





'''simple docstring'''


import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
 from transformers import WhisperFeatureExtractor

if is_torch_available():
 import torch

lowerCAmelCase					:     Tuple												=random.Random()
def 							UpperCAmelCase_  (						__lowerCamelCase					:					List[Any]	,__lowerCamelCase					:					Optional[int]=1.0	,__lowerCamelCase					:					Optional[int]=None	,__lowerCamelCase					:					Dict=None ):
   if rng is None:
      lowercase_			:List[Any]    			= global_rng

   lowercase_			:Optional[Any]    			= []
   for batch_idx in range(shape[0] ):
      values.append([] )
      for _ in range(shape[1] ):
         values[-1].append(rng.random() * scale )

   return values
@require_torch
@require_torchaudio
class       a_   (				unittest.TestCase							):





     def __init__( self		:					List[str]	,      lowercase		:					Tuple	,      lowercase		:					Tuple=7	,      lowercase		:					List[Any]=400	,      lowercase		:					Optional[Any]=2_000	,      lowercase		:					List[str]=10	,      lowercase		:					List[str]=160	,      lowercase		:					Optional[Any]=8	,      lowercase		:					List[Any]=0.0	,      lowercase		:					str=4_000	,      lowercase		:					Tuple=False	,      lowercase		:					int=True	,      ):





        """simple docstring"""




        lowercase_			:Any    			= parent
        lowercase_			:Tuple    			= batch_size
        lowercase_			:Any    			= min_seq_length
        lowercase_			:Dict    			= max_seq_length
        lowercase_			:str    			= (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        lowercase_			:Tuple    			= padding_value
        lowercase_			:Dict    			= sampling_rate
        lowercase_			:Any    			= return_attention_mask
        lowercase_			:Tuple    			= do_normalize
        lowercase_			:Optional[Any]    			= feature_size
        lowercase_			:int    			= chunk_length
        lowercase_			:str    			= hop_length





     def 							lowercase__      ( self		:					List[str]						):





        """simple docstring"""




        return {
            "feature_size": self.feature_size,
            "hop_length": self.hop_length,
            "chunk_length": self.chunk_length,
            "padding_value": self.padding_value,
            "sampling_rate": self.sampling_rate,
            "return_attention_mask": self.return_attention_mask,
            "do_normalize": self.do_normalize,
        }







     def 							lowercase__      ( self		:					int	,      lowercase		:					List[Any]=False	,      lowercase		:					Dict=False						):





        """simple docstring"""




        def _flatten(lowercase		:					List[str]						):
           return list(itertools.chain(*lowercase						)						)

        if equal_length:
           lowercase_			:List[Any]    			= [floats_list((self.max_seq_length, self.feature_size)						) for _ in range(self.batch_size						)]
        else:
           # make sure that inputs increase in size
           lowercase_			:Optional[Any]    			= [
               floats_list((x, self.feature_size)						)
               for x in range(self.min_seq_length	,      self.max_seq_length	,      self.seq_length_diff						)
           ]
        if numpify:
           lowercase_			:int    			= [np.asarray(lowercase						) for x in speech_inputs]
        return speech_inputs







@require_torch
@require_torchaudio
class       a_   (				_lowerCAmelCase	,						unittest.TestCase							):
     __A      =			WhisperFeatureExtractor if is_speech_available() else None





     def 							lowercase__      ( self		:					Optional[int]						):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= WhisperFeatureExtractionTester(self						)





     def 							lowercase__      ( self		:					Union[str, Any]						):





        """simple docstring"""




        lowercase_			:Union[str, Any]    			= self.feature_extraction_class(**self.feat_extract_dict						)

        with tempfile.TemporaryDirectory() as tmpdirname:
           lowercase_			:str    			= feat_extract_first.save_pretrained(lowercase						)[0]
           check_json_file_has_correct_format(lowercase						)
           lowercase_			:List[str]    			= self.feature_extraction_class.from_pretrained(lowercase						)

        lowercase_			:Optional[int]    			= feat_extract_first.to_dict()
        lowercase_			:Optional[Any]    			= feat_extract_second.to_dict()
        lowercase_			:str    			= feat_extract_first.mel_filters
        lowercase_			:Union[str, Any]    			= feat_extract_second.mel_filters
        self.assertTrue(np.allclose(lowercase	,      lowercase						)						)
        self.assertEqual(lowercase	,      lowercase						)





     def 							lowercase__      ( self		:					Optional[Any]						):





