


from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


a_					:    Tuple          =    "\\n@misc{wu2016googles,\n      title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n              and Jeffrey Dean},\n      year={2016},\n      eprint={1609.08144},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n"

a_					:    Any          =    "\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe 'GLEU score'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore's range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n"

a_					:    Any          =    "\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n    predictions (list of str): list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references (list of list of str): list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n    'google_bleu': google_bleu score\n\nExamples:\n    Example 1:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.44\n\n    Example 2:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.61\n\n    Example 3:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.53\n\n    Example 4:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.4\n"




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,				_KWARGS_DESCRIPTION       )
class  a     (     datasets.Metric       ):
 def 							__UpperCAmelCase		( self						)					->							MetricInfo:
      return datasets.MetricInfo(
          description=_DESCRIPTION      ,				citation=_CITATION      ,				inputs_description=_KWARGS_DESCRIPTION      ,				features=datasets.Features(
              {
                  'predictions': datasets.Sequence(datasets.Value('string'      ,				id='token'						)      ,				id='sequence'						),
                  'references': datasets.Sequence(
                      datasets.Sequence(datasets.Value('string'      ,				id='token'						)      ,				id='sequence'						)      ,				id='references'						),
              }						)      ,				)




 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = 1      ,				__magic_name__ = 4      ,				)					->							Dict[str, float]:
      return {
          "google_bleu": gleu_score.corpus_gleu(
              list_of_references=UpperCAmelCase__      ,				hypotheses=UpperCAmelCase__      ,				min_len=UpperCAmelCase__      ,				max_len=UpperCAmelCase__						)
      }





'''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 	_A				(lowerCAmelCase__  :Optional[int]				,						lowerCAmelCase__  :Optional[int]				,						lowerCAmelCase__  :Optional[int]=0		)   ->  Optional[int]:




     '''simple docstring'''



     if name is None:
          _a			      =							None
     else:
          _a			      =							'.' * max(0				,						spaces - 2		) + '# {:' + str(50 - spaces		) + 's}'
          _a			      =							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 	_A				(lowerCAmelCase__  :Optional[Any]				,						lowerCAmelCase__  :Optional[int]				,						lowerCAmelCase__  :Optional[Any]				,						lowerCAmelCase__  :Optional[Any]				,						lowerCAmelCase__  :List[str]		)   ->  Any:




     '''simple docstring'''



     _a			      =							param.size()
     if checkpoint_version == 1.0:
          # version 1.0 stores [num_heads * hidden_size * num_splits, :]
          _a			      =							(num_heads, hidden_size, num_splits) + input_shape[1:]
          _a			      =							param.view(*lowerCAmelCase__		)
          _a			      =							param.transpose(0				,						2		)
          _a			      =							param.transpose(1				,						2		).contiguous()
     elif checkpoint_version >= 2.0:
          # other versions store [num_heads * num_splits * hidden_size, :]
          _a			      =							(num_heads, num_splits, hidden_size) + input_shape[1:]
          _a			      =							param.view(*lowerCAmelCase__		)
          _a			      =							param.transpose(0				,						1		).contiguous()
     _a			      =							param.view(*lowerCAmelCase__		)
     return param






def 	_A				(lowerCAmelCase__  :List[Any]				,						lowerCAmelCase__  :Optional[Any]				,						lowerCAmelCase__  :List[str]		)   ->  Tuple:




     '''simple docstring'''



     _a			      =							{}

     # old versions did not store training args
     _a			      =							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))

          _a			      =							ds_args.padded_vocab_size
          _a			      =							ds_args.max_position_embeddings
          _a			      =							ds_args.hidden_size
          _a			      =							ds_args.num_layers
          _a			      =							ds_args.num_attention_heads
          _a			      =							ds_args.ffn_hidden_size
          # pprint(config)

     # The number of heads.
     _a			      =							config.n_head
     # The hidden_size per head.
     _a			      =							config.n_embd // config.n_head
     # Megatron-LM checkpoint version
     if "checkpoint_version" in input_state_dict.keys():
          _a			      =							input_state_dict['checkpoint_version']
     else:
          _a			      =							0.0

     # The model.
     _a			      =							input_state_dict['model']
     # The language model.
     _a			      =							model['language_model']
     # The embeddings.
     _a			      =							lm['embedding']

     # The word embeddings.
     _a			      =							embeddings['word_embeddings']['weight']
     # Truncate the embedding table to vocab_size rows.
     _a			      =							word_embeddings[: config.vocab_size, :]
     _a			      =							word_embeddings

