





'''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  SCREAMING_SNAKE_CASE    :


     def __init__(						self						,	_UpperCAmelCase						,	_UpperCAmelCase=13						,	_UpperCAmelCase=7						,	_UpperCAmelCase=True						,	_UpperCAmelCase=True						,	_UpperCAmelCase=True						,	_UpperCAmelCase=True						,	_UpperCAmelCase=99						,	_UpperCAmelCase=64						,	_UpperCAmelCase=5						,	_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						,	):

          '''simple docstring'''






          __A						: Any      =       parent
          __A						: Dict      =       batch_size
          __A						: Optional[int]      =       seq_length
          __A						: List[Any]      =       is_training
          __A						: Optional[Any]      =       use_input_mask
          __A						: List[str]      =       use_token_type_ids
          __A						: List[Any]      =       use_labels
          __A						: Union[str, Any]      =       vocab_size
          __A						: str      =       hidden_size
          __A						: Optional[Any]      =       num_hidden_layers
          __A						: Optional[Any]      =       num_attention_heads
          __A						: List[Any]      =       intermediate_size
          __A						: str      =       hidden_act
          __A						: List[str]      =       hidden_dropout_prob
          __A						: Optional[int]      =       attention_probs_dropout_prob
          __A						: Optional[Any]      =       max_position_embeddings
          __A						: List[Any]      =       type_vocab_size
          __A						: Tuple      =       type_sequence_label_size
          __A						: Union[str, Any]      =       initializer_range
          __A						: List[str]      =       num_labels
          __A						: Union[str, Any]      =       num_choices
          __A						: List[Any]      =       scope
          __A						: str      =       vocab_size - 1


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Tuple      =       ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size)

          __A						: int      =       None
          if self.use_input_mask:
               __A						: Union[str, Any]      =       random_attention_mask([self.batch_size, self.seq_length])

          __A						: Optional[Any]      =       None
          if self.use_labels:
               __A						: Optional[int]      =       ids_tensor([self.batch_size, self.seq_length]						,	self.num_labels)

          __A						: str      =       self.get_config()

          return config, input_ids, input_mask, token_labels


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          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=_UpperCAmelCase						,	initializer_range=self.initializer_range						,	pad_token_id=self.pad_token_id						,	)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A ,__A ,__A ,__A						: List[Any]      =       self.prepare_config_and_inputs()

          __A						: str      =       True

          return config, input_ids, input_mask, token_labels


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: str      =       GPTNeoXModel(config=_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()
          __A						: Union[str, Any]      =       model(_UpperCAmelCase						,	attention_mask=_UpperCAmelCase)
          __A						: Any      =       model(_UpperCAmelCase)
          self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size))


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: str      =       True
          __A						: Optional[Any]      =       GPTNeoXModel(_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()
          __A						: Union[str, Any]      =       model(_UpperCAmelCase						,	attention_mask=_UpperCAmelCase)
          self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size))


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: str      =       GPTNeoXForCausalLM(config=_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()
          __A						: Union[str, Any]      =       model(_UpperCAmelCase						,	attention_mask=_UpperCAmelCase						,	labels=_UpperCAmelCase)
          self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.vocab_size))


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: int      =       self.num_labels
          __A						: Any      =       GPTNeoXForQuestionAnswering(_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()
          __A						: Any      =       model(_UpperCAmelCase						,	attention_mask=_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 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: Any      =       self.num_labels
          __A						: Optional[Any]      =       GPTNeoXForSequenceClassification(_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()
          __A						: int      =       ids_tensor([self.batch_size]						,	self.type_sequence_label_size)
          __A						: Dict      =       model(_UpperCAmelCase						,	attention_mask=_UpperCAmelCase						,	labels=_UpperCAmelCase)
          self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels))


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: List[str]      =       self.num_labels
          __A						: Union[str, Any]      =       GPTNeoXForTokenClassification(_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()
          __A						: Any      =       model(_UpperCAmelCase						,	attention_mask=_UpperCAmelCase						,	labels=_UpperCAmelCase)
          self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.num_labels))


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: Union[str, Any]      =       True
          __A						: str      =       GPTNeoXForCausalLM(config=_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()

