


from __future__ import annotations

import unittest

from transformers import CTRLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers.models.ctrl.modeling_tf_ctrl import (
     TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFCTRLForSequenceClassification,
     TFCTRLLMHeadModel,
     TFCTRLModel,
 )




class A  (       _lowerCAmelCase					):






      def __init__( self						:					Optional[int]      ,       lowercase_						:					Any      ,       ) ->						str:
             """simple docstring"""




             _lowerCamelCase	:		Dict          =parent
             _lowerCamelCase	:		Dict          =13
             _lowerCamelCase	:		Dict          =7
             _lowerCamelCase	:		Optional[int]          =True
             _lowerCamelCase	:		Optional[int]          =True
             _lowerCamelCase	:		List[str]          =True
             _lowerCamelCase	:		Union[str, Any]          =True
             _lowerCamelCase	:		Dict          =True
             _lowerCamelCase	:		List[str]          =99
             _lowerCamelCase	:		Tuple          =32
             _lowerCamelCase	:		str          =2
             _lowerCamelCase	:		int          =4
             _lowerCamelCase	:		List[str]          =37
             _lowerCamelCase	:		Optional[Any]          ='gelu'
             _lowerCamelCase	:		Tuple          =0.1
             _lowerCamelCase	:		List[Any]          =0.1
             _lowerCamelCase	:		Tuple          =512
             _lowerCamelCase	:		Tuple          =16
             _lowerCamelCase	:		Union[str, Any]          =2
             _lowerCamelCase	:		Tuple          =0.02
             _lowerCamelCase	:		int          =3
             _lowerCamelCase	:		Dict          =4
             _lowerCamelCase	:		Tuple          =None
             _lowerCamelCase	:		List[Any]          =self.vocab_size - 1






      def 	lowerCamelCase	( self						:					str			) ->						Any:
             """simple docstring"""




             _lowerCamelCase	:		Tuple          =ids_tensor([self.batch_size, self.seq_length]      ,       self.vocab_size			)

             _lowerCamelCase	:		Optional[Any]          =None
             if self.use_input_mask:
                    _lowerCamelCase	:		Union[str, Any]          =random_attention_mask([self.batch_size, self.seq_length]			)

             _lowerCamelCase	:		Optional[Any]          =None
             if self.use_token_type_ids:
                    _lowerCamelCase	:		Optional[Any]          =ids_tensor([self.batch_size, self.seq_length]      ,       self.type_vocab_size			)

             _lowerCamelCase	:		Any          =None
             if self.use_mc_token_ids:
                    _lowerCamelCase	:		str          =ids_tensor([self.batch_size, self.num_choices]      ,       self.seq_length			)

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

             _lowerCamelCase	:		Dict          =CTRLConfig(
                 vocab_size=self.vocab_size      ,       n_embd=self.hidden_size      ,       n_layer=self.num_hidden_layers      ,       n_head=self.num_attention_heads      ,       n_positions=self.max_position_embeddings      ,       pad_token_id=self.pad_token_id      ,       )

             _lowerCamelCase	:		Tuple          =ids_tensor([self.num_hidden_layers, self.num_attention_heads]      ,       2			)

             return (
                 config,
                 input_ids,
                 input_mask,
                 head_mask,
                 token_type_ids,
                 mc_token_ids,
                 sequence_labels,
                 token_labels,
                 choice_labels,
             )






      def 	lowerCamelCase	( self						:					Optional[Any]      ,       lowercase_						:					Optional[int]      ,       lowercase_						:					Dict      ,       lowercase_						:					Union[str, Any]      ,       lowercase_						:					int      ,       lowercase_						:					List[Any]      ,       *lowercase_						:					List[Any]			) ->						Tuple:
             """simple docstring"""




             _lowerCamelCase	:		Optional[Any]          =TFCTRLModel(config=_lowerCamelCase			)
             _lowerCamelCase	:		List[str]          ={'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
             _lowerCamelCase	:		str          =model(_lowerCamelCase			)

