'''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       UpperCAmelCase  :





       '''simple docstring'''



       def __init__(						self     ,       __lowerCAmelCase     ,       )  ->  Optional[Any]:
        lowercase__    :   List[str]						= parent
        lowercase__    :   List[Any]						= 13
        lowercase__    :   Union[str, Any]						= 7
        lowercase__    :   Dict						= True
        lowercase__    :   Dict						= True
        lowercase__    :   str						= True
        lowercase__    :   str						= 99
        lowercase__    :   Optional[int]						= 32
        lowercase__    :   str						= 2
        lowercase__    :   str						= 4
        lowercase__    :   Any						= 37
        lowercase__    :   Any						= '''gelu'''
        lowercase__    :   List[Any]						= 0.1
        lowercase__    :   str						= 0.1
        lowercase__    :   Union[str, Any]						= 512
        lowercase__    :   Any						= 16
        lowercase__    :   Tuple						= 2
        lowercase__    :   List[str]						= 0.0_2
        lowercase__    :   str						= 3
        lowercase__    :   str						= 4
        lowercase__    :   str						= None

       def _lowerCAmelCase(						self			)  ->  Tuple:
        lowercase__    :   Tuple						= ids_tensor([self.batch_size, self.seq_length]     ,       self.vocab_size			)

        lowercase__    :   Optional[Any]						= None
        if self.use_input_mask:
         lowercase__    :   Tuple						= random_attention_mask([self.batch_size, self.seq_length]			)

        lowercase__    :   Dict						= None
        lowercase__    :   Any						= None
        lowercase__    :   str						= None
        if self.use_labels:
         lowercase__    :   List[str]						= ids_tensor([self.batch_size]     ,       self.type_sequence_label_size			)
         lowercase__    :   Any						= ids_tensor([self.batch_size, self.seq_length]     ,       self.num_labels			)
         lowercase__    :   Optional[Any]						= ids_tensor([self.batch_size]     ,       self.num_choices			)

        lowercase__    :   Optional[int]						= 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			)  ->  int:
        (
            (
            lowercase__
        )			,							(
            lowercase__
        )			,							(
            lowercase__
        )			,							(
            lowercase__
        )			,							(
            lowercase__
        )			,							(
            lowercase__
        )			,							
        )    :   int						= self.prepare_config_and_inputs()

        lowercase__    :   List[Any]						= True
        lowercase__    :   Tuple						= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
        lowercase__    :   str						= 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     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Dict:
        lowercase__    :   Optional[int]						= TFEsmModel(config=__lowerCAmelCase			)
        lowercase__    :   Union[str, Any]						= {'''input_ids''': input_ids, '''attention_mask''': input_mask}
        lowercase__    :   List[Any]						= model(__lowerCAmelCase			)

        lowercase__    :   List[str]						= [input_ids, input_mask]
        lowercase__    :   str						= model(__lowerCAmelCase			)

        lowercase__    :   List[Any]						= model(__lowerCAmelCase			)

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

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       )  ->  Union[str, Any]:
        lowercase__    :   List[Any]						= True

        lowercase__    :   Union[str, Any]						= TFEsmModel(config=__lowerCAmelCase			)
        lowercase__    :   Optional[int]						= {
            '''input_ids''': input_ids,
            '''attention_mask''': input_mask,
            '''encoder_hidden_states''': encoder_hidden_states,
            '''encoder_attention_mask''': encoder_attention_mask,
        }
        lowercase__    :   Dict						= model(__lowerCAmelCase			)

        lowercase__    :   int						= [input_ids, input_mask]
        lowercase__    :   Tuple						= model(__lowerCAmelCase     ,       encoder_hidden_states=__lowerCAmelCase			)

        # Also check the case where encoder outputs are not passed
        lowercase__    :   Any						= model(__lowerCAmelCase     ,       attention_mask=__lowerCAmelCase			)

