
"""simple docstring"""


import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


SCREAMING_SNAKE_CASE      =     random.Random()

if is_torch_available():
      import torch



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_=1.0		,      UpperCAmelCase_=None		,      UpperCAmelCase_=None				)->							Any:







       """simple docstring"""
       if rng is None:
              UpperCamelCase					 =		global_rng

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

       return values



class   __a  (      unittest.TestCase	):






       def __init__(  self :							int       ,      UpperCAmelCase_ :							Any       ,      UpperCAmelCase_ :							Optional[int]=7       ,      UpperCAmelCase_ :							Optional[int]=400       ,      UpperCAmelCase_ :							Union[str, Any]=2_000       ,      UpperCAmelCase_ :							Tuple=1       ,      UpperCAmelCase_ :							List[Any]=0.0       ,      UpperCAmelCase_ :							Any=16_000       ,      UpperCAmelCase_ :							List[str]=True       ,      UpperCAmelCase_ :							Union[str, Any]=True       ,      )->  Union[str, Any]:







              """simple docstring"""
              UpperCamelCase					 =		parent
              UpperCamelCase					 =		batch_size
              UpperCamelCase					 =		min_seq_length
              UpperCamelCase					 =		max_seq_length
              UpperCamelCase					 =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
              UpperCamelCase					 =		feature_size
              UpperCamelCase					 =		padding_value
              UpperCamelCase					 =		sampling_rate
              UpperCamelCase					 =		return_attention_mask
              UpperCamelCase					 =		do_normalize






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







              """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 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]       ,      UpperCAmelCase_ :							int=False       ,      UpperCAmelCase_ :							str=False			)->  Tuple:







              """simple docstring"""
              def _flatten(UpperCAmelCase_ :							List[Any]			):
                     return list(itertools.chain(*UpperCAmelCase_			)			)

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

              return speech_inputs





@require_torch
@require_torchaudio
class   __a  (      _lowerCAmelCase    ,     unittest.TestCase	):
       UpperCamelCase_		:					int   =			ASTFeatureExtractor






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







              """simple docstring"""
              UpperCamelCase					 =		ASTFeatureExtractionTester(self			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  Tuple:







              """simple docstring"""
              # Tests that all call wrap to encode_plus and batch_encode_plus
              UpperCamelCase					 =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
              # create three inputs of length 800, 1000, and 1200
              UpperCamelCase					 =		[floats_list((1, x)			)[0] for x in range(800       ,      1_400       ,      200			)]
              UpperCamelCase					 =		[np.asarray(UpperCAmelCase_			) for speech_input in speech_inputs]

              # Test not batched input
              UpperCamelCase					 =		feat_extract(speech_inputs[0]       ,      return_tensors="np"			).input_values
              UpperCamelCase					 =		feat_extract(np_speech_inputs[0]       ,      return_tensors="np"			).input_values
              self.assertTrue(np.allclose(UpperCAmelCase_       ,      UpperCAmelCase_       ,      atol=1e-3			)			)

              # Test batched
              UpperCamelCase					 =		feat_extract(UpperCAmelCase_       ,      padding=UpperCAmelCase_       ,      return_tensors="np"			).input_values
              UpperCamelCase					 =		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			)			)

              # Test 2-D numpy arrays are batched.
              UpperCamelCase					 =		[floats_list((1, x)			)[0] for x in (800, 800, 800)]
              UpperCamelCase					 =		np.asarray(UpperCAmelCase_			)
              UpperCamelCase					 =		feat_extract(UpperCAmelCase_       ,      return_tensors="np"			).input_values
              UpperCamelCase					 =		feat_extract(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			)			)






       @require_torch
       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  Optional[int]:







              """simple docstring"""
              import torch

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

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






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple       ,      UpperCAmelCase_ :							Any			)->  Optional[Any]:







              """simple docstring"""
              from datasets import load_dataset

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

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






       @require_torch
       def 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  Optional[int]:







              """simple docstring"""
              # fmt: off
              UpperCamelCase					 =		torch.tensor(
                  [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
                   -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
                   -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
                   -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869]			)
              # fmt: on

              UpperCamelCase					 =		self._load_datasamples(1			)
              UpperCamelCase					 =		ASTFeatureExtractor()
              UpperCamelCase					 =		feature_extractor(UpperCAmelCase_       ,      return_tensors="pt"			).input_values
              self.assertEquals(input_values.shape       ,      (1, 1_024, 128)			)
              self.assertTrue(torch.allclose(input_values[0, 0, :30]       ,      UpperCAmelCase_       ,      atol=1e-4			)			)