        """simple docstring"""




        lowercase_			:str    			= self.feature_extraction_class(**self.feat_extract_dict						)

        with tempfile.TemporaryDirectory() as tmpdirname:
           lowercase_			:str    			= os.path.join(lowercase	,      "feat_extract.json"						)
           feat_extract_first.to_json_file(lowercase						)
           lowercase_			:Dict    			= self.feature_extraction_class.from_json_file(lowercase						)

        lowercase_			:Optional[Any]    			= feat_extract_first.to_dict()
        lowercase_			:Dict    			= feat_extract_second.to_dict()
        lowercase_			:Union[str, Any]    			= feat_extract_first.mel_filters
        lowercase_			:str    			= feat_extract_second.mel_filters
        self.assertTrue(np.allclose(lowercase	,      lowercase						)						)
        self.assertEqual(lowercase	,      lowercase						)





     def 							lowercase__      ( self		:					List[str]						):





        """simple docstring"""




        lowercase_			:Optional[int]    			= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
        # create three inputs of length 800, 1000, and 1200
        lowercase_			:str    			= [floats_list((1, x)						)[0] for x in range(800	,      1_400	,      200						)]
        lowercase_			:Any    			= [np.asarray(lowercase						) for speech_input in speech_inputs]

        # Test feature size
        lowercase_			:Optional[int]    			= feature_extractor(lowercase	,      padding="max_length"	,      return_tensors="np"						).input_features
        self.assertTrue(input_features.ndim == 3						)
        self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames						)
        self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size						)

        # Test not batched input
        lowercase_			:List[str]    			= feature_extractor(speech_inputs[0]	,      return_tensors="np"						).input_features
        lowercase_			:Dict    			= feature_extractor(np_speech_inputs[0]	,      return_tensors="np"						).input_features
        self.assertTrue(np.allclose(lowercase	,      lowercase	,      atol=1e-3						)						)

        # Test batched
        lowercase_			:str    			= feature_extractor(lowercase	,      return_tensors="np"						).input_features
        lowercase_			:List[Any]    			= feature_extractor(lowercase	,      return_tensors="np"						).input_features
        for enc_seq_a, enc_seq_a in zip(lowercase	,      lowercase						):
           self.assertTrue(np.allclose(lowercase	,      lowercase	,      atol=1e-3						)						)

        # Test 2-D numpy arrays are batched.
        lowercase_			:Optional[Any]    			= [floats_list((1, x)						)[0] for x in (800, 800, 800)]
        lowercase_			:Dict    			= np.asarray(lowercase						)
        lowercase_			:str    			= feature_extractor(lowercase	,      return_tensors="np"						).input_features
        lowercase_			:int    			= feature_extractor(lowercase	,      return_tensors="np"						).input_features
        for enc_seq_a, enc_seq_a in zip(lowercase	,      lowercase						):
           self.assertTrue(np.allclose(lowercase	,      lowercase	,      atol=1e-3						)						)

        # Test truncation required
        lowercase_			:Union[str, Any]    			= [floats_list((1, x)						)[0] for x in range(200	,      (feature_extractor.n_samples + 500)	,      200						)]
        lowercase_			:Dict    			= [np.asarray(lowercase						) for speech_input in speech_inputs]

        lowercase_			:List[Any]    			= [x[: feature_extractor.n_samples] for x in speech_inputs]
        lowercase_			:List[str]    			= [np.asarray(lowercase						) for speech_input in speech_inputs_truncated]

        lowercase_			:int    			= feature_extractor(lowercase	,      return_tensors="np"						).input_features
        lowercase_			:Dict    			= feature_extractor(lowercase	,      return_tensors="np"						).input_features
        for enc_seq_a, enc_seq_a in zip(lowercase	,      lowercase						):
           self.assertTrue(np.allclose(lowercase	,      lowercase	,      atol=1e-3						)						)





     def 							lowercase__      ( self		:					Optional[int]						):





        """simple docstring"""




        import torch

        lowercase_			:Any    			= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
        lowercase_			:List[Any]    			= np.random.rand(100	,      32						).astype(np.floataa						)
        lowercase_			:List[str]    			= np_speech_inputs.tolist()

        for inputs in [py_speech_inputs, np_speech_inputs]:
           lowercase_			:Any    			= feature_extractor.pad([{"input_features": inputs}]	,      return_tensors="np"						)
           self.assertTrue(np_processed.input_features.dtype == np.floataa						)
           lowercase_			:Dict    			= feature_extractor.pad([{"input_features": inputs}]	,      return_tensors="pt"						)
           self.assertTrue(pt_processed.input_features.dtype == torch.floataa						)





     def 							lowercase__      ( self		:					Optional[Any]	,      lowercase		:					int						):





