





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


__A							:			str         =			logging.get_logger(__name__)







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			['input_values', 'attention_mask']




     def __init__(    self				,     snake_case_ = 1				,     snake_case_ = 1_6000				,     snake_case_ = 0.0				,     snake_case_ = False				,     snake_case_ = 80				,     snake_case_ = 16				,     snake_case_ = 64				,     snake_case_ = "hann_window"				,     snake_case_ = 1.0				,     snake_case_ = 80				,     snake_case_ = 7600				,     snake_case_ = 1E-10				,     snake_case_ = 2				,     snake_case_ = True				,     **snake_case_				,     ):
         super().__init__(feature_size=snake_case_				,     sampling_rate=snake_case_				,     padding_value=snake_case_				,     **snake_case_							)
         _A     =							do_normalize
         _A     =							return_attention_mask

         _A     =							num_mel_bins
         _A     =							hop_length
         _A     =							win_length
         _A     =							win_function
         _A     =							frame_signal_scale
         _A     =							fmin
         _A     =							fmax
         _A     =							mel_floor
         _A     =							reduction_factor

         _A     =							win_length * sampling_rate // 1000
         _A     =							hop_length * sampling_rate // 1000
         _A     =							optimal_fft_length(self.sample_size							)
         _A     =							(self.n_fft // 2) + 1

         _A     =							window_function(window_length=self.sample_size				,     name=self.win_function				,     periodic=snake_case_							)

         _A     =							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        lowerCAmelCase__		(    snake_case_				,     snake_case_				,     snake_case_ = 0.0							):
         if attention_mask is not None:
             _A     =							np.array(snake_case_				,     np.intaa							)
             _A     =							[]

             for vector, length in zip(snake_case_				,     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(snake_case_							)
         else:
             _A     =							[(x - x.mean()) / np.sqrt(x.var() + 1E-7							) for x in input_values]

         return normed_input_values




     def        lowerCAmelCase__		(    self				,     snake_case_				,     ):
         _A     =							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				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = False				,     snake_case_ = None				,     snake_case_ = False				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     **snake_case_				,     ):
         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:
             _A     =							self._process_audio(
                 snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     **snake_case_				,     )
         else:
             _A     =							None

         if audio_target is not None:
             _A     =							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:
                 _A     =							inputs_target['input_values']
                 _A     =							inputs_target.get('attention_mask'							)
                 if decoder_attention_mask is not None:
                     _A     =							decoder_attention_mask

         return inputs




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = False				,     snake_case_ = False				,     snake_case_ = None				,     snake_case_ = False				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     **snake_case_				,     ):
         _A     =							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}"							)
         _A     =							is_batched_numpy or (
             isinstance(snake_case_				,     (list, tuple)							) and (isinstance(speech[0]				,     (np.ndarray, tuple, list)							))
         )

         if is_batched:
             _A     =							[np.asarray(snake_case_				,     dtype=np.floataa							) for speech in speech]
         elif not is_batched and not isinstance(snake_case_				,     np.ndarray							):
             _A     =							np.asarray(snake_case_				,     dtype=np.floataa							)
         elif isinstance(snake_case_				,     np.ndarray							) and speech.dtype is np.dtype(np.floataa							):
             _A     =							speech.astype(np.floataa							)

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

         # needed to make pad() work on spectrogram inputs
         _A     =							self.feature_size

         # convert into correct format for padding
         if is_target:
             _A     =							[self._extract_mel_features(snake_case_							) for waveform in speech]
             _A     =							BatchFeature({'input_values': features}							)
             _A     =							self.num_mel_bins
         else:
             _A     =							BatchFeature({'input_values': speech}							)

         _A     =							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_				,     )

         _A     =							feature_size_hack

         # convert input values to correct format
         _A     =							padded_inputs['input_values']
         if not isinstance(input_values[0]				,     np.ndarray							):
             _A     =							[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							)
         ):
             _A     =							[array.astype(np.floataa							) for array in input_values]
         elif isinstance(snake_case_				,     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(snake_case_				,     dtype=np.intaa							) for array in attention_mask]

