import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
   from transformers import SpeechaTextFeatureExtractor

__lowerCamelCase      :			List[str]							       =		random.Random()



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Tuple			,					__UpperCamelCase   :	Any=1.0			,					__UpperCamelCase   :	List[str]=None			,					__UpperCamelCase   :	List[str]=None     )							-> List[str]:







  """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 __snake_case    (						unittest.TestCase		):




   def __init__(   self      :    Optional[Any]	,     _lowercase      :    Tuple	,     _lowercase      :    int=7	,     _lowercase      :    List[Any]=4_00	,     _lowercase      :    Tuple=20_00	,     _lowercase      :    Optional[Any]=24	,     _lowercase      :    str=24	,     _lowercase      :    Dict=0.0	,     _lowercase      :    List[str]=1_60_00	,     _lowercase      :    Any=True	,     _lowercase      :    Tuple=True	,     ):



     """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__      							=				feature_size
     SCREAMING_SNAKE_CASE__      							=				num_mel_bins
     SCREAMING_SNAKE_CASE__      							=				padding_value
     SCREAMING_SNAKE_CASE__      							=				sampling_rate
     SCREAMING_SNAKE_CASE__      							=				return_attention_mask
     SCREAMING_SNAKE_CASE__      							=				do_normalize




   def      __a						(   self      :    str				):



     """simple docstring"""






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




   def      __a						(   self      :    Tuple	,     _lowercase      :    Any=False	,     _lowercase      :    int=False				):



     """simple docstring"""






     def _flatten(_lowercase      :    Any				):
       return list(itertools.chain(*_lowercase				)				)

     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(_lowercase				) for x in speech_inputs]
     return speech_inputs







@require_torch
@require_torchaudio
class __snake_case    (						lowerCamelCase_       ,		unittest.TestCase		):
   lowerCAmelCase_														=							SpeechaTextFeatureExtractor if is_speech_available() else None




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				SpeechaTextFeatureExtractionTester(self				)




   def      __a						(   self      :    Optional[int]	,     _lowercase      :    List[Any]				):



     """simple docstring"""






     self.assertTrue(np.all(np.mean(_lowercase	,     axis=0				) < 1E-3				)				)
     self.assertTrue(np.all(np.abs(np.var(_lowercase	,     axis=0				) - 1				) < 1E-3				)				)




   def      __a						(   self      :    Optional[Any]				):



     """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(8_00	,     14_00	,     2_00				)]
     SCREAMING_SNAKE_CASE__      							=				[np.asarray(_lowercase				) for speech_input in speech_inputs]

     # Test feature size
     SCREAMING_SNAKE_CASE__      							=				feature_extractor(_lowercase	,     padding=_lowercase	,     return_tensors="""np"""				).input_features
     self.assertTrue(input_features.ndim == 3				)
     self.assertTrue(input_features.shape[-1] == 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(_lowercase	,     _lowercase	,     atol=1E-3				)				)

     # Test batched
     SCREAMING_SNAKE_CASE__      							=				feature_extractor(_lowercase	,     return_tensors="""np"""				).input_features
     SCREAMING_SNAKE_CASE__      							=				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.
     SCREAMING_SNAKE_CASE__      							=				[floats_list((1, x)				)[0] for x in (8_00, 8_00, 8_00)]
     SCREAMING_SNAKE_CASE__      							=				np.asarray(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				feature_extractor(_lowercase	,     return_tensors="""np"""				).input_features
     SCREAMING_SNAKE_CASE__      							=				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      __a						(   self      :    List[str]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
     SCREAMING_SNAKE_CASE__      							=				[floats_list((1, x)				)[0] for x in range(8_00	,     14_00	,     2_00				)]

     SCREAMING_SNAKE_CASE__      							=				["""longest""", """max_length""", """do_not_pad"""]
     SCREAMING_SNAKE_CASE__      							=				[None, 16, None]
     for max_length, padding in zip(_lowercase	,     _lowercase				):
       SCREAMING_SNAKE_CASE__      							=				feature_extractor(
           _lowercase	,     padding=_lowercase	,     max_length=_lowercase	,     return_attention_mask=_lowercase				)
       SCREAMING_SNAKE_CASE__      							=				inputs.input_features
       SCREAMING_SNAKE_CASE__      							=				inputs.attention_mask
       SCREAMING_SNAKE_CASE__      							=				[np.sum(_lowercase				) for x in attention_mask]

       self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]				)
       self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]				)
       self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]				)




   def      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
     SCREAMING_SNAKE_CASE__      							=				[floats_list((1, x)				)[0] for x in range(8_00	,     14_00	,     2_00				)]

     SCREAMING_SNAKE_CASE__      							=				["""longest""", """max_length""", """do_not_pad"""]
     SCREAMING_SNAKE_CASE__      							=				[None, 16, None]
     for max_length, padding in zip(_lowercase	,     _lowercase				):
       SCREAMING_SNAKE_CASE__      							=				feature_extractor(
           _lowercase	,     max_length=_lowercase	,     padding=_lowercase	,     return_tensors="""np"""	,     return_attention_mask=_lowercase				)
       SCREAMING_SNAKE_CASE__      							=				inputs.input_features
       SCREAMING_SNAKE_CASE__      							=				inputs.attention_mask
       SCREAMING_SNAKE_CASE__      							=				[np.sum(_lowercase				) for x in attention_mask]

       self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]				)
       self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6				)
       self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]				)
       self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6				)
       self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]				)




