

"""simple docstring"""

import unittest

from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import 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 (
        NystromformerForMaskedLM,
        NystromformerForMultipleChoice,
        NystromformerForQuestionAnswering,
        NystromformerForSequenceClassification,
        NystromformerForTokenClassification,
        NystromformerModel,
    )
    from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST






class 			__a					:





       """simple docstring"""


       def __init__(   self			,					snake_case			,					snake_case=13			,					snake_case=7			,					snake_case=True			,					snake_case=True			,					snake_case=True			,					snake_case=True			,					snake_case=99			,					snake_case=32			,					snake_case=5			,					snake_case=4			,					snake_case=37			,					snake_case="gelu"			,					snake_case=0.1			,					snake_case=0.1			,					snake_case=512			,					snake_case=16			,					snake_case=2			,					snake_case=0.02			,					snake_case=3			,					snake_case=4			,					snake_case=None			,					):





        """simple docstring"""

        lowerCAmelCase__   :				Any 			=				parent
        lowerCAmelCase__   :				int 			=				batch_size
        lowerCAmelCase__   :				str 			=				seq_length
        lowerCAmelCase__   :				int 			=				is_training
        lowerCAmelCase__   :				List[Any] 			=				use_input_mask
        lowerCAmelCase__   :				int 			=				use_token_type_ids
        lowerCAmelCase__   :				Optional[int] 			=				use_labels
        lowerCAmelCase__   :				List[str] 			=				vocab_size
        lowerCAmelCase__   :				int 			=				hidden_size
        lowerCAmelCase__   :				Tuple 			=				num_hidden_layers
        lowerCAmelCase__   :				Optional[Any] 			=				num_attention_heads
        lowerCAmelCase__   :				Tuple 			=				intermediate_size
        lowerCAmelCase__   :				List[Any] 			=				hidden_act
        lowerCAmelCase__   :				Optional[Any] 			=				hidden_dropout_prob
        lowerCAmelCase__   :				List[Any] 			=				attention_probs_dropout_prob
        lowerCAmelCase__   :				Dict 			=				max_position_embeddings
        lowerCAmelCase__   :				Dict 			=				type_vocab_size
        lowerCAmelCase__   :				Optional[int] 			=				type_sequence_label_size
        lowerCAmelCase__   :				int 			=				initializer_range
        lowerCAmelCase__   :				Tuple 			=				num_labels
        lowerCAmelCase__   :				Optional[Any] 			=				num_choices
        lowerCAmelCase__   :				Union[str, Any] 			=				scope






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Dict 			=				ids_tensor([self.batch_size, self.seq_length]			,					self.vocab_size   )

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

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

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

        lowerCAmelCase__   :				Tuple 			=				self.get_config()

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return NystromformerConfig(
            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			,					is_decoder=lowercase__			,					initializer_range=self.initializer_range			,					)






