



import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
     from transformers import SpeechaTextFeatureExtractor

SCREAMING_SNAKE_CASE_:Tuple					=						random.Random()




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase=1.0				,					_lowerCAmelCase=None				,					_lowerCAmelCase=None		)	->  Union[str, Any]:



    """simple docstring"""






    if rng is None:
        A			:	int										=       global_rng

    A			:	Tuple										=       []
    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   ):




    '''simple docstring'''






    def __init__(	self,		lowerCamelCase__,		lowerCamelCase__=7,		lowerCamelCase__=400,		lowerCamelCase__=2000,		lowerCamelCase__=24,		lowerCamelCase__=24,		lowerCamelCase__=0.0,		lowerCamelCase__=1_6000,		lowerCamelCase__=True,		lowerCamelCase__=True,		):
        A			:	Optional[Any]										=       parent
        A			:	Optional[Any]										=       batch_size
        A			:	int										=       min_seq_length
        A			:	Union[str, Any]										=       max_seq_length
        A			:	int										=       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        A			:	Dict										=       feature_size
        A			:	Any										=       num_mel_bins
        A			:	int										=       padding_value
        A			:	int										=       sampling_rate
        A			:	int										=       return_attention_mask
        A			:	List[str]										=       do_normalize






    def       _lowerCAmelCase (	self		):
        return {
            "feature_size": self.feature_size,
            "num_mel_bins": self.num_mel_bins,
            "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:
            A			:	Optional[Any]										=       [floats_list((self.max_seq_length, self.feature_size)		) for _ in range(self.batch_size		)]
        else:
            # make sure that inputs increase in size
            A			:	List[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:
            A			:	Dict										=       [np.asarray(lowerCamelCase__		) for x in speech_inputs]
        return speech_inputs






@require_torch
@require_torchaudio
class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__			,						unittest.TestCase   ):




    '''simple docstring'''






    __lowerCamelCase      :			Optional[Any] =				SpeechaTextFeatureExtractor if is_speech_available() else None






    def       _lowerCAmelCase (	self		):
        A			:	List[str]										=       SpeechaTextFeatureExtractionTester(self		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        self.assertTrue(np.all(np.mean(lowerCamelCase__,		axis=0		) < 1e-3		)		)
        self.assertTrue(np.all(np.abs(np.var(lowerCamelCase__,		axis=0		) - 1		) < 1e-3		)		)






    def       _lowerCAmelCase (	self		):
        # Tests that all call wrap to encode_plus and batch_encode_plus
        A			:	str										=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
        # create three inputs of length 800, 1000, and 1200
        A			:	Optional[Any]										=       [floats_list((1, x)		)[0] for x in range(800,		1400,		200		)]
        A			:	Optional[int]										=       [np.asarray(lowerCamelCase__		) for speech_input in speech_inputs]

        # Test feature size
        A			:	List[Any]										=       feature_extractor(lowerCamelCase__,		padding=lowerCamelCase__,		return_tensors="""np"""		).input_features
        self.assertTrue(input_features.ndim == 3		)
        self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size		)

        # Test not batched input
        A			:	Union[str, Any]										=       feature_extractor(speech_inputs[0],		return_tensors="""np"""		).input_features
        A			:	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
        A			:	int										=       feature_extractor(lowerCamelCase__,		return_tensors="""np"""		).input_features
        A			:	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.
        A			:	Union[str, Any]										=       [floats_list((1, x)		)[0] for x in (800, 800, 800)]
        A			:	Any										=       np.asarray(lowerCamelCase__		)
        A			:	Dict										=       feature_extractor(lowerCamelCase__,		return_tensors="""np"""		).input_features
        A			:	Tuple										=       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		):
        A			:	Optional[int]										=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
        A			:	List[Any]										=       [floats_list((1, x)		)[0] for x in range(800,		1400,		200		)]

        A			:	Dict										=       ["""longest""", """max_length""", """do_not_pad"""]
        A			:	Optional[int]										=       [None, 16, None]
        for max_length, padding in zip(lowerCamelCase__,		lowerCamelCase__		):
            A			:	Tuple										=       feature_extractor(
                lowerCamelCase__,		padding=lowerCamelCase__,		max_length=lowerCamelCase__,		return_attention_mask=lowerCamelCase__		)
            A			:	Tuple										=       inputs.input_features
            A			:	List[str]										=       inputs.attention_mask
            A			:	Any										=       [np.sum(lowerCamelCase__		) for x in attention_mask]

            self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]		)
            self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]		)
            self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]		)






    def       _lowerCAmelCase (	self		):
        A			:	int										=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
        A			:	Union[str, Any]										=       [floats_list((1, x)		)[0] for x in range(800,		1400,		200		)]

