

"""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__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase=0   ):

    '''simple docstring'''



    if name is None:
        _lowerCAmelCase       :   Optional[Any]  =    None
    else:
        _lowerCAmelCase       :   List[str]  =    '.' * max(0 ,   spaces - 2   ) + '# {:' + str(50 - spaces   ) + 's}'
        _lowerCAmelCase       :   Dict  =    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 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



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

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Union[str, Any]  =    {}

    # old versions did not store training args
    _lowerCAmelCase       :   Optional[int]  =    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))

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

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

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

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

    # The position embeddings.
    _lowerCAmelCase       :   Dict  =    embeddings['position_embeddings']['weight']
    # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
    _lowerCAmelCase       :   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.
    _lowerCAmelCase       :   List[Any]  =    pos_embeddings

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

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

    # The simple map of names for "automated" rules.
    _lowerCAmelCase       :   Tuple  =    {
        '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.
        _lowerCAmelCase       :   Optional[int]  =    layer_re.match(_lowerCamelCase   )

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

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

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

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

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

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

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

        # Copy the bias.
        elif weight_or_bias == "bias":
            _lowerCAmelCase       :   List[str]  =    megatron_to_transformers[op_name]
            _lowerCAmelCase       :   Union[str, Any]  =    val

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

    # The final layernorm.
    _lowerCAmelCase       :   Any  =    transformer['final_layernorm.weight']
    _lowerCAmelCase       :   Dict  =    transformer['final_layernorm.bias']

    # For LM head, transformers' wants the matrix to weight embeddings.
    _lowerCAmelCase       :   Tuple  =    word_embeddings

    # It should be done!
    return output_state_dict

def 		lowerCamelCase__		(					):

    '''simple docstring'''



    _lowerCAmelCase       :   Any  =    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.' ,   )
    _lowerCAmelCase       :   int  =    parser.parse_args()

    # Extract the basename.
    _lowerCAmelCase       :   Union[str, 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:
                _lowerCAmelCase       :   Dict  =    torch.load(_lowerCamelCase ,   map_location='cpu'   )
    else:
        _lowerCAmelCase       :   int  =    torch.load(args.path_to_checkpoint ,   map_location='cpu'   )

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

        # Spell out all parameters in case the defaults change.
        _lowerCAmelCase       :   List[Any]  =    GPTaConfig(
            vocab_size=50257 ,   n_positions=1024 ,   n_embd=1024 ,   n_layer=24 ,   n_head=16 ,   n_inner=4096 ,   activation_function=_lowerCamelCase ,   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=_lowerCamelCase ,   summary_activation=_lowerCamelCase ,   summary_proj_to_labels=_lowerCamelCase ,   summary_first_dropout=0.1 ,   scale_attn_weights=_lowerCamelCase ,   use_cache=_lowerCamelCase ,   bos_token_id=50256 ,   eos_token_id=50256 ,   )
    else:
        _lowerCAmelCase       :   Optional[Any]  =    GPTaConfig.from_json_file(args.config_file   )

    _lowerCAmelCase       :   Any  =    ['GPT2LMHeadModel']

    # Convert.
    print('Converting'   )
    _lowerCAmelCase       :   Any  =    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:
        _lowerCAmelCase       :   str  =    ds_args.tokenizer_type
        if tokenizer_type == "GPT2BPETokenizer":
            _lowerCAmelCase       :   Any  =    'gpt2'
        elif tokenizer_type == "PretrainedFromHF":
            _lowerCAmelCase       :   List[str]  =    ds_args.tokenizer_name_or_path
        else:
            raise ValueError(f"""Unrecognized tokenizer_type {tokenizer_type}"""   )
    else:
        _lowerCAmelCase       :   List[str]  =    'gpt2'

    _lowerCAmelCase       :   List[str]  =    AutoTokenizer.from_pretrained(_lowerCamelCase   )
    _lowerCAmelCase       :   List[str]  =    type(_lowerCamelCase   ).__name__
    _lowerCAmelCase       :   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.
    _lowerCAmelCase       :   Any  =    os.path.join(_lowerCamelCase ,   'pytorch_model.bin'   )
    print(f"""Saving checkpoint to \"{output_checkpoint_file}\""""   )
    torch.save(_lowerCamelCase ,   _lowerCamelCase   )


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

if __name__ == "__main__":
 main()

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








"""simple docstring"""






import gc
import unittest

import numpy as np
import torch

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 	__UpperCamelCase    (    a__							,							unittest.TestCase						):
  _UpperCAmelCase          =		DanceDiffusionPipeline
  _UpperCAmelCase          =		UNCONDITIONAL_AUDIO_GENERATION_PARAMS
  _UpperCAmelCase          =		PipelineTesterMixin.required_optional_params - {
      "callback",
      "latents",
      "callback_steps",
      "output_type",
      "num_images_per_prompt",
  }
  _UpperCAmelCase          =		UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
  _UpperCAmelCase          =		False
  _UpperCAmelCase          =		False

