




"""simple docstring"""






# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

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

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__=0	):
				# Format the message.
				if name is None:
								_lowerCamelCase  :    Union[str, Any]					=					None
				else:
								_lowerCamelCase  :    Tuple					=					'.' * max(0							,					spaces - 2	) + '# {:' + str(50 - spaces	) + 's}'
								_lowerCamelCase  :    List[Any]					=					fmt.format(lowercase__	)

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







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					lowercase__	):
				# 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.
				_lowerCamelCase  :    Optional[int]					=					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  :    Dict					=					param.view(*lowercase__	)
								_lowerCamelCase  :    Union[str, Any]					=					param.transpose(0							,					2	)
								_lowerCamelCase  :    str					=					param.transpose(1							,					2	).contiguous()
				elif checkpoint_version >= 2.0:
								# other versions store [num_heads * num_splits * hidden_size, :]
								_lowerCamelCase  :    Any					=					(num_heads, num_splits, hidden_size) + input_shape[1:]
								_lowerCamelCase  :    int					=					param.view(*lowercase__	)
								_lowerCamelCase  :    Dict					=					param.transpose(0							,					1	).contiguous()
				_lowerCamelCase  :    List[str]					=					param.view(*lowercase__	)
				return param







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				# The converted output model.
				_lowerCamelCase  :    str					=					{}

				# old versions did not store training args
				_lowerCamelCase  :    Optional[Any]					=					input_state_dict.get('args'							,					lowercase__	)
				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  :    Tuple					=					ds_args.padded_vocab_size
								_lowerCamelCase  :    Union[str, Any]					=					ds_args.max_position_embeddings
								_lowerCamelCase  :    Tuple					=					ds_args.hidden_size
								_lowerCamelCase  :    List[Any]					=					ds_args.num_layers
								_lowerCamelCase  :    Union[str, Any]					=					ds_args.num_attention_heads
								_lowerCamelCase  :    List[str]					=					ds_args.ffn_hidden_size
								# pprint(config)

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

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

				# The word embeddings.
				_lowerCamelCase  :    Optional[int]					=					embeddings['word_embeddings']['weight']
				# Truncate the embedding table to vocab_size rows.
				_lowerCamelCase  :    Tuple					=					word_embeddings[: config.vocab_size, :]
				_lowerCamelCase  :    Dict					=					word_embeddings

				# The position embeddings.
				_lowerCamelCase  :    Tuple					=					embeddings['position_embeddings']['weight']
				# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
				_lowerCamelCase  :    Any					=					pos_embeddings.size(0	)
				if n_positions != config.n_positions:
								raise ValueError(
								    f'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match'''	)
				# Store the position embeddings.
				_lowerCamelCase  :    Any					=					pos_embeddings

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

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

				# The simple map of names for "automated" rules.
				_lowerCamelCase  :    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.
								_lowerCamelCase  :    Any					=					layer_re.match(lowercase__	)

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

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

								# The name of the layer.
								_lowerCamelCase  :    int					=					f'''transformer.h.{layer_idx}'''

								# For layernorm(s), simply store the layer norm.
								if op_name.endswith('layernorm'	):
												_lowerCamelCase  :    List[str]					=					'ln_1' if op_name.startswith('input'	) else 'ln_2'
												_lowerCamelCase  :    Optional[int]					=					val

								# Transpose the QKV matrix.
								elif (
								    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
								) and weight_or_bias == "weight":
												# Insert a tensor of 1x1xDxD bias.
												_lowerCamelCase  :    List[Any]					=					torch.tril(torch.ones((n_positions, n_positions)							,					dtype=torch.floataa	)	).view(
												    1							,					1							,					lowercase__							,					lowercase__	)
												_lowerCamelCase  :    List[Any]					=					causal_mask

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

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

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

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

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

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

				# For LM head, transformers' wants the matrix to weight embeddings.
				_lowerCamelCase  :    Optional[int]					=					word_embeddings

