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


snake_case      =    [
    # 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_							(			lowerCAmelCase__  ):



					"""simple docstring"""

					for pegasus_name, hf_name in PATTERNS:
										_lowerCAmelCase			:		List[str]          = k.replace(UpperCAmelCase_						,	UpperCAmelCase_  )
					return k






def        UpperCamelCase_							(			lowerCAmelCase__						,	lowerCAmelCase__  ):



					"""simple docstring"""

					_lowerCAmelCase			:		List[str]          = DEFAULTS.copy()
					cfg_kwargs.update(UpperCAmelCase_  )
					_lowerCAmelCase			:		Optional[int]          = PegasusConfig(**UpperCAmelCase_  )
					_lowerCAmelCase			:		int          = PegasusForConditionalGeneration(UpperCAmelCase_  )
					_lowerCAmelCase			:		Optional[int]          = torch_model.model.state_dict()
					_lowerCAmelCase			:		List[str]          = {}
					for k, v in tf_weights.items():
										_lowerCAmelCase			:		Optional[int]          = 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[Any]          = v.T
										_lowerCAmelCase			:		Tuple          = 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			:		Dict          = torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1]  )
					_lowerCAmelCase			:		Any          = mapping['''shared.weight''']
					_lowerCAmelCase			:		Union[str, Any]          = 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			:		Dict          = torch_model.model.load_state_dict(UpperCAmelCase_						,	strict=UpperCAmelCase_  )
					_lowerCAmelCase			:		str          = [
					    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_							(			lowerCAmelCase__="./ckpt/aeslc/model.ckpt-32000"  ):



					"""simple docstring"""

					_lowerCAmelCase			:		Any          = tf.train.list_variables(UpperCAmelCase_  )
					_lowerCAmelCase			:		Optional[Any]          = {}
					_lowerCAmelCase			:		Optional[Any]          = ['''Adafactor''', '''global_step''']
					for name, shape in tqdm(UpperCAmelCase_						,	desc="converting tf checkpoint to dict"  ):
										_lowerCAmelCase			:		str          = any(pat in name for pat in ignore_name  )
										if skip_key:
															continue
										_lowerCAmelCase			:		Dict          = tf.train.load_variable(UpperCAmelCase_						,	UpperCAmelCase_  )
										_lowerCAmelCase			:		Dict          = array
					return tf_weights






def        UpperCamelCase_							(			lowerCAmelCase__						,	lowerCAmelCase__  ):



					"""simple docstring"""

					_lowerCAmelCase			:		Optional[Any]          = Path(UpperCAmelCase_  ).parent.name
					_lowerCAmelCase			:		Union[str, Any]          = task_specific_params[f"""summarization_{dataset}"""]['''max_position_embeddings''']
					_lowerCAmelCase			:		Union[str, Any]          = 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			:		List[Any]          = task_specific_params[f"""summarization_{dataset}"""]
					if dataset == "large":
										_lowerCAmelCase			:		str          = task_specific_params
					_lowerCAmelCase			:		Dict          = convert_pegasus(UpperCAmelCase_						,	UpperCAmelCase_  )
					torch_model.save_pretrained(UpperCAmelCase_  )
					_lowerCAmelCase			:		Optional[int]          = 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__":
						snake_case      =    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.")
						snake_case      =    parser.parse_args()
						if args.save_dir is None:
												snake_case      =    Path(args.tf_ckpt_path).parent.name
												snake_case      =    os.path.join("pegasus", dataset)
						convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)








import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


_lowercase											=    """\
@inproceedings{lin-2004-rouge,
    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",
    author = \"Lin, Chin-Yew\",
    booktitle = \"Text Summarization Branches Out\",
    month = jul,
    year = \"2004\",
    address = \"Barcelona, Spain\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W04-1013\",
    pages = \"74--81\",
}
"""

_lowercase											=    """\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
"""

_lowercase											=    """
Calculates average rouge scores for a list of hypotheses and references
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,
        `\"rougeL\"`: Longest common subsequence based scoring.
        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric('rouge')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
    >>> print(results[\"rouge1\"])
    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
    >>> print(results[\"rouge1\"].mid.fmeasure)
    1.0
"""


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


       def 	_snake_case				(    self				)						->    Union[str, Any]:
              return datasets.MetricInfo(
                  description=_DESCRIPTION						,       citation=_CITATION						,       inputs_description=_KWARGS_DESCRIPTION						,       features=datasets.Features(
                      {
                          '''predictions''': datasets.Value('''string'''						,       id='''sequence'''				),
                          '''references''': datasets.Value('''string'''						,       id='''sequence'''				),
                      }				)						,       codebase_urls=['''https://github.com/google-research/google-research/tree/master/rouge''']						,       reference_urls=[
                      '''https://en.wikipedia.org/wiki/ROUGE_(metric)''',
                      '''https://github.com/google-research/google-research/tree/master/rouge''',
                  ]						,       )





       def 	_snake_case				(    self						,       __A						,       __A						,       __A=None						,       __A=True						,       __A=False				)						->    List[str]:
              if rouge_types is None:
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=['''rouge1''', '''rouge2''', '''rougeL''', '''rougeLsum''']

              SCREAMING_SNAKE_CASE_							:				Tuple		=rouge_scorer.RougeScorer(rouge_types=__A						,       use_stemmer=__A				)
              if use_aggregator:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=scoring.BootstrapAggregator()
              else:
                     SCREAMING_SNAKE_CASE_							:				Tuple		=[]

              for ref, pred in zip(__A						,       __A				):
                     SCREAMING_SNAKE_CASE_							:				int		=scorer.score(__A						,       __A				)
                     if use_aggregator:
                            aggregator.add_scores(__A				)
                     else:
                            scores.append(__A				)

              if use_aggregator:
                     SCREAMING_SNAKE_CASE_							:				Tuple		=aggregator.aggregate()
              else:
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		={}
                     for key in scores[0]:
                            SCREAMING_SNAKE_CASE_							:				Tuple		=[score[key] for score in scores]

              return result
