'''simple docstring'''

import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
 from transformers.models.esm.modeling_esm import (
     ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
     EsmEmbeddings,
     create_position_ids_from_input_ids,
 )


class     __UpperCAmelCase       :





  def __init__(	self    ,     _lowerCamelCase    ,     _lowerCamelCase=13    ,     _lowerCamelCase=7    ,     _lowerCamelCase=False    ,     _lowerCamelCase=True    ,     _lowerCamelCase=False    ,     _lowerCamelCase=True    ,     _lowerCamelCase=33    ,     _lowerCamelCase=32    ,     _lowerCamelCase=5    ,     _lowerCamelCase=4    ,     _lowerCamelCase=37    ,     _lowerCamelCase="gelu"    ,     _lowerCamelCase=0.1    ,     _lowerCamelCase=0.1    ,     _lowerCamelCase=512    ,     _lowerCamelCase=16    ,     _lowerCamelCase=2    ,     _lowerCamelCase=0.02    ,     _lowerCamelCase=3    ,     _lowerCamelCase=4    ,     _lowerCamelCase=None    ,     ):
     lowerCAmelCase_			=      parent
     lowerCAmelCase_			=      batch_size
     lowerCAmelCase_			=      seq_length
     lowerCAmelCase_			=      is_training
     lowerCAmelCase_			=      use_input_mask
     lowerCAmelCase_			=      use_token_type_ids
     lowerCAmelCase_			=      use_labels
     lowerCAmelCase_			=      vocab_size
     lowerCAmelCase_			=      hidden_size
     lowerCAmelCase_			=      num_hidden_layers
     lowerCAmelCase_			=      num_attention_heads
     lowerCAmelCase_			=      intermediate_size
     lowerCAmelCase_			=      hidden_act
     lowerCAmelCase_			=      hidden_dropout_prob
     lowerCAmelCase_			=      attention_probs_dropout_prob
     lowerCAmelCase_			=      max_position_embeddings
     lowerCAmelCase_			=      type_vocab_size
     lowerCAmelCase_			=      type_sequence_label_size
     lowerCAmelCase_			=      initializer_range
     lowerCAmelCase_			=      num_labels
     lowerCAmelCase_			=      num_choices
     lowerCAmelCase_			=      scope





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size )

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

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

     lowerCAmelCase_			=      self.get_config()

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





  def 						UpperCAmelCase_	(	self ):
     return EsmConfig(
         vocab_size=self.vocab_size    ,     hidden_size=self.hidden_size    ,     pad_token_id=1    ,     num_hidden_layers=self.num_hidden_layers    ,     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    ,     )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      EsmModel(config=lowercase_ )
     model.to(lowercase_ )
     model.eval()
     lowerCAmelCase_			=      model(lowercase_    ,     attention_mask=lowercase_ )
     lowerCAmelCase_			=      model(lowercase_ )
     lowerCAmelCase_			=      model(lowercase_ )

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





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      EsmForMaskedLM(config=lowercase_ )
     model.to(lowercase_ )
     model.eval()
     lowerCAmelCase_			=      model(lowercase_    ,     attention_mask=lowercase_    ,     labels=lowercase_ )
     self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.seq_length, self.vocab_size) )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      self.num_labels
     lowerCAmelCase_			=      EsmForTokenClassification(config=lowercase_ )
     model.to(lowercase_ )
     model.eval()
     lowerCAmelCase_			=      model(lowercase_    ,     attention_mask=lowercase_    ,     labels=lowercase_ )
     self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.seq_length, self.num_labels) )







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






@require_torch
class     __UpperCAmelCase       ( SCREAMING_SNAKE_CASE_						,       SCREAMING_SNAKE_CASE_						,       unittest.TestCase		):
  __A :				Dict											= False

  __A :				List[str]											= (
      (
          EsmForMaskedLM,
          EsmModel,
          EsmForSequenceClassification,
          EsmForTokenClassification,
      )
      if is_torch_available()
      else ()
  )
  __A :				List[str]											= ()
  __A :				List[str]											= (
      {
          "feature-extraction": EsmModel,
          "fill-mask": EsmForMaskedLM,
          "text-classification": EsmForSequenceClassification,
          "token-classification": EsmForTokenClassification,
          "zero-shot": EsmForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  __A :				Optional[int]											= True





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      EsmModelTester(self )
     lowerCAmelCase_			=      ConfigTester(self    ,     config_class=lowercase_    ,     hidden_size=37 )





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





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_ )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs()
     for type in ["absolute", "relative_key", "relative_key_query"]:
        lowerCAmelCase_			=      type
        self.model_tester.create_and_check_model(*lowercase_ )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*lowercase_ )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*lowercase_ )





