



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    UpperCAmelCase__:

   '''simple docstring'''




   def __init__(    self	:      List[Any]      ,					lowerCAmelCase	:      Tuple      ,					lowerCAmelCase	:      str=13      ,					lowerCAmelCase	:      Union[str, Any]=7      ,					lowerCAmelCase	:      int=True      ,					lowerCAmelCase	:      Optional[Any]=True      ,					lowerCAmelCase	:      Tuple=True      ,					lowerCAmelCase	:      str=True      ,					lowerCAmelCase	:      Any=99      ,					lowerCAmelCase	:      str=64      ,					lowerCAmelCase	:      Optional[Any]=5      ,					lowerCAmelCase	:      Union[str, Any]=4      ,					lowerCAmelCase	:      List[Any]=37      ,					lowerCAmelCase	:      int="gelu"      ,					lowerCAmelCase	:      Any=0.1      ,					lowerCAmelCase	:      Any=0.1      ,					lowerCAmelCase	:      List[str]=5_12      ,					lowerCAmelCase	:      Tuple=16      ,					lowerCAmelCase	:      Tuple=2      ,					lowerCAmelCase	:      int=0.02      ,					lowerCAmelCase	:      Optional[int]=3      ,					lowerCAmelCase	:      Optional[int]=4      ,					lowerCAmelCase	:      Union[str, Any]=None      ,					)						->					Optional[int]:




         """simple docstring"""

         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       UpperCAmelCase		(    self	:      Tuple)						->					Optional[int]:




         """simple docstring"""

         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       UpperCAmelCase		(    self	:      Dict)						->					Optional[int]:




         """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=lowerCAmelCase      ,					initializer_range=self.initializer_range      ,					pad_token_id=self.pad_token_id      ,					)


   def       UpperCAmelCase		(    self	:      Union[str, Any])						->					Dict:




         """simple docstring"""

         lowercase__,						lowercase__,						lowercase__,						lowercase__  =	self.prepare_config_and_inputs()

         lowercase__  =	True

         return config, input_ids, input_mask, token_labels


   def       UpperCAmelCase		(    self	:      Optional[Any]      ,					lowerCAmelCase	:      Dict      ,					lowerCAmelCase	:      List[Any]      ,					lowerCAmelCase	:      Dict)						->					Tuple:




         """simple docstring"""

         lowercase__  =	GPTNeoXModel(config=lowerCAmelCase)
         model.to(lowerCAmelCase)
         model.eval()
         lowercase__  =	model(lowerCAmelCase      ,					attention_mask=lowerCAmelCase)
         lowercase__  =	model(lowerCAmelCase)
         self.parent.assertEqual(result.last_hidden_state.shape      ,					(self.batch_size, self.seq_length, self.hidden_size))


   def       UpperCAmelCase		(    self	:      str      ,					lowerCAmelCase	:      Optional[Any]      ,					lowerCAmelCase	:      Optional[Any]      ,					lowerCAmelCase	:      List[str])						->					List[Any]:




         """simple docstring"""

         lowercase__  =	True
         lowercase__  =	GPTNeoXModel(lowerCAmelCase)
         model.to(lowerCAmelCase)
         model.eval()
         lowercase__  =	model(lowerCAmelCase      ,					attention_mask=lowerCAmelCase)
         self.parent.assertEqual(result.last_hidden_state.shape      ,					(self.batch_size, self.seq_length, self.hidden_size))


   def       UpperCAmelCase		(    self	:      Optional[int]      ,					lowerCAmelCase	:      Optional[Any]      ,					lowerCAmelCase	:      Dict      ,					lowerCAmelCase	:      Dict      ,					lowerCAmelCase	:      Dict)						->					Optional[Any]:




         """simple docstring"""

         lowercase__  =	GPTNeoXForCausalLM(config=lowerCAmelCase)
         model.to(lowerCAmelCase)
         model.eval()
         lowercase__  =	model(lowerCAmelCase      ,					attention_mask=lowerCAmelCase      ,					labels=lowerCAmelCase)
         self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.seq_length, self.vocab_size))


   def       UpperCAmelCase		(    self	:      Optional[Any]      ,					lowerCAmelCase	:      Any      ,					lowerCAmelCase	:      str      ,					lowerCAmelCase	:      Any      ,					lowerCAmelCase	:      Any)						->					Optional[Any]:




