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		:







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







       def 				snake_case  (				self     :    Union[str, Any]		):
          lowerCamelCase						:Union[str, Any]						  =     ids_tensor([self.batch_size, self.seq_length]	,					self.vocab_size		)

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

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

          lowerCamelCase						:List[str]						  =     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 				snake_case  (				self     :    Union[str, Any]		):
          (
              (
              lowerCamelCase      
          )  ,    (
              lowerCamelCase      
          )  ,    (
              lowerCamelCase      
          )  ,    (
              lowerCamelCase      
          )  ,    (
              lowerCamelCase      
          )  ,    (
              lowerCamelCase      
          )  ,          
          )						:Optional[int]						  =     self.prepare_config_and_inputs()

          lowerCamelCase						:Optional[int]						  =     True
          lowerCamelCase						:List[Any]						  =     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
          lowerCamelCase						:int						  =     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 				snake_case  (				self     :    Optional[int]	,					__snake_case     :    int	,					__snake_case     :    List[str]	,					__snake_case     :    Optional[int]	,					__snake_case     :    List[Any]	,					__snake_case     :    Any	,					__snake_case     :    Any		):
          lowerCamelCase						:List[Any]						  =     TFEsmModel(config=A_		)
          lowerCamelCase						:List[Any]						  =     {'''input_ids''': input_ids, '''attention_mask''': input_mask}
          lowerCamelCase						:List[str]						  =     model(A_		)

          lowerCamelCase						:int						  =     [input_ids, input_mask]
          lowerCamelCase						:List[str]						  =     model(A_		)

          lowerCamelCase						:str						  =     model(A_		)

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







       def 				snake_case  (				self     :    Union[str, Any]	,					__snake_case     :    Any	,					__snake_case     :    Union[str, Any]	,					__snake_case     :    Tuple	,					__snake_case     :    int	,					__snake_case     :    List[Any]	,					__snake_case     :    Optional[int]	,					__snake_case     :    Optional[Any]	,					__snake_case     :    List[str]	,					):
          lowerCamelCase						:Union[str, Any]						  =     True

          lowerCamelCase						:str						  =     TFEsmModel(config=A_		)
          lowerCamelCase						:Tuple						  =     {
              '''input_ids''': input_ids,
              '''attention_mask''': input_mask,
              '''encoder_hidden_states''': encoder_hidden_states,
              '''encoder_attention_mask''': encoder_attention_mask,
          }
          lowerCamelCase						:Optional[Any]						  =     model(A_		)

          lowerCamelCase						:Union[str, Any]						  =     [input_ids, input_mask]
          lowerCamelCase						:List[str]						  =     model(A_	,					encoder_hidden_states=A_		)

          # Also check the case where encoder outputs are not passed
          lowerCamelCase						:Optional[int]						  =     model(A_	,					attention_mask=A_		)

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







       def 				snake_case  (				self     :    str	,					__snake_case     :    List[Any]	,					__snake_case     :    List[Any]	,					__snake_case     :    Optional[Any]	,					__snake_case     :    Optional[int]	,					__snake_case     :    Optional[Any]	,					__snake_case     :    List[Any]		):
          lowerCamelCase						:Any						  =     TFEsmForMaskedLM(config=A_		)
          lowerCamelCase						:Tuple						  =     model([input_ids, input_mask]		)
          self.parent.assertEqual(result.logits.shape	,					(self.batch_size, self.seq_length, self.vocab_size)		)







       def 				snake_case  (				self     :    Any	,					__snake_case     :    Optional[Any]	,					__snake_case     :    str	,					__snake_case     :    List[Any]	,					__snake_case     :    int	,					__snake_case     :    Tuple	,					__snake_case     :    Optional[int]		):
          lowerCamelCase						:List[Any]						  =     self.num_labels
          lowerCamelCase						:Any						  =     TFEsmForTokenClassification(config=A_		)
          lowerCamelCase						:Tuple						  =     {'''input_ids''': input_ids, '''attention_mask''': input_mask}
          lowerCamelCase						:Any						  =     model(A_		)
          self.parent.assertEqual(result.logits.shape	,					(self.batch_size, self.seq_length, self.num_labels)		)







       def 				snake_case  (				self     :    Any		):
          lowerCamelCase						:Union[str, Any]						  =     self.prepare_config_and_inputs()
          (
              (
              lowerCamelCase      
          )  ,    (
              lowerCamelCase      
          )  ,    (
              lowerCamelCase      
          )  ,    (
              lowerCamelCase      
          )  ,    (
              lowerCamelCase      
          )  ,    (
              lowerCamelCase      
          )  ,          
          )						:Tuple						  =     config_and_inputs
          lowerCamelCase						:Optional[Any]						  =     {'''input_ids''': input_ids, '''attention_mask''': input_mask}
          return config, inputs_dict