        """simple docstring"""




        lowercase_			:Optional[int]    			= load_dataset("hf-internal-testing/librispeech_asr_dummy"	,      "clean"	,      split="validation"						)
        # automatic decoding with librispeech
        lowercase_			:List[str]    			= ds.sort("id"						).select(range(lowercase						)						)[:num_samples]["audio"]

        return [x["array"] for x in speech_samples]





     def 							lowercase__      ( self		:					Union[str, Any]						):





        """simple docstring"""




        lowercase_			:Optional[int]    			= torch.tensor(
            [
                0.11_93, -0.09_46, -0.10_98, -0.01_96, 0.02_25, -0.06_90, -0.17_36, 0.09_51,
                0.09_71, -0.08_17, -0.07_02, 0.01_62, 0.02_60, 0.00_17, -0.01_92, -0.16_78,
                0.07_09, -0.18_67, -0.06_55, -0.02_74, -0.02_34, -0.18_84, -0.05_16, -0.05_54,
                -0.02_74, -0.14_25, -0.14_23, 0.08_37, 0.03_77, -0.08_54
            ]						)
        # fmt: on

        lowercase_			:Any    			= self._load_datasamples(1						)
        lowercase_			:List[Any]    			= WhisperFeatureExtractor()
        lowercase_			:str    			= feature_extractor(lowercase	,      return_tensors="pt"						).input_features
        self.assertEqual(input_features.shape	,      (1, 80, 3_000)						)
        self.assertTrue(torch.allclose(input_features[0, 0, :30]	,      lowercase	,      atol=1e-4						)						)







     def 							lowercase__      ( self		:					Any						):





        """simple docstring"""




        lowercase_			:List[str]    			= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
        lowercase_			:Union[str, Any]    			= self._load_datasamples(1						)[0]
        lowercase_			:Optional[int]    			= ((audio - audio.min()) / (audio.max() - audio.min())) * 65_535  # Rescale to [0, 65535] to show issue
        lowercase_			:Optional[Any]    			= feat_extract.zero_mean_unit_var_norm([audio]	,      attention_mask=lowercase						)[0]

        self.assertTrue(np.all(np.mean(lowercase						) < 1e-3						)						)
        self.assertTrue(np.all(np.abs(np.var(lowercase						) - 1						) < 1e-3						)						)









'''simple docstring'''


import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Optional[int]	,__lowerCamelCase					:					Any	,__lowerCamelCase					:					Any	,__lowerCamelCase					:					List[str]	,__lowerCamelCase					:					Dict=True	,__lowerCamelCase					:					List[Any]="pt" ):
   lowercase_			:Dict    			= {"add_prefix_space": True} if isinstance(__lowerCamelCase	,__lowerCamelCase ) and not line.startswith(" " ) else {}
   lowercase_			:str    			= padding_side
   return tokenizer(
       [line]	,max_length=__lowerCamelCase	,padding="max_length" if pad_to_max_length else None	,truncation=__lowerCamelCase	,return_tensors=__lowerCamelCase	,add_special_tokens=__lowerCamelCase	,**__lowerCamelCase	,)
def 							UpperCAmelCase_  (						__lowerCamelCase					:					List[str]	,__lowerCamelCase					:					Dict	,__lowerCamelCase					:					str=None	,):
   lowercase_			:Optional[int]    			= input_ids.ne(__lowerCamelCase ).any(dim=0 )
   if attention_mask is None:
      return input_ids[:, keep_column_mask]
   else:
      return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class       a_   (				_lowerCAmelCase							):





     def __init__( self		:					Optional[int]	,      lowercase		:					Any	,      lowercase		:					Tuple	,      lowercase		:					Union[str, Any]	,      lowercase		:					Tuple	,      lowercase		:					str="train"	,      lowercase		:					Dict=None	,      lowercase		:					Tuple=None	,      lowercase		:					List[str]=None	,      lowercase		:					int=""	,      ):





