



'''simple docstring'''





from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowerCAmelCase      						=	logging.get_logger(__name__)


_lowerCAmelCase      						=	{
    '''nielsr/canine-s''': 2048,
}

# Unicode defines 1,114,112 total “codepoints”
_lowerCAmelCase      						=	111_4112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
_lowerCAmelCase      						=	0

_lowerCAmelCase      						=	0xe_000
_lowerCAmelCase      						=	0xe_001
_lowerCAmelCase      						=	0xe_002
_lowerCAmelCase      						=	0xe_003
_lowerCAmelCase      						=	0xe_004

# Maps special codepoints to human-readable names.
_lowerCAmelCase      						=	{
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
_lowerCAmelCase      						=	{name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}






class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  __lowercase							:      int				=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




  def __init__(    self  ,__UpperCAmelCase=chr(__UpperCAmelCase      )  ,__UpperCAmelCase=chr(__UpperCAmelCase      )  ,__UpperCAmelCase=chr(__UpperCAmelCase      )  ,__UpperCAmelCase=chr(__UpperCAmelCase      )  ,__UpperCAmelCase=chr(__UpperCAmelCase      )  ,__UpperCAmelCase=chr(__UpperCAmelCase      )  ,__UpperCAmelCase=False  ,__UpperCAmelCase=2048  ,**__UpperCAmelCase  ,)    ->		Optional[int]:
      lowerCAmelCase__ :  Optional[int]							      =					AddedToken(__UpperCAmelCase  ,lstrip=__UpperCAmelCase  ,rstrip=__UpperCAmelCase      ) if isinstance(__UpperCAmelCase  ,__UpperCAmelCase      ) else bos_token
      lowerCAmelCase__ :  Dict							      =					AddedToken(__UpperCAmelCase  ,lstrip=__UpperCAmelCase  ,rstrip=__UpperCAmelCase      ) if isinstance(__UpperCAmelCase  ,__UpperCAmelCase      ) else eos_token
      lowerCAmelCase__ :  Union[str, Any]							      =					AddedToken(__UpperCAmelCase  ,lstrip=__UpperCAmelCase  ,rstrip=__UpperCAmelCase      ) if isinstance(__UpperCAmelCase  ,__UpperCAmelCase      ) else sep_token
      lowerCAmelCase__ :  int							      =					AddedToken(__UpperCAmelCase  ,lstrip=__UpperCAmelCase  ,rstrip=__UpperCAmelCase      ) if isinstance(__UpperCAmelCase  ,__UpperCAmelCase      ) else cls_token
      lowerCAmelCase__ :  int							      =					AddedToken(__UpperCAmelCase  ,lstrip=__UpperCAmelCase  ,rstrip=__UpperCAmelCase      ) if isinstance(__UpperCAmelCase  ,__UpperCAmelCase      ) else pad_token

      # Mask token behave like a normal word, i.e. include the space before it
      lowerCAmelCase__ :  str							      =					AddedToken(__UpperCAmelCase  ,lstrip=__UpperCAmelCase  ,rstrip=__UpperCAmelCase      ) if isinstance(__UpperCAmelCase  ,__UpperCAmelCase      ) else mask_token

      super().__init__(
          bos_token=__UpperCAmelCase  ,eos_token=__UpperCAmelCase  ,sep_token=__UpperCAmelCase  ,cls_token=__UpperCAmelCase  ,pad_token=__UpperCAmelCase  ,mask_token=__UpperCAmelCase  ,add_prefix_space=__UpperCAmelCase  ,model_max_length=__UpperCAmelCase  ,**__UpperCAmelCase  ,)

      # Creates a mapping for looking up the IDs of special symbols.
      lowerCAmelCase__ :  Dict[str, int]							      =					{}
      for codepoint, name in SPECIAL_CODEPOINTS.items():
          lowerCAmelCase__ :  Tuple							      =					codepoint

      # Creates a mapping for looking up the string forms of special symbol IDs.
      lowerCAmelCase__ :  Dict[int, str]							      =					{
          codepoint: name for name, codepoint in self._special_codepoints.items()
      }

      lowerCAmelCase__ :  Optional[Any]							      =					UNICODE_VOCAB_SIZE
      lowerCAmelCase__ :  Any							      =					len(self._special_codepoints      )




  @property
  def 							UpperCAmelCase_	(    self      )    ->		int:
      return self._unicode_vocab_size




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		List[str]:
      return list(__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		int:
      try:
          return ord(__UpperCAmelCase      )
      except TypeError:
          raise ValueError(F"""invalid token: '{token}'"""      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		str:
      try:
          if index in SPECIAL_CODEPOINTS:
              return SPECIAL_CODEPOINTS[index]
          return chr(__UpperCAmelCase      )
      except TypeError:
          raise ValueError(F"""invalid id: {index}"""      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		int:
      return "".join(__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = None      )    ->		List[int]:
      lowerCAmelCase__ :  str							      =					[self.sep_token_id]
      lowerCAmelCase__ :  Optional[int]							      =					[self.cls_token_id]

      lowerCAmelCase__ :  str							      =					cls + token_ids_a + sep
      if token_ids_a is not None:
          result += token_ids_a + sep
      return result




