





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

UpperCamelCase__     =	random.Random()





def       _UpperCamelCase       (a__				:Any				,     a__				:Union[str, Any]=1.0				,     a__				:Tuple=None				,     a__				:str=None    ):





							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

@require_torch
@require_torchaudio
class     __SCREAMING_SNAKE_CASE   (       unittest.TestCase    ):






							def __init__(  self					,	__lowerCAmelCase					,	__lowerCAmelCase=7					,	__lowerCAmelCase=400					,	__lowerCAmelCase=2000					,	__lowerCAmelCase=10					,	__lowerCAmelCase=160					,	__lowerCAmelCase=8					,	__lowerCAmelCase=0.0					,	__lowerCAmelCase=4000					,	__lowerCAmelCase=False					,	__lowerCAmelCase=True					,	):
														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__			       =     padding_value
														UpperCamelCase__			       =     sampling_rate
														UpperCamelCase__			       =     return_attention_mask
														UpperCamelCase__			       =     do_normalize
														UpperCamelCase__			       =     feature_size
														UpperCamelCase__			       =     chunk_length
														UpperCamelCase__			       =     hop_length






							def 			_lowerCamelCase						(  self					):
														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					,	__lowerCAmelCase=False					,	__lowerCAmelCase=False					):
														def _flatten(__lowerCAmelCase					):
																					return list(itertools.chain(*__lowerCAmelCase					)					)

														if equal_length:
																					UpperCamelCase__			       =     [floats_list((self.max_seq_length, self.feature_size)					) for _ in range(self.batch_size					)]
														else:
																					# make sure that inputs increase in size
																					UpperCamelCase__			       =     [
																					    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(__lowerCAmelCase					) for x in speech_inputs]
														return speech_inputs






@require_torch
@require_torchaudio
class     __SCREAMING_SNAKE_CASE   (       _a				,						unittest.TestCase    ):
							snake_case       : int         =    WhisperFeatureExtractor if is_speech_available() else None






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     WhisperFeatureExtractionTester(self					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.feature_extraction_class(**self.feat_extract_dict					)

														with tempfile.TemporaryDirectory() as tmpdirname:
																					UpperCamelCase__			       =     feat_extract_first.save_pretrained(__lowerCAmelCase					)[0]
																					check_json_file_has_correct_format(__lowerCAmelCase					)
																					UpperCamelCase__			       =     self.feature_extraction_class.from_pretrained(__lowerCAmelCase					)

														UpperCamelCase__			       =     feat_extract_first.to_dict()
														UpperCamelCase__			       =     feat_extract_second.to_dict()
														UpperCamelCase__			       =     feat_extract_first.mel_filters
														UpperCamelCase__			       =     feat_extract_second.mel_filters
														self.assertTrue(np.allclose(__lowerCAmelCase					,	__lowerCAmelCase					)					)
														self.assertEqual(__lowerCAmelCase					,	__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.feature_extraction_class(**self.feat_extract_dict					)

														with tempfile.TemporaryDirectory() as tmpdirname:
																					UpperCamelCase__			       =     os.path.join(__lowerCAmelCase					,	"""feat_extract.json"""					)
																					feat_extract_first.to_json_file(__lowerCAmelCase					)
																					UpperCamelCase__			       =     self.feature_extraction_class.from_json_file(__lowerCAmelCase					)

														UpperCamelCase__			       =     feat_extract_first.to_dict()
														UpperCamelCase__			       =     feat_extract_second.to_dict()
														UpperCamelCase__			       =     feat_extract_first.mel_filters
														UpperCamelCase__			       =     feat_extract_second.mel_filters
														self.assertTrue(np.allclose(__lowerCAmelCase					,	__lowerCAmelCase					)					)
														self.assertEqual(__lowerCAmelCase					,	__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														# 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					,	1400					,	200					)]
														UpperCamelCase__			       =     [np.asarray(__lowerCAmelCase					) for speech_input in speech_inputs]

														# Test feature size
														UpperCamelCase__			       =     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__			       =     feature_extractor(speech_inputs[0]					,	return_tensors="""np"""					).input_features
														UpperCamelCase__			       =     feature_extractor(np_speech_inputs[0]					,	return_tensors="""np"""					).input_features
														self.assertTrue(np.allclose(__lowerCAmelCase					,	__lowerCAmelCase					,	atol=1E-3					)					)

														# Test batched
														UpperCamelCase__			       =     feature_extractor(__lowerCAmelCase					,	return_tensors="""np"""					).input_features
														UpperCamelCase__			       =     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__			       =     [floats_list((1, x)					)[0] for x in (800, 800, 800)]
														UpperCamelCase__			       =     np.asarray(__lowerCAmelCase					)
														UpperCamelCase__			       =     feature_extractor(__lowerCAmelCase					,	return_tensors="""np"""					).input_features
														UpperCamelCase__			       =     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__			       =     [floats_list((1, x)					)[0] for x in range(200					,	(feature_extractor.n_samples + 500)					,	200					)]
														UpperCamelCase__			       =     [np.asarray(__lowerCAmelCase					) for speech_input in speech_inputs]

														UpperCamelCase__			       =     [x[: feature_extractor.n_samples] for x in speech_inputs]
														UpperCamelCase__			       =     [np.asarray(__lowerCAmelCase					) for speech_input in speech_inputs_truncated]

														UpperCamelCase__			       =     feature_extractor(__lowerCAmelCase					,	return_tensors="""np"""					).input_features
														UpperCamelCase__			       =     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					):
														import torch

														UpperCamelCase__			       =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
														UpperCamelCase__			       =     np.random.rand(100					,	32					).astype(np.floataa					)
														UpperCamelCase__			       =     np_speech_inputs.tolist()

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






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

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






							def 			_lowerCamelCase						(  self					):
														# fmt: off
														UpperCamelCase__			       =     torch.tensor(
														    [
														        0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
														        0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
														        0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
														        -0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
														    ]					)
														# fmt: on

														UpperCamelCase__			       =     self._load_datasamples(1					)
														UpperCamelCase__			       =     WhisperFeatureExtractor()
														UpperCamelCase__			       =     feature_extractor(__lowerCAmelCase					,	return_tensors="""pt"""					).input_features
														self.assertEqual(input_features.shape					,	(1, 80, 3000)					)
														self.assertTrue(torch.allclose(input_features[0, 0, :30]					,	__lowerCAmelCase					,	atol=1E-4					)					)







							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
														UpperCamelCase__			       =     self._load_datasamples(1					)[0]
														UpperCamelCase__			       =     ((audio - audio.min()) / (audio.max() - audio.min())) * 65535  # Rescale to [0, 65535] to show issue
														UpperCamelCase__			       =     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					)					)











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCamelCase__     =	{
    "configuration_swiftformer": [
        "SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SwiftFormerConfig",
        "SwiftFormerOnnxConfig",
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__     =	[
					    "SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "SwiftFormerForImageClassification",
					    "SwiftFormerModel",
					    "SwiftFormerPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_swiftformer import (
					    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    SwiftFormerConfig,
					    SwiftFormerOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_swiftformer import (
										    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SwiftFormerForImageClassification,
										    SwiftFormerModel,
										    SwiftFormerPreTrainedModel,
										)

else:
					import sys

					UpperCamelCase__     =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




