
"""simple docstring"""
import unittest

from transformers import MarkupLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        MarkupLMForQuestionAnswering,
        MarkupLMForSequenceClassification,
        MarkupLMForTokenClassification,
        MarkupLMModel,
    )

# TODO check dependencies
from transformers import MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer






class   _UpperCAmelCase					:
    def __init__(     self    ,     snake_case_    ,     snake_case_=13    ,     snake_case_=7    ,     snake_case_=True    ,     snake_case_=True    ,     snake_case_=True    ,     snake_case_=True    ,     snake_case_=99    ,     snake_case_=32    ,     snake_case_=5    ,     snake_case_=4    ,     snake_case_=37    ,     snake_case_="gelu"    ,     snake_case_=0.1    ,     snake_case_=0.1    ,     snake_case_=5_12    ,     snake_case_=16    ,     snake_case_=2    ,     snake_case_=0.02    ,     snake_case_=3    ,     snake_case_=None    ,     snake_case_=20    ,     snake_case_=30    ,     snake_case_=2    ,     snake_case_=2    ,     snake_case_=10    ,     ):
      _snake_case  :    Optional[Any]            = parent
      _snake_case  :    Tuple            = batch_size
      _snake_case  :    List[str]            = seq_length
      _snake_case  :    Optional[int]            = is_training
      _snake_case  :    List[Any]            = use_input_mask
      _snake_case  :    Tuple            = use_token_type_ids
      _snake_case  :    Union[str, Any]            = use_labels
      _snake_case  :    Union[str, Any]            = vocab_size
      _snake_case  :    int            = hidden_size
      _snake_case  :    Optional[Any]            = num_hidden_layers
      _snake_case  :    List[Any]            = num_attention_heads
      _snake_case  :    int            = intermediate_size
      _snake_case  :    Union[str, Any]            = hidden_act
      _snake_case  :    Union[str, Any]            = hidden_dropout_prob
      _snake_case  :    Optional[int]            = attention_probs_dropout_prob
      _snake_case  :    Union[str, Any]            = max_position_embeddings
      _snake_case  :    int            = type_vocab_size
      _snake_case  :    Tuple            = type_sequence_label_size
      _snake_case  :    Tuple            = initializer_range
      _snake_case  :    Optional[Any]            = num_labels
      _snake_case  :    Union[str, Any]            = scope
      _snake_case  :    int            = max_xpath_tag_unit_embeddings
      _snake_case  :    Optional[int]            = max_xpath_subs_unit_embeddings
      _snake_case  :    Optional[Any]            = tag_pad_id
      _snake_case  :    str            = subs_pad_id
      _snake_case  :    List[str]            = max_depth
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[Any]            = ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size  )

      _snake_case  :    List[str]            = ids_tensor(
          [self.batch_size, self.seq_length, self.max_depth]    ,     self.max_xpath_tag_unit_embeddings  )

      _snake_case  :    Any            = ids_tensor(
          [self.batch_size, self.seq_length, self.max_depth]    ,     self.max_xpath_subs_unit_embeddings  )

      _snake_case  :    Dict            = None
      if self.use_input_mask:
        _snake_case  :    Tuple            = ids_tensor([self.batch_size, self.seq_length]    ,     vocab_size=2  )

      _snake_case  :    int            = None
      if self.use_token_type_ids:
        _snake_case  :    Tuple            = ids_tensor([self.batch_size, self.seq_length]    ,     self.type_vocab_size  )

      _snake_case  :    List[str]            = None
      _snake_case  :    str            = None
      if self.use_labels:
        _snake_case  :    int            = ids_tensor([self.batch_size]    ,     self.type_sequence_label_size  )
        _snake_case  :    Union[str, Any]            = ids_tensor([self.batch_size, self.seq_length]    ,     self.num_labels  )

