



'''simple docstring'''




import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad

class 			UpperCamelCase_ (							unittest.TestCase			):





	def  _lowercase(  self    )			->			Any:
								UpperCAmelCase     :		Optional[int]							     =		10





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Dict							     =		[1, 2, 3, 4]
								UpperCAmelCase     :		List[Any]							     =		[1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
								self.assertEqual(truncate_or_pad(A		,		self.block_size		,		0    )		,		A    )





	def  _lowercase(  self    )			->			List[Any]:
								UpperCAmelCase     :		Dict							     =		[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
								UpperCAmelCase     :		List[Any]							     =		[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
								self.assertEqual(truncate_or_pad(A		,		self.block_size		,		0    )		,		A    )





	def  _lowercase(  self    )			->			Union[str, Any]:
								UpperCAmelCase     :		Union[str, Any]							     =		[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
								UpperCAmelCase     :		Optional[Any]							     =		[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
								self.assertEqual(truncate_or_pad(A		,		self.block_size		,		0    )		,		A    )





	def  _lowercase(  self    )			->			Any:
								UpperCAmelCase     :		List[Any]							     =		"""It was the year of Our Lord one thousand seven hundred and
        seventy-five.\n\nSpiritual revelations were conceded to England at that
        favoured period, as at this."""
								UpperCAmelCase     ,     UpperCAmelCase     :		List[str]							     =		process_story(A    )
								self.assertEqual(A		,		[]    )





	def  _lowercase(  self    )			->			Any:
								UpperCAmelCase     :		List[Any]							     =		""""""
								UpperCAmelCase     ,     UpperCAmelCase     :		Any							     =		process_story(A    )
								self.assertEqual(A		,		[]    )
								self.assertEqual(A		,		[]    )





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Any							     =		(
								    """It was the year of Our Lord one thousand seven hundred and """
								    """seventy-five\n\nSpiritual revelations were conceded to England """
								    """at that favoured period, as at this.\n@highlight\n\nIt was the best of times"""
								)
								UpperCAmelCase     ,     UpperCAmelCase     :		Optional[int]							     =		process_story(A    )

								UpperCAmelCase     :		Union[str, Any]							     =		[
								    """It was the year of Our Lord one thousand seven hundred and seventy-five.""",
								    """Spiritual revelations were conceded to England at that favoured period, as at this.""",
								]
								self.assertEqual(A		,		A    )

								UpperCAmelCase     :		Optional[Any]							     =		["""It was the best of times."""]
								self.assertEqual(A		,		A    )





	def  _lowercase(  self    )			->			Dict:
								UpperCAmelCase     :		List[Any]							     =		torch.tensor([1, 2, 3, 4]    )
								UpperCAmelCase     :		List[str]							     =		torch.tensor([1, 1, 1, 1]    )
								np.testing.assert_array_equal(build_mask(A		,		0    ).numpy()		,		expected.numpy()    )





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		List[Any]							     =		torch.tensor([1, 2, 3, 4, 23, 23, 23]    )
								UpperCAmelCase     :		Optional[Any]							     =		torch.tensor([1, 1, 1, 1, 0, 0, 0]    )
								np.testing.assert_array_equal(build_mask(A		,		23    ).numpy()		,		expected.numpy()    )





	def  _lowercase(  self    )			->			List[str]:
								UpperCAmelCase     :		Optional[int]							     =		torch.tensor([8, 2, 3, 4, 1, 1, 1]    )
								UpperCAmelCase     :		str							     =		torch.tensor([1, 1, 1, 1, 0, 0, 0]    )
								np.testing.assert_array_equal(build_mask(A		,		1    ).numpy()		,		expected.numpy()    )






	def  _lowercase(  self    )			->			List[Any]:
								UpperCAmelCase     :		Any							     =		101
								UpperCAmelCase     :		Any							     =		torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]    )
								UpperCAmelCase     :		List[Any]							     =		torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]    )

