





"""simple docstring"""
import copy
import inspect

from transformers.testing_utils import require_torch, torch_device
from transformers.utils.backbone_utils import BackboneType






@require_torch
class    _UpperCAmelCase  :
					UpperCamelCase						=				()
					UpperCamelCase						=				True


					def       lowerCamelCase			(   self     :List[Any]						):
												A						=    self.config_class

												# test default config
												A						=    config_class()
												self.assertIsNotNone(__UpperCamelCase						)
												A						=    ["stem"] + [f"stage{idx}" for idx in range(1	,			len(config.depths						) + 1						)]
												self.assertEqual(config.stage_names	,			__UpperCamelCase						)
												self.assertTrue(set(config.out_features						).issubset(set(config.stage_names						)						)						)

												# Test out_features and out_indices are correctly set
												# out_features and out_indices both None
												A						=    config_class(out_features=__UpperCamelCase	,			out_indices=__UpperCamelCase						)
												self.assertEqual(config.out_features	,			[config.stage_names[-1]]						)
												self.assertEqual(config.out_indices	,			[len(config.stage_names						) - 1]						)

												# out_features and out_indices both set
												A						=    config_class(out_features=["stem", "stage1"]	,			out_indices=[0, 1]						)
												self.assertEqual(config.out_features	,			["stem", "stage1"]						)
												self.assertEqual(config.out_indices	,			[0, 1]						)

												# Only out_features set
												A						=    config_class(out_features=["stage1", "stage3"]						)
												self.assertEqual(config.out_features	,			["stage1", "stage3"]						)
												self.assertEqual(config.out_indices	,			[1, 3]						)

												# Only out_indices set
												A						=    config_class(out_indices=[0, 2]						)
												self.assertEqual(config.out_features	,			[config.stage_names[0], config.stage_names[2]]						)
												self.assertEqual(config.out_indices	,			[0, 2]						)

												# Error raised when out_indices do not correspond to out_features
												with self.assertRaises(__UpperCamelCase						):
																			A						=    config_class(out_features=["stage1", "stage2"]	,			out_indices=[0, 2]						)


					def       lowerCamelCase			(   self     :Optional[Any]						):
												A,      A						=    self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			A						=    model_class(__UpperCamelCase						)
																			A						=    inspect.signature(model.forward						)
																			# signature.parameters is an OrderedDict => so arg_names order is deterministic
																			A						=    [*signature.parameters.keys()]
																			A						=    ["pixel_values"]
																			self.assertListEqual(arg_names[:1]	,			__UpperCamelCase						)


					def       lowerCamelCase			(   self     :List[Any]						):
												A,      A						=    self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			A						=    model_class(__UpperCamelCase						)
																			self.assertEqual(len(model.channels						)	,			len(config.out_features						)						)
																			A						=    model.num_features
																			A						=    [config.stage_names.index(__UpperCamelCase						) for feat in config.out_features]
																			A						=    [num_features[idx] for idx in out_indices]
																			self.assertListEqual(model.channels	,			__UpperCamelCase						)

																			A						=    copy.deepcopy(__UpperCamelCase						)
																			A						=    None
																			A						=    model_class(__UpperCamelCase						)
																			self.assertEqual(len(model.channels						)	,			1						)
																			self.assertListEqual(model.channels	,			[num_features[-1]]						)

																			A						=    copy.deepcopy(__UpperCamelCase						)
																			A						=    None
																			A						=    model_class(__UpperCamelCase						)
																			self.assertEqual(len(model.channels						)	,			1						)
																			self.assertListEqual(model.channels	,			[num_features[-1]]						)


					def       lowerCamelCase			(   self     :str						):
												A,      A						=    self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			A						=    model_class(__UpperCamelCase						)
																			model.to(__UpperCamelCase						)
																			model.eval()
																			A						=    model(**__UpperCamelCase						)

																			self.assertEqual(len(result.feature_maps						)	,			len(config.out_features						)						)
																			self.assertEqual(len(model.channels						)	,			len(config.out_features						)						)
																			self.assertEqual(len(result.feature_maps						)	,			len(config.out_indices						)						)
																			self.assertEqual(len(model.channels						)	,			len(config.out_indices						)						)

																			# Check output of last stage is taken if out_features=None, out_indices=None
																			A						=    copy.deepcopy(__UpperCamelCase						)
																			A						=    None
																			A						=    model_class(__UpperCamelCase						)
																			model.to(__UpperCamelCase						)
																			model.eval()
																			A						=    model(**__UpperCamelCase						)

																			self.assertEqual(len(result.feature_maps						)	,			1						)
																			self.assertEqual(len(model.channels						)	,			1						)

