			   =					masked_bias

																					__UpperCamelCase	:   List[Any]			   =					fix_query_key_value_ordering(_lowerCamelCase						,  _lowerCamelCase						,  3						,  _lowerCamelCase						,  _lowerCamelCase)
																					# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
																					__UpperCamelCase	:   Any			   =					out_val.transpose(0						,  1).contiguous()
																					# Store.
																					__UpperCamelCase	:   List[str]			   =					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":
																					__UpperCamelCase	:   Union[str, Any]			   =					fix_query_key_value_ordering(_lowerCamelCase						,  _lowerCamelCase						,  3						,  _lowerCamelCase						,  _lowerCamelCase)
																					# Store. No change of shape.
																					__UpperCamelCase	:   Optional[int]			   =					out_val

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

														# Copy the bias.
														elif weight_or_bias == "bias":
																					__UpperCamelCase	:   Union[str, Any]			   =					megatron_to_transformers[op_name]
																					__UpperCamelCase	:   Optional[int]			   =					val

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

							# The final layernorm.
							__UpperCamelCase	:   str			   =					transformer['final_layernorm.weight']
							__UpperCamelCase	:   str			   =					transformer['final_layernorm.bias']

							# For LM head, transformers' wants the matrix to weight embeddings.
							__UpperCamelCase	:   Optional[int]			   =					word_embeddings

							# It should be done!
							return output_state_dict




def 	_SCREAMING_SNAKE_CASE					(		)					->    Union[str, Any]:






							'''simple docstring'''





							__UpperCamelCase	:   Dict			   =					argparse.ArgumentParser()
							parser.add_argument("--print-checkpoint-structure"						,  action="store_true")
							parser.add_argument(
							    "path_to_checkpoint"						,  type=_lowerCamelCase						,  help="Path to the checkpoint file (.zip archive or direct .pt file)"						,  )
							parser.add_argument(
							    "--config_file"						,  default=""						,  type=_lowerCamelCase						,  help="An optional config json file describing the pre-trained model."						,  )
							__UpperCamelCase	:   List[Any]			   =					parser.parse_args()

							# Extract the basename.
							__UpperCamelCase	:   Dict			   =					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	:   List[str]			   =					torch.load(_lowerCamelCase						,  map_location="cpu")
							else:
														__UpperCamelCase	:   str			   =					torch.load(args.path_to_checkpoint						,  map_location="cpu")

							__UpperCamelCase	:   List[str]			   =					input_state_dict.get("args"						,  _lowerCamelCase)

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

														# Spell out all parameters in case the defaults change.
														__UpperCamelCase	:   str			   =					GPTaConfig(
														    vocab_size=50_257						,  n_positions=1_024						,  n_embd=1_024						,  n_layer=24						,  n_head=16						,  n_inner=4_096						,  activation_function=_lowerCamelCase						,  resid_pdrop=0.1						,  embd_pdrop=0.1						,  attn_pdrop=0.1						,  layer_norm_epsilon=1e-5						,  initializer_range=0.0_2						,  summary_type="cls_index"						,  summary_use_proj=_lowerCamelCase						,  summary_activation=_lowerCamelCase						,  summary_proj_to_labels=_lowerCamelCase						,  summary_first_dropout=0.1						,  scale_attn_weights=_lowerCamelCase						,  use_cache=_lowerCamelCase						,  bos_token_id=50_256						,  eos_token_id=50_256						,  )
							else:
														__UpperCamelCase	:   List[str]			   =					GPTaConfig.from_json_file(args.config_file)

							__UpperCamelCase	:   Optional[int]			   =					['GPT2LMHeadModel']

							# Convert.
							print("Converting")
							__UpperCamelCase	:   Tuple			   =					convert_megatron_checkpoint(_lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase)

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

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

							__UpperCamelCase	:   Tuple			   =					AutoTokenizer.from_pretrained(_lowerCamelCase)
							__UpperCamelCase	:   Union[str, Any]			   =					type(_lowerCamelCase).__name__
							__UpperCamelCase	:   List[str]			   =					tokenizer_class