      _snake_case  :    Optional[int]            = self.get_config()

      return (
          config,
          input_ids,
          xpath_tags_seq,
          xpath_subs_seq,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
      )
    def        lowerCamelCase__						(     self  ):
      return MarkupLMConfig(
          vocab_size=self.vocab_size    ,     hidden_size=self.hidden_size    ,     num_hidden_layers=self.num_hidden_layers    ,     num_attention_heads=self.num_attention_heads    ,     intermediate_size=self.intermediate_size    ,     hidden_act=self.hidden_act    ,     hidden_dropout_prob=self.hidden_dropout_prob    ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,     max_position_embeddings=self.max_position_embeddings    ,     type_vocab_size=self.type_vocab_size    ,     initializer_range=self.initializer_range    ,     max_xpath_tag_unit_embeddings=self.max_xpath_tag_unit_embeddings    ,     max_xpath_subs_unit_embeddings=self.max_xpath_subs_unit_embeddings    ,     tag_pad_id=self.tag_pad_id    ,     subs_pad_id=self.subs_pad_id    ,     max_depth=self.max_depth    ,     )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     ):
      _snake_case  :    Optional[int]            = MarkupLMModel(config=snake_case_  )
      model.to(snake_case_  )
      model.eval()
      print("Configs:"    ,     model.config.tag_pad_id    ,     model.config.subs_pad_id  )
      _snake_case  :    List[Any]            = model(snake_case_    ,     attention_mask=snake_case_    ,     token_type_ids=snake_case_  )
      _snake_case  :    List[Any]            = model(snake_case_    ,     token_type_ids=snake_case_  )
      _snake_case  :    Optional[int]            = model(snake_case_  )
      self.parent.assertEqual(result.last_hidden_state.shape    ,     (self.batch_size, self.seq_length, self.hidden_size)  )
      self.parent.assertEqual(result.pooler_output.shape    ,     (self.batch_size, self.hidden_size)  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     ):
      _snake_case  :    str            = self.num_labels
      _snake_case  :    int            = MarkupLMForSequenceClassification(snake_case_  )
      model.to(snake_case_  )
      model.eval()
      _snake_case  :    int            = model(
          snake_case_    ,     xpath_tags_seq=snake_case_    ,     xpath_subs_seq=snake_case_    ,     attention_mask=snake_case_    ,     token_type_ids=snake_case_    ,     labels=snake_case_    ,     )
      self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.num_labels)  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     ):
      _snake_case  :    int            = self.num_labels
      _snake_case  :    List[Any]            = MarkupLMForTokenClassification(config=snake_case_  )
      model.to(snake_case_  )
      model.eval()
      _snake_case  :    List[str]            = model(
          snake_case_    ,     xpath_tags_seq=snake_case_    ,     xpath_subs_seq=snake_case_    ,     attention_mask=snake_case_    ,     token_type_ids=snake_case_    ,     labels=snake_case_    ,     )
      self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.seq_length, self.num_labels)  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     ):
      _snake_case  :    Any            = MarkupLMForQuestionAnswering(config=snake_case_  )
      model.to(snake_case_  )
      model.eval()
      _snake_case  :    str            = model(
          snake_case_    ,     xpath_tags_seq=snake_case_    ,     xpath_subs_seq=snake_case_    ,     attention_mask=snake_case_    ,     token_type_ids=snake_case_    ,     start_positions=snake_case_    ,     end_positions=snake_case_    ,     )
      self.parent.assertEqual(result.start_logits.shape    ,     (self.batch_size, self.seq_length)  )
      self.parent.assertEqual(result.end_logits.shape    ,     (self.batch_size, self.seq_length)  )


    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Optional[Any]            = self.prepare_config_and_inputs()
      (
          (
          _snake_case
      )      ,       (
          _snake_case
      )      ,       (
          _snake_case
      )      ,       (
          _snake_case
      )      ,       (
          _snake_case
      )      ,       (
          _snake_case
      )      ,       (
          _snake_case
      )      ,       (
          _snake_case
      )      ,       
      )  :    Tuple            = config_and_inputs
      _snake_case  :    Optional[Any]            = {
          "input_ids": input_ids,
          "xpath_tags_seq": xpath_tags_seq,
          "xpath_subs_seq": xpath_subs_seq,
          "token_type_ids": token_type_ids,
          "attention_mask": input_mask,
      }
      return config, inputs_dict






