
"""simple docstring"""







import unittest

from transformers import LayoutLMConfig, 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           LayoutLMForMaskedLM,
           LayoutLMForQuestionAnswering,
           LayoutLMForSequenceClassification,
           LayoutLMForTokenClassification,
           LayoutLMModel,
       )



class 		UpperCAmelCase_	:
       """simple docstring"""




       def __init__( self       : Tuple     ,   a_       : Union[str, Any]     ,   a_       : Union[str, Any]=13     ,   a_       : Union[str, Any]=7     ,   a_       : Optional[int]=True     ,   a_       : List[str]=True     ,   a_       : Tuple=True     ,   a_       : List[str]=True     ,   a_       : int=99     ,   a_       : Optional[Any]=32     ,   a_       : Any=5     ,   a_       : Dict=4     ,   a_       : Dict=37     ,   a_       : Dict="gelu"     ,   a_       : Union[str, Any]=0.1     ,   a_       : Dict=0.1     ,   a_       : int=5_12     ,   a_       : int=16     ,   a_       : Dict=2     ,   a_       : Optional[int]=0.02     ,   a_       : int=3     ,   a_       : Tuple=4     ,   a_       : Dict=None     ,   a_       : Optional[Any]=10_00     ,   )->					str:



            """simple docstring"""

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




       def   a      ( self       : Dict )->					Any:



            """simple docstring"""

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

            UpperCAmelCase_						:    str    					=  ids_tensor([self.batch_size, self.seq_length, 4]     ,   self.range_bbox )
            # Ensure that bbox is legal
            for i in range(bbox.shape[0] ):
                 for j in range(bbox.shape[1] ):
                      if bbox[i, j, 3] < bbox[i, j, 1]:
                           UpperCAmelCase_						:    List[Any]    					=  bbox[i, j, 3]
                           UpperCAmelCase_						:    Tuple    					=  bbox[i, j, 1]
                           UpperCAmelCase_						:    Dict    					=  t
                      if bbox[i, j, 2] < bbox[i, j, 0]:
                           UpperCAmelCase_						:    Tuple    					=  bbox[i, j, 2]
                           UpperCAmelCase_						:    Any    					=  bbox[i, j, 0]
                           UpperCAmelCase_						:    Dict    					=  t

            UpperCAmelCase_						:    List[Any]    					=  None
            if self.use_input_mask:
                 UpperCAmelCase_						:    str    					=  ids_tensor([self.batch_size, self.seq_length]     ,   vocab_size=2 )

            UpperCAmelCase_						:    str    					=  None
            if self.use_token_type_ids:
                 UpperCAmelCase_						:    Union[str, Any]    					=  ids_tensor([self.batch_size, self.seq_length]     ,   self.type_vocab_size )

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

            UpperCAmelCase_						:    int    					=  self.get_config()

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




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



            """simple docstring"""

            return LayoutLMConfig(
                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     ,   )




       def   a      ( self       : Union[str, Any]     ,   a_       : str     ,   a_       : Tuple     ,   a_       : int     ,   a_       : Dict     ,   a_       : int     ,   a_       : Any     ,   a_       : Optional[int]     ,   a_       : int )->					List[str]:



            """simple docstring"""

            UpperCAmelCase_						:    Union[str, Any]    					=  LayoutLMModel(config=a_ )
            model.to(a_ )
            model.eval()
            UpperCAmelCase_						:    int    					=  model(a_     ,   a_     ,   attention_mask=a_     ,   token_type_ids=a_ )
            UpperCAmelCase_						:    Optional[int]    					=  model(a_     ,   a_     ,   token_type_ids=a_ )
            UpperCAmelCase_						:    str    					=  model(a_     ,   a_ )
            self.parent.assertEqual(result.last_hidden_state.shape     ,   (self.batch_size, self.seq_length, self.hidden_size) )
            self.parent.assertEqual(result.pooler_output.shape     ,   (self.batch_size, self.hidden_size) )




       def   a      ( self       : Union[str, Any]     ,   a_       : Optional[Any]     ,   a_       : Union[str, Any]     ,   a_       : List[str]     ,   a_       : str     ,   a_       : List[str]     ,   a_       : Optional[int]     ,   a_       : int     ,   a_       : List[Any] )->					List[Any]:



            """simple docstring"""

