





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


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



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    int:
		__lowercase											=     '\n'.join(snake_case							)
		Path(snake_case							).open('w'							).writelines(snake_case							)



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Union[str, Any]:
		for split in ["train", "val", "test"]:
				_dump_articles(os.path.join(snake_case						,   F"{split}.source"							)						,   snake_case							)
				_dump_articles(os.path.join(snake_case						,   F"{split}.target"							)						,   snake_case							)
		return tmp_dir


class 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''




	@parameterized.expand(
	    [
	        MBART_TINY,
	        MARIAN_TINY,
	        T5_TINY,
	        BART_TINY,
	        PEGASUS_XSUM,
	    ]					,      )
	@slow
	def    UpperCAmelCase			(						self							:     Union[str, Any]					,      __lowerCamelCase							:     Tuple   )       -> Optional[int]:

			'''simple docstring'''




			__lowercase											=     AutoTokenizer.from_pretrained(__lowerCamelCase   )
			__lowercase											=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
			__lowercase											=     max(len(tokenizer.encode(__lowerCamelCase   )   ) for a in ARTICLES   )
			__lowercase											=     max(len(tokenizer.encode(__lowerCamelCase   )   ) for a in SUMMARIES   )
			__lowercase											=     4
			__lowercase											=     8
			assert max_len_target > max_src_len  # Will be truncated
			assert max_len_source > max_src_len  # Will be truncated
			__lowercase						,      __lowercase											=     'ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
			__lowercase											=     SeqaSeqDataset(
			    __lowerCamelCase					,      data_dir=__lowerCamelCase					,      type_path='train'					,      max_source_length=__lowerCamelCase					,      max_target_length=__lowerCamelCase					,      src_lang=__lowerCamelCase					,      tgt_lang=__lowerCamelCase					,      )
			__lowercase											=     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
					__lowercase											=     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							:     Optional[int]					,      __lowerCamelCase							:     int   )       -> List[Any]:

			'''simple docstring'''




			__lowercase											=     AutoTokenizer.from_pretrained(__lowerCamelCase   )
			__lowercase											=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
			__lowercase											=     max(len(tokenizer.encode(__lowerCamelCase   )   ) for a in ARTICLES   )
			__lowercase											=     max(len(tokenizer.encode(__lowerCamelCase   )   ) for a in SUMMARIES   )
			__lowercase											=     4
			__lowercase											=     LegacySeqaSeqDataset(
			    __lowerCamelCase					,      data_dir=__lowerCamelCase					,      type_path='train'					,      max_source_length=20					,      max_target_length=__lowerCamelCase					,      )
			__lowercase											=     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 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							:     str   )       -> List[Any]:

			'''simple docstring'''




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

			__lowercase											=     Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )   )
			__lowercase											=     tmp_dir.joinpath('train.source'   ).open().readlines()
			__lowercase											=     Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )   )
			pack_data_dir(__lowerCamelCase					,      __lowerCamelCase					,      128					,      __lowerCamelCase   )
			__lowercase											=     {x.name for x in tmp_dir.iterdir()}
			__lowercase											=     {x.name for x in save_dir.iterdir()}
			__lowercase											=     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    UpperCAmelCase			(						self							:     Optional[int]   )       -> Optional[int]:

			'''simple docstring'''




			if not FAIRSEQ_AVAILABLE:
					return
			__lowercase						,      __lowercase						,      __lowercase											=     self._get_dataset(max_len=64   )
			__lowercase											=     64
			__lowercase											=     ds.make_dynamic_sampler(__lowerCamelCase					,      required_batch_size_multiple=__lowerCamelCase   )
			__lowercase											=     [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
			__lowercase											=     DataLoader(__lowerCamelCase					,      batch_sampler=__lowerCamelCase					,      collate_fn=ds.collate_fn					,      num_workers=2   )
			__lowercase											=     []
			__lowercase											=     []
			for batch in data_loader:
					__lowercase											=     batch['input_ids'].shape
					__lowercase											=     src_shape[0]
					assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
					__lowercase											=     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    UpperCAmelCase			(						self							:     int   )       -> Tuple:

			'''simple docstring'''




			__lowercase						,      __lowercase						,      __lowercase											=     self._get_dataset(max_len=512   )
			__lowercase											=     2
			__lowercase											=     ds.make_sortish_sampler(__lowerCamelCase					,      shuffle=__lowerCamelCase   )

