



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=0.02,			__a=3,			__a=4,			__a=None,			):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    parent
														_lowerCAmelCase     :							Optional[int]    =    batch_size
														_lowerCAmelCase     :							Optional[int]    =    seq_length
														_lowerCAmelCase     :							List[Any]    =    is_training
														_lowerCAmelCase     :							Any    =    use_input_mask
														_lowerCAmelCase     :							List[Any]    =    use_token_type_ids
														_lowerCAmelCase     :							str    =    use_labels
														_lowerCAmelCase     :							Optional[int]    =    vocab_size
														_lowerCAmelCase     :							Optional[Any]    =    hidden_size
														_lowerCAmelCase     :							int    =    num_hidden_layers
														_lowerCAmelCase     :							str    =    num_attention_heads
														_lowerCAmelCase     :							Any    =    intermediate_size
														_lowerCAmelCase     :							Optional[Any]    =    hidden_act
														_lowerCAmelCase     :							Optional[int]    =    hidden_dropout_prob
														_lowerCAmelCase     :							List[Any]    =    attention_probs_dropout_prob
														_lowerCAmelCase     :							List[Any]    =    max_position_embeddings
														_lowerCAmelCase     :							Union[str, Any]    =    type_vocab_size
														_lowerCAmelCase     :							str    =    type_sequence_label_size
														_lowerCAmelCase     :							Optional[Any]    =    initializer_range
														_lowerCAmelCase     :							Dict    =    num_labels
														_lowerCAmelCase     :							Any    =    num_choices
														_lowerCAmelCase     :							Optional[int]    =    scope
														_lowerCAmelCase     :							List[str]    =    vocab_size - 1







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    ids_tensor([self.batch_size, self.seq_length],			self.vocab_size)

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

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

														_lowerCAmelCase     :							Optional[Any]    =    self.get_config()

														return config, input_ids, input_mask, token_labels







							def    snake_case__      (   self):

														'''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=__a,			initializer_range=self.initializer_range,			pad_token_id=self.pad_token_id,			)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase     :							List[str]    =    self.prepare_config_and_inputs()

														_lowerCAmelCase     :							str    =    True

														return config, input_ids, input_mask, token_labels







							def    snake_case__      (   self,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							List[str]    =    GPTNeoXModel(config=__a)
														model.to(__a)
														model.eval()
														_lowerCAmelCase     :							Optional[int]    =    model(__a,			attention_mask=__a)
														_lowerCAmelCase     :							str    =    model(__a)
														self.parent.assertEqual(result.last_hidden_state.shape,			(self.batch_size, self.seq_length, self.hidden_size))







							def    snake_case__      (   self,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    True
														_lowerCAmelCase     :							List[Any]    =    GPTNeoXModel(__a)
														model.to(__a)
														model.eval()
														_lowerCAmelCase     :							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    snake_case__      (   self,			__a,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    GPTNeoXForCausalLM(config=__a)
														model.to(__a)
														model.eval()
														_lowerCAmelCase     :							Tuple    =    model(__a,			attention_mask=__a,			labels=__a)
														self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.seq_length, self.vocab_size))







							def    snake_case__      (   self,			__a,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    self.num_labels
														_lowerCAmelCase     :							Union[str, Any]    =    GPTNeoXForQuestionAnswering(__a)
														model.to(__a)
														model.eval()
														_lowerCAmelCase     :							Tuple    =    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    snake_case__      (   self,			__a,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							str    =    self.num_labels
														_lowerCAmelCase     :							str    =    GPTNeoXForSequenceClassification(__a)
														model.to(__a)
														model.eval()
														_lowerCAmelCase     :							List[str]    =    ids_tensor([self.batch_size],			self.type_sequence_label_size)
														_lowerCAmelCase     :							Dict    =    model(__a,			attention_mask=__a,			labels=__a)
														self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.num_labels))







							def    snake_case__      (   self,			__a,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							str    =    self.num_labels
														_lowerCAmelCase     :							Dict    =    GPTNeoXForTokenClassification(__a)
														model.to(__a)
														model.eval()
														_lowerCAmelCase     :							str    =    model(__a,			attention_mask=__a,			labels=__a)
														self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.seq_length, self.num_labels))







							def    snake_case__      (   self,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    True
														_lowerCAmelCase     :							Dict    =    GPTNeoXForCausalLM(config=__a)
														model.to(__a)
														model.eval()

