
'''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

a_											=						random.Random()

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE=1.0,		__SCREAMING_SNAKE_CASE=None,		__SCREAMING_SNAKE_CASE=None       ):
    """simple docstring"""






    if rng is None:
        snake_case_ :    List[str]     			=						global_rng

    snake_case_ :    Dict     			=						[]
    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					):





 def __init__(    self ,       lowercase_ ,       lowercase_=7 ,       lowercase_=4_00 ,       lowercase_=20_00 ,       lowercase_=10 ,       lowercase_=1_60 ,       lowercase_=8 ,       lowercase_=0.0 ,       lowercase_=40_00 ,       lowercase_=False ,       lowercase_=True ,       ):
     snake_case_ :    List[Any]     			=						parent
     snake_case_ :    Union[str, Any]     			=						batch_size
     snake_case_ :    Optional[Any]     			=						min_seq_length
     snake_case_ :    Optional[Any]     			=						max_seq_length
     snake_case_ :    Dict     			=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     snake_case_ :    Optional[Any]     			=						padding_value
     snake_case_ :    Union[str, Any]     			=						sampling_rate
     snake_case_ :    Any     			=						return_attention_mask
     snake_case_ :    Optional[Any]     			=						do_normalize
     snake_case_ :    Union[str, Any]     			=						feature_size
     snake_case_ :    int     			=						chunk_length
     snake_case_ :    int     			=						hop_length





 def        snake_case__		(    self):
     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        snake_case__		(    self ,       lowercase_=False ,       lowercase_=False):
     def _flatten(lowercase_):
         return list(itertools.chain(*lowercase_))

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






@require_torch
@require_torchaudio
class    UpperCAmelCase_				(	snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						WhisperFeatureExtractor if is_speech_available() else None





 def        snake_case__		(    self):
     snake_case_ :    List[str]     			=						WhisperFeatureExtractionTester(self)





 def        snake_case__		(    self):
     snake_case_ :    int     			=						self.feature_extraction_class(**self.feat_extract_dict)

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

     snake_case_ :    Dict     			=						feat_extract_first.to_dict()
     snake_case_ :    Tuple     			=						feat_extract_second.to_dict()
     snake_case_ :    List[str]     			=						feat_extract_first.mel_filters
     snake_case_ :    int     			=						feat_extract_second.mel_filters
     self.assertTrue(np.allclose(lowercase_ ,       lowercase_))
     self.assertEqual(lowercase_ ,       lowercase_)





 def        snake_case__		(    self):
     snake_case_ :    List[str]     			=						self.feature_extraction_class(**self.feat_extract_dict)

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

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





 def        snake_case__		(    self):
     # Tests that all call wrap to encode_plus and batch_encode_plus
     snake_case_ :    Tuple     			=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
     # create three inputs of length 800, 1000, and 1200
     snake_case_ :    Any     			=						[floats_list((1, x))[0] for x in range(8_00 ,       14_00 ,       2_00)]
     snake_case_ :    List[str]     			=						[np.asarray(lowercase_) for speech_input in speech_inputs]

     # Test feature size
     snake_case_ :    List[Any]     			=						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
     snake_case_ :    List[Any]     			=						feature_extractor(speech_inputs[0] ,       return_tensors="np").input_features
     snake_case_ :    List[Any]     			=						feature_extractor(np_speech_inputs[0] ,       return_tensors="np").input_features
     self.assertTrue(np.allclose(lowercase_ ,       lowercase_ ,       atol=1E-3))

     # Test batched
     snake_case_ :    Tuple     			=						feature_extractor(lowercase_ ,       return_tensors="np").input_features
     snake_case_ :    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.
     snake_case_ :    Dict     			=						[floats_list((1, x))[0] for x in (8_00, 8_00, 8_00)]
     snake_case_ :    Optional[int]     			=						np.asarray(lowercase_)
     snake_case_ :    Dict     			=						feature_extractor(lowercase_ ,       return_tensors="np").input_features
     snake_case_ :    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 truncation required
     snake_case_ :    Tuple     			=						[floats_list((1, x))[0] for x in range(2_00 ,       (feature_extractor.n_samples + 5_00) ,       2_00)]
     snake_case_ :    List[str]     			=						[np.asarray(lowercase_) for speech_input in speech_inputs]

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

     snake_case_ :    str     			=						feature_extractor(lowercase_ ,       return_tensors="np").input_features
     snake_case_ :    List[str]     			=						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        snake_case__		(    self):
     import torch

     snake_case_ :    Any     			=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
     snake_case_ :    Optional[Any]     			=						np.random.rand(1_00 ,       32).astype(np.floataa)
     snake_case_ :    int     			=						np_speech_inputs.tolist()

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





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

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





 def        snake_case__		(    self):
     # fmt: off
     snake_case_ :    Tuple     			=						torch.tensor(
         [
             0.1_193, -0.0_946, -0.1_098, -0.0_196, 0.0_225, -0.0_690, -0.1_736, 0.0_951,
             0.0_971, -0.0_817, -0.0_702, 0.0_162, 0.0_260, 0.0_017, -0.0_192, -0.1_678,
             0.0_709, -0.1_867, -0.0_655, -0.0_274, -0.0_234, -0.1_884, -0.0_516, -0.0_554,
             -0.0_274, -0.1_425, -0.1_423, 0.0_837, 0.0_377, -0.0_854
         ])
     # fmt: on

     snake_case_ :    Tuple     			=						self._load_datasamples(1)
     snake_case_ :    int     			=						WhisperFeatureExtractor()
     snake_case_ :    Optional[int]     			=						feature_extractor(lowercase_ ,       return_tensors="pt").input_features
     self.assertEqual(input_features.shape ,       (1, 80, 30_00))
     self.assertTrue(torch.allclose(input_features[0, 0, :30] ,       lowercase_ ,       atol=1E-4))





 def        snake_case__		(    self):
     snake_case_ :    List[str]     			=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
     snake_case_ :    Dict     			=						self._load_datasamples(1)[0]
     snake_case_ :    Union[str, Any]     			=						((audio - audio.min()) / (audio.max() - audio.min())) * 6_55_35  # Rescale to [0, 65535] to show issue
     snake_case_ :    Union[str, 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'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a_											=						{
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_											=						[
      "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "BioGptForCausalLM",
      "BioGptForTokenClassification",
      "BioGptForSequenceClassification",
      "BioGptModel",
      "BioGptPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
  from .tokenization_biogpt import BioGptTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_biogpt import (
        BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BioGptForCausalLM,
        BioGptForSequenceClassification,
        BioGptForTokenClassification,
        BioGptModel,
        BioGptPreTrainedModel,
    )


else:
  import sys

  a_											=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




