





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   : Any       =				'''bert-base-cased'''
A   : Dict       =				'''google/pegasus-xsum'''
A   : Optional[Any]       =				[''' 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   : Tuple       =				'''patrickvonplaten/t5-tiny-random'''
A   : Dict       =				'''sshleifer/bart-tiny-random'''
A   : Any       =				'''sshleifer/tiny-mbart'''
A   : Optional[Any]       =				'''sshleifer/tiny-marian-en-de'''







def        __lowerCamelCase (							__a       :Path   ,					__a       :list   )		->    Union[str, Any]:






		"""simple docstring"""

		A__  				=       """\n""".join(__a   )
		Path(__a   ).open("""w"""   ).writelines(__a   )







def        __lowerCamelCase (							__a       :Optional[Any]   )		->    Tuple:






		"""simple docstring"""

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






class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			@parameterized.expand(
			    [
			        MBART_TINY,
			        MARIAN_TINY,
			        T5_TINY,
			        BART_TINY,
			        PEGASUS_XSUM,
			    ]							,     )
			@slow
			def        a_	(      self       :   int							,     __lowerCAmelCase       :   List[str]       )     ->     Dict:







					"""simple docstring"""






					A__  				=       AutoTokenizer.from_pretrained(__lowerCAmelCase       )
					A__  				=       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
					A__  				=       max(len(tokenizer.encode(__lowerCAmelCase       )       ) for a in ARTICLES       )
					A__  				=       max(len(tokenizer.encode(__lowerCAmelCase       )       ) 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(
					    __lowerCAmelCase							,     data_dir=__lowerCAmelCase							,     type_path="""train"""							,     max_source_length=__lowerCAmelCase							,     max_target_length=__lowerCAmelCase							,     src_lang=__lowerCAmelCase							,     tgt_lang=__lowerCAmelCase							,     )
					A__  				=       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
							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        a_	(      self       :   List[str]							,     __lowerCAmelCase       :   Optional[Any]       )     ->     List[Any]:







					"""simple docstring"""






					A__  				=       AutoTokenizer.from_pretrained(__lowerCAmelCase       )
					A__  				=       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
					A__  				=       max(len(tokenizer.encode(__lowerCAmelCase       )       ) for a in ARTICLES       )
					A__  				=       max(len(tokenizer.encode(__lowerCAmelCase       )       ) for a in SUMMARIES       )
					A__  				=       4
					A__  				=       LegacySeqaSeqDataset(
					    __lowerCAmelCase							,     data_dir=__lowerCAmelCase							,     type_path="""train"""							,     max_source_length=20							,     max_target_length=__lowerCAmelCase							,     )
					A__  				=       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        a_	(      self       :   int       )     ->     Dict:







					"""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(__lowerCAmelCase							,     __lowerCAmelCase							,     1_28							,     __lowerCAmelCase       )
					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(__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       :   Union[str, Any]       )     ->     Union[str, Any]:







					"""simple docstring"""






					if not FAIRSEQ_AVAILABLE:
							return
					A__				,      A__				,      A__  				=       self._get_dataset(max_len=64       )
					A__  				=       64
					A__  				=       ds.make_dynamic_sampler(__lowerCAmelCase							,     required_batch_size_multiple=__lowerCAmelCase       )
					A__  				=       [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
					A__  				=       DataLoader(__lowerCAmelCase							,     batch_sampler=__lowerCAmelCase							,     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(__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       :   int       )     ->     List[str]:







					"""simple docstring"""






					A__				,      A__				,      A__  				=       self._get_dataset(max_len=5_12       )
					A__  				=       2
					A__  				=       ds.make_sortish_sampler(__lowerCAmelCase							,     shuffle=__lowerCAmelCase       )

					A__  				=       DataLoader(__lowerCAmelCase							,     batch_size=__lowerCAmelCase							,     collate_fn=ds.collate_fn							,     num_workers=2       )
					A__  				=       DataLoader(__lowerCAmelCase							,     batch_size=__lowerCAmelCase							,     collate_fn=ds.collate_fn							,     num_workers=2							,     sampler=__lowerCAmelCase       )

					A__  				=       tokenizer.pad_token_id

					def count_pad_tokens(__lowerCAmelCase       :   Union[str, Any]							,     __lowerCAmelCase       :   Any="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       :   int							,     __lowerCAmelCase       :   Any=10_00							,     __lowerCAmelCase       :   Optional[Any]=1_28       )     ->     List[str]:







					"""simple docstring"""






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

					A__  				=       AutoTokenizer.from_pretrained(__lowerCAmelCase       )
					A__  				=       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       :   Tuple       )     ->     Optional[int]:







