"""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        UpperCamelCase		:
   def __init__(			self     ,__UpperCamelCase     ,__UpperCamelCase=13     ,__UpperCamelCase=7     ,__UpperCamelCase=True     ,__UpperCamelCase=True     ,__UpperCamelCase=True     ,__UpperCamelCase=True     ,__UpperCamelCase=99     ,__UpperCamelCase=64     ,__UpperCamelCase=5     ,__UpperCamelCase=4     ,__UpperCamelCase=37     ,__UpperCamelCase="gelu"     ,__UpperCamelCase=0.1     ,__UpperCamelCase=0.1     ,__UpperCamelCase=512     ,__UpperCamelCase=16     ,__UpperCamelCase=2     ,__UpperCamelCase=0.02     ,__UpperCamelCase=3     ,__UpperCamelCase=4     ,__UpperCamelCase=None     ,)	->      List[str]:


        '''simple docstring'''


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


        '''simple docstring'''


        lowercase_	:				str													=	ids_tensor([self.batch_size, self.seq_length]     ,self.vocab_size			)

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

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

        lowercase_	:				Tuple													=	self.get_config()

        return config, input_ids, input_mask, token_labels
   def 			_UpperCAmelCase   (			self			)	->      Dict:


        '''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=__UpperCamelCase     ,initializer_range=self.initializer_range     ,pad_token_id=self.pad_token_id     ,)
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        lowercase_							,  lowercase_							,  lowercase_							,  lowercase_	:				Optional[Any]													=	self.prepare_config_and_inputs()

        lowercase_	:				Union[str, Any]													=	True

        return config, input_ids, input_mask, token_labels
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      str:


        '''simple docstring'''


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


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	True
        lowercase_	:				int													=	GPTNeoXModel(__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()
        lowercase_	:				int													=	model(__UpperCamelCase     ,attention_mask=__UpperCamelCase			)
        self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size)			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				Tuple													=	GPTNeoXForCausalLM(config=__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()
        lowercase_	:				Dict													=	model(__UpperCamelCase     ,attention_mask=__UpperCamelCase     ,labels=__UpperCamelCase			)
        self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length, self.vocab_size)			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Union[str, Any]:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	self.num_labels
        lowercase_	:				Dict													=	GPTNeoXForQuestionAnswering(__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()
        lowercase_	:				Any													=	model(__UpperCamelCase     ,attention_mask=__UpperCamelCase			)
        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     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      str:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	self.num_labels
        lowercase_	:				List[str]													=	GPTNeoXForSequenceClassification(__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()
        lowercase_	:				str													=	ids_tensor([self.batch_size]     ,self.type_sequence_label_size			)
        lowercase_	:				Optional[Any]													=	model(__UpperCamelCase     ,attention_mask=__UpperCamelCase     ,labels=__UpperCamelCase			)
        self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.num_labels)			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Union[str, Any]:


        '''simple docstring'''


        lowercase_	:				Tuple													=	self.num_labels
        lowercase_	:				Tuple													=	GPTNeoXForTokenClassification(__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()
        lowercase_	:				List[str]													=	model(__UpperCamelCase     ,attention_mask=__UpperCamelCase     ,labels=__UpperCamelCase			)
        self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length, self.num_labels)			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				Dict													=	True
        lowercase_	:				Union[str, Any]													=	GPTNeoXForCausalLM(config=__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()

        # first forward pass
        lowercase_	:				List[str]													=	model(__UpperCamelCase     ,attention_mask=__UpperCamelCase     ,use_cache=__UpperCamelCase			)
        lowercase_	:				Dict													=	outputs.past_key_values

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

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

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

        # select random slice
        lowercase_	:				int													=	ids_tensor((1,)     ,output_from_past.shape[-1]			).item()
        lowercase_	:				List[Any]													=	output_from_no_past[:, -3:, random_slice_idx].detach()
        lowercase_	:				Any													=	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(__UpperCamelCase     ,__UpperCamelCase     ,atol=1e-3			)			)







   def 			_UpperCAmelCase   (			self			)	->      Dict:


        '''simple docstring'''


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







@require_torch
class        UpperCamelCase		(			lowercase_						,      lowercase_						,      lowercase_						,      unittest.TestCase  ):
   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 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	GPTNeoXModelTester(self			)
        lowercase_	:				Any													=	ConfigTester(self     ,config_class=__UpperCamelCase     ,hidden_size=64     ,num_attention_heads=8			)
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        self.config_tester.run_common_tests()
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_							,  lowercase_							,  lowercase_							,  lowercase_	:				Tuple													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


        lowercase_							,  lowercase_							,  lowercase_							,  lowercase_	:				Optional[Any]													=	self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        lowercase_							,  lowercase_							,  lowercase_							,  lowercase_	:				Dict													=	self.model_tester.prepare_config_and_inputs_for_decoder()

        lowercase_	:				Optional[Any]													=	None

        self.model_tester.create_and_check_model_as_decoder(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_							,  lowercase_							,  lowercase_							,  lowercase_	:				int													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        lowercase_	:				str													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_causal_lm(*__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				Tuple													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        lowercase_	:				Tuple													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        lowercase_	:				List[str]													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase			)
   @unittest.skip(reason='Feed forward chunking is not implemented'			)
   def 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


        pass







   @parameterized.expand([('linear',), ('dynamic',)]			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      int:


        '''simple docstring'''


        lowercase_							,  lowercase_	:				Optional[int]													=	self.model_tester.prepare_config_and_inputs_for_common()
        lowercase_	:				Dict													=	ids_tensor([1, 10]     ,config.vocab_size			)
        lowercase_	:				List[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
        lowercase_	:				str													=	GPTNeoXModel(__UpperCamelCase			)
        original_model.to(__UpperCamelCase			)
        original_model.eval()
        lowercase_	:				int													=	original_model(__UpperCamelCase			).last_hidden_state
        lowercase_	:				Dict													=	original_model(__UpperCamelCase			).last_hidden_state

        set_seed(42			)  # Fixed seed at init time so the two models get the same random weights
        lowercase_	:				Any													=	{'type': scaling_type, 'factor': 10.0}
        lowercase_	:				int													=	GPTNeoXModel(__UpperCamelCase			)
        scaled_model.to(__UpperCamelCase			)
        scaled_model.eval()
        lowercase_	:				Optional[int]													=	scaled_model(__UpperCamelCase			).last_hidden_state
        lowercase_	:				Optional[int]													=	scaled_model(__UpperCamelCase			).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(__UpperCamelCase     ,__UpperCamelCase     ,atol=1e-5			)			)
        else:
             self.assertFalse(torch.allclose(__UpperCamelCase     ,__UpperCamelCase     ,atol=1e-5			)			)

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







@require_torch
class        UpperCamelCase		(			unittest.TestCase  ):
   @slow
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


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

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

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

             lowercase_	:				int													=	model.generate(**__UpperCamelCase     ,do_sample=__UpperCamelCase     ,max_new_tokens=20			)
             lowercase_	:				Optional[int]													=	tokenizer.batch_decode(__UpperCamelCase			)[0]

             self.assertEqual(__UpperCamelCase     ,__UpperCamelCase			)


"""simple docstring"""







import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

__SCREAMING_SNAKE_CASE    	="python tqdm regex requests packaging filelock numpy tokenizers".split()
if sys.version_info < (3, 7):
       pkgs_to_check_at_runtime.append("dataclasses")
if sys.version_info < (3, 8):
       pkgs_to_check_at_runtime.append("importlib_metadata")

for pkg in pkgs_to_check_at_runtime:
       if pkg in deps:
              if pkg == "tokenizers":
                     # must be loaded here, or else tqdm check may fail
                     from .utils import is_tokenizers_available

                     if not is_tokenizers_available():
                            continue  # not required, check version only if installed

              require_version_core(deps[pkg])
       else:
              raise ValueError(F"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py")



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     Union[str, Any]=None      ):
     require_version(deps[pkg]     ,			__SCREAMING_SNAKE_CASE      )

