import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
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


a  :      Union[str, Any]            =    random.Random()
def 		snake_case__	(		lowercase							,				lowercase=1.0							,				lowercase=None							,				lowercase=None					):
   if rng is None:
      lowerCAmelCase_:							Optional[int]       =	global_rng

   lowerCAmelCase_:							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 					_lowercase    (     unittest.TestCase			):






    '''simple docstring'''




    def __init__(    self       ,						lowerCamelCase__       ,						lowerCamelCase__=7       ,						lowerCamelCase__=400       ,						lowerCamelCase__=2_000       ,						lowerCamelCase__=1       ,						lowerCamelCase__=0.0       ,						lowerCamelCase__=16_000       ,						lowerCamelCase__=True       ,						lowerCamelCase__=80       ,						lowerCamelCase__=16       ,						lowerCamelCase__=64       ,						lowerCamelCase__="hann_window"       ,						lowerCamelCase__=80       ,						lowerCamelCase__=7_600       ,						lowerCamelCase__=1E-10       ,						lowerCamelCase__=True       ,						):
       lowerCAmelCase_:							Optional[int]       =	parent
       lowerCAmelCase_:							Any       =	batch_size
       lowerCAmelCase_:							Optional[Any]       =	min_seq_length
       lowerCAmelCase_:							Optional[int]       =	max_seq_length
       lowerCAmelCase_:							int       =	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       lowerCAmelCase_:							Dict       =	feature_size
       lowerCAmelCase_:							Dict       =	padding_value
       lowerCAmelCase_:							List[Any]       =	sampling_rate
       lowerCAmelCase_:							List[str]       =	do_normalize
       lowerCAmelCase_:							Any       =	num_mel_bins
       lowerCAmelCase_:							Dict       =	hop_length
       lowerCAmelCase_:							Any       =	win_length
       lowerCAmelCase_:							Optional[int]       =	win_function
       lowerCAmelCase_:							Any       =	fmin
       lowerCAmelCase_:							str       =	fmax
       lowerCAmelCase_:							List[str]       =	mel_floor
       lowerCAmelCase_:							Any       =	return_attention_mask




    def      _a    (    self ):
       return {
           "feature_size": self.feature_size,
           "padding_value": self.padding_value,
           "sampling_rate": self.sampling_rate,
           "do_normalize": self.do_normalize,
           "num_mel_bins": self.num_mel_bins,
           "hop_length": self.hop_length,
           "win_length": self.win_length,
           "win_function": self.win_function,
           "fmin": self.fmin,
           "fmax": self.fmax,
           "mel_floor": self.mel_floor,
           "return_attention_mask": self.return_attention_mask,
       }




    def      _a    (    self       ,						lowerCamelCase__=False       ,						lowerCamelCase__=False ):
       def _flatten(lowerCamelCase__ ):
          return list(itertools.chain(*lowerCamelCase__ ) )

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

       return speech_inputs




    def      _a    (    self       ,						lowerCamelCase__=False       ,						lowerCamelCase__=False ):
       if equal_length:
          lowerCAmelCase_:							int       =	[floats_list((self.max_seq_length, self.num_mel_bins) ) for _ in range(self.batch_size )]
       else:
          # make sure that inputs increase in size
          lowerCAmelCase_:							Union[str, Any]       =	[
              floats_list((x, self.num_mel_bins) )
              for x in range(self.min_seq_length       ,						self.max_seq_length       ,						self.seq_length_diff )
          ]

       if numpify:
          lowerCAmelCase_:							Optional[int]       =	[np.asarray(lowerCamelCase__ ) for x in speech_inputs]

       return speech_inputs





@require_torch
class 					_lowercase    (     UpperCAmelCase__ ,  unittest.TestCase			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Optional[int]          =    SpeechTaFeatureExtractor




    def      _a    (    self ):
       lowerCAmelCase_:							Union[str, Any]       =	SpeechTaFeatureExtractionTester(self )




    def      _a    (    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      _a    (    self ):
       # Tests that all call wrap to encode_plus and batch_encode_plus
       lowerCAmelCase_:							Optional[int]       =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
       # create three inputs of length 800, 1000, and 1200
       lowerCAmelCase_:							Tuple       =	[floats_list((1, x) )[0] for x in range(800       ,						1_400       ,						200 )]
       lowerCAmelCase_:							Optional[int]       =	[np.asarray(lowerCamelCase__ ) for speech_input in speech_inputs]