          # first forward pass
          __A						: Optional[Any]      =       model(_UpperCAmelCase						,	attention_mask=_UpperCAmelCase						,	use_cache=_UpperCAmelCase)
          __A						: str      =       outputs.past_key_values

          # create hypothetical multiple next token and extent to next_input_ids
          __A						: int      =       ids_tensor((self.batch_size, 3)						,	config.vocab_size)
          __A						: Optional[Any]      =       ids_tensor((self.batch_size, 3)						,	vocab_size=2)

          # append to next input_ids and
          __A						: List[Any]      =       torch.cat([input_ids, next_tokens]						,	dim=-1)
          __A						: Optional[int]      =       torch.cat([input_mask, next_mask]						,	dim=-1)

          __A						: int      =       model(_UpperCAmelCase						,	attention_mask=_UpperCAmelCase						,	output_hidden_states=_UpperCAmelCase)
          __A						: Tuple      =       output_from_no_past['hidden_states'][0]
          __A						: Any      =       model(
              _UpperCAmelCase						,	attention_mask=_UpperCAmelCase						,	past_key_values=_UpperCAmelCase						,	output_hidden_states=_UpperCAmelCase						,	)['hidden_states'][0]

          # select random slice
          __A						: Optional[int]      =       ids_tensor((1,)						,	output_from_past.shape[-1]).item()
          __A						: Dict      =       output_from_no_past[:, -3:, random_slice_idx].detach()
          __A						: Union[str, Any]      =       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(_UpperCAmelCase						,	_UpperCAmelCase						,	atol=1e-3))


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[Any]      =       self.prepare_config_and_inputs()
          __A ,__A ,__A ,__A						: Any      =       config_and_inputs
          __A						: str      =       {'input_ids': input_ids, 'attention_mask': input_mask}
          return config, inputs_dict




@require_torch
class  SCREAMING_SNAKE_CASE    (a__					,		a__					,		a__					,		unittest.TestCase							):
     lowerCAmelCase					=       (
         (
             GPTNeoXModel,
             GPTNeoXForCausalLM,
             GPTNeoXForQuestionAnswering,
             GPTNeoXForSequenceClassification,
             GPTNeoXForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     lowerCAmelCase					=       (GPTNeoXForCausalLM,) if is_torch_available() else ()
     lowerCAmelCase					=       (
         {
             '''feature-extraction''': GPTNeoXModel,
             '''question-answering''': GPTNeoXForQuestionAnswering,
             '''text-classification''': GPTNeoXForSequenceClassification,
             '''text-generation''': GPTNeoXForCausalLM,
             '''token-classification''': GPTNeoXForTokenClassification,
             '''zero-shot''': GPTNeoXForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     lowerCAmelCase					=       False
     lowerCAmelCase					=       False
     lowerCAmelCase					=       False
     lowerCAmelCase					=       False


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: str      =       GPTNeoXModelTester(self)
          __A						: Union[str, Any]      =       ConfigTester(self						,	config_class=_UpperCAmelCase						,	hidden_size=64						,	num_attention_heads=8)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          self.config_tester.run_common_tests()


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A ,__A ,__A ,__A						: int      =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A ,__A ,__A ,__A						: Optional[Any]      =       self.model_tester.prepare_config_and_inputs_for_decoder()
          self.model_tester.create_and_check_model_as_decoder(_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A ,__A ,__A ,__A						: str      =       self.model_tester.prepare_config_and_inputs_for_decoder()

          __A						: Dict      =       None

          self.model_tester.create_and_check_model_as_decoder(_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A ,__A ,__A ,__A						: Union[str, Any]      =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_decoder_model_past_large_inputs(_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Union[str, Any]      =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_causal_lm(*_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Union[str, Any]      =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[Any]      =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: int      =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase)