@require_torch
class   _UpperCAmelCase					(       _snake_case		,       _snake_case		,       unittest.TestCase):
    __lowercase				:			Union[str, Any]	=       (
        (
            MarkupLMModel,
            MarkupLMForSequenceClassification,
            MarkupLMForTokenClassification,
            MarkupLMForQuestionAnswering,
        )
        if is_torch_available()
        else None
    )
    __lowercase				:			Optional[Any]	=       (
        {
            """feature-extraction""": MarkupLMModel,
            """question-answering""": MarkupLMForQuestionAnswering,
            """text-classification""": MarkupLMForSequenceClassification,
            """token-classification""": MarkupLMForTokenClassification,
            """zero-shot""": MarkupLMForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      # ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]`
      # (batch of pretokenized examples).
      return True
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    str            = MarkupLMModelTester(self  )
      _snake_case  :    Optional[int]            = ConfigTester(self    ,     config_class=snake_case_    ,     hidden_size=37  )
    def        lowerCamelCase__						(     self  ):
      self.config_tester.run_common_tests()
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    int            = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*snake_case_  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    int            = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*snake_case_  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Optional[Any]            = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*snake_case_  )


    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Any            = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*snake_case_  )


def 					a__ ( ):

  """simple docstring"""







  _snake_case  :    int            = "\n    <!DOCTYPE html>\n    <html>\n    <head>\n    <title>Page Title</title>\n    </head>\n    <body>\n\n    <h1>This is a Heading</h1>\n    <p>This is a paragraph.</p>\n\n    </body>\n    </html>\n    "

  return html_string






@require_torch
class   _UpperCAmelCase					(       unittest.TestCase):
    @cached_property
    def        lowerCamelCase__						(     self  ):
      # TODO use from_pretrained here
      _snake_case  :    List[Any]            = MarkupLMFeatureExtractor()
      _snake_case  :    Optional[int]            = MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base"  )

      return MarkupLMProcessor(snake_case_    ,     snake_case_  )


    @slow
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[Any]            = MarkupLMModel.from_pretrained("microsoft/markuplm-base"  ).to(snake_case_  )

      _snake_case  :    List[str]            = self.default_processor

      _snake_case  :    List[str]            = processor(prepare_html_string()    ,     return_tensors="pt"  )
      _snake_case  :    Tuple            = inputs.to(snake_case_  )

      # forward pass
      with torch.no_grad():
        _snake_case  :    Optional[Any]            = model(**snake_case_  )

      # verify the last hidden states
      _snake_case  :    Tuple            = torch.Size([1, 14, 7_68]  )
      self.assertEqual(outputs.last_hidden_state.shape    ,     snake_case_  )

      _snake_case  :    Dict            = torch.tensor(
          [[0.0675, -0.0052, 0.5001], [-0.2281, 0.0802, 0.2192], [-0.0583, -0.3311, 0.1185]]  ).to(snake_case_  )

      self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]    ,     snake_case_    ,     atol=1E-4  )  )




"""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_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 							_lowerCAmelCase   (    UpperCAmelCase  :				Union[str, Any]   ,				UpperCAmelCase  :				Tuple   ,				UpperCAmelCase  :				Tuple=0					):


					'''simple docstring'''






					if name is None:
										UpperCamelCase__		:     Any								=None
					else:
										UpperCamelCase__		:     str								='''.''' * max(0   ,				spaces - 2					) + '''# {:''' + str(50 - spaces					) + '''s}'''
										UpperCamelCase__		:     int								=fmt.format(UpperCAmelCase					)

					# Print and recurse (if needed).
					if isinstance(UpperCAmelCase   ,				UpperCAmelCase					):
										if msg is not None:
															print(UpperCAmelCase					)
										for k in val.keys():
															recursive_print(UpperCAmelCase   ,				val[k]   ,				spaces + 2					)
					elif isinstance(UpperCAmelCase   ,				torch.Tensor					):
										print(UpperCAmelCase   ,				''':'''   ,				val.size()					)
					else:
										print(UpperCAmelCase   ,				''':'''   ,				UpperCAmelCase					)



def 							_lowerCAmelCase   (    UpperCAmelCase  :				List[str]   ,				UpperCAmelCase  :				Dict   ,				UpperCAmelCase  :				Union[str, Any]   ,				UpperCAmelCase  :				List[str]   ,				UpperCAmelCase  :				List[Any]					):