								UpperCAmelCase     :		Optional[int]							     =		compute_token_type_ids(A		,		A    )
								np.testing.assert_array_equal(A		,		A    )






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





	def  _lowercase(  self    )			->			Union[str, Any]:
								UpperCAmelCase     :		List[str]							     =		ids_tensor([self.batch_size, self.seq_length]		,		self.vocab_size    )

								UpperCAmelCase     :		Any							     =		None
								if self.use_input_mask:
															UpperCAmelCase     :		int							     =		random_attention_mask([self.batch_size, self.seq_length]    )

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

								UpperCAmelCase     :		Optional[int]							     =		self.get_config()

								return config, input_ids, input_mask, token_labels





	def  _lowercase(  self    )			->			Optional[Any]:
								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  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		List[str]							     =		self.prepare_config_and_inputs()

								UpperCAmelCase     :		Any							     =		True

								return config, input_ids, input_mask, token_labels





	def  _lowercase(  self		,		A		,		A		,		A    )			->			int:
								UpperCAmelCase     :		str							     =		GPTNeoXModel(config=A    )
								model.to(A    )
								model.eval()
								UpperCAmelCase     :		List[str]							     =		model(A		,		attention_mask=A    )
								UpperCAmelCase     :		List[str]							     =		model(A    )
								self.parent.assertEqual(result.last_hidden_state.shape		,		(self.batch_size, self.seq_length, self.hidden_size)    )





	def  _lowercase(  self		,		A		,		A		,		A    )			->			Optional[int]:
								UpperCAmelCase     :		str							     =		True
								UpperCAmelCase     :		Optional[Any]							     =		GPTNeoXModel(A    )
								model.to(A    )
								model.eval()
								UpperCAmelCase     :		List[Any]							     =		model(A		,		attention_mask=A    )
								self.parent.assertEqual(result.last_hidden_state.shape		,		(self.batch_size, self.seq_length, self.hidden_size)    )





	def  _lowercase(  self		,		A		,		A		,		A		,		A    )			->			List[str]:
								UpperCAmelCase     :		Tuple							     =		GPTNeoXForCausalLM(config=A    )
								model.to(A    )
								model.eval()
								UpperCAmelCase     :		str							     =		model(A		,		attention_mask=A		,		labels=A    )
								self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.seq_length, self.vocab_size)    )





	def  _lowercase(  self		,		A		,		A		,		A		,		A    )			->			Tuple:
								UpperCAmelCase     :		List[str]							     =		self.num_labels
								UpperCAmelCase     :		Any							     =		GPTNeoXForQuestionAnswering(A    )
								model.to(A    )
								model.eval()
								UpperCAmelCase     :		str							     =		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  _lowercase(  self		,		A		,		A		,		A		,		A    )			->			int:
								UpperCAmelCase     :		Tuple							     =		self.num_labels
								UpperCAmelCase     :		List[str]							     =		GPTNeoXForSequenceClassification(A    )
								model.to(A    )
								model.eval()
								UpperCAmelCase     :		List[Any]							     =		ids_tensor([self.batch_size]		,		self.type_sequence_label_size    )
								UpperCAmelCase     :		Tuple							     =		model(A		,		attention_mask=A		,		labels=A    )
								self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.num_labels)    )





	def  _lowercase(  self		,		A		,		A		,		A		,		A    )			->			str:
								UpperCAmelCase     :		List[Any]							     =		self.num_labels
								UpperCAmelCase     :		Tuple							     =		GPTNeoXForTokenClassification(A    )
								model.to(A    )
								model.eval()
								UpperCAmelCase     :		int							     =		model(A		,		attention_mask=A		,		labels=A    )
								self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.seq_length, self.num_labels)    )





	def  _lowercase(  self		,		A		,		A		,		A    )			->			Union[str, Any]:
								UpperCAmelCase     :		Optional[int]							     =		True
								UpperCAmelCase     :		str							     =		GPTNeoXForCausalLM(config=A    )
								model.to(A    )
								model.eval()

