


"""simple docstring"""



from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import (
      TFRoFormerForCausalLM,
      TFRoFormerForMaskedLM,
      TFRoFormerForMultipleChoice,
      TFRoFormerForQuestionAnswering,
      TFRoFormerForSequenceClassification,
      TFRoFormerForTokenClassification,
      TFRoFormerModel,
  )
  from transformers.models.roformer.modeling_tf_roformer import (
      TFRoFormerSelfAttention,
      TFRoFormerSinusoidalPositionalEmbedding,
  )







class _lowerCAmelCase							:







   def __init__(      self		,      UpperCamelCase__		,      UpperCamelCase__=13		,      UpperCamelCase__=7		,      UpperCamelCase__=True		,      UpperCamelCase__=True		,      UpperCamelCase__=True		,      UpperCamelCase__=True		,      UpperCamelCase__=99		,      UpperCamelCase__=32		,      UpperCamelCase__=2		,      UpperCamelCase__=4		,      UpperCamelCase__=37		,      UpperCamelCase__="gelu"		,      UpperCamelCase__=0.1		,      UpperCamelCase__=0.1		,      UpperCamelCase__=512		,      UpperCamelCase__=16		,      UpperCamelCase__=2		,      UpperCamelCase__=0.02		,      UpperCamelCase__=3		,      UpperCamelCase__=4		,      UpperCamelCase__=None		,      )      ->						str:

       '''simple docstring'''




       snake_case       : Optional[int]    =       parent
       snake_case       : Dict    =       13
       snake_case       : List[Any]    =       7
       snake_case       : Any    =       True
       snake_case       : Union[str, Any]    =       True
       snake_case       : Union[str, Any]    =       True
       snake_case       : Any    =       True
       snake_case       : Optional[int]    =       99
       snake_case       : List[Any]    =       32
       snake_case       : Optional[int]    =       2
       snake_case       : Any    =       4
       snake_case       : Tuple    =       37
       snake_case       : Optional[Any]    =       "gelu"
       snake_case       : int    =       0.1
       snake_case       : int    =       0.1
       snake_case       : Tuple    =       512
       snake_case       : Tuple    =       16
       snake_case       : str    =       2
       snake_case       : int    =       0.02
       snake_case       : Any    =       3
       snake_case       : str    =       4
       snake_case       : int    =       None







   def 					lowerCamelCase						(      self		)      ->						Union[str, Any]:

       '''simple docstring'''




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

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

       snake_case       : List[str]    =       None
       if self.use_token_type_ids:
           snake_case       : Dict    =       ids_tensor([self.batch_size, self.seq_length]		,      self.type_vocab_size		)

       snake_case       : Optional[int]    =       None
       snake_case       : List[Any]    =       None
       snake_case       : List[Any]    =       None
       if self.use_labels:
           snake_case       : List[str]    =       ids_tensor([self.batch_size]		,      self.type_sequence_label_size		)
           snake_case       : Any    =       ids_tensor([self.batch_size, self.seq_length]		,      self.num_labels		)
           snake_case       : Tuple    =       ids_tensor([self.batch_size]		,      self.num_choices		)

       snake_case       : str    =       RoFormerConfig(
           vocab_size=self.vocab_size		,      hidden_size=self.hidden_size		,      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		,      return_dict=UpperCamelCase__		,      )

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







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

       '''simple docstring'''




       snake_case       : Dict    =       TFRoFormerModel(config=UpperCamelCase__		)
       snake_case       : Dict    =       {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

       snake_case       : List[str]    =       [input_ids, input_mask]
       snake_case       : Union[str, Any]    =       model(UpperCamelCase__		)

       snake_case       : Tuple    =       model(UpperCamelCase__		)

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







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						int:

       '''simple docstring'''




       snake_case       : Optional[int]    =       True
       snake_case       : Union[str, Any]    =       TFRoFormerForCausalLM(config=UpperCamelCase__		)
       snake_case       : List[str]    =       {
           "input_ids": input_ids,
           "attention_mask": input_mask,
           "token_type_ids": token_type_ids,
       }
       snake_case       : Tuple    =       model(UpperCamelCase__		)["logits"]
       self.parent.assertListEqual(
           list(prediction_scores.numpy().shape		)		,      [self.batch_size, self.seq_length, self.vocab_size]		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						List[Any]:

       '''simple docstring'''




       snake_case       : Optional[Any]    =       TFRoFormerForMaskedLM(config=UpperCamelCase__		)
       snake_case       : Any    =       {
           "input_ids": input_ids,
           "attention_mask": input_mask,
           "token_type_ids": token_type_ids,
       }
       snake_case       : Optional[int]    =       model(UpperCamelCase__		)
       self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.seq_length, self.vocab_size)		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						List[Any]:

       '''simple docstring'''




       snake_case       : Union[str, Any]    =       self.num_labels
       snake_case       : int    =       TFRoFormerForSequenceClassification(config=UpperCamelCase__		)
       snake_case       : Optional[int]    =       {
           "input_ids": input_ids,
           "attention_mask": input_mask,
           "token_type_ids": token_type_ids,
       }

       snake_case       : Dict    =       model(UpperCamelCase__		)
       self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.num_labels)		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						Tuple:

       '''simple docstring'''




       snake_case       : Optional[int]    =       self.num_choices
       snake_case       : Dict    =       TFRoFormerForMultipleChoice(config=UpperCamelCase__		)
       snake_case       : Any    =       tf.tile(tf.expand_dims(UpperCamelCase__		,      1		)		,      (1, self.num_choices, 1)		)
       snake_case       : List[Any]    =       tf.tile(tf.expand_dims(UpperCamelCase__		,      1		)		,      (1, self.num_choices, 1)		)
       snake_case       : List[str]    =       tf.tile(tf.expand_dims(UpperCamelCase__		,      1		)		,      (1, self.num_choices, 1)		)
       snake_case       : Dict    =       {
           "input_ids": multiple_choice_inputs_ids,
           "attention_mask": multiple_choice_input_mask,
           "token_type_ids": multiple_choice_token_type_ids,
       }
       snake_case       : Optional[Any]    =       model(UpperCamelCase__		)
       self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.num_choices)		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						Union[str, Any]:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						Optional[Any]:

       '''simple docstring'''




       snake_case       : Optional[int]    =       TFRoFormerForQuestionAnswering(config=UpperCamelCase__		)
       snake_case       : Optional[Any]    =       {
           "input_ids": input_ids,
           "attention_mask": input_mask,
           "token_type_ids": token_type_ids,
       }

       snake_case       : Optional[int]    =       model(UpperCamelCase__		)
       self.parent.assertEqual(result.start_logits.shape		,      (self.batch_size, self.seq_length)		)
       self.parent.assertEqual(result.end_logits.shape		,      (self.batch_size, self.seq_length)		)







   def 					lowerCamelCase						(      self		)      ->						Optional[int]:

       '''simple docstring'''




       snake_case       : Optional[int]    =       self.prepare_config_and_inputs()
       (
           (
           snake_case
       )   ,(
           snake_case
       )   ,(
           snake_case
       )   ,(
           snake_case
       )   ,(
           snake_case
       )   ,(
           snake_case
       )   ,(
           snake_case
       )   ,
       )       : Optional[int]    =       config_and_inputs
       snake_case       : List[str]    =       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
       return config, inputs_dict







@require_tf
class _lowerCAmelCase							( snake_case_       ,				snake_case_       ,				unittest.TestCase      ):
   __UpperCAmelCase					:			str				    =    (
       (
           TFRoFormerModel,
           TFRoFormerForCausalLM,
           TFRoFormerForMaskedLM,
           TFRoFormerForQuestionAnswering,
           TFRoFormerForSequenceClassification,
           TFRoFormerForTokenClassification,
           TFRoFormerForMultipleChoice,
       )
       if is_tf_available()
       else ()
   )
   __UpperCAmelCase					:			int				    =    (
       {
           '''feature-extraction''': TFRoFormerModel,
           '''fill-mask''': TFRoFormerForMaskedLM,
           '''question-answering''': TFRoFormerForQuestionAnswering,
           '''text-classification''': TFRoFormerForSequenceClassification,
           '''text-generation''': TFRoFormerForCausalLM,
           '''token-classification''': TFRoFormerForTokenClassification,
           '''zero-shot''': TFRoFormerForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )

   __UpperCAmelCase					:			Tuple				    =    False
   __UpperCAmelCase					:			List[Any]				    =    False







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						int:

       '''simple docstring'''




       if pipeline_test_casse_name == "TextGenerationPipelineTests":
           return True

       return False







   def 					lowerCamelCase						(      self		)      ->						Optional[int]:

       '''simple docstring'''




       snake_case       : Tuple    =       TFRoFormerModelTester(self		)
       snake_case       : Dict    =       ConfigTester(self		,      config_class=UpperCamelCase__		,      hidden_size=37		)







   def 					lowerCamelCase						(      self		)      ->						Dict:

       '''simple docstring'''




       self.config_tester.run_common_tests()







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

       '''simple docstring'''




       snake_case       : List[Any]    =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						Tuple:

       '''simple docstring'''




       snake_case       : Any    =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						int:

       '''simple docstring'''




       snake_case       : Tuple    =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_lm_head(*UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						Any:

       '''simple docstring'''




       snake_case       : Dict    =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						Any:

       '''simple docstring'''




       snake_case       : Tuple    =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						Union[str, Any]:

       '''simple docstring'''




       snake_case       : Optional[Any]    =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						Dict:

       '''simple docstring'''




       snake_case       : Optional[int]    =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__		)







   @slow
   def 					lowerCamelCase						(      self		)      ->						List[str]:

       '''simple docstring'''




       snake_case       : int    =       TFRoFormerModel.from_pretrained("junnyu/roformer_chinese_base"		)
       self.assertIsNotNone(UpperCamelCase__		)







@require_tf
class _lowerCAmelCase							( unittest.TestCase      ):







   @slow
   def 					lowerCamelCase						(      self		)      ->						Tuple:

       '''simple docstring'''




       snake_case       : Dict    =       TFRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"		)
       snake_case       : str    =       tf.constant([[0, 1, 2, 3, 4, 5]]		)
       snake_case       : Tuple    =       model(UpperCamelCase__		)[0]

       # TODO Replace vocab size
       snake_case       : Dict    =       5_0000

       snake_case       : Optional[Any]    =       [1, 6, vocab_size]
       self.assertEqual(output.shape		,      UpperCamelCase__		)

       print(output[:, :3, :3]		)

       # TODO Replace values below with what was printed above.
       snake_case       : int    =       tf.constant(
           [
               [
                   [-0.12053341, -1.0264901, 0.29221946],
                   [-1.5133783, 0.197433, 0.15190607],
                   [-5.0135403, -3.900256, -0.84038764],
               ]
           ]		)
       tf.debugging.assert_near(output[:, :3, :3]		,      UpperCamelCase__		,      atol=1e-4		)







@require_tf
class _lowerCAmelCase							( unittest.TestCase      ):
   __UpperCAmelCase					:			int				    =    1E-4







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

       '''simple docstring'''




       snake_case       : List[str]    =       tf.constant([[4, 10]]		)
       snake_case       : Optional[Any]    =       TFRoFormerSinusoidalPositionalEmbedding(num_positions=6		,      embedding_dim=6		)

       snake_case       : str    =       emba(input_ids.shape		)
       snake_case       : int    =       tf.constant(
           [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]		)

       tf.debugging.assert_near(UpperCamelCase__		,      UpperCamelCase__		,      atol=self.tolerance		)







   def 					lowerCamelCase						(      self		)      ->						Optional[Any]:

       '''simple docstring'''




       snake_case       : Optional[int]    =       tf.constant(
           [
               [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
               [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
               [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
           ]		)
       snake_case       : Optional[Any]    =       TFRoFormerSinusoidalPositionalEmbedding(num_positions=512		,      embedding_dim=512		)
       emba([2, 16, 512]		)
       snake_case       : List[str]    =       emba.weight[:3, :5]

       tf.debugging.assert_near(UpperCamelCase__		,      UpperCamelCase__		,      atol=self.tolerance		)







@require_tf
class _lowerCAmelCase							( unittest.TestCase      ):
   __UpperCAmelCase					:			str				    =    1E-4







   def 					lowerCamelCase						(      self		)      ->						Tuple:

       '''simple docstring'''




       snake_case       : Optional[int]    =       tf.reshape(tf.range(2 * 12 * 16 * 64		,      dtype=tf.floataa		)		,      shape=(2, 12, 16, 64)		) / 100

       snake_case       : int    =       -tf.reshape(tf.range(2 * 12 * 16 * 64		,      dtype=tf.floataa		)		,      shape=(2, 12, 16, 64)		) / 100

       snake_case       : int    =       TFRoFormerSinusoidalPositionalEmbedding(num_positions=32		,      embedding_dim=64		)
       snake_case       : Tuple    =       embed_positions([2, 16, 768]		)[None, None, :, :]

       snake_case   ,snake_case       : Dict    =       TFRoFormerSelfAttention.apply_rotary_position_embeddings(
           UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)

       snake_case       : Optional[int]    =       tf.constant(
           [
               [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
               [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
               [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
               [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
               [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
               [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
           ]		)
       snake_case       : str    =       tf.constant(
           [
               [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
               [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
               [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
               [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
               [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
               [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
           ]		)

       tf.debugging.assert_near(query_layer[0, 0, :6, :8]		,      UpperCamelCase__		,      atol=self.tolerance		)
       tf.debugging.assert_near(key_layer[0, 0, :6, :8]		,      UpperCamelCase__		,      atol=self.tolerance		)









"""simple docstring"""



import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


__snake_case       =       logging.get_logger(__name__)







class _lowerCAmelCase							( snake_case_      ):







   def __init__(      self		,      *UpperCamelCase__		,      **UpperCamelCase__		)      ->						None:

       '''simple docstring'''




       warnings.warn(
           "The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
           " Please use SegformerImageProcessor instead."		,      UpperCamelCase__		,      )
       super().__init__(*UpperCamelCase__		,      **UpperCamelCase__		)





