

"""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   :






					"""simple docstring"""



					def __init__(			self     :							Optional[int]  ,lowercase_     :							Union[str, Any]  ,lowercase_     :							Optional[int]=1_3  ,lowercase_     :							List[str]=7  ,lowercase_     :							Union[str, Any]=True  ,lowercase_     :							Dict=True  ,lowercase_     :							Any=True  ,lowercase_     :							Optional[int]=True  ,lowercase_     :							Dict=9_9  ,lowercase_     :							List[str]=6_4  ,lowercase_     :							Optional[Any]=5  ,lowercase_     :							Any=4  ,lowercase_     :							Optional[int]=3_7  ,lowercase_     :							Tuple="gelu"  ,lowercase_     :							Optional[int]=0.1  ,lowercase_     :							Union[str, Any]=0.1  ,lowercase_     :							int=5_1_2  ,lowercase_     :							Optional[Any]=1_6  ,lowercase_     :							List[Any]=2  ,lowercase_     :							Any=0.02  ,lowercase_     :							Optional[Any]=3  ,lowercase_     :							str=4  ,lowercase_     :							Union[str, Any]=None  ,):
						lowerCAmelCase__      :		Tuple    							=      parent
						lowerCAmelCase__      :		Dict    							=      batch_size
						lowerCAmelCase__      :		Optional[int]    							=      seq_length
						lowerCAmelCase__      :		List[Any]    							=      is_training
						lowerCAmelCase__      :		int    							=      use_input_mask
						lowerCAmelCase__      :		List[str]    							=      use_token_type_ids
						lowerCAmelCase__      :		Optional[Any]    							=      use_labels
						lowerCAmelCase__      :		Tuple    							=      vocab_size
						lowerCAmelCase__      :		int    							=      hidden_size
						lowerCAmelCase__      :		str    							=      num_hidden_layers
						lowerCAmelCase__      :		List[str]    							=      num_attention_heads
						lowerCAmelCase__      :		List[Any]    							=      intermediate_size
						lowerCAmelCase__      :		List[Any]    							=      hidden_act
						lowerCAmelCase__      :		str    							=      hidden_dropout_prob
						lowerCAmelCase__      :		Optional[int]    							=      attention_probs_dropout_prob
						lowerCAmelCase__      :		List[Any]    							=      max_position_embeddings
						lowerCAmelCase__      :		int    							=      type_vocab_size
						lowerCAmelCase__      :		str    							=      type_sequence_label_size
						lowerCAmelCase__      :		Tuple    							=      initializer_range
						lowerCAmelCase__      :		Optional[Any]    							=      num_labels
						lowerCAmelCase__      :		List[str]    							=      num_choices
						lowerCAmelCase__      :		List[str]    							=      scope
						lowerCAmelCase__      :		Optional[int]    							=      vocab_size - 1







					def 			__lowerCAmelCase					(			self     :							Optional[Any]						):
						lowerCAmelCase__      :		Any    							=      ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size						)

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

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

						lowerCAmelCase__      :		Optional[Any]    							=      self.get_config()

						return config, input_ids, input_mask, token_labels







					def 			__lowerCAmelCase					(			self     :							List[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=lowercase_  ,initializer_range=self.initializer_range  ,pad_token_id=self.pad_token_id  ,)







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__      :		int    							=      self.prepare_config_and_inputs()

						lowerCAmelCase__      :		List[Any]    							=      True

						return config, input_ids, input_mask, token_labels







					def 			__lowerCAmelCase					(			self     :							str  ,lowercase_     :							Tuple  ,lowercase_     :							Optional[Any]  ,lowercase_     :							str						):
						lowerCAmelCase__      :		Any    							=      GPTNeoXModel(config=lowercase_						)
						model.to(lowercase_						)
						model.eval()
						lowerCAmelCase__      :		Optional[Any]    							=      model(lowercase_  ,attention_mask=lowercase_						)
						lowerCAmelCase__      :		List[Any]    							=      model(lowercase_						)
						self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.seq_length, self.hidden_size)						)







					def 			__lowerCAmelCase					(			self     :							Union[str, Any]  ,lowercase_     :							List[Any]  ,lowercase_     :							List[str]  ,lowercase_     :							Optional[Any]						):
						lowerCAmelCase__      :		str    							=      True
						lowerCAmelCase__      :		Tuple    							=      GPTNeoXModel(lowercase_						)
						model.to(lowercase_						)
						model.eval()
						lowerCAmelCase__      :		int    							=      model(lowercase_  ,attention_mask=lowercase_						)
						self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.seq_length, self.hidden_size)						)







					def 			__lowerCAmelCase					(			self     :							Union[str, Any]  ,lowercase_     :							Optional[int]  ,lowercase_     :							Optional[Any]  ,lowercase_     :							Tuple  ,lowercase_     :							Optional[int]						):
						lowerCAmelCase__      :		Union[str, Any]    							=      GPTNeoXForCausalLM(config=lowercase_						)
						model.to(lowercase_						)
						model.eval()
						lowerCAmelCase__      :		int    							=      model(lowercase_  ,attention_mask=lowercase_  ,labels=lowercase_						)
						self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.seq_length, self.vocab_size)						)