        A			:	Tuple										=       ["""longest""", """max_length""", """do_not_pad"""]
        A			:	Union[str, Any]										=       [None, 16, None]
        for max_length, padding in zip(lowerCamelCase__,		lowerCamelCase__		):
            A			:	int										=       feature_extractor(
                lowerCamelCase__,		max_length=lowerCamelCase__,		padding=lowerCamelCase__,		return_tensors="""np""",		return_attention_mask=lowerCamelCase__		)
            A			:	Optional[int]										=       inputs.input_features
            A			:	int										=       inputs.attention_mask
            A			:	List[Any]										=       [np.sum(lowerCamelCase__		) for x in attention_mask]

            self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]		)
            self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6		)
            self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]		)
            self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6		)
            self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]		)






    def       _lowerCAmelCase (	self		):
        A			:	List[str]										=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
        A			:	Any										=       [floats_list((1, x)		)[0] for x in range(800,		1400,		200		)]
        A			:	Dict										=       feature_extractor(
            lowerCamelCase__,		padding="""max_length""",		max_length=4,		truncation=lowerCamelCase__,		return_tensors="""np""",		return_attention_mask=lowerCamelCase__,		)
        A			:	Union[str, Any]										=       inputs.input_features
        A			:	Tuple										=       inputs.attention_mask
        A			:	List[Any]										=       np.sum(attention_mask == 1,		axis=1		)

        self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]		)
        self._check_zero_mean_unit_variance(input_features[1]		)
        self._check_zero_mean_unit_variance(input_features[2]		)






    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
        A			:	Tuple										=       [floats_list((1, x)		)[0] for x in range(800,		1400,		200		)]
        A			:	Any										=       feature_extractor(
            lowerCamelCase__,		padding="""longest""",		max_length=4,		truncation=lowerCamelCase__,		return_tensors="""np""",		return_attention_mask=lowerCamelCase__,		)
        A			:	Optional[Any]										=       inputs.input_features
        A			:	Optional[int]										=       inputs.attention_mask
        A			:	Tuple										=       np.sum(attention_mask == 1,		axis=1		)

        self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]		)
        self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]		)
        self._check_zero_mean_unit_variance(input_features[2]		)

        # make sure that if max_length < longest -> then pad to max_length
        self.assertEqual(input_features.shape,		(3, 4, 24)		)

        A			:	List[str]										=       [floats_list((1, x)		)[0] for x in range(800,		1400,		200		)]
        A			:	Any										=       feature_extractor(
            lowerCamelCase__,		padding="""longest""",		max_length=16,		truncation=lowerCamelCase__,		return_tensors="""np""",		return_attention_mask=lowerCamelCase__,		)
        A			:	int										=       inputs.input_features
        A			:	Optional[int]										=       inputs.attention_mask
        A			:	List[Any]										=       np.sum(attention_mask == 1,		axis=1		)

        self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]		)
        self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]		)
        self._check_zero_mean_unit_variance(input_features[2]		)

        # make sure that if max_length < longest -> then pad to max_length
        self.assertEqual(input_features.shape,		(3, 6, 24)		)






    def       _lowerCAmelCase (	self		):
        import torch

        A			:	Any										=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
        A			:	Dict										=       np.random.rand(100,		32		).astype(np.floataa		)
        A			:	List[Any]										=       np_speech_inputs.tolist()

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






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        from datasets import load_dataset

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

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






    def       _lowerCAmelCase (	self		):
        # fmt: off
        A			:	Optional[int]										=       np.array([
            -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
            -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
            -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
        ]		)
        # fmt: on

        A			:	Optional[Any]										=       self._load_datasamples(1		)
        A			:	int										=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
        A			:	List[Any]										=       feature_extractor(lowerCamelCase__,		return_tensors="""pt"""		).input_features
        self.assertEquals(input_features.shape,		(1, 584, 24)		)
        self.assertTrue(np.allclose(input_features[0, 0, :30],		lowerCamelCase__,		atol=1e-4		)		)







import os
from pathlib import Path




def      __UpperCamelCase     (  )	->  Any:



    """simple docstring"""






    from torch.utils.cpp_extension import load

    A			:	Any										=       Path(_lowerCAmelCase		).resolve().parent.parent.parent / """kernels""" / """deformable_detr"""
    A			:	int										=       [
        root / filename
        for filename in [
            """vision.cpp""",
            os.path.join("""cpu"""				,					"""ms_deform_attn_cpu.cpp"""		),
            os.path.join("""cuda"""				,					"""ms_deform_attn_cuda.cu"""		),
        ]
    ]

    load(
        """MultiScaleDeformableAttention"""				,					_lowerCAmelCase				,					with_cuda=_lowerCAmelCase				,					extra_include_paths=[str(_lowerCAmelCase		)]				,					extra_cflags=["""-DWITH_CUDA=1"""]				,					extra_cuda_cflags=[
            """-DCUDA_HAS_FP16=1""",
            """-D__CUDA_NO_HALF_OPERATORS__""",
            """-D__CUDA_NO_HALF_CONVERSIONS__""",
            """-D__CUDA_NO_HALF2_OPERATORS__""",
        ]				,					)

    import MultiScaleDeformableAttention as MSDA

    return MSDA


