



'''simple docstring'''






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput

@dataclass
class 							a__( __lowercase     ):
    lowercase__			=   42
    lowercase__			=   42

class 							a__( __lowercase		,			__lowercase     ):
    lowercase__			=   1







    @register_to_config
    def __init__(				self			:  Any						,      __snake_case			:  List[Any] = 20_00						,      __snake_case			:  str = 0.15						,      __snake_case			:  List[str] = 0.01						,      __snake_case			:  Dict = 13_48.0						,      __snake_case			:  Any = 1e-5						,      __snake_case			:  Any = 1						,      ):
      # standard deviation of the initial noise distribution
      a						: Union[str, Any]					       =					sigma_max

      # setable values
      a						: List[str]					       =					None

      self.set_sigmas(_a						,      _a						,      _a						,      _a     )







    def       lowercase_	(				self			:  int						,      __snake_case			:  str						,      __snake_case			:  List[str] = None     ):
      return sample







    def       lowercase_	(				self			:  Any						,      __snake_case			:  Dict						,      __snake_case			:  Union[str, Any] = None						,      __snake_case			:  List[str] = None     ):
      a						: List[str]					       =					sampling_eps if sampling_eps is not None else self.config.sampling_eps

      a						: Tuple					       =					torch.linspace(1						,      _a						,      _a						,      device=_a     )







    def       lowercase_	(				self			:  Tuple						,      __snake_case			:  Tuple						,      __snake_case			:  int = None						,      __snake_case			:  Dict = None						,      __snake_case			:  Tuple = None     ):
      a						: Union[str, Any]					       =					sigma_min if sigma_min is not None else self.config.sigma_min
      a						: Union[str, Any]					       =					sigma_max if sigma_max is not None else self.config.sigma_max
      a						: List[str]					       =					sampling_eps if sampling_eps is not None else self.config.sampling_eps
      if self.timesteps is None:
        self.set_timesteps(_a						,      _a     )

      a						: Union[str, Any]					       =					sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
      a						: Any					       =					torch.exp(torch.linspace(math.log(_a     )						,      math.log(_a     )						,      _a     )     )
      a						: Optional[Any]					       =					torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]     )







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  Any						,      __snake_case			:  List[Any]     ):
      return torch.where(
          timesteps == 0						,      torch.zeros_like(t.to(timesteps.device     )     )						,      self.discrete_sigmas[timesteps - 1].to(timesteps.device     )						,      )







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  List[str]						,      __snake_case			:  Dict						,      __snake_case			:  Any						,      __snake_case			:  int = None						,      __snake_case			:  Any = True						,      ):
      if self.timesteps is None:
        raise ValueError(
            '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'     )

      a						: str					       =					timestep * torch.ones(
          sample.shape[0]						,      device=sample.device     )  # torch.repeat_interleave(timestep, sample.shape[0])
      a						: str					       =					(timestep * (len(self.timesteps     ) - 1)).long()

      # mps requires indices to be in the same device, so we use cpu as is the default with cuda
      a						: Tuple					       =					timesteps.to(self.discrete_sigmas.device     )

      a						: int					       =					self.discrete_sigmas[timesteps].to(sample.device     )
      a						: Any					       =					self.get_adjacent_sigma(_a						,      _a     ).to(sample.device     )
      a						: Any					       =					torch.zeros_like(_a     )
      a						: Union[str, Any]					       =					(sigma**2 - adjacent_sigma**2) ** 0.5

      # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
      # also equation 47 shows the analog from SDE models to ancestral sampling methods
      a						: Optional[int]					       =					diffusion.flatten()
      while len(diffusion.shape     ) < len(sample.shape     ):
        a						: List[str]					       =					diffusion.unsqueeze(-1     )
      a						: int					       =					drift - diffusion**2 * model_output

