'''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     A__							:





   """simple docstring"""





   def __init__( self     :		Dict						,     lowerCAmelCase__     :		str						,     )       ->    Union[str, Any]:







      """simple docstring"""




      _UpperCAmelCase							:	Optional[int]							=   parent
      _UpperCAmelCase							:	List[str]							=   1_3
      _UpperCAmelCase							:	Tuple							=   7
      _UpperCAmelCase							:	Tuple							=   True
      _UpperCAmelCase							:	Tuple							=   True
      _UpperCAmelCase							:	str							=   True
      _UpperCAmelCase							:	Union[str, Any]							=   9_9
      _UpperCAmelCase							:	Dict							=   3_2
      _UpperCAmelCase							:	Optional[Any]							=   2
      _UpperCAmelCase							:	Tuple							=   4
      _UpperCAmelCase							:	str							=   3_7
      _UpperCAmelCase							:	Union[str, Any]							=   "gelu"
      _UpperCAmelCase							:	Tuple							=   0.1
      _UpperCAmelCase							:	List[str]							=   0.1
      _UpperCAmelCase							:	Optional[int]							=   5_1_2
      _UpperCAmelCase							:	List[Any]							=   1_6
      _UpperCAmelCase							:	Tuple							=   2
      _UpperCAmelCase							:	Dict							=   0.02
      _UpperCAmelCase							:	List[Any]							=   3
      _UpperCAmelCase							:	Union[str, Any]							=   4
      _UpperCAmelCase							:	str							=   None




   def 			_lowerCAmelCase		( self     :		Tuple			)       ->    int:







      """simple docstring"""




      _UpperCAmelCase							:	Tuple							=   ids_tensor([self.batch_size, self.seq_length]						,     self.vocab_size			)

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

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

      _UpperCAmelCase							:	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     :		Tuple			)       ->    Union[str, Any]:







      """simple docstring"""




      (
          (
          _UpperCAmelCase     
      )							,       (
          _UpperCAmelCase     
      )							,       (
          _UpperCAmelCase     
      )							,       (
          _UpperCAmelCase     
      )							,       (
          _UpperCAmelCase     
      )							,       (
          _UpperCAmelCase     
      )							,            
      )							:	int							=   self.prepare_config_and_inputs()

      _UpperCAmelCase							:	Optional[Any]							=   True
      _UpperCAmelCase							:	Dict							=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
      _UpperCAmelCase							:	Tuple							=   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     :		Any						,     lowerCAmelCase__     :		Any						,     lowerCAmelCase__     :		Optional[Any]						,     lowerCAmelCase__     :		int						,     lowerCAmelCase__     :		Any						,     lowerCAmelCase__     :		List[str]						,     lowerCAmelCase__     :		Dict			)       ->    int:







      """simple docstring"""




      _UpperCAmelCase							:	int							=   TFEsmModel(config=_a			)
      _UpperCAmelCase							:	Tuple							=   {"input_ids": input_ids, "attention_mask": input_mask}
      _UpperCAmelCase							:	Optional[int]							=   model(_a			)

      _UpperCAmelCase							:	Union[str, Any]							=   [input_ids, input_mask]
      _UpperCAmelCase							:	Union[str, Any]							=   model(_a			)

      _UpperCAmelCase							:	Dict							=   model(_a			)

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




   def 			_lowerCAmelCase		( self     :		List[str]						,     lowerCAmelCase__     :		List[str]						,     lowerCAmelCase__     :		Optional[int]						,     lowerCAmelCase__     :		Union[str, Any]						,     lowerCAmelCase__     :		Optional[int]						,     lowerCAmelCase__     :		Union[str, Any]						,     lowerCAmelCase__     :		Tuple						,     lowerCAmelCase__     :		int						,     lowerCAmelCase__     :		List[str]						,     )       ->    Optional[Any]:







      """simple docstring"""




      _UpperCAmelCase							:	Optional[Any]							=   True

      _UpperCAmelCase							:	Tuple							=   TFEsmModel(config=_a			)
      _UpperCAmelCase							:	str							=   {
          "input_ids": input_ids,
          "attention_mask": input_mask,
          "encoder_hidden_states": encoder_hidden_states,
          "encoder_attention_mask": encoder_attention_mask,
      }
      _UpperCAmelCase							:	int							=   model(_a			)

      _UpperCAmelCase							:	List[str]							=   [input_ids, input_mask]
      _UpperCAmelCase							:	str							=   model(_a						,     encoder_hidden_states=_a			)

      # Also check the case where encoder outputs are not passed
      _UpperCAmelCase							:	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 			_lowerCAmelCase		( self     :		Tuple						,     lowerCAmelCase__     :		Optional[Any]						,     lowerCAmelCase__     :		Tuple						,     lowerCAmelCase__     :		str						,     lowerCAmelCase__     :		int						,     lowerCAmelCase__     :		List[Any]						,     lowerCAmelCase__     :		str			)       ->    Tuple:







      """simple docstring"""




      _UpperCAmelCase							:	Union[str, Any]							=   TFEsmForMaskedLM(config=_a			)
      _UpperCAmelCase							:	List[Any]							=   model([input_ids, input_mask]			)
      self.parent.assertEqual(result.logits.shape						,     (self.batch_size, self.seq_length, self.vocab_size)			)




   def 			_lowerCAmelCase		( self     :		Any						,     lowerCAmelCase__     :		List[Any]						,     lowerCAmelCase__     :		Union[str, Any]						,     lowerCAmelCase__     :		Union[str, Any]						,     lowerCAmelCase__     :		Optional[Any]						,     lowerCAmelCase__     :		Dict						,     lowerCAmelCase__     :		Dict			)       ->    str:







      """simple docstring"""




      _UpperCAmelCase							:	int							=   self.num_labels
      _UpperCAmelCase							:	Optional[Any]							=   TFEsmForTokenClassification(config=_a			)
      _UpperCAmelCase							:	str							=   {"input_ids": input_ids, "attention_mask": input_mask}
      _UpperCAmelCase							:	Union[str, Any]							=   model(_a			)
      self.parent.assertEqual(result.logits.shape						,     (self.batch_size, self.seq_length, self.num_labels)			)




   def 			_lowerCAmelCase		( self     :		Tuple			)       ->    List[str]:







      """simple docstring"""




      _UpperCAmelCase							:	Optional[int]							=   self.prepare_config_and_inputs()
      (
          (
          _UpperCAmelCase     
      )							,       (
          _UpperCAmelCase     
      )							,       (
          _UpperCAmelCase     
      )							,       (
          _UpperCAmelCase     
      )							,       (
          _UpperCAmelCase     
      )							,       (
          _UpperCAmelCase     
      )							,            
      )							:	int							=   config_and_inputs
      _UpperCAmelCase							:	Any							=   {"input_ids": input_ids, "attention_mask": input_mask}
      return config, inputs_dict





@require_tf
class     A__							(       __SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		unittest.TestCase   ):





   """simple docstring"""





   UpperCamelCase_       :						int    				=     (
       (
           TFEsmModel,
           TFEsmForMaskedLM,
           TFEsmForSequenceClassification,
           TFEsmForTokenClassification,
       )
       if is_tf_available()
       else ()
   )
   UpperCamelCase_       :						Tuple    				=     (
       {
           'feature-extraction': TFEsmModel,
           'fill-mask': TFEsmForMaskedLM,
           'text-classification': TFEsmForSequenceClassification,
           'token-classification': TFEsmForTokenClassification,
           'zero-shot': TFEsmForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   UpperCamelCase_       :						Tuple    				=     False
   UpperCamelCase_       :						Union[str, Any]    				=     False




   def 			_lowerCAmelCase		( self     :		Any			)       ->    Dict:







      """simple docstring"""




      _UpperCAmelCase							:	Any							=   TFEsmModelTester(self			)
      _UpperCAmelCase							:	List[Any]							=   ConfigTester(self						,     config_class=_a						,     hidden_size=3_7			)




   def 			_lowerCAmelCase		( self     :		List[Any]			)       ->    int:







      """simple docstring"""




      self.config_tester.run_common_tests()




   def 			_lowerCAmelCase		( self     :		Any			)       ->    int:







      """simple docstring"""




      _UpperCAmelCase							:	Any							=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_a			)




   def 			_lowerCAmelCase		( self     :		Any			)       ->    Any:







      """simple docstring"""




      _UpperCAmelCase							:	Tuple							=   self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_model_as_decoder(*_a			)




   def 			_lowerCAmelCase		( self     :		Any			)       ->    List[Any]:







      """simple docstring"""




      _UpperCAmelCase							:	List[str]							=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*_a			)




   def 			_lowerCAmelCase		( self     :		Optional[int]			)       ->    Any:







      """simple docstring"""




      _UpperCAmelCase							:	Tuple							=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*_a			)




   @slow
   def 			_lowerCAmelCase		( self     :		Union[str, Any]			)       ->    Union[str, Any]:







      """simple docstring"""




      for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _UpperCAmelCase							:	Optional[Any]							=   TFEsmModel.from_pretrained(_a			)
         self.assertIsNotNone(_a			)




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







      """simple docstring"""




      pass




   @unittest.skip("Protein models do not support embedding resizing."			)
   def 			_lowerCAmelCase		( self     :		Optional[int]			)       ->    Tuple:







      """simple docstring"""




      pass




   def 			_lowerCAmelCase		( self     :		Tuple			)       ->    Optional[int]:







      """simple docstring"""




      _UpperCAmelCase							,       _UpperCAmelCase							:	List[Any]							=   self.model_tester.prepare_config_and_inputs_for_common()

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





@require_tf
class     A__							(       unittest.TestCase   ):





   """simple docstring"""





   @slow
   def 			_lowerCAmelCase		( self     :		Tuple			)       ->    Tuple:







      """simple docstring"""




      _UpperCAmelCase							:	Dict							=   TFEsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D"			)

      _UpperCAmelCase							:	int							=   tf.constant([[0, 1, 2, 3, 4, 5]]			)
      _UpperCAmelCase							:	List[Any]							=   model(_a			)[0]
      _UpperCAmelCase							:	List[Any]							=   [1, 6, 3_3]
      self.assertEqual(list(output.numpy().shape			)						,     _a			)
      # compare the actual values for a slice.
      _UpperCAmelCase							:	Union[str, Any]							=   tf.constant(
          [
              [
                  [8.92_1518, -10.58_9814, -6.467_1307],
                  [-6.396_7156, -13.91_1377, -1.121_1915],
                  [-7.78_1247, -13.95_1557, -3.74_0592],
              ]
          ]			)
      self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()						,     expected_slice.numpy()						,     atol=1e-2			)			)




   @slow
   def 			_lowerCAmelCase		( self     :		Tuple			)       ->    int:







      """simple docstring"""




      _UpperCAmelCase							:	int							=   TFEsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D"			)

      _UpperCAmelCase							:	Any							=   tf.constant([[0, 6, 4, 1_3, 5, 4, 1_6, 1_2, 1_1, 7, 2]]			)
      _UpperCAmelCase							:	Optional[Any]							=   model(_a			)[0]
      # compare the actual values for a slice.
      _UpperCAmelCase							:	int							=   tf.constant(
          [
              [
                  [0.1444_3092, 0.5412_5327, 0.324_7739],
                  [0.3034_0484, 0.0052_6676, 0.3107_7722],
                  [0.3227_8043, -0.2498_7096, 0.341_4628],
              ]
          ]			)
      self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()						,     expected_slice.numpy()						,     atol=1e-4			)			)



"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase_			     =  {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  ["RoFormerTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "RoFormerForCausalLM",
				    "RoFormerForMaskedLM",
				    "RoFormerForMultipleChoice",
				    "RoFormerForQuestionAnswering",
				    "RoFormerForSequenceClassification",
				    "RoFormerForTokenClassification",
				    "RoFormerLayer",
				    "RoFormerModel",
				    "RoFormerPreTrainedModel",
				    "load_tf_weights_in_roformer",
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFRoFormerForCausalLM",
				    "TFRoFormerForMaskedLM",
				    "TFRoFormerForMultipleChoice",
				    "TFRoFormerForQuestionAnswering",
				    "TFRoFormerForSequenceClassification",
				    "TFRoFormerForTokenClassification",
				    "TFRoFormerLayer",
				    "TFRoFormerModel",
				    "TFRoFormerPreTrainedModel",
				]


try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "FlaxRoFormerForMaskedLM",
				    "FlaxRoFormerForMultipleChoice",
				    "FlaxRoFormerForQuestionAnswering",
				    "FlaxRoFormerForSequenceClassification",
				    "FlaxRoFormerForTokenClassification",
				    "FlaxRoFormerModel",
				    "FlaxRoFormerPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
				from .tokenization_roformer import RoFormerTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_roformer_fast import RoFormerTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_roformer import (
								    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RoFormerForCausalLM,
								    RoFormerForMaskedLM,
								    RoFormerForMultipleChoice,
								    RoFormerForQuestionAnswering,
								    RoFormerForSequenceClassification,
								    RoFormerForTokenClassification,
								    RoFormerLayer,
								    RoFormerModel,
								    RoFormerPreTrainedModel,
								    load_tf_weights_in_roformer,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_roformer import (
								    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFRoFormerForCausalLM,
								    TFRoFormerForMaskedLM,
								    TFRoFormerForMultipleChoice,
								    TFRoFormerForQuestionAnswering,
								    TFRoFormerForSequenceClassification,
								    TFRoFormerForTokenClassification,
								    TFRoFormerLayer,
								    TFRoFormerModel,
								    TFRoFormerPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_roformer import (
								    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    FlaxRoFormerForMaskedLM,
								    FlaxRoFormerForMultipleChoice,
								    FlaxRoFormerForQuestionAnswering,
								    FlaxRoFormerForSequenceClassification,
								    FlaxRoFormerForTokenClassification,
								    FlaxRoFormerModel,
								    FlaxRoFormerPreTrainedModel,
								)


else:
				import sys

				lowercase_			     =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






