






"""simple docstring"""




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

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

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_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 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__=0				):



  '''simple docstring'''
  # Format the message.
  if name is None:
    _a  :       Any             =	None
  else:
    _a  :       int             =	""".""" * max(0     ,   spaces - 2				) + """# {:""" + str(5_0 - spaces				) + """s}"""
    _a  :       Optional[int]             =	fmt.format(UpperCamelCase__				)

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

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  # 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 GPT2.
  _a  :       List[Any]             =	param.size()
  if checkpoint_version == 1.0:
    # version 1.0 stores [num_heads * hidden_size * num_splits, :]
    _a  :       Union[str, Any]             =	(num_heads, hidden_size, num_splits) + input_shape[1:]
    _a  :       Union[str, Any]             =	param.view(*UpperCamelCase__				)
    _a  :       Optional[int]             =	param.transpose(0     ,   2				)
    _a  :       Union[str, Any]             =	param.transpose(1     ,   2				).contiguous()
  elif checkpoint_version >= 2.0:
    # other versions store [num_heads * num_splits * hidden_size, :]
    _a  :       List[Any]             =	(num_heads, num_splits, hidden_size) + input_shape[1:]
    _a  :       List[str]             =	param.view(*UpperCamelCase__				)
    _a  :       int             =	param.transpose(0     ,   1				).contiguous()
  _a  :       Any             =	param.view(*UpperCamelCase__				)
  return param

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  # The converted output model.
  _a  :       Any             =	{}

  # old versions did not store training args
  _a  :       int             =	input_state_dict.get("""args"""     ,   UpperCamelCase__				)
  if ds_args is not None:
    # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
    # from pprint import pprint
    # pprint(vars(ds_args))

    _a  :       List[str]             =	ds_args.padded_vocab_size
    _a  :       Tuple             =	ds_args.max_position_embeddings
    _a  :       Union[str, Any]             =	ds_args.hidden_size
    _a  :       List[str]             =	ds_args.num_layers
    _a  :       Any             =	ds_args.num_attention_heads
    _a  :       List[str]             =	ds_args.ffn_hidden_size
    # pprint(config)

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

  # The model.
  _a  :       Tuple             =	input_state_dict["""model"""]
  # The language model.
  _a  :       Dict             =	model["""language_model"""]
  # The embeddings.
  _a  :       Tuple             =	lm["""embedding"""]

  # The word embeddings.
  _a  :       Dict             =	embeddings["""word_embeddings"""]["""weight"""]
  # Truncate the embedding table to vocab_size rows.
  _a  :       List[Any]             =	word_embeddings[: config.vocab_size, :]
  _a  :       Optional[Any]             =	word_embeddings

  # The position embeddings.
  _a  :       Any             =	embeddings["""position_embeddings"""]["""weight"""]
  # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
  _a  :       int             =	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.
  _a  :       Optional[Any]             =	pos_embeddings

  # The transformer.
  _a  :       Union[str, Any]             =	lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]

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

  # The simple map of names for "automated" rules.
  _a  :       List[str]             =	{
      """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.
    _a  :       Dict             =	layer_re.match(UpperCamelCase__				)

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

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

    # The name of the layer.
    _a  :       str             =	F"""transformer.h.{layer_idx}"""

    # For layernorm(s), simply store the layer norm.
    if op_name.endswith("""layernorm"""				):
      _a  :       Union[str, Any]             =	"""ln_1""" if op_name.startswith("""input"""				) else """ln_2"""
      _a  :       List[Any]             =	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.
      _a  :       str             =	torch.tril(torch.ones((n_positions, n_positions)     ,   dtype=torch.floataa				)				).view(
          1     ,   1     ,   UpperCamelCase__     ,   UpperCamelCase__				)
      _a  :       List[str]             =	causal_mask

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

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

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

    # Copy the bias.
    elif weight_or_bias == "bias":
      _a  :       int             =	megatron_to_transformers[op_name]
      _a  :       int             =	val

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

  # The final layernorm.
  _a  :       Tuple             =	transformer["""final_layernorm.weight"""]
  _a  :       Dict             =	transformer["""final_layernorm.bias"""]

  # For LM head, transformers' wants the matrix to weight embeddings.
  _a  :       Union[str, Any]             =	word_embeddings

  # It should be done!
  return output_state_dict

def 			lowerCAmelCase__     (     ):



  '''simple docstring'''
  # Create the argument parser.
  _a  :       int             =	argparse.ArgumentParser()
  parser.add_argument("""--print-checkpoint-structure"""     ,   action="""store_true"""				)
  parser.add_argument(
      """path_to_checkpoint"""     ,   type=UpperCamelCase__     ,   help="""Path to the checkpoint file (.zip archive or direct .pt file)"""     ,   )
  parser.add_argument(
      """--config_file"""     ,   default=""""""     ,   type=UpperCamelCase__     ,   help="""An optional config json file describing the pre-trained model."""     ,   )
  _a  :       int             =	parser.parse_args()

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

  _a  :       int             =	input_state_dict.get("""args"""     ,   UpperCamelCase__				)

  # 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:
        _a  :       Tuple             =	"""gelu_fast"""
      elif ds_args.openai_gelu:
        _a  :       List[Any]             =	"""gelu_new"""
      else:
        _a  :       Optional[int]             =	"""gelu"""
    else:
      # in the very early days this used to be "gelu_new"
      _a  :       str             =	"""gelu_new"""

