




import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


_lowerCamelCase      :  Any      						=						[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ["memory_attention", "encoder_attn"],
    ["attention", "attn"],
    ["/", "."],
    [".LayerNorm.gamma", "_layer_norm.weight"],
    [".LayerNorm.beta", "_layer_norm.bias"],
    ["r.layer_", "r.layers."],
    ["output_proj", "out_proj"],
    ["ffn.dense_1.", "fc2."],
    ["ffn.dense.", "fc1."],
    ["ffn_layer_norm", "final_layer_norm"],
    ["kernel", "weight"],
    ["encoder_layer_norm.", "encoder.layer_norm."],
    ["decoder_layer_norm.", "decoder.layer_norm."],
    ["embeddings.weights", "shared.weight"],
]




def 							_UpperCAmelCase  (UpperCamelCase_ : Any       ):
				'''simple docstring'''
				for pegasus_name, hf_name in PATTERNS:
								_lowerCAmelCase    :    Optional[int]										=      k.replace(UpperCamelCase_      ,    UpperCamelCase_       )
				return k




def 							_UpperCAmelCase  (UpperCamelCase_ : dict      ,    UpperCamelCase_ : dict       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Any										=      DEFAULTS.copy()
				cfg_kwargs.update(UpperCamelCase_       )
				_lowerCAmelCase    :    Optional[int]										=      PegasusConfig(**UpperCamelCase_       )
				_lowerCAmelCase    :    Dict										=      PegasusForConditionalGeneration(UpperCamelCase_       )
				_lowerCAmelCase    :    str										=      torch_model.model.state_dict()
				_lowerCAmelCase    :    Tuple										=      {}
				for k, v in tf_weights.items():
								_lowerCAmelCase    :    Dict										=      rename_state_dict_key(UpperCamelCase_       )
								if new_k not in sd:
												raise ValueError(F"could not find new key {new_k} in state dict. (converted from {k})"       )

								if "dense" in k or "proj" in new_k:
												_lowerCAmelCase    :    List[str]										=      v.T
								_lowerCAmelCase    :    List[str]										=      torch.tensor(UpperCamelCase_      ,    dtype=sd[new_k].dtype       )
								assert v.shape == sd[new_k].shape, F"{new_k}, {k}, {v.shape}, {sd[new_k].shape}"
				# make sure embedding.padding_idx is respected
				_lowerCAmelCase    :    Union[str, Any]										=      torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1]       )
				_lowerCAmelCase    :    int										=      mapping["""shared.weight"""]
				_lowerCAmelCase    :    int										=      mapping["""shared.weight"""]
				_lowerCAmelCase    :    Tuple										=      {k: torch.zeros_like(UpperCamelCase_       ) for k, v in sd.items() if k.endswith("""bias"""       ) and k not in mapping}
				mapping.update(**UpperCamelCase_       )
				_lowerCAmelCase					,    _lowerCAmelCase    :    int										=      torch_model.model.load_state_dict(UpperCamelCase_      ,    strict=UpperCamelCase_       )
				_lowerCAmelCase    :    Optional[Any]										=      [
				    k for k in missing if k not in ["""encoder.embed_positions.weight""", """decoder.embed_positions.weight"""]
				]
				assert unexpected_missing == [], F"no matches found for the following torch keys {unexpected_missing}"
				assert extra == [], F"no matches found for the following tf keys {extra}"
				return torch_model




def 							_UpperCAmelCase  (UpperCamelCase_ : Optional[Any]="./ckpt/aeslc/model.ckpt-32000"       ):
				'''simple docstring'''
				_lowerCAmelCase    :    List[Any]										=      tf.train.list_variables(UpperCamelCase_       )
				_lowerCAmelCase    :    Union[str, Any]										=      {}
				_lowerCAmelCase    :    str										=      ["""Adafactor""", """global_step"""]
				for name, shape in tqdm(UpperCamelCase_      ,    desc="""converting tf checkpoint to dict"""       ):
								_lowerCAmelCase    :    Union[str, Any]										=      any(pat in name for pat in ignore_name       )
								if skip_key:
												continue
								_lowerCAmelCase    :    List[str]										=      tf.train.load_variable(UpperCamelCase_      ,    UpperCamelCase_       )
								_lowerCAmelCase    :    Dict										=      array
				return tf_weights




def 							_UpperCAmelCase  (UpperCamelCase_ : str      ,    UpperCamelCase_ : str       ):
				'''simple docstring'''
				# save tokenizer first
				_lowerCAmelCase    :    Optional[int]										=      Path(UpperCamelCase_       ).parent.name
				_lowerCAmelCase    :    List[str]										=      task_specific_params[F"summarization_{dataset}"]["""max_position_embeddings"""]
				_lowerCAmelCase    :    Optional[int]										=      PegasusTokenizer.from_pretrained("""sshleifer/pegasus"""      ,    model_max_length=UpperCamelCase_       )
				assert tok.model_max_length == desired_max_model_length
				tok.save_pretrained(UpperCamelCase_       )

