





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      lowerCAmelCase							:
 def __init__(   self						:   Dict    ,  UpperCAmelCase						:   List[Any]    ,  UpperCAmelCase						:   Dict=13    ,  UpperCAmelCase						:   Union[str, Any]=7    ,  UpperCAmelCase						:   List[Any]=True    ,  UpperCAmelCase						:   List[str]=True    ,  UpperCAmelCase						:   Tuple=True    ,  UpperCAmelCase						:   Dict=True    ,  UpperCAmelCase						:   Union[str, Any]=99    ,  UpperCAmelCase						:   int=64    ,  UpperCAmelCase						:   str=5    ,  UpperCAmelCase						:   Any=4    ,  UpperCAmelCase						:   Dict=37    ,  UpperCAmelCase						:   Optional[int]="gelu"    ,  UpperCAmelCase						:   Any=0.1    ,  UpperCAmelCase						:   Optional[Any]=0.1    ,  UpperCAmelCase						:   Any=512    ,  UpperCAmelCase						:   Dict=16    ,  UpperCAmelCase						:   int=2    ,  UpperCAmelCase						:   Union[str, Any]=0.0_2    ,  UpperCAmelCase						:   str=3    ,  UpperCAmelCase						:   int=4    ,  UpperCAmelCase						:   List[str]=None    ,  )    ->			List[str]:
     lowerCamelCase__					:   Optional[int]      				=	parent
     lowerCamelCase__					:   List[Any]      				=	batch_size
     lowerCamelCase__					:   Optional[int]      				=	seq_length
     lowerCamelCase__					:   int      				=	is_training
     lowerCamelCase__					:   int      				=	use_input_mask
     lowerCamelCase__					:   Tuple      				=	use_token_type_ids
     lowerCamelCase__					:   List[Any]      				=	use_labels
     lowerCamelCase__					:   int      				=	vocab_size
     lowerCamelCase__					:   str      				=	hidden_size
     lowerCamelCase__					:   int      				=	num_hidden_layers
     lowerCamelCase__					:   Tuple      				=	num_attention_heads
     lowerCamelCase__					:   Optional[Any]      				=	intermediate_size
     lowerCamelCase__					:   Optional[int]      				=	hidden_act
     lowerCamelCase__					:   Any      				=	hidden_dropout_prob
     lowerCamelCase__					:   Optional[Any]      				=	attention_probs_dropout_prob
     lowerCamelCase__					:   Union[str, Any]      				=	max_position_embeddings
     lowerCamelCase__					:   Optional[int]      				=	type_vocab_size
     lowerCamelCase__					:   List[Any]      				=	type_sequence_label_size
     lowerCamelCase__					:   Dict      				=	initializer_range
     lowerCamelCase__					:   int      				=	num_labels
     lowerCamelCase__					:   List[str]      				=	num_choices
     lowerCamelCase__					:   List[Any]      				=	scope
     lowerCamelCase__					:   Any      				=	vocab_size - 1
 def   A_			(   self						:   Tuple			)    ->			List[str]:
     lowerCamelCase__					:   List[Any]      				=	ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size			)

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

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

     lowerCamelCase__					:   Any      				=	self.get_config()

     return config, input_ids, input_mask, token_labels
 def   A_			(   self						:   int			)    ->			Tuple:
     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   A_			(   self						:   List[Any]			)    ->			int:
     lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__					:   Dict      				=	self.prepare_config_and_inputs()

     lowerCamelCase__					:   Any      				=	True

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

     # first forward pass
     lowerCamelCase__					:   int      				=	model(UpperCAmelCase    ,  attention_mask=UpperCAmelCase    ,  use_cache=UpperCAmelCase			)
     lowerCamelCase__					:   List[str]      				=	outputs.past_key_values

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

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

     lowerCamelCase__					:   List[str]      				=	model(UpperCAmelCase    ,  attention_mask=UpperCAmelCase    ,  output_hidden_states=UpperCAmelCase			)
     lowerCamelCase__					:   Any      				=	output_from_no_past['hidden_states'][0]
     lowerCamelCase__					:   Tuple      				=	model(
         UpperCAmelCase    ,  attention_mask=UpperCAmelCase    ,  past_key_values=UpperCAmelCase    ,  output_hidden_states=UpperCAmelCase    ,  )['hidden_states'][0]

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


@require_torch
class      lowerCAmelCase							(      __UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						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   A_			(   self						:   str			)    ->			int:
     lowerCamelCase__					:   Any      				=	GPTNeoXModelTester(self			)
     lowerCamelCase__					:   Optional[int]      				=	ConfigTester(self    ,  config_class=UpperCAmelCase    ,  hidden_size=64    ,  num_attention_heads=8			)
 def   A_			(   self						:   Tuple			)    ->			int:
     self.config_tester.run_common_tests()
 def   A_			(   self						:   Union[str, Any]			)    ->			Tuple:
     lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__					:   List[str]      				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase			)
 def   A_			(   self						:   int			)    ->			Dict:
     lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__					:   Union[str, Any]      				=	self.model_tester.prepare_config_and_inputs_for_decoder()
     self.model_tester.create_and_check_model_as_decoder(UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase			)
 def   A_			(   self						:   str			)    ->			List[Any]:
     # This regression test was failing with PyTorch < 1.3
     lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__					:   Union[str, Any]      				=	self.model_tester.prepare_config_and_inputs_for_decoder()

