

import math
import os
import sys






def     __lowercase					(   _A	)   ->  Any:
      SCREAMING_SNAKE_CASE       :					Any        =      ''''''
      try:
            with open(lowercase__  ,			"""rb"""	) as binary_file:
                  SCREAMING_SNAKE_CASE       :					int        =      binary_file.read()
            for dat in data:
                  SCREAMING_SNAKE_CASE       :					Union[str, Any]        =      F"{dat:08b}"
                  result += curr_byte
            return result
      except OSError:
            print("""File not accessible"""	)
            sys.exit()






def     __lowercase					(   _A  ,			_A  ,			_A  ,			_A	)   ->  int:
      lexicon.pop(lowercase__	)
      SCREAMING_SNAKE_CASE       :					str        =      last_match_id

      if math.loga(lowercase__	).is_integer():
            for curr_key in lexicon:
                  SCREAMING_SNAKE_CASE       :					Optional[int]        =      '''0''' + lexicon[curr_key]

      SCREAMING_SNAKE_CASE       :					Any        =      bin(lowercase__	)[2:]






def     __lowercase					(   _A	)   ->  Any:
      SCREAMING_SNAKE_CASE       :					int        =      {'''0''': '''0''', '''1''': '''1'''}
      SCREAMING_SNAKE_CASE       :					str        =      '''''', ''''''
      SCREAMING_SNAKE_CASE       :					Tuple        =      len(lowercase__	)

      for i in range(len(lowercase__	)	):
            curr_string += data_bits[i]
            if curr_string not in lexicon:
                  continue

            SCREAMING_SNAKE_CASE       :					Optional[int]        =      lexicon[curr_string]
            result += last_match_id
            add_key_to_lexicon(lowercase__  ,			lowercase__  ,			lowercase__  ,			lowercase__	)
            index += 1
            SCREAMING_SNAKE_CASE       :					Optional[int]        =      ''''''

      while curr_string != "" and curr_string not in lexicon:
            curr_string += "0"

      if curr_string != "":
            SCREAMING_SNAKE_CASE       :					Optional[int]        =      lexicon[curr_string]
            result += last_match_id

      return result






def     __lowercase					(   _A  ,			_A	)   ->  Tuple:
      SCREAMING_SNAKE_CASE       :					Dict        =      os.path.getsize(lowercase__	)
      SCREAMING_SNAKE_CASE       :					Optional[Any]        =      bin(lowercase__	)[2:]
      SCREAMING_SNAKE_CASE       :					Any        =      len(lowercase__	)

      return "0" * (length_length - 1) + file_length_binary + compressed






def     __lowercase					(   _A  ,			_A	)   ->  List[Any]:
      SCREAMING_SNAKE_CASE       :					Tuple        =      8
      try:
            with open(lowercase__  ,			"""wb"""	) as opened_file:
                  SCREAMING_SNAKE_CASE       :					Dict        =      [
                      to_write[i : i + byte_length]
                      for i in range(0  ,			len(lowercase__	)  ,			lowercase__	)
                  ]

                  if len(result_byte_array[-1]	) % byte_length == 0:
                        result_byte_array.append("""10000000"""	)
                  else:
                        result_byte_array[-1] += "1" + "0" * (
                            byte_length - len(result_byte_array[-1]	) - 1
                        )

                  for elem in result_byte_array:
                        opened_file.write(int(lowercase__  ,			2	).to_bytes(1  ,			byteorder="""big"""	)	)
      except OSError:
            print("""File not accessible"""	)
            sys.exit()






def     __lowercase					(   _A  ,			_A	)   ->  int:
      SCREAMING_SNAKE_CASE       :					List[str]        =      read_file_binary(lowercase__	)
      SCREAMING_SNAKE_CASE       :					List[Any]        =      compress_data(lowercase__	)
      SCREAMING_SNAKE_CASE       :					int        =      add_file_length(lowercase__  ,			lowercase__	)
      write_file_binary(lowercase__  ,			lowercase__	)


if __name__ == "__main__":
  compress(sys.argv[1], sys.argv[2])







import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


_UpperCamelCase 			=		random.Random()







def 		_lowercase (     lowercase__   ,	lowercase__=1.0   ,	lowercase__=None   ,	lowercase__=None						):
      if rng is None:
            __lowerCAmelCase					:						Optional[Any]					=					global_rng