			__lowercase											=     DataLoader(__lowerCamelCase					,      batch_size=__lowerCamelCase					,      collate_fn=ds.collate_fn					,      num_workers=2   )
			__lowercase											=     DataLoader(__lowerCamelCase					,      batch_size=__lowerCamelCase					,      collate_fn=ds.collate_fn					,      num_workers=2					,      sampler=__lowerCamelCase   )

			__lowercase											=     tokenizer.pad_token_id

			def count_pad_tokens(__lowerCamelCase							:     int					,      __lowerCamelCase							:     str="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    UpperCAmelCase			(						self							:     Dict					,      __lowerCamelCase							:     Dict=1_000					,      __lowerCamelCase							:     int=128   )       -> Tuple:

			'''simple docstring'''




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

			__lowercase											=     AutoTokenizer.from_pretrained(__lowerCamelCase   )
			__lowercase											=     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    UpperCAmelCase			(						self							:     str   )       -> Union[str, Any]:

			'''simple docstring'''




			__lowercase						,      __lowercase						,      __lowercase											=     self._get_dataset()
			__lowercase											=     set(DistributedSortishSampler(__lowerCamelCase					,      256					,      num_replicas=2					,      rank=0					,      add_extra_examples=__lowerCamelCase   )   )
			__lowercase											=     set(DistributedSortishSampler(__lowerCamelCase					,      256					,      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    UpperCAmelCase			(						self							:     int					,      __lowerCamelCase							:     Tuple   )       -> Any:

			'''simple docstring'''




			__lowercase											=     AutoTokenizer.from_pretrained(__lowerCamelCase					,      use_fast=__lowerCamelCase   )
			if tok_name == MBART_TINY:
					__lowercase											=     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'					,      )
					__lowercase											=     train_dataset.dataset_kwargs
					assert "src_lang" in kwargs and "tgt_lang" in kwargs
			else:
					__lowercase											=     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					,      )
					__lowercase											=     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











from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE_   :      Optional[int]			=   TypeVar('''T''')



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    int:
		return (position - 1) // 2



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    int:
		return (2 * position) + 1



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    int:
		return (2 * position) + 2

class 		snake_case_   (							Generic[T]						):







	'''simple docstring'''




	def __init__(						self							:     Any   )       -> None:

			'''simple docstring'''




			__lowercase											=     []
			__lowercase											=     {}
			__lowercase											=     0




	def __len__(						self							:     Optional[Any]   )       -> int:

			'''simple docstring'''




			return self.elements




	def __repr__(						self							:     Optional[Any]   )       -> str:

			'''simple docstring'''




			return str(self.heap   )




	def    UpperCAmelCase			(						self							:     Union[str, Any]   )       -> bool:

			'''simple docstring'''




			return self.elements == 0




	def    UpperCAmelCase			(						self							:     str					,      __lowerCamelCase							:     T					,      __lowerCamelCase							:     int   )       -> None:

			'''simple docstring'''




			self.heap.append((elem, weight)   )
			__lowercase											=     self.elements
			self.elements += 1
			self._bubble_up(__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Dict   )       -> T:

			'''simple docstring'''




			if self.elements > 1:
					self._swap_nodes(0					,      self.elements - 1   )
			__lowercase						,      __lowercase											=     self.heap.pop()
			del self.position_map[elem]
			self.elements -= 1
			if self.elements > 0:
					__lowercase						,      __lowercase											=     self.heap[0]
					self._bubble_down(__lowerCamelCase   )
			return elem




	def    UpperCAmelCase			(						self							:     int					,      __lowerCamelCase							:     T					,      __lowerCamelCase							:     int   )       -> None:

			'''simple docstring'''




			__lowercase											=     self.position_map[elem]
			__lowercase											=     (elem, weight)
			if position > 0:
					__lowercase											=     get_parent_position(__lowerCamelCase   )
					__lowercase						,      __lowercase											=     self.heap[parent_position]
					if parent_weight > weight:
							self._bubble_up(__lowerCamelCase   )
					else:
							self._bubble_down(__lowerCamelCase   )
			else:
					self._bubble_down(__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Tuple					,      __lowerCamelCase							:     T   )       -> None:

			'''simple docstring'''




