'''simple docstring'''

import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
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 (
     GPTNeoXForCausalLM,
     GPTNeoXForQuestionAnswering,
     GPTNeoXForSequenceClassification,
     GPTNeoXForTokenClassification,
     GPTNeoXModel,
 )







class 				_lowercase     :


 '''simple docstring'''






 def __init__(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	str      ,       SCREAMING_SNAKE_CASE__   :	List[str]=13      ,       SCREAMING_SNAKE_CASE__   :	Any=7      ,       SCREAMING_SNAKE_CASE__   :	Tuple=True      ,       SCREAMING_SNAKE_CASE__   :	str=True      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=True      ,       SCREAMING_SNAKE_CASE__   :	Tuple=True      ,       SCREAMING_SNAKE_CASE__   :	Tuple=99      ,       SCREAMING_SNAKE_CASE__   :	List[str]=64      ,       SCREAMING_SNAKE_CASE__   :	Tuple=5      ,       SCREAMING_SNAKE_CASE__   :	List[str]=4      ,       SCREAMING_SNAKE_CASE__   :	int=37      ,       SCREAMING_SNAKE_CASE__   :	Any="gelu"      ,       SCREAMING_SNAKE_CASE__   :	int=0.1      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=0.1      ,       SCREAMING_SNAKE_CASE__   :	Tuple=5_12      ,       SCREAMING_SNAKE_CASE__   :	str=16      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=2      ,       SCREAMING_SNAKE_CASE__   :	List[str]=0.0_2      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=3      ,       SCREAMING_SNAKE_CASE__   :	Any=4      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=None      ,       )  ->    int:
  __lowerCAmelCase											=						parent
  __lowerCAmelCase											=						batch_size
  __lowerCAmelCase											=						seq_length
  __lowerCAmelCase											=						is_training
  __lowerCAmelCase											=						use_input_mask
  __lowerCAmelCase											=						use_token_type_ids
  __lowerCAmelCase											=						use_labels
  __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											=						vocab_size - 1
 def a					(      self   :	str							)  ->    Any:
  __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_labels:
   __lowerCAmelCase											=						ids_tensor([self.batch_size, self.seq_length]      ,       self.num_labels							)

  __lowerCAmelCase											=						self.get_config()

  return config, input_ids, input_mask, token_labels
 def a					(      self   :	List[Any]							)  ->    Tuple:
  return GPTNeoXConfig(
      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=SCREAMING_SNAKE_CASE__      ,       initializer_range=self.initializer_range      ,       pad_token_id=self.pad_token_id      ,       )
 def a					(      self   :	Tuple							)  ->    Any:
  __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=						self.prepare_config_and_inputs()

  __lowerCAmelCase											=						True

  return config, input_ids, input_mask, token_labels
 def a					(      self   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	Dict      ,       SCREAMING_SNAKE_CASE__   :	List[str]							)  ->    str:
  __lowerCAmelCase											=						GPTNeoXModel(config=SCREAMING_SNAKE_CASE__							)
  model.to(SCREAMING_SNAKE_CASE__							)
  model.eval()
  __lowerCAmelCase											=						model(SCREAMING_SNAKE_CASE__      ,       attention_mask=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)							)
 def a					(      self   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	str      ,       SCREAMING_SNAKE_CASE__   :	Dict							)  ->    Any:
  __lowerCAmelCase											=						True
  __lowerCAmelCase											=						GPTNeoXModel(SCREAMING_SNAKE_CASE__							)
  model.to(SCREAMING_SNAKE_CASE__							)
  model.eval()
  __lowerCAmelCase											=						model(SCREAMING_SNAKE_CASE__      ,       attention_mask=SCREAMING_SNAKE_CASE__							)
  self.parent.assertEqual(result.last_hidden_state.shape      ,       (self.batch_size, self.seq_length, self.hidden_size)							)
 def a					(      self   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]							)  ->    List[str]:
  __lowerCAmelCase											=						GPTNeoXForCausalLM(config=SCREAMING_SNAKE_CASE__							)
  model.to(SCREAMING_SNAKE_CASE__							)
  model.eval()
  __lowerCAmelCase											=						model(SCREAMING_SNAKE_CASE__      ,       attention_mask=SCREAMING_SNAKE_CASE__      ,       labels=SCREAMING_SNAKE_CASE__							)
  self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.seq_length, self.vocab_size)							)
 def a					(      self   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	Dict      ,       SCREAMING_SNAKE_CASE__   :	str							)  ->    List[Any]:
  __lowerCAmelCase											=						self.num_labels
  __lowerCAmelCase											=						GPTNeoXForQuestionAnswering(SCREAMING_SNAKE_CASE__							)
  model.to(SCREAMING_SNAKE_CASE__							)
  model.eval()
  __lowerCAmelCase											=						model(SCREAMING_SNAKE_CASE__      ,       attention_mask=SCREAMING_SNAKE_CASE__							)
  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   :	str      ,       SCREAMING_SNAKE_CASE__   :	Any      ,       SCREAMING_SNAKE_CASE__   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	Dict      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]							)  ->    Optional[int]:
  __lowerCAmelCase											=						self.num_labels
  __lowerCAmelCase											=						GPTNeoXForSequenceClassification(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__      ,       attention_mask=SCREAMING_SNAKE_CASE__      ,       labels=SCREAMING_SNAKE_CASE__							)
  self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.num_labels)							)
 def a					(      self   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	str      ,       SCREAMING_SNAKE_CASE__   :	Dict      ,       SCREAMING_SNAKE_CASE__   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Tuple							)  ->    Union[str, Any]:
  __lowerCAmelCase											=						self.num_labels
  __lowerCAmelCase											=						GPTNeoXForTokenClassification(SCREAMING_SNAKE_CASE__							)
  model.to(SCREAMING_SNAKE_CASE__							)
  model.eval()
  __lowerCAmelCase											=						model(SCREAMING_SNAKE_CASE__      ,       attention_mask=SCREAMING_SNAKE_CASE__      ,       labels=SCREAMING_SNAKE_CASE__							)
  self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.seq_length, self.num_labels)							)
 def a					(      self   :	int      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]							)  ->    Tuple:
  __lowerCAmelCase											=						True
  __lowerCAmelCase											=						GPTNeoXForCausalLM(config=SCREAMING_SNAKE_CASE__							)
  model.to(SCREAMING_SNAKE_CASE__							)
  model.eval()