				# It should be done!
				return output_state_dict







def   _snake_case    (       ):
				# Create the argument parser.
				_lowerCamelCase  :    Union[str, Any]					=					argparse.ArgumentParser()
				parser.add_argument('--print-checkpoint-structure'							,					action='store_true'	)
				parser.add_argument(
				    'path_to_checkpoint'							,					type=lowercase__							,					help='Path to the checkpoint file (.zip archive or direct .pt file)'							,					)
				parser.add_argument(
				    '--config_file'							,					default=''							,					type=lowercase__							,					help='An optional config json file describing the pre-trained model.'							,					)
				_lowerCamelCase  :    int					=					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  :    Union[str, Any]					=					torch.load(lowercase__							,					map_location='cpu'	)
				else:
								_lowerCamelCase  :    Optional[Any]					=					torch.load(args.path_to_checkpoint							,					map_location='cpu'	)

				_lowerCamelCase  :    int					=					input_state_dict.get('args'							,					lowercase__	)

				# Read the config, or default to the model released by NVIDIA.
				if args.config_file == "":
								if ds_args is not None:
												if ds_args.bias_gelu_fusion:
																_lowerCamelCase  :    Optional[Any]					=					'gelu_fast'
												elif ds_args.openai_gelu:
																_lowerCamelCase  :    str					=					'gelu_new'
												else:
																_lowerCamelCase  :    Any					=					'gelu'
								else:
												# in the very early days this used to be "gelu_new"
												_lowerCamelCase  :    str					=					'gelu_new'

								# Spell out all parameters in case the defaults change.
								_lowerCamelCase  :    Optional[int]					=					GPTaConfig(
								    vocab_size=50257							,					n_positions=1024							,					n_embd=1024							,					n_layer=24							,					n_head=16							,					n_inner=4096							,					activation_function=lowercase__							,					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=lowercase__							,					summary_activation=lowercase__							,					summary_proj_to_labels=lowercase__							,					summary_first_dropout=0.1							,					scale_attn_weights=lowercase__							,					use_cache=lowercase__							,					bos_token_id=50256							,					eos_token_id=50256							,					)
				else:
								_lowerCamelCase  :    List[str]					=					GPTaConfig.from_json_file(args.config_file	)

				_lowerCamelCase  :    Optional[Any]					=					['GPT2LMHeadModel']

				# Convert.
				print('Converting'	)
				_lowerCamelCase  :    Optional[int]					=					convert_megatron_checkpoint(lowercase__							,					lowercase__							,					lowercase__	)

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

				# Add tokenizer class info to config
				# see https://github.com/huggingface/transformers/issues/13906)
				if ds_args is not None:
								_lowerCamelCase  :    Tuple					=					ds_args.tokenizer_type
								if tokenizer_type == "GPT2BPETokenizer":
												_lowerCamelCase  :    Dict					=					'gpt2'
								elif tokenizer_type == "PretrainedFromHF":
												_lowerCamelCase  :    Union[str, Any]					=					ds_args.tokenizer_name_or_path
								else:
												raise ValueError(f'''Unrecognized tokenizer_type {tokenizer_type}'''	)
				else:
								_lowerCamelCase  :    Optional[Any]					=					'gpt2'

				_lowerCamelCase  :    Optional[Any]					=					AutoTokenizer.from_pretrained(lowercase__	)
				_lowerCamelCase  :    str					=					type(lowercase__	).__name__
				_lowerCamelCase  :    str					=					tokenizer_class

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

				# Save tokenizer based on args
				print(f'''Adding {tokenizer_class} tokenizer files'''	)
				tokenizer.save_pretrained(lowercase__	)

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


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

if __name__ == "__main__":
			main()

