


"""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


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



def 	snake_case    (					_a:  Path		, _a:  list				)->		List[Any]:
      '''simple docstring'''


      lowerCamelCase__							     =		"""\n""".join(a__				)
      Path(a__				).open('w'				).writelines(a__				)



def 	snake_case    (					_a:  Union[str, Any]				)->		List[Any]:
      '''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   (   _lowercase			):
    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ]				,       )
    @slow
    def   _UpperCamelCase							(			self				:       List[Any]				,       SCREAMING_SNAKE_CASE__				:       Any					):
          lowerCamelCase__							     =		AutoTokenizer.from_pretrained(__A					)
          lowerCamelCase__							     =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
          lowerCamelCase__							     =		max(len(tokenizer.encode(__A					)					) for a in ARTICLES					)
          lowerCamelCase__							     =		max(len(tokenizer.encode(__A					)					) for a in SUMMARIES					)
          lowerCamelCase__							     =		4
          lowerCamelCase__							     =		8
          assert max_len_target > max_src_len  # Will be truncated
          assert max_len_source > max_src_len  # Will be truncated
          lowerCamelCase__							     =		"""ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
          lowerCamelCase__							     =		SeqaSeqDataset(
              __A				,       data_dir=__A				,       type_path='train'				,       max_source_length=__A				,       max_target_length=__A				,       src_lang=__A				,       tgt_lang=__A				,       )
          lowerCamelCase__							     =		DataLoader(__A				,       batch_size=2				,       collate_fn=train_dataset.collate_fn					)
          for batch in dataloader:
                assert isinstance(__A				,       __A					)
                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
                lowerCamelCase__							     =		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[Any]				,       SCREAMING_SNAKE_CASE__				:       Optional[int]					):
          lowerCamelCase__							     =		AutoTokenizer.from_pretrained(__A					)
          lowerCamelCase__							     =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
          lowerCamelCase__							     =		max(len(tokenizer.encode(__A					)					) for a in ARTICLES					)
          lowerCamelCase__							     =		max(len(tokenizer.encode(__A					)					) for a in SUMMARIES					)
          lowerCamelCase__							     =		4
          lowerCamelCase__							     =		LegacySeqaSeqDataset(
              __A				,       data_dir=__A				,       type_path='train'				,       max_source_length=20				,       max_target_length=__A				,       )
          lowerCamelCase__							     =		DataLoader(__A				,       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				:       List[Any]					):
          lowerCamelCase__							     =		AutoTokenizer.from_pretrained('facebook/mbart-large-cc25'					)

          lowerCamelCase__							     =		Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)					)
          lowerCamelCase__							     =		tmp_dir.joinpath('train.source'					).open().readlines()
          lowerCamelCase__							     =		Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)					)
          pack_data_dir(__A				,       __A				,       1_28				,       __A					)
          lowerCamelCase__							     =		{x.name for x in tmp_dir.iterdir()}
          lowerCamelCase__							     =		{x.name for x in save_dir.iterdir()}
          lowerCamelCase__							     =		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(__A					) < len(__A					)
          assert len(__A					) == 1
          assert len(packed_examples[0]					) == sum(len(__A					) 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]					):
          if not FAIRSEQ_AVAILABLE:
                return
          lowerCamelCase__							     =		self._get_dataset(max_len=64					)
          lowerCamelCase__							     =		64
          lowerCamelCase__							     =		ds.make_dynamic_sampler(__A				,       required_batch_size_multiple=__A					)
          lowerCamelCase__							     =		[len(__A					) for x in batch_sampler]
          assert len(set(__A					)					) > 1  # it's not dynamic batch size if every batch is the same length
          assert sum(__A					) == len(__A					)  # no dropped or added examples
          lowerCamelCase__							     =		DataLoader(__A				,       batch_sampler=__A				,       collate_fn=ds.collate_fn				,       num_workers=2					)
          lowerCamelCase__							     =		[]
          lowerCamelCase__							     =		[]
          for batch in data_loader:
                lowerCamelCase__							     =		batch["""input_ids"""].shape
                lowerCamelCase__							     =		src_shape[0]
                assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
                lowerCamelCase__							     =		np.product(batch['input_ids'].shape					)
                num_src_per_batch.append(__A					)
                if num_src_tokens > (max_tokens * 1.1):
                      failures.append(__A					)
          assert num_src_per_batch[0] == max(__A					)
          if failures:
                raise AssertionError(F'too many tokens in {len(__A					)} batches'					)
    def   _UpperCamelCase							(			self				:       List[Any]					):
          lowerCamelCase__							     =		self._get_dataset(max_len=5_12					)
          lowerCamelCase__							     =		2
          lowerCamelCase__							     =		ds.make_sortish_sampler(__A				,       shuffle=__A					)