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				NystromformerModel(config=lowercase__   )
        model.to(lowercase__   )
        model.eval()
        lowerCAmelCase__   :				int 			=				model(lowercase__			,					attention_mask=lowercase__			,					token_type_ids=lowercase__   )
        lowerCAmelCase__   :				Dict 			=				model(lowercase__			,					token_type_ids=lowercase__   )
        lowerCAmelCase__   :				List[str] 			=				model(lowercase__   )
        self.parent.assertEqual(result.last_hidden_state.shape			,					(self.batch_size, self.seq_length, self.hidden_size)   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				NystromformerForMaskedLM(config=lowercase__   )
        model.to(lowercase__   )
        model.eval()
        lowerCAmelCase__   :				Union[str, Any] 			=				model(lowercase__			,					attention_mask=lowercase__			,					token_type_ids=lowercase__			,					labels=lowercase__   )
        self.parent.assertEqual(result.logits.shape			,					(self.batch_size, self.seq_length, self.vocab_size)   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				Dict 			=				NystromformerForQuestionAnswering(config=lowercase__   )
        model.to(lowercase__   )
        model.eval()
        lowerCAmelCase__   :				Dict 			=				model(
            lowercase__			,					attention_mask=lowercase__			,					token_type_ids=lowercase__			,					start_positions=lowercase__			,					end_positions=lowercase__			,					)
        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 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[Any] 			=				self.num_labels
        lowerCAmelCase__   :				Any 			=				NystromformerForSequenceClassification(lowercase__   )
        model.to(lowercase__   )
        model.eval()
        lowerCAmelCase__   :				Union[str, Any] 			=				model(lowercase__			,					attention_mask=lowercase__			,					token_type_ids=lowercase__			,					labels=lowercase__   )
        self.parent.assertEqual(result.logits.shape			,					(self.batch_size, self.num_labels)   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				self.num_labels
        lowerCAmelCase__   :				str 			=				NystromformerForTokenClassification(config=lowercase__   )
        model.to(lowercase__   )
        model.eval()
        lowerCAmelCase__   :				Union[str, Any] 			=				model(lowercase__			,					attention_mask=lowercase__			,					token_type_ids=lowercase__			,					labels=lowercase__   )
        self.parent.assertEqual(result.logits.shape			,					(self.batch_size, self.seq_length, self.num_labels)   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				self.num_choices
        lowerCAmelCase__   :				int 			=				NystromformerForMultipleChoice(config=lowercase__   )
        model.to(lowercase__   )
        model.eval()
        lowerCAmelCase__   :				str 			=				input_ids.unsqueeze(1   ).expand(-1			,					self.num_choices			,					-1   ).contiguous()
        lowerCAmelCase__   :				Union[str, Any] 			=				token_type_ids.unsqueeze(1   ).expand(-1			,					self.num_choices			,					-1   ).contiguous()
        lowerCAmelCase__   :				Any 			=				input_mask.unsqueeze(1   ).expand(-1			,					self.num_choices			,					-1   ).contiguous()
        lowerCAmelCase__   :				Optional[int] 			=				model(
            lowercase__			,					attention_mask=lowercase__			,					token_type_ids=lowercase__			,					labels=lowercase__			,					)
        self.parent.assertEqual(result.logits.shape			,					(self.batch_size, self.num_choices)   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

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






@require_torch
class 			__a					(						snake_case_       ,	snake_case_       ,	unittest.TestCase       ):





       """simple docstring"""


       __UpperCamelCase					:      Optional[int]    =						(
           (
               NystromformerModel,
               NystromformerForMaskedLM,
               NystromformerForMultipleChoice,
               NystromformerForQuestionAnswering,
               NystromformerForSequenceClassification,
               NystromformerForTokenClassification,
           )
           if is_torch_available()
           else ()
       )
       __UpperCamelCase					:      Union[str, Any]    =						(
           {
               '''feature-extraction''': NystromformerModel,
               '''fill-mask''': NystromformerForMaskedLM,
               '''question-answering''': NystromformerForQuestionAnswering,
               '''text-classification''': NystromformerForSequenceClassification,
               '''token-classification''': NystromformerForTokenClassification,
               '''zero-shot''': NystromformerForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       __UpperCamelCase					:      Any    =						False
       __UpperCamelCase					:      str    =						False






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				NystromformerModelTester(self   )
        lowerCAmelCase__   :				Optional[Any] 			=				ConfigTester(self			,					config_class=lowercase__			,					hidden_size=37   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        self.config_tester.run_common_tests()






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				int 			=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase__   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
         lowerCAmelCase__   :				int 			=				type
         self.model_tester.create_and_check_model(*lowercase__   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[str] 			=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*lowercase__   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[str] 			=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*lowercase__   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Dict 			=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*lowercase__   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[int] 			=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*lowercase__   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[str] 			=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*lowercase__   )






       @slow
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowerCAmelCase__   :				Tuple 			=				NystromformerModel.from_pretrained(lowercase__   )
         self.assertIsNotNone(lowercase__   )






@require_torch
class 			__a					(						unittest.TestCase       ):





       """simple docstring"""


