





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


lowerCAmelCase :					Union[str, Any] 							='''bert-base-cased'''
lowerCAmelCase :					Optional[Any] 							='''google/pegasus-xsum'''
lowerCAmelCase :					Optional[int] 							=[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
lowerCAmelCase :					str 							=['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
lowerCAmelCase :					List[str] 							='''patrickvonplaten/t5-tiny-random'''
lowerCAmelCase :					Union[str, Any] 							='''sshleifer/bart-tiny-random'''
lowerCAmelCase :					Union[str, Any] 							='''sshleifer/tiny-mbart'''
lowerCAmelCase :					Any 							='''sshleifer/tiny-marian-en-de'''
def 							UpperCAmelCase_  (						__lowerCamelCase : Path     ,__lowerCamelCase : list			):
   lowercase_      :Optional[int]         =			"\n".join(__lowerCamelCase			)
   Path(__lowerCamelCase			).open("w"			).writelines(__lowerCamelCase			)
def 							UpperCAmelCase_  (						__lowerCamelCase : List[Any]			):
   for split in ["train", "val", "test"]:
      _dump_articles(os.path.join(__lowerCamelCase     ,F'{split}.source'			)     ,__lowerCamelCase			)
      _dump_articles(os.path.join(__lowerCamelCase     ,F'{split}.target'			)     ,__lowerCamelCase			)
   return tmp_dir


class 				a_       (    _lowerCAmelCase							):





     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ]     ,				)
     @slow
     def 							lowercase__				(			self		: Optional[int]     ,				lowercase		: str						):





        """simple docstring"""




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





        """simple docstring"""




        lowercase_      :int         =			AutoTokenizer.from_pretrained(lowercase						)
        lowercase_      :List[str]         =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)
        lowercase_      :Tuple         =			max(len(tokenizer.encode(lowercase						)						) for a in ARTICLES						)
        lowercase_      :Dict         =			max(len(tokenizer.encode(lowercase						)						) for a in SUMMARIES						)
        lowercase_      :Optional[Any]         =			4
        lowercase_      :Union[str, Any]         =			LegacySeqaSeqDataset(
            lowercase     ,				data_dir=lowercase     ,				type_path="train"     ,				max_source_length=20     ,				max_target_length=lowercase     ,				)
        lowercase_      :Tuple         =			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 							lowercase__				(			self		: List[Any]						):





        """simple docstring"""




        lowercase_      :Any         =			AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"						)

        lowercase_      :str         =			Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)						)
        lowercase_      :Optional[int]         =			tmp_dir.joinpath("train.source"						).open().readlines()
        lowercase_      :Optional[int]         =			Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)						)
        pack_data_dir(lowercase     ,				lowercase     ,				128     ,				lowercase						)
        lowercase_      :Optional[int]         =			{x.name for x in tmp_dir.iterdir()}
        lowercase_      :Optional[int]         =			{x.name for x in save_dir.iterdir()}
        lowercase_      :Union[str, Any]         =			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 							lowercase__				(			self		: Optional[int]						):





        """simple docstring"""




        if not FAIRSEQ_AVAILABLE:
           return
        lowercase_      :Any         =			self._get_dataset(max_len=64						)
        lowercase_      :Tuple         =			64
        lowercase_      :Dict         =			ds.make_dynamic_sampler(lowercase     ,				required_batch_size_multiple=lowercase						)
        lowercase_      :List[str]         =			[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
        lowercase_      :Dict         =			DataLoader(lowercase     ,				batch_sampler=lowercase     ,				collate_fn=ds.collate_fn     ,				num_workers=2						)
        lowercase_      :int         =			[]
        lowercase_      :Optional[int]         =			[]
        for batch in data_loader:
           lowercase_      :List[Any]         =			batch["input_ids"].shape
           lowercase_      :Dict         =			src_shape[0]
           assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
           lowercase_      :Optional[Any]         =			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 							lowercase__				(			self		: int						):





