
'''simple docstring'''
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


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

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    List[str]     			=						"\n".join(__SCREAMING_SNAKE_CASE       )
    Path(__SCREAMING_SNAKE_CASE       ).open("w"       ).writelines(__SCREAMING_SNAKE_CASE       )

def 						UpperCamelCase_     (	__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    UpperCAmelCase_				(	snake_case__					):





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

     snake_case_ :    int     			=						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
     snake_case_ :    int     			=						tmp_dir.joinpath("train.source").open().readlines()
     snake_case_ :    Any     			=						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
     pack_data_dir(lowercase_ ,       lowercase_ ,       1_28 ,       lowercase_)
     snake_case_ :    Optional[Any]     			=						{x.name for x in tmp_dir.iterdir()}
     snake_case_ :    List[str]     			=						{x.name for x in save_dir.iterdir()}
     snake_case_ :    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        snake_case__		(    self):
     if not FAIRSEQ_AVAILABLE:
         return
     snake_case_ :    Dict     			=						self._get_dataset(max_len=64)
     snake_case_ :    Union[str, Any]     			=						64
     snake_case_ :    str     			=						ds.make_dynamic_sampler(lowercase_ ,       required_batch_size_multiple=lowercase_)
     snake_case_ :    int     			=						[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
     snake_case_ :    int     			=						DataLoader(lowercase_ ,       batch_sampler=lowercase_ ,       collate_fn=ds.collate_fn ,       num_workers=2)
     snake_case_ :    Dict     			=						[]
     snake_case_ :    Dict     			=						[]
     for batch in data_loader:
         snake_case_ :    Union[str, Any]     			=						batch["input_ids"].shape
         snake_case_ :    Any     			=						src_shape[0]
         assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
         snake_case_ :    Dict     			=						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        snake_case__		(    self):
     snake_case_ :    str     			=						self._get_dataset(max_len=5_12)
     snake_case_ :    str     			=						2
     snake_case_ :    Dict     			=						ds.make_sortish_sampler(lowercase_ ,       shuffle=lowercase_)

     snake_case_ :    Optional[int]     			=						DataLoader(lowercase_ ,       batch_size=lowercase_ ,       collate_fn=ds.collate_fn ,       num_workers=2)
     snake_case_ :    List[str]     			=						DataLoader(lowercase_ ,       batch_size=lowercase_ ,       collate_fn=ds.collate_fn ,       num_workers=2 ,       sampler=lowercase_)

     snake_case_ :    Optional[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        snake_case__		(    self ,       lowercase_=10_00 ,       lowercase_=1_28):
     if os.getenv("USE_REAL_DATA" ,       lowercase_):
         snake_case_ :    List[Any]     			=						"examples/seq2seq/wmt_en_ro"
         snake_case_ :    Tuple     			=						max_len * 2 * 64
         if not Path(lowercase_).joinpath("train.len").exists():
             save_len_file(lowercase_ ,       lowercase_)
     else:
         snake_case_ :    Tuple     			=						"examples/seq2seq/test_data/wmt_en_ro"
         snake_case_ :    str     			=						max_len * 4
         save_len_file(lowercase_ ,       lowercase_)

     snake_case_ :    Optional[Any]     			=						AutoTokenizer.from_pretrained(lowercase_)
     snake_case_ :    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        snake_case__		(    self):
     snake_case_ :    Tuple     			=						self._get_dataset()
     snake_case_ :    Optional[int]     			=						set(DistributedSortishSampler(lowercase_ ,       2_56 ,       num_replicas=2 ,       rank=0 ,       add_extra_examples=lowercase_))
     snake_case_ :    List[str]     			=						set(DistributedSortishSampler(lowercase_ ,       2_56 ,       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        snake_case__		(    self ,       lowercase_):
     snake_case_ :    int     			=						AutoTokenizer.from_pretrained(lowercase_ ,       use_fast=lowercase_)
     if tok_name == MBART_TINY:
         snake_case_ :    Dict     			=						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" ,       )
         snake_case_ :    Dict     			=						train_dataset.dataset_kwargs
         assert "src_lang" in kwargs and "tgt_lang" in kwargs
     else:
         snake_case_ :    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 ,       )
         snake_case_ :    Tuple     			=						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


'''simple docstring'''
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


a_											=						logging.get_logger(__name__)

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    List[Any]     			=						r"\w+[.]\d+"
    snake_case_ :    Tuple     			=						re.findall(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    for pat in pats:
        snake_case_ :    Union[str, Any]     			=						key.replace(__SCREAMING_SNAKE_CASE,		"_".join(pat.split("."       )       )       )
    return key

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Tuple     			=						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)
    ):
        snake_case_ :    int     			=						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:
        snake_case_ :    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:
        snake_case_ :    List[Any]     			=						pt_tuple_key[:-1] + ("embedding",)
        return renamed_pt_tuple_key, pt_tensor

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

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

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

    # old PyTorch layer norm bias
    snake_case_ :    Union[str, Any]     			=						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 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE=4_2       ):
    """simple docstring"""






    snake_case_ :    int     			=						{k: v.numpy() for k, v in pt_state_dict.items()}

    # Step 2: Since the model is stateless, get random Flax params
    snake_case_ :    Optional[int]     			=						flax_model.init_weights(PRNGKey(__SCREAMING_SNAKE_CASE       )       )

    snake_case_ :    Union[str, Any]     			=						flatten_dict(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    str     			=						{}

    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
        snake_case_ :    List[str]     			=						rename_key(__SCREAMING_SNAKE_CASE       )
        snake_case_ :    Tuple     			=						tuple(renamed_pt_key.split("."       )       )

        # Correctly rename weight parameters
        snake_case_			, snake_case_ :    Dict     			=						rename_key_and_reshape_tensor(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

        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
        snake_case_ :    Any     			=						jnp.asarray(__SCREAMING_SNAKE_CASE       )

    return unflatten_dict(__SCREAMING_SNAKE_CASE       )