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
     SCREAMING_SNAKE_CASE__      							=				[floats_list((1, x)				)[0] for x in range(8_00	,     14_00	,     2_00				)]
     SCREAMING_SNAKE_CASE__      							=				feature_extractor(
         _lowercase	,     padding="""max_length"""	,     max_length=4	,     truncation=_lowercase	,     return_tensors="""np"""	,     return_attention_mask=_lowercase	,     )
     SCREAMING_SNAKE_CASE__      							=				inputs.input_features
     SCREAMING_SNAKE_CASE__      							=				inputs.attention_mask
     SCREAMING_SNAKE_CASE__      							=				np.sum(attention_mask == 1	,     axis=1				)

     self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]				)
     self._check_zero_mean_unit_variance(input_features[1]				)
     self._check_zero_mean_unit_variance(input_features[2]				)




   def      __a						(   self      :    Dict				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
     SCREAMING_SNAKE_CASE__      							=				[floats_list((1, x)				)[0] for x in range(8_00	,     14_00	,     2_00				)]
     SCREAMING_SNAKE_CASE__      							=				feature_extractor(
         _lowercase	,     padding="""longest"""	,     max_length=4	,     truncation=_lowercase	,     return_tensors="""np"""	,     return_attention_mask=_lowercase	,     )
     SCREAMING_SNAKE_CASE__      							=				inputs.input_features
     SCREAMING_SNAKE_CASE__      							=				inputs.attention_mask
     SCREAMING_SNAKE_CASE__      							=				np.sum(attention_mask == 1	,     axis=1				)

     self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]				)
     self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]				)
     self._check_zero_mean_unit_variance(input_features[2]				)

     # make sure that if max_length < longest -> then pad to max_length
     self.assertEqual(input_features.shape	,     (3, 4, 24)				)

     SCREAMING_SNAKE_CASE__      							=				[floats_list((1, x)				)[0] for x in range(8_00	,     14_00	,     2_00				)]
     SCREAMING_SNAKE_CASE__      							=				feature_extractor(
         _lowercase	,     padding="""longest"""	,     max_length=16	,     truncation=_lowercase	,     return_tensors="""np"""	,     return_attention_mask=_lowercase	,     )
     SCREAMING_SNAKE_CASE__      							=				inputs.input_features
     SCREAMING_SNAKE_CASE__      							=				inputs.attention_mask
     SCREAMING_SNAKE_CASE__      							=				np.sum(attention_mask == 1	,     axis=1				)

     self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]				)
     self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]				)
     self._check_zero_mean_unit_variance(input_features[2]				)

     # make sure that if max_length < longest -> then pad to max_length
     self.assertEqual(input_features.shape	,     (3, 6, 24)				)




   def      __a						(   self      :    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(1_00	,     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      __a						(   self      :    List[str]	,     _lowercase      :    Optional[Any]				):



     """simple docstring"""






     from datasets import load_dataset

     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(_lowercase				)				)[:num_samples]["""audio"""]

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




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				np.array([
         -1.57_45, -1.77_13, -1.70_20, -1.60_69, -1.22_50, -1.11_05, -0.90_72, -0.82_41,
         -1.23_10, -0.80_98, -0.33_20, -0.41_01, -0.79_85, -0.49_96, -0.82_13, -0.91_28,
         -1.04_20, -1.12_86, -1.04_40, -0.79_99, -0.84_05, -1.22_75, -1.54_43, -1.46_25,
     ]				)
     # fmt: on

     SCREAMING_SNAKE_CASE__      							=				self._load_datasamples(1				)
     SCREAMING_SNAKE_CASE__      							=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
     SCREAMING_SNAKE_CASE__      							=				feature_extractor(_lowercase	,     return_tensors="""pt"""				).input_features
     self.assertEquals(input_features.shape	,     (1, 5_84, 24)				)
     self.assertTrue(np.allclose(input_features[0, 0, :30]	,     _lowercase	,     atol=1E-4				)				)




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer







@require_torch
@require_sentencepiece
@require_tokenizers
class __snake_case    (						unittest.TestCase		):




   @slow
   def      __a						(   self      :    List[str]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""	,     return_dict=_lowercase				).to(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				AutoTokenizer.from_pretrained("""google/mt5-small"""				)

     SCREAMING_SNAKE_CASE__      							=				tokenizer("""Hello there"""	,     return_tensors="""pt"""				).input_ids
     SCREAMING_SNAKE_CASE__      							=				tokenizer("""Hi I am"""	,     return_tensors="""pt"""				).input_ids

     SCREAMING_SNAKE_CASE__      							=				model(input_ids.to(_lowercase				)	,     labels=labels.to(_lowercase				)				).loss
     SCREAMING_SNAKE_CASE__      							=				-(labels.shape[-1] * loss.item())

     SCREAMING_SNAKE_CASE__      							=				-84.91_27
     self.assertTrue(abs(mtf_score - EXPECTED_SCORE				) < 1E-4				)
