'''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  a			:



  """simple docstring"""







  def __init__(				self ,     snake_case_ ,     snake_case_=13 ,     snake_case_=7 ,     snake_case_=True ,     snake_case_=True ,     snake_case_=True ,     snake_case_=True ,     snake_case_=99 ,     snake_case_=64 ,     snake_case_=5 ,     snake_case_=4 ,     snake_case_=37 ,     snake_case_="gelu" ,     snake_case_=0.1 ,     snake_case_=0.1 ,     snake_case_=512 ,     snake_case_=16 ,     snake_case_=2 ,     snake_case_=0.0_2 ,     snake_case_=3 ,     snake_case_=4 ,     snake_case_=None ,     ):

    '''simple docstring'''







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







  def  lowercase_	(				self       ):

    '''simple docstring'''







    __UpperCAmelCase:       int		=			ids_tensor([self.batch_size, self.seq_length] ,     self.vocab_size       )

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

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

    __UpperCAmelCase:       Optional[int]		=			self.get_config()

    return config, input_ids, input_mask, token_labels







  def  lowercase_	(				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=snake_case_ ,     initializer_range=self.initializer_range ,     pad_token_id=self.pad_token_id ,     )







  def  lowercase_	(				self       ):

    '''simple docstring'''







    __UpperCAmelCase:       int		=			self.prepare_config_and_inputs()

    __UpperCAmelCase:       List[Any]		=			True

    return config, input_ids, input_mask, token_labels







  def  lowercase_	(				self ,     snake_case_ ,     snake_case_ ,     snake_case_       ):

    '''simple docstring'''







    __UpperCAmelCase:       int		=			GPTNeoXModel(config=snake_case_       )
    model.to(snake_case_       )
    model.eval()
    __UpperCAmelCase:       List[Any]		=			model(snake_case_ ,     attention_mask=snake_case_       )
    __UpperCAmelCase:       Optional[int]		=			model(snake_case_       )
    self.parent.assertEqual(result.last_hidden_state.shape ,     (self.batch_size, self.seq_length, self.hidden_size)       )







  def  lowercase_	(				self ,     snake_case_ ,     snake_case_ ,     snake_case_       ):

    '''simple docstring'''







    __UpperCAmelCase:       Tuple		=			True
    __UpperCAmelCase:       str		=			GPTNeoXModel(snake_case_       )
    model.to(snake_case_       )
    model.eval()
    __UpperCAmelCase:       Tuple		=			model(snake_case_ ,     attention_mask=snake_case_       )
    self.parent.assertEqual(result.last_hidden_state.shape ,     (self.batch_size, self.seq_length, self.hidden_size)       )







  def  lowercase_	(				self ,     snake_case_ ,     snake_case_ ,     snake_case_ ,     snake_case_       ):

    '''simple docstring'''







    __UpperCAmelCase:       List[Any]		=			GPTNeoXForCausalLM(config=snake_case_       )
    model.to(snake_case_       )
    model.eval()
    __UpperCAmelCase:       Tuple		=			model(snake_case_ ,     attention_mask=snake_case_ ,     labels=snake_case_       )
    self.parent.assertEqual(result.logits.shape ,     (self.batch_size, self.seq_length, self.vocab_size)       )







  def  lowercase_	(				self ,     snake_case_ ,     snake_case_ ,     snake_case_ ,     snake_case_       ):

    '''simple docstring'''







    __UpperCAmelCase:       int		=			self.num_labels
    __UpperCAmelCase:       Tuple		=			GPTNeoXForQuestionAnswering(snake_case_       )
    model.to(snake_case_       )
    model.eval()
    __UpperCAmelCase:       Union[str, Any]		=			model(snake_case_ ,     attention_mask=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  lowercase_	(				self ,     snake_case_ ,     snake_case_ ,     snake_case_ ,     snake_case_       ):

    '''simple docstring'''