         # zero-mean and unit-variance normalization
         if not is_target and self.do_normalize:
             _A     =							(
                 attention_mask
                 if self._get_padding_strategies(snake_case_				,     max_length=snake_case_							) is not PaddingStrategy.DO_NOT_PAD
                 else None
             )
             _A     =							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:
             _A     =							padded_inputs.convert_to_tensors(snake_case_							)

         return padded_inputs




     def        lowerCAmelCase__		(    self							):
         _A     =							super().to_dict()

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

         return output












from __future__ import annotations

import unittest

from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import numpy
 import tensorflow as tf

 from transformers.models.esm.modeling_tf_esm import (
     TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFEsmForMaskedLM,
     TFEsmForSequenceClassification,
     TFEsmForTokenClassification,
     TFEsmModel,
 )







class      lowerCamelCase:
     '''simple docstring'''


     def __init__(    self				,     snake_case_				,     ):
         _A     =							parent
         _A     =							13
         _A     =							7
         _A     =							True
         _A     =							True
         _A     =							True
         _A     =							99
         _A     =							32
         _A     =							2
         _A     =							4
         _A     =							37
         _A     =							'gelu'
         _A     =							0.1
         _A     =							0.1
         _A     =							512
         _A     =							16
         _A     =							2
         _A     =							0.02
         _A     =							3
         _A     =							4
         _A     =							None




     def        lowerCAmelCase__		(    self							):
         _A     =							ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size							)

         _A     =							None
         if self.use_input_mask:
             _A     =							random_attention_mask([self.batch_size, self.seq_length]							)

         _A     =							None
         _A     =							None
         _A     =							None
         if self.use_labels:
             _A     =							ids_tensor([self.batch_size]				,     self.type_sequence_label_size							)
             _A     =							ids_tensor([self.batch_size, self.seq_length]				,     self.num_labels							)
             _A     =							ids_tensor([self.batch_size]				,     self.num_choices							)

         _A     =							EsmConfig(
             vocab_size=self.vocab_size				,     hidden_size=self.hidden_size				,     num_hidden_layers=self.num_hidden_layers				,     pad_token_id=1				,     num_attention_heads=self.num_attention_heads				,     intermediate_size=self.intermediate_size				,     hidden_act=self.hidden_act				,     hidden_dropout_prob=self.hidden_dropout_prob				,     attention_probs_dropout_prob=self.attention_probs_dropout_prob				,     max_position_embeddings=self.max_position_embeddings				,     type_vocab_size=self.type_vocab_size				,     initializer_range=self.initializer_range				,     )

         return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels




     def        lowerCAmelCase__		(    self							):
         (
             (
             _A
         ),			(
             _A
         ),			(
             _A
         ),			(
             _A
         ),			(
             _A
         ),			(
             _A
         ),			
         )     =							self.prepare_config_and_inputs()

         _A     =							True
         _A     =							floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
         _A     =							ids_tensor([self.batch_size, self.seq_length]				,     vocab_size=2							)

         return (
             config,
             input_ids,
             input_mask,
             sequence_labels,
             token_labels,
             choice_labels,
             encoder_hidden_states,
             encoder_attention_mask,
         )




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							TFEsmModel(config=snake_case_							)
         _A     =							{'input_ids': input_ids, 'attention_mask': input_mask}
         _A     =							model(snake_case_							)

         _A     =							[input_ids, input_mask]
         _A     =							model(snake_case_							)

         _A     =							model(snake_case_							)

         self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size)							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     ):
         _A     =							True

         _A     =							TFEsmModel(config=snake_case_							)
         _A     =							{
             'input_ids': input_ids,
             'attention_mask': input_mask,
             'encoder_hidden_states': encoder_hidden_states,
             'encoder_attention_mask': encoder_attention_mask,
         }
         _A     =							model(snake_case_							)

         _A     =							[input_ids, input_mask]
         _A     =							model(snake_case_				,     encoder_hidden_states=snake_case_							)