         """simple docstring"""

         lowercase__  =	self.num_labels
         lowercase__  =	GPTNeoXForQuestionAnswering(lowerCAmelCase)
         model.to(lowerCAmelCase)
         model.eval()
         lowercase__  =	model(lowerCAmelCase      ,					attention_mask=lowerCAmelCase)
         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       UpperCAmelCase		(    self	:      str      ,					lowerCAmelCase	:      Optional[int]      ,					lowerCAmelCase	:      Optional[Any]      ,					lowerCAmelCase	:      Tuple      ,					lowerCAmelCase	:      List[str])						->					int:




         """simple docstring"""

         lowercase__  =	self.num_labels
         lowercase__  =	GPTNeoXForSequenceClassification(lowerCAmelCase)
         model.to(lowerCAmelCase)
         model.eval()
         lowercase__  =	ids_tensor([self.batch_size]      ,					self.type_sequence_label_size)
         lowercase__  =	model(lowerCAmelCase      ,					attention_mask=lowerCAmelCase      ,					labels=lowerCAmelCase)
         self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.num_labels))


   def       UpperCAmelCase		(    self	:      Optional[Any]      ,					lowerCAmelCase	:      Union[str, Any]      ,					lowerCAmelCase	:      Union[str, Any]      ,					lowerCAmelCase	:      Optional[Any]      ,					lowerCAmelCase	:      List[str])						->					str:




         """simple docstring"""

         lowercase__  =	self.num_labels
         lowercase__  =	GPTNeoXForTokenClassification(lowerCAmelCase)
         model.to(lowerCAmelCase)
         model.eval()
         lowercase__  =	model(lowerCAmelCase      ,					attention_mask=lowerCAmelCase      ,					labels=lowerCAmelCase)
         self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.seq_length, self.num_labels))


   def       UpperCAmelCase		(    self	:      str      ,					lowerCAmelCase	:      List[Any]      ,					lowerCAmelCase	:      Tuple      ,					lowerCAmelCase	:      Optional[int])						->					int:




         """simple docstring"""

         lowercase__  =	True
         lowercase__  =	GPTNeoXForCausalLM(config=lowerCAmelCase)
         model.to(lowerCAmelCase)
         model.eval()

         # first forward pass
         lowercase__  =	model(lowerCAmelCase      ,					attention_mask=lowerCAmelCase      ,					use_cache=lowerCAmelCase)
         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(lowerCAmelCase      ,					attention_mask=lowerCAmelCase      ,					output_hidden_states=lowerCAmelCase)
         lowercase__  =	output_from_no_past['hidden_states'][0]
         lowercase__  =	model(
             lowerCAmelCase      ,					attention_mask=lowerCAmelCase      ,					past_key_values=lowerCAmelCase      ,					output_hidden_states=lowerCAmelCase      ,					)['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(lowerCAmelCase      ,					lowerCAmelCase      ,					atol=1E-3))



   def       UpperCAmelCase		(    self	:      List[str])						->					Any:




         """simple docstring"""

         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    UpperCAmelCase__(				lowerCamelCase  ,				lowerCamelCase  ,				lowerCamelCase  ,				unittest.TestCase						):

   '''simple docstring'''




   A     :	Tuple												=     (
       (
           GPTNeoXModel,
           GPTNeoXForCausalLM,
           GPTNeoXForQuestionAnswering,
           GPTNeoXForSequenceClassification,
           GPTNeoXForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   A     :	Any												=     (GPTNeoXForCausalLM,) if is_torch_available() else ()
   A     :	Any												=     (
       {
           "feature-extraction": GPTNeoXModel,
           "question-answering": GPTNeoXForQuestionAnswering,
           "text-classification": GPTNeoXForSequenceClassification,
           "text-generation": GPTNeoXForCausalLM,
           "token-classification": GPTNeoXForTokenClassification,
           "zero-shot": GPTNeoXForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   A     :	Optional[int]												=     False
   A     :	Tuple												=     False
   A     :	Optional[Any]												=     False
   A     :	List[str]												=     False


   def       UpperCAmelCase		(    self	:      List[Any])						->					List[str]:




         """simple docstring"""

         lowercase__  =	GPTNeoXModelTester(self)
         lowercase__  =	ConfigTester(self      ,					config_class=lowerCAmelCase      ,					hidden_size=64      ,					num_attention_heads=8)


   def       UpperCAmelCase		(    self	:      List[str])						->					str:




         """simple docstring"""

         self.config_tester.run_common_tests()


   def       UpperCAmelCase		(    self	:      Tuple)						->					int:




         """simple docstring"""

         lowercase__,						lowercase__,						lowercase__,						lowercase__  =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      Tuple)						->					Any:




         """simple docstring"""

         lowercase__,						lowercase__,						lowercase__,						lowercase__  =	self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_model_as_decoder(lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      Optional[Any])						->					Tuple:




         """simple docstring"""

         lowercase__,						lowercase__,						lowercase__,						lowercase__  =	self.model_tester.prepare_config_and_inputs_for_decoder()

         lowercase__  =	None

         self.model_tester.create_and_check_model_as_decoder(lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      Optional[int])						->					List[Any]:




         """simple docstring"""

         lowercase__,						lowercase__,						lowercase__,						lowercase__  =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_decoder_model_past_large_inputs(lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      str)						->					Any:




         """simple docstring"""

         lowercase__  =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_causal_lm(*lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      Optional[int])						->					Union[str, Any]:




         """simple docstring"""

         lowercase__  =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      Union[str, Any])						->					str:




         """simple docstring"""

         lowercase__  =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      int)						->					Optional[Any]:




         """simple docstring"""

         lowercase__  =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase)


   @unittest.skip(reason='Feed forward chunking is not implemented')
   def       UpperCAmelCase		(    self	:      List[str])						->					List[Any]:




         """simple docstring"""

         pass



   @parameterized.expand([('linear',), ('dynamic',)])
   def       UpperCAmelCase		(    self	:      Dict      ,					lowerCAmelCase	:      List[str])						->					int:




         """simple docstring"""

         lowercase__,						lowercase__  =	self.model_tester.prepare_config_and_inputs_for_common()
         lowercase__  =	ids_tensor([1, 10]      ,					config.vocab_size)
         lowercase__  =	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
         lowercase__  =	GPTNeoXModel(lowerCAmelCase)
         original_model.to(lowerCAmelCase)
         original_model.eval()
         lowercase__  =	original_model(lowerCAmelCase).last_hidden_state
         lowercase__  =	original_model(lowerCAmelCase).last_hidden_state

         set_seed(42)  # Fixed seed at init time so the two models get the same random weights
         lowercase__  =	{'type': scaling_type, 'factor': 10.0}
         lowercase__  =	GPTNeoXModel(lowerCAmelCase)
         scaled_model.to(lowerCAmelCase)
         scaled_model.eval()
         lowercase__  =	scaled_model(lowerCAmelCase).last_hidden_state
         lowercase__  =	scaled_model(lowerCAmelCase).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(lowerCAmelCase      ,					lowerCAmelCase      ,					atol=1E-5))
         else:
               self.assertFalse(torch.allclose(lowerCAmelCase      ,					lowerCAmelCase      ,					atol=1E-5))

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



@require_torch
class    UpperCAmelCase__(				unittest.TestCase						):

   '''simple docstring'''




   @slow
   def       UpperCAmelCase		(    self	:      str)						->					Optional[Any]:




         """simple docstring"""

         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(lowerCAmelCase)

               lowercase__  =	tokenizer('My favorite food is'      ,					return_tensors='pt').to(lowerCAmelCase)
               # 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(**lowerCAmelCase      ,					do_sample=lowerCAmelCase      ,					max_new_tokens=20)
               lowercase__  =	tokenizer.batch_decode(lowerCAmelCase)[0]

               self.assertEqual(lowerCAmelCase      ,					lowerCAmelCase)





import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


a__   :					Optional[Any] 						=      logging.get_logger(__name__)



class    UpperCAmelCase__(				lowerCamelCase						):

   '''simple docstring'''




   def __init__(    self	:      Optional[Any]      ,					*lowerCAmelCase	:      Optional[Any]      ,					**lowerCAmelCase	:      List[Any])						->					None:




         """simple docstring"""

         warnings.warn(
             'The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
             ' use GLPNImageProcessor instead.'      ,					lowerCAmelCase      ,					)
         super().__init__(*lowerCAmelCase      ,					**lowerCAmelCase)
