

'''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   :     Tuple					,lowercase__   :     int					,lowercase__   :     str=1_3					,lowercase__   :     Union[str, Any]=7					,lowercase__   :     List[str]=True					,lowercase__   :     List[str]=True					,lowercase__   :     List[str]=True					,lowercase__   :     Any=True					,lowercase__   :     str=9_9					,lowercase__   :     List[Any]=6_4					,lowercase__   :     Tuple=5					,lowercase__   :     List[Any]=4					,lowercase__   :     Any=3_7					,lowercase__   :     str="gelu"					,lowercase__   :     Any=0.1					,lowercase__   :     Union[str, Any]=0.1					,lowercase__   :     List[Any]=5_1_2					,lowercase__   :     int=1_6					,lowercase__   :     Dict=2					,lowercase__   :     Dict=0.0_2					,lowercase__   :     str=3					,lowercase__   :     Optional[Any]=4					,lowercase__   :     int=None					,):
   __lowercase							  = parent
   __lowercase							  = batch_size
   __lowercase							  = seq_length
   __lowercase							  = is_training
   __lowercase							  = use_input_mask
   __lowercase							  = use_token_type_ids
   __lowercase							  = use_labels
   __lowercase							  = vocab_size
   __lowercase							  = hidden_size
   __lowercase							  = num_hidden_layers
   __lowercase							  = num_attention_heads
   __lowercase							  = intermediate_size
   __lowercase							  = hidden_act
   __lowercase							  = hidden_dropout_prob
   __lowercase							  = attention_probs_dropout_prob
   __lowercase							  = max_position_embeddings
   __lowercase							  = type_vocab_size
   __lowercase							  = type_sequence_label_size
   __lowercase							  = initializer_range
   __lowercase							  = num_labels
   __lowercase							  = num_choices
   __lowercase							  = scope
   __lowercase							  = vocab_size - 1






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   __lowercase							  = ids_tensor([self.batch_size, self.seq_length]					,self.vocab_size			)

   __lowercase							  = None
   if self.use_input_mask:
    __lowercase							  = random_attention_mask([self.batch_size, self.seq_length]			)

   __lowercase							  = None
   if self.use_labels:
    __lowercase							  = ids_tensor([self.batch_size, self.seq_length]					,self.num_labels			)

   __lowercase							  = self.get_config()

   return config, input_ids, input_mask, token_labels






  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   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=lowercase__					,initializer_range=self.initializer_range					,pad_token_id=self.pad_token_id					,)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   __lowercase  ,	__lowercase  ,	__lowercase  ,	__lowercase							  = self.prepare_config_and_inputs()

   __lowercase							  = True

   return config, input_ids, input_mask, token_labels






  def 					SCREAMING_SNAKE_CASE      (      self   :     str					,lowercase__   :     Tuple					,lowercase__   :     Dict					,lowercase__   :     Any			):
   __lowercase							  = GPTNeoXModel(config=lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(lowercase__					,attention_mask=lowercase__			)
   __lowercase							  = model(lowercase__			)
   self.parent.assertEqual(result.last_hidden_state.shape					,(self.batch_size, self.seq_length, self.hidden_size)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]					,lowercase__   :     Any					,lowercase__   :     Union[str, Any]					,lowercase__   :     int			):
   __lowercase							  = True
   __lowercase							  = GPTNeoXModel(lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(lowercase__					,attention_mask=lowercase__			)
   self.parent.assertEqual(result.last_hidden_state.shape					,(self.batch_size, self.seq_length, self.hidden_size)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]					,lowercase__   :     List[Any]					,lowercase__   :     Dict					,lowercase__   :     Optional[int]					,lowercase__   :     Dict			):
   __lowercase							  = GPTNeoXForCausalLM(config=lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(lowercase__					,attention_mask=lowercase__					,labels=lowercase__			)
   self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.seq_length, self.vocab_size)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]					,lowercase__   :     Optional[Any]					,lowercase__   :     Union[str, Any]					,lowercase__   :     Optional[Any]					,lowercase__   :     Optional[Any]			):
   __lowercase							  = self.num_labels
   __lowercase							  = GPTNeoXForQuestionAnswering(lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(lowercase__					,attention_mask=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   :     Tuple					,lowercase__   :     List[Any]					,lowercase__   :     Dict					,lowercase__   :     List[str]					,lowercase__   :     str			):
   __lowercase							  = self.num_labels
   __lowercase							  = GPTNeoXForSequenceClassification(lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = ids_tensor([self.batch_size]					,self.type_sequence_label_size			)
   __lowercase							  = model(lowercase__					,attention_mask=lowercase__					,labels=lowercase__			)
   self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.num_labels)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict					,lowercase__   :     Any					,lowercase__   :     str					,lowercase__   :     Union[str, Any]					,lowercase__   :     Optional[int]			):
   __lowercase							  = self.num_labels
   __lowercase							  = GPTNeoXForTokenClassification(lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(lowercase__					,attention_mask=lowercase__					,labels=lowercase__			)
   self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.seq_length, self.num_labels)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]					,lowercase__   :     str					,lowercase__   :     Optional[Any]					,lowercase__   :     Optional[int]			):
   __lowercase							  = True
   __lowercase							  = GPTNeoXForCausalLM(config=lowercase__			)
   model.to(lowercase__			)
   model.eval()