  @slow
  def 						UpperCAmelCase_	(	self ):
     for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowerCAmelCase_			=      EsmModel.from_pretrained(lowercase_ )
        self.assertIsNotNone(lowercase_ )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs()[0]
     lowerCAmelCase_			=      EsmEmbeddings(config=lowercase_ )

     lowerCAmelCase_			=      torch.as_tensor([[12, 31, 13, model.padding_idx]] )
     lowerCAmelCase_			=      torch.as_tensor(
         [
             [
                 0 + model.padding_idx + 1,
                 1 + model.padding_idx + 1,
                 2 + model.padding_idx + 1,
                 model.padding_idx,
             ]
         ] )
     lowerCAmelCase_			=      create_position_ids_from_input_ids(lowercase_    ,     model.padding_idx )
     self.assertEqual(position_ids.shape    ,     expected_positions.shape )
     self.assertTrue(torch.all(torch.eq(lowercase_    ,     lowercase_ ) ) )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs()[0]
     lowerCAmelCase_			=      EsmEmbeddings(config=lowercase_ )

     lowerCAmelCase_			=      torch.empty(2    ,     4    ,     30 )
     lowerCAmelCase_			=      [
         0 + embeddings.padding_idx + 1,
         1 + embeddings.padding_idx + 1,
         2 + embeddings.padding_idx + 1,
         3 + embeddings.padding_idx + 1,
     ]
     lowerCAmelCase_			=      torch.as_tensor([expected_single_positions, expected_single_positions] )
     lowerCAmelCase_			=      embeddings.create_position_ids_from_inputs_embeds(lowercase_ )
     self.assertEqual(position_ids.shape    ,     expected_positions.shape )
     self.assertTrue(torch.all(torch.eq(lowercase_    ,     lowercase_ ) ) )





  @unittest.skip('''Esm does not support embedding resizing''' )
  def 						UpperCAmelCase_	(	self ):
     pass





  @unittest.skip('''Esm does not support embedding resizing''' )
  def 						UpperCAmelCase_	(	self ):
     pass







  @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
  def 						UpperCAmelCase_	(	self ):
     pass






@require_torch
class     __UpperCAmelCase       ( SCREAMING_SNAKE_CASE_		):





  @slow
  def 						UpperCAmelCase_	(	self ):
     with torch.no_grad():
        lowerCAmelCase_			=      EsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D''' )
        model.eval()
        lowerCAmelCase_			=      torch.tensor([[0, 1, 2, 3, 4, 5]] )
        lowerCAmelCase_			=      model(lowercase_ )[0]

        lowerCAmelCase_			=      33

        lowerCAmelCase_			=      torch.Size((1, 6, vocab_size) )
        self.assertEqual(output.shape    ,     lowercase_ )

        lowerCAmelCase_			=      torch.tensor(
            [[[8.92_15, -10.5898, -6.46_71], [-6.39_67, -13.9114, -1.12_12], [-7.78_12, -13.9516, -3.74_06]]] )
        self.assertTrue(torch.allclose(output[:, :3, :3]    ,     lowercase_    ,     atol=1E-4 ) )







  @slow
  def 						UpperCAmelCase_	(	self ):
     with torch.no_grad():
        lowerCAmelCase_			=      EsmModel.from_pretrained('''facebook/esm2_t6_8M_UR50D''' )
        model.eval()

        lowerCAmelCase_			=      torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
        lowerCAmelCase_			=      model(lowercase_ )[0]
        # compare the actual values for a slice.
        lowerCAmelCase_			=      torch.tensor(
            [[[0.14_44, 0.54_13, 0.32_48], [0.30_34, 0.00_53, 0.31_08], [0.32_28, -0.24_99, 0.34_15]]] )
        self.assertTrue(torch.allclose(output[:, :3, :3]    ,     lowercase_    ,     atol=1E-4 ) )







"""simple docstring"""

from __future__ import annotations

from collections.abc import Iterator
class   _UpperCAmelCase				:




      def __init__(				self ,							lowercase_				)    ->       None:
          UpperCAmelCase          =       value
          UpperCAmelCase          =       None
          UpperCAmelCase          =       None

class   _UpperCAmelCase				:




      def __init__(				self ,							lowercase_				)    ->       None:
          UpperCAmelCase          =       tree




      def      a_   (				self ,							lowercase_				)    ->       int:
          if node is None:
              return 0
          return node.value + (
              self.depth_first_search(node.left				) + self.depth_first_search(node.right				)
          )




      def __iter__(				self				)    ->       Iterator[int]:
          yield self.depth_first_search(self.tree				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



