


"""simple docstring"""






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    ,				__UpperCAmelCase    ,				)       ->							int:
          _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 							_UpperCAmelCase      (      self							)       ->							str:
          _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 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          (
              _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 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							Dict:
          _a    	=				TFEsmModel(config=lowerCamelCase_							)
          _a    	=				{'''input_ids''': input_ids, '''attention_mask''': input_mask}
          _a    	=				model(lowerCamelCase_							)

          _a    	=				[input_ids, input_mask]
          _a    	=				model(lowerCamelCase_							)

          _a    	=				model(lowerCamelCase_							)

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


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				)       ->							str:
          _a    	=				True

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

          _a    	=				[input_ids, input_mask]
          _a    	=				model(lowerCamelCase_    ,				encoder_hidden_states=lowerCamelCase_							)

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

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


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							Any:
          _a    	=				TFEsmForMaskedLM(config=lowerCamelCase_							)
          _a    	=				model([input_ids, input_mask]							)
          self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.seq_length, self.vocab_size)							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							int:
          _a    	=				self.num_labels
          _a    	=				TFEsmForTokenClassification(config=lowerCamelCase_							)
          _a    	=				{'''input_ids''': input_ids, '''attention_mask''': input_mask}
          _a    	=				model(lowerCamelCase_							)
          self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.seq_length, self.num_labels)							)





     def 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          _a    	=				self.prepare_config_and_inputs()
          (
              _a					
          )    	=				config_and_inputs
          _a    	=				{'''input_ids''': input_ids, '''attention_mask''': input_mask}
          return config, inputs_dict







@require_tf
class     __lowerCamelCase  (					_UpperCAmelCase  ,					_UpperCAmelCase  ,					unittest.TestCase				):

     '''simple docstring'''
     A_       :	int    =		(
         (
             TFEsmModel,
             TFEsmForMaskedLM,
             TFEsmForSequenceClassification,
             TFEsmForTokenClassification,
         )
         if is_tf_available()
         else ()
     )
     A_       :	Optional[int]    =		(
         {
             'feature-extraction': TFEsmModel,
             'fill-mask': TFEsmForMaskedLM,
             'text-classification': TFEsmForSequenceClassification,
             'token-classification': TFEsmForTokenClassification,
             'zero-shot': TFEsmForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     A_       :	List[Any]    =		False
     A_       :	Optional[int]    =		False


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          _a    	=				TFEsmModelTester(self							)
          _a    	=				ConfigTester(self    ,				config_class=lowerCamelCase_    ,				hidden_size=37							)


     def 							_UpperCAmelCase      (      self							)       ->							str:
          self.config_tester.run_common_tests()


     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCamelCase_							)


     def 							_UpperCAmelCase      (      self							)       ->							List[str]:
          _a    	=				self.model_tester.prepare_config_and_inputs_for_decoder()
          self.model_tester.create_and_check_model_as_decoder(*lowerCamelCase_							)


     def 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase_							)


     def 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_							)


     @slow
     def 							_UpperCAmelCase      (      self							)       ->							str:




          for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _a    	=				TFEsmModel.from_pretrained(lowerCamelCase_							)
               self.assertIsNotNone(lowerCamelCase_							)


     @unittest.skip('''Protein models do not support embedding resizing.'''							)
     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          pass


     @unittest.skip('''Protein models do not support embedding resizing.'''							)
     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          pass





     def 							_UpperCAmelCase      (      self							)       ->							List[Any]:
          _a    	=				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               _a    	=				model_class(lowerCamelCase_							)
               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(lowerCamelCase_    ,				lowerCamelCase_							)
                    for k, v in name.items():
                         assert isinstance(lowerCamelCase_    ,				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 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          _a    	=				TFEsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D'''							)

          _a    	=				tf.constant([[0, 1, 2, 3, 4, 5]]							)
          _a    	=				model(lowerCamelCase_							)[0]
          _a    	=				[1, 6, 33]
          self.assertEqual(list(output.numpy().shape							)    ,				lowerCamelCase_							)
          # compare the actual values for a slice.
          _a    	=				tf.constant(
              [
                  [
                      [8.921518, -10.589814, -6.4671307],
                      [-6.3967156, -13.911377, -1.1211915],
                      [-7.781247, -13.951557, -3.740592],
                  ]
              ]							)
          self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()    ,				expected_slice.numpy()    ,				atol=1e-2							)							)