    __UpperCAmelCase:       Optional[int]		=			self.num_labels
    __UpperCAmelCase:       List[Any]		=			GPTNeoXForSequenceClassification(snake_case_       )
    model.to(snake_case_       )
    model.eval()
    __UpperCAmelCase:       List[str]		=			ids_tensor([self.batch_size] ,     self.type_sequence_label_size       )
    __UpperCAmelCase:       Union[str, Any]		=			model(snake_case_ ,     attention_mask=snake_case_ ,     labels=snake_case_       )
    self.parent.assertEqual(result.logits.shape ,     (self.batch_size, self.num_labels)       )







  def  lowercase_	(				self ,     snake_case_ ,     snake_case_ ,     snake_case_ ,     snake_case_       ):

    '''simple docstring'''







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







  def  lowercase_	(				self ,     snake_case_ ,     snake_case_ ,     snake_case_       ):

    '''simple docstring'''







    __UpperCAmelCase:       Tuple		=			True
    __UpperCAmelCase:       Optional[Any]		=			GPTNeoXForCausalLM(config=snake_case_       )
    model.to(snake_case_       )
    model.eval()

    # first forward pass
    __UpperCAmelCase:       Dict		=			model(snake_case_ ,     attention_mask=snake_case_ ,     use_cache=snake_case_       )
    __UpperCAmelCase:       Dict		=			outputs.past_key_values

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

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

    __UpperCAmelCase:       List[str]		=			model(snake_case_ ,     attention_mask=snake_case_ ,     output_hidden_states=snake_case_       )
    __UpperCAmelCase:       str		=			output_from_no_past['hidden_states'][0]
    __UpperCAmelCase:       Optional[int]		=			model(
        snake_case_ ,     attention_mask=snake_case_ ,     past_key_values=snake_case_ ,     output_hidden_states=snake_case_ ,     )['hidden_states'][0]

    # select random slice
    __UpperCAmelCase:       int		=			ids_tensor((1,) ,     output_from_past.shape[-1]       ).item()
    __UpperCAmelCase:       Tuple		=			output_from_no_past[:, -3:, random_slice_idx].detach()
    __UpperCAmelCase:       List[str]		=			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(snake_case_ ,     snake_case_ ,     atol=1e-3       )       )







  def  lowercase_	(				self       ):

    '''simple docstring'''







    __UpperCAmelCase:       Dict		=			self.prepare_config_and_inputs()
    __UpperCAmelCase:       Dict		=			config_and_inputs
    __UpperCAmelCase:       List[str]		=			{'input_ids': input_ids, 'attention_mask': input_mask}
    return config, inputs_dict




@require_torch
class  a			(							snake_case__    ,	snake_case__    ,	snake_case__    ,	unittest.TestCase					):



  """simple docstring"""
  __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  lowercase_	(				self       ):

    '''simple docstring'''







    __UpperCAmelCase:       Union[str, Any]		=			GPTNeoXModelTester(self       )
    __UpperCAmelCase:       List[Any]		=			ConfigTester(self ,     config_class=snake_case_ ,     hidden_size=64 ,     num_attention_heads=8       )







  def  lowercase_	(				self       ):

    '''simple docstring'''







    self.config_tester.run_common_tests()







  def  lowercase_	(				self       ):

    '''simple docstring'''







    __UpperCAmelCase:       Union[str, Any]		=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(snake_case_ ,     snake_case_ ,     snake_case_       )







  def  lowercase_	(				self       ):

    '''simple docstring'''







    __UpperCAmelCase:       Any		=			self.model_tester.prepare_config_and_inputs_for_decoder()
    self.model_tester.create_and_check_model_as_decoder(snake_case_ ,     snake_case_ ,     snake_case_       )







  def  lowercase_	(				self       ):

    '''simple docstring'''







