



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


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




def 		SCREAMING_SNAKE_CASE     (     snake_case__				,   snake_case__      )   ->	List[Any]:
    __UpperCAmelCase			 ="""\n""".join(__lowerCAmelCase      )
    Path(__lowerCAmelCase      ).open('''w'''      ).writelines(__lowerCAmelCase      )




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



class        _SCREAMING_SNAKE_CASE		(       UpperCAmelCase_ ):





   @parameterized.expand(
       [
           MBART_TINY,
           MARIAN_TINY,
           T5_TINY,
           BART_TINY,
           PEGASUS_XSUM,
       ]    ,  )
   @slow
   def 	A__      (self    ,  UpperCAmelCase):
       '''simple docstring'''







       __UpperCAmelCase			 =AutoTokenizer.from_pretrained(lowerCamelCase__)
       __UpperCAmelCase			 =make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
       __UpperCAmelCase			 =max(len(tokenizer.encode(lowerCamelCase__)) for a in ARTICLES)
       __UpperCAmelCase			 =max(len(tokenizer.encode(lowerCamelCase__)) for a in SUMMARIES)
       __UpperCAmelCase			 =4
       __UpperCAmelCase			 =8
       assert max_len_target > max_src_len  # Will be truncated
       assert max_len_source > max_src_len  # Will be truncated
       __UpperCAmelCase			 ="""ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
       __UpperCAmelCase			 =SeqaSeqDataset(
           lowerCamelCase__    ,  data_dir=lowerCamelCase__    ,  type_path='''train'''    ,  max_source_length=lowerCamelCase__    ,  max_target_length=lowerCamelCase__    ,  src_lang=lowerCamelCase__    ,  tgt_lang=lowerCamelCase__    ,  )
       __UpperCAmelCase			 =DataLoader(lowerCamelCase__    ,  batch_size=2    ,  collate_fn=train_dataset.collate_fn)
       for batch in dataloader:
           assert isinstance(lowerCamelCase__    ,  lowerCamelCase__)
           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			 =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 	A__      (self    ,  UpperCAmelCase):
       '''simple docstring'''







       __UpperCAmelCase			 =AutoTokenizer.from_pretrained(lowerCamelCase__)
       __UpperCAmelCase			 =make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
       __UpperCAmelCase			 =max(len(tokenizer.encode(lowerCamelCase__)) for a in ARTICLES)
       __UpperCAmelCase			 =max(len(tokenizer.encode(lowerCamelCase__)) for a in SUMMARIES)
       __UpperCAmelCase			 =4
       __UpperCAmelCase			 =LegacySeqaSeqDataset(
           lowerCamelCase__    ,  data_dir=lowerCamelCase__    ,  type_path='''train'''    ,  max_source_length=2_0    ,  max_target_length=lowerCamelCase__    ,  )
       __UpperCAmelCase			 =DataLoader(lowerCamelCase__    ,  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 2_0 >= 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 	A__      (self):
       '''simple docstring'''







       __UpperCAmelCase			 =AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25''')

       __UpperCAmelCase			 =Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
       __UpperCAmelCase			 =tmp_dir.joinpath('''train.source''').open().readlines()
       __UpperCAmelCase			 =Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
       pack_data_dir(lowerCamelCase__    ,  lowerCamelCase__    ,  1_2_8    ,  lowerCamelCase__)
       __UpperCAmelCase			 ={x.name for x in tmp_dir.iterdir()}
       __UpperCAmelCase			 ={x.name for x in save_dir.iterdir()}
       __UpperCAmelCase			 =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(lowerCamelCase__) < len(lowerCamelCase__)
       assert len(lowerCamelCase__) == 1
       assert len(packed_examples[0]) == sum(len(lowerCamelCase__) for x in orig_examples)
       assert orig_paths == new_paths





   @pytest.mark.skipif(not FAIRSEQ_AVAILABLE    ,  reason='''This test requires fairseq''')
   def 	A__      (self):
       '''simple docstring'''