         # Also check the case where encoder outputs are not passed
         _A     =							model(snake_case_				,     attention_mask=snake_case_							)

         self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size)							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							TFEsmForMaskedLM(config=snake_case_							)
         _A     =							model([input_ids, input_mask]							)
         self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.vocab_size)							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							self.num_labels
         _A     =							TFEsmForTokenClassification(config=snake_case_							)
         _A     =							{'input_ids': input_ids, 'attention_mask': input_mask}
         _A     =							model(snake_case_							)
         self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.num_labels)							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.prepare_config_and_inputs()
         (
             (
             _A
         ),			(
             _A
         ),			(
             _A
         ),			(
             _A
         ),			(
             _A
         ),			(
             _A
         ),			
         )     =							config_and_inputs
         _A     =							{'input_ids': input_ids, 'attention_mask': input_mask}
         return config, inputs_dict







@require_tf
class      lowerCamelCase(						__snake_case			, __snake_case			, unittest.TestCase	):
     '''simple docstring'''


     __magic_name__   =			(
         (
             TFEsmModel,
             TFEsmForMaskedLM,
             TFEsmForSequenceClassification,
             TFEsmForTokenClassification,
         )
         if is_tf_available()
         else ()
     )
     __magic_name__   =			(
         {
             'feature-extraction': TFEsmModel,
             'fill-mask': TFEsmForMaskedLM,
             'text-classification': TFEsmForSequenceClassification,
             'token-classification': TFEsmForTokenClassification,
             'zero-shot': TFEsmForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     __magic_name__   =			False
     __magic_name__   =			False




     def        lowerCAmelCase__		(    self							):
         _A     =							TFEsmModelTester(self							)
         _A     =							ConfigTester(self				,     config_class=snake_case_				,     hidden_size=37							)




     def        lowerCAmelCase__		(    self							):
         self.config_tester.run_common_tests()




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_model_as_decoder(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*snake_case_							)




     @slow
     def        lowerCAmelCase__		(    self							):
         for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _A     =							TFEsmModel.from_pretrained(snake_case_							)
             self.assertIsNotNone(snake_case_							)




     @unittest.skip('Protein models do not support embedding resizing.'							)
     def        lowerCAmelCase__		(    self							):
         pass




     @unittest.skip('Protein models do not support embedding resizing.'							)
     def        lowerCAmelCase__		(    self							):
         pass




     def        lowerCAmelCase__		(    self							):
         _A,			_A     =							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             _A     =							model_class(snake_case_							)
             assert isinstance(model.get_input_embeddings()				,     tf.keras.layers.Layer							)
             if model_class is TFEsmForMaskedLM:
                 # Output embedding test differs from the main test because they're a matrix, not a layer
                 _A     =							model.get_bias()
                 assert isinstance(snake_case_				,     snake_case_							)
                 for k, v in name.items():
                     assert isinstance(snake_case_				,     tf.Variable							)
             else:
                 _A     =							model.get_output_embeddings()
                 assert x is None
                 _A     =							model.get_bias()
                 assert name is None







@require_tf
class      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


     @slow
     def        lowerCAmelCase__		(    self							):
         _A     =							TFEsmForMaskedLM.from_pretrained('facebook/esm2_t6_8M_UR50D'							)

         _A     =							tf.constant([[0, 1, 2, 3, 4, 5]]							)
         _A     =							model(snake_case_							)[0]
         _A     =							[1, 6, 33]
         self.assertEqual(list(output.numpy().shape							)				,     snake_case_							)
         # compare the actual values for a slice.
         _A     =							tf.constant(
             [
                 [
                     [8.92_1518, -10.58_9814, -6.467_1307],
                     [-6.396_7156, -13.91_1377, -1.121_1915],
                     [-7.78_1247, -13.95_1557, -3.74_0592],
                 ]
             ]							)
         self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()				,     expected_slice.numpy()				,     atol=1E-2							)							)




     @slow
     def        lowerCAmelCase__		(    self							):
         _A     =							TFEsmModel.from_pretrained('facebook/esm2_t6_8M_UR50D'							)

         _A     =							tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]							)
         _A     =							model(snake_case_							)[0]
         # compare the actual values for a slice.
         _A     =							tf.constant(
             [
                 [
                     [0.1444_3092, 0.5412_5327, 0.324_7739],
                     [0.3034_0484, 0.0052_6676, 0.3107_7722],
                     [0.3227_8043, -0.2498_7096, 0.341_4628],
                 ]
             ]							)
         self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()				,     expected_slice.numpy()				,     atol=1E-4							)							)