            UpperCAmelCase_						:    Optional[Any]    					=  LayoutLMForMaskedLM(config=a_ )
            model.to(a_ )
            model.eval()
            UpperCAmelCase_						:    int    					=  model(a_     ,   a_     ,   attention_mask=a_     ,   token_type_ids=a_     ,   labels=a_ )
            self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.seq_length, self.vocab_size) )




       def   a      ( self       : Optional[Any]     ,   a_       : Optional[int]     ,   a_       : Union[str, Any]     ,   a_       : Dict     ,   a_       : List[str]     ,   a_       : Union[str, Any]     ,   a_       : Tuple     ,   a_       : int     ,   a_       : str )->					List[str]:



            """simple docstring"""

            UpperCAmelCase_						:    List[Any]    					=  self.num_labels
            UpperCAmelCase_						:    List[Any]    					=  LayoutLMForSequenceClassification(a_ )
            model.to(a_ )
            model.eval()
            UpperCAmelCase_						:    str    					=  model(
                a_     ,   a_     ,   attention_mask=a_     ,   token_type_ids=a_     ,   labels=a_ )
            self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.num_labels) )




       def   a      ( self       : str     ,   a_       : Any     ,   a_       : Any     ,   a_       : List[Any]     ,   a_       : Any     ,   a_       : str     ,   a_       : Union[str, Any]     ,   a_       : List[Any]     ,   a_       : Optional[int] )->					int:



            """simple docstring"""

            UpperCAmelCase_						:    int    					=  self.num_labels
            UpperCAmelCase_						:    Union[str, Any]    					=  LayoutLMForTokenClassification(config=a_ )
            model.to(a_ )
            model.eval()
            UpperCAmelCase_						:    int    					=  model(a_     ,   a_     ,   attention_mask=a_     ,   token_type_ids=a_     ,   labels=a_ )
            self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.seq_length, self.num_labels) )




       def   a      ( self       : List[str]     ,   a_       : Any     ,   a_       : List[str]     ,   a_       : List[Any]     ,   a_       : Dict     ,   a_       : int     ,   a_       : Union[str, Any]     ,   a_       : Dict     ,   a_       : Optional[int] )->					str:



            """simple docstring"""

            UpperCAmelCase_						:    Optional[Any]    					=  LayoutLMForQuestionAnswering(config=a_ )
            model.to(a_ )
            model.eval()
            UpperCAmelCase_						:    List[Any]    					=  model(
                a_     ,   bbox=a_     ,   attention_mask=a_     ,   token_type_ids=a_     ,   start_positions=a_     ,   end_positions=a_     ,   )
            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   a      ( self       : int )->					str:



            """simple docstring"""

            UpperCAmelCase_						:    List[str]    					=  self.prepare_config_and_inputs()
            (
                UpperCAmelCase_
            )						:    Tuple    					=  config_and_inputs
            UpperCAmelCase_						:    List[str]    					=  {
                """input_ids""": input_ids,
                """bbox""": bbox,
                """token_type_ids""": token_type_ids,
                """attention_mask""": input_mask,
            }
            return config, inputs_dict



@require_torch
class 		UpperCAmelCase_	(lowerCamelCase_  , lowerCamelCase_  , unittest.TestCase						):
       """simple docstring"""




       UpperCamelCase_						:     List[Any]				   =    (
           (
               LayoutLMModel,
               LayoutLMForMaskedLM,
               LayoutLMForSequenceClassification,
               LayoutLMForTokenClassification,
               LayoutLMForQuestionAnswering,
           )
           if is_torch_available()
           else None
       )
       UpperCamelCase_						:     Optional[int]				   =    (
           {
               """document-question-answering""": LayoutLMForQuestionAnswering,
               """feature-extraction""": LayoutLMModel,
               """fill-mask""": LayoutLMForMaskedLM,
               """text-classification""": LayoutLMForSequenceClassification,
               """token-classification""": LayoutLMForTokenClassification,
               """zero-shot""": LayoutLMForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       UpperCamelCase_						:     List[str]				   =    True




       def   a      ( self       : int )->					Any:



            """simple docstring"""

            UpperCAmelCase_						:    Union[str, Any]    					=  LayoutLMModelTester(self )
            UpperCAmelCase_						:    Tuple    					=  ConfigTester(self     ,   config_class=a_     ,   hidden_size=37 )




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



            """simple docstring"""

            self.config_tester.run_common_tests()




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



            """simple docstring"""

