

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


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

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Path     ,					SCREAMING_SNAKE_CASE_		:   list      ):



     '''simple docstring'''





     lowercase__   :       Union[str, Any]				=	'\n'.join(SCREAMING_SNAKE_CASE_      )
     Path(SCREAMING_SNAKE_CASE_      ).open('w'      ).writelines(SCREAMING_SNAKE_CASE_      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   List[Any]      ):



     '''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 				SCREAMING_SNAKE_CASE__ (__snake_case					):





    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ]						,       )
    @slow
    def 				snake_case_    (				self						,       a):
         lowercase__   :       Optional[Any]				=	AutoTokenizer.from_pretrained(a)
         lowercase__   :       Any				=	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
         lowercase__   :       int				=	max(len(tokenizer.encode(a)) for a in ARTICLES)
         lowercase__   :       Any				=	max(len(tokenizer.encode(a)) for a in SUMMARIES)
         lowercase__   :       int				=	4
         lowercase__   :       Optional[int]				=	8
         assert max_len_target > max_src_len  # Will be truncated
         assert max_len_source > max_src_len  # Will be truncated
         lowercase__				,			lowercase__   :       List[str]				=	'ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
         lowercase__   :       List[str]				=	SeqaSeqDataset(
             a						,       data_dir=a						,       type_path='train'						,       max_source_length=a						,       max_target_length=a						,       src_lang=a						,       tgt_lang=a						,       )
         lowercase__   :       int				=	DataLoader(a						,       batch_size=2						,       collate_fn=train_dataset.collate_fn)
         for batch in dataloader:
              assert isinstance(a						,       a)
              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
              lowercase__   :       Tuple				=	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 				snake_case_    (				self						,       a):
         lowercase__   :       Optional[Any]				=	AutoTokenizer.from_pretrained(a)
         lowercase__   :       Optional[int]				=	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
         lowercase__   :       List[Any]				=	max(len(tokenizer.encode(a)) for a in ARTICLES)
         lowercase__   :       str				=	max(len(tokenizer.encode(a)) for a in SUMMARIES)
         lowercase__   :       Optional[Any]				=	4
         lowercase__   :       Tuple				=	LegacySeqaSeqDataset(
             a						,       data_dir=a						,       type_path='train'						,       max_source_length=20						,       max_target_length=a						,       )
         lowercase__   :       Optional[int]				=	DataLoader(a						,       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 				snake_case_    (				self):
         lowercase__   :       Any				=	AutoTokenizer.from_pretrained('facebook/mbart-large-cc25')

         lowercase__   :       Any				=	Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
         lowercase__   :       List[Any]				=	tmp_dir.joinpath('train.source').open().readlines()
         lowercase__   :       Optional[Any]				=	Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
         pack_data_dir(a						,       a						,       128						,       a)
         lowercase__   :       Optional[int]				=	{x.name for x in tmp_dir.iterdir()}
         lowercase__   :       Optional[int]				=	{x.name for x in save_dir.iterdir()}
         lowercase__   :       Tuple				=	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(a) < len(a)
         assert len(a) == 1
         assert len(packed_examples[0]) == sum(len(a) for x in orig_examples)
         assert orig_paths == new_paths





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





    def 				snake_case_    (				self):
         lowercase__				,			lowercase__				,			lowercase__   :       Optional[Any]				=	self._get_dataset(max_len=512)
         lowercase__   :       List[str]				=	2
         lowercase__   :       Union[str, Any]				=	ds.make_sortish_sampler(a						,       shuffle=a)

         lowercase__   :       Tuple				=	DataLoader(a						,       batch_size=a						,       collate_fn=ds.collate_fn						,       num_workers=2)
         lowercase__   :       str				=	DataLoader(a						,       batch_size=a						,       collate_fn=ds.collate_fn						,       num_workers=2						,       sampler=a)

         lowercase__   :       Union[str, Any]				=	tokenizer.pad_token_id

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

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





    def 				snake_case_    (				self						,       a=1000						,       a=128):
         if os.getenv('USE_REAL_DATA'						,       a):
              lowercase__   :       Optional[Any]				=	'examples/seq2seq/wmt_en_ro'
              lowercase__   :       Tuple				=	max_len * 2 * 64
              if not Path(a).joinpath('train.len').exists():
                   save_len_file(a						,       a)
         else:
              lowercase__   :       Any				=	'examples/seq2seq/test_data/wmt_en_ro'
              lowercase__   :       str				=	max_len * 4
              save_len_file(a						,       a)

         lowercase__   :       str				=	AutoTokenizer.from_pretrained(a)
         lowercase__   :       Any				=	SeqaSeqDataset(
             a						,       data_dir=a						,       type_path='train'						,       max_source_length=a						,       max_target_length=a						,       n_obs=a						,       )
         return ds, max_tokens, tokenizer





    def 				snake_case_    (				self):
         lowercase__				,			lowercase__				,			lowercase__   :       Union[str, Any]				=	self._get_dataset()
         lowercase__   :       Dict				=	set(DistributedSortishSampler(a						,       256						,       num_replicas=2						,       rank=0						,       add_extra_examples=a))
         lowercase__   :       Any				=	set(DistributedSortishSampler(a						,       256						,       num_replicas=2						,       rank=1						,       add_extra_examples=a))
         assert idsa.intersection(a) == set()