     @slow
     def 							_UpperCAmelCase      (      self							)       ->							List[str]:
          _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(lowerCamelCase_							)[0]
          # compare the actual values for a slice.
          _a    	=				tf.constant(
              [
                  [
                      [0.14443092, 0.54125327, 0.3247739],
                      [0.30340484, 0.00526676, 0.31077722],
                      [0.32278043, -0.24987096, 0.3414628],
                  ]
              ]							)
          self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()    ,				expected_slice.numpy()    ,				atol=1e-4							)							)



"""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		=       logging.get_logger(__name__)


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	Union[str, Any]    =		['input_values', 'padding_mask']


     def __init__(      self    ,				__UpperCAmelCase = 1    ,				__UpperCAmelCase = 24000    ,				__UpperCAmelCase = 0.0    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							List[str]:
          super().__init__(feature_size=__UpperCAmelCase    ,				sampling_rate=__UpperCAmelCase    ,				padding_value=__UpperCAmelCase    ,				**__UpperCAmelCase							)
          _a    	=				chunk_length_s
          _a    	=				overlap


     @property
     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          if self.chunk_length_s is None:
               return None
          else:
               return int(self.chunk_length_s * self.sampling_rate							)


     @property
     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          if self.chunk_length_s is None or self.overlap is None:
               return None
          else:
               return max(1    ,				int((1.0 - self.overlap) * self.chunk_length							)							)





     def __call__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = False    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				)       ->							BatchFeature:
          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 padding and truncation:
               raise ValueError('''Both padding and truncation were set. Make sure you only set one.'''							)
          elif padding is None:
               # by default let's pad the inputs
               _a    	=				True

          _a    	=				bool(
              isinstance(__UpperCAmelCase    ,				(list, tuple)							) and (isinstance(raw_audio[0]    ,				(np.ndarray, tuple, list)							))							)

          if is_batched:
               _a    	=				[np.asarray(__UpperCAmelCase    ,				dtype=np.floataa							).T for audio in raw_audio]
          elif not is_batched and not isinstance(__UpperCAmelCase    ,				np.ndarray							):
               _a    	=				np.asarray(__UpperCAmelCase    ,				dtype=np.floataa							)
          elif isinstance(__UpperCAmelCase    ,				np.ndarray							) and raw_audio.dtype is np.dtype(np.floataa							):
               _a    	=				raw_audio.astype(np.floataa							)

          # always return batch
          if not is_batched:
               _a    	=				[np.asarray(__UpperCAmelCase							).T]

          # verify inputs are valid
          for idx, example in enumerate(__UpperCAmelCase							):
               if example.ndim > 2:
                    raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}'							)
               if self.feature_size == 1 and example.ndim != 1:
                    raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels'							)
               if self.feature_size == 2 and example.shape[-1] != 2:
                    raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels'							)

          _a    	=				None
          _a    	=				BatchFeature({'''input_values''': raw_audio}							)
          if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
               if truncation:
                    _a    	=				min(array.shape[0] for array in raw_audio							)
                    _a    	=				int(np.floor(max_length / self.chunk_stride							)							)
                    _a    	=				(nb_step - 1) * self.chunk_stride + self.chunk_length
               elif padding:
                    _a    	=				max(array.shape[0] for array in raw_audio							)
                    _a    	=				int(np.ceil(max_length / self.chunk_stride							)							)
                    _a    	=				(nb_step - 1) * self.chunk_stride + self.chunk_length
                    _a    	=				'''max_length'''
               else:
                    _a    	=				input_values

        # normal padding on batch
          if padded_inputs is None:
               _a    	=				self.pad(
                   __UpperCAmelCase    ,				max_length=__UpperCAmelCase    ,				truncation=__UpperCAmelCase    ,				padding=__UpperCAmelCase    ,				return_attention_mask=__UpperCAmelCase    ,				)
               if padding:
                    _a    	=				padded_inputs.pop('''attention_mask'''							)

          _a    	=				[]
          for example in padded_inputs.pop('''input_values'''							):
               if self.feature_size == 1:
                    _a    	=				example[..., None]
               input_values.append(example.T							)

          _a    	=				input_values
          if return_tensors is not None:
               _a    	=				padded_inputs.convert_to_tensors(__UpperCAmelCase							)

          return padded_inputs