



'''simple docstring'''

import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


UpperCAmelCase     		=   [
    # tf -> hf
    ('/', '.'),
    ('layer_', 'layers.'),
    ('kernel', 'weight'),
    ('beta', 'bias'),
    ('gamma', 'weight'),
    ('pegasus', 'model'),
]
UpperCAmelCase     		=   [
    ('.output.dense', '.fc2'),
    ('intermediate.LayerNorm', 'final_layer_norm'),
    ('intermediate.dense', 'fc1'),
]

UpperCAmelCase     		=   (
    INIT_COMMON
    + [
        ('attention.self.LayerNorm', 'self_attn_layer_norm'),
        ('attention.output.dense', 'self_attn.out_proj'),
        ('attention.self', 'self_attn'),
        ('attention.encdec.LayerNorm', 'encoder_attn_layer_norm'),
        ('attention.encdec_output.dense', 'encoder_attn.out_proj'),
        ('attention.encdec', 'encoder_attn'),
        ('key', 'k_proj'),
        ('value', 'v_proj'),
        ('query', 'q_proj'),
        ('decoder.LayerNorm', 'decoder.layernorm_embedding'),
    ]
    + END_COMMON
)

UpperCAmelCase     		=   (
    INIT_COMMON
    + [
        ('embeddings.word_embeddings', 'shared.weight'),
        ('embeddings.position_embeddings', 'embed_positions.weight'),
        ('attention.self.LayerNorm', 'self_attn_layer_norm'),
        ('attention.output.dense', 'self_attn.output'),
        ('attention.self', 'self_attn.self'),
        ('encoder.LayerNorm', 'encoder.layernorm_embedding'),
    ]
    + END_COMMON
)

UpperCAmelCase     		=   [
    'encdec/key/bias',
    'encdec/query/bias',
    'encdec/value/bias',
    'self/key/bias',
    'self/query/bias',
    'self/value/bias',
    'encdec_output/dense/bias',
    'attention/output/dense/bias',
]






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[Any]							,		_SCREAMING_SNAKE_CASE					:						int							)      ->						Union[str, Any]:







	"""simple docstring"""
	for tf_name, hf_name in patterns:
		lowerCAmelCase   =     k.replace(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
	return k






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						dict							,		_SCREAMING_SNAKE_CASE					:						dict							)      ->						BigBirdPegasusForConditionalGeneration:







	"""simple docstring"""
	lowerCAmelCase   =     BigBirdPegasusConfig(**_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     BigBirdPegasusForConditionalGeneration(_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     torch_model.state_dict()
	lowerCAmelCase   =     {}

	# separating decoder weights
	lowerCAmelCase   =     {k: tf_weights[k] for k in tf_weights if k.startswith("""pegasus/decoder"""							)}
	lowerCAmelCase   =     {k: tf_weights[k] for k in tf_weights if not k.startswith("""pegasus/decoder"""							)}

	for k, v in tqdm(decoder_weights.items()							,		"""tf -> hf conversion"""							):
		lowerCAmelCase   =     [k.endswith(_SCREAMING_SNAKE_CASE							) for ending in KEYS_TO_IGNORE]
		if any(_SCREAMING_SNAKE_CASE							):
			continue
		lowerCAmelCase   =     DECODER_PATTERNS
		lowerCAmelCase   =     rename_state_dict_key(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
		if new_k not in state_dict:
			raise ValueError(f'could not find new key {new_k} in state dict. (converted from {k})'							)
		if any(True if i in k else False for i in ["""dense""", """query""", """key""", """value"""]							):
			lowerCAmelCase   =     v.T
		lowerCAmelCase   =     torch.from_numpy(_SCREAMING_SNAKE_CASE							)
		assert v.shape == state_dict[new_k].shape, f'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'

	for k, v in tqdm(remaining_weights.items()							,		"""tf -> hf conversion"""							):
		lowerCAmelCase   =     [k.endswith(_SCREAMING_SNAKE_CASE							) for ending in KEYS_TO_IGNORE]
		if any(_SCREAMING_SNAKE_CASE							):
			continue
		lowerCAmelCase   =     REMAINING_PATTERNS
		lowerCAmelCase   =     rename_state_dict_key(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
		if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
			raise ValueError(f'could not find new key {new_k} in state dict. (converted from {k})'							)
		if any(True if i in k else False for i in ["""dense""", """query""", """key""", """value"""]							):
			lowerCAmelCase   =     v.T
		lowerCAmelCase   =     torch.from_numpy(_SCREAMING_SNAKE_CASE							)
		if k != "pegasus/embeddings/position_embeddings":
			assert v.shape == state_dict[new_k].shape, f'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'

	lowerCAmelCase   =     mapping["""model.embed_positions.weight"""]
	lowerCAmelCase   =     mapping.pop("""model.embed_positions.weight"""							)
	lowerCAmelCase,       lowerCAmelCase   =     torch_model.load_state_dict(_SCREAMING_SNAKE_CASE							,		strict=_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     [
	    k
	    for k in missing
	    if k
	    not in [
	        """final_logits_bias""",
	        """model.encoder.embed_tokens.weight""",
	        """model.decoder.embed_tokens.weight""",
	        """lm_head.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 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[Any]							)      ->						Dict:







	"""simple docstring"""
	lowerCAmelCase   =     tf.train.list_variables(_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     {}
	lowerCAmelCase   =     ["""global_step"""]
	for name, shape in tqdm(_SCREAMING_SNAKE_CASE							,		desc="""converting tf checkpoint to dict"""							):
		lowerCAmelCase   =     any(pat in name for pat in ignore_name							)
		if skip_key:
			continue
		lowerCAmelCase   =     tf.train.load_variable(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
		lowerCAmelCase   =     array
	return tf_weights