        """simple docstring"""




        super().__init__()
        lowercase_			:List[Any]    			= Path(lowercase						).joinpath(type_path + ".source"						)
        lowercase_			:Dict    			= Path(lowercase						).joinpath(type_path + ".target"						)
        lowercase_			:Optional[int]    			= self.get_char_lens(self.src_file						)
        lowercase_			:List[str]    			= max_source_length
        lowercase_			:str    			= max_target_length
        assert min(self.src_lens						) > 0, F'found empty line in {self.src_file}'
        lowercase_			:int    			= tokenizer
        lowercase_			:Dict    			= prefix
        if n_obs is not None:
           lowercase_			:Union[str, Any]    			= self.src_lens[:n_obs]
        lowercase_			:Optional[int]    			= src_lang
        lowercase_			:str    			= tgt_lang





     def __len__( self		:					Tuple						):





        """simple docstring"""




        return len(self.src_lens						)





     def __getitem__( self		:					str	,      lowercase		:					Dict						):





        """simple docstring"""




        lowercase_			:Tuple    			= index + 1  # linecache starts at 1
        lowercase_			:Optional[Any]    			= self.prefix + linecache.getline(str(self.src_file						)	,      lowercase						).rstrip("\n"						)
        lowercase_			:List[str]    			= linecache.getline(str(self.tgt_file						)	,      lowercase						).rstrip("\n"						)
        assert source_line, F'empty source line for index {index}'
        assert tgt_line, F'empty tgt line for index {index}'

        # Need to add eos token manually for T5
        if isinstance(self.tokenizer	,      lowercase						):
           source_line += self.tokenizer.eos_token
           tgt_line += self.tokenizer.eos_token

        # Pad source and target to the right
        lowercase_			:List[str]    			= (
            self.tokenizer.question_encoder if isinstance(self.tokenizer	,      lowercase						) else self.tokenizer
        )
        lowercase_			:int    			= self.tokenizer.generator if isinstance(self.tokenizer	,      lowercase						) else self.tokenizer

        lowercase_			:List[str]    			= encode_line(lowercase	,      lowercase	,      self.max_source_length	,      "right"						)
        lowercase_			:Any    			= encode_line(lowercase	,      lowercase	,      self.max_target_length	,      "right"						)

        lowercase_			:Dict    			= source_inputs["input_ids"].squeeze()
        lowercase_			:Tuple    			= target_inputs["input_ids"].squeeze()
        lowercase_			:Optional[int]    			= source_inputs["attention_mask"].squeeze()
        return {
            "input_ids": source_ids,
            "attention_mask": src_mask,
            "decoder_input_ids": target_ids,
        }





     @staticmethod
     def 							lowercase__      ( lowercase		:					Union[str, Any]						):





        """simple docstring"""




        return [len(lowercase						) for x in Path(lowercase						).open().readlines()]







     def 							lowercase__      ( self		:					str	,      lowercase		:					List[Any]						):





        """simple docstring"""




        lowercase_			:Optional[int]    			= torch.stack([x["input_ids"] for x in batch]						)
        lowercase_			:Dict    			= torch.stack([x["attention_mask"] for x in batch]						)
        lowercase_			:List[str]    			= torch.stack([x["decoder_input_ids"] for x in batch]						)
        lowercase_			:Any    			= (
            self.tokenizer.generator.pad_token_id
            if isinstance(self.tokenizer	,      lowercase						)
            else self.tokenizer.pad_token_id
        )
        lowercase_			:str    			= (
            self.tokenizer.question_encoder.pad_token_id
            if isinstance(self.tokenizer	,      lowercase						)
            else self.tokenizer.pad_token_id
        )
        lowercase_			:Union[str, Any]    			= trim_batch(lowercase	,      lowercase						)
        lowercase_			,							lowercase_			:Optional[Any]    			= trim_batch(lowercase	,      lowercase	,      attention_mask=lowercase						)
        lowercase_			:Tuple    			= {
            "input_ids": source_ids,
            "attention_mask": source_mask,
            "decoder_input_ids": y,
        }
        return batch







