


'''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

lowerCamelCase__    	=     random.Random()






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase=1.0      , __lowerCAmelCase=None      , __lowerCAmelCase=None   ):
		if rng is None:
				_UpperCAmelCase		:       Tuple								=      global_rng

		_UpperCAmelCase		:       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
@require_torchaudio
class 	lowerCAmelCase__			(			unittest.TestCase	):




			def __init__(		self  :     List[str]	,  lowerCamelCase__  :     Optional[Any]	,  lowerCamelCase__  :     Tuple=7	,  lowerCamelCase__  :     List[str]=4_00	,  lowerCamelCase__  :     List[str]=20_00	,  lowerCamelCase__  :     Tuple=10	,  lowerCamelCase__  :     str=1_60	,  lowerCamelCase__  :     int=8	,  lowerCamelCase__  :     Union[str, Any]=0.0	,  lowerCamelCase__  :     Optional[Any]=40_00	,  lowerCamelCase__  :     Optional[Any]=False	,  lowerCamelCase__  :     Tuple=True	,  )   ->List[Any]:



					'''simple docstring'''







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




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



					'''simple docstring'''







					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 							lowerCAmelCase__    (		self  :     Optional[Any]	,  lowerCamelCase__  :     Dict=False	,  lowerCamelCase__  :     Tuple=False )   ->Optional[int]:



					'''simple docstring'''







					def _flatten(lowerCamelCase__  :     str ):
							return list(itertools.chain(*lowerCamelCase__ ) )

					if equal_length:
							_UpperCAmelCase		:       Tuple								=      [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
					else:
							# make sure that inputs increase in size
							_UpperCAmelCase		:       Optional[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:
							_UpperCAmelCase		:       Tuple								=      [np.asarray(lowerCamelCase__ ) for x in speech_inputs]
					return speech_inputs






@require_torch
@require_torchaudio
class 	lowerCAmelCase__			(			UpperCAmelCase__    ,       unittest.TestCase	):
			lowerCAmelCase :	Dict          =     WhisperFeatureExtractor if is_speech_available() else None




			def 							lowerCAmelCase__    (		self  :     Any )   ->str:



					'''simple docstring'''







					_UpperCAmelCase		:       List[str]								=      WhisperFeatureExtractionTester(self )




			def 							lowerCAmelCase__    (		self  :     Any )   ->str:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      self.feature_extraction_class(**self.feat_extract_dict )

					with tempfile.TemporaryDirectory() as tmpdirname:
							_UpperCAmelCase		:       Dict								=      feat_extract_first.save_pretrained(lowerCamelCase__ )[0]
							check_json_file_has_correct_format(lowerCamelCase__ )
							_UpperCAmelCase		:       int								=      self.feature_extraction_class.from_pretrained(lowerCamelCase__ )

					_UpperCAmelCase		:       Dict								=      feat_extract_first.to_dict()
					_UpperCAmelCase		:       Any								=      feat_extract_second.to_dict()
					_UpperCAmelCase		:       List[Any]								=      feat_extract_first.mel_filters
					_UpperCAmelCase		:       Dict								=      feat_extract_second.mel_filters
					self.assertTrue(np.allclose(lowerCamelCase__	,  lowerCamelCase__ ) )
					self.assertEqual(lowerCamelCase__	,  lowerCamelCase__ )




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



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      self.feature_extraction_class(**self.feat_extract_dict )

					with tempfile.TemporaryDirectory() as tmpdirname:
							_UpperCAmelCase		:       Dict								=      os.path.join(lowerCamelCase__	,  "feat_extract.json" )
							feat_extract_first.to_json_file(lowerCamelCase__ )
							_UpperCAmelCase		:       Tuple								=      self.feature_extraction_class.from_json_file(lowerCamelCase__ )

					_UpperCAmelCase		:       Tuple								=      feat_extract_first.to_dict()
					_UpperCAmelCase		:       Optional[Any]								=      feat_extract_second.to_dict()
					_UpperCAmelCase		:       Optional[int]								=      feat_extract_first.mel_filters
					_UpperCAmelCase		:       Tuple								=      feat_extract_second.mel_filters
					self.assertTrue(np.allclose(lowerCamelCase__	,  lowerCamelCase__ ) )
					self.assertEqual(lowerCamelCase__	,  lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     List[str] )   ->Dict:



					'''simple docstring'''







					_UpperCAmelCase		:       List[Any]								=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
					# create three inputs of length 800, 1000, and 1200
					_UpperCAmelCase		:       Union[str, Any]								=      [floats_list((1, x) )[0] for x in range(8_00	,  14_00	,  2_00 )]
					_UpperCAmelCase		:       Tuple								=      [np.asarray(lowerCamelCase__ ) for speech_input in speech_inputs]

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

					# Test batched
					_UpperCAmelCase		:       int								=      feature_extractor(lowerCamelCase__	,  return_tensors="np" ).input_features
					_UpperCAmelCase		:       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 2-D numpy arrays are batched.
					_UpperCAmelCase		:       List[Any]								=      [floats_list((1, x) )[0] for x in (8_00, 8_00, 8_00)]
					_UpperCAmelCase		:       Dict								=      np.asarray(lowerCamelCase__ )
					_UpperCAmelCase		:       Optional[int]								=      feature_extractor(lowerCamelCase__	,  return_tensors="np" ).input_features
					_UpperCAmelCase		:       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
					_UpperCAmelCase		:       Union[str, Any]								=      [floats_list((1, x) )[0] for x in range(2_00	,  (feature_extractor.n_samples + 5_00)	,  2_00 )]
					_UpperCAmelCase		:       Any								=      [np.asarray(lowerCamelCase__ ) for speech_input in speech_inputs]