        """simple docstring"""




        lowercase_      :int         =			self._get_dataset(max_len=512						)
        lowercase_      :Tuple         =			2
        lowercase_      :Optional[int]         =			ds.make_sortish_sampler(lowercase     ,				shuffle=lowercase						)

        lowercase_      :List[Any]         =			DataLoader(lowercase     ,				batch_size=lowercase     ,				collate_fn=ds.collate_fn     ,				num_workers=2						)
        lowercase_      :Dict         =			DataLoader(lowercase     ,				batch_size=lowercase     ,				collate_fn=ds.collate_fn     ,				num_workers=2     ,				sampler=lowercase						)

        lowercase_      :int         =			tokenizer.pad_token_id

        def count_pad_tokens(lowercase		: Union[str, Any]     ,				lowercase		: int="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 							lowercase__				(			self		: Any     ,				lowercase		: Optional[int]=1_000     ,				lowercase		: List[str]=128						):





        """simple docstring"""




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

        lowercase_      :Any         =			AutoTokenizer.from_pretrained(lowercase						)
        lowercase_      :List[str]         =			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 							lowercase__				(			self		: List[Any]						):





        """simple docstring"""




        lowercase_      :str         =			self._get_dataset()
        lowercase_      :Optional[Any]         =			set(DistributedSortishSampler(lowercase     ,				256     ,				num_replicas=2     ,				rank=0     ,				add_extra_examples=lowercase						)						)
        lowercase_      :Any         =			set(DistributedSortishSampler(lowercase     ,				256     ,				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 							lowercase__				(			self		: str     ,				lowercase		: List[Any]						):





        """simple docstring"""




        lowercase_      :Optional[int]         =			AutoTokenizer.from_pretrained(lowercase     ,				use_fast=lowercase						)
        if tok_name == MBART_TINY:
           lowercase_      :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"     ,				)
           lowercase_      :str         =			train_dataset.dataset_kwargs
           assert "src_lang" in kwargs and "tgt_lang" in kwargs
        else:
           lowercase_      :List[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     ,				)
           lowercase_      :Union[str, Any]         =			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 copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


lowerCAmelCase :					Optional[int] 							=logging.get_logger(__name__)
class 				a_       (    _lowerCAmelCase							):
     __A								=      ["input_features"]





     def __init__(			self		: Any     ,				lowercase		: Tuple=80     ,				lowercase		: Optional[int]=16_000     ,				lowercase		: Optional[Any]=160     ,				lowercase		: Optional[int]=30     ,				lowercase		: List[Any]=400     ,				lowercase		: Dict=0.0     ,				lowercase		: Tuple=False     ,				**lowercase		: Optional[int]     ,				):





        """simple docstring"""




        super().__init__(
            feature_size=lowercase     ,				sampling_rate=lowercase     ,				padding_value=lowercase     ,				return_attention_mask=lowercase     ,				**lowercase     ,				)
        lowercase_      :Optional[int]         =			n_fft
        lowercase_      :List[Any]         =			hop_length
        lowercase_      :Tuple         =			chunk_length
        lowercase_      :List[str]         =			chunk_length * sampling_rate
        lowercase_      :Optional[Any]         =			self.n_samples // hop_length
        lowercase_      :Any         =			sampling_rate
        lowercase_      :List[Any]         =			mel_filter_bank(
            num_frequency_bins=1 + n_fft // 2     ,				num_mel_filters=lowercase     ,				min_frequency=0.0     ,				max_frequency=80_00.0     ,				sampling_rate=lowercase     ,				norm="slaney"     ,				mel_scale="slaney"     ,				)





     def 							lowercase__				(			self		: str     ,				lowercase		: np.array						):





        """simple docstring"""




        lowercase_      :Any         =			spectrogram(
            lowercase     ,				window_function(self.n_fft     ,				"hann"						)     ,				frame_length=self.n_fft     ,				hop_length=self.hop_length     ,				power=2.0     ,				mel_filters=self.mel_filters     ,				log_mel="log10"     ,				)
        lowercase_      :Any         =			log_spec[:, :-1]
        lowercase_      :List[Any]         =			np.maximum(lowercase     ,				log_spec.max() - 8.0						)
        lowercase_      :Dict         =			(log_spec + 4.0) / 4.0
        return log_spec





     @staticmethod
     # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
     def 							lowercase__				(			lowercase		: List[np.ndarray]     ,				lowercase		: List[np.ndarray]     ,				lowercase		: float = 0.0						):





