





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

SCREAMING_SNAKE_CASE_						   =       random.Random()



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE=1.0 ,						_SCREAMING_SNAKE_CASE=None ,						_SCREAMING_SNAKE_CASE=None	) ->   Any:


      '''simple docstring'''


      if rng is None:
            SCREAMING_SNAKE_CASE		=						global_rng

      SCREAMING_SNAKE_CASE		=						[]
      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 		UpperCamelCase__ (      unittest.TestCase   ):



     '''simple docstring'''

     def __init__(							self						:    Optional[Any]						,lowerCamelCase__						:    Optional[int]						,lowerCamelCase__						:    Union[str, Any]=7						,lowerCamelCase__						:    Optional[int]=400						,lowerCamelCase__						:    Tuple=2000						,lowerCamelCase__						:    Any=10						,lowerCamelCase__						:    Tuple=160						,lowerCamelCase__						:    List[Any]=8						,lowerCamelCase__						:    Dict=0.0						,lowerCamelCase__						:    str=4000						,lowerCamelCase__						:    Dict=False						,lowerCamelCase__						:    Tuple=True						,)     ->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						parent
           SCREAMING_SNAKE_CASE		=						batch_size
           SCREAMING_SNAKE_CASE		=						min_seq_length
           SCREAMING_SNAKE_CASE		=						max_seq_length
           SCREAMING_SNAKE_CASE		=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
           SCREAMING_SNAKE_CASE		=						padding_value
           SCREAMING_SNAKE_CASE		=						sampling_rate
           SCREAMING_SNAKE_CASE		=						return_attention_mask
           SCREAMING_SNAKE_CASE		=						do_normalize
           SCREAMING_SNAKE_CASE		=						feature_size
           SCREAMING_SNAKE_CASE		=						chunk_length
           SCREAMING_SNAKE_CASE		=						hop_length






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




           '''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 				SCREAMING_SNAKE_CASE__  (							self						:    int						,lowerCamelCase__						:    int=False						,lowerCamelCase__						:    Optional[Any]=False       )     ->		str:




           '''simple docstring'''


           def _flatten(lowerCamelCase__						:    List[Any]       ):
                 return list(itertools.chain(*lowerCamelCase__       )       )

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

@require_torch
@require_torchaudio
class 		UpperCamelCase__ (      lowerCAmelCase_							,      unittest.TestCase   ):



     '''simple docstring'''

     __snake_case    :	Any			=			WhisperFeatureExtractor if is_speech_available() else None






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						WhisperFeatureExtractionTester(self       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.feature_extraction_class(**self.feat_extract_dict       )

           with tempfile.TemporaryDirectory() as tmpdirname:
                 SCREAMING_SNAKE_CASE		=						feat_extract_first.save_pretrained(lowerCamelCase__       )[0]
                 check_json_file_has_correct_format(lowerCamelCase__       )
                 SCREAMING_SNAKE_CASE		=						self.feature_extraction_class.from_pretrained(lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						feat_extract_first.to_dict()
           SCREAMING_SNAKE_CASE		=						feat_extract_second.to_dict()
           SCREAMING_SNAKE_CASE		=						feat_extract_first.mel_filters
           SCREAMING_SNAKE_CASE		=						feat_extract_second.mel_filters
           self.assertTrue(np.allclose(lowerCamelCase__						,lowerCamelCase__       )       )
           self.assertEqual(lowerCamelCase__						,lowerCamelCase__       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    int       )     ->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.feature_extraction_class(**self.feat_extract_dict       )

           with tempfile.TemporaryDirectory() as tmpdirname:
                 SCREAMING_SNAKE_CASE		=						os.path.join(lowerCamelCase__						,"""feat_extract.json"""       )
                 feat_extract_first.to_json_file(lowerCamelCase__       )
                 SCREAMING_SNAKE_CASE		=						self.feature_extraction_class.from_json_file(lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						feat_extract_first.to_dict()
           SCREAMING_SNAKE_CASE		=						feat_extract_second.to_dict()
           SCREAMING_SNAKE_CASE		=						feat_extract_first.mel_filters
           SCREAMING_SNAKE_CASE		=						feat_extract_second.mel_filters
           self.assertTrue(np.allclose(lowerCamelCase__						,lowerCamelCase__       )       )
           self.assertEqual(lowerCamelCase__						,lowerCamelCase__       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[Any]       )     ->		Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
           # create three inputs of length 800, 1000, and 1200
           SCREAMING_SNAKE_CASE		=						[floats_list((1, x)       )[0] for x in range(800						,1400						,200       )]
           SCREAMING_SNAKE_CASE		=						[np.asarray(lowerCamelCase__       ) for speech_input in speech_inputs]