            UpperCAmelCase_						:    Union[str, Any]    					=  self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*a_ )




       def   a      ( self       : Optional[int] )->					List[str]:



            """simple docstring"""

            UpperCAmelCase_						:    Optional[Any]    					=  self.model_tester.prepare_config_and_inputs()
            for type in ["absolute", "relative_key", "relative_key_query"]:
                 UpperCAmelCase_						:    Union[str, Any]    					=  type
                 self.model_tester.create_and_check_model(*a_ )




       def   a      ( self       : List[Any] )->					List[str]:



            """simple docstring"""

            UpperCAmelCase_						:    Union[str, Any]    					=  self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_lm(*a_ )




       def   a      ( self       : List[str] )->					int:



            """simple docstring"""

            UpperCAmelCase_						:    Union[str, Any]    					=  self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_sequence_classification(*a_ )




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



            """simple docstring"""

            UpperCAmelCase_						:    int    					=  self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(*a_ )




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



            """simple docstring"""

            UpperCAmelCase_						:    int    					=  self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_question_answering(*a_ )






       @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
       def   a      ( self       : List[str] )->					List[str]:



            """simple docstring"""

            pass




def 			A_			(					)     ->							int:


     """simple docstring"""







     UpperCAmelCase_						:    Optional[Any]    					=  torch.tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]]						, device=lowercase      )  # noqa: E231
     UpperCAmelCase_						:    str    					=  torch.tensor([[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, 1, 1, 1, 1, 1],]						, device=lowercase      )  # noqa: E231
     UpperCAmelCase_						:    Dict    					=  torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]						, device=lowercase      )  # noqa: E231
     UpperCAmelCase_						:    List[str]    					=  torch.tensor([[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]]						, device=lowercase      )  # noqa: E231
     # these are sequence labels (i.e. at the token level)
     UpperCAmelCase_						:    Tuple    					=  torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]						, device=lowercase      )  # noqa: E231
     # fmt: on

     return input_ids, attention_mask, bbox, token_type_ids, labels



@require_torch
class 		UpperCAmelCase_	(unittest.TestCase						):
       """simple docstring"""




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



            """simple docstring"""

            UpperCAmelCase_						:    Optional[Any]    					=  LayoutLMModel.from_pretrained("""microsoft/layoutlm-base-uncased""" ).to(a_ )

            UpperCAmelCase_						:    List[str]    					=  prepare_layoutlm_batch_inputs()

            # forward pass
            UpperCAmelCase_						:    Union[str, Any]    					=  model(input_ids=a_     ,   bbox=a_     ,   attention_mask=a_     ,   token_type_ids=a_ )

            # test the sequence output on [0, :3, :3]
            UpperCAmelCase_						:    str    					=  torch.tensor(
                [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]]     ,   device=a_     ,   )

            self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]     ,   a_     ,   atol=1E-3 ) )

            # test the pooled output on [1, :3]
            UpperCAmelCase_						:    str    					=  torch.tensor([-0.6580, -0.0214, 0.8552]     ,   device=a_ )

            self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]     ,   a_     ,   atol=1E-3 ) )




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



            """simple docstring"""

            UpperCAmelCase_						:    Any    					=  LayoutLMForSequenceClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""     ,   num_labels=2 ).to(
                a_ )

            UpperCAmelCase_						:    str    					=  prepare_layoutlm_batch_inputs()

            # forward pass
            UpperCAmelCase_						:    Dict    					=  model(
                input_ids=a_     ,   bbox=a_     ,   attention_mask=a_     ,   token_type_ids=a_     ,   labels=torch.tensor([1, 1]     ,   device=a_ )     ,   )

            # test whether we get a loss as a scalar
            UpperCAmelCase_						:    Optional[int]    					=  outputs.loss
            UpperCAmelCase_						:    List[Any]    					=  torch.Size([] )
            self.assertEqual(loss.shape     ,   a_ )

            # test the shape of the logits
            UpperCAmelCase_						:    int    					=  outputs.logits
            UpperCAmelCase_						:    Union[str, Any]    					=  torch.Size((2, 2) )
            self.assertEqual(logits.shape     ,   a_ )




       @slow
       def   a      ( self       : List[Any] )->					Dict:



            """simple docstring"""

            UpperCAmelCase_						:    int    					=  LayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""     ,   num_labels=13 ).to(
                a_ )