             _lowerCamelCase	:		List[Any]          =[input_ids, None, input_mask]  # None is the input for 'past'
             _lowerCamelCase	:		Union[str, Any]          =model(_lowerCamelCase			)

             _lowerCamelCase	:		int          =model(_lowerCamelCase			)

             self.parent.assertEqual(result.last_hidden_state.shape      ,       (self.batch_size, self.seq_length, self.hidden_size)			)






      def 	lowerCamelCase	( self						:					int      ,       lowercase_						:					Dict      ,       lowercase_						:					Optional[Any]      ,       lowercase_						:					Union[str, Any]      ,       lowercase_						:					Any      ,       lowercase_						:					Optional[Any]      ,       *lowercase_						:					Tuple			) ->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase	:		Tuple          =TFCTRLLMHeadModel(config=_lowerCamelCase			)
             _lowerCamelCase	:		Any          ={'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
             _lowerCamelCase	:		Any          =model(_lowerCamelCase			)
             self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.seq_length, self.vocab_size)			)






      def 	lowerCamelCase	( self						:					Optional[int]      ,       lowercase_						:					Optional[Any]      ,       lowercase_						:					Dict      ,       lowercase_						:					Tuple      ,       lowercase_						:					Dict      ,       lowercase_						:					List[str]      ,       *lowercase_						:					List[str]			) ->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase	:		Dict          =self.num_labels
             _lowerCamelCase	:		Any          =ids_tensor([self.batch_size]      ,       self.type_sequence_label_size			)
             _lowerCamelCase	:		Tuple          ={
                 'input_ids': input_ids,
                 'token_type_ids': token_type_ids,
                 'labels': sequence_labels,
             }
             _lowerCamelCase	:		Optional[Any]          =TFCTRLForSequenceClassification(_lowerCamelCase			)
             _lowerCamelCase	:		str          =model(_lowerCamelCase			)
             self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.num_labels)			)






      def 	lowerCamelCase	( self						:					Tuple			) ->						str:
             """simple docstring"""




             _lowerCamelCase	:		Union[str, Any]          =self.prepare_config_and_inputs()

             (
                 (
                 _lowerCamelCase   
             )     ,					(
                 _lowerCamelCase   
             )     ,					(
                 _lowerCamelCase   
             )     ,					(
                 _lowerCamelCase   
             )     ,					(
                 _lowerCamelCase   
             )     ,					(
                 _lowerCamelCase   
             )     ,					(
                 _lowerCamelCase   
             )     ,					(
                 _lowerCamelCase   
             )     ,					(
                 _lowerCamelCase   
             )     ,					   
             )	:		List[Any]          =config_and_inputs

             _lowerCamelCase	:		Optional[int]          ={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
             return config, inputs_dict




@require_tf
class A  (       _lowerCAmelCase   ,							_lowerCAmelCase   ,							unittest.TestCase					):
      UpperCamelCase__							:  Optional[int]				=(TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
      UpperCamelCase__							:  str				=(TFCTRLLMHeadModel,) if is_tf_available() else ()
      UpperCamelCase__							:  int				=(
          {
              "feature-extraction": TFCTRLModel,
              "text-classification": TFCTRLForSequenceClassification,
              "text-generation": TFCTRLLMHeadModel,
              "zero-shot": TFCTRLForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      UpperCamelCase__							:  Union[str, Any]				=False
      UpperCamelCase__							:  Dict				=False






      def 	lowerCamelCase	( self						:					Optional[int]      ,       lowercase_						:					Union[str, Any]      ,       lowercase_						:					int      ,       lowercase_						:					List[str]      ,       lowercase_						:					str      ,       lowercase_						:					str			) ->						Tuple:
             """simple docstring"""




             if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
                    # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
                    # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
                    # config could not be created.
                    return True

             return False






      def 	lowerCamelCase	( self						:					Tuple			) ->						Tuple:
             """simple docstring"""