       # Test not batched input
       lowerCAmelCase_:							int       =	feat_extract(speech_inputs[0]       ,						return_tensors="np" ).input_values
       lowerCAmelCase_:							Dict       =	feat_extract(np_speech_inputs[0]       ,						return_tensors="np" ).input_values
       self.assertTrue(np.allclose(lowerCamelCase__       ,						lowerCamelCase__       ,						atol=1E-3 ) )

       # Test batched
       lowerCAmelCase_:							List[Any]       =	feat_extract(lowerCamelCase__       ,						return_tensors="np" ).input_values
       lowerCAmelCase_:							Any       =	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 ) )




    def      _a    (    self ):
       lowerCAmelCase_:							str       =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
       lowerCAmelCase_:							List[Any]       =	[floats_list((1, x) )[0] for x in range(800       ,						1_400       ,						200 )]

       lowerCAmelCase_:							List[Any]       =	["longest", "max_length", "do_not_pad"]
       lowerCAmelCase_:							Union[str, Any]       =	[None, 1_600, None]
       for max_length, padding in zip(lowerCamelCase__       ,						lowerCamelCase__ ):
          lowerCAmelCase_:							Optional[int]       =	feat_extract(lowerCamelCase__       ,						padding=lowerCamelCase__       ,						max_length=lowerCamelCase__       ,						return_tensors="np" )
          lowerCAmelCase_:							Tuple       =	processed.input_values

          self._check_zero_mean_unit_variance(input_values[0][:800] )
          self.assertTrue(input_values[0][800:].sum() < 1E-6 )
          self._check_zero_mean_unit_variance(input_values[1][:1_000] )
          self.assertTrue(input_values[0][1_000:].sum() < 1E-6 )
          self._check_zero_mean_unit_variance(input_values[2][:1_200] )




    def      _a    (    self ):
       lowerCAmelCase_:							Union[str, Any]       =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
       lowerCAmelCase_:							List[Any]       =	range(800       ,						1_400       ,						200 )
       lowerCAmelCase_:							Tuple       =	[floats_list((1, x) )[0] for x in lengths]

       lowerCAmelCase_:							str       =	["longest", "max_length", "do_not_pad"]
       lowerCAmelCase_:							Dict       =	[None, 1_600, None]

       for max_length, padding in zip(lowerCamelCase__       ,						lowerCamelCase__ ):
          lowerCAmelCase_:							Any       =	feat_extract(lowerCamelCase__       ,						max_length=lowerCamelCase__       ,						padding=lowerCamelCase__ )
          lowerCAmelCase_:							List[str]       =	processed.input_values

          self._check_zero_mean_unit_variance(input_values[0][:800] )
          self._check_zero_mean_unit_variance(input_values[1][:1_000] )
          self._check_zero_mean_unit_variance(input_values[2][:1_200] )




    def      _a    (    self ):
       lowerCAmelCase_:							str       =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
       lowerCAmelCase_:							List[str]       =	[floats_list((1, x) )[0] for x in range(800       ,						1_400       ,						200 )]
       lowerCAmelCase_:							List[str]       =	feat_extract(
           lowerCamelCase__       ,						truncation=lowerCamelCase__       ,						max_length=1_000       ,						padding="max_length"       ,						return_tensors="np" )
       lowerCAmelCase_:							Dict       =	processed.input_values

       self._check_zero_mean_unit_variance(input_values[0, :800] )
       self._check_zero_mean_unit_variance(input_values[1] )
       self._check_zero_mean_unit_variance(input_values[2] )




    def      _a    (    self ):
       lowerCAmelCase_:							Any       =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
       lowerCAmelCase_:							Optional[int]       =	[floats_list((1, x) )[0] for x in range(800       ,						1_400       ,						200 )]
       lowerCAmelCase_:							str       =	feat_extract(
           lowerCamelCase__       ,						truncation=lowerCamelCase__       ,						max_length=1_000       ,						padding="longest"       ,						return_tensors="np" )
       lowerCAmelCase_:							str       =	processed.input_values

       self._check_zero_mean_unit_variance(input_values[0, :800] )
       self._check_zero_mean_unit_variance(input_values[1, :1_000] )
       self._check_zero_mean_unit_variance(input_values[2] )