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







       def 				snake_case  (				self     :    List[Any]		):
          lowerCamelCase						:Optional[int]						  =     TFEsmModelTester(self		)
          lowerCamelCase						:Dict						  =     ConfigTester(self	,					config_class=A_	,					hidden_size=37		)







       def 				snake_case  (				self     :    Any		):
          self.config_tester.run_common_tests()







       def 				snake_case  (				self     :    Union[str, Any]		):
          lowerCamelCase						:Union[str, Any]						  =     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A_		)







       def 				snake_case  (				self     :    List[Any]		):
          lowerCamelCase						:str						  =     self.model_tester.prepare_config_and_inputs_for_decoder()
          self.model_tester.create_and_check_model_as_decoder(*A_		)







       def 				snake_case  (				self     :    str		):
          lowerCamelCase						:Dict						  =     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*A_		)







       def 				snake_case  (				self     :    List[Any]		):
          lowerCamelCase						:Union[str, Any]						  =     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*A_		)







       @slow
       def 				snake_case  (				self     :    Tuple		):
          for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowerCamelCase						:Dict						  =     TFEsmModel.from_pretrained(A_		)
             self.assertIsNotNone(A_		)







       @unittest.skip('''Protein models do not support embedding resizing.'''		)
       def 				snake_case  (				self     :    Optional[int]		):
          pass







       @unittest.skip('''Protein models do not support embedding resizing.'''		)
       def 				snake_case  (				self     :    Tuple		):
          pass







       def 				snake_case  (				self     :    str		):
          lowerCamelCase  ,    lowerCamelCase						:int						  =     self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             lowerCamelCase						:Any						  =     model_class(A_		)
             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
                lowerCamelCase						:Dict						  =     model.get_bias()
                assert isinstance(A_	,					A_		)
                for k, v in name.items():
                   assert isinstance(A_	,					tf.Variable		)
             else:
                lowerCamelCase						:int						  =     model.get_output_embeddings()
                assert x is None
                lowerCamelCase						:Tuple						  =     model.get_bias()
                assert name is None


@require_tf
class   _lowerCAmelCase		(     unittest.TestCase				):







       @slow
       def 				snake_case  (				self     :    Optional[Any]		):
          lowerCamelCase						:Any						  =     TFEsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D'''		)

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

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




import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE      ,   unittest.TestCase				):
							_UpperCAmelCase						    =     DebertaTokenizer
							_UpperCAmelCase						    =     True
							_UpperCAmelCase						    =     DebertaTokenizerFast







							def 				snake_case  (				self     :    List[str]		):
										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										lowerCamelCase						:Dict						  =     [
										    '''l''',
										    '''o''',
										    '''w''',
										    '''e''',
										    '''r''',
										    '''s''',
										    '''t''',
										    '''i''',
										    '''d''',
										    '''n''',
										    '''\u0120''',
										    '''\u0120l''',
										    '''\u0120n''',
										    '''\u0120lo''',
										    '''\u0120low''',
										    '''er''',
										    '''\u0120lowest''',
										    '''\u0120newer''',
										    '''\u0120wider''',
										    '''[UNK]''',
										]
										lowerCamelCase						:List[str]						  =     dict(zip(__snake_case	,					range(len(__snake_case		)		)		)		)
										lowerCamelCase						:Any						  =     ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
										lowerCamelCase						:Dict						  =     {'''unk_token''': '''[UNK]'''}

										lowerCamelCase						:Any						  =     os.path.join(self.tmpdirname	,					VOCAB_FILES_NAMES['''vocab_file''']		)
										lowerCamelCase						:List[str]						  =     os.path.join(self.tmpdirname	,					VOCAB_FILES_NAMES['''merges_file''']		)
										with open(self.vocab_file	,					'''w'''	,					encoding='''utf-8'''		) as fp:
													fp.write(json.dumps(__snake_case		) + '''\n'''		)
										with open(self.merges_file	,					'''w'''	,					encoding='''utf-8'''		) as fp:
													fp.write('''\n'''.join(__snake_case		)		)