    # Spell out all parameters in case the defaults change.
    _a  :       Any             =	GPTaConfig(
        vocab_size=5_0_2_5_7     ,   n_positions=1_0_2_4     ,   n_embd=1_0_2_4     ,   n_layer=2_4     ,   n_head=1_6     ,   n_inner=4_0_9_6     ,   activation_function=UpperCamelCase__     ,   resid_pdrop=0.1     ,   embd_pdrop=0.1     ,   attn_pdrop=0.1     ,   layer_norm_epsilon=1e-5     ,   initializer_range=0.02     ,   summary_type="""cls_index"""     ,   summary_use_proj=UpperCamelCase__     ,   summary_activation=UpperCamelCase__     ,   summary_proj_to_labels=UpperCamelCase__     ,   summary_first_dropout=0.1     ,   scale_attn_weights=UpperCamelCase__     ,   use_cache=UpperCamelCase__     ,   bos_token_id=5_0_2_5_6     ,   eos_token_id=5_0_2_5_6     ,   )
  else:
    _a  :       Optional[Any]             =	GPTaConfig.from_json_file(args.config_file				)

  _a  :       Optional[Any]             =	["""GPT2LMHeadModel"""]

  # Convert.
  print("""Converting"""				)
  _a  :       Dict             =	convert_megatron_checkpoint(UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				)

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

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

  _a  :       Tuple             =	AutoTokenizer.from_pretrained(UpperCamelCase__				)
  _a  :       Any             =	type(UpperCamelCase__				).__name__
  _a  :       int             =	tokenizer_class

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

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

  # Store the state_dict to file.
  _a  :       Optional[Any]             =	os.path.join(UpperCamelCase__     ,   """pytorch_model.bin"""				)
  print(F"""Saving checkpoint to \"{output_checkpoint_file}\""""				)
  torch.save(UpperCamelCase__     ,   UpperCamelCase__				)


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

if __name__ == "__main__":
 main()

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









"""simple docstring"""




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_snake_case          =   False






class        UpperCamelCase			(	unittest.TestCase      ):
 pass







@nightly
@require_torch_gpu
class        UpperCamelCase			(	unittest.TestCase      ):


 def _lowercase	(     self		:						Optional[Any]							)					->       Optional[Any]:
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()


 def _lowercase	(     self		:						Tuple							)					->       List[Any]:
   _a  :       Tuple             =	VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""							,						torch_dtype=torch.floataa							)
   pipe.to(UpperCAmelCase__							)
   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       List[str]             =	load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""							)

   _a  :       Optional[Any]             =	torch.manual_seed(0							)
   _a  :       Union[str, Any]             =	pipe.dual_guided(
       prompt="""first prompt"""							,						image=UpperCAmelCase__							,						text_to_image_strength=0.7_5							,						generator=UpperCAmelCase__							,						guidance_scale=7.5							,						num_inference_steps=2							,						output_type="""numpy"""							,						).images

   with tempfile.TemporaryDirectory() as tmpdirname:
     pipe.save_pretrained(UpperCAmelCase__							)
     _a  :       Dict             =	VersatileDiffusionPipeline.from_pretrained(UpperCAmelCase__							,						torch_dtype=torch.floataa							)
   pipe.to(UpperCAmelCase__							)
   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       Optional[Any]             =	generator.manual_seed(0							)
   _a  :       str             =	pipe.dual_guided(
       prompt="""first prompt"""							,						image=UpperCAmelCase__							,						text_to_image_strength=0.7_5							,						generator=UpperCAmelCase__							,						guidance_scale=7.5							,						num_inference_steps=2							,						output_type="""numpy"""							,						).images

   assert np.abs(image - new_image							).sum() < 1E-5, "Models don't have the same forward pass"







 def _lowercase	(     self		:						Optional[int]							)					->       Optional[int]:
   _a  :       Optional[int]             =	VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""							,						torch_dtype=torch.floataa							)
   pipe.to(UpperCAmelCase__							)
   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       int             =	"""cyberpunk 2077"""
   _a  :       Tuple             =	load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""							)
   _a  :       Tuple             =	torch.manual_seed(0							)
   _a  :       Any             =	pipe.dual_guided(
       prompt=UpperCAmelCase__							,						image=UpperCAmelCase__							,						text_to_image_strength=0.7_5							,						generator=UpperCAmelCase__							,						guidance_scale=7.5							,						num_inference_steps=50							,						output_type="""numpy"""							,						).images

   _a  :       List[str]             =	image[0, 253:256, 253:256, -1]

   assert image.shape == (1, 512, 512, 3)
   _a  :       Optional[int]             =	np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]							)

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

   _a  :       int             =	"""A painting of a squirrel eating a burger """
   _a  :       Tuple             =	torch.manual_seed(0							)
   _a  :       Union[str, Any]             =	pipe.text_to_image(
       prompt=UpperCAmelCase__							,						generator=UpperCAmelCase__							,						guidance_scale=7.5							,						num_inference_steps=50							,						output_type="""numpy"""							).images

   _a  :       int             =	image[0, 253:256, 253:256, -1]

   assert image.shape == (1, 512, 512, 3)
   _a  :       int             =	np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]							)

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

   _a  :       str             =	pipe.image_variation(UpperCAmelCase__							,						generator=UpperCAmelCase__							,						output_type="""numpy"""							).images

   _a  :       str             =	image[0, 253:256, 253:256, -1]

   assert image.shape == (1, 512, 512, 3)
   _a  :       Optional[Any]             =	np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]							)

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