    __UpperCAmelCase:       Optional[int]		=			self.model_tester.prepare_config_and_inputs_for_decoder()

    __UpperCAmelCase:       Optional[Any]		=			None

    self.model_tester.create_and_check_model_as_decoder(snake_case_ ,     snake_case_ ,     snake_case_       )







  def  lowercase_	(				self       ):

    '''simple docstring'''







    __UpperCAmelCase:       Dict		=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_decoder_model_past_large_inputs(snake_case_ ,     snake_case_ ,     snake_case_       )







  def  lowercase_	(				self       ):

    '''simple docstring'''







    __UpperCAmelCase:       Optional[Any]		=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_causal_lm(*snake_case_       )







  def  lowercase_	(				self       ):

    '''simple docstring'''







    __UpperCAmelCase:       Tuple		=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_question_answering(*snake_case_       )







  def  lowercase_	(				self       ):

    '''simple docstring'''







    __UpperCAmelCase:       Tuple		=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(*snake_case_       )







  def  lowercase_	(				self       ):

    '''simple docstring'''







    __UpperCAmelCase:       str		=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(*snake_case_       )







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

    '''simple docstring'''







    pass







  @parameterized.expand([("""linear""",), ("""dynamic""",)]       )
  def  lowercase_	(				self ,     snake_case_       ):

    '''simple docstring'''







    __UpperCAmelCase:       Tuple		=			self.model_tester.prepare_config_and_inputs_for_common()
    __UpperCAmelCase:       str		=			ids_tensor([1, 10] ,     config.vocab_size       )
    __UpperCAmelCase:       Any		=			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
    __UpperCAmelCase:       Tuple		=			GPTNeoXModel(snake_case_       )
    original_model.to(snake_case_       )
    original_model.eval()
    __UpperCAmelCase:       int		=			original_model(snake_case_       ).last_hidden_state
    __UpperCAmelCase:       List[Any]		=			original_model(snake_case_       ).last_hidden_state

    set_seed(42       )  # Fixed seed at init time so the two models get the same random weights
    __UpperCAmelCase:       str		=			{'type': scaling_type, 'factor': 1_0.0}
    __UpperCAmelCase:       Any		=			GPTNeoXModel(snake_case_       )
    scaled_model.to(snake_case_       )
    scaled_model.eval()
    __UpperCAmelCase:       Tuple		=			scaled_model(snake_case_       ).last_hidden_state
    __UpperCAmelCase:       List[Any]		=			scaled_model(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(snake_case_ ,     snake_case_ ,     atol=1e-5       )       )
    else:
      self.assertFalse(torch.allclose(snake_case_ ,     snake_case_ ,     atol=1e-5       )       )

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




@require_torch
class  a			(							unittest.TestCase					):



  """simple docstring"""







  @slow
  def  lowercase_	(				self       ):

    '''simple docstring'''







    __UpperCAmelCase:       Tuple		=			AutoTokenizer.from_pretrained("""EleutherAI/pythia-410m-deduped"""       )
    for checkpointing in [True, False]:
      __UpperCAmelCase:       Optional[int]		=			GPTNeoXForCausalLM.from_pretrained("""EleutherAI/pythia-410m-deduped"""       )

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

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

      __UpperCAmelCase:       Tuple		=			model.generate(**snake_case_ ,     do_sample=snake_case_ ,     max_new_tokens=20       )
      __UpperCAmelCase:       List[str]		=			tokenizer.batch_decode(snake_case_       )[0]

      self.assertEqual(snake_case_ ,     snake_case_       )






import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


_lowercase		:  int			       =logging.get_logger(__name__)






class 						UpperCamelCase_  (  snake_case__							):



					def __init__(		self     :     Tuple       ,      *lowerCamelCase     :     List[Any]       ,      **lowerCamelCase     :     Union[str, Any]						):
							warnings.warn(
							    'The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
							    ' use YolosImageProcessor instead.'       ,      lowerCamelCase       ,      )
							super().__init__(*lowerCamelCase       ,      **lowerCamelCase						)
