'''simple docstring'''

import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 (
     CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
     CTRLForSequenceClassification,
     CTRLLMHeadModel,
     CTRLModel,
 )







class 				_lowercase     :


 '''simple docstring'''






 def __init__(      self   :	str      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	Dict=14      ,       SCREAMING_SNAKE_CASE__   :	Tuple=7      ,       SCREAMING_SNAKE_CASE__   :	Tuple=True      ,       SCREAMING_SNAKE_CASE__   :	List[str]=True      ,       SCREAMING_SNAKE_CASE__   :	Tuple=True      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=True      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=True      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=99      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=32      ,       SCREAMING_SNAKE_CASE__   :	str=5      ,       SCREAMING_SNAKE_CASE__   :	int=4      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=37      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]="gelu"      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=0.1      ,       SCREAMING_SNAKE_CASE__   :	int=0.1      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=5_12      ,       SCREAMING_SNAKE_CASE__   :	Any=16      ,       SCREAMING_SNAKE_CASE__   :	Dict=2      ,       SCREAMING_SNAKE_CASE__   :	int=0.0_2      ,       SCREAMING_SNAKE_CASE__   :	Dict=3      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=4      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=None      ,       )  ->    Optional[Any]:
  __lowerCAmelCase											=						parent
  __lowerCAmelCase											=						batch_size
  __lowerCAmelCase											=						seq_length
  __lowerCAmelCase											=						is_training
  __lowerCAmelCase											=						use_token_type_ids
  __lowerCAmelCase											=						use_input_mask
  __lowerCAmelCase											=						use_labels
  __lowerCAmelCase											=						use_mc_token_ids
  __lowerCAmelCase											=						vocab_size
  __lowerCAmelCase											=						hidden_size
  __lowerCAmelCase											=						num_hidden_layers
  __lowerCAmelCase											=						num_attention_heads
  __lowerCAmelCase											=						intermediate_size
  __lowerCAmelCase											=						hidden_act
  __lowerCAmelCase											=						hidden_dropout_prob
  __lowerCAmelCase											=						attention_probs_dropout_prob
  __lowerCAmelCase											=						max_position_embeddings
  __lowerCAmelCase											=						type_vocab_size
  __lowerCAmelCase											=						type_sequence_label_size
  __lowerCAmelCase											=						initializer_range
  __lowerCAmelCase											=						num_labels
  __lowerCAmelCase											=						num_choices
  __lowerCAmelCase											=						scope
  __lowerCAmelCase											=						self.vocab_size - 1
 def a					(      self   :	Any							)  ->    Dict:
  __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
  if self.use_token_type_ids:
   __lowerCAmelCase											=						ids_tensor([self.batch_size, self.seq_length]      ,       self.type_vocab_size							)

  __lowerCAmelCase											=						None
  if self.use_mc_token_ids:
   __lowerCAmelCase											=						ids_tensor([self.batch_size, self.num_choices]      ,       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											=						self.get_config()

  __lowerCAmelCase											=						ids_tensor([self.num_hidden_layers, self.num_attention_heads]      ,       2							)

  return (
      config,
      input_ids,
      input_mask,
      head_mask,
      token_type_ids,
      mc_token_ids,
      sequence_labels,
      token_labels,
      choice_labels,
  )
 def a					(      self   :	int							)  ->    Tuple:
  return CTRLConfig(
      vocab_size=self.vocab_size      ,       n_embd=self.hidden_size      ,       n_layer=self.num_hidden_layers      ,       n_head=self.num_attention_heads      ,       n_positions=self.max_position_embeddings      ,       pad_token_id=self.pad_token_id      ,       )
 def a					(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	Dict      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]      ,       *SCREAMING_SNAKE_CASE__   :	str							)  ->    List[str]:
  __lowerCAmelCase											=						CTRLModel(config=SCREAMING_SNAKE_CASE__							)
  model.to(SCREAMING_SNAKE_CASE__							)
  model.eval()

  model(SCREAMING_SNAKE_CASE__      ,       token_type_ids=SCREAMING_SNAKE_CASE__      ,       head_mask=SCREAMING_SNAKE_CASE__							)
  model(SCREAMING_SNAKE_CASE__      ,       token_type_ids=SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						model(SCREAMING_SNAKE_CASE__							)
  self.parent.assertEqual(result.last_hidden_state.shape      ,       (self.batch_size, self.seq_length, self.hidden_size)							)
  self.parent.assertEqual(len(result.past_key_values							)      ,       config.n_layer							)
 def a					(      self   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]      ,       SCREAMING_SNAKE_CASE__   :	Dict      ,       *SCREAMING_SNAKE_CASE__   :	Dict							)  ->    Any:
  __lowerCAmelCase											=						CTRLLMHeadModel(SCREAMING_SNAKE_CASE__							)
  model.to(SCREAMING_SNAKE_CASE__							)
  model.eval()

  __lowerCAmelCase											=						model(SCREAMING_SNAKE_CASE__      ,       token_type_ids=SCREAMING_SNAKE_CASE__      ,       labels=SCREAMING_SNAKE_CASE__							)
  self.parent.assertEqual(result.loss.shape      ,       ()							)
  self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.seq_length, self.vocab_size)							)
 def a					(      self   :	str							)  ->    List[Any]:
  __lowerCAmelCase											=						self.prepare_config_and_inputs()

  (
      (
      __lowerCAmelCase
  )							,       (
      __lowerCAmelCase
  )							,       (
      __lowerCAmelCase
  )							,       (
      __lowerCAmelCase
  )							,       (
      __lowerCAmelCase
  )							,       (
      __lowerCAmelCase
  )							,       (
      __lowerCAmelCase
  )							,       (
      __lowerCAmelCase
  )							,       (
      __lowerCAmelCase
  )							,       
  )											=						config_and_inputs

