





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


__UpperCAmelCase						=      random.Random()
def 			__UpperCamelCase				(			lowercase__	:       Optional[int]		,    lowercase__	:       Dict=1.0		,    lowercase__	:       Any=None		,    lowercase__	:       Union[str, Any]=None		)	->      List[Any]:
    '''simple docstring'''





    if rng is None:
        lowerCAmelCase_					:	Optional[int]						=   global_rng

    lowerCAmelCase_					:	List[str]						=   []
    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 		__a							(	unittest.TestCase      ):

       '''simple docstring'''





       def __init__(     self	:   List[str]					,			UpperCAmelCase	:   List[str]					,			UpperCAmelCase	:   Any=7					,			UpperCAmelCase	:   List[Any]=4_00					,			UpperCAmelCase	:   Optional[int]=20_00					,			UpperCAmelCase	:   Dict=1					,			UpperCAmelCase	:   Any=0.0					,			UpperCAmelCase	:   List[Any]=2_40_00					,			UpperCAmelCase	:   Optional[int]=True					,			):
           lowerCAmelCase_					:	Dict						=   parent
           lowerCAmelCase_					:	List[Any]						=   batch_size
           lowerCAmelCase_					:	List[Any]						=   min_seq_length
           lowerCAmelCase_					:	List[Any]						=   max_seq_length
           lowerCAmelCase_					:	str						=   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
           lowerCAmelCase_					:	Dict						=   feature_size
           lowerCAmelCase_					:	Optional[Any]						=   padding_value
           lowerCAmelCase_					:	Optional[Any]						=   sampling_rate
           lowerCAmelCase_					:	Union[str, Any]						=   return_attention_mask



       def  A							(     self	:   int			):
           return {
               "feature_size": self.feature_size,
               "padding_value": self.padding_value,
               "sampling_rate": self.sampling_rate,
               "return_attention_mask": self.return_attention_mask,
           }



       def  A							(     self	:   Optional[Any]					,			UpperCAmelCase	:   str=False					,			UpperCAmelCase	:   Dict=False			):
           def _flatten(UpperCAmelCase	:   Dict			):
               return list(itertools.chain(*UpperCAmelCase			)			)

           if equal_length:
               lowerCAmelCase_					:	Optional[int]						=   floats_list((self.batch_size, self.max_seq_length)			)
           else:
               # make sure that inputs increase in size
               lowerCAmelCase_					:	Union[str, Any]						=   [
                   _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_					:	Dict						=   [np.asarray(UpperCAmelCase			) for x in audio_inputs]

           return audio_inputs






@require_torch
class 		__a							(	__UpperCamelCase			,unittest.TestCase      ):

       '''simple docstring'''





       __snake_case						:   Optional[Any]      =		EncodecFeatureExtractor



       def  A							(     self	:   Optional[int]			):
           lowerCAmelCase_					:	Tuple						=   EnCodecFeatureExtractionTester(self			)



       def  A							(     self	:   Optional[int]			):
           # Tests that all call wrap to encode_plus and batch_encode_plus
           lowerCAmelCase_					:	str						=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
           # create three inputs of length 800, 1000, and 1200
           lowerCAmelCase_					:	Dict						=   [floats_list((1, x)			)[0] for x in range(8_00					,			14_00					,			2_00			)]
           lowerCAmelCase_					:	List[str]						=   [np.asarray(UpperCAmelCase			) for audio_input in audio_inputs]

           # Test not batched input
           lowerCAmelCase_					:	Tuple						=   feat_extract(audio_inputs[0]					,			return_tensors="""np"""			).input_values
           lowerCAmelCase_					:	Union[str, Any]						=   feat_extract(np_audio_inputs[0]					,			return_tensors="""np"""			).input_values
           self.assertTrue(np.allclose(UpperCAmelCase					,			UpperCAmelCase					,			atol=1e-3			)			)

           # Test batched
           lowerCAmelCase_					:	str						=   feat_extract(UpperCAmelCase					,			padding=UpperCAmelCase					,			return_tensors="""np"""			).input_values
           lowerCAmelCase_					:	List[Any]						=   feat_extract(UpperCAmelCase					,			padding=UpperCAmelCase					,			return_tensors="""np"""			).input_values
           for enc_seq_a, enc_seq_a in zip(UpperCAmelCase					,			UpperCAmelCase			):
               self.assertTrue(np.allclose(UpperCAmelCase					,			UpperCAmelCase					,			atol=1e-3			)			)



       def  A							(     self	:   Dict			):
           lowerCAmelCase_					:	Tuple						=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
           lowerCAmelCase_					:	List[Any]						=   np.random.rand(1_00			).astype(np.floataa			)
           lowerCAmelCase_					:	Union[str, Any]						=   np_audio_inputs.tolist()