     # The position embeddings.
     _a			      =							embeddings['position_embeddings']['weight']
     # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
     _a			      =							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.
     _a			      =							pos_embeddings

     # The transformer.
     _a			      =							lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']

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

     # The simple map of names for "automated" rules.
     _a			      =							{
         '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.
          _a			      =							layer_re.match(lowerCAmelCase__		)

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

          # The index of the layer.
          _a			      =							int(m.group(1		)		)
          # The name of the operation.
          _a			      =							m.group(2		)
          # Is it a weight or a bias?
          _a			      =							m.group(3		)

          # The name of the layer.
          _a			      =							f'transformer.h.{layer_idx}'

          # For layernorm(s), simply store the layer norm.
          if op_name.endswith('layernorm'		):
               _a			      =							'ln_1' if op_name.startswith('input'		) else 'ln_2'
               _a			      =							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.
               _a			      =							torch.tril(torch.ones((n_positions, n_positions)				,						dtype=torch.floataa		)		).view(
                   1				,						1				,						lowerCAmelCase__				,						lowerCAmelCase__		)
               _a			      =							causal_mask

               # Insert a "dummy" tensor for masked_bias.
               _a			      =							torch.tensor(-1E4				,						dtype=torch.floataa		)
               _a			      =							masked_bias

               _a			      =							fix_query_key_value_ordering(lowerCAmelCase__				,						lowerCAmelCase__				,						3				,						lowerCAmelCase__				,						lowerCAmelCase__		)
               # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
               _a			      =							out_val.transpose(0				,						1		).contiguous()
               # Store.
               _a			      =							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":
               _a			      =							fix_query_key_value_ordering(lowerCAmelCase__				,						lowerCAmelCase__				,						3				,						lowerCAmelCase__				,						lowerCAmelCase__		)
               # Store. No change of shape.
               _a			      =							out_val

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

          # Copy the bias.
          elif weight_or_bias == "bias":
               _a			      =							megatron_to_transformers[op_name]
               _a			      =							val

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

     # The final layernorm.
     _a			      =							transformer['final_layernorm.weight']
     _a			      =							transformer['final_layernorm.bias']

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

     # It should be done!
     return output_state_dict






def 	_A				()   ->  Any:




     '''simple docstring'''



     _a			      =							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.'				,						)
     _a			      =							parser.parse_args()

     # Extract the basename.
     _a			      =							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:
                    _a			      =							torch.load(lowerCAmelCase__				,						map_location='cpu'		)
     else:
          _a			      =							torch.load(args.path_to_checkpoint				,						map_location='cpu'		)

     _a			      =							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:
                    _a			      =							'gelu_fast'
               elif ds_args.openai_gelu:
                    _a			      =							'gelu_new'
               else:
                    _a			      =							'gelu'
          else:
               # in the very early days this used to be "gelu_new"
               _a			      =							'gelu_new'

          # Spell out all parameters in case the defaults change.
          _a			      =							GPTaConfig(
              vocab_size=5_02_57				,						n_positions=10_24				,						n_embd=10_24				,						n_layer=24				,						n_head=16				,						n_inner=40_96				,						activation_function=lowerCAmelCase__				,						resid_pdrop=0.1				,						embd_pdrop=0.1				,						attn_pdrop=0.1				,						layer_norm_epsilon=1E-5				,						initializer_range=0.0_2				,						summary_type='cls_index'				,						summary_use_proj=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_02_56				,						eos_token_id=5_02_56				,						)
     else:
          _a			      =							GPTaConfig.from_json_file(args.config_file		)

     _a			      =							['GPT2LMHeadModel']

     # Convert.
     print('Converting'		)
     _a			      =							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:
          _a			      =							ds_args.tokenizer_type
          if tokenizer_type == "GPT2BPETokenizer":
               _a			      =							'gpt2'
          elif tokenizer_type == "PretrainedFromHF":
               _a			      =							ds_args.tokenizer_name_or_path
          else:
               raise ValueError(f'Unrecognized tokenizer_type {tokenizer_type}'		)
     else:
          _a			      =							'gpt2'

     _a			      =							AutoTokenizer.from_pretrained(lowerCAmelCase__		)
     _a			      =							type(lowerCAmelCase__		).__name__
     _a			      =							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.
     _a			      =							os.path.join(lowerCAmelCase__				,						'pytorch_model.bin'		)
     print(f'Saving checkpoint to "{output_checkpoint_file}"'		)
     torch.save(lowerCAmelCase__				,						lowerCAmelCase__		)


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

if __name__ == "__main__":
 main()

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