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      torch.manual_seed(0      )
      _lowerCAmelCase       :   List[Any]  =    UNetaDModel(
          block_out_channels=(32, 32, 64)    ,extra_in_channels=16    ,sample_size=512    ,sample_rate=1_6000    ,in_channels=2    ,out_channels=2    ,flip_sin_to_cos=_A    ,use_timestep_embedding=_A    ,time_embedding_type='fourier'    ,mid_block_type='UNetMidBlock1D'    ,down_block_types=('DownBlock1DNoSkip', 'DownBlock1D', 'AttnDownBlock1D')    ,up_block_types=('AttnUpBlock1D', 'UpBlock1D', 'UpBlock1DNoSkip')    ,)
      _lowerCAmelCase       :   int  =    IPNDMScheduler()

      _lowerCAmelCase       :   Union[str, Any]  =    {
          'unet': unet,
          'scheduler': scheduler,
      }
      return components

  def __lowerCamelCase		(		self    ,_A    ,_A=0      ):
      '''simple docstring'''

      if str(_A      ).startswith('mps'      ):
          _lowerCAmelCase       :   str  =    torch.manual_seed(_A      )
      else:
          _lowerCAmelCase       :   Optional[Any]  =    torch.Generator(device=_A      ).manual_seed(_A      )
      _lowerCAmelCase       :   int  =    {
          'batch_size': 1,
          'generator': generator,
          'num_inference_steps': 4,
      }
      return inputs

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    'cpu'  # ensure determinism for the device-dependent torch.Generator
      _lowerCAmelCase       :   int  =    self.get_dummy_components()
      _lowerCAmelCase       :   Optional[Any]  =    DanceDiffusionPipeline(**_A      )
      _lowerCAmelCase       :   int  =    pipe.to(_A      )
      pipe.set_progress_bar_config(disable=_A      )

      _lowerCAmelCase       :   Union[str, Any]  =    self.get_dummy_inputs(_A      )
      _lowerCAmelCase       :   List[str]  =    pipe(**_A      )
      _lowerCAmelCase       :   List[Any]  =    output.audios

      _lowerCAmelCase       :   List[str]  =    audio[0, -3:, -3:]

      assert audio.shape == (1, 2, components["unet"].sample_size)
      _lowerCAmelCase       :   Optional[Any]  =    np.array([-0.7_2_6_5, 1.0_0_0_0, -0.8_3_8_8, 0.1_1_7_5, 0.9_4_9_8, -1.0_0_0_0]      )
      assert np.abs(audio_slice.flatten() - expected_slice      ).max() < 1E-2

  @skip_mps
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return super().test_save_load_local()

  @skip_mps
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3      )

  @skip_mps
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return super().test_save_load_optional_components()

  @skip_mps
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return super().test_attention_slicing_forward_pass()







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      super().test_inference_batch_single_identical(expected_max_diff=3E-3      )







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

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    torch_device

      _lowerCAmelCase       :   int  =    DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k'      )
      _lowerCAmelCase       :   int  =    pipe.to(_A      )
      pipe.set_progress_bar_config(disable=_A      )

      _lowerCAmelCase       :   Optional[int]  =    torch.manual_seed(0      )
      _lowerCAmelCase       :   str  =    pipe(generator=_A    ,num_inference_steps=100    ,audio_length_in_s=4.0_9_6      )
      _lowerCAmelCase       :   str  =    output.audios

      _lowerCAmelCase       :   List[str]  =    audio[0, -3:, -3:]

      assert audio.shape == (1, 2, pipe.unet.sample_size)
      _lowerCAmelCase       :   Union[str, Any]  =    np.array([-0.0_1_9_2, -0.0_2_3_1, -0.0_3_1_8, -0.0_0_5_9, 0.0_0_0_2, -0.0_0_2_0]      )

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







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    torch_device

      _lowerCAmelCase       :   Tuple  =    DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k'    ,torch_dtype=torch.floataa      )
      _lowerCAmelCase       :   Optional[int]  =    pipe.to(_A      )
      pipe.set_progress_bar_config(disable=_A      )

      _lowerCAmelCase       :   Union[str, Any]  =    torch.manual_seed(0      )
      _lowerCAmelCase       :   Optional[int]  =    pipe(generator=_A    ,num_inference_steps=100    ,audio_length_in_s=4.0_9_6      )
      _lowerCAmelCase       :   Union[str, Any]  =    output.audios

      _lowerCAmelCase       :   int  =    audio[0, -3:, -3:]

      assert audio.shape == (1, 2, pipe.unet.sample_size)
      _lowerCAmelCase       :   List[str]  =    np.array([-0.0_3_6_7, -0.0_4_8_8, -0.0_7_7_1, -0.0_5_2_5, -0.0_4_4_4, -0.0_3_4_1]      )

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