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



       def  A							(     self	:   List[str]					,			UpperCAmelCase	:   List[Any]			):
           from datasets import load_dataset

           lowerCAmelCase_					:	List[Any]						=   load_dataset("""hf-internal-testing/librispeech_asr_dummy"""					,			"""clean"""					,			split="""validation"""			)
           # automatic decoding with librispeech
           lowerCAmelCase_					:	str						=   ds.sort("""id"""			).select(range(UpperCAmelCase			)			)[:num_samples]["""audio"""]

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



       def  A							(     self	:   List[str]			):
           # fmt: off
           lowerCAmelCase_					:	List[Any]						=   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
           lowerCAmelCase_					:	List[Any]						=   self._load_datasamples(1			)
           lowerCAmelCase_					:	int						=   EncodecFeatureExtractor()
           lowerCAmelCase_					:	Optional[int]						=   feature_extractor(UpperCAmelCase					,			return_tensors="""pt"""			).input_values
           self.assertEquals(input_values.shape					,			(1, 1, 9_36_80)			)
           self.assertTrue(torch.allclose(input_values[0, 0, :30]					,			UpperCAmelCase					,			atol=1e-6			)			)



       def  A							(     self	:   Union[str, Any]			):
           # fmt: off
           lowerCAmelCase_					:	Dict						=   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
           lowerCAmelCase_					:	int						=   self._load_datasamples(1			)
           lowerCAmelCase_					:	Optional[Any]						=   [np.tile(input_audio[0][None]					,			reps=(2, 1)			)]
           input_audio[0][1] *= 0.5
           lowerCAmelCase_					:	Optional[int]						=   EncodecFeatureExtractor(feature_size=2			)
           lowerCAmelCase_					:	Optional[int]						=   feature_extractor(UpperCAmelCase					,			return_tensors="""pt"""			).input_values
           self.assertEquals(input_values.shape					,			(1, 2, 9_36_80)			)
           self.assertTrue(torch.allclose(input_values[0, 0, :30]					,			UpperCAmelCase					,			atol=1e-6			)			)
           self.assertTrue(torch.allclose(input_values[0, 1, :30]					,			EXPECTED_INPUT_VALUES * 0.5					,			atol=1e-6			)			)



       def  A							(     self	:   Dict			):
           lowerCAmelCase_					:	Optional[Any]						=   self._load_datasamples(2			)
           # would be easier if the stride was like
           lowerCAmelCase_					:	Optional[int]						=   EncodecFeatureExtractor(feature_size=1					,			chunk_length_s=1					,			overlap=0.01			)

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

           # truncate to chunk
           lowerCAmelCase_					:	Union[str, Any]						=   feature_extractor(UpperCAmelCase					,			truncation=UpperCAmelCase					,			return_tensors="""pt"""			).input_values
           self.assertEquals(truncated_outputs.shape					,			(2, 1, 7_15_20)			)  # 2 chunks

           # force truncate to max_length
           lowerCAmelCase_					:	List[str]						=   feature_extractor(
               UpperCAmelCase					,			truncation=UpperCAmelCase					,			max_length=4_80_00					,			return_tensors="""pt"""			).input_values
           self.assertEquals(truncated_outputs.shape					,			(2, 1, 4_80_00)			)

           # pad to chunk
           lowerCAmelCase_					:	Union[str, Any]						=   feature_extractor(UpperCAmelCase					,			padding=UpperCAmelCase					,			return_tensors="""pt"""			).input_values
           self.assertEquals(padded_outputs.shape					,			(2, 1, 9_52_80)			)

           # pad to chunk
           lowerCAmelCase_					:	Tuple						=   feature_extractor(UpperCAmelCase					,			return_tensors="""pt"""			).input_values
           self.assertEquals(truncated_outputs.shape					,			(2, 1, 9_52_80)			)

           # force pad to max length
           lowerCAmelCase_					:	List[str]						=   feature_extractor(
               UpperCAmelCase					,			padding="""max_length"""					,			max_length=10_00_00					,			return_tensors="""pt"""			).input_values
           self.assertEquals(truncated_outputs.shape					,			(2, 1, 10_00_00)			)

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

           lowerCAmelCase_					:	Tuple						=   feature_extractor(input_audio[0]					,			padding=UpperCAmelCase					,			return_tensors="""pt"""			).input_values
           self.assertEquals(truncated_outputs.shape					,			(1, 1, 9_36_80)			)

