





import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


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      )					->  Union[str, 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
class 						UpperCamelCase__					( unittest.TestCase  ):



     '''simple docstring'''

     def __init__(				self		:       Optional[int]		,lowerCamelCase__		:       Optional[Any]		,lowerCamelCase__		:       Tuple=7		,lowerCamelCase__		:       int=400		,lowerCamelCase__		:       List[str]=2000		,lowerCamelCase__		:       List[str]=1		,lowerCamelCase__		:       Union[str, Any]=0.0		,lowerCamelCase__		:       List[str]=24000		,lowerCamelCase__		:       str=True		,)	->						List[Any]:




           '''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						=		padding_value
           SCREAMING_SNAKE_CASE						=		sampling_rate
           SCREAMING_SNAKE_CASE						=		return_attention_mask






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




           '''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,
           }






     def 							SCREAMING_SNAKE_CASE__    (				self		:       int		,lowerCamelCase__		:       List[Any]=False		,lowerCamelCase__		:       Dict=False			)	->						Union[str, Any]:




           '''simple docstring'''


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

           if equal_length:
                 SCREAMING_SNAKE_CASE						=		floats_list((self.batch_size, self.max_seq_length)			)
           else:
                 # make sure that inputs increase in size
                 SCREAMING_SNAKE_CASE						=		[
                     _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:
                 SCREAMING_SNAKE_CASE						=		[np.asarray(lowerCamelCase__			) for x in audio_inputs]

           return audio_inputs

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



     '''simple docstring'''

     __snake_case       :      Optional[int]						      =     EncodecFeatureExtractor






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		EnCodecFeatureExtractionTester(self			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Dict			)	->						Union[str, 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(800		,1400		,200			)]
           SCREAMING_SNAKE_CASE						=		[np.asarray(lowerCamelCase__			) for audio_input in audio_inputs]

           # Test not batched input
           SCREAMING_SNAKE_CASE						=		feat_extract(audio_inputs[0]		,return_tensors="""np"""			).input_values
           SCREAMING_SNAKE_CASE						=		feat_extract(np_audio_inputs[0]		,return_tensors="""np"""			).input_values
           self.assertTrue(np.allclose(lowerCamelCase__		,lowerCamelCase__		,atol=1e-3			)			)

           # Test batched
           SCREAMING_SNAKE_CASE						=		feat_extract(lowerCamelCase__		,padding=lowerCamelCase__		,return_tensors="""np"""			).input_values
           SCREAMING_SNAKE_CASE						=		feat_extract(lowerCamelCase__		,padding=lowerCamelCase__		,return_tensors="""np"""			).input_values
           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		:       Tuple			)	->						str:




           '''simple docstring'''


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

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






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




           '''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(lowerCamelCase__			)			)[:num_samples]["""audio"""]

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






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		torch.tensor(
               [2.3_8_0_4e-0_3, 2.0_7_5_2e-0_3, 1.9_8_3_6e-0_3, 2.1_0_5_7e-0_3, 1.6_1_7_4e-0_3,
                3.0_5_1_8e-0_4, 9.1_5_5_3e-0_5, 3.3_5_6_9e-0_4, 9.7_6_5_6e-0_4, 1.8_3_1_1e-0_3,
                2.0_1_4_2e-0_3, 2.1_0_5_7e-0_3, 1.7_3_9_5e-0_3, 4.5_7_7_6e-0_4, -3.9_6_7_3e-0_4,
                4.5_7_7_6e-0_4, 1.0_0_7_1e-0_3, 9.1_5_5_3e-0_5, 4.8_8_2_8e-0_4, 1.1_5_9_7e-0_3,
                7.3_2_4_2e-0_4, 9.4_6_0_4e-0_4, 1.8_0_0_5e-0_3, 1.8_3_1_1e-0_3, 8.8_5_0_1e-0_4,
                4.2_7_2_5e-0_4, 4.8_8_2_8e-0_4, 7.3_2_4_2e-0_4, 1.0_9_8_6e-0_3, 2.1_0_5_7e-0_3]			)
           # fmt: on
           SCREAMING_SNAKE_CASE						=		self._load_datasamples(1			)
           SCREAMING_SNAKE_CASE						=		EncodecFeatureExtractor()
           SCREAMING_SNAKE_CASE						=		feature_extractor(lowerCamelCase__		,return_tensors="""pt"""			).input_values
           self.assertEquals(input_values.shape		,(1, 1, 93680)			)
           self.assertTrue(torch.allclose(input_values[0, 0, :30]		,lowerCamelCase__		,atol=1e-6			)			)