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

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Optional[Any]:
        lowercase__    :   List[str]						= TFEsmForMaskedLM(config=__lowerCAmelCase			)
        lowercase__    :   int						= model([input_ids, input_mask]			)
        self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.seq_length, self.vocab_size)			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  List[str]:
        lowercase__    :   Tuple						= self.num_labels
        lowercase__    :   Any						= TFEsmForTokenClassification(config=__lowerCAmelCase			)
        lowercase__    :   Union[str, Any]						= {'''input_ids''': input_ids, '''attention_mask''': input_mask}
        lowercase__    :   str						= model(__lowerCAmelCase			)
        self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.seq_length, self.num_labels)			)


       def _lowerCAmelCase(						self			)  ->  int:
        lowercase__    :   List[str]						= self.prepare_config_and_inputs()
        (
            (
            lowercase__
        )			,							(
            lowercase__
        )			,							(
            lowercase__
        )			,							(
            lowercase__
        )			,							(
            lowercase__
        )			,							(
            lowercase__
        )			,							
        )    :   Any						= config_and_inputs
        lowercase__    :   List[str]						= {'''input_ids''': input_ids, '''attention_mask''': input_mask}
        return config, inputs_dict



@require_tf
class       UpperCAmelCase  (						a__   ,						a__   ,						unittest.TestCase ):





       '''simple docstring'''



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

       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        lowercase__    :   str						= TFEsmModelTester(self			)
        lowercase__    :   List[str]						= ConfigTester(self     ,       config_class=__lowerCAmelCase     ,       hidden_size=37			)

       def _lowerCAmelCase(						self			)  ->  Dict:
        self.config_tester.run_common_tests()

       def _lowerCAmelCase(						self			)  ->  Tuple:
        lowercase__    :   List[str]						= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowerCAmelCase			)

       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        lowercase__    :   str						= self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(*__lowerCAmelCase			)

       def _lowerCAmelCase(						self			)  ->  int:
        lowercase__    :   Tuple						= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase			)

       def _lowerCAmelCase(						self			)  ->  int:
        lowercase__    :   Union[str, Any]						= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase			)

       @slow
       def _lowerCAmelCase(						self			)  ->  Tuple:
        for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowercase__    :   int						= TFEsmModel.from_pretrained(__lowerCAmelCase			)
         self.assertIsNotNone(__lowerCAmelCase			)

       @unittest.skip('''Protein models do not support embedding resizing.'''			)
       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        pass

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


       def _lowerCAmelCase(						self			)  ->  List[Any]:
        lowercase__			,							lowercase__    :   int						= self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         lowercase__    :   Any						= 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
          lowercase__    :   int						= model.get_bias()
          assert isinstance(__lowerCAmelCase     ,       __lowerCAmelCase			)
          for k, v in name.items():
           assert isinstance(__lowerCAmelCase     ,       tf.Variable			)
         else:
          lowercase__    :   Any						= model.get_output_embeddings()
          assert x is None
          lowercase__    :   Optional[int]						= model.get_bias()
          assert name is None



@require_tf
class       UpperCAmelCase  (						unittest.TestCase ):





       '''simple docstring'''



       @slow
       def _lowerCAmelCase(						self			)  ->  Dict:
        lowercase__    :   Any						= TFEsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D'''			)

        lowercase__    :   List[str]						= tf.constant([[0, 1, 2, 3, 4, 5]]			)
        lowercase__    :   Optional[Any]						= model(__lowerCAmelCase			)[0]
        lowercase__    :   List[Any]						= [1, 6, 33]
        self.assertEqual(list(output.numpy().shape			)     ,       __lowerCAmelCase			)
        # compare the actual values for a slice.
        lowercase__    :   Union[str, Any]						= 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			)  ->  str:
        lowercase__    :   Any						= TFEsmModel.from_pretrained('''facebook/esm2_t6_8M_UR50D'''			)

        lowercase__    :   Optional[Any]						= tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]			)
        lowercase__    :   Optional[Any]						= model(__lowerCAmelCase			)[0]
        # compare the actual values for a slice.
        lowercase__    :   int						= 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'''

import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size'''		,					[None, 400 * 2**20, 600 * 2**20]					)
@pytest.mark.parametrize('''input_in_memory_max_size'''		,					['''default''', 0, 100 * 2**20, 900 * 2**20]					)
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):
 if input_in_memory_max_size != "default":
  monkeypatch.setattr(datasets.config		,					'''IN_MEMORY_MAX_SIZE'''		,					UpperCAmelCase					)
 lowercase__    :   List[Any]						= datasets.config.IN_MEMORY_MAX_SIZE
 if input_in_memory_max_size == "default":
  assert in_memory_max_size == 0
 else:
  assert in_memory_max_size == input_in_memory_max_size
 if dataset_size and in_memory_max_size:
  lowercase__    :   str						= dataset_size < in_memory_max_size
 else:
  lowercase__    :   Optional[int]						= False
 lowercase__    :   Optional[Any]						= is_small_dataset(UpperCAmelCase					)
 assert result == expected