																			A						=    copy.deepcopy(__UpperCamelCase						)
																			A						=    None
																			A						=    model_class(__UpperCamelCase						)
																			model.to(__UpperCamelCase						)
																			model.eval()
																			A						=    model(**__UpperCamelCase						)

																			self.assertEqual(len(result.feature_maps						)	,			1						)
																			self.assertEqual(len(model.channels						)	,			1						)

																			# Check backbone can be initialized with fresh weights
																			A						=    copy.deepcopy(__UpperCamelCase						)
																			A						=    False
																			A						=    model_class(__UpperCamelCase						)
																			model.to(__UpperCamelCase						)
																			model.eval()
																			A						=    model(**__UpperCamelCase						)


					def       lowerCamelCase			(   self     :Dict						):
												A,      A						=    self.model_tester.prepare_config_and_inputs_for_common()

												for backbone_class in self.all_model_classes:
																			A						=    backbone_class(__UpperCamelCase						)

																			self.assertTrue(hasattr(__UpperCamelCase	,			"backbone_type"						)						)
																			self.assertTrue(hasattr(__UpperCamelCase	,			"stage_names"						)						)
																			self.assertTrue(hasattr(__UpperCamelCase	,			"num_features"						)						)
																			self.assertTrue(hasattr(__UpperCamelCase	,			"out_indices"						)						)
																			self.assertTrue(hasattr(__UpperCamelCase	,			"out_features"						)						)
																			self.assertTrue(hasattr(__UpperCamelCase	,			"out_feature_channels"						)						)
																			self.assertTrue(hasattr(__UpperCamelCase	,			"channels"						)						)

																			self.assertIsInstance(backbone.backbone_type	,			__UpperCamelCase						)
																			# Verify num_features has been initialized in the backbone init
																			self.assertIsNotNone(backbone.num_features						)
																			self.assertTrue(len(backbone.channels						) == len(backbone.out_indices						)						)
																			self.assertTrue(len(backbone.stage_names						) == len(backbone.num_features						)						)
																			self.assertTrue(len(backbone.channels						) <= len(backbone.num_features						)						)
																			self.assertTrue(len(backbone.out_feature_channels						) == len(backbone.stage_names						)						)


					def       lowerCamelCase			(   self     :List[str]						):
												A,      A						=    self.model_tester.prepare_config_and_inputs_for_common()
												A						=    inputs_dict["pixel_values"].shape[0]

												for backbone_class in self.all_model_classes:
																			A						=    backbone_class(__UpperCamelCase						)
																			backbone.to(__UpperCamelCase						)
																			backbone.eval()

																			A						=    backbone(**__UpperCamelCase						)

																			# Test default outputs and verify feature maps
																			self.assertIsInstance(outputs.feature_maps	,			__UpperCamelCase						)
																			self.assertTrue(len(outputs.feature_maps						) == len(backbone.channels						)						)
																			for feature_map, n_channels in zip(outputs.feature_maps	,			backbone.channels						):
																										self.assertTrue(feature_map.shape[:2]	,			(batch_size, n_channels)						)
																			self.assertIsNone(outputs.hidden_states						)
																			self.assertIsNone(outputs.attentions						)

																			# Test output_hidden_states=True
																			A						=    backbone(**__UpperCamelCase	,			output_hidden_states=__UpperCamelCase						)
																			self.assertIsNotNone(outputs.hidden_states						)
																			self.assertTrue(len(outputs.hidden_states						)	,			len(backbone.stage_names						)						)
																			for hidden_state, n_channels in zip(outputs.hidden_states	,			backbone.channels						):
																										self.assertTrue(hidden_state.shape[:2]	,			(batch_size, n_channels)						)

																			# Test output_attentions=True
																			if self.has_attentions:
																										A						=    backbone(**__UpperCamelCase	,			output_attentions=__UpperCamelCase						)
																										self.assertIsNotNone(outputs.attentions						)









"""simple docstring"""
from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_snake_case	:						List[str]					=					logging.get_logger(__name__)






class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				['''input_values''', '''attention_mask''']


					def __init__(   self     :str	,			__UpperCamelCase     :List[str]=1	,			__UpperCamelCase     :Tuple=1_60_00	,			__UpperCamelCase     :Optional[int]=0.0	,			__UpperCamelCase     :Optional[int]=False	,			__UpperCamelCase     :Optional[Any]=True	,			**__UpperCamelCase     :Optional[Any]	,			):
												super().__init__(feature_size=__UpperCamelCase	,			sampling_rate=__UpperCamelCase	,			padding_value=__UpperCamelCase	,			**__UpperCamelCase						)
												A						=    return_attention_mask
												A						=    do_normalize