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						dict							)      ->						Tuple:







	"""simple docstring"""
	lowerCAmelCase   =     get_tf_weights_as_numpy(_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     convert_bigbird_pegasus(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
	torch_model.save_pretrained(_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
				UpperCAmelCase     		=   argparse.ArgumentParser()
				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.')
				UpperCAmelCase     		=   parser.parse_args()
				UpperCAmelCase     		=   {}
				convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)




'''simple docstring'''

import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
				raise RuntimeError('At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training')

# TF training parameters
UpperCAmelCase     		=   False
UpperCAmelCase     		=   False






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Namespace							)      ->						Tuple:







	"""simple docstring"""
	return TrainCommand(_SCREAMING_SNAKE_CASE							)





class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						@staticmethod
						def     __snake_case   ( A_				)     ->     Optional[int]:
							lowerCAmelCase   =     parser.add_parser("""train"""     ,	help="""CLI tool to train a model on a task."""				)

							train_parser.add_argument(
							    """--train_data"""     ,	type=A_     ,	required=A_     ,	help="""path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."""     ,	)
							train_parser.add_argument(
							    """--column_label"""     ,	type=A_     ,	default=0     ,	help="""Column of the dataset csv file with example labels."""				)
							train_parser.add_argument(
							    """--column_text"""     ,	type=A_     ,	default=1     ,	help="""Column of the dataset csv file with example texts."""				)
							train_parser.add_argument(
							    """--column_id"""     ,	type=A_     ,	default=2     ,	help="""Column of the dataset csv file with example ids."""				)
							train_parser.add_argument(
							    """--skip_first_row"""     ,	action="""store_true"""     ,	help="""Skip the first row of the csv file (headers)."""				)

							train_parser.add_argument("""--validation_data"""     ,	type=A_     ,	default=""""""     ,	help="""path to validation dataset."""				)
							train_parser.add_argument(
							    """--validation_split"""     ,	type=A_     ,	default=0.1     ,	help="""if validation dataset is not provided, fraction of train dataset to use as validation dataset."""     ,	)

							train_parser.add_argument("""--output"""     ,	type=A_     ,	default="""./"""     ,	help="""path to saved the trained model."""				)

							train_parser.add_argument(
							    """--task"""     ,	type=A_     ,	default="""text_classification"""     ,	help="""Task to train the model on."""				)
							train_parser.add_argument(
							    """--model"""     ,	type=A_     ,	default="""bert-base-uncased"""     ,	help="""Model's name or path to stored model."""				)
							train_parser.add_argument("""--train_batch_size"""     ,	type=A_     ,	default=32     ,	help="""Batch size for training."""				)
							train_parser.add_argument("""--valid_batch_size"""     ,	type=A_     ,	default=64     ,	help="""Batch size for validation."""				)
							train_parser.add_argument("""--learning_rate"""     ,	type=A_     ,	default=3e-5     ,	help="""Learning rate."""				)
							train_parser.add_argument("""--adam_epsilon"""     ,	type=A_     ,	default=1e-08     ,	help="""Epsilon for Adam optimizer."""				)
							train_parser.set_defaults(func=A_				)


						def __init__( self     ,	A_				)     ->     Tuple:
							lowerCAmelCase   =     logging.get_logger("""transformers-cli/training"""				)

							lowerCAmelCase   =     """tf""" if is_tf_available() else """torch"""

							os.makedirs(args.output     ,	exist_ok=A_				)
							lowerCAmelCase   =     args.output

							lowerCAmelCase   =     args.column_label
							lowerCAmelCase   =     args.column_text
							lowerCAmelCase   =     args.column_id

							self.logger.info(f'Loading {args.task} pipeline for {args.model}'				)
							if args.task == "text_classification":
								lowerCAmelCase   =     TextClassificationPipeline.from_pretrained(args.model				)
							elif args.task == "token_classification":
								raise NotImplementedError
							elif args.task == "question_answering":
								raise NotImplementedError

							self.logger.info(f'Loading dataset from {args.train_data}'				)
							lowerCAmelCase   =     Processor.create_from_csv(
							    args.train_data     ,	column_label=args.column_label     ,	column_text=args.column_text     ,	column_id=args.column_id     ,	skip_first_row=args.skip_first_row     ,	)
							lowerCAmelCase   =     None
							if args.validation_data:
								self.logger.info(f'Loading validation dataset from {args.validation_data}'				)
								lowerCAmelCase   =     Processor.create_from_csv(
								    args.validation_data     ,	column_label=args.column_label     ,	column_text=args.column_text     ,	column_id=args.column_id     ,	skip_first_row=args.skip_first_row     ,	)

							lowerCAmelCase   =     args.validation_split
							lowerCAmelCase   =     args.train_batch_size
							lowerCAmelCase   =     args.valid_batch_size
							lowerCAmelCase   =     args.learning_rate
							lowerCAmelCase   =     args.adam_epsilon


						def     __snake_case   ( self				)     ->     Optional[int]:
							if self.framework == "tf":
								return self.run_tf()
							return self.run_torch()


						def     __snake_case   ( self				)     ->     Tuple:
							raise NotImplementedError




						def     __snake_case   ( self				)     ->     Tuple:
							self.pipeline.fit(
							    self.train_dataset     ,	validation_data=self.valid_dataset     ,	validation_split=self.validation_split     ,	learning_rate=self.learning_rate     ,	adam_epsilon=self.adam_epsilon     ,	train_batch_size=self.train_batch_size     ,	valid_batch_size=self.valid_batch_size     ,	)

							# Save trained pipeline
							self.pipeline.save_pretrained(self.output				)