							def 				snake_case  (				self     :    str	,					**__snake_case     :    Dict		):
										kwargs.update(self.special_tokens_map		)
										return self.tokenizer_class.from_pretrained(self.tmpdirname	,					**__snake_case		)







							def 				snake_case  (				self     :    Optional[Any]	,					__snake_case     :    int		):
										lowerCamelCase						:List[Any]						  =     '''lower newer'''
										lowerCamelCase						:List[str]						  =     '''lower newer'''
										return input_text, output_text







							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:Optional[int]						  =     self.get_tokenizer()
										lowerCamelCase						:Union[str, Any]						  =     '''lower newer'''
										lowerCamelCase						:str						  =     ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
										lowerCamelCase						:Optional[int]						  =     tokenizer.tokenize(__snake_case		)
										self.assertListEqual(__snake_case	,					__snake_case		)

										lowerCamelCase						:List[str]						  =     tokens + [tokenizer.unk_token]
										lowerCamelCase						:Optional[int]						  =     [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case		)	,					__snake_case		)







							def 				snake_case  (				self     :    Optional[int]		):
										lowerCamelCase						:List[str]						  =     self.get_tokenizer()
										lowerCamelCase						:Optional[int]						  =     tokenizer('''Hello'''	,					'''World'''		)
										lowerCamelCase						:List[str]						  =     [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
										self.assertListEqual(tokd['''token_type_ids''']	,					__snake_case		)







							@slow
							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:Union[str, Any]						  =     self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''		)

										lowerCamelCase						:Optional[Any]						  =     tokenizer.encode('''sequence builders'''	,					add_special_tokens=__snake_case		)
										lowerCamelCase						:Tuple						  =     tokenizer.encode('''multi-sequence build'''	,					add_special_tokens=__snake_case		)

										lowerCamelCase						:Union[str, Any]						  =     tokenizer.encode(
										    '''sequence builders'''	,					add_special_tokens=__snake_case	,					add_prefix_space=__snake_case		)
										lowerCamelCase						:str						  =     tokenizer.encode(
										    '''sequence builders'''	,					'''multi-sequence build'''	,					add_special_tokens=__snake_case	,					add_prefix_space=__snake_case		)

										lowerCamelCase						:Any						  =     tokenizer.build_inputs_with_special_tokens(__snake_case		)
										lowerCamelCase						:Dict						  =     tokenizer.build_inputs_with_special_tokens(__snake_case	,					__snake_case		)

										assert encoded_sentence == encoded_text_from_decode
										assert encoded_pair == encoded_pair_from_decode







							@slow
							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:List[str]						  =     [self.tokenizer_class]
										if self.test_rust_tokenizer:
													tokenizer_classes.append(self.rust_tokenizer_class		)

										for tokenizer_class in tokenizer_classes:
													lowerCamelCase						:int						  =     tokenizer_class.from_pretrained('''microsoft/deberta-base'''		)

													lowerCamelCase						:Tuple						  =     [
													    '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
													    '''ALBERT incorporates two parameter reduction techniques''',
													    '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
													    ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
													    ''' vocabulary embedding.''',
													]

													lowerCamelCase						:List[Any]						  =     tokenizer(__snake_case	,					padding=__snake_case		)
													lowerCamelCase						:Union[str, Any]						  =     [tokenizer.decode(__snake_case	,					skip_special_tokens=__snake_case		) for seq in encoding['''input_ids''']]

													# fmt: off
													lowerCamelCase						:Any						  =     {
													    '''input_ids''': [
													        [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
													        [1, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
													        [1, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 4, 2]
													    ],
													    '''token_type_ids''': [
													        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
													        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
													        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
													    ],
													    '''attention_mask''': [
													        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
													        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
													        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
													    ]
													}
													# fmt: on

													lowerCamelCase						:Optional[int]						  =     [
													    '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
													    '''ALBERT incorporates two parameter reduction techniques''',
													    '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
													    ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
													    ''' vocabulary embedding.''',
													]

													self.assertDictEqual(encoding.data	,					__snake_case		)

													for expected, decoded in zip(__snake_case	,					__snake_case		):
																self.assertEqual(__snake_case	,					__snake_case		)



