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

lowercase_  : Union[str, Any]								=					random.Random()







def       A__   (						snake_case_			:		int							,					snake_case_			:		Any=1.0							,					snake_case_			:		str=None							,					snake_case_			:		List[Any]=None   ):
 if rng is None:
  SCREAMING_SNAKE_CASE__: str=   global_rng

 SCREAMING_SNAKE_CASE__: 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 		_lowerCamelCase		( unittest.TestCase     ):
    def __init__(	self			,			lowerCAmelCase			,			lowerCAmelCase=7			,			lowerCAmelCase=400			,			lowerCAmelCase=2000			,			lowerCAmelCase=10			,			lowerCAmelCase=160			,			lowerCAmelCase=8			,			lowerCAmelCase=0.0			,			lowerCAmelCase=4000			,			lowerCAmelCase=False			,			lowerCAmelCase=True			,			)							->       List[str]:
     SCREAMING_SNAKE_CASE__: List[Any]=   parent
     SCREAMING_SNAKE_CASE__: Optional[Any]=   batch_size
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   min_seq_length
     SCREAMING_SNAKE_CASE__: List[str]=   max_seq_length
     SCREAMING_SNAKE_CASE__: Optional[int]=   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     SCREAMING_SNAKE_CASE__: str=   padding_value
     SCREAMING_SNAKE_CASE__: List[Any]=   sampling_rate
     SCREAMING_SNAKE_CASE__: Dict=   return_attention_mask
     SCREAMING_SNAKE_CASE__: int=   do_normalize
     SCREAMING_SNAKE_CASE__: List[Any]=   feature_size
     SCREAMING_SNAKE_CASE__: List[Any]=   chunk_length
     SCREAMING_SNAKE_CASE__: List[Any]=   hop_length
    def     UpperCamelCase_					(	self )							->       Optional[Any]:
     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     UpperCamelCase_					(	self			,			lowerCAmelCase=False			,			lowerCAmelCase=False )							->       Union[str, Any]:
     def _flatten(lowerCAmelCase ):
      return list(itertools.chain(*lowerCAmelCase ) )

     if equal_length:
      SCREAMING_SNAKE_CASE__: Dict=   [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__: 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:
      SCREAMING_SNAKE_CASE__: List[str]=   [np.asarray(lowerCAmelCase ) for x in speech_inputs]
     return speech_inputs






@require_torch
@require_torchaudio
class 		_lowerCamelCase		( UpperCamelCase_     ,  unittest.TestCase     ):
    __a						=   WhisperFeatureExtractor if is_speech_available() else None
    def     UpperCamelCase_					(	self )							->       Tuple:
     SCREAMING_SNAKE_CASE__: Any=   WhisperFeatureExtractionTester(self )
    def     UpperCamelCase_					(	self )							->       int:
     SCREAMING_SNAKE_CASE__: List[Any]=   self.feature_extraction_class(**self.feat_extract_dict )

     with tempfile.TemporaryDirectory() as tmpdirname:
      SCREAMING_SNAKE_CASE__: List[Any]=   feat_extract_first.save_pretrained(lowerCAmelCase )[0]
      check_json_file_has_correct_format(lowerCAmelCase )
      SCREAMING_SNAKE_CASE__: Optional[Any]=   self.feature_extraction_class.from_pretrained(lowerCAmelCase )

     SCREAMING_SNAKE_CASE__: str=   feat_extract_first.to_dict()
     SCREAMING_SNAKE_CASE__: List[str]=   feat_extract_second.to_dict()
     SCREAMING_SNAKE_CASE__: Optional[Any]=   feat_extract_first.mel_filters
     SCREAMING_SNAKE_CASE__: Any=   feat_extract_second.mel_filters
     self.assertTrue(np.allclose(lowerCAmelCase			,			lowerCAmelCase ) )
     self.assertEqual(lowerCAmelCase			,			lowerCAmelCase )
    def     UpperCamelCase_					(	self )							->       List[Any]:
     SCREAMING_SNAKE_CASE__: Any=   self.feature_extraction_class(**self.feat_extract_dict )

     with tempfile.TemporaryDirectory() as tmpdirname:
      SCREAMING_SNAKE_CASE__: Any=   os.path.join(lowerCAmelCase			,			'''feat_extract.json''' )
      feat_extract_first.to_json_file(lowerCAmelCase )
      SCREAMING_SNAKE_CASE__: Optional[Any]=   self.feature_extraction_class.from_json_file(lowerCAmelCase )

     SCREAMING_SNAKE_CASE__: List[Any]=   feat_extract_first.to_dict()
     SCREAMING_SNAKE_CASE__: Optional[int]=   feat_extract_second.to_dict()
     SCREAMING_SNAKE_CASE__: List[str]=   feat_extract_first.mel_filters
     SCREAMING_SNAKE_CASE__: Dict=   feat_extract_second.mel_filters
     self.assertTrue(np.allclose(lowerCAmelCase			,			lowerCAmelCase ) )
     self.assertEqual(lowerCAmelCase			,			lowerCAmelCase )
    def     UpperCamelCase_					(	self )							->       int:
     # Tests that all call wrap to encode_plus and batch_encode_plus
     SCREAMING_SNAKE_CASE__: int=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
     # create three inputs of length 800, 1000, and 1200
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   [floats_list((1, x) )[0] for x in range(800			,			1400			,			200 )]
     SCREAMING_SNAKE_CASE__: Tuple=   [np.asarray(lowerCAmelCase ) for speech_input in speech_inputs]