   # first forward pass
   __lowercase							  = model(lowercase__					,attention_mask=lowercase__					,use_cache=lowercase__			)
   __lowercase							  = outputs.past_key_values

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

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

   __lowercase							  = model(lowercase__					,attention_mask=lowercase__					,output_hidden_states=lowercase__			)
   __lowercase							  = output_from_no_past['''hidden_states'''][0]
   __lowercase							  = model(
       lowercase__					,attention_mask=lowercase__					,past_key_values=lowercase__					,output_hidden_states=lowercase__					,)['''hidden_states'''][0]

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






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   __lowercase							  = self.prepare_config_and_inputs()
   __lowercase  ,	__lowercase  ,	__lowercase  ,	__lowercase							  = config_and_inputs
   __lowercase							  = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
   return config, inputs_dict






@require_torch
class 			lowercase_	(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       unittest.TestCase      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Optional[Any]							=			(
      (
          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    :  Union[str, Any]							=			(
      {
          '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    :  List[str]							=			False
  SCREAMING_SNAKE_CASE    :  Optional[int]							=			False
  SCREAMING_SNAKE_CASE    :  List[str]							=			False
  SCREAMING_SNAKE_CASE    :  Dict							=			False






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   __lowercase							  = GPTNeoXModelTester(self			)
   __lowercase							  = ConfigTester(self					,config_class=lowercase__					,hidden_size=6_4					,num_attention_heads=8			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   self.config_tester.run_common_tests()






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   __lowercase  ,	__lowercase  ,	__lowercase  ,	__lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(lowercase__					,lowercase__					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   __lowercase  ,	__lowercase  ,	__lowercase  ,	__lowercase							  = self.model_tester.prepare_config_and_inputs_for_decoder()
   self.model_tester.create_and_check_model_as_decoder(lowercase__					,lowercase__					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   # This regression test was failing with PyTorch < 1.3
   __lowercase  ,	__lowercase  ,	__lowercase  ,	__lowercase							  = self.model_tester.prepare_config_and_inputs_for_decoder()

   __lowercase							  = None

   self.model_tester.create_and_check_model_as_decoder(lowercase__					,lowercase__					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   __lowercase  ,	__lowercase  ,	__lowercase  ,	__lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_decoder_model_past_large_inputs(lowercase__					,lowercase__					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_causal_lm(*lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_question_answering(*lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_sequence_classification(*lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_token_classification(*lowercase__			)






  @unittest.skip(reason='''Feed forward chunking is not implemented'''			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   pass






