




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   :    Union[str, Any]		,				A_   :    int		,				A_   :    Union[str, Any]=13		,				A_   :    Union[str, Any]=7		,				A_   :    int=True		,				A_   :    Union[str, Any]=True		,				A_   :    List[Any]=True		,				A_   :    Optional[Any]=True		,				A_   :    List[Any]=99		,				A_   :    List[str]=64		,				A_   :    Optional[Any]=5		,				A_   :    Any=4		,				A_   :    int=37		,				A_   :    Any="gelu"		,				A_   :    List[str]=0.1		,				A_   :    int=0.1		,				A_   :    Union[str, Any]=512		,				A_   :    Dict=16		,				A_   :    Union[str, Any]=2		,				A_   :    str=0.02		,				A_   :    Union[str, Any]=3		,				A_   :    List[str]=4		,				A_   :    Any=None		,				)  ->      Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_							=       parent
       lowerCamelCase_							=       batch_size
       lowerCamelCase_							=       seq_length
       lowerCamelCase_							=       is_training
       lowerCamelCase_							=       use_input_mask
       lowerCamelCase_							=       use_token_type_ids
       lowerCamelCase_							=       use_labels
       lowerCamelCase_							=       vocab_size
       lowerCamelCase_							=       hidden_size
       lowerCamelCase_							=       num_hidden_layers
       lowerCamelCase_							=       num_attention_heads
       lowerCamelCase_							=       intermediate_size
       lowerCamelCase_							=       hidden_act
       lowerCamelCase_							=       hidden_dropout_prob
       lowerCamelCase_							=       attention_probs_dropout_prob
       lowerCamelCase_							=       max_position_embeddings
       lowerCamelCase_							=       type_vocab_size
       lowerCamelCase_							=       type_sequence_label_size
       lowerCamelCase_							=       initializer_range
       lowerCamelCase_							=       num_labels
       lowerCamelCase_							=       num_choices
       lowerCamelCase_							=       scope
       lowerCamelCase_							=       vocab_size - 1

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

       """simple docstring"""






       lowerCamelCase_							=       ids_tensor([self.batch_size, self.seq_length]		,				self.vocab_size    )

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

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

       lowerCamelCase_							=       self.get_config()

       return config, input_ids, input_mask, token_labels

 def 							a__					(							self   :    str    )  ->      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=A_		,				initializer_range=self.initializer_range		,				pad_token_id=self.pad_token_id		,				)

 def 							a__					(							self   :    Dict    )  ->      str:

       """simple docstring"""






       lowerCamelCase_	,							lowerCamelCase_	,							lowerCamelCase_	,							lowerCamelCase_							=       self.prepare_config_and_inputs()

       lowerCamelCase_							=       True

       return config, input_ids, input_mask, token_labels

 def 							a__					(							self   :    Optional[Any]		,				A_   :    int		,				A_   :    Tuple		,				A_   :    Any    )  ->      Optional[int]:

       """simple docstring"""






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

 def 							a__					(							self   :    Optional[Any]		,				A_   :    str		,				A_   :    List[str]		,				A_   :    Union[str, Any]    )  ->      List[str]:

       """simple docstring"""






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

 def 							a__					(							self   :    Union[str, Any]		,				A_   :    Any		,				A_   :    List[str]		,				A_   :    str		,				A_   :    Dict    )  ->      int:

       """simple docstring"""






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

 def 							a__					(							self   :    str		,				A_   :    Union[str, Any]		,				A_   :    str		,				A_   :    Any		,				A_   :    Dict    )  ->      Optional[Any]:

       """simple docstring"""






       lowerCamelCase_							=       self.num_labels
       lowerCamelCase_							=       GPTNeoXForQuestionAnswering(A_    )
       model.to(A_    )
       model.eval()
       lowerCamelCase_							=       model(A_		,				attention_mask=A_    )
       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 							a__					(							self   :    Any		,				A_   :    Dict		,				A_   :    Tuple		,				A_   :    Optional[Any]		,				A_   :    Union[str, Any]    )  ->      List[Any]:

       """simple docstring"""






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

 def 							a__					(							self   :    int		,				A_   :    Optional[int]		,				A_   :    str		,				A_   :    Optional[int]		,				A_   :    List[Any]    )  ->      int:

       """simple docstring"""






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

 def 							a__					(							self   :    str		,				A_   :    Any		,				A_   :    Any		,				A_   :    Union[str, Any]    )  ->      int:

       """simple docstring"""






       lowerCamelCase_							=       True
       lowerCamelCase_							=       GPTNeoXForCausalLM(config=A_    )
       model.to(A_    )
       model.eval()