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

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

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


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

if __name__ == "__main__":
		main()

####################################################################################################
import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
   import jax
   import jax.numpy as jnp
   from flax.jax_utils import replicate
   from flax.training.common_utils import shard





@slow
@require_flax
class 		_lowercase						(					unittest.TestCase       ):

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						str:

  """simple docstring"""





  super().tearDown()
  gc.collect()

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_, UpperCamelCase_					:						str =					FlaxStableDiffusionPipeline.from_pretrained(
      'stabilityai/stable-diffusion-2'   ,      revision='bf16'   ,      dtype=jnp.bfloataa   ,      )

  UpperCamelCase_					:						str =					'A painting of a squirrel eating a burger'
  UpperCamelCase_					:						Any =					jax.device_count()
  UpperCamelCase_					:						List[str] =					num_samples * [prompt]
  UpperCamelCase_					:						List[Any] =					sd_pipe.prepare_inputs(snake_case			)

  UpperCamelCase_					:						Dict =					replicate(snake_case			)
  UpperCamelCase_					:						Optional[Any] =					shard(snake_case			)

  UpperCamelCase_					:						Dict =					jax.random.PRNGKey(0			)
  UpperCamelCase_					:						Tuple =					jax.random.split(snake_case   ,      jax.device_count()			)

  UpperCamelCase_					:						Optional[Any] =					sd_pipe(snake_case   ,      snake_case   ,      snake_case   ,      num_inference_steps=2_5   ,      jit=snake_case			)[0]
  assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

  UpperCamelCase_					:						int =					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]			)
  UpperCamelCase_					:						Tuple =					images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

  UpperCamelCase_					:						str =					jnp.asarray(jax.device_get(image_slice.flatten()			)			)
  UpperCamelCase_					:						List[str] =					jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.45508, 0.4512]			)
  print(f"output_slice: {output_slice}"			)
  assert jnp.abs(output_slice - expected_slice			).max() < 1e-2


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					'stabilityai/stable-diffusion-2'
  UpperCamelCase_, UpperCamelCase_					:						Tuple =					FlaxDPMSolverMultistepScheduler.from_pretrained(snake_case   ,      subfolder='scheduler'			)
  UpperCamelCase_, UpperCamelCase_					:						Union[str, Any] =					FlaxStableDiffusionPipeline.from_pretrained(
      snake_case   ,      scheduler=snake_case   ,      revision='bf16'   ,      dtype=jnp.bfloataa   ,      )
  UpperCamelCase_					:						Optional[int] =					scheduler_params

  UpperCamelCase_					:						Optional[Any] =					'A painting of a squirrel eating a burger'
  UpperCamelCase_					:						Union[str, Any] =					jax.device_count()
  UpperCamelCase_					:						Union[str, Any] =					num_samples * [prompt]
  UpperCamelCase_					:						Tuple =					sd_pipe.prepare_inputs(snake_case			)

  UpperCamelCase_					:						List[Any] =					replicate(snake_case			)
  UpperCamelCase_					:						Optional[Any] =					shard(snake_case			)

  UpperCamelCase_					:						Tuple =					jax.random.PRNGKey(0			)
  UpperCamelCase_					:						str =					jax.random.split(snake_case   ,      jax.device_count()			)

  UpperCamelCase_					:						str =					sd_pipe(snake_case   ,      snake_case   ,      snake_case   ,      num_inference_steps=2_5   ,      jit=snake_case			)[0]
  assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

  UpperCamelCase_					:						str =					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]			)
  UpperCamelCase_					:						int =					images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

  UpperCamelCase_					:						int =					jnp.asarray(jax.device_get(image_slice.flatten()			)			)
  UpperCamelCase_					:						Union[str, Any] =					jnp.array([0.4336, 0.42969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297]			)
  print(f"output_slice: {output_slice}"			)
  assert jnp.abs(output_slice - expected_slice			).max() < 1e-2