"""simple docstring"""


from __future__ import annotations

from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE      =     TypeVar("""T""")



class   __a  (      Generic[T]	):






       def __init__(  self :							List[Any]       ,      UpperCAmelCase_ :							T			)->  None:







              """simple docstring"""
              UpperCamelCase					 =		data
              UpperCamelCase					 =		self
              UpperCamelCase					 =		0





class   __a  (      Generic[T]	):






       def __init__(  self :							int			)->  None:







              """simple docstring"""
              # map from node name to the node object
              UpperCamelCase					 =		{}






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]       ,      UpperCAmelCase_ :							T			)->  None:







              """simple docstring"""
              # create a new set with x as its member
              UpperCamelCase					 =		DisjointSetTreeNode(UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]       ,      UpperCAmelCase_ :							T			)->  DisjointSetTreeNode[T]:







              """simple docstring"""
              # find the set x belongs to (with path-compression)
              UpperCamelCase					 =		self.map[data]
              if elem_ref != elem_ref.parent:
                     UpperCamelCase					 =		self.find_set(elem_ref.parent.data			)
              return elem_ref.parent






       def 	_SCREAMING_SNAKE_CASE       (  self :							Any       ,      UpperCAmelCase_ :							DisjointSetTreeNode[T]       ,      UpperCAmelCase_ :							DisjointSetTreeNode[T]			)->  None:







              """simple docstring"""
              # helper function for union operation
              if nodea.rank > nodea.rank:
                     UpperCamelCase					 =		nodea
              else:
                     UpperCamelCase					 =		nodea
                     if nodea.rank == nodea.rank:
                            nodea.rank += 1






       def 	_SCREAMING_SNAKE_CASE       (  self :							int       ,      UpperCAmelCase_ :							T       ,      UpperCAmelCase_ :							T			)->  None:







              """simple docstring"""
              # merge 2 disjoint sets
              self.link(self.find_set(UpperCAmelCase_			)       ,      self.find_set(UpperCAmelCase_			)			)





class   __a  (      Generic[T]	):






       def __init__(  self :							Dict			)->  None:







              """simple docstring"""
              # connections: map from the node to the neighbouring nodes (with weights)
              UpperCamelCase					 =		{}






       def 	_SCREAMING_SNAKE_CASE       (  self :							Any       ,      UpperCAmelCase_ :							T			)->  None:







              """simple docstring"""
              # add a node ONLY if its not present in the graph
              if node not in self.connections:
                     UpperCamelCase					 =		{}






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]       ,      UpperCAmelCase_ :							T       ,      UpperCAmelCase_ :							T       ,      UpperCAmelCase_ :							int			)->  None:







              """simple docstring"""
              # add an edge with the given weight
              self.add_node(UpperCAmelCase_			)
              self.add_node(UpperCAmelCase_			)
              UpperCamelCase					 =		weight
              UpperCamelCase					 =		weight






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  GraphUndirectedWeighted[T]:







              """simple docstring"""
              UpperCamelCase					 =		[]
              UpperCamelCase					 =		set()
              for start in self.connections:
                     for end in self.connections[start]:
                            if (start, end) not in seen:
                                   seen.add((end, start)			)
                                   edges.append((start, end, self.connections[start][end])			)
              edges.sort(key=lambda UpperCAmelCase_			: x[2]			)

              # creating the disjoint set
              UpperCamelCase					 =		DisjointSetTree[T]()
              for node in self.connections:
                     disjoint_set.make_set(UpperCAmelCase_			)

              # MST generation
              UpperCamelCase					 =		0
              UpperCamelCase					 =		0
              UpperCamelCase					 =		GraphUndirectedWeighted[T]()
              while num_edges < len(self.connections			) - 1:
                     UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase					 =		edges[index]
                     index += 1
                     UpperCamelCase					 =		disjoint_set.find_set(UpperCAmelCase_			)
                     UpperCamelCase					 =		disjoint_set.find_set(UpperCAmelCase_			)
                     if parent_u != parent_v:
                            num_edges += 1
                            graph.add_edge(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_			)
                            disjoint_set.union(UpperCAmelCase_       ,      UpperCAmelCase_			)
              return graph