      #  equation 6: sample noise for the diffusion term of
      a						: List[Any]					       =					randn_tensor(
          sample.shape						,      layout=sample.layout						,      generator=_a						,      device=sample.device						,      dtype=sample.dtype     )
      a						: List[Any]					       =					sample - drift  # subtract because `dt` is a small negative timestep
      # TODO is the variable diffusion the correct scaling term for the noise?
      a						: List[Any]					       =					prev_sample_mean + diffusion * noise  # add impact of diffusion field g

      if not return_dict:
        return (prev_sample, prev_sample_mean)

      return SdeVeOutput(prev_sample=_a						,      prev_sample_mean=_a     )







    def       lowercase_	(				self			:  str						,      __snake_case			:  Optional[int]						,      __snake_case			:  List[Any]						,      __snake_case			:  Dict = None						,      __snake_case			:  str = True						,      ):
      if self.timesteps is None:
        raise ValueError(
            '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'     )

      # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
      # sample noise for correction
      a						: Any					       =					randn_tensor(sample.shape						,      layout=sample.layout						,      generator=_a     ).to(sample.device     )

      # compute step size from the model_output, the noise, and the snr
      a						: List[str]					       =					torch.norm(model_output.reshape(model_output.shape[0]						,      -1     )						,      dim=-1     ).mean()
      a						: int					       =					torch.norm(noise.reshape(noise.shape[0]						,      -1     )						,      dim=-1     ).mean()
      a						: Dict					       =					(self.config.snr * noise_norm / grad_norm) ** 2 * 2
      a						: Tuple					       =					step_size * torch.ones(sample.shape[0]     ).to(sample.device     )
      # self.repeat_scalar(step_size, sample.shape[0])

      # compute corrected sample: model_output term and noise term
      a						: Dict					       =					step_size.flatten()
      while len(step_size.shape     ) < len(sample.shape     ):
        a						: Union[str, Any]					       =					step_size.unsqueeze(-1     )
      a						: List[str]					       =					sample + step_size * model_output
      a						: str					       =					prev_sample_mean + ((step_size * 2) ** 0.5) * noise

      if not return_dict:
        return (prev_sample,)

      return SchedulerOutput(prev_sample=_a     )







    def       lowercase_	(				self			:  str						,      __snake_case			:  Dict						,      __snake_case			:  Union[str, Any]						,      __snake_case			:  Optional[Any]						,      ):
      # Make sure sigmas and timesteps have the same device and dtype as original_samples
      a						: List[str]					       =					timesteps.to(original_samples.device     )
      a						: List[str]					       =					self.discrete_sigmas.to(original_samples.device     )[timesteps]
      a						: List[Any]					       =					(
          noise * sigmas[:, None, None, None]
          if noise is not None
          else torch.randn_like(_a     ) * sigmas[:, None, None, None]
      )
      a						: List[Any]					       =					noise + original_samples
      return noisy_samples







    def __len__(				self			:  int     ):
      return self.config.num_train_timesteps




'''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__	(						_A			,      _A			,      _A=0    ):
  # Format the message.
  if name is None:
    a						: Tuple					       =					None
  else:
    a						: Dict					       =					'.' * max(0			,      spaces - 2    ) + '# {:' + str(50 - spaces    ) + 's}'
    a						: Tuple					       =					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 GPT2.
  a						: str					       =					param.size()
  if checkpoint_version == 1.0:
    # version 1.0 stores [num_heads * hidden_size * num_splits, :]
    a						: List[Any]					       =					(num_heads, hidden_size, num_splits) + input_shape[1:]
    a						: int					       =					param.view(*_A    )
    a						: List[str]					       =					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						: Union[str, Any]					       =					(num_heads, num_splits, hidden_size) + input_shape[1:]
    a						: List[str]					       =					param.view(*_A    )
    a						: Union[str, Any]					       =					param.transpose(0			,      1    ).contiguous()
  a						: List[Any]					       =					param.view(*_A    )
  return param
def 				lowerCamelCase__	(						_A			,      _A			,      _A    ):
  # The converted output model.
  a						: Optional[Any]					       =					{}

  # old versions did not store training args
  a						: Dict					       =					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))

    a						: Union[str, Any]					       =					ds_args.padded_vocab_size
    a						: str					       =					ds_args.max_position_embeddings
    a						: Dict					       =					ds_args.hidden_size
    a						: Union[str, Any]					       =					ds_args.num_layers
    a						: Dict					       =					ds_args.num_attention_heads
    a						: int					       =					ds_args.ffn_hidden_size
    # pprint(config)

  # The number of heads.
  a						: Any					       =					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						: Any					       =					input_state_dict['checkpoint_version']
  else:
    a						: Any					       =					0.0

