import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__lowercase										= (
    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
)

__lowercase										= (
    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
)

__lowercase										= [
    '''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 lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
				'''simple docstring'''







				for tf_name, hf_name in patterns:
								__UpperCamelCase					:Optional[Any]        =		k.replace(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
				return k



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
				'''simple docstring'''







				__UpperCamelCase					:Optional[int]        =		BigBirdPegasusConfig(**SCREAMING_SNAKE_CASE     )
				__UpperCamelCase					:List[str]        =		BigBirdPegasusForConditionalGeneration(SCREAMING_SNAKE_CASE     )
				__UpperCamelCase					:Any        =		torch_model.state_dict()
				__UpperCamelCase					:Optional[Any]        =		{}

				# separating decoder weights
				__UpperCamelCase					:int        =		{k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder'''     )}
				__UpperCamelCase					:Any        =		{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					:List[Any]        =		[k.endswith(SCREAMING_SNAKE_CASE     ) for ending in KEYS_TO_IGNORE]
								if any(SCREAMING_SNAKE_CASE     ):
												continue
								__UpperCamelCase					:Union[str, Any]        =		DECODER_PATTERNS
								__UpperCamelCase					:Union[str, Any]        =		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					:List[str]        =		v.T
								__UpperCamelCase					:Any        =		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					:int        =		[k.endswith(SCREAMING_SNAKE_CASE     ) for ending in KEYS_TO_IGNORE]
								if any(SCREAMING_SNAKE_CASE     ):
												continue
								__UpperCamelCase					:List[str]        =		REMAINING_PATTERNS
								__UpperCamelCase					:Optional[int]        =		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					:List[Any]        =		v.T
								__UpperCamelCase					:str        =		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					:Optional[int]        =		mapping['''model.embed_positions.weight''']
				__UpperCamelCase					:Optional[Any]        =		mapping.pop('''model.embed_positions.weight'''     )
				__UpperCamelCase					:List[Any]        =		torch_model.load_state_dict(SCREAMING_SNAKE_CASE  ,      strict=SCREAMING_SNAKE_CASE     )
				__UpperCamelCase					:Dict        =		[
				    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 lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
				'''simple docstring'''







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



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
				'''simple docstring'''







				__UpperCamelCase					:Optional[int]        =		get_tf_weights_as_numpy(SCREAMING_SNAKE_CASE     )
				__UpperCamelCase					:Tuple        =		convert_bigbird_pegasus(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
				torch_model.save_pretrained(SCREAMING_SNAKE_CASE     )


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






import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


__lowercase										= '''bert-base-cased'''
__lowercase										= '''google/pegasus-xsum'''
__lowercase										= [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
__lowercase										= ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
__lowercase										= '''patrickvonplaten/t5-tiny-random'''
__lowercase										= '''sshleifer/bart-tiny-random'''
__lowercase										= '''sshleifer/tiny-mbart'''
__lowercase										= '''sshleifer/tiny-marian-en-de'''



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:str        =		'''\n'''.join(SCREAMING_SNAKE_CASE     )
    Path(SCREAMING_SNAKE_CASE     ).open('''w'''     ).writelines(SCREAMING_SNAKE_CASE     )



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    for split in ["train", "val", "test"]:
        _dump_articles(os.path.join(SCREAMING_SNAKE_CASE  ,      f"""{split}.source"""     )  ,      SCREAMING_SNAKE_CASE     )
        _dump_articles(os.path.join(SCREAMING_SNAKE_CASE  ,      f"""{split}.target"""     )  ,      SCREAMING_SNAKE_CASE     )
    return tmp_dir


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ]			,			)
    @slow
    def       UpperCamelCase__       (     self			,			__lowercase)	->     List[Any]:
        __UpperCamelCase					:Dict        =		AutoTokenizer.from_pretrained(__lowercase)
        __UpperCamelCase					:Optional[Any]        =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
        __UpperCamelCase					:List[Any]        =		max(len(tokenizer.encode(__lowercase)) for a in ARTICLES)
        __UpperCamelCase					:Optional[int]        =		max(len(tokenizer.encode(__lowercase)) for a in SUMMARIES)
        __UpperCamelCase					:int        =		4
        __UpperCamelCase					:Any        =		8
        assert max_len_target > max_src_len  # Will be truncated
        assert max_len_source > max_src_len  # Will be truncated
        __UpperCamelCase  ,	__UpperCamelCase					:Tuple        =		'''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
        __UpperCamelCase					:str        =		SeqaSeqDataset(
            __lowercase			,			data_dir=__lowercase			,			type_path='''train'''			,			max_source_length=__lowercase			,			max_target_length=__lowercase			,			src_lang=__lowercase			,			tgt_lang=__lowercase			,			)
        __UpperCamelCase					:Any        =		DataLoader(__lowercase			,			batch_size=2			,			collate_fn=train_dataset.collate_fn)
        for batch in dataloader:
            assert isinstance(__lowercase			,			__lowercase)
            assert batch["attention_mask"].shape == batch["input_ids"].shape
            # show that articles were trimmed.
            assert batch["input_ids"].shape[1] == max_src_len
            # show that targets are the same len
            assert batch["labels"].shape[1] == max_tgt_len
            if tok_name != MBART_TINY:
                continue
            # check language codes in correct place
            __UpperCamelCase					:Optional[int]        =		shift_tokens_right(batch['''labels''']			,			tokenizer.pad_token_id)
            assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
            assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
            assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
            assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