														# first forward pass
														_lowerCAmelCase     :							List[Any]    =    model(__a,			attention_mask=__a,			use_cache=__a)
														_lowerCAmelCase     :							Optional[int]    =    outputs.past_key_values

														# create hypothetical multiple next token and extent to next_input_ids
														_lowerCAmelCase     :							Optional[Any]    =    ids_tensor((self.batch_size, 3),			config.vocab_size)
														_lowerCAmelCase     :							Dict    =    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     :							str    =    torch.cat([input_mask, next_mask],			dim=-1)

														_lowerCAmelCase     :							List[str]    =    model(__a,			attention_mask=__a,			output_hidden_states=__a)
														_lowerCAmelCase     :							List[str]    =    output_from_no_past["hidden_states"][0]
														_lowerCAmelCase     :							List[Any]    =    model(
														    __a,			attention_mask=__a,			past_key_values=__a,			output_hidden_states=__a,			)["hidden_states"][0]

														# select random slice
														_lowerCAmelCase     :							int    =    ids_tensor((1,),			output_from_past.shape[-1]).item()
														_lowerCAmelCase     :							str    =    output_from_no_past[:, -3:, random_slice_idx].detach()
														_lowerCAmelCase     :							Union[str, Any]    =    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    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    self.prepare_config_and_inputs()
														_lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase     :							List[str]    =    config_and_inputs
														_lowerCAmelCase     :							Optional[Any]    =    {"input_ids": input_ids, "attention_mask": input_mask}
														return config, inputs_dict



@require_torch
class   UpperCAmelCase_    (       a   ,					a   ,					a   ,					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    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    GPTNeoXModelTester(self)
														_lowerCAmelCase     :							Union[str, Any]    =    ConfigTester(self,			config_class=__a,			hidden_size=64,			num_attention_heads=8)







							def    snake_case__      (   self):

														'''simple docstring'''

														self.config_tester.run_common_tests()







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase     :							Dict    =    self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(__a,			__a,			__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_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(__a,			__a,			__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[Any]    =    self.model_tester.prepare_config_and_inputs_for_decoder()

														_lowerCAmelCase     :							List[str]    =    None

														self.model_tester.create_and_check_model_as_decoder(__a,			__a,			__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase     :							Tuple    =    self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_decoder_model_past_large_inputs(__a,			__a,			__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_causal_lm(*__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_question_answering(*__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_sequence_classification(*__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    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    snake_case__      (   self):

														'''simple docstring'''

														pass







							@parameterized.expand([("linear",), ("dynamic",)])
							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase   ,    _lowerCAmelCase     :							Any    =    self.model_tester.prepare_config_and_inputs_for_common()
														_lowerCAmelCase     :							Optional[Any]    =    ids_tensor([1, 10],			config.vocab_size)
														_lowerCAmelCase     :							List[str]    =    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[Any]    =    GPTNeoXModel(__a)
														original_model.to(__a)
														original_model.eval()
														_lowerCAmelCase     :							Union[str, Any]    =    original_model(__a).last_hidden_state
														_lowerCAmelCase     :							Union[str, Any]    =    original_model(__a).last_hidden_state

														set_seed(42)  # Fixed seed at init time so the two models get the same random weights
														_lowerCAmelCase     :							int    =    {"type": scaling_type, "factor": 10.0}
														_lowerCAmelCase     :							Optional[int]    =    GPTNeoXModel(__a)
														scaled_model.to(__a)
														scaled_model.eval()
														_lowerCAmelCase     :							Optional[Any]    =    scaled_model(__a).last_hidden_state
														_lowerCAmelCase     :							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    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    AutoTokenizer.from_pretrained("EleutherAI/pythia-410m-deduped")
														for checkpointing in [True, False]:
																					_lowerCAmelCase     :							List[str]    =    GPTNeoXForCausalLM.from_pretrained("EleutherAI/pythia-410m-deduped")

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

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

																					_lowerCAmelCase     :							Any    =    model.generate(**__a,			do_sample=__a,			max_new_tokens=20)
																					_lowerCAmelCase     :							str    =    tokenizer.batch_decode(__a)[0]

																					self.assertEqual(__a,			__a)





def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							if density <= 0:
														raise ValueError("Impossible fluid density"  )
							if bulk_modulus <= 0:
														raise ValueError("Impossible bulk modulus"  )

							return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
						import doctest

						doctest.testmod()
