
import os
import sys
import unittest


UpperCamelCase_						:     Union[str, Any]	       =							os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


UpperCamelCase_						:     Any	       =							os.path.join("""tests""", """models""", """bert""", """test_modeling_bert.py""")
UpperCamelCase_						:     Any	       =							os.path.join("""tests""", """models""", """blip""", """test_modeling_blip.py""")




class   __lowercase						(						unittest.TestCase       ):






	def       _a(self				: Any    ) ->			List[Any]:
							_lowercase       :	Tuple           =   get_test_to_tester_mapping(snake_case    )
							_lowercase       :	Tuple           =   get_test_to_tester_mapping(snake_case    )

							_lowercase       :	int           =   {"BertModelTest": "BertModelTester"}

							_lowercase       :	Any           =   {
							    "BlipModelTest": "BlipModelTester",
							    "BlipTextImageModelTest": "BlipTextImageModelsModelTester",
							    "BlipTextModelTest": "BlipTextModelTester",
							    "BlipTextRetrievalModelTest": "BlipTextRetrievalModelTester",
							    "BlipVQAModelTest": "BlipVQAModelTester",
							    "BlipVisionModelTest": "BlipVisionModelTester",
							}

							self.assertEqual(get_test_info.to_json(snake_case    )      ,	snake_case    )
							self.assertEqual(get_test_info.to_json(snake_case    )      ,	snake_case    )






	def       _a(self				: Optional[Any]    ) ->			Optional[Any]:
							_lowercase       :	str           =   get_model_to_test_mapping(snake_case    )
							_lowercase       :	List[str]           =   get_model_to_test_mapping(snake_case    )

							_lowercase       :	Any           =   {
							    "BertForMaskedLM": ["BertModelTest"],
							    "BertForMultipleChoice": ["BertModelTest"],
							    "BertForNextSentencePrediction": ["BertModelTest"],
							    "BertForPreTraining": ["BertModelTest"],
							    "BertForQuestionAnswering": ["BertModelTest"],
							    "BertForSequenceClassification": ["BertModelTest"],
							    "BertForTokenClassification": ["BertModelTest"],
							    "BertLMHeadModel": ["BertModelTest"],
							    "BertModel": ["BertModelTest"],
							}

							_lowercase       :	Dict           =   {
							    "BlipForConditionalGeneration": ["BlipTextImageModelTest"],
							    "BlipForImageTextRetrieval": ["BlipTextRetrievalModelTest"],
							    "BlipForQuestionAnswering": ["BlipVQAModelTest"],
							    "BlipModel": ["BlipModelTest"],
							    "BlipTextModel": ["BlipTextModelTest"],
							    "BlipVisionModel": ["BlipVisionModelTest"],
							}

							self.assertEqual(get_test_info.to_json(snake_case    )      ,	snake_case    )
							self.assertEqual(get_test_info.to_json(snake_case    )      ,	snake_case    )






	def       _a(self				: int    ) ->			Any:
							_lowercase       :	Optional[int]           =   get_model_to_tester_mapping(snake_case    )
							_lowercase       :	Tuple           =   get_model_to_tester_mapping(snake_case    )

							_lowercase       :	Union[str, Any]           =   {
							    "BertForMaskedLM": ["BertModelTester"],
							    "BertForMultipleChoice": ["BertModelTester"],
							    "BertForNextSentencePrediction": ["BertModelTester"],
							    "BertForPreTraining": ["BertModelTester"],
							    "BertForQuestionAnswering": ["BertModelTester"],
							    "BertForSequenceClassification": ["BertModelTester"],
							    "BertForTokenClassification": ["BertModelTester"],
							    "BertLMHeadModel": ["BertModelTester"],
							    "BertModel": ["BertModelTester"],
							}

