


"""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   snake_case		(     UpperCamelCase__       :						Optional[int]			,				UpperCamelCase__       :						Dict			,				UpperCamelCase__       :						List[str]=0    )   ->      Optional[Any]:




      if name is None:
            lowerCamelCase					:					Any        =	None
      else:
            lowerCamelCase					:					Dict        =	""".""" * max(0			,				spaces - 2    ) + """# {:""" + str(50 - spaces    ) + """s}"""
            lowerCamelCase					:					Dict        =	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   snake_case		(     UpperCamelCase__       :						Any			,				UpperCamelCase__       :						List[Any]			,				UpperCamelCase__       :						int			,				UpperCamelCase__       :						str			,				UpperCamelCase__       :						Tuple    )   ->      Optional[int]:
      lowerCamelCase					:					str        =	param.size()
      if checkpoint_version == 1.0:
            # version 1.0 stores [num_heads * hidden_size * num_splits, :]
            lowerCamelCase					:					Tuple        =	(num_heads, hidden_size, num_splits) + input_shape[1:]
            lowerCamelCase					:					Tuple        =	param.view(*__a    )
            lowerCamelCase					:					Dict        =	param.transpose(0			,				2    )
            lowerCamelCase					:					Tuple        =	param.transpose(1			,				2    ).contiguous()
      elif checkpoint_version >= 2.0:
            # other versions store [num_heads * num_splits * hidden_size, :]
            lowerCamelCase					:					int        =	(num_heads, num_splits, hidden_size) + input_shape[1:]
            lowerCamelCase					:					str        =	param.view(*__a    )
            lowerCamelCase					:					List[Any]        =	param.transpose(0			,				1    ).contiguous()
      lowerCamelCase					:					Tuple        =	param.view(*__a    )
      return param




def   snake_case		(     UpperCamelCase__       :						Dict			,				UpperCamelCase__       :						Any			,				UpperCamelCase__       :						List[str]    )   ->      Any:
      lowerCamelCase					:					Dict        =	{}

      # old versions did not store training args
      lowerCamelCase					:					List[str]        =	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))

            lowerCamelCase					:					Any        =	ds_args.padded_vocab_size
            lowerCamelCase					:					List[Any]        =	ds_args.max_position_embeddings
            lowerCamelCase					:					Any        =	ds_args.hidden_size
            lowerCamelCase					:					Tuple        =	ds_args.num_layers
            lowerCamelCase					:					Any        =	ds_args.num_attention_heads
            lowerCamelCase					:					Optional[int]        =	ds_args.ffn_hidden_size
            # pprint(config)

      # The number of heads.
      lowerCamelCase					:					Union[str, Any]        =	config.n_head
      # The hidden_size per head.
      lowerCamelCase					:					List[Any]        =	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					:					str        =	0.0

      # The model.
      lowerCamelCase					:					Optional[int]        =	input_state_dict["""model"""]
      # The language model.
      lowerCamelCase					:					Union[str, Any]        =	model["""language_model"""]
      # The embeddings.
      lowerCamelCase					:					str        =	lm["""embedding"""]

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

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

      # The transformer.
      lowerCamelCase					:					int        =	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					:					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.
            lowerCamelCase					:					Optional[Any]        =	layer_re.match(__a    )

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

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

            # The name of the layer.
            lowerCamelCase					:					List[Any]        =	F'transformer.h.{layer_idx}'

            # For layernorm(s), simply store the layer norm.
            if op_name.endswith("""layernorm"""    ):
                  lowerCamelCase					:					int        =	"""ln_1""" if op_name.startswith("""input"""    ) else """ln_2"""
                  lowerCamelCase					:					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.
                  lowerCamelCase					:					Dict        =	torch.tril(torch.ones((n_positions, n_positions)			,				dtype=torch.floataa    )    ).view(
                      1			,				1			,				__a			,				__a    )
                  lowerCamelCase					:					str        =	causal_mask

