

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     __lowercase :
 def __init__(				self ,				A_ ,				A_=13 ,				A_=7 ,				A_=False ,				A_=True ,				A_=False ,				A_=True ,				A_=33 ,				A_=32 ,				A_=5 ,				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 ,				)   ->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase   :		List[Any]   =   parent
       __lowerCAmelCase   :		Union[str, Any]   =   batch_size
       __lowerCAmelCase   :		str   =   seq_length
       __lowerCAmelCase   :		Optional[int]   =   is_training
       __lowerCAmelCase   :		str   =   use_input_mask
       __lowerCAmelCase   :		Optional[int]   =   use_token_type_ids
       __lowerCAmelCase   :		str   =   use_labels
       __lowerCAmelCase   :		Any   =   vocab_size
       __lowerCAmelCase   :		int   =   hidden_size
       __lowerCAmelCase   :		Optional[int]   =   num_hidden_layers
       __lowerCAmelCase   :		Tuple   =   num_attention_heads
       __lowerCAmelCase   :		Tuple   =   intermediate_size
       __lowerCAmelCase   :		Union[str, Any]   =   hidden_act
       __lowerCAmelCase   :		Dict   =   hidden_dropout_prob
       __lowerCAmelCase   :		Any   =   attention_probs_dropout_prob
       __lowerCAmelCase   :		List[str]   =   max_position_embeddings
       __lowerCAmelCase   :		Optional[Any]   =   type_vocab_size
       __lowerCAmelCase   :		Dict   =   type_sequence_label_size
       __lowerCAmelCase   :		Dict   =   initializer_range
       __lowerCAmelCase   :		List[str]   =   num_labels
       __lowerCAmelCase   :		Tuple   =   num_choices
       __lowerCAmelCase   :		List[Any]   =   scope
 def       UpperCamelCase__    (				self   )   ->Tuple:



       '''simple docstring'''





       __lowerCAmelCase   :		int   =   ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size   )

       __lowerCAmelCase   :		int   =   None
       if self.use_input_mask:
             __lowerCAmelCase   :		Any   =   random_attention_mask([self.batch_size, self.seq_length]   )

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

       __lowerCAmelCase   :		Union[str, Any]   =   self.get_config()

       return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
 def       UpperCamelCase__    (				self   )   ->str:



       '''simple docstring'''





       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 ,				A_ ,				A_ ,				A_ ,				A_ ,				A_ ,				A_   )   ->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase   :		Any   =   EsmModel(config=__lowerCamelCase   )
       model.to(__lowerCamelCase   )
       model.eval()
       __lowerCAmelCase   :		List[str]   =   model(__lowerCamelCase ,				attention_mask=__lowerCamelCase   )
       __lowerCAmelCase   :		Dict   =   model(__lowerCamelCase   )
       __lowerCAmelCase   :		Optional[Any]   =   model(__lowerCamelCase   )

       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 ,				A_ ,				A_ ,				A_ ,				A_ ,				A_ ,				A_   )   ->int:



       '''simple docstring'''





       __lowerCAmelCase   :		Optional[Any]   =   EsmForMaskedLM(config=__lowerCamelCase   )
       model.to(__lowerCamelCase   )
       model.eval()
       __lowerCAmelCase   :		Dict   =   model(__lowerCamelCase ,				attention_mask=__lowerCamelCase ,				labels=__lowerCamelCase   )
       self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)   )
 def       UpperCamelCase__    (				self ,				A_ ,				A_ ,				A_ ,				A_ ,				A_ ,				A_   )   ->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase   :		str   =   self.num_labels
       __lowerCAmelCase   :		Dict   =   EsmForTokenClassification(config=__lowerCamelCase   )
       model.to(__lowerCamelCase   )
       model.eval()
       __lowerCAmelCase   :		str   =   model(__lowerCamelCase ,				attention_mask=__lowerCamelCase ,				labels=__lowerCamelCase   )
       self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.num_labels)   )





 def       UpperCamelCase__    (				self   )   ->List[str]:



       '''simple docstring'''