lowerCAmelCase					:     List[str]												=getLogger(__name__)
def 							UpperCAmelCase_  (						__lowerCamelCase					:					List[List] ):
   return list(itertools.chain.from_iterable(__lowerCamelCase ) )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					str ):
   lowercase_			:List[str]    			= get_git_info()
   save_json(__lowerCamelCase	,os.path.join(__lowerCamelCase	,"git_log.json" ) )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Any	,__lowerCamelCase					:					Any	,__lowerCamelCase					:					List[Any]=4	,**__lowerCamelCase					:					List[str] ):
   with open(__lowerCamelCase	,"w" ) as f:
      json.dump(__lowerCamelCase	,__lowerCamelCase	,indent=__lowerCamelCase	,**__lowerCamelCase )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Tuple ):
   with open(__lowerCamelCase ) as f:
      return json.load(__lowerCamelCase )
def 							UpperCAmelCase_  (						):
   lowercase_			:Dict    			= git.Repo(search_parent_directories=__lowerCamelCase )
   lowercase_			:List[str]    			= {
       "repo_id": str(__lowerCamelCase ),
       "repo_sha": str(repo.head.object.hexsha ),
       "repo_branch": str(repo.active_branch ),
       "hostname": str(socket.gethostname() ),
   }
   return repo_infos
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Callable	,__lowerCamelCase					:					Iterable ):
   return list(map(__lowerCamelCase	,__lowerCamelCase ) )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Union[str, Any]	,__lowerCamelCase					:					List[str] ):
   with open(__lowerCamelCase	,"wb" ) as f:
      return pickle.dump(__lowerCamelCase	,__lowerCamelCase )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					str ):

   def remove_articles(__lowerCamelCase					:					Optional[int] ):
      return re.sub(r"\b(a|an|the)\b"	," "	,__lowerCamelCase )

   def white_space_fix(__lowerCamelCase					:					Dict ):
      return " ".join(text.split() )

   def remove_punc(__lowerCamelCase					:					Optional[Any] ):
      lowercase_			:Any    			= set(string.punctuation )
      return "".join(ch for ch in text if ch not in exclude )

   def lower(__lowerCamelCase					:					List[Any] ):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(__lowerCamelCase ) ) ) )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Optional[int]	,__lowerCamelCase					:					Optional[int] ):
   lowercase_			:Tuple    			= normalize_answer(__lowerCamelCase ).split()
   lowercase_			:Dict    			= normalize_answer(__lowerCamelCase ).split()
   lowercase_			:Tuple    			= Counter(__lowerCamelCase ) & Counter(__lowerCamelCase )
   lowercase_			:Tuple    			= sum(common.values() )
   if num_same == 0:
      return 0
   lowercase_			:Union[str, Any]    			= 1.0 * num_same / len(__lowerCamelCase )
   lowercase_			:List[Any]    			= 1.0 * num_same / len(__lowerCamelCase )
   lowercase_			:Tuple    			= (2 * precision * recall) / (precision + recall)
   return fa
def 							UpperCAmelCase_  (						__lowerCamelCase					:					int	,__lowerCamelCase					:					Union[str, Any] ):
   return normalize_answer(__lowerCamelCase ) == normalize_answer(__lowerCamelCase )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					List[str]	,__lowerCamelCase					:					List[str] ):
   assert len(__lowerCamelCase ) == len(__lowerCamelCase )
   lowercase_			:Any    			= 0
   for hypo, pred in zip(__lowerCamelCase	,__lowerCamelCase ):
      em += exact_match_score(__lowerCamelCase	,__lowerCamelCase )
   if len(__lowerCamelCase ) > 0:
      em /= len(__lowerCamelCase )
   return {"em": em}
def 							UpperCAmelCase_  (						__lowerCamelCase					:					str ):
   return model_prefix.startswith("rag" )


def 							UpperCAmelCase_  (						__lowerCamelCase					:					List[str]	,__lowerCamelCase					:					List[Any]	,__lowerCamelCase					:					int ):
   lowercase_			:Dict    			= {p: p for p in extra_params}
   # T5 models don't have `dropout` param, they have `dropout_rate` instead
   lowercase_			:List[str]    			= "dropout_rate"
   for p in extra_params:
      if getattr(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase ):
         if not hasattr(__lowerCamelCase	,__lowerCamelCase ) and not hasattr(__lowerCamelCase	,equivalent_param[p] ):
            logger.info("config doesn't have a `{}` attribute".format(__lowerCamelCase ) )
            delattr(__lowerCamelCase	,__lowerCamelCase )
            continue
         lowercase_			:List[Any]    			= p if hasattr(__lowerCamelCase	,__lowerCamelCase ) else equivalent_param[p]
         setattr(__lowerCamelCase	,__lowerCamelCase	,getattr(__lowerCamelCase	,__lowerCamelCase ) )
         delattr(__lowerCamelCase	,__lowerCamelCase )
   return hparams, config


