
"""simple docstring"""
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


lowercase		:						Optional[int] =					random.Random()


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_=1.0	,				lowerCamelCase_=None	,				lowerCamelCase_=None )     ->			Any:


     '''simple docstring'''
     if rng is None:
          __a:						int		    =					global_rng

     __a:						Optional[Any]		    =					[]
     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    _snake_case  (						unittest.TestCase       ):






       def __init__(					self						:			Any						, snake_case__						:			Union[str, Any]						, snake_case__						:			Tuple=7						, snake_case__						:			Optional[Any]=4_0_0						, snake_case__						:			Dict=2_0_0_0						, snake_case__						:			List[str]=1						, snake_case__						:			List[str]=0.0						, snake_case__						:			Tuple=2_4_0_0_0						, snake_case__						:			Tuple=True						, ):






            """simple docstring"""



            __a:						Any		    =					parent
            __a:						Optional[Any]		    =					batch_size
            __a:						Optional[Any]		    =					min_seq_length
            __a:						Any		    =					max_seq_length
            __a:						Union[str, Any]		    =					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
            __a:						List[Any]		    =					feature_size
            __a:						Any		    =					padding_value
            __a:						Tuple		    =					sampling_rate
            __a:						Optional[Any]		    =					return_attention_mask






       def 							__lowercase  (					self						:			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,
            }






       def 							__lowercase  (					self						:			Tuple						, snake_case__						:			Union[str, Any]=False						, snake_case__						:			int=False    ):






            """simple docstring"""



            def _flatten(snake_case__						:			Optional[Any]    ):
                 return list(itertools.chain(*snake_case__    )    )

            if equal_length:
                 __a:						Tuple		    =					floats_list((self.batch_size, self.max_seq_length)    )
            else:
                 # make sure that inputs increase in size
                 __a:						int		    =					[
                     _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:						Union[str, Any]		    =					[np.asarray(snake_case__    ) for x in audio_inputs]

            return audio_inputs






@require_torch
class    _snake_case  (						_lowercase ,				unittest.TestCase       ):
       SCREAMING_SNAKE_CASE		     =						EncodecFeatureExtractor






       def 							__lowercase  (					self						:			Dict    ):






            """simple docstring"""



            __a:						List[Any]		    =					EnCodecFeatureExtractionTester(self    )






       def 							__lowercase  (					self						:			Dict    ):






            """simple docstring"""



            __a:						Optional[Any]		    =					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(8_0_0						, 1_4_0_0						, 2_0_0    )]
            __a:						List[Any]		    =					[np.asarray(snake_case__    ) for audio_input in audio_inputs]

            # Test not batched input
            __a:						Tuple		    =					feat_extract(audio_inputs[0]						, return_tensors="""np"""    ).input_values
            __a:						Union[str, Any]		    =					feat_extract(np_audio_inputs[0]						, return_tensors="""np"""    ).input_values
            self.assertTrue(np.allclose(snake_case__						, snake_case__						, atol=1e-3    )    )

            # Test batched
            __a:						Tuple		    =					feat_extract(snake_case__						, padding=snake_case__						, return_tensors="""np"""    ).input_values
            __a:						int		    =					feat_extract(snake_case__						, padding=snake_case__						, return_tensors="""np"""    ).input_values
            for enc_seq_a, enc_seq_a in zip(snake_case__						, snake_case__    ):
                 self.assertTrue(np.allclose(snake_case__						, snake_case__						, atol=1e-3    )    )






       def 							__lowercase  (					self						:			Dict    ):






            """simple docstring"""



            __a:						Optional[Any]		    =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
            __a:						List[Any]		    =					np.random.rand(1_0_0    ).astype(np.floataa    )
            __a:						int		    =					np_audio_inputs.tolist()

            for inputs in [py_audio_inputs, np_audio_inputs]:
                 __a:						Union[str, Any]		    =					feature_extractor.pad([{"""input_values""": inputs}]						, return_tensors="""np"""    )
                 self.assertTrue(np_processed.input_values.dtype == np.floataa    )
                 __a:						Optional[int]		    =					feature_extractor.pad([{"""input_values""": inputs}]						, return_tensors="""pt"""    )
                 self.assertTrue(pt_processed.input_values.dtype == torch.floataa    )






       def 							__lowercase  (					self						:			Dict						, snake_case__						:			int    ):






            """simple docstring"""



            from datasets import load_dataset

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

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






       def 							__lowercase  (					self						:			str    ):






            """simple docstring"""



            __a:						Union[str, Any]		    =					torch.tensor(
                [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
                 3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
                 2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
                 4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
                 7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
                 4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]    )
            # fmt: on
            __a:						List[Any]		    =					self._load_datasamples(1    )
            __a:						Optional[int]		    =					EncodecFeatureExtractor()
            __a:						Optional[int]		    =					feature_extractor(snake_case__						, return_tensors="""pt"""    ).input_values
            self.assertEquals(input_values.shape						, (1, 1, 9_3_6_8_0)    )
            self.assertTrue(torch.allclose(input_values[0, 0, :3_0]						, snake_case__						, atol=1e-6    )    )






       def 							__lowercase  (					self						:			Optional[Any]    ):






            """simple docstring"""