  __lowerCAmelCase											=						{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """head_mask""": head_mask}

  return config, inputs_dict





 def a					(      self   :	str      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	Dict      ,       *SCREAMING_SNAKE_CASE__   :	str							)  ->    Tuple:
  __lowerCAmelCase											=						self.num_labels
  __lowerCAmelCase											=						CTRLForSequenceClassification(SCREAMING_SNAKE_CASE__							)
  model.to(SCREAMING_SNAKE_CASE__							)
  model.eval()
  __lowerCAmelCase											=						ids_tensor([self.batch_size]      ,       self.type_sequence_label_size							)
  __lowerCAmelCase											=						model(SCREAMING_SNAKE_CASE__      ,       token_type_ids=SCREAMING_SNAKE_CASE__      ,       labels=SCREAMING_SNAKE_CASE__							)
  self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.num_labels)							)







@require_torch
class 				_lowercase     (				UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  ,							unittest.TestCase			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       List[Any]      	=					(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
 _SCREAMING_SNAKE_CASE     :       List[Any]      	=					(CTRLLMHeadModel,) if is_torch_available() else ()
 _SCREAMING_SNAKE_CASE     :       Tuple      	=					(
     {
         """feature-extraction""": CTRLModel,
         """text-classification""": CTRLForSequenceClassification,
         """text-generation""": CTRLLMHeadModel,
         """zero-shot""": CTRLForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 _SCREAMING_SNAKE_CASE     :       int      	=					True
 _SCREAMING_SNAKE_CASE     :       List[Any]      	=					False
 _SCREAMING_SNAKE_CASE     :       Tuple      	=					False
 def a					(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Dict      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]							)  ->    str:
  if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
   # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
   # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
   # config could not be created.
   return True

  return False
 def a					(      self   :	Tuple							)  ->    Tuple:
  __lowerCAmelCase											=						CTRLModelTester(self							)
  __lowerCAmelCase											=						ConfigTester(self      ,       config_class=SCREAMING_SNAKE_CASE__      ,       n_embd=37							)
 def a					(      self   :	List[str]							)  ->    str:
  super().tearDown()
  # clean-up as much as possible GPU memory occupied by PyTorch
  gc.collect()
  torch.cuda.empty_cache()
 def a					(      self   :	List[str]							)  ->    Tuple:
  self.config_tester.run_common_tests()
 def a					(      self   :	int							)  ->    Optional[Any]:
  __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_ctrl_model(*SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	List[str]							)  ->    Dict:
  __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_lm_head_model(*SCREAMING_SNAKE_CASE__							)
 @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""							)
 def a					(      self   :	Union[str, Any]							)  ->    int:
  pass
 @slow
 def a					(      self   :	Union[str, Any]							)  ->    List[str]:
  for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
   __lowerCAmelCase											=						CTRLModel.from_pretrained(SCREAMING_SNAKE_CASE__							)
   self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)





 @unittest.skip("""The model doesn't support left padding"""							)  # and it's not used enough to be worth fixing :)
 def a					(      self   :	Tuple							)  ->    Optional[int]:
  pass







@require_torch
class 				_lowercase     (				unittest.TestCase			):


 '''simple docstring'''






 def a					(      self   :	Dict							)  ->    Optional[int]:
  super().tearDown()
  # clean-up as much as possible GPU memory occupied by PyTorch
  gc.collect()
  torch.cuda.empty_cache()





 @slow
 def a					(      self   :	Union[str, Any]							)  ->    List[str]:
  __lowerCAmelCase											=						CTRLLMHeadModel.from_pretrained("""ctrl"""							)
  model.to(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						torch.tensor(
      [[1_18_59, 0, 16_11, 8]]      ,       dtype=torch.long      ,       device=SCREAMING_SNAKE_CASE__							)  # Legal the president is
  __lowerCAmelCase											=						[
      1_18_59,
      0,
      16_11,
      8,
      5,
      1_50,
      2_64_49,
      2,
      19,
      3_48,
      4_69,
      3,
      25_95,
      48,
      2_07_40,
      24_65_33,
      24_65_33,
      19,
      30,
      5,
  ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

  __lowerCAmelCase											=						model.generate(SCREAMING_SNAKE_CASE__      ,       do_sample=SCREAMING_SNAKE_CASE__							)
  self.assertListEqual(output_ids[0].tolist()      ,       SCREAMING_SNAKE_CASE__							)







'''simple docstring'''

from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
_A			:      List[Any]					      =				299792458

# Symbols
_A      ,     _A      ,     _A      ,     _A			:      Union[str, Any]					      =				symbols('''ct x y z''')







def      UpperCamelCase_						(					snake_case_       :       float      )					->     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      UpperCamelCase_						(					snake_case_       :       float      )					->     float:






 '''simple docstring'''

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







def      UpperCamelCase_						(					snake_case_       :       float      )					->     np.ndarray:






 '''simple docstring'''

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







def      UpperCamelCase_						(					snake_case_       :       float						,		snake_case_       :       np.ndarray | None = None      )					->     np.ndarray:






 '''simple docstring'''

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

 return transformation_matrix(snake_case_      ) @ event


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # Example of symbolic vector:
 _A			:      str					      =				transform(29979245)
 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
 _A			:      int					      =				{ct: c, x: 1, y: 1, z: 1}
 _A			:      Any					      =				[four_vector[i].subs(sub_dict) for i in range(4)]

 print(f'\n{numerical_vector}')