  @parameterized.expand([('''linear''',), ('''dynamic''',)]			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     str					,lowercase__   :     Union[str, Any]			):
   __lowercase  ,	__lowercase							  = self.model_tester.prepare_config_and_inputs_for_common()
   __lowercase							  = ids_tensor([1, 1_0]					,config.vocab_size			)
   __lowercase							  = ids_tensor([1, int(config.max_position_embeddings * 1.5			)]					,config.vocab_size			)

   set_seed(4_2			)  # Fixed seed at init time so the two models get the same random weights
   __lowercase							  = GPTNeoXModel(lowercase__			)
   original_model.to(lowercase__			)
   original_model.eval()
   __lowercase							  = original_model(lowercase__			).last_hidden_state
   __lowercase							  = original_model(lowercase__			).last_hidden_state

   set_seed(4_2			)  # Fixed seed at init time so the two models get the same random weights
   __lowercase							  = {'''type''': scaling_type, '''factor''': 1_0.0}
   __lowercase							  = GPTNeoXModel(lowercase__			)
   scaled_model.to(lowercase__			)
   scaled_model.eval()
   __lowercase							  = scaled_model(lowercase__			).last_hidden_state
   __lowercase							  = scaled_model(lowercase__			).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(lowercase__					,lowercase__					,atol=1e-5			)			)
   else:
    self.assertFalse(torch.allclose(lowercase__					,lowercase__					,atol=1e-5			)			)

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






@require_torch
class 			lowercase_	(unittest.TestCase      ):




  """simple docstring"""




  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   __lowercase							  = AutoTokenizer.from_pretrained('''EleutherAI/pythia-410m-deduped'''			)
   for checkpointing in [True, False]:
    __lowercase							  = GPTNeoXForCausalLM.from_pretrained('''EleutherAI/pythia-410m-deduped'''			)

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

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

    __lowercase							  = model.generate(**lowercase__					,do_sample=lowercase__					,max_new_tokens=2_0			)
    __lowercase							  = tokenizer.batch_decode(lowercase__			)[0]

    self.assertEqual(lowercase__					,lowercase__			)




'''simple docstring'''






import colorsys

from PIL import Image  # type: ignore


def 		_A     (    A__     ,				A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = x
 __lowercase							  = y
 for step in range(A__      ):  # noqa: B007
  __lowercase							  = a * a - b * b + x
  __lowercase							  = 2 * a * b + y
  __lowercase							  = a_new

  # divergence happens for all complex number with an absolute value
  # greater than 4
  if a * a + b * b > 4:
   break
 return step / (max_step - 1)


def 		_A     (    A__      ):






 """simple docstring"""



 if distance == 1:
  return (0, 0, 0)
 else:
  return (255, 255, 255)


def 		_A     (    A__      ):






 """simple docstring"""



 if distance == 1:
  return (0, 0, 0)
 else:
  return tuple(round(i * 255      ) for i in colorsys.hsv_to_rgb(A__     ,				1     ,				1      )      )


def 		_A     (    A__ = 800     ,				A__ = 600     ,				A__ = -0.6     ,				A__ = 0     ,				A__ = 3.2     ,				A__ = 50     ,				A__ = True     ,				):






 """simple docstring"""



 __lowercase							  = Image.new('''RGB'''     ,				(image_width, image_height)      )
 __lowercase							  = img.load()

 # loop through the image-coordinates
 for image_x in range(A__      ):
  for image_y in range(A__      ):
   # determine the figure-coordinates based on the image-coordinates
   __lowercase							  = figure_width / image_width * image_height
   __lowercase							  = figure_center_x + (image_x / image_width - 0.5) * figure_width
   __lowercase							  = figure_center_y + (image_y / image_height - 0.5) * figure_height

   __lowercase							  = get_distance(A__     ,				A__     ,				A__      )

   # color the corresponding pixel based on the selected coloring-function
   if use_distance_color_coding:
    __lowercase							  = get_color_coded_rgb(A__      )
   else:
    __lowercase							  = get_black_and_white_rgb(A__      )

 return img


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # colored version, full figure
 lowerCAmelCase__											=	get_image()

 # uncomment for colored version, different section, zoomed in
 # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
 # figure_width = 0.8)

 # uncomment for black and white version, full figure
 # img = get_image(use_distance_color_coding = False)

 # uncomment to save the image
 # img.save("mandelbrot.png")

 img.show()