							_lowercase       :	Tuple           =   {
							    "BlipForConditionalGeneration": ["BlipTextImageModelsModelTester"],
							    "BlipForImageTextRetrieval": ["BlipTextRetrievalModelTester"],
							    "BlipForQuestionAnswering": ["BlipVQAModelTester"],
							    "BlipModel": ["BlipModelTester"],
							    "BlipTextModel": ["BlipTextModelTester"],
							    "BlipVisionModel": ["BlipVisionModelTester"],
							}

							self.assertEqual(get_test_info.to_json(snake_case    )      ,	snake_case    )
							self.assertEqual(get_test_info.to_json(snake_case    )      ,	snake_case    )






import warnings
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


UpperCamelCase_						:     Union[str, Any]	       =							logging.get_logger(__name__)




class   __lowercase						(						__snake_case       ):
	_A       =   ["input_values", "attention_mask"]






	def __init__(self				: int      ,	snake_case				: int = 1      ,	snake_case				: int = 1_6000      ,	snake_case				: float = 0.0      ,	snake_case				: bool = False      ,	snake_case				: int = 80      ,	snake_case				: int = 16      ,	snake_case				: int = 64      ,	snake_case				: str = "hann_window"      ,	snake_case				: float = 1.0      ,	snake_case				: float = 80      ,	snake_case				: float = 7600      ,	snake_case				: float = 1e-10      ,	snake_case				: int = 2      ,	snake_case				: bool = True      ,	**snake_case				: Any      ,	) ->			str:
							super().__init__(feature_size=snake_case      ,	sampling_rate=snake_case      ,	padding_value=snake_case      ,	**snake_case    )
							_lowercase       :	int           =   do_normalize
							_lowercase       :	Any           =   return_attention_mask

							_lowercase       :	str           =   num_mel_bins
							_lowercase       :	str           =   hop_length
							_lowercase       :	List[Any]           =   win_length
							_lowercase       :	Tuple           =   win_function
							_lowercase       :	Any           =   frame_signal_scale
							_lowercase       :	Any           =   fmin
							_lowercase       :	Any           =   fmax
							_lowercase       :	List[str]           =   mel_floor
							_lowercase       :	str           =   reduction_factor

							_lowercase       :	str           =   win_length * sampling_rate // 1000
							_lowercase       :	Optional[int]           =   hop_length * sampling_rate // 1000
							_lowercase       :	Tuple           =   optimal_fft_length(self.sample_size    )
							_lowercase       :	int           =   (self.n_fft // 2) + 1

							_lowercase       :	Tuple           =   window_function(window_length=self.sample_size      ,	name=self.win_function      ,	periodic=snake_case    )

							_lowercase       :	Optional[int]           =   mel_filter_bank(
							    num_frequency_bins=self.n_freqs      ,	num_mel_filters=self.num_mel_bins      ,	min_frequency=self.fmin      ,	max_frequency=self.fmax      ,	sampling_rate=self.sampling_rate      ,	norm="slaney"      ,	mel_scale="slaney"      ,	)

							if frame_signal_scale != 1.0:
													warnings.warn(
													    "The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers"      ,	snake_case      ,	)
							if reduction_factor != 2.0:
													warnings.warn(
													    "The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"      ,	snake_case      ,	)






	@staticmethod
	# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
	def       _a(snake_case				: List[np.ndarray]      ,	snake_case				: List[np.ndarray]      ,	snake_case				: float = 0.0    ) ->			List[np.ndarray]:
							if attention_mask is not None:
													_lowercase       :	List[str]           =   np.array(snake_case      ,	np.intaa    )
													_lowercase       :	Dict           =   []

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

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

							return normed_input_values






	def       _a(self				: Optional[Any]      ,	snake_case				: np.ndarray      ,	) ->			np.ndarray:
							_lowercase       :	Tuple           =   spectrogram(
							    snake_case      ,	window=self.window      ,	frame_length=self.sample_size      ,	hop_length=self.sample_stride      ,	fft_length=self.n_fft      ,	mel_filters=self.mel_filters      ,	mel_floor=self.mel_floor      ,	log_mel="log10"      ,	)
							return log_mel_spec.T






	def __call__(self				: Tuple      ,	snake_case				: Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None      ,	snake_case				: Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None      ,	snake_case				: Union[bool, str, PaddingStrategy] = False      ,	snake_case				: Optional[int] = None      ,	snake_case				: bool = False      ,	snake_case				: Optional[int] = None      ,	snake_case				: Optional[bool] = None      ,	snake_case				: Optional[Union[str, TensorType]] = None      ,	snake_case				: Optional[int] = None      ,	**snake_case				: Union[str, Any]      ,	) ->			BatchFeature:
							if audio is None and audio_target is None:
													raise ValueError("You must provide either `audio` or `audio_target` values."    )