       # make sure that if max_length < longest -> then pad to max_length
       self.assertTrue(input_values.shape == (3, 1_000) )

       lowerCAmelCase_:							Any       =	[floats_list((1, x) )[0] for x in range(800       ,						1_400       ,						200 )]
       lowerCAmelCase_:							Any       =	feat_extract(
           lowerCamelCase__       ,						truncation=lowerCamelCase__       ,						max_length=2_000       ,						padding="longest"       ,						return_tensors="np" )
       lowerCAmelCase_:							Union[str, Any]       =	processed.input_values

       self._check_zero_mean_unit_variance(input_values[0, :800] )
       self._check_zero_mean_unit_variance(input_values[1, :1_000] )
       self._check_zero_mean_unit_variance(input_values[2] )

       # make sure that if max_length > longest -> then pad to longest
       self.assertTrue(input_values.shape == (3, 1_200) )




    def      _a    (    self ):
       lowerCAmelCase_:							Any       =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
       lowerCAmelCase_:							List[Any]       =	np.random.rand(100 ).astype(np.floataa )
       lowerCAmelCase_:							Tuple       =	np_speech_inputs.tolist()

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




    def      _a    (    self ):
       # Tests that all call wrap to encode_plus and batch_encode_plus
       lowerCAmelCase_:							Dict       =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
       # create three inputs of length 800, 1000, and 1200
       lowerCAmelCase_:							Tuple       =	[floats_list((1, x) )[0] for x in range(800       ,						1_400       ,						200 )]
       lowerCAmelCase_:							Tuple       =	[np.asarray(lowerCamelCase__ ) for speech_input in speech_inputs]

       # Test feature size
       lowerCAmelCase_:							Optional[int]       =	feature_extractor(audio_target=lowerCamelCase__       ,						padding=lowerCamelCase__       ,						return_tensors="np" ).input_values
       self.assertTrue(input_values.ndim == 3 )
       self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins )

       # Test not batched input
       lowerCAmelCase_:							List[Any]       =	feature_extractor(speech_inputs[0]       ,						return_tensors="np" ).input_values
       lowerCAmelCase_:							Dict       =	feature_extractor(np_speech_inputs[0]       ,						return_tensors="np" ).input_values
       self.assertTrue(np.allclose(lowerCamelCase__       ,						lowerCamelCase__       ,						atol=1E-3 ) )

       # Test batched
       lowerCAmelCase_:							Tuple       =	feature_extractor(lowerCamelCase__       ,						return_tensors="np" ).input_values
       lowerCAmelCase_:							Optional[Any]       =	feature_extractor(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.
       lowerCAmelCase_:							int       =	[floats_list((1, x) )[0] for x in (800, 800, 800)]
       lowerCAmelCase_:							Tuple       =	np.asarray(lowerCamelCase__ )
       lowerCAmelCase_:							Union[str, Any]       =	feature_extractor(lowerCamelCase__       ,						return_tensors="np" ).input_values
       lowerCAmelCase_:							int       =	feature_extractor(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 ) )




    def      _a    (    self ):
       lowerCAmelCase_:							Union[str, Any]       =	self.feat_extract_tester.prepare_inputs_for_target()
       lowerCAmelCase_:							int       =	self.feature_extraction_class(**self.feat_extract_dict )
       lowerCAmelCase_:							List[Any]       =	feat_extract.model_input_names[0]

       lowerCAmelCase_:							List[Any]       =	BatchFeature({input_name: speech_inputs} )

       self.assertTrue(all(len(lowerCamelCase__ ) == len(lowerCamelCase__ ) for x, y in zip(lowerCamelCase__       ,						processed_features[input_name] ) ) )

       lowerCAmelCase_:							List[str]       =	self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowerCamelCase__ )
       lowerCAmelCase_:							str       =	BatchFeature({input_name: speech_inputs}       ,						tensor_type="np" )

       lowerCAmelCase_:							Dict       =	processed_features[input_name]

       if len(batch_features_input.shape ) < 3:
          lowerCAmelCase_:							Dict       =	batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )




    @require_torch
    def      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowerCamelCase__ )
       lowerCAmelCase_:							Union[str, Any]       =	self.feature_extraction_class(**self.feat_extract_dict )
       lowerCAmelCase_:							str       =	feat_extract.model_input_names[0]

       lowerCAmelCase_:							int       =	BatchFeature({input_name: speech_inputs}       ,						tensor_type="pt" )

       lowerCAmelCase_:							int       =	processed_features[input_name]

       if len(batch_features_input.shape ) < 3:
          lowerCAmelCase_:							Dict       =	batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )




    @require_torch
    def      _a    (    self ):
       lowerCAmelCase_:							Optional[Any]       =	self.feature_extraction_class(**self.feat_extract_dict )
       lowerCAmelCase_:							Dict       =	self.feat_extract_tester.prepare_inputs_for_target()
       lowerCAmelCase_:							Tuple       =	feat_extract.model_input_names[0]

       lowerCAmelCase_:							Union[str, Any]       =	BatchFeature({input_name: speech_inputs} )

       lowerCAmelCase_:							int       =	feat_extract.num_mel_bins  # hack!

       lowerCAmelCase_:							Tuple       =	feat_extract.pad(lowerCamelCase__       ,						padding="longest"       ,						return_tensors="np" )[input_name]
       lowerCAmelCase_:							Dict       =	feat_extract.pad(lowerCamelCase__       ,						padding="longest"       ,						return_tensors="pt" )[input_name]

       self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1E-2 )




    def      _a    (    self ):
       lowerCAmelCase_:							List[str]       =	self.feat_extract_dict
       lowerCAmelCase_:							Tuple       =	True
       lowerCAmelCase_:							Union[str, Any]       =	self.feature_extraction_class(**lowerCamelCase__ )
       lowerCAmelCase_:							Tuple       =	self.feat_extract_tester.prepare_inputs_for_target()
       lowerCAmelCase_:							str       =	[len(lowerCamelCase__ ) for x in speech_inputs]
       lowerCAmelCase_:							int       =	feat_extract.model_input_names[0]

       lowerCAmelCase_:							List[str]       =	BatchFeature({input_name: speech_inputs} )

       lowerCAmelCase_:							Tuple       =	feat_extract.num_mel_bins  # hack!

       lowerCAmelCase_:							int       =	feat_extract.pad(lowerCamelCase__       ,						padding="longest"       ,						return_tensors="np" )
       self.assertIn("attention_mask"       ,						lowerCamelCase__ )
       self.assertListEqual(list(processed.attention_mask.shape )       ,						list(processed[input_name].shape[:2] ) )
       self.assertListEqual(processed.attention_mask.sum(-1 ).tolist()       ,						lowerCamelCase__ )




    def      _a    (    self ):
       lowerCAmelCase_:							Dict       =	self.feat_extract_dict
       lowerCAmelCase_:							Dict       =	True
       lowerCAmelCase_:							Optional[int]       =	self.feature_extraction_class(**lowerCamelCase__ )
       lowerCAmelCase_:							int       =	self.feat_extract_tester.prepare_inputs_for_target()
       lowerCAmelCase_:							str       =	[len(lowerCamelCase__ ) for x in speech_inputs]
       lowerCAmelCase_:							Union[str, Any]       =	feat_extract.model_input_names[0]

       lowerCAmelCase_:							Optional[Any]       =	BatchFeature({input_name: speech_inputs} )
       lowerCAmelCase_:							List[str]       =	min(lowerCamelCase__ )

       lowerCAmelCase_:							Optional[int]       =	feat_extract.num_mel_bins  # hack!

       lowerCAmelCase_:							Dict       =	feat_extract.pad(
           lowerCamelCase__       ,						padding="max_length"       ,						max_length=lowerCamelCase__       ,						truncation=lowerCamelCase__       ,						return_tensors="np" )
       self.assertIn("attention_mask"       ,						lowerCamelCase__ )
       self.assertListEqual(
           list(processed_pad.attention_mask.shape )       ,						[processed_pad[input_name].shape[0], max_length] )
       self.assertListEqual(
           processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist()       ,						[max_length for x in speech_inputs] )




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

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

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




    def      _a    (    self ):
       # fmt: off
       lowerCAmelCase_:							List[Any]       =	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

       lowerCAmelCase_:							Tuple       =	self._load_datasamples(1 )
       lowerCAmelCase_:							Dict       =	SpeechTaFeatureExtractor()
       lowerCAmelCase_:							Dict       =	feature_extractor(lowerCamelCase__       ,						return_tensors="pt" ).input_values
       self.assertEquals(input_values.shape       ,						(1, 93_680) )
       self.assertTrue(torch.allclose(input_values[0, :30]       ,						lowerCamelCase__       ,						atol=1E-6 ) )