    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ]						,       )
    def 				snake_case_    (				self						,       a):
         lowercase__   :       Optional[int]				=	AutoTokenizer.from_pretrained(a						,       use_fast=a)
         if tok_name == MBART_TINY:
              lowercase__   :       Tuple				=	SeqaSeqDataset(
                  a						,       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'						,       )
              lowercase__   :       Optional[int]				=	train_dataset.dataset_kwargs
              assert "src_lang" in kwargs and "tgt_lang" in kwargs
         else:
              lowercase__   :       Optional[int]				=	SeqaSeqDataset(
                  a						,       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						,       )
              lowercase__   :       Any				=	train_dataset.dataset_kwargs
              assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
              assert len(a) == 1 if tok_name == BART_TINY else len(a) == 0




import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   BertModel     ,					SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   str      ):



     '''simple docstring'''





     lowercase__   :       Tuple				=	('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

     lowercase__   :       Dict				=	(
         ('layer.', 'layer_'),
         ('word_embeddings.weight', 'word_embeddings'),
         ('position_embeddings.weight', 'position_embeddings'),
         ('token_type_embeddings.weight', 'token_type_embeddings'),
         ('.', '/'),
         ('LayerNorm/weight', 'LayerNorm/gamma'),
         ('LayerNorm/bias', 'LayerNorm/beta'),
         ('weight', 'kernel'),
     )

     if not os.path.isdir(SCREAMING_SNAKE_CASE_      ):
          os.makedirs(SCREAMING_SNAKE_CASE_      )

     lowercase__   :       str				=	model.state_dict()

     def to_tf_var_name(SCREAMING_SNAKE_CASE_		:   str      ):
          for patt, repl in iter(SCREAMING_SNAKE_CASE_      ):
               lowercase__   :       Optional[int]				=	name.replace(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
          return f"""bert/{name}"""

     def create_tf_var(SCREAMING_SNAKE_CASE_		:   np.ndarray     ,					SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   tf.Session      ):
          lowercase__   :       Optional[Any]				=	tf.dtypes.as_dtype(tensor.dtype      )
          lowercase__   :       List[str]				=	tf.get_variable(dtype=SCREAMING_SNAKE_CASE_     ,					shape=tensor.shape     ,					name=SCREAMING_SNAKE_CASE_     ,					initializer=tf.zeros_initializer()      )
          session.run(tf.variables_initializer([tf_var]      )      )
          session.run(SCREAMING_SNAKE_CASE_      )
          return tf_var

     tf.reset_default_graph()
     with tf.Session() as session:
          for var_name in state_dict:
               lowercase__   :       Tuple				=	to_tf_var_name(SCREAMING_SNAKE_CASE_      )
               lowercase__   :       str				=	state_dict[var_name].numpy()
               if any(x in var_name for x in tensors_to_transpose      ):
                    lowercase__   :       str				=	torch_tensor.T
               lowercase__   :       Any				=	create_tf_var(tensor=SCREAMING_SNAKE_CASE_     ,					name=SCREAMING_SNAKE_CASE_     ,					session=SCREAMING_SNAKE_CASE_      )
               tf.keras.backend.set_value(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
               lowercase__   :       Optional[int]				=	session.run(SCREAMING_SNAKE_CASE_      )
               print(f"""Successfully created {tf_name}: {np.allclose(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )}"""      )

          lowercase__   :       Tuple				=	tf.train.Saver(tf.trainable_variables()      )
          saver.save(SCREAMING_SNAKE_CASE_     ,					os.path.join(SCREAMING_SNAKE_CASE_     ,					model_name.replace('-'     ,					'_'      ) + '.ckpt'      )      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Optional[Any]=None      ):



     '''simple docstring'''





     lowercase__   :       List[str]				=	argparse.ArgumentParser()
     parser.add_argument('--model_name'     ,					type=SCREAMING_SNAKE_CASE_     ,					required=SCREAMING_SNAKE_CASE_     ,					help='model name e.g. bert-base-uncased'      )
     parser.add_argument(
         '--cache_dir'     ,					type=SCREAMING_SNAKE_CASE_     ,					default=SCREAMING_SNAKE_CASE_     ,					required=SCREAMING_SNAKE_CASE_     ,					help='Directory containing pytorch model'      )
     parser.add_argument('--pytorch_model_path'     ,					type=SCREAMING_SNAKE_CASE_     ,					required=SCREAMING_SNAKE_CASE_     ,					help='/path/to/<pytorch-model-name>.bin'      )
     parser.add_argument('--tf_cache_dir'     ,					type=SCREAMING_SNAKE_CASE_     ,					required=SCREAMING_SNAKE_CASE_     ,					help='Directory in which to save tensorflow model'      )
     lowercase__   :       List[str]				=	parser.parse_args(SCREAMING_SNAKE_CASE_      )

     lowercase__   :       int				=	BertModel.from_pretrained(
         pretrained_model_name_or_path=args.model_name     ,					state_dict=torch.load(args.pytorch_model_path      )     ,					cache_dir=args.cache_dir     ,					)

     convert_pytorch_checkpoint_to_tf(model=SCREAMING_SNAKE_CASE_     ,					ckpt_dir=args.tf_cache_dir     ,					model_name=args.model_name      )


if __name__ == "__main__":
       main()