       __lowerCAmelCase   :		Any   =   self.prepare_config_and_inputs()
       (
           (
           __lowerCAmelCase      
       ),				(
           __lowerCAmelCase      
       ),				(
           __lowerCAmelCase      
       ),				(
           __lowerCAmelCase      
       ),				(
           __lowerCAmelCase      
       ),				(
           __lowerCAmelCase      
       ),				      
       )   :		List[str]   =   config_and_inputs
       __lowerCAmelCase   :		Optional[Any]   =   {'''input_ids''': input_ids, '''attention_mask''': input_mask}
       return config, inputs_dict







@require_torch
class     __lowercase (_a						,  _a						,  unittest.TestCase					):
 _UpperCamelCase         =    False

 _UpperCamelCase         =    (
     (
         EsmForMaskedLM,
         EsmModel,
         EsmForSequenceClassification,
         EsmForTokenClassification,
     )
     if is_torch_available()
     else ()
 )
 _UpperCamelCase         =    ()
 _UpperCamelCase         =    (
     {
         "feature-extraction": EsmModel,
         "fill-mask": EsmForMaskedLM,
         "text-classification": EsmForSequenceClassification,
         "token-classification": EsmForTokenClassification,
         "zero-shot": EsmForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 _UpperCamelCase         =    True
 def       UpperCamelCase__    (				self   )   ->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase   :		int   =   EsmModelTester(self   )
       __lowerCAmelCase   :		List[str]   =   ConfigTester(self ,				config_class=__lowerCamelCase ,				hidden_size=37   )
 def       UpperCamelCase__    (				self   )   ->Union[str, Any]:



       '''simple docstring'''





       self.config_tester.run_common_tests()
 def       UpperCamelCase__    (				self   )   ->List[str]:



       '''simple docstring'''





       __lowerCAmelCase   :		Optional[Any]   =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowerCamelCase   )
 def       UpperCamelCase__    (				self   )   ->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase   :		List[Any]   =   self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
             __lowerCAmelCase   :		List[Any]   =   type
             self.model_tester.create_and_check_model(*__lowerCamelCase   )
 def       UpperCamelCase__    (				self   )   ->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase   :		Union[str, Any]   =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase   )
 def       UpperCamelCase__    (				self   )   ->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase   :		Optional[Any]   =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase   )
 @slow
 def       UpperCamelCase__    (				self   )   ->Optional[Any]:



       '''simple docstring'''





       for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             __lowerCAmelCase   :		Union[str, Any]   =   EsmModel.from_pretrained(__lowerCamelCase   )
             self.assertIsNotNone(__lowerCamelCase   )
 def       UpperCamelCase__    (				self   )   ->str:



       '''simple docstring'''





       __lowerCAmelCase   :		Tuple   =   self.model_tester.prepare_config_and_inputs()[0]
       __lowerCAmelCase   :		Any   =   EsmEmbeddings(config=__lowerCamelCase   )

       __lowerCAmelCase   :		str   =   torch.as_tensor([[12, 31, 13, model.padding_idx]]   )
       __lowerCAmelCase   :		Any   =   torch.as_tensor(
           [
               [
                   0 + model.padding_idx + 1,
                   1 + model.padding_idx + 1,
                   2 + model.padding_idx + 1,
                   model.padding_idx,
               ]
           ]   )
       __lowerCAmelCase   :		Union[str, Any]   =   create_position_ids_from_input_ids(__lowerCamelCase ,				model.padding_idx   )
       self.assertEqual(position_ids.shape ,				expected_positions.shape   )
       self.assertTrue(torch.all(torch.eq(__lowerCamelCase ,				__lowerCamelCase   )   )   )
 def       UpperCamelCase__    (				self   )   ->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase   :		Optional[int]   =   self.model_tester.prepare_config_and_inputs()[0]
       __lowerCAmelCase   :		str   =   EsmEmbeddings(config=__lowerCamelCase   )

       __lowerCAmelCase   :		Tuple   =   torch.empty(2 ,				4 ,				30   )
       __lowerCAmelCase   :		Union[str, Any]   =   [
           0 + embeddings.padding_idx + 1,
           1 + embeddings.padding_idx + 1,
           2 + embeddings.padding_idx + 1,
           3 + embeddings.padding_idx + 1,
       ]
       __lowerCAmelCase   :		Dict   =   torch.as_tensor([expected_single_positions, expected_single_positions]   )
       __lowerCAmelCase   :		str   =   embeddings.create_position_ids_from_inputs_embeds(__lowerCamelCase   )
       self.assertEqual(position_ids.shape ,				expected_positions.shape   )
       self.assertTrue(torch.all(torch.eq(__lowerCamelCase ,				__lowerCamelCase   )   )   )
 @unittest.skip('''Esm does not support embedding resizing'''   )
 def       UpperCamelCase__    (				self   )   ->str:



       '''simple docstring'''





       pass
 @unittest.skip('''Esm does not support embedding resizing'''   )
 def       UpperCamelCase__    (				self   )   ->Tuple:



       '''simple docstring'''





       pass





 @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''   )
 def       UpperCamelCase__    (				self   )   ->Optional[int]:



       '''simple docstring'''





       pass







@require_torch
class     __lowercase (_a					):
 @slow
 def       UpperCamelCase__    (				self   )   ->Tuple:



       '''simple docstring'''





       with torch.no_grad():
             __lowerCAmelCase   :		Tuple   =   EsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D'''   )
             model.eval()
             __lowerCAmelCase   :		Tuple   =   torch.tensor([[0, 1, 2, 3, 4, 5]]   )
             __lowerCAmelCase   :		Optional[Any]   =   model(__lowerCamelCase   )[0]

             __lowerCAmelCase   :		Optional[int]   =   33

             __lowerCAmelCase   :		Optional[Any]   =   torch.Size((1, 6, vocab_size)   )
             self.assertEqual(output.shape ,				__lowerCamelCase   )

             __lowerCAmelCase   :		Any   =   torch.tensor(
                 [[[8.9_215, -10.5_898, -6.4_671], [-6.3_967, -13.9_114, -1.1_212], [-7.7_812, -13.9_516, -3.7_406]]]   )
             self.assertTrue(torch.allclose(output[:, :3, :3] ,				__lowerCamelCase ,				atol=1e-4   )   )





 @slow
 def       UpperCamelCase__    (				self   )   ->Tuple:



       '''simple docstring'''





       with torch.no_grad():
             __lowerCAmelCase   :		List[str]   =   EsmModel.from_pretrained('''facebook/esm2_t6_8M_UR50D'''   )
             model.eval()

             __lowerCAmelCase   :		Union[str, Any]   =   torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]   )
             __lowerCAmelCase   :		List[Any]   =   model(__lowerCamelCase   )[0]
             # compare the actual values for a slice.
             __lowerCAmelCase   :		Tuple   =   torch.tensor(
                 [[[0.1_444, 0.5_413, 0.3_248], [0.3_034, 0.0_053, 0.3_108], [0.3_228, -0.2_499, 0.3_415]]]   )
             self.assertTrue(torch.allclose(output[:, :3, :3] ,				__lowerCamelCase ,				atol=1e-4   )   )








from __future__ import annotations
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase = None				):
  lowerCAmelCase__										=      word_bank or []
  # create a table
  lowerCAmelCase__										=      len(__lowerCAmelCase				) + 1

  lowerCAmelCase__										=      []
  for _ in range(__lowerCAmelCase				):
    table.append([]				)
  # seed value
  lowerCAmelCase__										=      [[]]  # because empty string has empty combination

  # iterate through the indices
  for i in range(__lowerCAmelCase				):
    # condition
    if table[i] != []:
      for word in word_bank:
        # slice condition
        if target[i : i + len(__lowerCAmelCase				)] == word:
          lowerCAmelCase__										=      [
              [word, *way] for way in table[i]
          ]
          # adds the word to every combination the current position holds
          # now,push that combination to the table[i+len(word)]
          table[i + len(__lowerCAmelCase				)] += new_combinations

    # combinations are in reverse order so reverse for better output
  for combination in table[len(__lowerCAmelCase				)]:
    combination.reverse()

  return table[len(__lowerCAmelCase				)]


if __name__ == "__main__":
      print(all_construct("""jwajalapa""", ["""jwa""", """j""", """w""", """a""", """la""", """lapa"""]))
      print(all_construct("""rajamati""", ["""s""", """raj""", """amat""", """raja""", """ma""", """i""", """t"""]))
      print(
          all_construct(
              """hexagonosaurus""",
              ["""h""", """ex""", """hex""", """ag""", """ago""", """ru""", """auru""", """rus""", """go""", """no""", """o""", """s"""],
          )
      )