					'''simple docstring'''






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



def 							_lowerCAmelCase   (    UpperCAmelCase  :				Optional[int]   ,				UpperCAmelCase  :				Tuple   ,				UpperCAmelCase  :				int					):


					'''simple docstring'''






					UpperCamelCase__		:     int								={}

					# old versions did not store training args
					UpperCamelCase__		:     Optional[int]								=input_state_dict.get('''args'''   ,				UpperCAmelCase					)
					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))

										UpperCamelCase__		:     List[str]								=ds_args.tokenizer_type
										UpperCamelCase__		:     str								=ds_args.padded_vocab_size
										UpperCamelCase__		:     List[str]								=ds_args.max_position_embeddings
										UpperCamelCase__		:     Any								=ds_args.hidden_size
										UpperCamelCase__		:     Optional[Any]								=ds_args.num_layers
										UpperCamelCase__		:     Optional[Any]								=ds_args.num_attention_heads
										UpperCamelCase__		:     Optional[Any]								=ds_args.ffn_hidden_size if '''ffn_hidden_size''' in ds_args else 4 * ds_args.hidden_size
										# pprint(config)

					# The number of heads.
					UpperCamelCase__		:     List[str]								=config.num_attention_heads
					# The hidden_size per head.
					UpperCamelCase__		:     Union[str, Any]								=config.hidden_size // heads
					# Megatron-LM checkpoint version
					if "checkpoint_version" in input_state_dict.keys():
										UpperCamelCase__		:     Optional[Any]								=input_state_dict['''checkpoint_version''']
					else:
										UpperCamelCase__		:     int								=0.0

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

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

					# The position embeddings.
					UpperCamelCase__		:     Dict								=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.
					UpperCamelCase__		:     List[str]								=pos_embeddings

					# The token-type embeddings.
					UpperCamelCase__		:     str								=embeddings['''tokentype_embeddings''']['''weight''']
					# Store the position embeddings.
					UpperCamelCase__		:     List[str]								=tokentype_embeddings

					# The transformer.
					UpperCamelCase__		:     Optional[int]								=lm['''transformer'''] if '''transformer''' in lm.keys() else lm['''encoder''']

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

					# The simple map of names for "automated" rules.
					UpperCamelCase__		:     List[Any]								={
					    '''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.
					UpperCamelCase__		:     Tuple								=None

					# Extract the layers.
					for key, val in transformer.items():
										# Match the name.
										UpperCamelCase__		:     Union[str, Any]								=layer_re.match(UpperCAmelCase					)

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

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

										# The name of the layer.
										UpperCamelCase__		:     str								=F'''bert.encoder.layer.{layer_idx}'''

										# For layernorm(s), simply store the layer norm.
										if op_name.endswith('''layernorm'''					):
															UpperCamelCase__		:     int								='''attention.ln''' if op_name.startswith('''input'''					) else '''ln'''
															UpperCamelCase__		:     Tuple								=val

										# Transpose the QKV matrix.
										elif (
										    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
										) and weight_or_bias == "weight":
															# Make sure the QKV pointer is nil.
															assert attention_qkv_weight is None, ""

															UpperCamelCase__		:     Any								=fix_query_key_value_ordering(UpperCAmelCase   ,				UpperCAmelCase   ,				3   ,				UpperCAmelCase   ,				UpperCAmelCase					)
															# Store the tensor as we need the bias as well to interleave QKV and biases.
															UpperCamelCase__		:     Tuple								=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.
															UpperCamelCase__		:     Tuple								=attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
															UpperCamelCase__		:     Tuple								=attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
															UpperCamelCase__		:     str								=attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

															UpperCamelCase__		:     int								=fix_query_key_value_ordering(UpperCAmelCase   ,				UpperCAmelCase   ,				3   ,				UpperCAmelCase   ,				UpperCAmelCase					)
															# Split the bias.
															UpperCamelCase__		:     Any								=out_val[0 * config.hidden_size : 1 * config.hidden_size]
															UpperCamelCase__		:     Any								=out_val[1 * config.hidden_size : 2 * config.hidden_size]
															UpperCamelCase__		:     List[Any]								=out_val[2 * config.hidden_size : 3 * config.hidden_size]

