

"""simple docstring"""


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Tuple       = (DEISMultistepScheduler,)
     _SCREAMING_SNAKE_CASE						:List[Any]       = (("""num_inference_steps""", 25),)

     def 					_a			(						self	,    **_a  )  ->      Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	{
               """num_train_timesteps""": 1_000,
               """beta_start""": 0.0_001,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
               """solver_order""": 2,
           }

           config.update(**_a  )
           return config

     def 					_a			(						self	,    _a=0	,    **_a  )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Dict      		=	dict(self.forward_default_kwargs  )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	kwargs.pop("""num_inference_steps"""	,    _a  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.dummy_sample
           SCREAMING_SNAKE_CASE__   :      str      		=	0.1 * sample
           SCREAMING_SNAKE_CASE__   :      str      		=	[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.get_scheduler_config(**_a  )
                 SCREAMING_SNAKE_CASE__   :      Dict      		=	scheduler_class(**_a  )
                 scheduler.set_timesteps(_a  )
                 # copy over dummy past residuals
                 SCREAMING_SNAKE_CASE__   :      Any      		=	dummy_past_residuals[: scheduler.config.solver_order]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(_a  )
                       SCREAMING_SNAKE_CASE__   :      Tuple      		=	scheduler_class.from_pretrained(_a  )
                       new_scheduler.set_timesteps(_a  )
                       # copy over dummy past residuals
                       SCREAMING_SNAKE_CASE__   :      str      		=	dummy_past_residuals[: new_scheduler.config.solver_order]

                 SCREAMING_SNAKE_CASE__   :      Any      		=	sample, sample
                 for t in range(_a	,    time_step + scheduler.config.solver_order + 1  ):
                       SCREAMING_SNAKE_CASE__   :      str      		=	scheduler.step(_a	,    _a	,    _a	,    **_a  ).prev_sample
                       SCREAMING_SNAKE_CASE__   :      Any      		=	new_scheduler.step(_a	,    _a	,    _a	,    **_a  ).prev_sample

                       assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"

     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           pass

     def 					_a			(						self	,    _a=0	,    **_a  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	dict(self.forward_default_kwargs  )
           SCREAMING_SNAKE_CASE__   :      int      		=	kwargs.pop("""num_inference_steps"""	,    _a  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	self.dummy_sample
           SCREAMING_SNAKE_CASE__   :      Any      		=	0.1 * sample
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                 SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.get_scheduler_config()
                 SCREAMING_SNAKE_CASE__   :      int      		=	scheduler_class(**_a  )
                 scheduler.set_timesteps(_a  )

                 # copy over dummy past residuals (must be after setting timesteps)
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	dummy_past_residuals[: scheduler.config.solver_order]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(_a  )
                       SCREAMING_SNAKE_CASE__   :      Tuple      		=	scheduler_class.from_pretrained(_a  )
                       # copy over dummy past residuals
                       new_scheduler.set_timesteps(_a  )

                       # copy over dummy past residual (must be after setting timesteps)
                       SCREAMING_SNAKE_CASE__   :      Any      		=	dummy_past_residuals[: new_scheduler.config.solver_order]

                 SCREAMING_SNAKE_CASE__   :      List[Any]      		=	scheduler.step(_a	,    _a	,    _a	,    **_a  ).prev_sample
                 SCREAMING_SNAKE_CASE__   :      str      		=	new_scheduler.step(_a	,    _a	,    _a	,    **_a  ).prev_sample

                 assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"

     def 					_a			(						self	,    _a=None	,    **_a  )  ->      Union[str, Any]:


           """simple docstring"""


           if scheduler is None:
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.scheduler_classes[0]
                 SCREAMING_SNAKE_CASE__   :      Any      		=	self.get_scheduler_config(**_a  )
                 SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.get_scheduler_config(**_a  )
           SCREAMING_SNAKE_CASE__   :      str      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      str      		=	10
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.dummy_model()
           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.dummy_sample_deter
           scheduler.set_timesteps(_a  )

           for i, t in enumerate(scheduler.timesteps  ):
                 SCREAMING_SNAKE_CASE__   :      str      		=	model(_a	,    _a  )
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	scheduler.step(_a	,    _a	,    _a  ).prev_sample

           return sample

     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	dict(self.forward_default_kwargs  )