  # The model.
  a						: Optional[int]					       =					input_state_dict['model']
  # The language model.
  a						: Optional[Any]					       =					model['language_model']
  # The embeddings.
  a						: List[str]					       =					lm['embedding']

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

  # The position embeddings.
  a						: Tuple					       =					embeddings['position_embeddings']['weight']
  # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
  a						: List[str]					       =					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						: List[Any]					       =					re.compile(r'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'    )

  # The simple map of names for "automated" rules.
  a						: Optional[Any]					       =					{
      '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						: Tuple					       =					layer_re.match(_A    )

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

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

    # The name of the layer.
    a						: Any					       =					f"""transformer.h.{layer_idx}"""

    # For layernorm(s), simply store the layer norm.
    if op_name.endswith('layernorm'    ):
      a						: str					       =					'ln_1' if op_name.startswith('input'    ) else 'ln_2'
      a						: Tuple					       =					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						: Dict					       =					torch.tril(torch.ones((n_positions, n_positions)			,      dtype=torch.floataa    )    ).view(
          1			,      1			,      _A			,      _A    )
      a						: Optional[Any]					       =					causal_mask

      # Insert a "dummy" tensor for masked_bias.
      a						: List[Any]					       =					torch.tensor(-1E4			,      dtype=torch.floataa    )
      a						: List[str]					       =					masked_bias

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

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

    # Copy the bias.
    elif weight_or_bias == "bias":
      a						: Dict					       =					megatron_to_transformers[op_name]
      a						: Optional[Any]					       =					val

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

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

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

  # It should be done!
  return output_state_dict
def 				lowerCamelCase__	(						):
  # Create the argument parser.
  a						: Dict					       =					argparse.ArgumentParser()
  parser.add_argument('--print-checkpoint-structure'			,      action='store_true'    )
  parser.add_argument(
      'path_to_checkpoint'			,      type=_A			,      help='Path to the checkpoint file (.zip archive or direct .pt file)'			,      )
  parser.add_argument(
      '--config_file'			,      default=''			,      type=_A			,      help='An optional config json file describing the pre-trained model.'			,      )
  a						: Union[str, Any]					       =					parser.parse_args()

  # Extract the basename.
  a						: Optional[Any]					       =					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						: Union[str, Any]					       =					torch.load(_A			,      map_location='cpu'    )
  else:
    a						: Any					       =					torch.load(args.path_to_checkpoint			,      map_location='cpu'    )

  a						: List[Any]					       =					input_state_dict.get('args'			,      _A    )

  # 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						: int					       =					'gelu_fast'
      elif ds_args.openai_gelu:
        a						: Dict					       =					'gelu_new'
      else:
        a						: Any					       =					'gelu'
    else:
      # in the very early days this used to be "gelu_new"
      a						: Any					       =					'gelu_new'

    # Spell out all parameters in case the defaults change.
    a						: Tuple					       =					GPTaConfig(
        vocab_size=5_0257			,      n_positions=1024			,      n_embd=1024			,      n_layer=24			,      n_head=16			,      n_inner=4096			,      activation_function=_A			,      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=_A			,      summary_activation=_A			,      summary_proj_to_labels=_A			,      summary_first_dropout=0.1			,      scale_attn_weights=_A			,      use_cache=_A			,      bos_token_id=5_0256			,      eos_token_id=5_0256			,      )
  else:
    a						: str					       =					GPTaConfig.from_json_file(args.config_file    )

  a						: Any					       =					['GPT2LMHeadModel']

  # Convert.
  print('Converting'    )
  a						: Union[str, Any]					       =					convert_megatron_checkpoint(_A			,      _A			,      _A    )

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

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

  a						: Tuple					       =					AutoTokenizer.from_pretrained(_A    )
  a						: str					       =					type(_A    ).__name__
  a						: List[str]					       =					tokenizer_class

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

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

  # Store the state_dict to file.
  a						: Optional[int]					       =					os.path.join(_A			,      'pytorch_model.bin'    )
  print(f"""Saving checkpoint to \"{output_checkpoint_file}\""""    )
  torch.save(_A			,      _A    )


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

if __name__ == "__main__":
     main()

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