															# Store.
															UpperCamelCase__		:     int								=q
															UpperCamelCase__		:     Dict								=q_bias
															UpperCamelCase__		:     List[Any]								=k
															UpperCamelCase__		:     Optional[int]								=k_bias
															UpperCamelCase__		:     Tuple								=v
															UpperCamelCase__		:     Optional[Any]								=v_bias

															# Clear the stored tensor.
															UpperCamelCase__		:     Optional[Any]								=None

										# Copy weights and biases as is.
										elif weight_or_bias in ["weight", "bias"]:
															UpperCamelCase__		:     str								=megatron_to_transformers[op_name]
															UpperCamelCase__		:     Any								=val

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

					# The pooler.
					UpperCamelCase__		:     str								=lm['''pooler''']

					# Store the matrix and the bias.
					UpperCamelCase__		:     Optional[Any]								=pooler['''dense.weight''']
					UpperCamelCase__		:     Tuple								=pooler['''dense.bias''']

					# The LM head from Megatron (for RACE).
					UpperCamelCase__		:     Tuple								=model['''lm_head''']

					# The transform matrix.
					UpperCamelCase__		:     Optional[Any]								=lm_head['''dense.weight''']
					UpperCamelCase__		:     Union[str, Any]								=lm_head['''dense.bias''']

					# The transform LN.
					UpperCamelCase__		:     Optional[int]								=lm_head['''layernorm.weight''']
					UpperCamelCase__		:     Dict								=lm_head['''layernorm.bias''']

					# For the decoder, we replicate the weights.
					UpperCamelCase__		:     Any								=word_embeddings
					UpperCamelCase__		:     Optional[Any]								=lm_head['''bias''']

					# The classifier from Megatron (for MLNI).
					UpperCamelCase__		:     Union[str, Any]								=model['''binary_head''']

					# Store the classifier.
					UpperCamelCase__		:     Union[str, Any]								=binary_head['''weight''']
					UpperCamelCase__		:     int								=binary_head['''bias''']

					# It should be done!
					return output_state_dict



def 							_lowerCAmelCase   (    ):


					'''simple docstring'''






					UpperCamelCase__		:     Dict								=argparse.ArgumentParser()
					parser.add_argument('''--print-checkpoint-structure'''   ,				action='''store_true'''					)
					parser.add_argument('''path_to_checkpoint'''   ,				type=UpperCAmelCase   ,				help='''Path to the ZIP file containing the checkpoint'''					)
					parser.add_argument(
					    '''--config_file'''   ,				default=''''''   ,				type=UpperCAmelCase   ,				help='''An optional config json file describing the pre-trained model.'''   ,				)
					UpperCamelCase__		:     Dict								=parser.parse_args()

					# Extract the basename.
					UpperCamelCase__		:     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:
																				UpperCamelCase__		:     Optional[int]								=torch.load(UpperCAmelCase   ,				map_location='''cpu'''					)
					else:
										UpperCamelCase__		:     Optional[Any]								=torch.load(args.path_to_checkpoint   ,				map_location='''cpu'''					)

					if args.config_file == "":
										# Default config of megatron-bert 345m
										UpperCamelCase__		:     Union[str, Any]								=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
										UpperCamelCase__		:     Tuple								=input_state_dict['''model''']['''lm_head''']['''bias'''].numel()
					else:
										UpperCamelCase__		:     Any								=MegatronBertConfig.from_json_file(args.config_file					)

					# Convert.
					print('''Converting'''					)
					UpperCamelCase__		:     str								=convert_megatron_checkpoint(UpperCAmelCase   ,				UpperCAmelCase   ,				UpperCAmelCase					)

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

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

					# Store the state_dict to file.
					UpperCamelCase__		:     Union[str, Any]								=os.path.join(UpperCAmelCase   ,				'''pytorch_model.bin'''					)
					print(F'''Saving checkpoint to "{output_checkpoint_file}"'''					)
					torch.save(UpperCAmelCase   ,				UpperCAmelCase					)


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

if __name__ == "__main__":
				main()

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


