

"""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        _lowercase					:







     def __init__(      self ,						UpperCamelCase_ ,						):
      __magic_name__     =      parent
      __magic_name__     =      13
      __magic_name__     =      7
      __magic_name__     =      True
      __magic_name__     =      True
      __magic_name__     =      True
      __magic_name__     =      99
      __magic_name__     =      32
      __magic_name__     =      2
      __magic_name__     =      4
      __magic_name__     =      37
      __magic_name__     =      '''gelu'''
      __magic_name__     =      0.1
      __magic_name__     =      0.1
      __magic_name__     =      512
      __magic_name__     =      16
      __magic_name__     =      2
      __magic_name__     =      0.0_2
      __magic_name__     =      3
      __magic_name__     =      4
      __magic_name__     =      None







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

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

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

      __magic_name__     =      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       ):
      (
          (
          __magic_name__
      )   ,						(
          __magic_name__
      )   ,						(
          __magic_name__
      )   ,						(
          __magic_name__
      )   ,						(
          __magic_name__
      )   ,						(
          __magic_name__
      )   ,						
      )     =      self.prepare_config_and_inputs()

      __magic_name__     =      True
      __magic_name__     =      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
      __magic_name__     =      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 ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      __magic_name__     =      TFEsmModel(config=UpperCamelCase_       )
      __magic_name__     =      {'''input_ids''': input_ids, '''attention_mask''': input_mask}
      __magic_name__     =      model(UpperCamelCase_       )

      __magic_name__     =      [input_ids, input_mask]
      __magic_name__     =      model(UpperCamelCase_       )

      __magic_name__     =      model(UpperCamelCase_       )

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







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						):
      __magic_name__     =      True

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

      __magic_name__     =      [input_ids, input_mask]
      __magic_name__     =      model(UpperCamelCase_ ,						encoder_hidden_states=UpperCamelCase_       )

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

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







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







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







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


@require_tf
class        _lowercase					(     __UpperCAmelCase   , __UpperCAmelCase   , unittest.TestCase     ):
     _lowerCamelCase     =       (
         (
             TFEsmModel,
             TFEsmForMaskedLM,
             TFEsmForSequenceClassification,
             TFEsmForTokenClassification,
         )
         if is_tf_available()
         else ()
     )
     _lowerCamelCase     =       (
         {
             '''feature-extraction''': TFEsmModel,
             '''fill-mask''': TFEsmForMaskedLM,
             '''text-classification''': TFEsmForSequenceClassification,
             '''token-classification''': TFEsmForTokenClassification,
             '''zero-shot''': TFEsmForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     _lowerCamelCase     =       False
     _lowerCamelCase     =       False







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      TFEsmModelTester(self       )
      __magic_name__     =      ConfigTester(self ,						config_class=UpperCamelCase_ ,						hidden_size=37       )







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







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_model_as_decoder(*UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*UpperCamelCase_       )







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







     @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       ):
      __magic_name__   ,						__magic_name__     =      self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __magic_name__     =      model_class(UpperCamelCase_       )
       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
        __magic_name__     =      model.get_bias()
        assert isinstance(UpperCamelCase_ ,						UpperCamelCase_       )
        for k, v in name.items():
         assert isinstance(UpperCamelCase_ ,						tf.Variable       )
       else:
        __magic_name__     =      model.get_output_embeddings()
        assert x is None
        __magic_name__     =      model.get_bias()
        assert name is None


@require_tf
class        _lowercase					(     unittest.TestCase     ):







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

      __magic_name__     =      tf.constant([[0, 1, 2, 3, 4, 5]]       )
      __magic_name__     =      model(UpperCamelCase_       )[0]
      __magic_name__     =      [1, 6, 33]
      self.assertEqual(list(output.numpy().shape       ) ,						UpperCamelCase_       )
      # compare the actual values for a slice.
      __magic_name__     =      tf.constant(
          [
              [
                  [8.9_2_1_5_1_8, -1_0.5_8_9_8_1_4, -6.4_6_7_1_3_0_7],
                  [-6.3_9_6_7_1_5_6, -1_3.9_1_1_3_7_7, -1.1_2_1_1_9_1_5],
                  [-7.7_8_1_2_4_7, -1_3.9_5_1_5_5_7, -3.7_4_0_5_9_2],
              ]
          ]       )
      self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() ,						expected_slice.numpy() ,						atol=1E-2       )       )







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

      __magic_name__     =      tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]       )
      __magic_name__     =      model(UpperCamelCase_       )[0]
      # compare the actual values for a slice.
      __magic_name__     =      tf.constant(
          [
              [
                  [0.1_4_4_4_3_0_9_2, 0.5_4_1_2_5_3_2_7, 0.3_2_4_7_7_3_9],
                  [0.3_0_3_4_0_4_8_4, 0.0_0_5_2_6_6_7_6, 0.3_1_0_7_7_7_2_2],
                  [0.3_2_2_7_8_0_4_3, -0.2_4_9_8_7_0_9_6, 0.3_4_1_4_6_2_8],
              ]
          ]       )
      self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() ,						expected_slice.numpy() ,						atol=1E-4       )       )




"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase										=		logging.get_logger(__name__)

__lowerCamelCase										=		{
    "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
    "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json"
    # See all FNet models at https://huggingface.co/models?filter=fnet
}
class        _lowercase					(     __UpperCAmelCase     ):
     _lowerCamelCase     =       '''fnet'''







     def __init__(      self ,						UpperCamelCase_=3_2000 ,						UpperCamelCase_=768 ,						UpperCamelCase_=12 ,						UpperCamelCase_=3072 ,						UpperCamelCase_="gelu_new" ,						UpperCamelCase_=0.1 ,						UpperCamelCase_=512 ,						UpperCamelCase_=4 ,						UpperCamelCase_=0.0_2 ,						UpperCamelCase_=1E-1_2 ,						UpperCamelCase_=False ,						UpperCamelCase_=512 ,						UpperCamelCase_=3 ,						UpperCamelCase_=1 ,						UpperCamelCase_=2 ,						**UpperCamelCase_ ,						):
      super().__init__(pad_token_id=UpperCamelCase_ ,						bos_token_id=UpperCamelCase_ ,						eos_token_id=UpperCamelCase_ ,						**UpperCamelCase_       )

      __magic_name__     =      vocab_size
      __magic_name__     =      max_position_embeddings
      __magic_name__     =      hidden_size
      __magic_name__     =      num_hidden_layers
      __magic_name__     =      intermediate_size
      __magic_name__     =      hidden_act
      __magic_name__     =      hidden_dropout_prob
      __magic_name__     =      initializer_range
      __magic_name__     =      type_vocab_size
      __magic_name__     =      layer_norm_eps
      __magic_name__     =      use_tpu_fourier_optimizations
      __magic_name__     =      tpu_short_seq_length