					_UpperCAmelCase		:       str								=      [x[: feature_extractor.n_samples] for x in speech_inputs]
					_UpperCAmelCase		:       int								=      [np.asarray(lowerCamelCase__ ) for speech_input in speech_inputs_truncated]

					_UpperCAmelCase		:       List[str]								=      feature_extractor(lowerCamelCase__	,  return_tensors="np" ).input_features
					_UpperCAmelCase		:       Optional[Any]								=      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 							lowerCAmelCase__    (		self  :     List[Any] )   ->Optional[int]:



					'''simple docstring'''







					import torch

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

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




			def 							lowerCAmelCase__    (		self  :     str	,  lowerCamelCase__  :     Union[str, Any] )   ->Tuple:



					'''simple docstring'''







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

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




			def 							lowerCAmelCase__    (		self  :     Any )   ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[int]								=      torch.tensor(
					    [
					        0.1_1_9_3, -0.0_9_4_6, -0.1_0_9_8, -0.0_1_9_6, 0.0_2_2_5, -0.0_6_9_0, -0.1_7_3_6, 0.0_9_5_1,
					        0.0_9_7_1, -0.0_8_1_7, -0.0_7_0_2, 0.0_1_6_2, 0.0_2_6_0, 0.0_0_1_7, -0.0_1_9_2, -0.1_6_7_8,
					        0.0_7_0_9, -0.1_8_6_7, -0.0_6_5_5, -0.0_2_7_4, -0.0_2_3_4, -0.1_8_8_4, -0.0_5_1_6, -0.0_5_5_4,
					        -0.0_2_7_4, -0.1_4_2_5, -0.1_4_2_3, 0.0_8_3_7, 0.0_3_7_7, -0.0_8_5_4
					    ] )
					# fmt: on

					_UpperCAmelCase		:       Union[str, Any]								=      self._load_datasamples(1 )
					_UpperCAmelCase		:       int								=      WhisperFeatureExtractor()
					_UpperCAmelCase		:       List[Any]								=      feature_extractor(lowerCamelCase__	,  return_tensors="pt" ).input_features
					self.assertEqual(input_features.shape	,  (1, 80, 30_00) )
					self.assertTrue(torch.allclose(input_features[0, 0, :30]	,  lowerCamelCase__	,  atol=1E-4 ) )






			def 							lowerCAmelCase__    (		self  :     Dict )   ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:       List[str]								=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
					_UpperCAmelCase		:       List[str]								=      self._load_datasamples(1 )[0]
					_UpperCAmelCase		:       Optional[int]								=      ((audio - audio.min()) / (audio.max() - audio.min())) * 6_55_35  # Rescale to [0, 65535] to show issue
					_UpperCAmelCase		:       List[str]								=      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 __future__ import annotations

lowerCamelCase__    	=     {
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}






class 	lowerCAmelCase__			:




			def __init__(		self  :     int	,  lowerCamelCase__  :     dict[str, list[str]]	,  lowerCamelCase__  :     str )   ->None:



					'''simple docstring'''







					_UpperCAmelCase		:       Dict								=      graph
					# mapping node to its parent in resulting breadth first tree
					_UpperCAmelCase		:       dict[str, str | None]								=      {}
					_UpperCAmelCase		:       List[Any]								=      source_vertex




			def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->None:



					'''simple docstring'''







					_UpperCAmelCase		:       List[Any]								=      {self.source_vertex}
					_UpperCAmelCase		:       List[Any]								=      None
					_UpperCAmelCase		:       List[str]								=      [self.source_vertex]  # first in first out queue

					while queue:
							_UpperCAmelCase		:       int								=      queue.pop(0 )
							for adjacent_vertex in self.graph[vertex]:
									if adjacent_vertex not in visited:
											visited.add(lowerCamelCase__ )
											_UpperCAmelCase		:       List[Any]								=      vertex
											queue.append(lowerCamelCase__ )






			def 							lowerCAmelCase__    (		self  :     Tuple	,  lowerCamelCase__  :     str )   ->str:



					'''simple docstring'''







					if target_vertex == self.source_vertex:
							return self.source_vertex

					_UpperCAmelCase		:       int								=      self.parent.get(lowerCamelCase__ )
					if target_vertex_parent is None:
							_UpperCAmelCase		:       Tuple								=      (
							    F"""No path from vertex: {self.source_vertex} to vertex: {target_vertex}"""
							)
							raise ValueError(lowerCamelCase__ )

					return self.shortest_path(lowerCamelCase__ ) + F"""->{target_vertex}"""


if __name__ == "__main__":
						lowerCamelCase__    	=     Graph(graph, 'G')
						g.breath_first_search()
						print(g.shortest_path('D'))
						print(g.shortest_path('G'))
						print(g.shortest_path('Foo'))