       if not FAIRSEQ_AVAILABLE:
           return
       __UpperCAmelCase			 =self._get_dataset(max_len=6_4)
       __UpperCAmelCase			 =6_4
       __UpperCAmelCase			 =ds.make_dynamic_sampler(lowerCamelCase__    ,  required_batch_size_multiple=lowerCamelCase__)
       __UpperCAmelCase			 =[len(lowerCamelCase__) for x in batch_sampler]
       assert len(set(lowerCamelCase__)) > 1  # it's not dynamic batch size if every batch is the same length
       assert sum(lowerCamelCase__) == len(lowerCamelCase__)  # no dropped or added examples
       __UpperCAmelCase			 =DataLoader(lowerCamelCase__    ,  batch_sampler=lowerCamelCase__    ,  collate_fn=ds.collate_fn    ,  num_workers=2)
       __UpperCAmelCase			 =[]
       __UpperCAmelCase			 =[]
       for batch in data_loader:
           __UpperCAmelCase			 =batch["""input_ids"""].shape
           __UpperCAmelCase			 =src_shape[0]
           assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
           __UpperCAmelCase			 =np.product(batch['''input_ids'''].shape)
           num_src_per_batch.append(lowerCamelCase__)
           if num_src_tokens > (max_tokens * 1.1):
               failures.append(lowerCamelCase__)
       assert num_src_per_batch[0] == max(lowerCamelCase__)
       if failures:
           raise AssertionError(f"""too many tokens in {len(lowerCamelCase__)} batches""")





   def 	A__      (self):
       '''simple docstring'''







       __UpperCAmelCase			 =self._get_dataset(max_len=5_1_2)
       __UpperCAmelCase			 =2
       __UpperCAmelCase			 =ds.make_sortish_sampler(lowerCamelCase__    ,  shuffle=lowerCamelCase__)

       __UpperCAmelCase			 =DataLoader(lowerCamelCase__    ,  batch_size=lowerCamelCase__    ,  collate_fn=ds.collate_fn    ,  num_workers=2)
       __UpperCAmelCase			 =DataLoader(lowerCamelCase__    ,  batch_size=lowerCamelCase__    ,  collate_fn=ds.collate_fn    ,  num_workers=2    ,  sampler=lowerCamelCase__)

       __UpperCAmelCase			 =tokenizer.pad_token_id

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

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





   def 	A__      (self    ,  UpperCAmelCase=1_0_0_0    ,  UpperCAmelCase=1_2_8):
       '''simple docstring'''







       if os.getenv('''USE_REAL_DATA'''    ,  lowerCamelCase__):
           __UpperCAmelCase			 ="""examples/seq2seq/wmt_en_ro"""
           __UpperCAmelCase			 =max_len * 2 * 6_4
           if not Path(lowerCamelCase__).joinpath('''train.len''').exists():
               save_len_file(lowerCamelCase__    ,  lowerCamelCase__)
       else:
           __UpperCAmelCase			 ="""examples/seq2seq/test_data/wmt_en_ro"""
           __UpperCAmelCase			 =max_len * 4
           save_len_file(lowerCamelCase__    ,  lowerCamelCase__)

       __UpperCAmelCase			 =AutoTokenizer.from_pretrained(lowerCamelCase__)
       __UpperCAmelCase			 =SeqaSeqDataset(
           lowerCamelCase__    ,  data_dir=lowerCamelCase__    ,  type_path='''train'''    ,  max_source_length=lowerCamelCase__    ,  max_target_length=lowerCamelCase__    ,  n_obs=lowerCamelCase__    ,  )
       return ds, max_tokens, tokenizer





   def 	A__      (self):
       '''simple docstring'''







       __UpperCAmelCase			 =self._get_dataset()
       __UpperCAmelCase			 =set(DistributedSortishSampler(lowerCamelCase__    ,  2_5_6    ,  num_replicas=2    ,  rank=0    ,  add_extra_examples=lowerCamelCase__))
       __UpperCAmelCase			 =set(DistributedSortishSampler(lowerCamelCase__    ,  2_5_6    ,  num_replicas=2    ,  rank=1    ,  add_extra_examples=lowerCamelCase__))
       assert idsa.intersection(lowerCamelCase__) == set()






   @parameterized.expand(
       [
           MBART_TINY,
           MARIAN_TINY,
           T5_TINY,
           BART_TINY,
           PEGASUS_XSUM,
       ]    ,  )
   def 	A__      (self    ,  UpperCAmelCase):
       '''simple docstring'''