			__lowercase											=     self.position_map[elem]
			if curr_pos == 0:
					return None
			__lowercase											=     get_parent_position(__lowerCamelCase   )
			__lowercase						,      __lowercase											=     self.heap[curr_pos]
			__lowercase						,      __lowercase											=     self.heap[parent_position]
			if parent_weight > weight:
					self._swap_nodes(__lowerCamelCase					,      __lowerCamelCase   )
					return self._bubble_up(__lowerCamelCase   )
			return None




	def    UpperCAmelCase			(						self							:     List[str]					,      __lowerCamelCase							:     T   )       -> None:

			'''simple docstring'''




			__lowercase											=     self.position_map[elem]
			__lowercase						,      __lowercase											=     self.heap[curr_pos]
			__lowercase											=     get_child_left_position(__lowerCamelCase   )
			__lowercase											=     get_child_right_position(__lowerCamelCase   )
			if child_left_position < self.elements and child_right_position < self.elements:
					__lowercase						,      __lowercase											=     self.heap[child_left_position]
					__lowercase						,      __lowercase											=     self.heap[child_right_position]
					if child_right_weight < child_left_weight and child_right_weight < weight:
							self._swap_nodes(__lowerCamelCase					,      __lowerCamelCase   )
							return self._bubble_down(__lowerCamelCase   )
			if child_left_position < self.elements:
					__lowercase						,      __lowercase											=     self.heap[child_left_position]
					if child_left_weight < weight:
							self._swap_nodes(__lowerCamelCase					,      __lowerCamelCase   )
							return self._bubble_down(__lowerCamelCase   )
			else:
					return None
			if child_right_position < self.elements:
					__lowercase						,      __lowercase											=     self.heap[child_right_position]
					if child_right_weight < weight:
							self._swap_nodes(__lowerCamelCase					,      __lowerCamelCase   )
							return self._bubble_down(__lowerCamelCase   )
			return None






	def    UpperCAmelCase			(						self							:     Optional[int]					,      __lowerCamelCase							:     int					,      __lowerCamelCase							:     int   )       -> None:

			'''simple docstring'''




			__lowercase											=     self.heap[nodea_pos][0]
			__lowercase											=     self.heap[nodea_pos][0]
			__lowercase						,      __lowercase											=     (
			    self.heap[nodea_pos],
			    self.heap[nodea_pos],
			)
			__lowercase											=     nodea_pos
			__lowercase											=     nodea_pos





class 		snake_case_   (							Generic[T]						):







	'''simple docstring'''




	def __init__(						self							:     Optional[Any]   )       -> None:

			'''simple docstring'''




			__lowercase											=     {}
			__lowercase											=     0




	def __repr__(						self							:     Union[str, Any]   )       -> str:

			'''simple docstring'''




			return str(self.connections   )




	def __len__(						self							:     int   )       -> int:

			'''simple docstring'''




			return self.nodes




	def    UpperCAmelCase			(						self							:     Optional[int]					,      __lowerCamelCase							:     T   )       -> None:

			'''simple docstring'''




			if node not in self.connections:
					__lowercase											=     {}
					self.nodes += 1






	def    UpperCAmelCase			(						self							:     List[Any]					,      __lowerCamelCase							:     T					,      __lowerCamelCase							:     T					,      __lowerCamelCase							:     int   )       -> None:

			'''simple docstring'''




			self.add_node(__lowerCamelCase   )
			self.add_node(__lowerCamelCase   )
			__lowercase											=     weight
			__lowercase											=     weight





def  SCREAMING_SNAKE_CASE				(      snake_case						,   )		->    tuple[dict[T, int], dict[T, T | None]]:
		__lowercase											=     {node: maxsize for node in graph.connections}
		__lowercase											=     {node: None for node in graph.connections}

		__lowercase											=     MinPriorityQueue()
		for node, weight in dist.items():
				priority_queue.push(snake_case						,   snake_case							)

		if priority_queue.is_empty():
				return dist, parent

		# initialization
		__lowercase											=     priority_queue.extract_min()
		__lowercase											=     0
		for neighbour in graph.connections[node]:
				if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
						__lowercase											=     dist[node] + graph.connections[node][neighbour]
						priority_queue.update_key(snake_case						,   dist[neighbour]							)
						__lowercase											=     node

    # running prim's algorithm
		while not priority_queue.is_empty():
				__lowercase											=     priority_queue.extract_min()
				for neighbour in graph.connections[node]:
						if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
								__lowercase											=     dist[node] + graph.connections[node][neighbour]
								priority_queue.update_key(snake_case						,   dist[neighbour]							)
								__lowercase											=     node
		return dist, parent




