


'''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  a     :
	def __init__( self      ,				__magic_name__      ,				__magic_name__=13      ,				__magic_name__=7      ,				__magic_name__=True      ,				__magic_name__=True      ,				__magic_name__=True      ,				__magic_name__=True      ,				__magic_name__=99      ,				__magic_name__=64      ,				__magic_name__=5      ,				__magic_name__=4      ,				__magic_name__=37      ,				__magic_name__="gelu"      ,				__magic_name__=0.1      ,				__magic_name__=0.1      ,				__magic_name__=5_12      ,				__magic_name__=16      ,				__magic_name__=2      ,				__magic_name__=0.0_2      ,				__magic_name__=3      ,				__magic_name__=4      ,				__magic_name__=None      ,				)					->							List[str]:
						_a			      =							parent
						_a			      =							batch_size
						_a			      =							seq_length
						_a			      =							is_training
						_a			      =							use_input_mask
						_a			      =							use_token_type_ids
						_a			      =							use_labels
						_a			      =							vocab_size
						_a			      =							hidden_size
						_a			      =							num_hidden_layers
						_a			      =							num_attention_heads
						_a			      =							intermediate_size
						_a			      =							hidden_act
						_a			      =							hidden_dropout_prob
						_a			      =							attention_probs_dropout_prob
						_a			      =							max_position_embeddings
						_a			      =							type_vocab_size
						_a			      =							type_sequence_label_size
						_a			      =							initializer_range
						_a			      =							num_labels
						_a			      =							num_choices
						_a			      =							scope
						_a			      =							vocab_size - 1
	def 							__UpperCAmelCase		( self						)					->							int:
						_a			      =							ids_tensor([self.batch_size, self.seq_length]      ,				self.vocab_size						)

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

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

						_a			      =							self.get_config()

						return config, input_ids, input_mask, token_labels
	def 							__UpperCAmelCase		( self						)					->							List[str]:
						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 							__UpperCAmelCase		( self						)					->							int:
						_a			      =							self.prepare_config_and_inputs()

						_a			      =							True

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

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

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

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

						_a			      =							model(A_      ,				attention_mask=A_      ,				output_hidden_states=A_						)
						_a			      =							output_from_no_past["hidden_states"][0]
						_a			      =							model(
						    A_      ,				attention_mask=A_      ,				past_key_values=A_      ,				output_hidden_states=A_      ,				)["hidden_states"][0]

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





@require_torch
class  a     (     __snake_case  ,				__snake_case  ,				__snake_case  ,				unittest.TestCase       ):
	_lowerCAmelCase 					=	(
	    (
	        GPTNeoXModel,
	        GPTNeoXForCausalLM,
	        GPTNeoXForQuestionAnswering,
	        GPTNeoXForSequenceClassification,
	        GPTNeoXForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	_lowerCAmelCase 					=	(GPTNeoXForCausalLM,) if is_torch_available() else ()
	_lowerCAmelCase 					=	(
	    {
	        'feature-extraction': GPTNeoXModel,
	        'question-answering': GPTNeoXForQuestionAnswering,
	        'text-classification': GPTNeoXForSequenceClassification,
	        'text-generation': GPTNeoXForCausalLM,
	        'token-classification': GPTNeoXForTokenClassification,
	        'zero-shot': GPTNeoXForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	_lowerCAmelCase 					=	False
	_lowerCAmelCase 					=	False
	_lowerCAmelCase 					=	False
	_lowerCAmelCase 					=	False
	def 							__UpperCAmelCase		( self						)					->							Any:
						_a			      =							GPTNeoXModelTester(self						)
						_a			      =							ConfigTester(self      ,				config_class=A_      ,				hidden_size=64      ,				num_attention_heads=8						)
	def 							__UpperCAmelCase		( self						)					->							Tuple:
						self.config_tester.run_common_tests()
	def 							__UpperCAmelCase		( self						)					->							Tuple:
						_a			      =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(A_      ,				A_      ,				A_						)
	def 							__UpperCAmelCase		( self						)					->							Tuple:
						_a			      =							self.model_tester.prepare_config_and_inputs_for_decoder()
						self.model_tester.create_and_check_model_as_decoder(A_      ,				A_      ,				A_						)
	def 							__UpperCAmelCase		( self						)					->							List[str]:
						_a			      =							self.model_tester.prepare_config_and_inputs_for_decoder()

						_a			      =							None

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




	@parameterized.expand([('linear',), ('dynamic',)]						)
	def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Union[str, Any]:
						_a			      =							self.model_tester.prepare_config_and_inputs_for_common()
						_a			      =							ids_tensor([1, 10]      ,				config.vocab_size						)
						_a			      =							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
						_a			      =							GPTNeoXModel(A_						)
						original_model.to(A_						)
						original_model.eval()
						_a			      =							original_model(A_						).last_hidden_state
						_a			      =							original_model(A_						).last_hidden_state

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

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

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

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

											self.assertEqual(A_      ,				A_						)





'''simple docstring'''






def 	_A				(lowerCAmelCase__  :Union[str, Any]		)   ->  List[str]:




     '''simple docstring'''



     return [
         {
             0: [1, 2],
             1: [0, 2],
             2: [0, 1, 3, 5],
             3: [2, 4],
             4: [3],
             5: [2, 6, 8],
             6: [5, 7],
             7: [6, 8],
             8: [5, 7],
         },
         {
             0: [6],
             1: [9],
             2: [4, 5],
             3: [4],
             4: [2, 3],
             5: [2],
             6: [0, 7],
             7: [6],
             8: [],
             9: [1],
         },
         {
             0: [4],
             1: [6],
             2: [],
             3: [5, 6, 7],
             4: [0, 6],
             5: [3, 8, 9],
             6: [1, 3, 4, 7],
             7: [3, 6, 8, 9],
             8: [5, 7],
             9: [5, 7],
         },
         {
             0: [1, 3],
             1: [0, 2, 4],
             2: [1, 3, 4],
             3: [0, 2, 4],
             4: [1, 2, 3],
         },
     ][index]






def 	_A				(lowerCAmelCase__  :dict[int, list[int]]		)   ->  list[tuple[int, int]]:




     '''simple docstring'''



     _a			      =							0
     _a			      =							len(lowerCAmelCase__		)  # No of vertices in graph
     _a			      =							[0] * n
     _a			      =							[False] * n

     def dfs(lowerCAmelCase__  :Dict				,						lowerCAmelCase__  :str				,						lowerCAmelCase__  :Dict				,						lowerCAmelCase__  :Union[str, Any]		):
          _a			      =							True
          _a			      =							id_
          id_ += 1
          for to in graph[at]:
               if to == parent:
                    pass
               elif not visited[to]:
                    dfs(lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__				,						id_		)
                    _a			      =							min(low[at]				,						low[to]		)
                    if id_ <= low[to]:
                         bridges.append((at, to) if at < to else (to, at)		)
               else:
                    # This edge is a back edge and cannot be a bridge
                    _a			      =							min(low[at]				,						low[to]		)

     _a			      =							[]
     for i in range(lowerCAmelCase__		):
          if not visited[i]:
               dfs(lowerCAmelCase__				,						-1				,						lowerCAmelCase__				,						id_		)
     return bridges


if __name__ == "__main__":
 import doctest

 doctest.testmod()

