



"""simple docstring"""


import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


__A   :							Tuple										=   logging.get_logger(__name__)
@dataclass
class 			_a :
			"""simple docstring"""
			UpperCamelCase__			      =	field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys())})
			UpperCamelCase__			      =	field(
			    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""})
			UpperCamelCase__			      =	field(
			    default=128			,							metadata={
			        """help""": (
			            """The maximum total input sequence length after tokenization. Sequences longer """
			            """than this will be truncated, sequences shorter will be padded."""
			        )
			    }			,							)
			UpperCamelCase__			      =	field(
			    default=lowerCAmelCase			,							metadata={"""help""": """Overwrite the cached training and evaluation sets"""})



			def  lowercase__						(		self     :    List[str]   )->int:
							_UpperCAmelCase					=    self.task_name.lower()



class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	"""train"""
			UpperCamelCase__			      =	"""dev"""
			UpperCamelCase__			      =	"""test"""



class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	42
			UpperCamelCase__			      =	42
			UpperCamelCase__			      =	42



			def __init__(		self     :    Any				,   __UpperCamelCase     :    GlueDataTrainingArguments				,   __UpperCamelCase     :    PreTrainedTokenizerBase				,   __UpperCamelCase     :    Optional[int] = None				,   __UpperCamelCase     :    Union[str, Split] = Split.train				,   __UpperCamelCase     :    Optional[str] = None				,   )->Optional[int]:
							warnings.warn(
							    '''This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '''
							    '''library. You can have a look at this example script for pointers: '''
							    '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''				,   __UpperCamelCase				,   )
							_UpperCAmelCase					=    args
							_UpperCAmelCase					=    glue_processors[args.task_name]()
							_UpperCAmelCase					=    glue_output_modes[args.task_name]
							if isinstance(__UpperCamelCase				,   __UpperCamelCase   ):
											try:
															_UpperCAmelCase					=    Split[mode]
											except KeyError:
															raise KeyError('''mode is not a valid split name'''   )
        # Load data features from cache or dataset file
							_UpperCAmelCase					=    os.path.join(
							    cache_dir if cache_dir is not None else args.data_dir				,   F'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'				,   )
							_UpperCAmelCase					=    self.processor.get_labels()
							if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
							    "RobertaTokenizer",
							    "RobertaTokenizerFast",
							    "XLMRobertaTokenizer",
							    "BartTokenizer",
							    "BartTokenizerFast",
							):
											# HACK(label indices are swapped in RoBERTa pretrained model)
											_UpperCAmelCase		,						_UpperCAmelCase					=    label_list[2], label_list[1]
							_UpperCAmelCase					=    label_list

							# Make sure only the first process in distributed training processes the dataset,
							# and the others will use the cache.
							_UpperCAmelCase					=    cached_features_file + '''.lock'''
							with FileLock(__UpperCamelCase   ):
											if os.path.exists(__UpperCamelCase   ) and not args.overwrite_cache:
															_UpperCAmelCase					=    time.time()
															_UpperCAmelCase					=    torch.load(__UpperCamelCase   )
															logger.info(
															    F'Loading features from cached file {cached_features_file} [took %.3f s]'				,   time.time() - start   )
											else:
															logger.info(F'Creating features from dataset file at {args.data_dir}'   )

															if mode == Split.dev:
																			_UpperCAmelCase					=    self.processor.get_dev_examples(args.data_dir   )
															elif mode == Split.test:
																			_UpperCAmelCase					=    self.processor.get_test_examples(args.data_dir   )
															else:
																			_UpperCAmelCase					=    self.processor.get_train_examples(args.data_dir   )
															if limit_length is not None:
																			_UpperCAmelCase					=    examples[:limit_length]
															_UpperCAmelCase					=    glue_convert_examples_to_features(
															    __UpperCamelCase				,   __UpperCamelCase				,   max_length=args.max_seq_length				,   label_list=__UpperCamelCase				,   output_mode=self.output_mode				,   )
															_UpperCAmelCase					=    time.time()
															torch.save(self.features				,   __UpperCamelCase   )
															# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
															logger.info(
															    F'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'   )



			def __len__(		self     :    Optional[Any]   )->Any:
							return len(self.features   )



			def __getitem__(		self     :    Union[str, Any]				,   __UpperCamelCase     :    Dict   )->InputFeatures:
							return self.features[i]



			def  lowercase__						(		self     :    Optional[Any]   )->List[Any]:
							return self.label_list








"""simple docstring"""


import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__A   :							Tuple										=   (
    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
)

__A   :							Optional[int]										=   (
    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
)

__A   :							Union[str, Any]										=   [
    "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  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[int]					,     _SCREAMING_SNAKE_CASE   :						List[Any]				):






				'''simple docstring'''



				for tf_name, hf_name in patterns:
								_UpperCAmelCase					=    k.replace(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
				return k







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						dict					,     _SCREAMING_SNAKE_CASE   :						dict				):






				'''simple docstring'''



				_UpperCAmelCase					=    BigBirdPegasusConfig(**_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    BigBirdPegasusForConditionalGeneration(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    torch_model.state_dict()
				_UpperCAmelCase					=    {}

				# separating decoder weights
				_UpperCAmelCase					=    {k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder'''				)}
				_UpperCAmelCase					=    {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'''				):
								_UpperCAmelCase					=    [k.endswith(_SCREAMING_SNAKE_CASE				) for ending in KEYS_TO_IGNORE]
								if any(_SCREAMING_SNAKE_CASE				):
												continue
								_UpperCAmelCase					=    DECODER_PATTERNS
								_UpperCAmelCase					=    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''']				):
												_UpperCAmelCase					=    v.T
								_UpperCAmelCase					=    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'''				):
								_UpperCAmelCase					=    [k.endswith(_SCREAMING_SNAKE_CASE				) for ending in KEYS_TO_IGNORE]
								if any(_SCREAMING_SNAKE_CASE				):
												continue
								_UpperCAmelCase					=    REMAINING_PATTERNS
								_UpperCAmelCase					=    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''']				):
												_UpperCAmelCase					=    v.T
								_UpperCAmelCase					=    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}'

				_UpperCAmelCase					=    mapping['''model.embed_positions.weight''']
				_UpperCAmelCase					=    mapping.pop('''model.embed_positions.weight'''				)
				_UpperCAmelCase		,						_UpperCAmelCase					=    torch_model.load_state_dict(_SCREAMING_SNAKE_CASE					,     strict=_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    [
				    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  lowercase	(      _SCREAMING_SNAKE_CASE   :						Any				):






				'''simple docstring'''



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







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						dict				):






				'''simple docstring'''



				_UpperCAmelCase					=    get_tf_weights_as_numpy(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    convert_bigbird_pegasus(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
				torch_model.save_pretrained(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
					__A   :							Tuple										=   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.")
					__A   :							Dict										=   parser.parse_args()
					__A   :							Tuple										=   {}
					convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)