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

      return values







class   __lowercase      (unittest.TestCase ):
 def __init__(							self      ,							A_      ,							A_=7      ,							A_=400      ,							A_=2000      ,							A_=1      ,							A_=0.0      ,							A_=1_6000      ,							A_=True      ,							A_=True      ,							)					->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					parent
       __lowerCAmelCase					:						Optional[int]					=					batch_size
       __lowerCAmelCase					:						Any					=					min_seq_length
       __lowerCAmelCase					:						Tuple					=					max_seq_length
       __lowerCAmelCase					:						Tuple					=					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       __lowerCAmelCase					:						Dict					=					feature_size
       __lowerCAmelCase					:						Optional[int]					=					padding_value
       __lowerCAmelCase					:						Tuple					=					sampling_rate
       __lowerCAmelCase					:						Union[str, Any]					=					return_attention_mask
       __lowerCAmelCase					:						Dict					=					do_normalize
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       return {
           "feature_size": self.feature_size,
           "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      ,							A_=False      ,							A_=False					)					->Union[str, Any]:



       '''simple docstring'''





       def _flatten(A_					):
             return list(itertools.chain(*A_					)					)

       if equal_length:
             __lowerCAmelCase					:						Dict					=					floats_list((self.batch_size, self.max_seq_length)					)
       else:
             # make sure that inputs increase in size
             __lowerCAmelCase					:						Tuple					=					[
                 _flatten(floats_list((x, self.feature_size)					)					)
                 for x in range(self.min_seq_length      ,							self.max_seq_length      ,							self.seq_length_diff					)
             ]

       if numpify:
             __lowerCAmelCase					:						Tuple					=					[np.asarray(A_					) for x in speech_inputs]

       return speech_inputs







class   __lowercase      (_UpperCAmelCase		,   unittest.TestCase ):
 _UpperCamelCase			  =  WavaVecaFeatureExtractor
 def 	UpperCamelCase__  (							self					)					->Dict:



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					WavaVecaFeatureExtractionTester(self					)
 def 	UpperCamelCase__  (							self      ,							A_					)					->Optional[Any]:



       '''simple docstring'''





       self.assertTrue(np.all(np.mean(A_      ,							axis=0					) < 1e-3					)					)
       self.assertTrue(np.all(np.abs(np.var(A_      ,							axis=0					) - 1					) < 1e-3					)					)
 def 	UpperCamelCase__  (							self					)					->Tuple:



       '''simple docstring'''





       __lowerCAmelCase					:						List[str]					=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
       # create three inputs of length 800, 1000, and 1200
       __lowerCAmelCase					:						Optional[Any]					=					[floats_list((1, x)					)[0] for x in range(800      ,							1400      ,							200					)]
       __lowerCAmelCase					:						Any					=					[np.asarray(A_					) for speech_input in speech_inputs]

       # Test not batched input
       __lowerCAmelCase					:						Optional[Any]					=					feat_extract(speech_inputs[0]      ,							return_tensors='''np'''					).input_values
       __lowerCAmelCase					:						Dict					=					feat_extract(np_speech_inputs[0]      ,							return_tensors='''np'''					).input_values
       self.assertTrue(np.allclose(A_      ,							A_      ,							atol=1e-3					)					)

       # Test batched
       __lowerCAmelCase					:						Dict					=					feat_extract(A_      ,							return_tensors='''np'''					).input_values
       __lowerCAmelCase					:						Dict					=					feat_extract(A_      ,							return_tensors='''np'''					).input_values
       for enc_seq_a, enc_seq_a in zip(A_      ,							A_					):
             self.assertTrue(np.allclose(A_      ,							A_      ,							atol=1e-3					)					)

       # Test 2-D numpy arrays are batched.
       __lowerCAmelCase					:						int					=					[floats_list((1, x)					)[0] for x in (800, 800, 800)]
       __lowerCAmelCase					:						List[Any]					=					np.asarray(A_					)
       __lowerCAmelCase					:						Any					=					feat_extract(A_      ,							return_tensors='''np'''					).input_values
       __lowerCAmelCase					:						Union[str, Any]					=					feat_extract(A_      ,							return_tensors='''np'''					).input_values
       for enc_seq_a, enc_seq_a in zip(A_      ,							A_					):
             self.assertTrue(np.allclose(A_      ,							A_      ,							atol=1e-3					)					)
 def 	UpperCamelCase__  (							self					)					->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
       __lowerCAmelCase					:						str					=					[floats_list((1, x)					)[0] for x in range(800      ,							1400      ,							200					)]

       __lowerCAmelCase					:						str					=					['''longest''', '''max_length''', '''do_not_pad''']
       __lowerCAmelCase					:						str					=					[None, 1600, None]
       for max_length, padding in zip(A_      ,							A_					):
             __lowerCAmelCase					:						Optional[int]					=					feat_extract(A_      ,							padding=A_      ,							max_length=A_      ,							return_tensors='''np'''					)
             __lowerCAmelCase					:						Optional[Any]					=					processed.input_values

             self._check_zero_mean_unit_variance(input_values[0][:800]					)
             self.assertTrue(input_values[0][800:].sum() < 1e-6					)
             self._check_zero_mean_unit_variance(input_values[1][:1000]					)
             self.assertTrue(input_values[0][1000:].sum() < 1e-6					)
             self._check_zero_mean_unit_variance(input_values[2][:1200]					)
 def 	UpperCamelCase__  (							self					)					->Dict:



       '''simple docstring'''





       __lowerCAmelCase					:						List[str]					=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
       __lowerCAmelCase					:						Optional[int]					=					range(800      ,							1400      ,							200					)
       __lowerCAmelCase					:						Union[str, Any]					=					[floats_list((1, x)					)[0] for x in lengths]

       __lowerCAmelCase					:						int					=					['''longest''', '''max_length''', '''do_not_pad''']
       __lowerCAmelCase					:						List[str]					=					[None, 1600, None]

       for max_length, padding in zip(A_      ,							A_					):
             __lowerCAmelCase					:						Union[str, Any]					=					feat_extract(A_      ,							max_length=A_      ,							padding=A_					)
             __lowerCAmelCase					:						Union[str, Any]					=					processed.input_values

             self._check_zero_mean_unit_variance(input_values[0][:800]					)
             self._check_zero_mean_unit_variance(input_values[1][:1000]					)
             self._check_zero_mean_unit_variance(input_values[2][:1200]					)
 def 	UpperCamelCase__  (							self					)					->Tuple:



       '''simple docstring'''





       __lowerCAmelCase					:						Dict					=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
       __lowerCAmelCase					:						Optional[Any]					=					[floats_list((1, x)					)[0] for x in range(800      ,							1400      ,							200					)]
       __lowerCAmelCase					:						List[str]					=					feat_extract(
           A_      ,							truncation=A_      ,							max_length=1000      ,							padding='''max_length'''      ,							return_tensors='''np'''					)
       __lowerCAmelCase					:						int					=					processed.input_values

       self._check_zero_mean_unit_variance(input_values[0, :800]					)
       self._check_zero_mean_unit_variance(input_values[1]					)
       self._check_zero_mean_unit_variance(input_values[2]					)
 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						Dict					=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
       __lowerCAmelCase					:						int					=					[floats_list((1, x)					)[0] for x in range(800      ,							1400      ,							200					)]
       __lowerCAmelCase					:						int					=					feat_extract(
           A_      ,							truncation=A_      ,							max_length=1000      ,							padding='''longest'''      ,							return_tensors='''np'''					)
       __lowerCAmelCase					:						Optional[Any]					=					processed.input_values

       self._check_zero_mean_unit_variance(input_values[0, :800]					)
       self._check_zero_mean_unit_variance(input_values[1, :1000]					)
       self._check_zero_mean_unit_variance(input_values[2]					)

       # make sure that if max_length < longest -> then pad to max_length
       self.assertTrue(input_values.shape == (3, 1000)					)

       __lowerCAmelCase					:						Any					=					[floats_list((1, x)					)[0] for x in range(800      ,							1400      ,							200					)]
       __lowerCAmelCase					:						Optional[int]					=					feat_extract(
           A_      ,							truncation=A_      ,							max_length=2000      ,							padding='''longest'''      ,							return_tensors='''np'''					)
       __lowerCAmelCase					:						List[str]					=					processed.input_values

       self._check_zero_mean_unit_variance(input_values[0, :800]					)
       self._check_zero_mean_unit_variance(input_values[1, :1000]					)
       self._check_zero_mean_unit_variance(input_values[2]					)

       # make sure that if max_length > longest -> then pad to longest
       self.assertTrue(input_values.shape == (3, 1200)					)
 @require_torch
 def 	UpperCamelCase__  (							self					)					->Any:



       '''simple docstring'''





       import torch

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

       for inputs in [py_speech_inputs, np_speech_inputs]:
             __lowerCAmelCase					:						Any					=					feature_extractor.pad([{'''input_values''': inputs}]      ,							return_tensors='''np'''					)
             self.assertTrue(np_processed.input_values.dtype == np.floataa					)
             __lowerCAmelCase					:						List[str]					=					feature_extractor.pad([{'''input_values''': inputs}]      ,							return_tensors='''pt'''					)
             self.assertTrue(pt_processed.input_values.dtype == torch.floataa					)





 @slow
 @require_torch
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
             __lowerCAmelCase					:						Any					=					WavaVecaConfig.from_pretrained(A_					)
             __lowerCAmelCase					:						Tuple					=					WavaVecaFeatureExtractor.from_pretrained(A_					)

             # only "layer" feature extraction norm should make use of
             # attention_mask
             self.assertEqual(feat_extract.return_attention_mask      ,							config.feat_extract_norm == '''layer'''					)