     lowerCamelCase__					:   Tuple      				=	None

     self.model_tester.create_and_check_model_as_decoder(UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase			)
 def   A_			(   self						:   List[Any]			)    ->			Tuple:
     lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__					:   Optional[int]      				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_decoder_model_past_large_inputs(UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase			)
 def   A_			(   self						:   int			)    ->			Optional[Any]:
     lowerCamelCase__					:   Dict      				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_causal_lm(*UpperCAmelCase			)
 def   A_			(   self						:   List[str]			)    ->			int:
     lowerCamelCase__					:   Optional[int]      				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase			)
 def   A_			(   self						:   Optional[Any]			)    ->			Optional[int]:
     lowerCamelCase__					:   List[Any]      				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase			)
 def   A_			(   self						:   str			)    ->			Union[str, Any]:
     lowerCamelCase__					:   Any      				=	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   A_			(   self						:   List[str]			)    ->			Any:
     pass







 @parameterized.expand([('linear',), ('dynamic',)]			)
 def   A_			(   self						:   str    ,  UpperCAmelCase						:   Optional[int]			)    ->			Tuple:
     lowerCamelCase__				,							lowerCamelCase__					:   Optional[int]      				=	self.model_tester.prepare_config_and_inputs_for_common()
     lowerCamelCase__					:   int      				=	ids_tensor([1, 10]    ,  config.vocab_size			)
     lowerCamelCase__					:   Optional[int]      				=	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__					:   List[str]      				=	GPTNeoXModel(UpperCAmelCase			)
     original_model.to(UpperCAmelCase			)
     original_model.eval()
     lowerCamelCase__					:   str      				=	original_model(UpperCAmelCase			).last_hidden_state
     lowerCamelCase__					:   Union[str, Any]      				=	original_model(UpperCAmelCase			).last_hidden_state

     set_seed(42			)  # Fixed seed at init time so the two models get the same random weights
     lowerCamelCase__					:   Any      				=	{'type': scaling_type, 'factor': 1_0.0}
     lowerCamelCase__					:   Tuple      				=	GPTNeoXModel(UpperCAmelCase			)
     scaled_model.to(UpperCAmelCase			)
     scaled_model.eval()
     lowerCamelCase__					:   List[Any]      				=	scaled_model(UpperCAmelCase			).last_hidden_state
     lowerCamelCase__					:   Tuple      				=	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      lowerCAmelCase							(      unittest.TestCase    ):
 @slow
 def   A_			(   self						:   str			)    ->			str:
     lowerCamelCase__					:   Tuple      				=	AutoTokenizer.from_pretrained('EleutherAI/pythia-410m-deduped'			)
     for checkpointing in [True, False]:
         lowerCamelCase__					:   Dict      				=	GPTNeoXForCausalLM.from_pretrained('EleutherAI/pythia-410m-deduped'			)

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

         lowerCamelCase__					:   List[str]      				=	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
         lowerCamelCase__					:   str      				=	'My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'

         lowerCamelCase__					:   Optional[Any]      				=	model.generate(**UpperCAmelCase    ,  do_sample=UpperCAmelCase    ,  max_new_tokens=20			)
         lowerCamelCase__					:   Dict      				=	tokenizer.batch_decode(UpperCAmelCase			)[0]

         self.assertEqual(UpperCAmelCase    ,  UpperCAmelCase			)












from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

_UpperCAmelCase       : Union[str, Any]			     =       (3, 9, -11, 0, 7, 5, 1, -1)
_UpperCAmelCase       : Union[str, Any]			     =       (4, 6, 2, 0, 8, 10, 3, -2)

@dataclass
class      lowerCAmelCase							:
 UpperCAmelCase__									=	42
 UpperCAmelCase__									=	42


class      lowerCAmelCase							:
 def __init__(   self						:   Any    ,  UpperCAmelCase						:   Iterable[int]			)    ->			None:
     lowerCamelCase__					:   Node | None      				=	None
     for i in sorted(UpperCAmelCase    ,  reverse=UpperCAmelCase			):
         lowerCamelCase__					:   Tuple      				=	Node(UpperCAmelCase    ,  self.head			)
 def __iter__(   self						:   Optional[int]			)    ->			Iterator[int]:
     lowerCamelCase__					:   str      				=	self.head
     while node:
         yield node.data
         lowerCamelCase__					:   List[str]      				=	node.next_node
 def __len__(   self						:   List[str]			)    ->			int:
     return sum(1 for _ in self			)







 def __str__(   self						:   Any			)    ->			str:
     return " -> ".join([str(UpperCAmelCase			) for node in self]			)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase				)    ->  SortedLinkedList:
    return SortedLinkedList(list(_UpperCAmelCase				) + list(_UpperCAmelCase				)				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    _UpperCAmelCase       : Optional[Any]			     =       SortedLinkedList
    print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))