                  # Insert a "dummy" tensor for masked_bias.
                  lowerCamelCase					:					Dict        =	torch.tensor(-1E4			,				dtype=torch.floataa    )
                  lowerCamelCase					:					int        =	masked_bias

                  lowerCamelCase					:					Tuple        =	fix_query_key_value_ordering(__a			,				__a			,				3			,				__a			,				__a    )
                  # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
                  lowerCamelCase					:					List[str]        =	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					:					int        =	fix_query_key_value_ordering(__a			,				__a			,				3			,				__a			,				__a    )
                  # Store. No change of shape.
                  lowerCamelCase					:					Optional[int]        =	out_val

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

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

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

      # The final layernorm.
      lowerCamelCase					:					List[Any]        =	transformer["""final_layernorm.weight"""]
      lowerCamelCase					:					Optional[Any]        =	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   snake_case		(     )   ->      List[str]:
      lowerCamelCase					:					Optional[Any]        =	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."""			,				)
      lowerCamelCase					:					Dict        =	parser.parse_args()

      # Extract the basename.
      lowerCamelCase					:					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:
                        lowerCamelCase					:					List[Any]        =	torch.load(__a			,				map_location="""cpu"""    )
      else:
            lowerCamelCase					:					str        =	torch.load(args.path_to_checkpoint			,				map_location="""cpu"""    )

      lowerCamelCase					:					int        =	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:
                        lowerCamelCase					:					Tuple        =	"""gelu_fast"""
                  elif ds_args.openai_gelu:
                        lowerCamelCase					:					Union[str, Any]        =	"""gelu_new"""
                  else:
                        lowerCamelCase					:					Tuple        =	"""gelu"""
            else:
                  # in the very early days this used to be "gelu_new"
                  lowerCamelCase					:					Tuple        =	"""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=__a			,				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=__a			,				summary_activation=__a			,				summary_proj_to_labels=__a			,				summary_first_dropout=0.1			,				scale_attn_weights=__a			,				use_cache=__a			,				bos_token_id=50256			,				eos_token_id=50256			,				)
      else:
            lowerCamelCase					:					Any        =	GPTaConfig.from_json_file(args.config_file    )

      lowerCamelCase					:					List[Any]        =	["""GPT2LMHeadModel"""]

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

      lowerCamelCase					:					List[Any]        =	AutoTokenizer.from_pretrained(__a    )
      lowerCamelCase					:					Dict        =	type(__a    ).__name__
      lowerCamelCase					:					Dict        =	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.
      lowerCamelCase					:					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()

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








"""simple docstring"""


import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					Tuple													=(KDPMaDiscreteScheduler,)
     snake_case__	:					Tuple													=10

     def 		a__						(	self:						List[Any]     ,		**__a:						Optional[int]	)->    Union[str, Any]:
           lowerCamelCase					:					int        =	{
               """num_train_timesteps""": 1_100,
               """beta_start""": 0.00_01,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
           }

           config.update(**__a	)
           return config

     def 		a__						(	self:						Union[str, Any]	)->    Any:
           for timesteps in [10, 50, 100, 1_000]:
                 self.check_over_configs(num_train_timesteps=__a	)

     def 		a__						(	self:						str	)->    int:
           for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]     ,		[0.00_02, 0.0_02, 0.02]	):
                 self.check_over_configs(beta_start=__a     ,		beta_end=__a	)

     def 		a__						(	self:						int	)->    Union[str, Any]:
           for schedule in ["linear", "scaled_linear"]:
                 self.check_over_configs(beta_schedule=__a	)

     def 		a__						(	self:						List[Any]	)->    List[Any]:
           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=__a	)

     def 		a__						(	self:						Union[str, Any]	)->    int:
           lowerCamelCase					:					List[str]        =	self.scheduler_classes[0]
           lowerCamelCase					:					Union[str, Any]        =	self.get_scheduler_config(prediction_type="""v_prediction"""	)
           lowerCamelCase					:					List[str]        =	scheduler_class(**__a	)

           scheduler.set_timesteps(self.num_inference_steps	)

           lowerCamelCase					:					Dict        =	self.dummy_model()
           lowerCamelCase					:					List[Any]        =	self.dummy_sample_deter * scheduler.init_noise_sigma
           lowerCamelCase					:					List[Any]        =	sample.to(__a	)

           for i, t in enumerate(scheduler.timesteps	):
                 lowerCamelCase					:					Optional[Any]        =	scheduler.scale_model_input(__a     ,		__a	)