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		torch.tensor(
               [2.3_8_0_4e-0_3, 2.0_7_5_2e-0_3, 1.9_8_3_6e-0_3, 2.1_0_5_7e-0_3, 1.6_1_7_4e-0_3,
                3.0_5_1_8e-0_4, 9.1_5_5_3e-0_5, 3.3_5_6_9e-0_4, 9.7_6_5_6e-0_4, 1.8_3_1_1e-0_3,
                2.0_1_4_2e-0_3, 2.1_0_5_7e-0_3, 1.7_3_9_5e-0_3, 4.5_7_7_6e-0_4, -3.9_6_7_3e-0_4,
                4.5_7_7_6e-0_4, 1.0_0_7_1e-0_3, 9.1_5_5_3e-0_5, 4.8_8_2_8e-0_4, 1.1_5_9_7e-0_3,
                7.3_2_4_2e-0_4, 9.4_6_0_4e-0_4, 1.8_0_0_5e-0_3, 1.8_3_1_1e-0_3, 8.8_5_0_1e-0_4,
                4.2_7_2_5e-0_4, 4.8_8_2_8e-0_4, 7.3_2_4_2e-0_4, 1.0_9_8_6e-0_3, 2.1_0_5_7e-0_3]			)
           # fmt: on
           SCREAMING_SNAKE_CASE						=		self._load_datasamples(1			)
           SCREAMING_SNAKE_CASE						=		[np.tile(input_audio[0][None]		,reps=(2, 1)			)]
           input_audio[0][1] *= 0.5
           SCREAMING_SNAKE_CASE						=		EncodecFeatureExtractor(feature_size=2			)
           SCREAMING_SNAKE_CASE						=		feature_extractor(lowerCamelCase__		,return_tensors="""pt"""			).input_values
           self.assertEquals(input_values.shape		,(1, 2, 93680)			)
           self.assertTrue(torch.allclose(input_values[0, 0, :30]		,lowerCamelCase__		,atol=1e-6			)			)
           self.assertTrue(torch.allclose(input_values[0, 1, :30]		,EXPECTED_INPUT_VALUES * 0.5		,atol=1e-6			)			)






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self._load_datasamples(2			)
           # would be easier if the stride was like
           SCREAMING_SNAKE_CASE						=		EncodecFeatureExtractor(feature_size=1		,chunk_length_s=1		,overlap=0.01			)

           # pad and trunc raise an error ?
           with self.assertRaisesRegex(
               lowerCamelCase__		,"""^Both padding and truncation were set. Make sure you only set one.$"""		,):
                 SCREAMING_SNAKE_CASE						=		feature_extractor(
                     lowerCamelCase__		,padding="""max_length"""		,truncation=lowerCamelCase__		,return_tensors="""pt"""			).input_values

           # truncate to chunk
           SCREAMING_SNAKE_CASE						=		feature_extractor(lowerCamelCase__		,truncation=lowerCamelCase__		,return_tensors="""pt"""			).input_values
           self.assertEquals(truncated_outputs.shape		,(2, 1, 71520)			)  # 2 chunks

           # force truncate to max_length
           SCREAMING_SNAKE_CASE						=		feature_extractor(
               lowerCamelCase__		,truncation=lowerCamelCase__		,max_length=48000		,return_tensors="""pt"""			).input_values
           self.assertEquals(truncated_outputs.shape		,(2, 1, 48000)			)

           # pad to chunk
           SCREAMING_SNAKE_CASE						=		feature_extractor(lowerCamelCase__		,padding=lowerCamelCase__		,return_tensors="""pt"""			).input_values
           self.assertEquals(padded_outputs.shape		,(2, 1, 95280)			)

           # pad to chunk
           SCREAMING_SNAKE_CASE						=		feature_extractor(lowerCamelCase__		,return_tensors="""pt"""			).input_values
           self.assertEquals(truncated_outputs.shape		,(2, 1, 95280)			)

