




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      :
						def __init__(			self   :str ,      lowerCamelCase   :Optional[int] ,      lowerCamelCase   :Any=13 ,      lowerCamelCase   :Tuple=7 ,      lowerCamelCase   :List[str]=True ,      lowerCamelCase   :int=True ,      lowerCamelCase   :int=True ,      lowerCamelCase   :Tuple=True ,      lowerCamelCase   :Optional[int]=99 ,      lowerCamelCase   :Union[str, Any]=64 ,      lowerCamelCase   :Any=5 ,      lowerCamelCase   :List[Any]=4 ,      lowerCamelCase   :Tuple=37 ,      lowerCamelCase   :Tuple="gelu" ,      lowerCamelCase   :Optional[int]=0.1 ,      lowerCamelCase   :List[Any]=0.1 ,      lowerCamelCase   :Dict=512 ,      lowerCamelCase   :List[str]=16 ,      lowerCamelCase   :List[Any]=2 ,      lowerCamelCase   :List[str]=0.02 ,      lowerCamelCase   :int=3 ,      lowerCamelCase   :str=4 ,      lowerCamelCase   :Tuple=None ,      )     ->			List[Any]:
							UpperCAmelCase__							 =						parent
							UpperCAmelCase__							 =						batch_size
							UpperCAmelCase__							 =						seq_length
							UpperCAmelCase__							 =						is_training
							UpperCAmelCase__							 =						use_input_mask
							UpperCAmelCase__							 =						use_token_type_ids
							UpperCAmelCase__							 =						use_labels
							UpperCAmelCase__							 =						vocab_size
							UpperCAmelCase__							 =						hidden_size
							UpperCAmelCase__							 =						num_hidden_layers
							UpperCAmelCase__							 =						num_attention_heads
							UpperCAmelCase__							 =						intermediate_size
							UpperCAmelCase__							 =						hidden_act
							UpperCAmelCase__							 =						hidden_dropout_prob
							UpperCAmelCase__							 =						attention_probs_dropout_prob
							UpperCAmelCase__							 =						max_position_embeddings
							UpperCAmelCase__							 =						type_vocab_size
							UpperCAmelCase__							 =						type_sequence_label_size
							UpperCAmelCase__							 =						initializer_range
							UpperCAmelCase__							 =						num_labels
							UpperCAmelCase__							 =						num_choices
							UpperCAmelCase__							 =						scope
							UpperCAmelCase__							 =						vocab_size - 1
						def 	UpperCAmelCase_						(			self   :Optional[Any]      )     ->			List[Any]:
							UpperCAmelCase__							 =						ids_tensor([self.batch_size, self.seq_length] ,      self.vocab_size      )

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

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

							UpperCAmelCase__							 =						self.get_config()

							return config, input_ids, input_mask, token_labels
						def 	UpperCAmelCase_						(			self   :Optional[int]      )     ->			Any:
							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]      )     ->			Optional[Any]:
							UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__							 =						self.prepare_config_and_inputs()

							UpperCAmelCase__							 =						True

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

							# first forward pass
							UpperCAmelCase__							 =						model(lowerCamelCase ,      attention_mask=lowerCamelCase ,      use_cache=lowerCamelCase      )
							UpperCAmelCase__							 =						outputs.past_key_values

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

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

							UpperCAmelCase__							 =						model(lowerCamelCase ,      attention_mask=lowerCamelCase ,      output_hidden_states=lowerCamelCase      )
							UpperCAmelCase__							 =						output_from_no_past["hidden_states"][0]
							UpperCAmelCase__							 =						model(
							    lowerCamelCase ,      attention_mask=lowerCamelCase ,      past_key_values=lowerCamelCase ,      output_hidden_states=lowerCamelCase ,      )["hidden_states"][0]

							# select random slice
							UpperCAmelCase__							 =						ids_tensor((1,) ,      output_from_past.shape[-1]      ).item()
							UpperCAmelCase__							 =						output_from_no_past[:, -3:, random_slice_idx].detach()
							UpperCAmelCase__							 =						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   :Tuple      )     ->			Tuple:
							UpperCAmelCase__							 =						self.prepare_config_and_inputs()
							UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__							 =						config_and_inputs
							UpperCAmelCase__							 =						{"input_ids": input_ids, "attention_mask": input_mask}
							return config, inputs_dict



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

							UpperCAmelCase__							 =						None

							self.model_tester.create_and_check_model_as_decoder(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase      )
						def 	UpperCAmelCase_						(			self   :Any      )     ->			Optional[int]:
							UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__							 =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_decoder_model_past_large_inputs(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase      )
						def 	UpperCAmelCase_						(			self   :Optional[int]      )     ->			Union[str, Any]:
							UpperCAmelCase__							 =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_causal_lm(*lowerCamelCase      )
						def 	UpperCAmelCase_						(			self   :int      )     ->			Optional[Any]:
							UpperCAmelCase__							 =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*lowerCamelCase      )
						def 	UpperCAmelCase_						(			self   :Any      )     ->			Dict:
							UpperCAmelCase__							 =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase      )
						def 	UpperCAmelCase_						(			self   :int      )     ->			Any:
							UpperCAmelCase__							 =						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   :Any      )     ->			Union[str, Any]:
							pass







						@parameterized.expand([("linear",), ("dynamic",)]      )
						def 	UpperCAmelCase_						(			self   :Any ,      lowerCamelCase   :Optional[Any]      )     ->			Tuple:
							UpperCAmelCase__ , UpperCAmelCase__							 =						self.model_tester.prepare_config_and_inputs_for_common()
							UpperCAmelCase__							 =						ids_tensor([1, 10] ,      config.vocab_size      )
							UpperCAmelCase__							 =						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__							 =						GPTNeoXModel(lowerCamelCase      )
							original_model.to(lowerCamelCase      )
							original_model.eval()
							UpperCAmelCase__							 =						original_model(lowerCamelCase      ).last_hidden_state
							UpperCAmelCase__							 =						original_model(lowerCamelCase      ).last_hidden_state

							set_seed(42      )  # Fixed seed at init time so the two models get the same random weights
							UpperCAmelCase__							 =						{"type": scaling_type, "factor": 10.0}
							UpperCAmelCase__							 =						GPTNeoXModel(lowerCamelCase      )
							scaled_model.to(lowerCamelCase      )
							scaled_model.eval()
							UpperCAmelCase__							 =						scaled_model(lowerCamelCase      ).last_hidden_state
							UpperCAmelCase__							 =						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		):
						@slow
						def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Optional[int]:
							UpperCAmelCase__							 =						AutoTokenizer.from_pretrained("EleutherAI/pythia-410m-deduped"      )
							for checkpointing in [True, False]:
								UpperCAmelCase__							 =						GPTNeoXForCausalLM.from_pretrained("EleutherAI/pythia-410m-deduped"      )

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

								UpperCAmelCase__							 =						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
								UpperCAmelCase__							 =						"My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"

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

								self.assertEqual(lowerCamelCase ,      lowerCamelCase      )











import warnings

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


_lowerCAmelCase						:   int		=   logging.get_logger(__name__)
class 			_UpperCamelCase      (				lowerCAmelCase		):
      def __init__(			self   :Optional[Any] ,      *lowerCamelCase   :List[str] ,      **lowerCamelCase   :Tuple      )     ->			None:
       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      )