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





"""simple docstring"""






import comet  # From: unbabel-comet
import torch

import datasets


lowercase__  		=		datasets.logging.get_logger(__name__)

lowercase__  		=		"""\
@inproceedings{rei-EtAl:2020:WMT,
   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},
   title     = {Unbabel's Participation in the WMT20 Metrics Shared Task},
   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},
   month          = {November},
   year           = {2020},
   address        = {Online},
   publisher      = {Association for Computational Linguistics},
   pages     = {909--918},
}
@inproceedings{rei-etal-2020-comet,
   title = \"{COMET}: A Neural Framework for {MT} Evaluation\",
   author = \"Rei, Ricardo  and
      Stewart, Craig  and
      Farinha, Ana C  and
      Lavie, Alon\",
   booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",
   month = nov,
   year = \"2020\",
   address = \"Online\",
   publisher = \"Association for Computational Linguistics\",
   url = \"https://www.aclweb.org/anthology/2020.emnlp-main.213\",
   pages = \"2685--2702\",
}
"""

lowercase__  		=		"""\
Crosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA's or MQM).
With the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.

See the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.
"""

lowercase__  		=		"""
COMET score.

Args:

`sources` (list of str): Source sentences
`predictions` (list of str): candidate translations
`references` (list of str): reference translations
`cuda` (bool): If set to True, runs COMET using GPU
`show_progress` (bool): Shows progress
`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.

Returns:
    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.
    `scores`: List of scores.

Examples:

    >>> comet_metric = datasets.load_metric('comet')
    >>> # comet_metric = load_metric('comet', 'wmt20-comet-da')  # you can also choose which model to use
    >>> source = [\"Dem Feuer konnte Einhalt geboten werden\", \"Schulen und Kindergärten wurden eröffnet.\"]
    >>> hypothesis = [\"The fire could be stopped\", \"Schools and kindergartens were open\"]
    >>> reference = [\"They were able to control the fire.\", \"Schools and kindergartens opened\"]
    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)
    >>> print([round(v, 2) for v in results[\"scores\"]])
    [0.19, 0.92]
"""






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,			_KWARGS_DESCRIPTION		)
class lowerCAmelCase__			(  datasets.Metric		):






			'''simple docstring'''



			def       A_					(						self						):
							return datasets.MetricInfo(
							    description=_DESCRIPTION						,   citation=_CITATION						,   homepage='https://unbabel.github.io/COMET/html/index.html'						,   inputs_description=_KWARGS_DESCRIPTION						,   features=datasets.Features(
							        {
							            'sources': datasets.Value('string'						,   id='sequence'						),
							            'predictions': datasets.Value('string'						,   id='sequence'						),
							            'references': datasets.Value('string'						,   id='sequence'						),
							        }						)						,   codebase_urls=['https://github.com/Unbabel/COMET']						,   reference_urls=[
							        'https://github.com/Unbabel/COMET',
							        'https://www.aclweb.org/anthology/2020.emnlp-main.213/',
							        'http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6',
							    ]						,   )
			def       A_					(						self						,   lowercase						):
							if self.config_name == "default":
											_lowerCamelCase  :    List[Any]					=					comet.load_from_checkpoint(comet.download_model('wmt20-comet-da'						)						)
							else:
											_lowerCamelCase  :    Tuple					=					comet.load_from_checkpoint(comet.download_model(self.config_name						)						)






			def       A_					(						self						,   lowercase						,   lowercase						,   lowercase						,   lowercase=None						,   lowercase=False						):
							if gpus is None:
											_lowerCamelCase  :    Tuple					=					1 if torch.cuda.is_available() else 0
							_lowerCamelCase  :    List[Any]					=					{'src': sources, 'mt': predictions, 'ref': references}
							_lowerCamelCase  :    int					=					[dict(zip(lowercase						,   lowercase						)						) for t in zip(*data.values()						)]
							_lowerCamelCase,				_lowerCamelCase  :    List[str]					=					self.scorer.predict(lowercase						,   gpus=lowercase						,   progress_bar=lowercase						)
							return {"mean_score": mean_score, "scores": scores}