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

          lowerCamelCase__							     =		tokenizer.pad_token_id

          def count_pad_tokens(SCREAMING_SNAKE_CASE__				:       str				,       SCREAMING_SNAKE_CASE__				:       Union[str, Any]="input_ids"					):
                return [batch[k].eq(__A					).sum().item() for batch in data_loader]

          assert sum(count_pad_tokens(__A				,       k='labels'					)					) < sum(count_pad_tokens(__A				,       k='labels'					)					)
          assert sum(count_pad_tokens(__A					)					) < sum(count_pad_tokens(__A					)					)
          assert len(__A					) == len(__A					)
    def   _UpperCamelCase							(			self				:       Tuple				,       SCREAMING_SNAKE_CASE__				:       Union[str, Any]=10_00				,       SCREAMING_SNAKE_CASE__				:       List[Any]=1_28					):
          if os.getenv('USE_REAL_DATA'				,       __A					):
                lowerCamelCase__							     =		"""examples/seq2seq/wmt_en_ro"""
                lowerCamelCase__							     =		max_len * 2 * 64
                if not Path(__A					).joinpath('train.len'					).exists():
                      save_len_file(__A				,       __A					)
          else:
                lowerCamelCase__							     =		"""examples/seq2seq/test_data/wmt_en_ro"""
                lowerCamelCase__							     =		max_len * 4
                save_len_file(__A				,       __A					)

          lowerCamelCase__							     =		AutoTokenizer.from_pretrained(__A					)
          lowerCamelCase__							     =		SeqaSeqDataset(
              __A				,       data_dir=__A				,       type_path='train'				,       max_source_length=__A				,       max_target_length=__A				,       n_obs=__A				,       )
          return ds, max_tokens, tokenizer
    def   _UpperCamelCase							(			self				:       List[str]					):
          lowerCamelCase__							     =		self._get_dataset()
          lowerCamelCase__							     =		set(DistributedSortishSampler(__A				,       2_56				,       num_replicas=2				,       rank=0				,       add_extra_examples=__A					)					)
          lowerCamelCase__							     =		set(DistributedSortishSampler(__A				,       2_56				,       num_replicas=2				,       rank=1				,       add_extra_examples=__A					)					)
          assert idsa.intersection(__A					) == set()


    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ]				,       )
    def   _UpperCamelCase							(			self				:       List[Any]				,       SCREAMING_SNAKE_CASE__				:       Union[str, Any]					):
          lowerCamelCase__							     =		AutoTokenizer.from_pretrained(__A				,       use_fast=__A					)
          if tok_name == MBART_TINY:
                lowerCamelCase__							     =		SeqaSeqDataset(
                    __A				,       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'				,       )
                lowerCamelCase__							     =		train_dataset.dataset_kwargs
                assert "src_lang" in kwargs and "tgt_lang" in kwargs
          else:
                lowerCamelCase__							     =		SeqaSeqDataset(
                    __A				,       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				,       )
                lowerCamelCase__							     =		train_dataset.dataset_kwargs
                assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
                assert len(__A					) == 1 if tok_name == BART_TINY else len(__A					) == 0