     @unittest.skip(reason='Feed forward chunking is not implemented')
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     @parameterized.expand([('linear',), ('dynamic',)])
     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          __A ,__A						: Any      =       self.model_tester.prepare_config_and_inputs_for_common()
          __A						: Tuple      =       ids_tensor([1, 10]						,	config.vocab_size)
          __A						: Tuple      =       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
          __A						: Union[str, Any]      =       GPTNeoXModel(_UpperCAmelCase)
          original_model.to(_UpperCAmelCase)
          original_model.eval()
          __A						: Tuple      =       original_model(_UpperCAmelCase).last_hidden_state
          __A						: Optional[int]      =       original_model(_UpperCAmelCase).last_hidden_state

          set_seed(42)  # Fixed seed at init time so the two models get the same random weights
          __A						: Union[str, Any]      =       {'type': scaling_type, 'factor': 10.0}
          __A						: int      =       GPTNeoXModel(_UpperCAmelCase)
          scaled_model.to(_UpperCAmelCase)
          scaled_model.eval()
          __A						: List[str]      =       scaled_model(_UpperCAmelCase).last_hidden_state
          __A						: Any      =       scaled_model(_UpperCAmelCase).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(_UpperCAmelCase						,	_UpperCAmelCase						,	atol=1e-5))
          else:
               self.assertFalse(torch.allclose(_UpperCAmelCase						,	_UpperCAmelCase						,	atol=1e-5))

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




@require_torch
class  SCREAMING_SNAKE_CASE    (unittest.TestCase							):


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[str]      =       AutoTokenizer.from_pretrained('EleutherAI/pythia-410m-deduped')
          for checkpointing in [True, False]:
               __A						: Dict      =       GPTNeoXForCausalLM.from_pretrained('EleutherAI/pythia-410m-deduped')

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

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

               __A						: Optional[int]      =       model.generate(**_UpperCAmelCase						,	do_sample=_UpperCAmelCase						,	max_new_tokens=20)
               __A						: Tuple      =       tokenizer.batch_decode(_UpperCAmelCase)[0]

               self.assertEqual(_UpperCAmelCase						,	_UpperCAmelCase)






'''simple docstring'''




from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros






def        _lowerCAmelCase			(  __snake_case  : Optional[Any] ,							__snake_case  : Optional[int]			)     ->     List[str]:
     __A						: List[str]      =       k_size // 2
     __A ,__A						: List[Any]      =       mgrid[0 - center : k_size - center, 0 - center : k_size - center]
     __A						: List[Any]      =       1 / (2 * pi * sigma) * exp(-(square(__snake_case			) + square(__snake_case			)) / (2 * square(__snake_case			))			)
     return g






def        _lowerCAmelCase			(  __snake_case  : List[str] ,							__snake_case  : List[str] ,							__snake_case  : int			)     ->     Union[str, Any]:
     __A ,__A						: Tuple      =       image.shape[0], image.shape[1]
     # dst image height and width
     __A						: Tuple      =       height - k_size + 1
     __A						: Optional[Any]      =       width - k_size + 1

     # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
     __A						: str      =       zeros((dst_height * dst_width, k_size * k_size)			)
     __A						: Optional[Any]      =       0
     for i, j in product(range(__snake_case			) ,							range(__snake_case			)			):
          __A						: int      =       ravel(image[i : i + k_size, j : j + k_size]			)
          __A						: List[str]      =       window
          row += 1

     #  turn the kernel into shape(k*k, 1)
     __A						: List[Any]      =       gen_gaussian_kernel(__snake_case ,							__snake_case			)
     __A						: Any      =       ravel(__snake_case			)

     # reshape and get the dst image
     __A						: Dict      =       dot(__snake_case ,							__snake_case			).reshape(__snake_case ,							__snake_case			).astype(__snake_case			)

     return dst


if __name__ == "__main__":
  # read original image
  lowercase__    :      List[Any]						=    imread(r'''../image_data/lena.jpg''')
  # turn image in gray scale value
  lowercase__    :      Dict						=    cvtColor(img, COLOR_BGR2GRAY)

  # get values with two different mask size
  lowercase__    :      Any						=    gaussian_filter(gray, 3, sigma=1)
  lowercase__    :      str						=    gaussian_filter(gray, 5, sigma=0.8)

  # show result images
  imshow('''gaussian filter with 3x3 mask''', gaussianaxa)
  imshow('''gaussian filter with 5x5 mask''', gaussianaxa)
  waitKey()