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

           lowerCAmelCase_					:	List[str]						=   feature_extractor(input_audio[0]					,			padding=UpperCAmelCase					,			return_tensors="""pt"""			).input_values
           self.assertEquals(truncated_outputs.shape					,			(1, 1, 9_36_80)			)

           # no pad if no overlap
           lowerCAmelCase_					:	Optional[Any]						=   2
           lowerCAmelCase_					:	List[str]						=   None
           with self.assertRaisesRegex(
               UpperCAmelCase					,			"""^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""					,			):
               lowerCAmelCase_					:	Dict						=   feature_extractor(UpperCAmelCase					,			padding=UpperCAmelCase					,			return_tensors="""pt"""			).input_values

           lowerCAmelCase_					:	Dict						=   feature_extractor(input_audio[0]					,			padding=UpperCAmelCase					,			return_tensors="""pt"""			).input_values
           self.assertEquals(truncated_outputs.shape					,			(1, 1, 9_36_80)			)









from __future__ import annotations

import sys

class 		__a							:



       def __init__(     self	:   Any					,			UpperCAmelCase	:   str					,			UpperCAmelCase	:   int			):
           lowerCAmelCase_					:	str						=   letter
           lowerCAmelCase_					:	int						=   freq
           lowerCAmelCase_					:	dict[str, str]						=   {}



       def __repr__(     self	:   Optional[int]			):
           return F'{self.letter}:{self.freq}'






class 		__a							:



       def __init__(     self	:   Any					,			UpperCAmelCase	:   int					,			UpperCAmelCase	:   Letter | TreeNode					,			UpperCAmelCase	:   Letter | TreeNode			):
           lowerCAmelCase_					:	int						=   freq
           lowerCAmelCase_					:	Letter | TreeNode						=   left
           lowerCAmelCase_					:	Letter | TreeNode						=   right






def 			__UpperCamelCase				(			lowercase__	:       str		)	->      list[Letter]:
    '''simple docstring'''





    lowerCAmelCase_					:	dict[str, int]						=   {}
    with open(lowercase__		) as f:
        while True:
            lowerCAmelCase_					:	int						=   f.read(1		)
            if not c:
                break
            lowerCAmelCase_					:	Optional[Any]						=   chars[c] + 1 if c in chars else 1
    return sorted((Letter(lowercase__		,    lowercase__		) for c, f in chars.items())		,    key=lambda lowercase__		: x.freq		)
def 			__UpperCamelCase				(			lowercase__	:       list[Letter]		)	->      Letter | TreeNode:
    '''simple docstring'''





    lowerCAmelCase_					:	list[Letter | TreeNode]						=   letters  # type: ignore
    while len(lowercase__		) > 1:
        lowerCAmelCase_					:	str						=   response.pop(0		)
        lowerCAmelCase_					:	Any						=   response.pop(0		)
        lowerCAmelCase_					:	Union[str, Any]						=   left.freq + right.freq
        lowerCAmelCase_					:	Union[str, Any]						=   TreeNode(lowercase__		,    lowercase__		,    lowercase__		)
        response.append(lowercase__		)
        response.sort(key=lambda lowercase__		: x.freq		)
    return response[0]
def 			__UpperCamelCase				(			lowercase__	:       Letter | TreeNode		,    lowercase__	:       str		)	->      list[Letter]:
    '''simple docstring'''





    if isinstance(lowercase__		,    lowercase__		):
        lowerCAmelCase_					:	Dict						=   bitstring
        return [root]
    lowerCAmelCase_					:	TreeNode						=   root  # type: ignore
    lowerCAmelCase_					:	Union[str, Any]						=   []
    letters += traverse_tree(treenode.left		,    bitstring + """0"""		)
    letters += traverse_tree(treenode.right		,    bitstring + """1"""		)
    return letters
def 			__UpperCamelCase				(			lowercase__	:       str		)	->      None:
    '''simple docstring'''





    lowerCAmelCase_					:	Any						=   parse_file(lowercase__		)
    lowerCAmelCase_					:	int						=   build_tree(lowercase__		)
    lowerCAmelCase_					:	Tuple						=   {
        k: v for letter in traverse_tree(lowercase__		,    """"""		) for k, v in letter.bitstring.items()
    }
    print(f'Huffman Coding  of {file_path}: '		)
    with open(lowercase__		) as f:
        while True:
            lowerCAmelCase_					:	List[str]						=   f.read(1		)
            if not c:
                break
            print(letters[c]		,    end=""" """		)
    print()


if __name__ == "__main__":
  # pass the file path to the huffman function
  huffman(sys.argv[1])


