




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   :						int				=			[
    # 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 		lowercase__   (		__A:	Optional[int]				):





					'''simple docstring'''






					for pegasus_name, hf_name in PATTERNS:
										__magic_name__							:	str    							=							k.replace(__A							,__A				)
					return k







def 		lowercase__   (		__A:	dict							,__A:	dict				):





					'''simple docstring'''






					__magic_name__							:	Optional[int]    							=							DEFAULTS.copy()
					cfg_kwargs.update(__A				)
					__magic_name__							:	Any    							=							PegasusConfig(**__A				)
					__magic_name__							:	Any    							=							PegasusForConditionalGeneration(__A				)
					__magic_name__							:	List[str]    							=							torch_model.model.state_dict()
					__magic_name__							:	int    							=							{}
					for k, v in tf_weights.items():
										__magic_name__							:	Any    							=							rename_state_dict_key(__A				)
										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:
															__magic_name__							:	Optional[int]    							=							v.T
										__magic_name__							:	Dict    							=							torch.tensor(__A							,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
					__magic_name__							:	str    							=							torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1]				)
					__magic_name__							:	Union[str, Any]    							=							mapping['''shared.weight''']
					__magic_name__							:	Optional[int]    							=							mapping['''shared.weight''']
					__magic_name__							:	Any    							=							{k: torch.zeros_like(__A				) for k, v in sd.items() if k.endswith('''bias'''				) and k not in mapping}
					mapping.update(**__A				)
					__magic_name__							,    __magic_name__							:	List[str]    							=							torch_model.model.load_state_dict(__A							,strict=__A				)
					__magic_name__							:	Dict    							=							[
					    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 		lowercase__   (		__A:	Optional[Any]="./ckpt/aeslc/model.ckpt-32000"				):





					'''simple docstring'''






					__magic_name__							:	List[Any]    							=							tf.train.list_variables(__A				)
					__magic_name__							:	List[str]    							=							{}
					__magic_name__							:	List[Any]    							=							['''Adafactor''', '''global_step''']
					for name, shape in tqdm(__A							,desc='''converting tf checkpoint to dict'''				):
										__magic_name__							:	Union[str, Any]    							=							any(pat in name for pat in ignore_name				)
										if skip_key:
															continue
										__magic_name__							:	Tuple    							=							tf.train.load_variable(__A							,__A				)
										__magic_name__							:	str    							=							array
					return tf_weights







def 		lowercase__   (		__A:	str							,__A:	str				):





					'''simple docstring'''






					__magic_name__							:	List[Any]    							=							Path(__A				).parent.name
					__magic_name__							:	Dict    							=							task_specific_params[F'''summarization_{dataset}''']['''max_position_embeddings''']
					__magic_name__							:	List[str]    							=							PegasusTokenizer.from_pretrained('''sshleifer/pegasus'''							,model_max_length=__A				)
					assert tok.model_max_length == desired_max_model_length
					tok.save_pretrained(__A				)

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


if __name__ == "__main__":
	__lowerCamelCase   :						Any				=			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   :						Any				=			parser.parse_args()
	if args.save_dir is None:
		__lowerCamelCase   :						str				=			Path(args.tf_ckpt_path).parent.name
		__lowerCamelCase   :						Dict				=			os.path.join('''pegasus''', dataset)
	convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)









from sklearn.metrics import mean_squared_error

import datasets


__lowerCamelCase   :						List[str]				=			'''\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
'''

__lowerCamelCase   :						Optional[int]				=			'''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''


__lowerCamelCase   :						str				=			'''
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

        "raw_values" : Returns a full set of errors in case of multioutput input.

        "uniform_average" : Errors of all outputs are averaged with uniform weight.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric("mse")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mse\': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {\'mse\': 0.6123724356957945}

    If you\'re using multi-dimensional lists, then set the config as follows :

    >>> mse_metric = datasets.load_metric("mse", "multilist")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mse\': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {\'mse\': array([0.41666667, 1. ])}
'''


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






			'''simple docstring'''





			def 							UpperCAmelCase__       ( self			:   Dict  )						->   Tuple:
								return datasets.MetricInfo(
								    description=_DESCRIPTION       ,						citation=_CITATION       ,						inputs_description=_KWARGS_DESCRIPTION       ,						features=datasets.Features(self._get_feature_types()  )       ,						reference_urls=[
								        '''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'''
								    ]       ,						)

			def 							UpperCAmelCase__       ( self			:   List[Any]  )						->   Any:
								if self.config_name == "multilist":
													return {
													    "predictions": datasets.Sequence(datasets.Value('''float'''  )  ),
													    "references": datasets.Sequence(datasets.Value('''float'''  )  ),
													}
								else:
													return {
													    "predictions": datasets.Value('''float'''  ),
													    "references": datasets.Value('''float'''  ),
													}


			def 							UpperCAmelCase__       ( self			:   Union[str, Any]       ,						lowerCamelCase_			:   Optional[Any]       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   int=None       ,						lowerCamelCase_			:   List[Any]="uniform_average"       ,						lowerCamelCase_			:   List[str]=True  )						->   List[str]:
								__magic_name__							:	Tuple    							=							mean_squared_error(
								    lowerCamelCase_       ,						lowerCamelCase_       ,						sample_weight=lowerCamelCase_       ,						multioutput=lowerCamelCase_       ,						squared=lowerCamelCase_  )

								return {"mse": mse}



