code: 
  
  
  
  
  # 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     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_=0       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if name is None:
            snake_case					=       None
       else:
            snake_case					=       '''.''' * max(0							,spaces - 2       ) + '''# {:''' + str(50 - spaces       ) + '''s}'''
            snake_case					=       fmt.format(lowercase__       )
  
       # Print and recurse (if needed).
       if isinstance(lowercase__							,lowercase__       ):
            if msg is not None:
                 print(lowercase__       )
            for k in val.keys():
                 recursive_print(lowercase__							,val[k]							,spaces + 2       )
       elif isinstance(lowercase__							,torch.Tensor       ):
            print(lowercase__							,''':'''							,val.size()       )
       else:
            print(lowercase__							,''':'''							,lowercase__       )
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       param.size()
       if checkpoint_version == 1.0:
            # version 1.0 stores [num_heads * hidden_size * num_splits, :]
            snake_case					=       (num_heads, hidden_size, num_splits) + input_shape[1:]
            snake_case					=       param.view(*lowercase__       )
            snake_case					=       param.transpose(0							,2       )
            snake_case					=       param.transpose(1							,2       ).contiguous()
       elif checkpoint_version >= 2.0:
            # other versions store [num_heads * num_splits * hidden_size, :]
            snake_case					=       (num_heads, num_splits, hidden_size) + input_shape[1:]
            snake_case					=       param.view(*lowercase__       )
            snake_case					=       param.transpose(0							,1       ).contiguous()
       snake_case					=       param.view(*lowercase__       )
       return param
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       {}
  
       # old versions did not store training args
       snake_case					=       input_state_dict.get('''args'''							,lowercase__       )
       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))
  
            snake_case					=       ds_args.tokenizer_type
            snake_case					=       ds_args.padded_vocab_size
            snake_case					=       ds_args.max_position_embeddings
            snake_case					=       ds_args.hidden_size
            snake_case					=       ds_args.num_layers
            snake_case					=       ds_args.num_attention_heads
            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.
       snake_case					=       config.num_attention_heads
       # The hidden_size per head.
       snake_case					=       config.hidden_size // heads
       # Megatron-LM checkpoint version
       if "checkpoint_version" in input_state_dict.keys():
            snake_case					=       input_state_dict['''checkpoint_version''']
       else:
            snake_case					=       0.0
  
       # The model.
       snake_case					=       input_state_dict['''model''']
       # The language model.
       snake_case					=       model['''language_model''']
       # The embeddings.
       snake_case					=       lm['''embedding''']
  
       # The word embeddings.
       snake_case					=       embeddings['''word_embeddings''']['''weight''']
       # Truncate the embedding table to vocab_size rows.
       snake_case					=       word_embeddings[: config.vocab_size, :]
       # Store the word embeddings.
       snake_case					=       word_embeddings
  
       # The position embeddings.
       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.
       snake_case					=       pos_embeddings
  
       # The token-type embeddings.
       snake_case					=       embeddings['''tokentype_embeddings''']['''weight''']
       # Store the position embeddings.
       snake_case					=       tokentype_embeddings
  
       # The transformer.
       snake_case					=       lm['''transformer'''] if '''transformer''' in lm.keys() else lm['''encoder''']
  
       # The regex to extract layer names.
       snake_case					=       re.compile(r'''layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'''       )
  
       # The simple map of names for "automated" rules.
       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.
       snake_case					=       None
  
       # Extract the layers.
       for key, val in transformer.items():
            # Match the name.
            snake_case					=       layer_re.match(lowercase__       )
  
            # Stop if that's not a layer
            if m is None:
                 break
  
            # The index of the layer.
            snake_case					=       int(m.group(1       )       )
            # The name of the operation.
            snake_case					=       m.group(2       )
            # Is it a weight or a bias?
            snake_case					=       m.group(3       )
  