  # first forward pass
  __lowerCAmelCase											=						model(SCREAMING_SNAKE_CASE__      ,       attention_mask=SCREAMING_SNAKE_CASE__      ,       use_cache=SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						outputs.past_key_values

  # create hypothetical multiple next token and extent to next_input_ids
  __lowerCAmelCase											=						ids_tensor((self.batch_size, 3)      ,       config.vocab_size							)
  __lowerCAmelCase											=						ids_tensor((self.batch_size, 3)      ,       vocab_size=2							)

  # append to next input_ids and
  __lowerCAmelCase											=						torch.cat([input_ids, next_tokens]      ,       dim=-1							)
  __lowerCAmelCase											=						torch.cat([input_mask, next_mask]      ,       dim=-1							)

  __lowerCAmelCase											=						model(SCREAMING_SNAKE_CASE__      ,       attention_mask=SCREAMING_SNAKE_CASE__      ,       output_hidden_states=SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						output_from_no_past["""hidden_states"""][0]
  __lowerCAmelCase											=						model(
      SCREAMING_SNAKE_CASE__      ,       attention_mask=SCREAMING_SNAKE_CASE__      ,       past_key_values=SCREAMING_SNAKE_CASE__      ,       output_hidden_states=SCREAMING_SNAKE_CASE__      ,       )["""hidden_states"""][0]

  # select random slice
  __lowerCAmelCase											=						ids_tensor((1,)      ,       output_from_past.shape[-1]							).item()
  __lowerCAmelCase											=						output_from_no_past[:, -3:, random_slice_idx].detach()
  __lowerCAmelCase											=						output_from_past[:, :, random_slice_idx].detach()

  self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]							)

  # test that outputs are equal for slice
  self.parent.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       atol=1e-3							)							)





 def a					(      self   :	List[Any]							)  ->    int:
  __lowerCAmelCase											=						self.prepare_config_and_inputs()
  __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=						config_and_inputs
  __lowerCAmelCase											=						{"""input_ids""": input_ids, """attention_mask""": input_mask}
  return config, inputs_dict







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


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       str      	=					(
     (
         GPTNeoXModel,
         GPTNeoXForCausalLM,
         GPTNeoXForQuestionAnswering,
         GPTNeoXForSequenceClassification,
         GPTNeoXForTokenClassification,
     )
     if is_torch_available()
     else ()
 )
 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					(GPTNeoXForCausalLM,) if is_torch_available() else ()
 _SCREAMING_SNAKE_CASE     :       int      	=					(
     {
         """feature-extraction""": GPTNeoXModel,
         """question-answering""": GPTNeoXForQuestionAnswering,
         """text-classification""": GPTNeoXForSequenceClassification,
         """text-generation""": GPTNeoXForCausalLM,
         """token-classification""": GPTNeoXForTokenClassification,
         """zero-shot""": GPTNeoXForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 _SCREAMING_SNAKE_CASE     :       Dict      	=					False
 _SCREAMING_SNAKE_CASE     :       Tuple      	=					False
 _SCREAMING_SNAKE_CASE     :       List[Any]      	=					False
 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					False
 def a					(      self   :	Optional[Any]							)  ->    Union[str, Any]:
  __lowerCAmelCase											=						GPTNeoXModelTester(self							)
  __lowerCAmelCase											=						ConfigTester(self      ,       config_class=SCREAMING_SNAKE_CASE__      ,       hidden_size=64      ,       num_attention_heads=8							)
 def a					(      self   :	List[str]							)  ->    Optional[int]:
  self.config_tester.run_common_tests()
 def a					(      self   :	int							)  ->    Optional[Any]:
  __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	Optional[Any]							)  ->    Union[str, Any]:
  __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs_for_decoder()
  self.model_tester.create_and_check_model_as_decoder(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	int							)  ->    Optional[int]:
  # This regression test was failing with PyTorch < 1.3
  __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs_for_decoder()