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	kwargs.pop("""num_inference_steps"""	,    _a  )

           for scheduler_class in self.scheduler_classes:
                 SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.get_scheduler_config()
                 SCREAMING_SNAKE_CASE__   :      str      		=	scheduler_class(**_a  )

                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.dummy_sample
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	0.1 * sample

                 if num_inference_steps is not None and hasattr(_a	,    """set_timesteps"""  ):
                       scheduler.set_timesteps(_a  )
                 elif num_inference_steps is not None and not hasattr(_a	,    """set_timesteps"""  ):
                       SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	num_inference_steps

                 # copy over dummy past residuals (must be done after set_timesteps)
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[residual + 0.2, residual + 0.15, residual + 0.10]
                 SCREAMING_SNAKE_CASE__   :      List[Any]      		=	dummy_past_residuals[: scheduler.config.solver_order]

                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	scheduler.timesteps[5]
                 SCREAMING_SNAKE_CASE__   :      Any      		=	scheduler.timesteps[6]

                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	scheduler.step(_a	,    _a	,    _a	,    **_a  ).prev_sample
                 SCREAMING_SNAKE_CASE__   :      str      		=	scheduler.step(_a	,    _a	,    _a	,    **_a  ).prev_sample

                 self.assertEqual(output_a.shape	,    sample.shape  )
                 self.assertEqual(output_a.shape	,    output_a.shape  )

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	DEISMultistepScheduler(**self.get_scheduler_config()  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.full_loop(scheduler=_a  )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	torch.mean(torch.abs(_a  )  )

           assert abs(result_mean.item() - 0.23_916  ) < 1E-3

           SCREAMING_SNAKE_CASE__   :      Dict      		=	DPMSolverSinglestepScheduler.from_config(scheduler.config  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	DPMSolverMultistepScheduler.from_config(scheduler.config  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	UniPCMultistepScheduler.from_config(scheduler.config  )
           SCREAMING_SNAKE_CASE__   :      str      		=	DEISMultistepScheduler.from_config(scheduler.config  )

           SCREAMING_SNAKE_CASE__   :      str      		=	self.full_loop(scheduler=_a  )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	torch.mean(torch.abs(_a  )  )

           assert abs(result_mean.item() - 0.23_916  ) < 1E-3

     def 					_a			(						self  )  ->      List[str]:


           """simple docstring"""


           for timesteps in [25, 50, 100, 999, 1_000]:
                 self.check_over_configs(num_train_timesteps=_a  )

     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           self.check_over_configs(thresholding=_a  )
           for order in [1, 2, 3]:
                 for solver_type in ["logrho"]:
                       for threshold in [0.5, 1.0, 2.0]:
                             for prediction_type in ["epsilon", "sample"]:
                                   self.check_over_configs(
                                       thresholding=_a	,    prediction_type=_a	,    sample_max_value=_a	,    algorithm_type="""deis"""	,    solver_order=_a	,    solver_type=_a	,    )

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=_a  )

     def 					_a			(						self  )  ->      Union[str, Any]:


           """simple docstring"""


           for algorithm_type in ["deis"]:
                 for solver_type in ["logrho"]:
                       for order in [1, 2, 3]:
                             for prediction_type in ["epsilon", "sample"]:
                                   self.check_over_configs(
                                       solver_order=_a	,    solver_type=_a	,    prediction_type=_a	,    algorithm_type=_a	,    )
                                   SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.full_loop(
                                       solver_order=_a	,    solver_type=_a	,    prediction_type=_a	,    algorithm_type=_a	,    )
                                   assert not torch.isnan(_a  ).any(), "Samples have nan numbers"

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           self.check_over_configs(lower_order_final=_a  )
           self.check_over_configs(lower_order_final=_a  )

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
                 self.check_over_forward(num_inference_steps=_a	,    time_step=0  )