            break  # No need to test every batch







    @parameterized.expand([BART_TINY, BERT_BASE_CASED])
    def       UpperCamelCase__       (     self			,			__lowercase)	->     int:
        __UpperCamelCase					:Union[str, Any]        =		AutoTokenizer.from_pretrained(__lowercase)
        __UpperCamelCase					:Union[str, Any]        =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
        __UpperCamelCase					:int        =		max(len(tokenizer.encode(__lowercase)) for a in ARTICLES)
        __UpperCamelCase					:Dict        =		max(len(tokenizer.encode(__lowercase)) for a in SUMMARIES)
        __UpperCamelCase					:Union[str, Any]        =		4
        __UpperCamelCase					:List[str]        =		LegacySeqaSeqDataset(
            __lowercase			,			data_dir=__lowercase			,			type_path='''train'''			,			max_source_length=20			,			max_target_length=__lowercase			,			)
        __UpperCamelCase					:Dict        =		DataLoader(__lowercase			,			batch_size=2			,			collate_fn=train_dataset.collate_fn)
        for batch in dataloader:
            assert batch["attention_mask"].shape == batch["input_ids"].shape
            # show that articles were trimmed.
            assert batch["input_ids"].shape[1] == max_len_source
            assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
            # show that targets were truncated
            assert batch["labels"].shape[1] == trunc_target  # Truncated
            assert max_len_target > trunc_target  # Truncated
            break  # No need to test every batch







    def       UpperCamelCase__       (     self)	->     Dict:
        __UpperCamelCase					:List[Any]        =		AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25''')

        __UpperCamelCase					:Union[str, Any]        =		Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
        __UpperCamelCase					:str        =		tmp_dir.joinpath('''train.source''').open().readlines()
        __UpperCamelCase					:int        =		Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
        pack_data_dir(__lowercase			,			__lowercase			,			128			,			__lowercase)
        __UpperCamelCase					:Union[str, Any]        =		{x.name for x in tmp_dir.iterdir()}
        __UpperCamelCase					:int        =		{x.name for x in save_dir.iterdir()}
        __UpperCamelCase					:Optional[int]        =		save_dir.joinpath('''train.source''').open().readlines()
        # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
        # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
        assert len(__lowercase) < len(__lowercase)
        assert len(__lowercase) == 1
        assert len(packed_examples[0]) == sum(len(__lowercase) for x in orig_examples)
        assert orig_paths == new_paths







    @pytest.mark.skipif(not FAIRSEQ_AVAILABLE			,			reason='''This test requires fairseq''')
    def       UpperCamelCase__       (     self)	->     List[Any]:
        if not FAIRSEQ_AVAILABLE:
            return
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Any        =		self._get_dataset(max_len=64)
        __UpperCamelCase					:Union[str, Any]        =		64
        __UpperCamelCase					:Tuple        =		ds.make_dynamic_sampler(__lowercase			,			required_batch_size_multiple=__lowercase)
        __UpperCamelCase					:List[str]        =		[len(__lowercase) for x in batch_sampler]
        assert len(set(__lowercase)) > 1  # it's not dynamic batch size if every batch is the same length
        assert sum(__lowercase) == len(__lowercase)  # no dropped or added examples
        __UpperCamelCase					:int        =		DataLoader(__lowercase			,			batch_sampler=__lowercase			,			collate_fn=ds.collate_fn			,			num_workers=2)
        __UpperCamelCase					:List[str]        =		[]
        __UpperCamelCase					:int        =		[]
        for batch in data_loader:
            __UpperCamelCase					:List[Any]        =		batch['''input_ids'''].shape
            __UpperCamelCase					:Dict        =		src_shape[0]
            assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
            __UpperCamelCase					:Optional[int]        =		np.product(batch['''input_ids'''].shape)
            num_src_per_batch.append(__lowercase)
            if num_src_tokens > (max_tokens * 1.1):
                failures.append(__lowercase)
        assert num_src_per_batch[0] == max(__lowercase)
        if failures:
            raise AssertionError(f"""too many tokens in {len(__lowercase)} batches""")