  __lowerCAmelCase											=						None

  self.model_tester.create_and_check_model_as_decoder(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	List[str]							)  ->    Optional[int]:
  __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_decoder_model_past_large_inputs(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	Optional[int]							)  ->    Tuple:
  __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_causal_lm(*SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	Union[str, Any]							)  ->    int:
  __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	List[str]							)  ->    Dict:
  __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	str							)  ->    Optional[int]:
  __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE__							)
 @unittest.skip(reason="""Feed forward chunking is not implemented"""							)
 def a					(      self   :	str							)  ->    List[str]:
  pass





 @parameterized.expand([("""linear""",), ("""dynamic""",)]							)
 def a					(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]							)  ->    Union[str, Any]:
  __lowerCAmelCase							,       __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs_for_common()
  __lowerCAmelCase											=						ids_tensor([1, 10]      ,       config.vocab_size							)
  __lowerCAmelCase											=						ids_tensor([1, int(config.max_position_embeddings * 1.5							)]      ,       config.vocab_size							)

  set_seed(42							)  # Fixed seed at init time so the two models get the same random weights
  __lowerCAmelCase											=						GPTNeoXModel(SCREAMING_SNAKE_CASE__							)
  original_model.to(SCREAMING_SNAKE_CASE__							)
  original_model.eval()
  __lowerCAmelCase											=						original_model(SCREAMING_SNAKE_CASE__							).last_hidden_state
  __lowerCAmelCase											=						original_model(SCREAMING_SNAKE_CASE__							).last_hidden_state

  set_seed(42							)  # Fixed seed at init time so the two models get the same random weights
  __lowerCAmelCase											=						{"""type""": scaling_type, """factor""": 1_0.0}
  __lowerCAmelCase											=						GPTNeoXModel(SCREAMING_SNAKE_CASE__							)
  scaled_model.to(SCREAMING_SNAKE_CASE__							)
  scaled_model.eval()
  __lowerCAmelCase											=						scaled_model(SCREAMING_SNAKE_CASE__							).last_hidden_state
  __lowerCAmelCase											=						scaled_model(SCREAMING_SNAKE_CASE__							).last_hidden_state

  # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
  # maximum sequence length, so the outputs for the short input should match.
  if scaling_type == "dynamic":
   self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       atol=1e-5							)							)
  else:
   self.assertFalse(torch.allclose(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       atol=1e-5							)							)

  # The output should be different for long inputs
  self.assertFalse(torch.allclose(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       atol=1e-5							)							)







@require_torch
class 				_lowercase     (				unittest.TestCase			):


 '''simple docstring'''






 @slow
 def a					(      self   :	Dict							)  ->    List[Any]:
  __lowerCAmelCase											=						AutoTokenizer.from_pretrained("""EleutherAI/pythia-410m-deduped"""							)
  for checkpointing in [True, False]:
   __lowerCAmelCase											=						GPTNeoXForCausalLM.from_pretrained("""EleutherAI/pythia-410m-deduped"""							)

   if checkpointing:
    model.gradient_checkpointing_enable()
   else:
    model.gradient_checkpointing_disable()
   model.to(SCREAMING_SNAKE_CASE__							)

   __lowerCAmelCase											=						tokenizer("""My favorite food is"""      ,       return_tensors="""pt"""							).to(SCREAMING_SNAKE_CASE__							)
   # The hub repo. is updated on 2023-04-04, resulting in poor outputs.
   # See: https://github.com/huggingface/transformers/pull/24193
   __lowerCAmelCase											=						"""My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"""

   __lowerCAmelCase											=						model.generate(**SCREAMING_SNAKE_CASE__      ,       do_sample=SCREAMING_SNAKE_CASE__      ,       max_new_tokens=20							)
   __lowerCAmelCase											=						tokenizer.batch_decode(SCREAMING_SNAKE_CASE__							)[0]

   self.assertEqual(SCREAMING_SNAKE_CASE__      ,       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}')