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







					def 			__lowerCAmelCase					(			self     :							Optional[Any]  ,lowercase_     :							Dict  ,lowercase_     :							str  ,lowercase_     :							Any  ,lowercase_     :							Tuple						):
						lowerCAmelCase__      :		Tuple    							=      self.num_labels
						lowerCAmelCase__      :		Dict    							=      GPTNeoXForTokenClassification(lowercase_						)
						model.to(lowercase_						)
						model.eval()
						lowerCAmelCase__      :		Optional[Any]    							=      model(lowercase_  ,attention_mask=lowercase_  ,labels=lowercase_						)
						self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.seq_length, self.num_labels)						)







					def 			__lowerCAmelCase					(			self     :							Dict  ,lowercase_     :							Dict  ,lowercase_     :							Tuple  ,lowercase_     :							Optional[int]						):
						lowerCAmelCase__      :		Optional[int]    							=      True
						lowerCAmelCase__      :		Tuple    							=      GPTNeoXForCausalLM(config=lowercase_						)
						model.to(lowercase_						)
						model.eval()

						# first forward pass
						lowerCAmelCase__      :		List[Any]    							=      model(lowercase_  ,attention_mask=lowercase_  ,use_cache=lowercase_						)
						lowerCAmelCase__      :		int    							=      outputs.past_key_values

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

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

						lowerCAmelCase__      :		Union[str, Any]    							=      model(lowercase_  ,attention_mask=lowercase_  ,output_hidden_states=lowercase_						)
						lowerCAmelCase__      :		int    							=      output_from_no_past['''hidden_states'''][0]
						lowerCAmelCase__      :		Any    							=      model(
						    lowercase_  ,attention_mask=lowercase_  ,past_key_values=lowercase_  ,output_hidden_states=lowercase_  ,)['''hidden_states'''][0]

						# select random slice
						lowerCAmelCase__      :		Any    							=      ids_tensor((1,)  ,output_from_past.shape[-1]						).item()
						lowerCAmelCase__      :		int    							=      output_from_no_past[:, -3:, random_slice_idx].detach()
						lowerCAmelCase__      :		Dict    							=      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 			__lowerCAmelCase					(			self     :							Optional[Any]						):
						lowerCAmelCase__      :		List[Any]    							=      self.prepare_config_and_inputs()
						lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__      :		Dict    							=      config_and_inputs
						lowerCAmelCase__      :		Union[str, Any]    							=      {'''input_ids''': input_ids, '''attention_mask''': input_mask}
						return config, inputs_dict







@require_torch
class 							SCREAMING_SNAKE_CASE   (						a_       ,				a_       ,				a_       ,				unittest.TestCase			):






					"""simple docstring"""



					lowercase__												=			(
					    (
					        GPTNeoXModel,
					        GPTNeoXForCausalLM,
					        GPTNeoXForQuestionAnswering,
					        GPTNeoXForSequenceClassification,
					        GPTNeoXForTokenClassification,
					    )
					    if is_torch_available()
					    else ()
					)
					lowercase__												=			(GPTNeoXForCausalLM,) if is_torch_available() else ()
					lowercase__												=			(
					    {
					        "feature-extraction": GPTNeoXModel,
					        "question-answering": GPTNeoXForQuestionAnswering,
					        "text-classification": GPTNeoXForSequenceClassification,
					        "text-generation": GPTNeoXForCausalLM,
					        "token-classification": GPTNeoXForTokenClassification,
					        "zero-shot": GPTNeoXForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					lowercase__												=			False
					lowercase__												=			False
					lowercase__												=			False
					lowercase__												=			False







					def 			__lowerCAmelCase					(			self     :							Optional[int]						):
						lowerCAmelCase__      :		Optional[Any]    							=      GPTNeoXModelTester(self						)
						lowerCAmelCase__      :		Any    							=      ConfigTester(self  ,config_class=lowercase_  ,hidden_size=6_4  ,num_attention_heads=8						)







					def 			__lowerCAmelCase					(			self     :							str						):
						self.config_tester.run_common_tests()