            # The name of the layer.
            snake_case					=       F'''bert.encoder.layer.{layer_idx}'''
  
            # For layernorm(s), simply store the layer norm.
            if op_name.endswith('''layernorm'''       ):
                 snake_case					=       '''attention.ln''' if op_name.startswith('''input'''       ) else '''ln'''
                 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, ""
  
                 snake_case					=       fix_query_key_value_ordering(lowercase__							,lowercase__							,3							,lowercase__							,lowercase__       )
                 # Store the tensor as we need the bias as well to interleave QKV and biases.
                 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.
                 snake_case					=       attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
                 snake_case					=       attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
                 snake_case					=       attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]
  
                 snake_case					=       fix_query_key_value_ordering(lowercase__							,lowercase__							,3							,lowercase__							,lowercase__       )
                 # Split the bias.
                 snake_case					=       out_val[0 * config.hidden_size : 1 * config.hidden_size]
                 snake_case					=       out_val[1 * config.hidden_size : 2 * config.hidden_size]
                 snake_case					=       out_val[2 * config.hidden_size : 3 * config.hidden_size]
  
                 # Store.
                 snake_case					=       q
                 snake_case					=       q_bias
                 snake_case					=       k
                 snake_case					=       k_bias
                 snake_case					=       v
                 snake_case					=       v_bias
  
                 # Clear the stored tensor.
                 snake_case					=       None
  
            # Copy weights and biases as is.
            elif weight_or_bias in ["weight", "bias"]:
                 snake_case					=       megatron_to_transformers[op_name]
                 snake_case					=       val
  
      # The final layernorm.
       snake_case					=       transformer['''final_layernorm.weight''']
       snake_case					=       transformer['''final_layernorm.bias''']
  
       # The pooler.
       snake_case					=       lm['''pooler''']
  
       # Store the matrix and the bias.
       snake_case					=       pooler['''dense.weight''']
       snake_case					=       pooler['''dense.bias''']
  
       # The LM head from Megatron (for RACE).
       snake_case					=       model['''lm_head''']
  
       # The transform matrix.
       snake_case					=       lm_head['''dense.weight''']
       snake_case					=       lm_head['''dense.bias''']
  
       # The transform LN.
       snake_case					=       lm_head['''layernorm.weight''']
       snake_case					=       lm_head['''layernorm.bias''']
  
       # For the decoder, we replicate the weights.
       snake_case					=       word_embeddings
       snake_case					=       lm_head['''bias''']
  
       # The classifier from Megatron (for MLNI).
       snake_case					=       model['''binary_head''']
  
       # Store the classifier.
       snake_case					=       binary_head['''weight''']
       snake_case					=       binary_head['''bias''']
  
       # It should be done!
       return output_state_dict
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       argparse.ArgumentParser()
       parser.add_argument('''--print-checkpoint-structure'''							,action='''store_true'''       )
       parser.add_argument('''path_to_checkpoint'''							,type=lowercase__							,help='''Path to the ZIP file containing the checkpoint'''       )
       parser.add_argument(
           '''--config_file'''							,default=''''''							,type=lowercase__							,help='''An optional config json file describing the pre-trained model.'''							,)
       snake_case					=       parser.parse_args()
  
       # Extract the basename.
       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:
                      snake_case					=       torch.load(lowercase__							,map_location='''cpu'''       )
       else:
            snake_case					=       torch.load(args.path_to_checkpoint							,map_location='''cpu'''       )
  
       if args.config_file == "":
            # Default config of megatron-bert 345m
            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
            snake_case					=       input_state_dict['''model''']['''lm_head''']['''bias'''].numel()
       else:
            snake_case					=       MegatronBertConfig.from_json_file(args.config_file       )
  
       # Convert.
       print('''Converting'''       )
       snake_case					=       convert_megatron_checkpoint(lowercase__							,lowercase__							,lowercase__       )
  
       # Print the structure of converted state dict.
       if args.print_checkpoint_structure:
            recursive_print(lowercase__							,lowercase__       )
  
       # Store the config to file.
       print('''Saving config'''       )
       config.save_pretrained(lowercase__       )
  
       # Store the state_dict to file.
       snake_case					=       os.path.join(lowercase__							,'''pytorch_model.bin'''       )
       print(F'''Saving checkpoint to "{output_checkpoint_file}"'''       )
       torch.save(lowercase__							,lowercase__       )
  
