


'''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					:				Tuple									=		random.Random()

if is_torch_available():
							import torch







def       a_				(	UpperCamelCase_						,  UpperCamelCase_=1.0						,  UpperCamelCase_=None						,  UpperCamelCase_=None ):
							if rng is None:
														A_			    =		global_rng

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

							return values






class 					__lowerCAmelCase   (   unittest.TestCase						):

						"""simple docstring"""






						def __init__(  self							:      str ,  lowerCAmelCase							:      List[str] ,  lowerCAmelCase							:      str=7 ,  lowerCAmelCase							:      str=4_00 ,  lowerCAmelCase							:      str=20_00 ,  lowerCAmelCase							:      Tuple=1 ,  lowerCAmelCase							:      Optional[int]=0.0 ,  lowerCAmelCase							:      Any=1_60_00 ,  lowerCAmelCase							:      Tuple=True ,  lowerCAmelCase							:      Tuple=True ,  ):
													A_			    =		parent
													A_			    =		batch_size
													A_			    =		min_seq_length
													A_			    =		max_seq_length
													A_			    =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
													A_			    =		feature_size
													A_			    =		padding_value
													A_			    =		sampling_rate
													A_			    =		return_attention_mask
													A_			    =		do_normalize






						def 				_UpperCAmelCase       (  self							:      str	):
													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							:      int ,  lowerCAmelCase							:      int=False ,  lowerCAmelCase							:      Optional[int]=False	):
													def _flatten(lowerCAmelCase							:      List[Any]	):
																				return list(itertools.chain(*lowerCAmelCase	)	)

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

													return speech_inputs






@require_torch
@require_torchaudio
class 					__lowerCAmelCase   (   lowercase				,			unittest.TestCase						):

						"""simple docstring"""






						_UpperCAmelCase		:       List[str]   						=ASTFeatureExtractor






						def 				_UpperCAmelCase       (  self							:      int	):
													A_			    =		ASTFeatureExtractionTester(self	)






						def 				_UpperCAmelCase       (  self							:      str	):
													# Tests that all call wrap to encode_plus and batch_encode_plus
													A_			    =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
													# create three inputs of length 800, 1000, and 1200
													A_			    =		[floats_list((1, x)	)[0] for x in range(8_00 ,  14_00 ,  2_00	)]
													A_			    =		[np.asarray(lowerCAmelCase	) for speech_input in speech_inputs]

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

													# Test batched
													A_			    =		feat_extract(lowerCAmelCase ,  padding=lowerCAmelCase ,  return_tensors="np"	).input_values
													A_			    =		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	)	)

													# Test 2-D numpy arrays are batched.
													A_			    =		[floats_list((1, x)	)[0] for x in (8_00, 8_00, 8_00)]
													A_			    =		np.asarray(lowerCAmelCase	)
													A_			    =		feat_extract(lowerCAmelCase ,  return_tensors="np"	).input_values
													A_			    =		feat_extract(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	)	)






						@require_torch
						def 				_UpperCAmelCase       (  self							:      List[str]	):
													import torch

													A_			    =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
													A_			    =		np.random.rand(1_00	).astype(np.floataa	)
													A_			    =		np_speech_inputs.tolist()

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






						def 				_UpperCAmelCase       (  self							:      List[Any] ,  lowerCAmelCase							:      Optional[Any]	):
													from datasets import load_dataset

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

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






						@require_torch
						def 				_UpperCAmelCase       (  self							:      int	):
													# fmt: off
													A_			    =		torch.tensor(
													    [-0.9_8_9_4, -1.2_7_7_6, -0.9_0_6_6, -1.2_7_7_6, -0.9_3_4_9, -1.2_6_0_9, -1.0_3_8_6, -1.2_7_7_6,
													     -1.1_5_6_1, -1.2_7_7_6, -1.2_0_5_2, -1.2_7_2_3, -1.2_1_9_0, -1.2_1_3_2, -1.2_7_7_6, -1.1_1_3_3,
													     -1.1_9_5_3, -1.1_3_4_3, -1.1_5_8_4, -1.2_2_0_3, -1.1_7_7_0, -1.2_4_7_4, -1.2_3_8_1, -1.1_9_3_6,
													     -0.9_2_7_0, -0.8_3_1_7, -0.8_0_4_9, -0.7_7_0_6, -0.7_5_6_5, -0.7_8_6_9]	)
													# fmt: on

													A_			    =		self._load_datasamples(1	)
													A_			    =		ASTFeatureExtractor()
													A_			    =		feature_extractor(lowerCAmelCase ,  return_tensors="pt"	).input_values
													self.assertEquals(input_values.shape ,  (1, 10_24, 1_28)	)
													self.assertTrue(torch.allclose(input_values[0, 0, :30] ,  lowerCAmelCase ,  atol=1e-4	)	)









'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__SCREAMING_SNAKE_CASE					:				List[str]									=		{
    '''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE					:				Optional[Any]									=		[
							    '''GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''GraphormerForGraphClassification''',
							    '''GraphormerModel''',
							    '''GraphormerPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_graphormer import (
														    GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GraphormerForGraphClassification,
														    GraphormerModel,
														    GraphormerPreTrainedModel,
														)


else:
							import sys

							__SCREAMING_SNAKE_CASE					:				Optional[int]									=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