       # first forward pass
       lowerCamelCase_							=       model(A_		,				attention_mask=A_		,				use_cache=A_    )
       lowerCamelCase_							=       outputs.past_key_values

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

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

       lowerCamelCase_							=       model(A_		,				attention_mask=A_		,				output_hidden_states=A_    )
       lowerCamelCase_							=       output_from_no_past['hidden_states'][0]
       lowerCamelCase_							=       model(
           A_		,				attention_mask=A_		,				past_key_values=A_		,				output_hidden_states=A_		,				)['hidden_states'][0]

       # select random slice
       lowerCamelCase_							=       ids_tensor((1,)		,				output_from_past.shape[-1]    ).item()
       lowerCamelCase_							=       output_from_no_past[:, -3:, random_slice_idx].detach()
       lowerCamelCase_							=       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(A_		,				A_		,				atol=1E-3    )    )





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

       """simple docstring"""






       lowerCamelCase_							=       self.prepare_config_and_inputs()
       lowerCamelCase_	,							lowerCamelCase_	,							lowerCamelCase_	,							lowerCamelCase_							=       config_and_inputs
       lowerCamelCase_							=       {'input_ids': input_ids, 'attention_mask': input_mask}
       return config, inputs_dict







@require_torch
class     A(		UpperCamelCase				, UpperCamelCase				, UpperCamelCase				, unittest.TestCase  ):



 '''simple docstring'''


 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 							a__					(							self   :    List[Any]    )  ->      Optional[Any]:

       """simple docstring"""






       lowerCamelCase_							=       GPTNeoXModelTester(self    )
       lowerCamelCase_							=       ConfigTester(self		,				config_class=A_		,				hidden_size=64		,				num_attention_heads=8    )

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

       """simple docstring"""






       self.config_tester.run_common_tests()

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

       """simple docstring"""






       lowerCamelCase_	,							lowerCamelCase_	,							lowerCamelCase_	,							lowerCamelCase_							=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(A_		,				A_		,				A_    )

 def 							a__					(							self   :    Optional[int]    )  ->      Dict:

       """simple docstring"""






       lowerCamelCase_	,							lowerCamelCase_	,							lowerCamelCase_	,							lowerCamelCase_							=       self.model_tester.prepare_config_and_inputs_for_decoder()
       self.model_tester.create_and_check_model_as_decoder(A_		,				A_		,				A_    )

 def 							a__					(							self   :    int    )  ->      Any:

       """simple docstring"""






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

       lowerCamelCase_							=       None

       self.model_tester.create_and_check_model_as_decoder(A_		,				A_		,				A_    )

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

       """simple docstring"""






       lowerCamelCase_	,							lowerCamelCase_	,							lowerCamelCase_	,							lowerCamelCase_							=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_decoder_model_past_large_inputs(A_		,				A_		,				A_    )

 def 							a__					(							self   :    Optional[Any]    )  ->      str:

       """simple docstring"""






       lowerCamelCase_							=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_causal_lm(*A_    )

 def 							a__					(							self   :    Tuple    )  ->      Any:

       """simple docstring"""






       lowerCamelCase_							=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*A_    )

 def 							a__					(							self   :    int    )  ->      str:

       """simple docstring"""






       lowerCamelCase_							=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*A_    )

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

       """simple docstring"""






       lowerCamelCase_							=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*A_    )

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

       """simple docstring"""






       pass





 @parameterized.expand([('linear',), ('dynamic',)]    )
 def 							a__					(							self   :    Dict		,				A_   :    Tuple    )  ->      Optional[Any]:

       """simple docstring"""






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

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

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







@require_torch
class     A(		unittest.TestCase  ):



 '''simple docstring'''


 @slow
 def 							a__					(							self   :    str    )  ->      str:

       """simple docstring"""






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

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

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

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

             self.assertEqual(A_		,				A_    )











from __future__ import annotations



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      list[int]      ,					__lowercase :      int     )    ->   bool:





    """simple docstring"""

    if len(__lowercase     ) == 0:
        return False
    __A       =						len(__lowercase     ) // 2
    if a_list[midpoint] == item:
        return True
    if item < a_list[midpoint]:
        return binary_search(a_list[:midpoint]      ,					__lowercase     )
    else:
        return binary_search(a_list[midpoint + 1 :]      ,					__lowercase     )


if __name__ == "__main__":
  __a   :							Tuple						= input("Enter numbers separated by comma:\n").strip()
  __a   :							Any						= [int(item.strip()) for item in user_input.split(",")]
  __a   :							List[Any]						= int(input("Enter the number to be found in the list:\n").strip())
  __a   :							Optional[int]						= "" if binary_search(sequence, target) else "not "
  print(f"""{target} was {not_str}found in {sequence}""")