             _lowerCamelCase	:		List[Any]          =TFCTRLModelTester(self			)
             _lowerCamelCase	:		List[Any]          =ConfigTester(self      ,       config_class=_lowerCamelCase      ,       n_embd=37			)






      def 	lowerCamelCase	( self						:					List[str]			) ->						int:
             """simple docstring"""




             self.config_tester.run_common_tests()






      def 	lowerCamelCase	( self						:					List[Any]			) ->						Any:
             """simple docstring"""




             _lowerCamelCase	:		str          =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_ctrl_model(*_lowerCamelCase			)






      def 	lowerCamelCase	( self						:					List[str]			) ->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase	:		Any          =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_ctrl_lm_head(*_lowerCamelCase			)






      def 	lowerCamelCase	( self						:					Optional[Any]			) ->						List[str]:
             """simple docstring"""




             _lowerCamelCase	:		Tuple          =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_ctrl_for_sequence_classification(*_lowerCamelCase			)






      def 	lowerCamelCase	( self						:					str			) ->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase     ,					_lowerCamelCase	:		Optional[Any]          =self.model_tester.prepare_config_and_inputs_for_common()
             _lowerCamelCase	:		Optional[int]          =[TFCTRLLMHeadModel]
             _lowerCamelCase	:		Optional[Any]          =[TFCTRLForSequenceClassification]

             for model_class in self.all_model_classes:
                    _lowerCamelCase	:		Dict          =model_class(_lowerCamelCase			)
                    model.build()  # may be needed for the get_bias() call below
                    assert isinstance(model.get_input_embeddings()      ,       tf.keras.layers.Layer			)

                    if model_class in list_lm_models:
                           _lowerCamelCase	:		str          =model.get_output_embeddings()
                           assert isinstance(_lowerCamelCase      ,       tf.keras.layers.Layer			)
                           _lowerCamelCase	:		Tuple          =model.get_bias()
                           assert isinstance(_lowerCamelCase      ,       _lowerCamelCase			)
                           for k, v in name.items():
                                  assert isinstance(_lowerCamelCase      ,       tf.Variable			)
                    elif model_class in list_other_models_with_output_ebd:
                           _lowerCamelCase	:		Dict          =model.get_output_embeddings()
                           assert isinstance(_lowerCamelCase      ,       tf.keras.layers.Layer			)
                           _lowerCamelCase	:		List[Any]          =model.get_bias()
                           assert name is None
                    else:
                           _lowerCamelCase	:		Union[str, Any]          =model.get_output_embeddings()
                           assert x is None
                           _lowerCamelCase	:		int          =model.get_bias()
                           assert name is None






      @slow
      def 	lowerCamelCase	( self						:					str			) ->						Any:
             """simple docstring"""




             for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    _lowerCamelCase	:		List[Any]          =TFCTRLModel.from_pretrained(_lowerCamelCase			)
                    self.assertIsNotNone(_lowerCamelCase			)




@require_tf
class A  (       unittest.TestCase					):






      @slow
      def 	lowerCamelCase	( self						:					Dict			) ->						Tuple:
             """simple docstring"""




             _lowerCamelCase	:		Tuple          =TFCTRLLMHeadModel.from_pretrained('ctrl'			)
             _lowerCamelCase	:		List[Any]          =tf.convert_to_tensor([[1_1859, 0, 1611, 8]]      ,       dtype=tf.intaa			)  # Legal the president is
             _lowerCamelCase	:		Optional[Any]          =[
                 1_1859,
                 0,
                 1611,
                 8,
                 5,
                 150,
                 2_6449,
                 2,
                 19,
                 348,
                 469,
                 3,
                 2595,
                 48,
                 2_0740,
                 24_6533,
                 24_6533,
                 19,
                 30,
                 5,
             ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

             _lowerCamelCase	:		List[str]          =model.generate(_lowerCamelCase      ,       do_sample=_lowerCamelCase			)
             self.assertListEqual(output_ids[0].numpy().tolist()      ,       _lowerCamelCase			)






# 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      a_				(   SCREAMING_SNAKE_CASE__ :				List[Any]		,      SCREAMING_SNAKE_CASE__ :				str		,      SCREAMING_SNAKE_CASE__ :				Dict=0					):
       '''simple docstring'''