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	self.full_loop()
           SCREAMING_SNAKE_CASE__   :      Dict      		=	torch.mean(torch.abs(_a  )  )

           assert abs(result_mean.item() - 0.23_916  ) < 1E-3

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.full_loop(prediction_type="""v_prediction"""  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	torch.mean(torch.abs(_a  )  )

           assert abs(result_mean.item() - 0.091  ) < 1E-3





     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.get_scheduler_config(thresholding=_a	,    dynamic_thresholding_ratio=0  )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	10
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.dummy_model()
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.dummy_sample_deter.half()
           scheduler.set_timesteps(_a  )

           for i, t in enumerate(scheduler.timesteps  ):
                 SCREAMING_SNAKE_CASE__   :      List[Any]      		=	model(_a	,    _a  )
                 SCREAMING_SNAKE_CASE__   :      int      		=	scheduler.step(_a	,    _a	,    _a  ).prev_sample

           assert sample.dtype == torch.floataa







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




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Tuple:
      # The converted output model.
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	{}

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

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

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

      # The model.
      SCREAMING_SNAKE_CASE__   :      Any      		=	input_state_dict["""model"""]
      # The language model.
      SCREAMING_SNAKE_CASE__   :      Any      		=	model["""language_model"""]
      # The embeddings.
      SCREAMING_SNAKE_CASE__   :      str      		=	lm["""embedding"""]

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

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

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

      # The regex to extract layer names.
      SCREAMING_SNAKE_CASE__   :      str      		=	re.compile(r"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"""							)

      # The simple map of names for "automated" rules.
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	{
          """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.
            SCREAMING_SNAKE_CASE__   :      str      		=	layer_re.match(__lowerCAmelCase							)

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

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

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

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

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

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

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

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

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

      # The final layernorm.
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	transformer["""final_layernorm.weight"""]
      SCREAMING_SNAKE_CASE__   :      str      		=	transformer["""final_layernorm.bias"""]

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

      # It should be done!
      return output_state_dict




def  _lowercase					(    )       ->				List[Any]:
      # Create the argument parser.
      SCREAMING_SNAKE_CASE__   :      str      		=	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."""  ,					)
      SCREAMING_SNAKE_CASE__   :      Dict      		=	parser.parse_args()

      # Extract the basename.
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	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:
                        SCREAMING_SNAKE_CASE__   :      List[Any]      		=	torch.load(__lowerCAmelCase  ,					map_location="""cpu"""							)
      else:
            SCREAMING_SNAKE_CASE__   :      str      		=	torch.load(args.path_to_checkpoint  ,					map_location="""cpu"""							)

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

            # Spell out all parameters in case the defaults change.
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	GPTaConfig(
                vocab_size=5_0257  ,					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=5_0256  ,					eos_token_id=5_0256  ,					)
      else:
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	GPTaConfig.from_json_file(args.config_file							)

      SCREAMING_SNAKE_CASE__   :      Tuple      		=	["""GPT2LMHeadModel"""]

      # Convert.
      print("""Converting"""							)
      SCREAMING_SNAKE_CASE__   :      Optional[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:
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	ds_args.tokenizer_type
            if tokenizer_type == "GPT2BPETokenizer":
                  SCREAMING_SNAKE_CASE__   :      Any      		=	"""gpt2"""
            elif tokenizer_type == "PretrainedFromHF":
                  SCREAMING_SNAKE_CASE__   :      Any      		=	ds_args.tokenizer_name_or_path
            else:
                  raise ValueError(F'''Unrecognized tokenizer_type {tokenizer_type}'''							)
      else:
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	"""gpt2"""

      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	AutoTokenizer.from_pretrained(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	type(__lowerCAmelCase							).__name__
      SCREAMING_SNAKE_CASE__   :      Dict      		=	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.
      SCREAMING_SNAKE_CASE__   :      Any      		=	os.path.join(__lowerCAmelCase  ,					"""pytorch_model.bin"""							)
      print(F'''Saving checkpoint to "{output_checkpoint_file}"'''							)
      torch.save(__lowerCAmelCase  ,					__lowerCAmelCase							)


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

if __name__ == "__main__":
  main()

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