					"""simple docstring"""






					A__				,      A__				,      A__  				=       self._get_dataset()
					A__  				=       set(DistributedSortishSampler(__lowerCAmelCase							,     2_56							,     num_replicas=2							,     rank=0							,     add_extra_examples=__lowerCAmelCase       )       )
					A__  				=       set(DistributedSortishSampler(__lowerCAmelCase							,     2_56							,     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       :   str							,     __lowerCAmelCase       :   Dict       )     ->     Union[str, Any]:







					"""simple docstring"""






					A__  				=       AutoTokenizer.from_pretrained(__lowerCAmelCase							,     use_fast=__lowerCAmelCase       )
					if tok_name == MBART_TINY:
							A__  				=       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"""							,     )
							A__  				=       train_dataset.dataset_kwargs
							assert "src_lang" in kwargs and "tgt_lang" in kwargs
					else:
							A__  				=       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							,     )
							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(__lowerCAmelCase       ) == 1 if tok_name == BART_TINY else len(__lowerCAmelCase       ) == 0










# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce







def        __lowerCamelCase (							__a       :List[str]   )		->    Tuple:






		"""simple docstring"""

		return (torch.arange(state.num_processes   ) + 1.0 + (state.num_processes * state.process_index)).to(state.device   )







def        __lowerCamelCase (							__a       :int   )		->    Optional[int]:






		"""simple docstring"""

		A__  				=       create_tensor(__a   )
		A__  				=       gather(__a   )
		assert gathered_tensor.tolist() == list(range(1   ,					state.num_processes**2 + 1   )   )







def        __lowerCamelCase (							__a       :Any   )		->    Any:






		"""simple docstring"""

		A__  				=       [state.process_index]
		A__  				=       gather_object(__a   )
		assert len(__a   ) == state.num_processes, F'{gathered_obj}, {len(__a   )} != {state.num_processes}'
		assert gathered_obj == list(range(state.num_processes   )   ), F'{gathered_obj} != {list(range(state.num_processes   )   )}'







def        __lowerCamelCase (							__a       :List[str]   )		->    List[str]:






		"""simple docstring"""

		A__  				=       create_tensor(__a   )
		A__  				=       broadcast(__a   )
		assert broadcasted_tensor.shape == torch.Size([state.num_processes]   )
		assert broadcasted_tensor.tolist() == list(range(1   ,					state.num_processes + 1   )   )







def        __lowerCamelCase (							__a       :Any   )		->    Any:






		"""simple docstring"""

		if state.is_main_process:
				A__  				=       torch.arange(state.num_processes + 1   ).to(state.device   )
		else:
				A__  				=       torch.arange(state.num_processes   ).to(state.device   )
		A__  				=       pad_across_processes(__a   )
		assert padded_tensor.shape == torch.Size([state.num_processes + 1]   )
		if not state.is_main_process:
				assert padded_tensor.tolist() == list(range(0   ,					state.num_processes   )   ) + [0]







def        __lowerCamelCase (							__a       :Union[str, Any]   )		->    str:






		"""simple docstring"""

		if state.num_processes != 2:
				return
		A__  				=       create_tensor(__a   )
		A__  				=       reduce(__a   ,					"""sum"""   )
		A__  				=       torch.tensor([4.0, 6]   ).to(state.device   )
		assert torch.allclose(__a   ,					__a   ), F'{reduced_tensor} != {truth_tensor}'







def        __lowerCamelCase (							__a       :List[Any]   )		->    List[str]:






		"""simple docstring"""

		if state.num_processes != 2:
				return
		A__  				=       create_tensor(__a   )
		A__  				=       reduce(__a   ,					"""mean"""   )
		A__  				=       torch.tensor([2.0, 3]   ).to(state.device   )
		assert torch.allclose(__a   ,					__a   ), F'{reduced_tensor} != {truth_tensor}'







def        __lowerCamelCase (							__a       :str   )		->    Optional[int]:






		"""simple docstring"""

		main()







def        __lowerCamelCase (							)		->    str:






		"""simple docstring"""

		A__  				=       PartialState()
		state.print(F'State: {state}'   )
		state.print("""testing gather"""   )
		test_gather(__a   )
		state.print("""testing gather_object"""   )
		test_gather_object(__a   )
		state.print("""testing broadcast"""   )
		test_broadcast(__a   )
		state.print("""testing pad_across_processes"""   )
		test_pad_across_processes(__a   )
		state.print("""testing reduce_sum"""   )
		test_reduce_sum(__a   )
		state.print("""testing reduce_mean"""   )
		test_reduce_mean(__a   )


if __name__ == "__main__":
					main()