                 lowerCamelCase					:					Optional[int]        =	model(__a     ,		__a	)

                 lowerCamelCase					:					Tuple        =	scheduler.step(__a     ,		__a     ,		__a	)
                 lowerCamelCase					:					Optional[Any]        =	output.prev_sample

           lowerCamelCase					:					List[str]        =	torch.sum(torch.abs(__a	)	)
           lowerCamelCase					:					Tuple        =	torch.mean(torch.abs(__a	)	)

           if torch_device in ["cpu", "mps"]:
                 assert abs(result_sum.item() - 4.6_9_3_4e-0_7	) < 1e-2
                 assert abs(result_mean.item() - 6.1_1_1_2e-1_0	) < 1e-3
           else:
                 # CUDA
                 assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2e-0_7	) < 1e-2
                 assert abs(result_mean.item() - 0.00_02	) < 1e-3

     def 		a__						(	self:						Any	)->    Any:
           if torch_device == "mps":
                 return
           lowerCamelCase					:					Dict        =	self.scheduler_classes[0]
           lowerCamelCase					:					Dict        =	self.get_scheduler_config()
           lowerCamelCase					:					int        =	scheduler_class(**__a	)

           scheduler.set_timesteps(self.num_inference_steps	)

           lowerCamelCase					:					List[Any]        =	self.dummy_model()
           lowerCamelCase					:					Optional[Any]        =	self.dummy_sample_deter * scheduler.init_noise_sigma
           lowerCamelCase					:					Optional[int]        =	sample.to(__a	)

           for i, t in enumerate(scheduler.timesteps	):
                 lowerCamelCase					:					Dict        =	scheduler.scale_model_input(__a     ,		__a	)

                 lowerCamelCase					:					Optional[Any]        =	model(__a     ,		__a	)

                 lowerCamelCase					:					Tuple        =	scheduler.step(__a     ,		__a     ,		__a	)
                 lowerCamelCase					:					str        =	output.prev_sample

           lowerCamelCase					:					Tuple        =	torch.sum(torch.abs(__a	)	)
           lowerCamelCase					:					Tuple        =	torch.mean(torch.abs(__a	)	)

           if torch_device in ["cpu", "mps"]:
                 assert abs(result_sum.item() - 20.41_25	) < 1e-2
                 assert abs(result_mean.item() - 0.02_66	) < 1e-3
           else:
                 # CUDA
                 assert abs(result_sum.item() - 20.41_25	) < 1e-2
                 assert abs(result_mean.item() - 0.02_66	) < 1e-3







     def 		a__						(	self:						Optional[Any]	)->    List[Any]:
           if torch_device == "mps":
                 return
           lowerCamelCase					:					Any        =	self.scheduler_classes[0]
           lowerCamelCase					:					Union[str, Any]        =	self.get_scheduler_config()
           lowerCamelCase					:					Optional[Any]        =	scheduler_class(**__a	)

           scheduler.set_timesteps(self.num_inference_steps     ,		device=__a	)

           lowerCamelCase					:					Union[str, Any]        =	self.dummy_model()
           lowerCamelCase					:					List[str]        =	self.dummy_sample_deter.to(__a	) * scheduler.init_noise_sigma

           for t in scheduler.timesteps:
                 lowerCamelCase					:					Union[str, Any]        =	scheduler.scale_model_input(__a     ,		__a	)

                 lowerCamelCase					:					Optional[int]        =	model(__a     ,		__a	)

                 lowerCamelCase					:					int        =	scheduler.step(__a     ,		__a     ,		__a	)
                 lowerCamelCase					:					int        =	output.prev_sample

           lowerCamelCase					:					Union[str, Any]        =	torch.sum(torch.abs(__a	)	)
           lowerCamelCase					:					int        =	torch.mean(torch.abs(__a	)	)

           if str(__a	).startswith("""cpu"""	):
                 # The following sum varies between 148 and 156 on mps. Why?
                 assert abs(result_sum.item() - 20.41_25	) < 1e-2
                 assert abs(result_mean.item() - 0.02_66	) < 1e-3
           else:
                 # CUDA
                 assert abs(result_sum.item() - 20.41_25	) < 1e-2
                 assert abs(result_mean.item() - 0.02_66	) < 1e-3




