






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



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Path      ,					__lowercase :      list     )    ->   Union[str, Any]:





    """simple docstring"""

    __A       =						"""\n""".join(__lowercase     )
    Path(__lowercase     ).open("""w"""     ).writelines(__lowercase     )



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Any     )    ->   Optional[int]:





    """simple docstring"""

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






class    __lowercase   (						lowercase_	):







     '''simple docstring'''




     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ]			,						)
     @slow
     def 		lowerCAmelCase_  (					self							:      Dict			,						UpperCamelCase_							:      Tuple					):


         """simple docstring"""



         __A       =						AutoTokenizer.from_pretrained(UpperCamelCase_					)
         __A       =						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
         __A       =						max(len(tokenizer.encode(UpperCamelCase_					)					) for a in ARTICLES					)
         __A       =						max(len(tokenizer.encode(UpperCamelCase_					)					) for a in SUMMARIES					)
         __A       =						4
         __A       =						8
         assert max_len_target > max_src_len  # Will be truncated
         assert max_len_source > max_src_len  # Will be truncated
         __A     ,						__A       =						"""ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
         __A       =						SeqaSeqDataset(
             UpperCamelCase_			,						data_dir=UpperCamelCase_			,						type_path="""train"""			,						max_source_length=UpperCamelCase_			,						max_target_length=UpperCamelCase_			,						src_lang=UpperCamelCase_			,						tgt_lang=UpperCamelCase_			,						)
         __A       =						DataLoader(UpperCamelCase_			,						batch_size=2			,						collate_fn=train_dataset.collate_fn					)
         for batch in dataloader:
             assert isinstance(UpperCamelCase_			,						UpperCamelCase_					)
             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
             __A       =						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 		lowerCAmelCase_  (					self							:      List[str]			,						UpperCamelCase_							:      Any					):


         """simple docstring"""



         __A       =						AutoTokenizer.from_pretrained(UpperCamelCase_					)
         __A       =						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
         __A       =						max(len(tokenizer.encode(UpperCamelCase_					)					) for a in ARTICLES					)
         __A       =						max(len(tokenizer.encode(UpperCamelCase_					)					) for a in SUMMARIES					)
         __A       =						4
         __A       =						LegacySeqaSeqDataset(
             UpperCamelCase_			,						data_dir=UpperCamelCase_			,						type_path="""train"""			,						max_source_length=20			,						max_target_length=UpperCamelCase_			,						)
         __A       =						DataLoader(UpperCamelCase_			,						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 		lowerCAmelCase_  (					self							:      Tuple					):


         """simple docstring"""



         __A       =						AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25"""					)

         __A       =						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)					)
         __A       =						tmp_dir.joinpath("""train.source"""					).open().readlines()
         __A       =						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)					)
         pack_data_dir(UpperCamelCase_			,						UpperCamelCase_			,						128			,						UpperCamelCase_					)
         __A       =						{x.name for x in tmp_dir.iterdir()}
         __A       =						{x.name for x in save_dir.iterdir()}
         __A       =						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(UpperCamelCase_					) < len(UpperCamelCase_					)
         assert len(UpperCamelCase_					) == 1
         assert len(packed_examples[0]					) == sum(len(UpperCamelCase_					) for x in orig_examples					)
         assert orig_paths == new_paths




     @pytest.mark.skipif(not FAIRSEQ_AVAILABLE			,						reason="""This test requires fairseq"""					)
     def 		lowerCAmelCase_  (					self							:      Dict					):


         """simple docstring"""



         if not FAIRSEQ_AVAILABLE:
             return
         __A     ,						__A     ,						__A       =						self._get_dataset(max_len=64					)
         __A       =						64
         __A       =						ds.make_dynamic_sampler(UpperCamelCase_			,						required_batch_size_multiple=UpperCamelCase_					)
         __A       =						[len(UpperCamelCase_					) for x in batch_sampler]
         assert len(set(UpperCamelCase_					)					) > 1  # it's not dynamic batch size if every batch is the same length
         assert sum(UpperCamelCase_					) == len(UpperCamelCase_					)  # no dropped or added examples
         __A       =						DataLoader(UpperCamelCase_			,						batch_sampler=UpperCamelCase_			,						collate_fn=ds.collate_fn			,						num_workers=2					)
         __A       =						[]
         __A       =						[]
         for batch in data_loader:
             __A       =						batch["""input_ids"""].shape
             __A       =						src_shape[0]
             assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
             __A       =						np.product(batch["""input_ids"""].shape					)
             num_src_per_batch.append(UpperCamelCase_					)
             if num_src_tokens > (max_tokens * 1.1):
                 failures.append(UpperCamelCase_					)
         assert num_src_per_batch[0] == max(UpperCamelCase_					)
         if failures:
             raise AssertionError(F"too many tokens in {len(UpperCamelCase_					)} batches"					)




     def 		lowerCAmelCase_  (					self							:      Tuple					):


         """simple docstring"""



         __A     ,						__A     ,						__A       =						self._get_dataset(max_len=512					)
         __A       =						2
         __A       =						ds.make_sortish_sampler(UpperCamelCase_			,						shuffle=UpperCamelCase_					)

         __A       =						DataLoader(UpperCamelCase_			,						batch_size=UpperCamelCase_			,						collate_fn=ds.collate_fn			,						num_workers=2					)
         __A       =						DataLoader(UpperCamelCase_			,						batch_size=UpperCamelCase_			,						collate_fn=ds.collate_fn			,						num_workers=2			,						sampler=UpperCamelCase_					)