					@staticmethod
					def       lowerCamelCase			(   __UpperCamelCase     :List[np.ndarray]	,			__UpperCamelCase     :List[np.ndarray]	,			__UpperCamelCase     :float = 0.0						):
												if attention_mask is not None:
																			A						=    np.array(__UpperCamelCase	,			np.intaa						)
																			A						=    []

																			for vector, length in zip(__UpperCamelCase	,			attention_mask.sum(-1						)						):
																										A						=    (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7						)
																										if length < normed_slice.shape[0]:
																																	A						=    padding_value

																										normed_input_values.append(__UpperCamelCase						)
												else:
																			A						=    [(x - x.mean()) / np.sqrt(x.var() + 1e-7						) for x in input_values]

												return normed_input_values


					def __call__(   self     :Tuple	,			__UpperCamelCase     :Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,			__UpperCamelCase     :Union[bool, str, PaddingStrategy] = False	,			__UpperCamelCase     :Optional[int] = None	,			__UpperCamelCase     :bool = False	,			__UpperCamelCase     :Optional[int] = None	,			__UpperCamelCase     :Optional[bool] = None	,			__UpperCamelCase     :Optional[Union[str, TensorType]] = None	,			__UpperCamelCase     :Optional[int] = None	,			**__UpperCamelCase     :List[str]	,			):

												if sampling_rate is not None:
																			if sampling_rate != self.sampling_rate:
																										raise ValueError(
																										    f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
																										    f" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"
																										    f" {self.sampling_rate} and not {sampling_rate}."						)
												else:
																			logger.warning(
																			    "It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
																			    "Failing to do so can result in silent errors that might be hard to debug."						)

												A						=    isinstance(__UpperCamelCase	,			np.ndarray						) and len(raw_speech.shape						) > 1
												if is_batched_numpy and len(raw_speech.shape						) > 2:
																			raise ValueError(f"Only mono-channel audio is supported for input to {self}"						)
												A						=    is_batched_numpy or (
												    isinstance(__UpperCamelCase	,			(list, tuple)						) and (isinstance(raw_speech[0]	,			(np.ndarray, tuple, list)						))
												)

												# always return batch
												if not is_batched:
																			A						=    [raw_speech]

												# convert into correct format for padding
												A						=    BatchFeature({"input_values": raw_speech}						)

												A						=    self.pad(
												    __UpperCamelCase	,			padding=__UpperCamelCase	,			max_length=__UpperCamelCase	,			truncation=__UpperCamelCase	,			pad_to_multiple_of=__UpperCamelCase	,			return_attention_mask=__UpperCamelCase	,			)

												# convert input values to correct format
												A						=    padded_inputs["input_values"]
												if not isinstance(input_values[0]	,			np.ndarray						):
																			A						=    [np.asarray(__UpperCamelCase	,			dtype=np.floataa						) for array in input_values]
												elif (
												    not isinstance(__UpperCamelCase	,			np.ndarray						)
												    and isinstance(input_values[0]	,			np.ndarray						)
												    and input_values[0].dtype is np.dtype(np.floataa						)
												):
																			A						=    [array.astype(np.floataa						) for array in input_values]
												elif isinstance(__UpperCamelCase	,			np.ndarray						) and input_values.dtype is np.dtype(np.floataa						):
																			A						=    input_values.astype(np.floataa						)

												# convert attention_mask to correct format
												A						=    padded_inputs.get("attention_mask"						)
												if attention_mask is not None:
																			A						=    [np.asarray(__UpperCamelCase	,			dtype=np.intaa						) for array in attention_mask]

												# zero-mean and unit-variance normalization
												if self.do_normalize:
																			A						=    (
																			    attention_mask
																			    if self._get_padding_strategies(__UpperCamelCase	,			max_length=__UpperCamelCase						) is not PaddingStrategy.DO_NOT_PAD
																			    else None
																			)
																			A						=    self.zero_mean_unit_var_norm(
																			    padded_inputs["input_values"]	,			attention_mask=__UpperCamelCase	,			padding_value=self.padding_value						)

												if return_tensors is not None:
																			A						=    padded_inputs.convert_to_tensors(__UpperCamelCase						)

												return padded_inputs


