


import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
    import torch


__snake_case     =		random.Random()






def 						_A						(						_lowercase			,       _lowercase=1.0			,       _lowercase=None			,       _lowercase=None				)			->				Tuple:


     """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



@require_torch
class 					__lowerCamelCase	(unittest.TestCase		):







       def __init__(	self:				str,A_:				List[str],A_:				int=7,A_:				Union[str, Any]=400,A_:				Any=2000,A_:				Any=1,A_:				Union[str, Any]=0.0,A_:				Dict=2_4000,A_:				int=True,):






            '''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







       def 						snake_case_					(	self:				Tuple			):






            '''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,
            }







       def 						snake_case_					(	self:				List[Any],A_:				Optional[int]=False,A_:				List[str]=False			):






            '''simple docstring'''




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

            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(A_			) for x in audio_inputs]

            return audio_inputs



@require_torch
class 					__lowerCamelCase	(_a							,     unittest.TestCase		):
       _lowercase    							=      EncodecFeatureExtractor







       def 						snake_case_					(	self:				Optional[Any]			):






            '''simple docstring'''




            __UpperCamelCase				=	EnCodecFeatureExtractionTester(self			)







       def 						snake_case_					(	self:				Dict			):






            '''simple docstring'''




            __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(A_			) for audio_input in audio_inputs]

            # Test not batched input
            __UpperCamelCase				=	feat_extract(audio_inputs[0],return_tensors='np'			).input_values
            __UpperCamelCase				=	feat_extract(np_audio_inputs[0],return_tensors='np'			).input_values
            self.assertTrue(np.allclose(A_,A_,atol=1E-3			)			)

            # Test batched
            __UpperCamelCase				=	feat_extract(A_,padding=A_,return_tensors='np'			).input_values
            __UpperCamelCase				=	feat_extract(A_,padding=A_,return_tensors='np'			).input_values
            for enc_seq_a, enc_seq_a in zip(A_,A_			):
                 self.assertTrue(np.allclose(A_,A_,atol=1E-3			)			)







       def 						snake_case_					(	self:				int			):






            '''simple docstring'''




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

            for inputs in [py_audio_inputs, np_audio_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 						snake_case_					(	self:				Tuple,A_:				str			):






            '''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(A_			)			)[:num_samples]['audio']

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







       def 						snake_case_					(	self:				int			):






            '''simple docstring'''




            __UpperCamelCase				=	torch.tensor(
                [2.3_804E-03, 2.0_752E-03, 1.9_836E-03, 2.1_057E-03, 1.6_174E-03,
                 3.0_518E-04, 9.1_553E-05, 3.3_569E-04, 9.7_656E-04, 1.8_311E-03,
                 2.0_142E-03, 2.1_057E-03, 1.7_395E-03, 4.5_776E-04, -3.9_673E-04,
                 4.5_776E-04, 1.0_071E-03, 9.1_553E-05, 4.8_828E-04, 1.1_597E-03,
                 7.3_242E-04, 9.4_604E-04, 1.8_005E-03, 1.8_311E-03, 8.8_501E-04,
                 4.2_725E-04, 4.8_828E-04, 7.3_242E-04, 1.0_986E-03, 2.1_057E-03]			)
            # fmt: on
            __UpperCamelCase				=	self._load_datasamples(1			)
            __UpperCamelCase				=	EncodecFeatureExtractor()
            __UpperCamelCase				=	feature_extractor(A_,return_tensors='pt'			).input_values
            self.assertEquals(input_values.shape,(1, 1, 9_3680)			)
            self.assertTrue(torch.allclose(input_values[0, 0, :30],A_,atol=1E-6			)			)







       def 						snake_case_					(	self:				Optional[Any]			):






            '''simple docstring'''




            __UpperCamelCase				=	torch.tensor(
                [2.3_804E-03, 2.0_752E-03, 1.9_836E-03, 2.1_057E-03, 1.6_174E-03,
                 3.0_518E-04, 9.1_553E-05, 3.3_569E-04, 9.7_656E-04, 1.8_311E-03,
                 2.0_142E-03, 2.1_057E-03, 1.7_395E-03, 4.5_776E-04, -3.9_673E-04,
                 4.5_776E-04, 1.0_071E-03, 9.1_553E-05, 4.8_828E-04, 1.1_597E-03,
                 7.3_242E-04, 9.4_604E-04, 1.8_005E-03, 1.8_311E-03, 8.8_501E-04,
                 4.2_725E-04, 4.8_828E-04, 7.3_242E-04, 1.0_986E-03, 2.1_057E-03]			)
            # fmt: on
            __UpperCamelCase				=	self._load_datasamples(1			)
            __UpperCamelCase				=	[np.tile(input_audio[0][None],reps=(2, 1)			)]
            input_audio[0][1] *= 0.5
            __UpperCamelCase				=	EncodecFeatureExtractor(feature_size=2			)
            __UpperCamelCase				=	feature_extractor(A_,return_tensors='pt'			).input_values
            self.assertEquals(input_values.shape,(1, 2, 9_3680)			)
            self.assertTrue(torch.allclose(input_values[0, 0, :30],A_,atol=1E-6			)			)
            self.assertTrue(torch.allclose(input_values[0, 1, :30],EXPECTED_INPUT_VALUES * 0.5,atol=1E-6			)			)







