



'''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        SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(		self    :  Optional[Any]	,    __snake_case    :  Tuple	,    __snake_case    :  Union[str, Any]=13	,    __snake_case    :  List[Any]=7	,    __snake_case    :  List[Any]=True	,    __snake_case    :  Tuple=True	,    __snake_case    :  int=True	,    __snake_case    :  List[str]=True	,    __snake_case    :  Tuple=99	,    __snake_case    :  Optional[Any]=64	,    __snake_case    :  int=5	,    __snake_case    :  List[Any]=4	,    __snake_case    :  Optional[Any]=37	,    __snake_case    :  Optional[int]="gelu"	,    __snake_case    :  Union[str, Any]=0.1	,    __snake_case    :  Optional[Any]=0.1	,    __snake_case    :  Dict=512	,    __snake_case    :  str=16	,    __snake_case    :  Any=2	,    __snake_case    :  Union[str, Any]=0.02	,    __snake_case    :  List[str]=3	,    __snake_case    :  Any=4	,    __snake_case    :  str=None	,    )    ->	Optional[int]:
								UpperCAmelCase				: List[Any]											=	parent
								UpperCAmelCase				: Tuple											=	batch_size
								UpperCAmelCase				: Tuple											=	seq_length
								UpperCAmelCase				: str											=	is_training
								UpperCAmelCase				: Optional[int]											=	use_input_mask
								UpperCAmelCase				: Optional[Any]											=	use_token_type_ids
								UpperCAmelCase				: Dict											=	use_labels
								UpperCAmelCase				: Optional[Any]											=	vocab_size
								UpperCAmelCase				: Any											=	hidden_size
								UpperCAmelCase				: int											=	num_hidden_layers
								UpperCAmelCase				: Optional[Any]											=	num_attention_heads
								UpperCAmelCase				: Dict											=	intermediate_size
								UpperCAmelCase				: str											=	hidden_act
								UpperCAmelCase				: Any											=	hidden_dropout_prob
								UpperCAmelCase				: Union[str, Any]											=	attention_probs_dropout_prob
								UpperCAmelCase				: List[Any]											=	max_position_embeddings
								UpperCAmelCase				: Union[str, Any]											=	type_vocab_size
								UpperCAmelCase				: Dict											=	type_sequence_label_size
								UpperCAmelCase				: List[str]											=	initializer_range
								UpperCAmelCase				: str											=	num_labels
								UpperCAmelCase				: List[Any]											=	num_choices
								UpperCAmelCase				: Optional[int]											=	scope
								UpperCAmelCase				: Tuple											=	vocab_size - 1







	def   A			(		self    :  Optional[Any]	)    ->	Any:
								UpperCAmelCase				: Any											=	ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size	)

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

								UpperCAmelCase				: Tuple											=	None
								if self.use_labels:
															UpperCAmelCase				: List[Any]											=	ids_tensor([self.batch_size, self.seq_length]	,    self.num_labels	)

								UpperCAmelCase				: Tuple											=	self.get_config()

								return config, input_ids, input_mask, token_labels







	def   A			(		self    :  int	)    ->	int:
								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=__snake_case	,    initializer_range=self.initializer_range	,    pad_token_id=self.pad_token_id	,    )







	def   A			(		self    :  List[Any]	)    ->	Optional[int]:
								UpperCAmelCase				: int											=	self.prepare_config_and_inputs()

								UpperCAmelCase				: Union[str, Any]											=	True

								return config, input_ids, input_mask, token_labels







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







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







	def   A			(		self    :  Union[str, Any]	,    __snake_case    :  Optional[Any]	,    __snake_case    :  Dict	,    __snake_case    :  Optional[Any]	,    __snake_case    :  Optional[int]	)    ->	Optional[int]:
								UpperCAmelCase				: str											=	GPTNeoXForCausalLM(config=__snake_case	)
								model.to(__snake_case	)
								model.eval()
								UpperCAmelCase				: Optional[int]											=	model(__snake_case	,    attention_mask=__snake_case	,    labels=__snake_case	)
								self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.seq_length, self.vocab_size)	)







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







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







	def   A			(		self    :  List[Any]	,    __snake_case    :  List[Any]	,    __snake_case    :  Tuple	,    __snake_case    :  Any	)    ->	List[str]:
								UpperCAmelCase				: Optional[Any]											=	True
								UpperCAmelCase				: Any											=	GPTNeoXForCausalLM(config=__snake_case	)
								model.to(__snake_case	)
								model.eval()