    def       UpperCamelCase__       (     self)	->     Tuple:
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Optional[Any]        =		self._get_dataset(max_len=512)
        __UpperCamelCase					:Any        =		2
        __UpperCamelCase					:List[Any]        =		ds.make_sortish_sampler(__lowercase			,			shuffle=__lowercase)

        __UpperCamelCase					:List[Any]        =		DataLoader(__lowercase			,			batch_size=__lowercase			,			collate_fn=ds.collate_fn			,			num_workers=2)
        __UpperCamelCase					:Tuple        =		DataLoader(__lowercase			,			batch_size=__lowercase			,			collate_fn=ds.collate_fn			,			num_workers=2			,			sampler=__lowercase)

        __UpperCamelCase					:int        =		tokenizer.pad_token_id

        def count_pad_tokens(__lowercase			,			__lowercase="input_ids"):
            return [batch[k].eq(__lowercase).sum().item() for batch in data_loader]

        assert sum(count_pad_tokens(__lowercase			,			k='''labels''')) < sum(count_pad_tokens(__lowercase			,			k='''labels'''))
        assert sum(count_pad_tokens(__lowercase)) < sum(count_pad_tokens(__lowercase))
        assert len(__lowercase) == len(__lowercase)







    def       UpperCamelCase__       (     self			,			__lowercase=1_000			,			__lowercase=128)	->     List[Any]:
        if os.getenv('''USE_REAL_DATA'''			,			__lowercase):
            __UpperCamelCase					:Optional[Any]        =		'''examples/seq2seq/wmt_en_ro'''
            __UpperCamelCase					:Dict        =		max_len * 2 * 64
            if not Path(__lowercase).joinpath('''train.len''').exists():
                save_len_file(__lowercase			,			__lowercase)
        else:
            __UpperCamelCase					:Union[str, Any]        =		'''examples/seq2seq/test_data/wmt_en_ro'''
            __UpperCamelCase					:Optional[int]        =		max_len * 4
            save_len_file(__lowercase			,			__lowercase)

        __UpperCamelCase					:str        =		AutoTokenizer.from_pretrained(__lowercase)
        __UpperCamelCase					:List[Any]        =		SeqaSeqDataset(
            __lowercase			,			data_dir=__lowercase			,			type_path='''train'''			,			max_source_length=__lowercase			,			max_target_length=__lowercase			,			n_obs=__lowercase			,			)
        return ds, max_tokens, tokenizer







    def       UpperCamelCase__       (     self)	->     Tuple:
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:List[Any]        =		self._get_dataset()
        __UpperCamelCase					:List[str]        =		set(DistributedSortishSampler(__lowercase			,			256			,			num_replicas=2			,			rank=0			,			add_extra_examples=__lowercase))
        __UpperCamelCase					:Tuple        =		set(DistributedSortishSampler(__lowercase			,			256			,			num_replicas=2			,			rank=1			,			add_extra_examples=__lowercase))
        assert idsa.intersection(__lowercase) == set()







    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ]			,			)
    def       UpperCamelCase__       (     self			,			__lowercase)	->     List[Any]:
        __UpperCamelCase					:List[Any]        =		AutoTokenizer.from_pretrained(__lowercase			,			use_fast=__lowercase)
        if tok_name == MBART_TINY:
            __UpperCamelCase					:Optional[Any]        =		SeqaSeqDataset(
                __lowercase			,			data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())			,			type_path='''train'''			,			max_source_length=4			,			max_target_length=8			,			src_lang='''EN'''			,			tgt_lang='''FR'''			,			)
            __UpperCamelCase					:Tuple        =		train_dataset.dataset_kwargs
            assert "src_lang" in kwargs and "tgt_lang" in kwargs
        else:
            __UpperCamelCase					:Tuple        =		SeqaSeqDataset(
                __lowercase			,			data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())			,			type_path='''train'''			,			max_source_length=4			,			max_target_length=8			,			)
            __UpperCamelCase					:Optional[int]        =		train_dataset.dataset_kwargs
            assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
            assert len(__lowercase) == 1 if tok_name == BART_TINY else len(__lowercase) == 0