       def 						snake_case_					(	self:				str			):






            '''simple docstring'''




            __UpperCamelCase				=	self._load_datasamples(2			)
            # would be easier if the stride was like
            __UpperCamelCase				=	EncodecFeatureExtractor(feature_size=1,chunk_length_s=1,overlap=0.0_1			)

            # pad and trunc raise an error ?
            with self.assertRaisesRegex(
                A_,'^Both padding and truncation were set. Make sure you only set one.$',):
                 __UpperCamelCase				=	feature_extractor(
                     A_,padding='max_length',truncation=A_,return_tensors='pt'			).input_values

            # truncate to chunk
            __UpperCamelCase				=	feature_extractor(A_,truncation=A_,return_tensors='pt'			).input_values
            self.assertEquals(truncated_outputs.shape,(2, 1, 7_1520)			)  # 2 chunks

            # force truncate to max_length
            __UpperCamelCase				=	feature_extractor(
                A_,truncation=A_,max_length=4_8000,return_tensors='pt'			).input_values
            self.assertEquals(truncated_outputs.shape,(2, 1, 4_8000)			)

            # pad to chunk
            __UpperCamelCase				=	feature_extractor(A_,padding=A_,return_tensors='pt'			).input_values
            self.assertEquals(padded_outputs.shape,(2, 1, 9_5280)			)

            # pad to chunk
            __UpperCamelCase				=	feature_extractor(A_,return_tensors='pt'			).input_values
            self.assertEquals(truncated_outputs.shape,(2, 1, 9_5280)			)

            # force pad to max length
            __UpperCamelCase				=	feature_extractor(
                A_,padding='max_length',max_length=10_0000,return_tensors='pt'			).input_values
            self.assertEquals(truncated_outputs.shape,(2, 1, 10_0000)			)

            # force no pad
            with self.assertRaisesRegex(
                A_,'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$',):
                 __UpperCamelCase				=	feature_extractor(A_,padding=A_,return_tensors='pt'			).input_values

            __UpperCamelCase				=	feature_extractor(input_audio[0],padding=A_,return_tensors='pt'			).input_values
            self.assertEquals(truncated_outputs.shape,(1, 1, 9_3680)			)

            # no pad if no chunk_length_s
            __UpperCamelCase				=	None
            with self.assertRaisesRegex(
                A_,'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$',):
                 __UpperCamelCase				=	feature_extractor(A_,padding=A_,return_tensors='pt'			).input_values

            __UpperCamelCase				=	feature_extractor(input_audio[0],padding=A_,return_tensors='pt'			).input_values
            self.assertEquals(truncated_outputs.shape,(1, 1, 9_3680)			)

            # no pad if no overlap
            __UpperCamelCase				=	2
            __UpperCamelCase				=	None
            with self.assertRaisesRegex(
                A_,'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$',):
                 __UpperCamelCase				=	feature_extractor(A_,padding=A_,return_tensors='pt'			).input_values

            __UpperCamelCase				=	feature_extractor(input_audio[0],padding=A_,return_tensors='pt'			).input_values
            self.assertEquals(truncated_outputs.shape,(1, 1, 9_3680)			)






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__snake_case     =		{
    '''configuration_convnextv2''': [
        '''CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ConvNextV2Config''',
    ]
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case     =		[
        '''CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''ConvNextV2ForImageClassification''',
        '''ConvNextV2Model''',
        '''ConvNextV2PreTrainedModel''',
        '''ConvNextV2Backbone''',
    ]


if TYPE_CHECKING:
    from .configuration_convnextva import (
        CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
        ConvNextVaConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_convnextva import (
            CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
            ConvNextVaBackbone,
            ConvNextVaForImageClassification,
            ConvNextVaModel,
            ConvNextVaPreTrainedModel,
        )

else:
    import sys

    __snake_case     =		_LazyModule(__name__, globals()['''__file__'''], _import_structure)