  
  ####################################################################################################
  
  if __name__ == "__main__":
   main()
  
  ####################################################################################################
  
  
  
  
  
style_context: 
  
  
  import gc
  import unittest
  
  import numpy as np
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  
  from diffusers import (
      AutoencoderKL,
      DDIMParallelScheduler,
      DDPMParallelScheduler,
      StableDiffusionParadigmsPipeline,
      UNetaDConditionModel,
  )
  from diffusers.utils import slow, torch_device
  from diffusers.utils.testing_utils import (
      enable_full_determinism,
      require_torch_gpu,
  )
  
  from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
  
  
  enable_full_determinism()
  
  class 						A__      (					snake_case__ ,      snake_case__ ,      unittest.TestCase			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      __magic_name__    						=	StableDiffusionParadigmsPipeline
      __magic_name__    						=	TEXT_TO_IMAGE_PARAMS
      __magic_name__    						=	TEXT_TO_IMAGE_BATCH_PARAMS
      __magic_name__    						=	TEXT_TO_IMAGE_IMAGE_PARAMS
      __magic_name__    						=	TEXT_TO_IMAGE_IMAGE_PARAMS
      def 	a_    ( self					):
           torch.manual_seed(0					)
           snake_case					=       UNetaDConditionModel(
               block_out_channels=(3_2, 6_4)    ,      layers_per_block=2    ,      sample_size=3_2    ,      in_channels=4    ,      out_channels=4    ,      down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')    ,      up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')    ,      cross_attention_dim=3_2    ,      attention_head_dim=(2, 4)    ,      use_linear_projection=__snake_case    ,      )
           snake_case					=       DDIMParallelScheduler(
               beta_start=0.0_0085    ,      beta_end=0.012    ,      beta_schedule='''scaled_linear'''    ,      clip_sample=__snake_case    ,      set_alpha_to_one=__snake_case    ,      )
           torch.manual_seed(0					)
           snake_case					=       AutoencoderKL(
               block_out_channels=[3_2, 6_4]    ,      in_channels=3    ,      out_channels=3    ,      down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']    ,      up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']    ,      latent_channels=4    ,      sample_size=1_2_8    ,      )
           torch.manual_seed(0					)
           snake_case					=       CLIPTextConfig(
               bos_token_id=0    ,      eos_token_id=2    ,      hidden_size=3_2    ,      intermediate_size=3_7    ,      layer_norm_eps=1E-05    ,      num_attention_heads=4    ,      num_hidden_layers=5    ,      pad_token_id=1    ,      vocab_size=1_0_0_0    ,      hidden_act='''gelu'''    ,      projection_dim=5_1_2    ,      )
           snake_case					=       CLIPTextModel(__snake_case					)
           snake_case					=       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)
  
           snake_case					=       {
               '''unet''': unet,
               '''scheduler''': scheduler,
               '''vae''': vae,
               '''text_encoder''': text_encoder,
               '''tokenizer''': tokenizer,
               '''safety_checker''': None,
               '''feature_extractor''': None,
           }
           return components
      def 	a_    ( self    ,      __snake_case    ,      __snake_case=0					):
           if str(__snake_case					).startswith('''mps'''					):
                snake_case					=       torch.manual_seed(__snake_case					)
           else:
                snake_case					=       torch.Generator(device=__snake_case					).manual_seed(__snake_case					)
           snake_case					=       {
               '''prompt''': '''a photograph of an astronaut riding a horse''',
               '''generator''': generator,
               '''num_inference_steps''': 1_0,
               '''guidance_scale''': 6.0,
               '''output_type''': '''numpy''',
               '''parallel''': 3,
               '''debug''': True,
           }
           return inputs
      def 	a_    ( self					):
           snake_case					=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           snake_case					=       self.get_dummy_components()
           snake_case					=       StableDiffusionParadigmsPipeline(**__snake_case					)
           snake_case					=       sd_pipe.to(__snake_case					)
           sd_pipe.set_progress_bar_config(disable=__snake_case					)
  
           snake_case					=       self.get_dummy_inputs(__snake_case					)
           snake_case					=       sd_pipe(**__snake_case					).images
           snake_case					=       image[0, -3:, -3:, -1]
           assert image.shape == (1, 6_4, 6_4, 3)
  