				# convert model
				_lowerCAmelCase    :    List[Any]										=      get_tf_weights_as_numpy(UpperCamelCase_       )
				_lowerCAmelCase    :    int										=      task_specific_params[F"summarization_{dataset}"]
				if dataset == "large":
								_lowerCAmelCase    :    Dict										=      task_specific_params
				_lowerCAmelCase    :    Optional[int]										=      convert_pegasus(UpperCamelCase_      ,    UpperCamelCase_       )
				torch_model.save_pretrained(UpperCamelCase_       )
				_lowerCAmelCase    :    List[str]										=      torch_model.state_dict()
				sd.pop("""model.decoder.embed_positions.weight"""       )
				sd.pop("""model.encoder.embed_positions.weight"""       )
				torch.save(UpperCamelCase_      ,    Path(UpperCamelCase_       ) / """pytorch_model.bin"""       )


if __name__ == "__main__":
	_lowerCamelCase      :  Dict      						=						argparse.ArgumentParser()
	# Required parameters
	parser.add_argument("tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
	parser.add_argument("save_dir", default=None, type=str, help="Path to the output PyTorch model.")
	_lowerCamelCase      :  Optional[int]      						=						parser.parse_args()
	if args.save_dir is None:
		_lowerCamelCase      :  List[str]      						=						Path(args.tf_ckpt_path).parent.name
		_lowerCamelCase      :  Union[str, Any]      						=						os.path.join("pegasus", dataset)
	convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)







import comet  # From: unbabel-comet
import torch

import datasets


_lowerCamelCase      :  List[Any]      						=						datasets.logging.get_logger(__name__)

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

_lowerCamelCase      :  Union[str, Any]      						=						"\\nCrosslingual 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).\nWith 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.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n"

_lowerCamelCase      :  Optional[int]      						=						"\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric('comet')\n    >>> # comet_metric = load_metric('comet', 'wmt20-comet-da')  # you can also choose which model to use\n    >>> source = [\"Dem Feuer konnte Einhalt geboten werden\", \"Schulen und Kindergärten wurden eröffnet.\"]\n    >>> hypothesis = [\"The fire could be stopped\", \"Schools and kindergartens were open\"]\n    >>> reference = [\"They were able to control the fire.\", \"Schools and kindergartens opened\"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results[\"scores\"]])\n    [0.19, 0.92]\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,	_KWARGS_DESCRIPTION		)
class        __snake_case  (datasets.Metric		):



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    Tuple:





								'''simple docstring'''




								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       SCREAMING_SNAKE_CASE       (      self      :		Dict		,	_UpperCAmelCase      :		List[str]      )  ->    Dict:





								'''simple docstring'''




								if self.config_name == "default":
												_lowerCAmelCase    :    Union[str, Any]										=      comet.load_from_checkpoint(comet.download_model("""wmt20-comet-da"""      )      )
								else:
												_lowerCAmelCase    :    Dict										=      comet.load_from_checkpoint(comet.download_model(self.config_name      )      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		Dict		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		Union[str, Any]		,	_UpperCAmelCase      :		Dict=None		,	_UpperCAmelCase      :		Tuple=False      )  ->    int:





								'''simple docstring'''




								if gpus is None:
												_lowerCAmelCase    :    Tuple										=      1 if torch.cuda.is_available() else 0
								_lowerCAmelCase    :    Optional[int]										=      {"""src""": sources, """mt""": predictions, """ref""": references}
								_lowerCAmelCase    :    List[Any]										=      [dict(zip(_UpperCAmelCase		,	_UpperCAmelCase      )      ) for t in zip(*data.values()      )]
								_lowerCAmelCase					,    _lowerCAmelCase    :    Optional[Any]										=      self.scorer.predict(_UpperCAmelCase		,	gpus=_UpperCAmelCase		,	progress_bar=_UpperCAmelCase      )
								return {"mean_score": mean_score, "scores": scores}