        """simple docstring"""




        if attention_mask is not None:
           lowercase_      :Optional[int]         =			np.array(lowercase     ,				np.intaa						)
           lowercase_      :Any         =			[]

           for vector, length in zip(lowercase     ,				attention_mask.sum(-1						)						):
              lowercase_      :Dict         =			(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7						)
              if length < normed_slice.shape[0]:
                 lowercase_      :List[Any]         =			padding_value

              normed_input_values.append(lowercase						)
        else:
           lowercase_      :List[Any]         =			[(x - x.mean()) / np.sqrt(x.var() + 1e-7						) for x in input_values]

        return normed_input_values





     def __call__(			self		: Tuple     ,				lowercase		: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     ,				lowercase		: bool = True     ,				lowercase		: Optional[int] = None     ,				lowercase		: Optional[Union[str, TensorType]] = None     ,				lowercase		: Optional[bool] = None     ,				lowercase		: Optional[str] = "max_length"     ,				lowercase		: Optional[int] = None     ,				lowercase		: Optional[int] = None     ,				lowercase		: Optional[bool] = None     ,				**lowercase		: Union[str, Any]     ,				):





        """simple docstring"""




        if sampling_rate is not None:
           if sampling_rate != self.sampling_rate:
              raise ValueError(
                  F'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
                  F' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
                  F' was sampled with {self.sampling_rate} and not {sampling_rate}.'						)
        else:
           logger.warning(
               "It is strongly recommended to pass the `sampling_rate` argument to this function. "
               "Failing to do so can result in silent errors that might be hard to debug."						)

        lowercase_      :List[str]         =			isinstance(lowercase     ,				np.ndarray						) and len(raw_speech.shape						) > 1
        if is_batched_numpy and len(raw_speech.shape						) > 2:
           raise ValueError(F'Only mono-channel audio is supported for input to {self}'						)
        lowercase_      :Optional[Any]         =			is_batched_numpy or (
            isinstance(lowercase     ,				(list, tuple)						) and (isinstance(raw_speech[0]     ,				(np.ndarray, tuple, list)						))
        )

        if is_batched:
           lowercase_      :Any         =			[np.asarray([speech]     ,				dtype=np.floataa						).T for speech in raw_speech]
        elif not is_batched and not isinstance(lowercase     ,				np.ndarray						):
           lowercase_      :List[Any]         =			np.asarray(lowercase     ,				dtype=np.floataa						)
        elif isinstance(lowercase     ,				np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
           lowercase_      :Union[str, Any]         =			raw_speech.astype(np.floataa						)

        # always return batch
        if not is_batched:
           lowercase_      :Optional[int]         =			[np.asarray([raw_speech]						).T]

        lowercase_      :int         =			BatchFeature({"input_features": raw_speech}						)

        # convert into correct format for padding

        lowercase_      :Tuple         =			self.pad(
            lowercase     ,				padding=lowercase     ,				max_length=max_length if max_length else self.n_samples     ,				truncation=lowercase     ,				pad_to_multiple_of=lowercase     ,				return_attention_mask=return_attention_mask or do_normalize     ,				)

        # zero-mean and unit-variance normalization
        if do_normalize:
           lowercase_      :Union[str, Any]         =			self.zero_mean_unit_var_norm(
               padded_inputs["input_features"]     ,				attention_mask=padded_inputs["attention_mask"]     ,				padding_value=self.padding_value     ,				)
           lowercase_      :List[Any]         =			np.stack(padded_inputs["input_features"]     ,				axis=0						)

        # make sure list is in array format
        lowercase_      :Union[str, Any]         =			padded_inputs.get("input_features"						).transpose(2     ,				0     ,				1						)

        lowercase_      :List[str]         =			[self._np_extract_fbank_features(lowercase						) for waveform in input_features[0]]

        if isinstance(input_features[0]     ,				lowercase						):
           lowercase_      :Tuple         =			[np.asarray(lowercase     ,				dtype=np.floataa						) for feature in input_features]
        else:
           lowercase_      :Union[str, Any]         =			input_features

        if return_attention_mask:
           # rescale from sample (48000) to feature (3000)
           lowercase_      :Dict         =			padded_inputs["attention_mask"][:, :: self.hop_length]

        if return_tensors is not None:
           lowercase_      :Tuple         =			padded_inputs.convert_to_tensors(lowercase						)

        return padded_inputs







     def 							lowercase__				(			self		: List[str]						):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			copy.deepcopy(self.__dict__						)
        lowercase_      :List[str]         =			self.__class__.__name__
        if "mel_filters" in output:
           del output["mel_filters"]
        return output