       @slow
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Tuple 			=				NystromformerModel.from_pretrained("uw-madison/nystromformer-512"   )
        lowerCAmelCase__   :				Optional[int] 			=				torch.tensor([[0, 1, 2, 3, 4, 5]]   )

        with torch.no_grad():
         lowerCAmelCase__   :				List[Any] 			=				model(lowercase__   )[0]

        lowerCAmelCase__   :				Dict 			=				torch.Size((1, 6, 768)   )
        self.assertEqual(output.shape			,					lowercase__   )

        lowerCAmelCase__   :				Dict 			=				torch.tensor(
            [[[-0.4_532, -0.0_936, 0.5_137], [-0.2_676, 0.0_628, 0.6_186], [-0.3_629, -0.1_726, 0.4_716]]]   )

        self.assertTrue(torch.allclose(output[:, :3, :3]			,					lowercase__			,					atol=1e-4   )   )






       @slow
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[str] 			=				"""the [MASK] of Belgium is Brussels"""

        lowerCAmelCase__   :				Dict 			=				AutoTokenizer.from_pretrained("uw-madison/nystromformer-512"   )
        lowerCAmelCase__   :				Optional[int] 			=				NystromformerForMaskedLM.from_pretrained("uw-madison/nystromformer-512"   )

        lowerCAmelCase__   :				List[str] 			=				tokenizer(lowercase__			,					return_tensors="pt"   )

        with torch.no_grad():
         lowerCAmelCase__   :				Optional[int] 			=				model(encoding.input_ids   ).logits

        lowerCAmelCase__   :				Union[str, Any] 			=				token_logits[:, 2, :].argmax(-1   )[0]

        self.assertEqual(tokenizer.decode(lowercase__   )			,					"capital"   )












'''simple docstring'''


from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
lowerCAmelCase_      :  List[Any]				     =  2_9979_2458

# Symbols
lowerCAmelCase_     ,   lowerCAmelCase_     ,   lowerCAmelCase_     ,   lowerCAmelCase_      :  Tuple				     =  symbols('ct x y z')



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float				):




     """simple docstring"""


     if velocity > c:
          raise ValueError("""Speed must not exceed light speed 299,792,458 [m/s]!"""				)
     elif velocity < 1:
          # Usually the speed should be much higher than 1 (c order of magnitude)
          raise ValueError("""Speed must be greater than or equal to 1!"""				)

     return velocity / c



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float				):




     """simple docstring"""


     return 1 / sqrt(1 - beta(UpperCamelCase__				) ** 2				)



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float				):




     """simple docstring"""


     return np.array(
         [
             [gamma(UpperCamelCase__				), -gamma(UpperCamelCase__				) * beta(UpperCamelCase__				), 0, 0],
             [-gamma(UpperCamelCase__				) * beta(UpperCamelCase__				), gamma(UpperCamelCase__				), 0, 0],
             [0, 0, 1, 0],
             [0, 0, 0, 1],
         ]				)



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float ,    UpperCamelCase__    :					np.ndarray | None = None				):




     """simple docstring"""


     if event is None:
          a_     :							str     						=  np.array([ct, x, y, z]				)  # Symbolic four vector
     else:
          event[0] *= c  # x0 is ct (speed of light * time)

     return transformation_matrix(UpperCamelCase__				) @ event


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # Example of symbolic vector:
 lowerCAmelCase_      :  Union[str, Any]				     =  transform(2997_9245)
 print('Example of four vector: ')
 print(f"ct' = {four_vector[0]}")
 print(f"x' = {four_vector[1]}")
 print(f"y' = {four_vector[2]}")
 print(f"z' = {four_vector[3]}")

 # Substitute symbols with numerical values
 lowerCAmelCase_      :  Tuple				     =  {ct: c, x: 1, y: 1, z: 1}
 lowerCAmelCase_      :  Optional[int]				     =  [four_vector[i].subs(sub_dict) for i in range(4)]

 print(f"\n{numerical_vector}")