            __a:						List[str]		    =					torch.tensor(
                [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
                 3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
                 2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
                 4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
                 7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
                 4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]    )
            # fmt: on
            __a:						Tuple		    =					self._load_datasamples(1    )
            __a:						Tuple		    =					[np.tile(input_audio[0][None]						, reps=(2, 1)    )]
            input_audio[0][1] *= 0.5
            __a:						Optional[Any]		    =					EncodecFeatureExtractor(feature_size=2    )
            __a:						List[Any]		    =					feature_extractor(snake_case__						, return_tensors="""pt"""    ).input_values
            self.assertEquals(input_values.shape						, (1, 2, 9_3_6_8_0)    )
            self.assertTrue(torch.allclose(input_values[0, 0, :3_0]						, snake_case__						, atol=1e-6    )    )
            self.assertTrue(torch.allclose(input_values[0, 1, :3_0]						, EXPECTED_INPUT_VALUES * 0.5						, atol=1e-6    )    )






       def 							__lowercase  (					self						:			str    ):






            """simple docstring"""



            __a:						Union[str, Any]		    =					self._load_datasamples(2    )
            # would be easier if the stride was like
            __a:						Optional[Any]		    =					EncodecFeatureExtractor(feature_size=1						, chunk_length_s=1						, overlap=0.01    )

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

            # truncate to chunk
            __a:						Optional[int]		    =					feature_extractor(snake_case__						, truncation=snake_case__						, return_tensors="""pt"""    ).input_values
            self.assertEquals(truncated_outputs.shape						, (2, 1, 7_1_5_2_0)    )  # 2 chunks

            # force truncate to max_length
            __a:						Any		    =					feature_extractor(
                snake_case__						, truncation=snake_case__						, max_length=4_8_0_0_0						, return_tensors="""pt"""    ).input_values
            self.assertEquals(truncated_outputs.shape						, (2, 1, 4_8_0_0_0)    )

            # pad to chunk
            __a:						List[Any]		    =					feature_extractor(snake_case__						, padding=snake_case__						, return_tensors="""pt"""    ).input_values
            self.assertEquals(padded_outputs.shape						, (2, 1, 9_5_2_8_0)    )

            # pad to chunk
            __a:						int		    =					feature_extractor(snake_case__						, return_tensors="""pt"""    ).input_values
            self.assertEquals(truncated_outputs.shape						, (2, 1, 9_5_2_8_0)    )

            # force pad to max length
            __a:						List[str]		    =					feature_extractor(
                snake_case__						, padding="""max_length"""						, max_length=1_0_0_0_0_0						, return_tensors="""pt"""    ).input_values
            self.assertEquals(truncated_outputs.shape						, (2, 1, 1_0_0_0_0_0)    )

            # force no pad
            with self.assertRaisesRegex(
                snake_case__						, """^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""						, ):
                 __a:						Optional[int]		    =					feature_extractor(snake_case__						, padding=snake_case__						, return_tensors="""pt"""    ).input_values

            __a:						Optional[int]		    =					feature_extractor(input_audio[0]						, padding=snake_case__						, return_tensors="""pt"""    ).input_values
            self.assertEquals(truncated_outputs.shape						, (1, 1, 9_3_6_8_0)    )

            # no pad if no chunk_length_s
            __a:						Union[str, Any]		    =					None
            with self.assertRaisesRegex(
                snake_case__						, """^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""						, ):
                 __a:						Any		    =					feature_extractor(snake_case__						, padding=snake_case__						, return_tensors="""pt"""    ).input_values

            __a:						str		    =					feature_extractor(input_audio[0]						, padding=snake_case__						, return_tensors="""pt"""    ).input_values
            self.assertEquals(truncated_outputs.shape						, (1, 1, 9_3_6_8_0)    )

            # no pad if no overlap
            __a:						str		    =					2
            __a:						Optional[Any]		    =					None
            with self.assertRaisesRegex(
                snake_case__						, """^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""						, ):
                 __a:						str		    =					feature_extractor(snake_case__						, padding=snake_case__						, return_tensors="""pt"""    ).input_values

            __a:						str		    =					feature_extractor(input_audio[0]						, padding=snake_case__						, return_tensors="""pt"""    ).input_values
            self.assertEquals(truncated_outputs.shape						, (1, 1, 9_3_6_8_0)    )


"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase		:						int =					logging.get_logger(__name__)

lowercase		:						Any =					{
    'facebook/regnet-y-040': 'https://huggingface.co/facebook/regnet-y-040/blob/main/config.json',
}




class    _snake_case  (						_lowercase       ):
							SCREAMING_SNAKE_CASE		     =						'''regnet'''
							SCREAMING_SNAKE_CASE		     =						['''x''', '''y''']






							def __init__(					self						:			Union[str, Any]						, snake_case__						:			Any=3						, snake_case__						:			str=3_2						, snake_case__						:			Tuple=[1_2_8, 1_9_2, 5_1_2, 1_0_8_8]						, snake_case__						:			Union[str, Any]=[2, 6, 1_2, 2]						, snake_case__						:			str=6_4						, snake_case__						:			Optional[int]="y"						, snake_case__						:			Tuple="relu"						, **snake_case__						:			str						, ):






												"""simple docstring"""



												super().__init__(**snake_case__    )
												if layer_type not in self.layer_types:
																	raise ValueError(f"""layer_type={layer_type} is not one of {','.join(self.layer_types    )}"""    )
												__a:						Any		    =					num_channels
												__a:						List[Any]		    =					embedding_size
												__a:						Optional[int]		    =					hidden_sizes
												__a:						Optional[int]		    =					depths
												__a:						Optional[Any]		    =					groups_width
												__a:						Tuple		    =					layer_type
												__a:						str		    =					hidden_act
												# always downsample in the first stage
												__a:						List[str]		    =					True