           # Test feature size
           SCREAMING_SNAKE_CASE		=						feature_extractor(lowerCamelCase__						,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
           SCREAMING_SNAKE_CASE		=						feature_extractor(speech_inputs[0]						,return_tensors="""np"""       ).input_features
           SCREAMING_SNAKE_CASE		=						feature_extractor(np_speech_inputs[0]						,return_tensors="""np"""       ).input_features
           self.assertTrue(np.allclose(lowerCamelCase__						,lowerCamelCase__						,atol=1e-3       )       )

           # Test batched
           SCREAMING_SNAKE_CASE		=						feature_extractor(lowerCamelCase__						,return_tensors="""np"""       ).input_features
           SCREAMING_SNAKE_CASE		=						feature_extractor(lowerCamelCase__						,return_tensors="""np"""       ).input_features
           for enc_seq_a, enc_seq_a in zip(lowerCamelCase__						,lowerCamelCase__       ):
                 self.assertTrue(np.allclose(lowerCamelCase__						,lowerCamelCase__						,atol=1e-3       )       )

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

           # Test truncation required
           SCREAMING_SNAKE_CASE		=						[floats_list((1, x)       )[0] for x in range(200						,(feature_extractor.n_samples + 500)						,200       )]
           SCREAMING_SNAKE_CASE		=						[np.asarray(lowerCamelCase__       ) for speech_input in speech_inputs]

           SCREAMING_SNAKE_CASE		=						[x[: feature_extractor.n_samples] for x in speech_inputs]
           SCREAMING_SNAKE_CASE		=						[np.asarray(lowerCamelCase__       ) for speech_input in speech_inputs_truncated]

           SCREAMING_SNAKE_CASE		=						feature_extractor(lowerCamelCase__						,return_tensors="""np"""       ).input_features
           SCREAMING_SNAKE_CASE		=						feature_extractor(lowerCamelCase__						,return_tensors="""np"""       ).input_features
           for enc_seq_a, enc_seq_a in zip(lowerCamelCase__						,lowerCamelCase__       ):
                 self.assertTrue(np.allclose(lowerCamelCase__						,lowerCamelCase__						,atol=1e-3       )       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		List[str]:




           '''simple docstring'''


           import torch

           SCREAMING_SNAKE_CASE		=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
           SCREAMING_SNAKE_CASE		=						np.random.rand(100						,32       ).astype(np.floataa       )
           SCREAMING_SNAKE_CASE		=						np_speech_inputs.tolist()

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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Any						,lowerCamelCase__						:    Any       )     ->		Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						load_dataset("""hf-internal-testing/librispeech_asr_dummy"""						,"""clean"""						,split="""validation"""       )
           # automatic decoding with librispeech
           SCREAMING_SNAKE_CASE		=						ds.sort("""id"""       ).select(range(lowerCamelCase__       )       )[:num_samples]["""audio"""]

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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[str]       )     ->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						torch.tensor(
               [
                   0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
                   0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
                   0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
                   -0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
               ]       )
           # fmt: on

           SCREAMING_SNAKE_CASE		=						self._load_datasamples(1       )
           SCREAMING_SNAKE_CASE		=						WhisperFeatureExtractor()
           SCREAMING_SNAKE_CASE		=						feature_extractor(lowerCamelCase__						,return_tensors="""pt"""       ).input_features
           self.assertEqual(input_features.shape						,(1, 80, 3000)       )
           self.assertTrue(torch.allclose(input_features[0, 0, :30]						,lowerCamelCase__						,atol=1e-4       )       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Dict       )     ->		Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
           SCREAMING_SNAKE_CASE		=						self._load_datasamples(1       )[0]
           SCREAMING_SNAKE_CASE		=						((audio - audio.min()) / (audio.max() - audio.min())) * 65535  # Rescale to [0, 65535] to show issue
           SCREAMING_SNAKE_CASE		=						feat_extract.zero_mean_unit_var_norm([audio]						,attention_mask=lowerCamelCase__       )[0]

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









import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


SCREAMING_SNAKE_CASE_						   =       logging.getLogger(__name__)

@dataclass
class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	Optional[float]			=			field(
         default=0.0							,      metadata={"help": "The label smoothing epsilon to apply (if not zero)."}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Whether to SortishSamler or not."}   )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "whether to use adafactor"}   )
     __snake_case    :	Optional[float]			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Encoder layer dropout probability. Goes into model.config."}   )
     __snake_case    :	Optional[float]			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Decoder layer dropout probability. Goes into model.config."}   )
     __snake_case    :	Optional[float]			=			field(default=lowerCAmelCase_							,      metadata={"help": "Dropout probability. Goes into model.config."}   )
     __snake_case    :	Optional[float]			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Attention dropout probability. Goes into model.config."}   )
     __snake_case    :	Optional[str]			=			field(
         default="linear"							,      metadata={"help": F"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()   )}"}							,      )