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

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




def      a_				(   SCREAMING_SNAKE_CASE__ :				Tuple		,      SCREAMING_SNAKE_CASE__ :				str		,      SCREAMING_SNAKE_CASE__ :				str		,      SCREAMING_SNAKE_CASE__ :				Optional[int]		,      SCREAMING_SNAKE_CASE__ :				Union[str, Any]					):
       '''simple docstring'''





       _lowerCamelCase	:		Tuple          =param.size()
       if checkpoint_version == 1.0:
              # version 1.0 stores [num_heads * hidden_size * num_splits, :]
              _lowerCamelCase	:		List[Any]          =(num_heads, hidden_size, num_splits) + input_shape[1:]
              _lowerCamelCase	:		int          =param.view(*SCREAMING_SNAKE_CASE__					)
              _lowerCamelCase	:		Union[str, Any]          =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	:		str          =(num_heads, num_splits, hidden_size) + input_shape[1:]
              _lowerCamelCase	:		Dict          =param.view(*SCREAMING_SNAKE_CASE__					)
              _lowerCamelCase	:		List[Any]          =param.transpose(0		,      1					).contiguous()
       _lowerCamelCase	:		Optional[int]          =param.view(*SCREAMING_SNAKE_CASE__					)
       return param




def      a_				(   SCREAMING_SNAKE_CASE__ :				Union[str, Any]		,      SCREAMING_SNAKE_CASE__ :				Dict		,      SCREAMING_SNAKE_CASE__ :				Any					):
       '''simple docstring'''





       _lowerCamelCase	:		Union[str, Any]          ={}

       # old versions did not store training args
       _lowerCamelCase	:		Tuple          =input_state_dict.get('args'		,      SCREAMING_SNAKE_CASE__					)
       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	:		Dict          =ds_args.tokenizer_type
              _lowerCamelCase	:		str          =ds_args.padded_vocab_size
              _lowerCamelCase	:		List[str]          =ds_args.max_position_embeddings
              _lowerCamelCase	:		List[Any]          =ds_args.hidden_size
              _lowerCamelCase	:		Dict          =ds_args.num_layers
              _lowerCamelCase	:		str          =ds_args.num_attention_heads
              _lowerCamelCase	:		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.
       _lowerCamelCase	:		List[str]          =config.num_attention_heads
       # The hidden_size per head.
       _lowerCamelCase	:		Dict          =config.hidden_size // heads
       # Megatron-LM checkpoint version
       if "checkpoint_version" in input_state_dict.keys():
              _lowerCamelCase	:		Any          =input_state_dict['checkpoint_version']
       else:
              _lowerCamelCase	:		Any          =0.0

       # The model.
       _lowerCamelCase	:		List[Any]          =input_state_dict['model']
       # The language model.
       _lowerCamelCase	:		Any          =model['language_model']
       # The embeddings.
       _lowerCamelCase	:		Any          =lm['embedding']

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

       # The position embeddings.
       _lowerCamelCase	:		int          =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.
       _lowerCamelCase	:		str          =pos_embeddings

       # The token-type embeddings.
       _lowerCamelCase	:		Optional[Any]          =embeddings['tokentype_embeddings']['weight']
       # Store the position embeddings.
       _lowerCamelCase	:		List[Any]          =tokentype_embeddings

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

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

       # The simple map of names for "automated" rules.
       _lowerCamelCase	:		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.
       _lowerCamelCase	:		Tuple          =None

       # Extract the layers.
       for key, val in transformer.items():
              # Match the name.
              _lowerCamelCase	:		Optional[int]          =layer_re.match(SCREAMING_SNAKE_CASE__					)

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

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

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

              # For layernorm(s), simply store the layer norm.
              if op_name.endswith('layernorm'					):
                     _lowerCamelCase	:		Any          ='attention.ln' if op_name.startswith('input'					) else 'ln'
                     _lowerCamelCase	:		List[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":
                     # Make sure the QKV pointer is nil.
                     assert attention_qkv_weight is None, ""