					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__      :		List[Any]    							=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(lowercase_  ,lowercase_  ,lowercase_						)







					def 			__lowerCAmelCase					(			self     :							Dict						):
						lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__      :		Optional[int]    							=      self.model_tester.prepare_config_and_inputs_for_decoder()
						self.model_tester.create_and_check_model_as_decoder(lowercase_  ,lowercase_  ,lowercase_						)







					def 			__lowerCAmelCase					(			self     :							Optional[int]						):
						# This regression test was failing with PyTorch < 1.3
						lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__      :		Any    							=      self.model_tester.prepare_config_and_inputs_for_decoder()

						lowerCAmelCase__      :		Optional[Any]    							=      None

						self.model_tester.create_and_check_model_as_decoder(lowercase_  ,lowercase_  ,lowercase_						)







					def 			__lowerCAmelCase					(			self     :							List[Any]						):
						lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__      :		Optional[Any]    							=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_decoder_model_past_large_inputs(lowercase_  ,lowercase_  ,lowercase_						)







					def 			__lowerCAmelCase					(			self     :							Optional[Any]						):
						lowerCAmelCase__      :		List[Any]    							=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_causal_lm(*lowercase_						)







					def 			__lowerCAmelCase					(			self     :							str						):
						lowerCAmelCase__      :		List[str]    							=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*lowercase_						)







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						lowerCAmelCase__      :		List[str]    							=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*lowercase_						)







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						lowerCAmelCase__      :		Optional[int]    							=      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 			__lowerCAmelCase					(			self     :							Optional[Any]						):
						pass







					@parameterized.expand([('''linear''',), ('''dynamic''',)]						)
					def 			__lowerCAmelCase					(			self     :							Any  ,lowercase_     :							Optional[int]						):
						lowerCAmelCase__							,lowerCAmelCase__      :		Tuple    							=      self.model_tester.prepare_config_and_inputs_for_common()
						lowerCAmelCase__      :		Any    							=      ids_tensor([1, 1_0]  ,config.vocab_size						)
						lowerCAmelCase__      :		Any    							=      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
						lowerCAmelCase__      :		List[Any]    							=      GPTNeoXModel(lowercase_						)
						original_model.to(lowercase_						)
						original_model.eval()
						lowerCAmelCase__      :		List[str]    							=      original_model(lowercase_						).last_hidden_state
						lowerCAmelCase__      :		Dict    							=      original_model(lowercase_						).last_hidden_state

						set_seed(4_2						)  # Fixed seed at init time so the two models get the same random weights
						lowerCAmelCase__      :		Optional[int]    							=      {'''type''': scaling_type, '''factor''': 10.0}
						lowerCAmelCase__      :		List[Any]    							=      GPTNeoXModel(lowercase_						)
						scaled_model.to(lowercase_						)
						scaled_model.eval()
						lowerCAmelCase__      :		Tuple    							=      scaled_model(lowercase_						).last_hidden_state
						lowerCAmelCase__      :		List[Any]    							=      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 							SCREAMING_SNAKE_CASE   (						unittest.TestCase			):






					"""simple docstring"""



					@slow
					def 			__lowerCAmelCase					(			self     :							Dict						):
						lowerCAmelCase__      :		Union[str, Any]    							=      AutoTokenizer.from_pretrained('''EleutherAI/pythia-410m-deduped'''						)
						for checkpointing in [True, False]:
							lowerCAmelCase__      :		int    							=      GPTNeoXForCausalLM.from_pretrained('''EleutherAI/pythia-410m-deduped'''						)

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

							lowerCAmelCase__      :		str    							=      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
							lowerCAmelCase__      :		Any    							=      '''My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'''

							lowerCAmelCase__      :		Optional[int]    							=      model.generate(**lowercase_  ,do_sample=lowercase_  ,max_new_tokens=2_0						)
							lowerCAmelCase__      :		List[str]    							=      tokenizer.batch_decode(lowercase_						)[0]

							self.assertEqual(lowercase_  ,lowercase_						)







"""simple docstring"""





import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


__UpperCamelCase    : List[str]				 =						logging.get_logger(__name__)







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					def __init__(			self     :							Any  ,*lowercase_     :							Dict  ,**lowercase_     :							List[str]						):
						warnings.warn(
						    '''The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
						    ''' Please use ImageGPTImageProcessor instead.'''  ,lowercase_  ,)
						super().__init__(*lowercase_  ,**lowercase_						)