            UpperCAmelCase_						:    Optional[int]    					=  prepare_layoutlm_batch_inputs()

            # forward pass
            UpperCAmelCase_						:    Optional[int]    					=  model(
                input_ids=a_     ,   bbox=a_     ,   attention_mask=a_     ,   token_type_ids=a_     ,   labels=a_ )

            # test the loss calculation to be around 2.65
            # expected_loss = torch.tensor(2.65, device=torch_device)

            # The loss is currently somewhat random and can vary between 0.1-0.3 atol.
            # self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

            # test the shape of the logits
            UpperCAmelCase_						:    Optional[Any]    					=  outputs.logits
            UpperCAmelCase_						:    Union[str, Any]    					=  torch.Size((2, 25, 13) )
            self.assertEqual(logits.shape     ,   a_ )






       @slow
       def   a      ( self       : Tuple )->					Optional[Any]:



            """simple docstring"""

            UpperCAmelCase_						:    str    					=  LayoutLMForQuestionAnswering.from_pretrained("""microsoft/layoutlm-base-uncased""" ).to(a_ )

            UpperCAmelCase_						:    Optional[Any]    					=  prepare_layoutlm_batch_inputs()

            # forward pass
            UpperCAmelCase_						:    Union[str, Any]    					=  model(input_ids=a_     ,   bbox=a_     ,   attention_mask=a_     ,   token_type_ids=a_ )

            # test the shape of the logits
            UpperCAmelCase_						:    Union[str, Any]    					=  torch.Size((2, 25) )
            self.assertEqual(outputs.start_logits.shape     ,   a_ )
            self.assertEqual(outputs.end_logits.shape     ,   a_ )



"""simple docstring"""







from math import radians as angle_to_radians
from math import sin

# Acceleration Constant on Earth (unit m/s^2)
lowercase_						    =					9.8_0665




def 			A_			(					lowercase						, lowercase      )     ->							None:


     """simple docstring"""







     if not isinstance(lowercase						, (int, float)      ):
          raise TypeError("""Invalid velocity. Should be a positive number."""      )

     if not isinstance(lowercase						, (int, float)      ):
          raise TypeError("""Invalid angle. Range is 1-90 degrees."""      )

     # Ensure valid angle
     if angle > 90 or angle < 1:
          raise ValueError("""Invalid angle. Range is 1-90 degrees."""      )

     # Ensure valid velocity
     if init_velocity < 0:
          raise ValueError("""Invalid velocity. Should be a positive number."""      )




def 			A_			(					lowercase						, lowercase      )     ->							float:


     """simple docstring"""







     check_args(lowercase						, lowercase      )
     UpperCAmelCase_						:    Any    					=  angle_to_radians(2 * angle      )
     return round(init_velocity**2 * sin(lowercase      ) / g						, 2      )




def 			A_			(					lowercase						, lowercase      )     ->							float:


     """simple docstring"""







     check_args(lowercase						, lowercase      )
     UpperCAmelCase_						:    Union[str, Any]    					=  angle_to_radians(lowercase      )
     return round(init_velocity**2 * sin(lowercase      ) ** 2 / (2 * g)						, 2      )




def 			A_			(					lowercase						, lowercase      )     ->							float:


     """simple docstring"""







     check_args(lowercase						, lowercase      )
     UpperCAmelCase_						:    Optional[int]    					=  angle_to_radians(lowercase      )
     return round(2 * init_velocity * sin(lowercase      ) / g						, 2      )




def 			A_			(					)     ->							None:


     """simple docstring"""







     UpperCAmelCase_					,UpperCAmelCase_						:    Optional[int]    					=  25, 20
     assert horizontal_distance(lowercase						, lowercase      ) == 40.97
     assert max_height(lowercase						, lowercase      ) == 3.73
     assert total_time(lowercase						, lowercase      ) == 1.74


if __name__ == "__main__":
       from doctest import testmod

       testmod()

       # Get input from user
       lowercase_						    =					float(input("Initial Velocity: ").strip())

       # Get input from user
       lowercase_						    =					float(input("angle: ").strip())

       # Print results
       print()
       print("Results: ")
       print(f"""Horizontal Distance: {horizontal_distance(init_vel, angle)!s} [m]""")
       print(f"""Maximum Height: {max_height(init_vel, angle)!s} [m]""")
       print(f"""Total Time: {total_time(init_vel, angle)!s} [s]""")