                     _lowerCamelCase	:		Union[str, Any]          =fix_query_key_value_ordering(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      3		,      SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
                     # Store the tensor as we need the bias as well to interleave QKV and biases.
                     _lowerCamelCase	:		List[str]          =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.
                     _lowerCamelCase	:		str          =attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
                     _lowerCamelCase	:		Optional[int]          =attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
                     _lowerCamelCase	:		List[Any]          =attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

                     _lowerCamelCase	:		Any          =fix_query_key_value_ordering(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      3		,      SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
                     # Split the bias.
                     _lowerCamelCase	:		Tuple          =out_val[0 * config.hidden_size : 1 * config.hidden_size]
                     _lowerCamelCase	:		str          =out_val[1 * config.hidden_size : 2 * config.hidden_size]
                     _lowerCamelCase	:		Any          =out_val[2 * config.hidden_size : 3 * config.hidden_size]

                     # Store.
                     _lowerCamelCase	:		Dict          =q
                     _lowerCamelCase	:		Union[str, Any]          =q_bias
                     _lowerCamelCase	:		str          =k
                     _lowerCamelCase	:		str          =k_bias
                     _lowerCamelCase	:		int          =v
                     _lowerCamelCase	:		Tuple          =v_bias

                     # Clear the stored tensor.
                     _lowerCamelCase	:		Any          =None

              # Copy weights and biases as is.
              elif weight_or_bias in ["weight", "bias"]:
                     _lowerCamelCase	:		Tuple          =megatron_to_transformers[op_name]
                     _lowerCamelCase	:		Dict          =val

    # The final layernorm.
       _lowerCamelCase	:		List[Any]          =transformer['final_layernorm.weight']
       _lowerCamelCase	:		Optional[int]          =transformer['final_layernorm.bias']

       # The pooler.
       _lowerCamelCase	:		Any          =lm['pooler']

       # Store the matrix and the bias.
       _lowerCamelCase	:		List[Any]          =pooler['dense.weight']
       _lowerCamelCase	:		int          =pooler['dense.bias']

       # The LM head from Megatron (for RACE).
       _lowerCamelCase	:		List[str]          =model['lm_head']

       # The transform matrix.
       _lowerCamelCase	:		int          =lm_head['dense.weight']
       _lowerCamelCase	:		List[Any]          =lm_head['dense.bias']

       # The transform LN.
       _lowerCamelCase	:		Optional[int]          =lm_head['layernorm.weight']
       _lowerCamelCase	:		List[str]          =lm_head['layernorm.bias']

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

       # The classifier from Megatron (for MLNI).
       _lowerCamelCase	:		Optional[int]          =model['binary_head']

       # Store the classifier.
       _lowerCamelCase	:		int          =binary_head['weight']
       _lowerCamelCase	:		int          =binary_head['bias']

       # It should be done!
       return output_state_dict




def      a_				(   ):
       '''simple docstring'''





       _lowerCamelCase	:		Union[str, Any]          =argparse.ArgumentParser()
       parser.add_argument('--print-checkpoint-structure'		,      action='store_true'					)
       parser.add_argument('path_to_checkpoint'		,      type=SCREAMING_SNAKE_CASE__		,      help='Path to the ZIP file containing the checkpoint'					)
       parser.add_argument(
           '--config_file'		,      default=''		,      type=SCREAMING_SNAKE_CASE__		,      help='An optional config json file describing the pre-trained model.'		,      )
       _lowerCamelCase	:		Optional[Any]          =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(SCREAMING_SNAKE_CASE__		,      map_location='cpu'					)
       else:
              _lowerCamelCase	:		str          =torch.load(args.path_to_checkpoint		,      map_location='cpu'					)

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

       # Convert.
       print('Converting'					)
       _lowerCamelCase	:		int          =convert_megatron_checkpoint(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)

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

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

       # Store the state_dict to file.
       _lowerCamelCase	:		str          =os.path.join(SCREAMING_SNAKE_CASE__		,      'pytorch_model.bin'					)
       print(F'''Saving checkpoint to "{output_checkpoint_file}"'''					)
       torch.save(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)


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

if __name__ == "__main__":
 main()

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