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

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

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

								UpperCAmelCase     :		Dict							     =		model(A		,		attention_mask=A		,		output_hidden_states=A    )
								UpperCAmelCase     :		Any							     =		output_from_no_past["""hidden_states"""][0]
								UpperCAmelCase     :		List[str]							     =		model(
								    A		,		attention_mask=A		,		past_key_values=A		,		output_hidden_states=A		,		)["""hidden_states"""][0]

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




@require_torch
class 			UpperCamelCase_ (							__magic_name__      ,     __magic_name__      ,     __magic_name__      ,     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  _lowercase(  self    )			->			Union[str, Any]:
								UpperCAmelCase     :		str							     =		GPTNeoXModelTester(self    )
								UpperCAmelCase     :		Optional[Any]							     =		ConfigTester(self		,		config_class=A		,		hidden_size=64		,		num_attention_heads=8    )





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





	def  _lowercase(  self    )			->			str:
								UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		Any							     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(A		,		A		,		A    )





	def  _lowercase(  self    )			->			str:
								UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		Union[str, Any]							     =		self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_model_as_decoder(A		,		A		,		A    )





	def  _lowercase(  self    )			->			Optional[Any]:
								# This regression test was failing with PyTorch < 1.3
								UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		Optional[Any]							     =		self.model_tester.prepare_config_and_inputs_for_decoder()

								UpperCAmelCase     :		Optional[Any]							     =		None

								self.model_tester.create_and_check_model_as_decoder(A		,		A		,		A    )





	def  _lowercase(  self    )			->			str:
								UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		List[str]							     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_decoder_model_past_large_inputs(A		,		A		,		A    )





	def  _lowercase(  self    )			->			int:
								UpperCAmelCase     :		Any							     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_causal_lm(*A    )





	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Tuple							     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*A    )





	def  _lowercase(  self    )			->			Any:
								UpperCAmelCase     :		Tuple							     =		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*A    )





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Optional[Any]							     =		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  _lowercase(  self    )			->			Optional[int]:
								pass






	@parameterized.expand([("""linear""",), ("""dynamic""",)]    )
	def  _lowercase(  self		,		A    )			->			str:
								UpperCAmelCase     ,     UpperCAmelCase     :		List[Any]							     =		self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase     :		int							     =		ids_tensor([1, 10]		,		config.vocab_size    )
								UpperCAmelCase     :		Optional[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
								UpperCAmelCase     :		Dict							     =		GPTNeoXModel(A    )
								original_model.to(A    )
								original_model.eval()
								UpperCAmelCase     :		List[str]							     =		original_model(A    ).last_hidden_state
								UpperCAmelCase     :		Any							     =		original_model(A    ).last_hidden_state

								set_seed(42    )  # Fixed seed at init time so the two models get the same random weights
								UpperCAmelCase     :		Any							     =		{"""type""": scaling_type, """factor""": 1_0.0}
								UpperCAmelCase     :		str							     =		GPTNeoXModel(A    )
								scaled_model.to(A    )
								scaled_model.eval()
								UpperCAmelCase     :		Optional[Any]							     =		scaled_model(A    ).last_hidden_state
								UpperCAmelCase     :		Optional[Any]							     =		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 			UpperCamelCase_ (							unittest.TestCase			):





	@slow
	def  _lowercase(  self    )			->			List[Any]:
								UpperCAmelCase     :		str							     =		AutoTokenizer.from_pretrained("""EleutherAI/pythia-410m-deduped"""    )
								for checkpointing in [True, False]:
															UpperCAmelCase     :		int							     =		GPTNeoXForCausalLM.from_pretrained("""EleutherAI/pythia-410m-deduped"""    )

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

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

															UpperCAmelCase     :		Union[str, Any]							     =		model.generate(**A		,		do_sample=A		,		max_new_tokens=20    )
															UpperCAmelCase     :		Tuple							     =		tokenizer.batch_decode(A    )[0]

															self.assertEqual(A		,		A    )