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = None  ,__UpperCAmelCase = False      )    ->		List[int]:
      if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=__UpperCAmelCase  ,token_ids_a=__UpperCAmelCase  ,already_has_special_tokens=__UpperCAmelCase      )

      lowerCAmelCase__ :  Union[str, Any]							      =					[1] + ([0] * len(__UpperCAmelCase      )) + [1]
      if token_ids_a is not None:
          result += ([0] * len(__UpperCAmelCase      )) + [1]
      return result




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = None      )    ->		List[int]:
      lowerCAmelCase__ :  Optional[int]							      =					[self.sep_token_id]
      lowerCAmelCase__ :  List[str]							      =					[self.cls_token_id]

      lowerCAmelCase__ :  Optional[int]							      =					len(cls + token_ids_a + sep      ) * [0]
      if token_ids_a is not None:
          result += len(token_ids_a + sep      ) * [1]
      return result






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = None      )    ->		int:
      return ()




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     _SCREAMING_SNAKE_CASE     :



	def __init__(    self						,  lowercase						,  )				->       Optional[int]:
					lowerCamelCase_						=			parent
					lowerCamelCase_						=			13
					lowerCamelCase_						=			7
					lowerCamelCase_						=			True
					lowerCamelCase_						=			True
					lowerCamelCase_						=			True
					lowerCamelCase_						=			99
					lowerCamelCase_						=			32
					lowerCamelCase_						=			2
					lowerCamelCase_						=			4
					lowerCamelCase_						=			37
					lowerCamelCase_						=			"gelu"
					lowerCamelCase_						=			0.1
					lowerCamelCase_						=			0.1
					lowerCamelCase_						=			512
					lowerCamelCase_						=			16
					lowerCamelCase_						=			2
					lowerCamelCase_						=			0.0_2
					lowerCamelCase_						=			3
					lowerCamelCase_						=			4
					lowerCamelCase_						=			None



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					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_						=			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     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					(
					    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    
					)						=			self.prepare_config_and_inputs()

					lowerCamelCase_						=			True
					lowerCamelCase_						=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
					lowerCamelCase_						=			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     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase )				->       Any:
					lowerCamelCase_						=			TFEsmModel(config=lowercase )
					lowerCamelCase_						=			{"input_ids": input_ids, "attention_mask": input_mask}
					lowerCamelCase_						=			model(lowercase )

					lowerCamelCase_						=			[input_ids, input_mask]
					lowerCamelCase_						=			model(lowercase )

					lowerCamelCase_						=			model(lowercase )

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



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  )				->       Tuple:
					lowerCamelCase_						=			True

					lowerCamelCase_						=			TFEsmModel(config=lowercase )
					lowerCamelCase_						=			{
					    "input_ids": input_ids,
					    "attention_mask": input_mask,
					    "encoder_hidden_states": encoder_hidden_states,
					    "encoder_attention_mask": encoder_attention_mask,
					}
					lowerCamelCase_						=			model(lowercase )

					lowerCamelCase_						=			[input_ids, input_mask]
					lowerCamelCase_						=			model(lowercase						,  encoder_hidden_states=lowercase )

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

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



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase )				->       Optional[int]:
					lowerCamelCase_						=			TFEsmForMaskedLM(config=lowercase )
					lowerCamelCase_						=			model([input_ids, input_mask] )
					self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.seq_length, self.vocab_size) )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase )				->       int:
					lowerCamelCase_						=			self.num_labels
					lowerCamelCase_						=			TFEsmForTokenClassification(config=lowercase )
					lowerCamelCase_						=			{"input_ids": input_ids, "attention_mask": input_mask}
					lowerCamelCase_						=			model(lowercase )
					self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.seq_length, self.num_labels) )



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					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_tf
class     _SCREAMING_SNAKE_CASE     (      snake_case_			,       snake_case_			,       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     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					lowerCamelCase_						=			TFEsmModelTester(self )
					lowerCamelCase_						=			ConfigTester(self						,  config_class=lowercase						,  hidden_size=37 )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					self.config_tester.run_common_tests()



	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs_for_decoder()
					self.model_tester.create_and_check_model_as_decoder(*lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*lowercase )



	@slow
	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCamelCase_						=			TFEsmModel.from_pretrained(lowercase )
									self.assertIsNotNone(lowercase )



	@unittest.skip("Protein models do not support embedding resizing." )
	def     SCREAMING_SNAKE_CASE_(    self )				->       List[Any]:
					pass



	@unittest.skip("Protein models do not support embedding resizing." )
	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					pass



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_	,    lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs_for_common()

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




@require_tf
class     _SCREAMING_SNAKE_CASE     (      unittest.TestCase						):



	@slow
	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_						=			TFEsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D" )

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

					lowerCamelCase_						=			tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
					lowerCamelCase_						=			model(lowercase )[0]
					# compare the actual values for a slice.
					lowerCamelCase_						=			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 ) )


