

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      ,      lowercase      ,      lowercase=13      ,      lowercase=7      ,      lowercase=True      ,      lowercase=True      ,      lowercase=True      ,      lowercase=True      ,      lowercase=99      ,      lowercase=64      ,      lowercase=5      ,      lowercase=4      ,      lowercase=37      ,      lowercase="gelu"      ,      lowercase=0.1      ,      lowercase=0.1      ,      lowercase=512      ,      lowercase=16      ,      lowercase=2      ,      lowercase=0.02      ,      lowercase=3      ,      lowercase=4      ,      lowercase=None      ,      )  ->						Any:
							'''simple docstring'''






							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       )  ->						List[str]:
							'''simple docstring'''






							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[Any]:
							'''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=lowercase      ,      initializer_range=self.initializer_range      ,      pad_token_id=self.pad_token_id      ,      )


			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							A__    ,				A__    ,				A__    ,				A__        =			self.prepare_config_and_inputs()

							A__        =			True

							return config, input_ids, input_mask, token_labels


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						Dict:
							'''simple docstring'''






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


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						List[Any]:
							'''simple docstring'''






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


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						Union[str, Any]:
							'''simple docstring'''






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


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						Optional[Any]:
							'''simple docstring'''






							A__        =			self.num_labels
							A__        =			GPTNeoXForQuestionAnswering(lowercase       )
							model.to(lowercase       )
							model.eval()
							A__        =			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      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						Union[str, Any]:
							'''simple docstring'''






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


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						Union[str, Any]:
							'''simple docstring'''






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


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						Dict:
							'''simple docstring'''






							A__        =			True
							A__        =			GPTNeoXForCausalLM(config=lowercase       )
							model.to(lowercase       )
							model.eval()

							# first forward pass
							A__        =			model(lowercase      ,      attention_mask=lowercase      ,      use_cache=lowercase       )
							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(lowercase      ,      attention_mask=lowercase      ,      output_hidden_states=lowercase       )
							A__        =			output_from_no_past["hidden_states"][0]
							A__        =			model(
							    lowercase      ,      attention_mask=lowercase      ,      past_key_values=lowercase      ,      output_hidden_states=lowercase      ,      )["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(lowercase      ,      lowercase      ,      atol=1e-3       )       )


			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''simple docstring'''






							A__        =			self.prepare_config_and_inputs()
							A__    ,				A__    ,				A__    ,				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			):
			"""simple docstring"""







			__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       )  ->						List[str]:
							'''simple docstring'''






							A__        =			GPTNeoXModelTester(self       )
							A__        =			ConfigTester(self      ,      config_class=lowercase      ,      hidden_size=64      ,      num_attention_heads=8       )


			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							self.config_tester.run_common_tests()


			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''simple docstring'''






							A__    ,				A__    ,				A__    ,				A__        =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(lowercase      ,      lowercase      ,      lowercase       )


			def      UpperCamelCase		(    self       )  ->						Dict:
							'''simple docstring'''






							A__    ,				A__    ,				A__    ,				A__        =			self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_model_as_decoder(lowercase      ,      lowercase      ,      lowercase       )


			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''simple docstring'''






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

							A__        =			None

							self.model_tester.create_and_check_model_as_decoder(lowercase      ,      lowercase      ,      lowercase       )


			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''simple docstring'''






							A__    ,				A__    ,				A__    ,				A__        =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_decoder_model_past_large_inputs(lowercase      ,      lowercase      ,      lowercase       )


			def      UpperCamelCase		(    self       )  ->						Dict:
							'''simple docstring'''






							A__        =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_causal_lm(*lowercase       )


			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							A__        =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*lowercase       )


			def      UpperCamelCase		(    self       )  ->						str:
							'''simple docstring'''






							A__        =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*lowercase       )


			def      UpperCamelCase		(    self       )  ->						str:
							'''simple docstring'''






							A__        =			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      UpperCamelCase		(    self       )  ->						Any:
							'''simple docstring'''






							pass


			@parameterized.expand([("linear",), ("dynamic",)]       )
			def      UpperCamelCase		(    self      ,      lowercase       )  ->						Union[str, Any]:
							'''simple docstring'''






							A__    ,				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(lowercase       )
							original_model.to(lowercase       )
							original_model.eval()
							A__        =			original_model(lowercase       ).last_hidden_state
							A__        =			original_model(lowercase       ).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": 10.0}
							A__        =			GPTNeoXModel(lowercase       )
							scaled_model.to(lowercase       )
							scaled_model.eval()
							A__        =			scaled_model(lowercase       ).last_hidden_state
							A__        =			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 					a__						(					unittest.TestCase			):
			"""simple docstring"""







			@slow
			def      UpperCamelCase		(    self       )  ->						Optional[Any]:
							'''simple docstring'''






							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(lowercase       )

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

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

											self.assertEqual(lowercase      ,      lowercase       )




def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int      )			->      int:




				'''simple docstring'''





				if divisor % 5 == 0 or divisor % 2 == 0:
								return 0
				A__        =			1
				A__        =			1
				while repunit:
								A__        =			(1_0 * repunit + 1) % divisor
								repunit_index += 1
				return repunit_index



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int = 1_0_0_0_0_0_0      )			->      int:




				'''simple docstring'''





				A__        =			limit - 1
				if divisor % 2 == 0:
								divisor += 1
				while least_divisible_repunit(SCREAMING_SNAKE_CASE_      ) <= limit:
								divisor += 2
				return divisor


if __name__ == "__main__":
	print(f"""{solution() = }""")