    def      _a    (    self ):
       # fmt: off
       lowerCAmelCase_:							Dict       =	torch.tensor(
           [-2.6_8_7_0, -3.0_1_0_4, -3.1_3_5_6, -3.5_3_5_2, -3.0_0_4_4, -3.0_3_5_3, -3.4_7_1_9, -3.6_7_7_7,
            -3.1_5_2_0, -2.9_4_3_5, -2.6_5_5_3, -2.8_7_9_5, -2.9_9_4_4, -2.5_9_2_1, -3.0_2_7_9, -3.0_3_8_6,
            -3.0_8_6_4, -3.1_2_9_1, -3.2_3_5_3, -2.7_4_4_4, -2.6_8_3_1, -2.7_2_8_7, -3.1_7_6_1, -3.1_5_7_1,
            -3.2_7_2_6, -3.0_5_8_2, -3.1_0_0_7, -3.4_5_3_3, -3.4_6_9_5, -3.0_9_9_8] )
       # fmt: on

       lowerCAmelCase_:							Tuple       =	self._load_datasamples(1 )
       lowerCAmelCase_:							Any       =	SpeechTaFeatureExtractor()
       lowerCAmelCase_:							Union[str, Any]       =	feature_extractor(audio_target=lowerCamelCase__       ,						return_tensors="pt" ).input_values
       self.assertEquals(input_values.shape       ,						(1, 366, 80) )
       self.assertTrue(torch.allclose(input_values[0, 0, :30]       ,						lowerCamelCase__       ,						atol=1E-4 ) )
from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a  :      Optional[int]            =    logging.get_logger(__name__)

a  :      Optional[Any]            =    {
    """google/efficientnet-b7""": """https://huggingface.co/google/efficientnet-b7/resolve/main/config.json""",
}

class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Dict          =    'efficientnet'




    def __init__(    self       ,						lowerCamelCase__ = 3       ,						lowerCamelCase__ = 600       ,						lowerCamelCase__ = 2.0       ,						lowerCamelCase__ = 3.1       ,						lowerCamelCase__ = 8       ,						lowerCamelCase__ = [3, 3, 5, 3, 5, 5, 3]       ,						lowerCamelCase__ = [32, 16, 24, 40, 80, 112, 192]       ,						lowerCamelCase__ = [16, 24, 40, 80, 112, 192, 320]       ,						lowerCamelCase__ = []       ,						lowerCamelCase__ = [1, 2, 2, 2, 1, 2, 1]       ,						lowerCamelCase__ = [1, 2, 2, 3, 3, 4, 1]       ,						lowerCamelCase__ = [1, 6, 6, 6, 6, 6, 6]       ,						lowerCamelCase__ = 0.2_5       ,						lowerCamelCase__ = "swish"       ,						lowerCamelCase__ = 2_560       ,						lowerCamelCase__ = "mean"       ,						lowerCamelCase__ = 0.0_2       ,						lowerCamelCase__ = 0.0_0_1       ,						lowerCamelCase__ = 0.9_9       ,						lowerCamelCase__ = 0.5       ,						lowerCamelCase__ = 0.2       ,						**lowerCamelCase__       ,						):
       super().__init__(**lowerCamelCase__ )

       lowerCAmelCase_:							str       =	num_channels
       lowerCAmelCase_:							str       =	image_size
       lowerCAmelCase_:							int       =	width_coefficient
       lowerCAmelCase_:							Union[str, Any]       =	depth_coefficient
       lowerCAmelCase_:							int       =	depth_divisor
       lowerCAmelCase_:							List[str]       =	kernel_sizes
       lowerCAmelCase_:							Tuple       =	in_channels
       lowerCAmelCase_:							List[str]       =	out_channels
       lowerCAmelCase_:							List[str]       =	depthwise_padding
       lowerCAmelCase_:							Optional[int]       =	strides
       lowerCAmelCase_:							List[str]       =	num_block_repeats
       lowerCAmelCase_:							Any       =	expand_ratios
       lowerCAmelCase_:							List[Any]       =	squeeze_expansion_ratio
       lowerCAmelCase_:							Optional[int]       =	hidden_act
       lowerCAmelCase_:							Optional[int]       =	hidden_dim
       lowerCAmelCase_:							Dict       =	pooling_type
       lowerCAmelCase_:							Optional[Any]       =	initializer_range
       lowerCAmelCase_:							int       =	batch_norm_eps
       lowerCAmelCase_:							List[str]       =	batch_norm_momentum
       lowerCAmelCase_:							List[Any]       =	dropout_rate
       lowerCAmelCase_:							Union[str, Any]       =	drop_connect_rate
       lowerCAmelCase_:							Tuple       =	sum(lowerCamelCase__ ) * 4





class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    version.parse('1.11'			)




    @property
    def      _a    (    self ):
       return OrderedDict(
           [
               ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
           ] )




    @property
    def      _a    (    self ):
       return 1E-5