         __A       =						tokenizer.pad_token_id

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

         assert sum(count_pad_tokens(UpperCamelCase_			,						k="""labels"""					)					) < sum(count_pad_tokens(UpperCamelCase_			,						k="""labels"""					)					)
         assert sum(count_pad_tokens(UpperCamelCase_					)					) < sum(count_pad_tokens(UpperCamelCase_					)					)
         assert len(UpperCamelCase_					) == len(UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      str			,						UpperCamelCase_							:      Tuple=1_000			,						UpperCamelCase_							:      Dict=128					):


         """simple docstring"""



         if os.getenv("""USE_REAL_DATA"""			,						UpperCamelCase_					):
             __A       =						"""examples/seq2seq/wmt_en_ro"""
             __A       =						max_len * 2 * 64
             if not Path(UpperCamelCase_					).joinpath("""train.len"""					).exists():
                 save_len_file(UpperCamelCase_			,						UpperCamelCase_					)
         else:
             __A       =						"""examples/seq2seq/test_data/wmt_en_ro"""
             __A       =						max_len * 4
             save_len_file(UpperCamelCase_			,						UpperCamelCase_					)

         __A       =						AutoTokenizer.from_pretrained(UpperCamelCase_					)
         __A       =						SeqaSeqDataset(
             UpperCamelCase_			,						data_dir=UpperCamelCase_			,						type_path="""train"""			,						max_source_length=UpperCamelCase_			,						max_target_length=UpperCamelCase_			,						n_obs=UpperCamelCase_			,						)
         return ds, max_tokens, tokenizer




     def 		lowerCAmelCase_  (					self							:      List[str]					):


         """simple docstring"""



         __A     ,						__A     ,						__A       =						self._get_dataset()
         __A       =						set(DistributedSortishSampler(UpperCamelCase_			,						256			,						num_replicas=2			,						rank=0			,						add_extra_examples=UpperCamelCase_					)					)
         __A       =						set(DistributedSortishSampler(UpperCamelCase_			,						256			,						num_replicas=2			,						rank=1			,						add_extra_examples=UpperCamelCase_					)					)
         assert idsa.intersection(UpperCamelCase_					) == set()





     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ]			,						)
     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      Any					):


         """simple docstring"""



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












from __future__ import annotations

from typing import Generic, TypeVar

__a   :							str						= TypeVar("T")


class    __lowercase   (						Generic[T]	):







     '''simple docstring'''




     def __init__(					self							:      Any			,						UpperCamelCase_							:      T					):


         """simple docstring"""



         __A       =						data
         __A       =						self
         __A       =						0


class    __lowercase   (						Generic[T]	):







     '''simple docstring'''




     def __init__(					self							:      Optional[Any]					):


         """simple docstring"""



         __A       =						{}




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]			,						UpperCamelCase_							:      T					):


         """simple docstring"""



         __A       =						DisjointSetTreeNode(UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      List[Any]			,						UpperCamelCase_							:      T					):


         """simple docstring"""



         __A       =						self.map[data]
         if elem_ref != elem_ref.parent:
             __A       =						self.find_set(elem_ref.parent.data					)
         return elem_ref.parent




     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      DisjointSetTreeNode[T]			,						UpperCamelCase_							:      DisjointSetTreeNode[T]					):


         """simple docstring"""



         if nodea.rank > nodea.rank:
             __A       =						nodea
         else:
             __A       =						nodea
             if nodea.rank == nodea.rank:
                 nodea.rank += 1





     def 		lowerCAmelCase_  (					self							:      Tuple			,						UpperCamelCase_							:      T			,						UpperCamelCase_							:      T					):


         """simple docstring"""



         self.link(self.find_set(UpperCamelCase_					)			,						self.find_set(UpperCamelCase_					)					)


class    __lowercase   (						Generic[T]	):







     '''simple docstring'''




     def __init__(					self							:      Optional[int]					):


         """simple docstring"""



         __A       =						{}




     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      T					):


         """simple docstring"""



         if node not in self.connections:
             __A       =						{}




     def 		lowerCAmelCase_  (					self							:      Dict			,						UpperCamelCase_							:      T			,						UpperCamelCase_							:      T			,						UpperCamelCase_							:      int					):


         """simple docstring"""



         self.add_node(UpperCamelCase_					)
         self.add_node(UpperCamelCase_					)
         __A       =						weight
         __A       =						weight





     def 		lowerCAmelCase_  (					self							:      Dict					):


         """simple docstring"""



         __A       =						[]
         __A       =						set()
         for start in self.connections:
             for end in self.connections[start]:
                 if (start, end) not in seen:
                     seen.add((end, start)					)
                     edges.append((start, end, self.connections[start][end])					)
         edges.sort(key=lambda UpperCamelCase_					: x[2]					)

         # creating the disjoint set
         __A       =						DisjointSetTree[T]()
         for node in self.connections:
             disjoint_set.make_set(UpperCamelCase_					)

         # MST generation
         __A       =						0
         __A       =						0
         __A       =						GraphUndirectedWeighted[T]()
         while num_edges < len(self.connections					) - 1:
             __A     ,						__A     ,						__A       =						edges[index]
             index += 1
             __A       =						disjoint_set.find_set(UpperCamelCase_					)
             __A       =						disjoint_set.find_set(UpperCamelCase_					)
             if parent_u != parent_v:
                 num_edges += 1
                 graph.add_edge(UpperCamelCase_			,						UpperCamelCase_			,						UpperCamelCase_					)
                 disjoint_set.union(UpperCamelCase_			,						UpperCamelCase_					)
         return graph