           snake_case					=       np.array([0.4773, 0.5417, 0.4723, 0.4925, 0.5631, 0.4752, 0.5240, 0.4935, 0.5023]					)
  
           assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
      def 	a_    ( self					):
           snake_case					=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           snake_case					=       self.get_dummy_components()
           torch.manual_seed(0					)
           snake_case					=       DDPMParallelScheduler()
           torch.manual_seed(0					)
           snake_case					=       StableDiffusionParadigmsPipeline(**__snake_case					)
           snake_case					=       sd_pipe.to(__snake_case					)
           sd_pipe.set_progress_bar_config(disable=__snake_case					)
  
           snake_case					=       self.get_dummy_inputs(__snake_case					)
           snake_case					=       sd_pipe(**__snake_case					).images
           snake_case					=       image[0, -3:, -3:, -1]
           assert image.shape == (1, 6_4, 6_4, 3)
  
           snake_case					=       np.array([0.3573, 0.4420, 0.4960, 0.4799, 0.3796, 0.3879, 0.4819, 0.4365, 0.4468]					)
  
           assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
      def 	a_    ( self					):
           super().test_inference_batch_consistent(batch_sizes=[1, 2]					)
      def 	a_    ( self					):
           super().test_inference_batch_single_identical(batch_size=2    ,      expected_max_diff=3E-3					)
  
  
  
  
      def 	a_    ( self					):
           snake_case					=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           snake_case					=       self.get_dummy_components()
           snake_case					=       StableDiffusionParadigmsPipeline(**__snake_case					)
           snake_case					=       sd_pipe.to(__snake_case					)
           sd_pipe.set_progress_bar_config(disable=__snake_case					)
  
           snake_case					=       self.get_dummy_inputs(__snake_case					)
           snake_case					=       '''french fries'''
           snake_case					=       sd_pipe(**__snake_case    ,      negative_prompt=__snake_case					)
           snake_case					=       output.images
           snake_case					=       image[0, -3:, -3:, -1]
  
           assert image.shape == (1, 6_4, 6_4, 3)
  
           snake_case					=       np.array([0.4771, 0.5420, 0.4683, 0.4918, 0.5636, 0.4725, 0.5230, 0.4923, 0.5015]					)
  
           assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
  
  
  
  
  
  
  @slow
  @require_torch_gpu
  class 						A__      (					unittest.TestCase			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      def 	a_    ( self					):
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
      def 	a_    ( self    ,      __snake_case=0					):
           snake_case					=       torch.Generator(device=__snake_case					).manual_seed(__snake_case					)
           snake_case					=       {
               '''prompt''': '''a photograph of an astronaut riding a horse''',
               '''generator''': generator,
               '''num_inference_steps''': 1_0,
               '''guidance_scale''': 7.5,
               '''output_type''': '''numpy''',
               '''parallel''': 3,
               '''debug''': True,
           }
           return inputs
  
  
  
  
      def 	a_    ( self					):
           snake_case					=       '''stabilityai/stable-diffusion-2-base'''
           snake_case					=       DDIMParallelScheduler.from_pretrained(__snake_case    ,      subfolder='''scheduler'''					)
           snake_case					=       StableDiffusionParadigmsPipeline.from_pretrained(__snake_case    ,      scheduler=__snake_case    ,      safety_checker=__snake_case					)
           pipe.to(__snake_case					)
           pipe.set_progress_bar_config(disable=__snake_case					)
           pipe.enable_attention_slicing()
  
           snake_case					=       self.get_inputs()
           snake_case					=       pipe(**__snake_case					).images
           snake_case					=       image[0, -3:, -3:, -1].flatten()
  
           assert image.shape == (1, 5_1_2, 5_1_2, 3)
  
           snake_case					=       np.array([0.9622, 0.9602, 0.9748, 0.9591, 0.9630, 0.9691, 0.9661, 0.9631, 0.9741]					)
  
           assert np.abs(expected_slice - image_slice					).max() < 1E-2
  
  
  
  
  