								# first forward pass
								UpperCAmelCase				: List[Any]											=	model(__snake_case	,    attention_mask=__snake_case	,    use_cache=__snake_case	)
								UpperCAmelCase				: List[str]											=	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				: Optional[int]											=	ids_tensor((self.batch_size, 3)	,    vocab_size=2	)

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

								UpperCAmelCase				: Optional[int]											=	model(__snake_case	,    attention_mask=__snake_case	,    output_hidden_states=__snake_case	)
								UpperCAmelCase				: Dict											=	output_from_no_past["hidden_states"][0]
								UpperCAmelCase				: Union[str, Any]											=	model(
								    __snake_case	,    attention_mask=__snake_case	,    past_key_values=__snake_case	,    output_hidden_states=__snake_case	,    )["hidden_states"][0]

								# select random slice
								UpperCAmelCase				: str											=	ids_tensor((1,)	,    output_from_past.shape[-1]	).item()
								UpperCAmelCase				: Union[str, Any]											=	output_from_no_past[:, -3:, random_slice_idx].detach()
								UpperCAmelCase				: 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(__snake_case	,    __snake_case	,    atol=1E-3	)	)







	def   A			(		self    :  List[Any]	)    ->	Optional[int]:
								UpperCAmelCase				: int											=	self.prepare_config_and_inputs()
								UpperCAmelCase				: Tuple											=	config_and_inputs
								UpperCAmelCase				: Dict											=	{"input_ids": input_ids, "attention_mask": input_mask}
								return config, inputs_dict

@require_torch
class        SCREAMING_SNAKE_CASE(							_UpperCamelCase							,	_UpperCamelCase							,	_UpperCamelCase							,	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   A			(		self    :  Dict	)    ->	Optional[int]:
								UpperCAmelCase				: List[Any]											=	GPTNeoXModelTester(self	)
								UpperCAmelCase				: Union[str, Any]											=	ConfigTester(self	,    config_class=__snake_case	,    hidden_size=64	,    num_attention_heads=8	)







	def   A			(		self    :  Union[str, Any]	)    ->	List[Any]:
								self.config_tester.run_common_tests()







	def   A			(		self    :  str	)    ->	str:
								UpperCAmelCase				: Union[str, Any]											=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(__snake_case	,    __snake_case	,    __snake_case	)







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







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

								UpperCAmelCase				: List[Any]											=	None

								self.model_tester.create_and_check_model_as_decoder(__snake_case	,    __snake_case	,    __snake_case	)







	def   A			(		self    :  str	)    ->	Any:
								UpperCAmelCase				: Optional[int]											=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_decoder_model_past_large_inputs(__snake_case	,    __snake_case	,    __snake_case	)







	def   A			(		self    :  str	)    ->	str:
								UpperCAmelCase				: Optional[Any]											=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_causal_lm(*__snake_case	)







	def   A			(		self    :  Optional[int]	)    ->	Any:
								UpperCAmelCase				: str											=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*__snake_case	)







	def   A			(		self    :  Optional[Any]	)    ->	Optional[Any]:
								UpperCAmelCase				: int											=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*__snake_case	)







	def   A			(		self    :  Optional[int]	)    ->	Union[str, Any]:
								UpperCAmelCase				: List[str]											=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*__snake_case	)







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







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

								set_seed(42	)  # Fixed seed at init time so the two models get the same random weights
								UpperCAmelCase				: Tuple											=	{"type": scaling_type, "factor": 10.0}
								UpperCAmelCase				: List[str]											=	GPTNeoXModel(__snake_case	)
								scaled_model.to(__snake_case	)
								scaled_model.eval()
								UpperCAmelCase				: Tuple											=	scaled_model(__snake_case	).last_hidden_state
								UpperCAmelCase				: Union[str, Any]											=	scaled_model(__snake_case	).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(__snake_case	,    __snake_case	,    atol=1E-5	)	)
								else:
															self.assertFalse(torch.allclose(__snake_case	,    __snake_case	,    atol=1E-5	)	)

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

@require_torch
class        SCREAMING_SNAKE_CASE(							unittest.TestCase    ):







	"""simple docstring"""






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

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

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

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

															self.assertEqual(__snake_case	,    __snake_case	)










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__:     Union[str, Any]										=    {
    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:     List[str]										=    [
						    "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "LiltForQuestionAnswering",
						    "LiltForSequenceClassification",
						    "LiltForTokenClassification",
						    "LiltModel",
						    "LiltPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_lilt import (
												    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LiltForQuestionAnswering,
												    LiltForSequenceClassification,
												    LiltForTokenClassification,
												    LiltModel,
												    LiltPreTrainedModel,
												)

else:
						import sys

						UpperCamelCase__:     Optional[Any]										=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