							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 audio 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."    )

							if audio is not None:
													_lowercase       :	Union[str, Any]           =   self._process_audio(
													    snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	**snake_case      ,	)
							else:
													_lowercase       :	str           =   None

							if audio_target is not None:
													_lowercase       :	Union[str, Any]           =   self._process_audio(
													    snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	**snake_case      ,	)

													if inputs is None:
																			return inputs_target
													else:
																			_lowercase       :	int           =   inputs_target["input_values"]
																			_lowercase       :	Optional[int]           =   inputs_target.get("attention_mask"    )
																			if decoder_attention_mask is not None:
																									_lowercase       :	List[str]           =   decoder_attention_mask

							return inputs






	def       _a(self				: Optional[Any]      ,	snake_case				: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      ,	snake_case				: bool = False      ,	snake_case				: Union[bool, str, PaddingStrategy] = False      ,	snake_case				: Optional[int] = None      ,	snake_case				: bool = False      ,	snake_case				: Optional[int] = None      ,	snake_case				: Optional[bool] = None      ,	snake_case				: Optional[Union[str, TensorType]] = None      ,	**snake_case				: int      ,	) ->			BatchFeature:
							_lowercase       :	int           =   isinstance(snake_case      ,	np.ndarray    ) and len(speech.shape    ) > 1
							if is_batched_numpy and len(speech.shape    ) > 2:
													raise ValueError(F"""Only mono-channel audio is supported for input to {self}"""    )
							_lowercase       :	List[str]           =   is_batched_numpy or (
							    isinstance(snake_case      ,	(list, tuple)    ) and (isinstance(speech[0]      ,	(np.ndarray, tuple, list)    ))
							)

							if is_batched:
													_lowercase       :	List[Any]           =   [np.asarray(snake_case      ,	dtype=np.floataa    ) for speech in speech]
							elif not is_batched and not isinstance(snake_case      ,	np.ndarray    ):
													_lowercase       :	Optional[Any]           =   np.asarray(snake_case      ,	dtype=np.floataa    )
							elif isinstance(snake_case      ,	np.ndarray    ) and speech.dtype is np.dtype(np.floataa    ):
													_lowercase       :	Optional[int]           =   speech.astype(np.floataa    )

							# always return batch
							if not is_batched:
													_lowercase       :	int           =   [speech]

							# needed to make pad() work on spectrogram inputs
							_lowercase       :	Tuple           =   self.feature_size

							# convert into correct format for padding
							if is_target:
													_lowercase       :	List[str]           =   [self._extract_mel_features(snake_case    ) for waveform in speech]
													_lowercase       :	int           =   BatchFeature({"input_values": features}    )
													_lowercase       :	str           =   self.num_mel_bins
							else:
													_lowercase       :	Optional[int]           =   BatchFeature({"input_values": speech}    )

							_lowercase       :	List[Any]           =   self.pad(
							    snake_case      ,	padding=snake_case      ,	max_length=snake_case      ,	truncation=snake_case      ,	pad_to_multiple_of=snake_case      ,	return_attention_mask=snake_case      ,	**snake_case      ,	)

							_lowercase       :	Optional[Any]           =   feature_size_hack

							# convert input values to correct format
							_lowercase       :	int           =   padded_inputs["input_values"]
							if not isinstance(input_values[0]      ,	np.ndarray    ):
													_lowercase       :	Any           =   [np.asarray(snake_case      ,	dtype=np.floataa    ) for array in input_values]
							elif (
							    not isinstance(snake_case      ,	np.ndarray    )
							    and isinstance(input_values[0]      ,	np.ndarray    )
							    and input_values[0].dtype is np.dtype(np.floataa    )
							):
													_lowercase       :	Union[str, Any]           =   [array.astype(np.floataa    ) for array in input_values]
							elif isinstance(snake_case      ,	np.ndarray    ) and input_values.dtype is np.dtype(np.floataa    ):
													_lowercase       :	Tuple           =   input_values.astype(np.floataa    )

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

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

							if return_tensors is not None:
													_lowercase       :	Any           =   padded_inputs.convert_to_tensors(snake_case    )

							return padded_inputs






	def       _a(self				: Dict    ) ->			Dict[str, Any]:
							_lowercase       :	str           =   super().to_dict()

							# Don't serialize these as they are derived from the other properties.
							_lowercase       :	Optional[Any]           =   ["window", "mel_filters", "sample_size", "sample_stride", "n_fft", "n_freqs"]
							for name in names:
													if name in output:
																			del output[name]

							return output