           # force pad to max length
           SCREAMING_SNAKE_CASE						=		feature_extractor(
               lowerCamelCase__		,padding="""max_length"""		,max_length=100000		,return_tensors="""pt"""			).input_values
           self.assertEquals(truncated_outputs.shape		,(2, 1, 100000)			)

           # force no pad
           with self.assertRaisesRegex(
               lowerCamelCase__		,"""^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""		,):
                 SCREAMING_SNAKE_CASE						=		feature_extractor(lowerCamelCase__		,padding=lowerCamelCase__		,return_tensors="""pt"""			).input_values

           SCREAMING_SNAKE_CASE						=		feature_extractor(input_audio[0]		,padding=lowerCamelCase__		,return_tensors="""pt"""			).input_values
           self.assertEquals(truncated_outputs.shape		,(1, 1, 93680)			)

           # no pad if no chunk_length_s
           SCREAMING_SNAKE_CASE						=		None
           with self.assertRaisesRegex(
               lowerCamelCase__		,"""^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""		,):
                 SCREAMING_SNAKE_CASE						=		feature_extractor(lowerCamelCase__		,padding=lowerCamelCase__		,return_tensors="""pt"""			).input_values

           SCREAMING_SNAKE_CASE						=		feature_extractor(input_audio[0]		,padding=lowerCamelCase__		,return_tensors="""pt"""			).input_values
           self.assertEquals(truncated_outputs.shape		,(1, 1, 93680)			)

           # no pad if no overlap
           SCREAMING_SNAKE_CASE						=		2
           SCREAMING_SNAKE_CASE						=		None
           with self.assertRaisesRegex(
               lowerCamelCase__		,"""^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""		,):
                 SCREAMING_SNAKE_CASE						=		feature_extractor(lowerCamelCase__		,padding=lowerCamelCase__		,return_tensors="""pt"""			).input_values

           SCREAMING_SNAKE_CASE						=		feature_extractor(input_audio[0]		,padding=lowerCamelCase__		,return_tensors="""pt"""			).input_values
           self.assertEquals(truncated_outputs.shape		,(1, 1, 93680)			)









import os
from string import ascii_letters

SCREAMING_SNAKE_CASE_		  =			ascii_letters + """ \t\n"""



def   __lowercase  ( )					->  dict[str, None]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		os.path.split(os.path.realpath(_SCREAMING_SNAKE_CASE      )      )
      SCREAMING_SNAKE_CASE						=		{}
      with open(path[0] + """/dictionary.txt"""      ) as dictionary_file:
            for word in dictionary_file.read().split("""\n"""      ):
                  SCREAMING_SNAKE_CASE						=		None
      return english_words


SCREAMING_SNAKE_CASE_		  =			load_dictionary()



def   __lowercase  ( _SCREAMING_SNAKE_CASE      )					->  float:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		message.upper()
      SCREAMING_SNAKE_CASE						=		remove_non_letters(_SCREAMING_SNAKE_CASE      )
      SCREAMING_SNAKE_CASE						=		message.split()
      SCREAMING_SNAKE_CASE						=		len([word for word in possible_words if word in ENGLISH_WORDS]      )
      return float(_SCREAMING_SNAKE_CASE      ) / len(_SCREAMING_SNAKE_CASE      )



def   __lowercase  ( _SCREAMING_SNAKE_CASE      )					->  str:


      '''simple docstring'''


      return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE      )



def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE = 20					,		_SCREAMING_SNAKE_CASE = 85      )					->  bool:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		get_english_count(_SCREAMING_SNAKE_CASE      ) * 1_00 >= word_percentage
      SCREAMING_SNAKE_CASE						=		len(remove_non_letters(_SCREAMING_SNAKE_CASE      )      )
      SCREAMING_SNAKE_CASE						=		(float(_SCREAMING_SNAKE_CASE      ) / len(_SCREAMING_SNAKE_CASE      )) * 1_00
      SCREAMING_SNAKE_CASE						=		message_letters_percentage >= letter_percentage
      return words_match and letters_match


if __name__ == "__main__":
      import doctest

      doctest.testmod()