from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   Union[str, Any]     					=				'''EncodecFeatureExtractor'''
	_lowercase		:   Any     					=				('''T5Tokenizer''', '''T5TokenizerFast''')




	def __init__(     self						:     List[Any]					,      __A						:     Any					,      __A						:     Tuple							):
		super().__init__(__A					,      __A							)
		__A :			Dict		    =			self.feature_extractor
		__A :			List[str]		    =			False




	def        lowerCAmelCase_       (     self						:     Union[str, Any]					,      __A						:     str=None					,      __A						:     Tuple=None					,      __A						:     Dict=True							):
		return self.tokenizer.get_decoder_prompt_ids(task=__A					,      language=__A					,      no_timestamps=__A							)




	def __call__(     self						:     Optional[Any]					,      *__A						:     Tuple					,      **__A						:     Tuple							):
		# For backward compatibility
		if self._in_target_context_manager:
			return self.current_processor(*__A					,      **__A							)

		__A :			str		    =			kwargs.pop("""audio"""					,      __A							)
		__A :			Optional[Any]		    =			kwargs.pop("""sampling_rate"""					,      __A							)
		__A :			int		    =			kwargs.pop("""text"""					,      __A							)
		if len(__A							) > 0:
			__A :			int		    =			args[0]
			__A :			Dict		    =			args[1:]

		if audio is None and text is None:
			raise ValueError("""You need to specify either an `audio` or `text` input to process."""							)

		if text is not None:
			__A :			Dict		    =			self.tokenizer(__A					,      **__A							)

		if audio is not None:
			__A :			Optional[int]		    =			self.feature_extractor(__A					,      *__A					,      sampling_rate=__A					,      **__A							)

		if audio is None:
			return inputs

		elif text is None:
			return audio_inputs

		else:
			__A :			List[Any]		    =			audio_inputs["""input_values"""]
			if "padding_mask" in audio_inputs:
				__A :			int		    =			audio_inputs["""padding_mask"""]
			return inputs




	def        lowerCAmelCase_       (     self						:     List[str]					,      *__A						:     int					,      **__A						:     Tuple							):
		__A :			Optional[int]		    =			kwargs.pop("""audio"""					,      __A							)
		__A :			List[str]		    =			kwargs.pop("""padding_mask"""					,      __A							)

		if len(__A							) > 0:
			__A :			Dict		    =			args[0]
			__A :			Optional[int]		    =			args[1:]

		if audio_values is not None:
			return self._decode_audio(__A					,      padding_mask=__A							)
		else:
			return self.tokenizer.batch_decode(*__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      *__A						:     Dict					,      **__A						:     Any							):
		return self.tokenizer.decode(*__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     Tuple					,      __A						:     Union[str, Any]					,      __A						:     Optional = None							):
		__A :			List[str]		    =			to_numpy(__A							)
		__A    ,				__A    ,				__A :			Tuple		    =			audio_values.shape

		if padding_mask is None:
			return list(__A							)

		__A :			Union[str, Any]		    =			to_numpy(__A							)

		# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
		# token (so that the generated audio values are **not** treated as padded tokens)
		__A :			List[str]		    =			seq_len - padding_mask.shape[-1]
		__A :			Tuple		    =			1 - self.feature_extractor.padding_value
		__A :			Optional[int]		    =			np.pad(__A					,      ((0, 0), (0, difference))					,      """constant"""					,      constant_values=__A							)

		__A :			int		    =			audio_values.tolist()
		for i in range(__A							):
			__A :			str		    =			np.asarray(audio_values[i]							)[
			    padding_mask[i][None, :] != self.feature_extractor.padding_value
			]
			__A :			List[Any]		    =			sliced_audio.reshape(__A					,      -1							)

		return audio_values