     # Test feature size
     SCREAMING_SNAKE_CASE__: List[str]=   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__: str=   feature_extractor(speech_inputs[0]			,			return_tensors='''np''' ).input_features
     SCREAMING_SNAKE_CASE__: str=   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__: str=   feature_extractor(lowerCAmelCase			,			return_tensors='''np''' ).input_features
     SCREAMING_SNAKE_CASE__: int=   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__: Tuple=   [floats_list((1, x) )[0] for x in (800, 800, 800)]
     SCREAMING_SNAKE_CASE__: str=   np.asarray(lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Dict=   feature_extractor(lowerCAmelCase			,			return_tensors='''np''' ).input_features
     SCREAMING_SNAKE_CASE__: str=   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__: List[Any]=   [floats_list((1, x) )[0] for x in range(200			,			(feature_extractor.n_samples + 500)			,			200 )]
     SCREAMING_SNAKE_CASE__: int=   [np.asarray(lowerCAmelCase ) for speech_input in speech_inputs]

     SCREAMING_SNAKE_CASE__: Any=   [x[: feature_extractor.n_samples] for x in speech_inputs]
     SCREAMING_SNAKE_CASE__: Tuple=   [np.asarray(lowerCAmelCase ) for speech_input in speech_inputs_truncated]

     SCREAMING_SNAKE_CASE__: str=   feature_extractor(lowerCAmelCase			,			return_tensors='''np''' ).input_features
     SCREAMING_SNAKE_CASE__: int=   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     UpperCamelCase_					(	self )							->       Dict:
     import torch

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

     for inputs in [py_speech_inputs, np_speech_inputs]:
      SCREAMING_SNAKE_CASE__: Optional[Any]=   feature_extractor.pad([{'''input_features''': inputs}]			,			return_tensors='''np''' )
      self.assertTrue(np_processed.input_features.dtype == np.floataa )
      SCREAMING_SNAKE_CASE__: Optional[Any]=   feature_extractor.pad([{'''input_features''': inputs}]			,			return_tensors='''pt''' )
      self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase )							->       str:
     SCREAMING_SNAKE_CASE__: Tuple=   load_dataset('''hf-internal-testing/librispeech_asr_dummy'''			,			'''clean'''			,			split='''validation''' )
     # automatic decoding with librispeech
     SCREAMING_SNAKE_CASE__: Any=   ds.sort('''id''' ).select(range(lowerCAmelCase ) )[:num_samples]['''audio''']

     return [x["array"] for x in speech_samples]
    def     UpperCamelCase_					(	self )							->       Dict:
     # fmt: off
     SCREAMING_SNAKE_CASE__: List[Any]=   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__: Any=   self._load_datasamples(1 )
     SCREAMING_SNAKE_CASE__: List[str]=   WhisperFeatureExtractor()
     SCREAMING_SNAKE_CASE__: Optional[int]=   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     UpperCamelCase_					(	self )							->       Any:
     SCREAMING_SNAKE_CASE__: Tuple=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
     SCREAMING_SNAKE_CASE__: Tuple=   self._load_datasamples(1 )[0]
     SCREAMING_SNAKE_CASE__: Optional[Any]=   ((audio - audio.min()) / (audio.max() - audio.min())) * 65535  # Rescale to [0, 65535] to show issue
     SCREAMING_SNAKE_CASE__: Dict=   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 ) )







'''simple docstring'''


from unittest.mock import Mock, patch

from file_transfer.send_file import send_file



@patch("""socket.socket"""				)
@patch("""builtins.open"""				)
def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Dict ,    UpperCamelCase__    :					Tuple				):




     """simple docstring"""


     a_     :							Dict     						=  Mock()
     a_     :							List[str]     						=  conn, Mock()
     a_     :							Optional[int]     						=  iter([1, None]				)
     a_     :							List[str]     						=  lambda UpperCamelCase__				: next(UpperCamelCase__				)

     # ===== invoke =====
     send_file(filename="""mytext.txt""" ,    testing=UpperCamelCase__				)

     # ===== ensurance =====
     sock.assert_called_once()
     sock.return_value.bind.assert_called_once()
     sock.return_value.listen.assert_called_once()
     sock.return_value.accept.assert_called_once()
     conn.recv.assert_called_once()

     file.return_value.__enter__.assert_called_once()
     file.return_value.__enter__.return_value.read.assert_called()

     conn.send.assert_called_once()
     conn.close.assert_called_once()
     sock.return_value.shutdown.assert_called_once()
     sock.return_value.close.assert_called_once()