       __UpperCAmelCase			 =AutoTokenizer.from_pretrained(lowerCamelCase__    ,  use_fast=lowerCamelCase__)
       if tok_name == MBART_TINY:
           __UpperCAmelCase			 =SeqaSeqDataset(
               lowerCamelCase__    ,  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			 =train_dataset.dataset_kwargs
           assert "src_lang" in kwargs and "tgt_lang" in kwargs
       else:
           __UpperCAmelCase			 =SeqaSeqDataset(
               lowerCamelCase__    ,  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			 =train_dataset.dataset_kwargs
           assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
           assert len(lowerCamelCase__) == 1 if tok_name == BART_TINY else len(lowerCamelCase__) == 0











import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


UpperCamelCase    							=      logging.get_logger(__name__)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Tuple							)     ->  List[str]:
   __UpperCamelCase	:			List[str]					=					R"""\w+[.]\d+"""
   __UpperCamelCase	:			Optional[int]					=					re.findall(__lowerCAmelCase					,							__lowerCAmelCase							)
   for pat in pats:
      __UpperCamelCase	:			str					=					key.replace(__lowerCAmelCase					,							"""_""".join(pat.split("""."""							)							)							)
   return key


def 				__lowerCamelCase     (							__lowerCAmelCase							:					int					,							__lowerCAmelCase							:					Optional[int]					,							__lowerCAmelCase							:					Union[str, Any]							)     ->  str:
   __UpperCamelCase	:			Dict					=					pt_tuple_key[:-1] + ("""scale""",)
   if (
       any("""norm""" in str_ for str_ in pt_tuple_key							)
       and (pt_tuple_key[-1] == "bias")
       and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
       and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
   ):
      __UpperCamelCase	:			List[Any]					=					pt_tuple_key[:-1] + ("""scale""",)
      return renamed_pt_tuple_key, pt_tensor
   elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
      __UpperCamelCase	:			Dict					=					pt_tuple_key[:-1] + ("""scale""",)
      return renamed_pt_tuple_key, pt_tensor

   # embedding
   if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
      __UpperCamelCase	:			Any					=					pt_tuple_key[:-1] + ("""embedding""",)
      return renamed_pt_tuple_key, pt_tensor

   # conv layer
   __UpperCamelCase	:			List[Any]					=					pt_tuple_key[:-1] + ("""kernel""",)
   if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
      __UpperCamelCase	:			Dict					=					pt_tensor.transpose(2					,							3					,							1					,							0							)
      return renamed_pt_tuple_key, pt_tensor

   # linear layer
   __UpperCamelCase	:			Union[str, Any]					=					pt_tuple_key[:-1] + ("""kernel""",)
   if pt_tuple_key[-1] == "weight":
      __UpperCamelCase	:			int					=					pt_tensor.T
      return renamed_pt_tuple_key, pt_tensor

   # old PyTorch layer norm weight
   __UpperCamelCase	:			List[Any]					=					pt_tuple_key[:-1] + ("""weight""",)
   if pt_tuple_key[-1] == "gamma":
      return renamed_pt_tuple_key, pt_tensor

   # old PyTorch layer norm bias
   __UpperCamelCase	:			Tuple					=					pt_tuple_key[:-1] + ("""bias""",)
   if pt_tuple_key[-1] == "beta":
      return renamed_pt_tuple_key, pt_tensor

   return pt_tuple_key, pt_tensor



def 				__lowerCamelCase     (							__lowerCAmelCase							:					Dict					,							__lowerCAmelCase							:					List[Any]					,							__lowerCAmelCase							:					Optional[int]=42							)     ->  int:
   # Step 1: Convert pytorch tensor to numpy
   __UpperCamelCase	:			Dict					=					{k: v.numpy() for k, v in pt_state_dict.items()}

   # Step 2: Since the model is stateless, get random Flax params
   __UpperCamelCase	:			str					=					flax_model.init_weights(PRNGKey(__lowerCAmelCase							)							)

   __UpperCamelCase	:			Union[str, Any]					=					flatten_dict(__lowerCAmelCase							)
   __UpperCamelCase	:			List[str]					=					{}

   # Need to change some parameters name to match Flax names
   for pt_key, pt_tensor in pt_state_dict.items():
      __UpperCamelCase	:			str					=					rename_key(__lowerCAmelCase							)
      __UpperCamelCase	:			Any					=					tuple(renamed_pt_key.split("""."""							)							)

      # Correctly rename weight parameters
      __UpperCamelCase	,     __UpperCamelCase	:			Tuple					=					rename_key_and_reshape_tensor(__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase							)

      if flax_key in random_flax_state_dict:
         if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
            raise ValueError(
                f'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
                f'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'							)

        # also add unexpected weight so that warning is thrown
      __UpperCamelCase	:			Any					=					jnp.asarray(__lowerCAmelCase							)

   return unflatten_dict(__lowerCAmelCase							)



