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      __lowercase					:

						def __init__(	self				:      List[str]							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      int=13							,							__lowerCamelCase				:      int=7							,							__lowerCamelCase				:      Dict=True							,							__lowerCamelCase				:      str=True							,							__lowerCamelCase				:      List[Any]=True							,							__lowerCamelCase				:      Any=True							,							__lowerCamelCase				:      Dict=99							,							__lowerCamelCase				:      Optional[Any]=64							,							__lowerCamelCase				:      Optional[int]=5							,							__lowerCamelCase				:      str=4							,							__lowerCamelCase				:      int=37							,							__lowerCamelCase				:      List[str]="gelu"							,							__lowerCamelCase				:      Optional[Any]=0.1							,							__lowerCamelCase				:      Dict=0.1							,							__lowerCamelCase				:      List[str]=5_12							,							__lowerCamelCase				:      int=16							,							__lowerCamelCase				:      int=2							,							__lowerCamelCase				:      Dict=0.02							,							__lowerCamelCase				:      List[Any]=3							,							__lowerCamelCase				:      str=4							,							__lowerCamelCase				:      Any=None							,							)							->		int:


									'''simple docstring'''

									lowercase							=		parent
									lowercase							=		batch_size
									lowercase							=		seq_length
									lowercase							=		is_training
									lowercase							=		use_input_mask
									lowercase							=		use_token_type_ids
									lowercase							=		use_labels
									lowercase							=		vocab_size
									lowercase							=		hidden_size
									lowercase							=		num_hidden_layers
									lowercase							=		num_attention_heads
									lowercase							=		intermediate_size
									lowercase							=		hidden_act
									lowercase							=		hidden_dropout_prob
									lowercase							=		attention_probs_dropout_prob
									lowercase							=		max_position_embeddings
									lowercase							=		type_vocab_size
									lowercase							=		type_sequence_label_size
									lowercase							=		initializer_range
									lowercase							=		num_labels
									lowercase							=		num_choices
									lowercase							=		scope
									lowercase							=		vocab_size - 1

						def        __a		(	self				:      List[str]   )							->		Union[str, Any]:


									'''simple docstring'''

									lowercase							=		ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size   )

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

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

									lowercase							=		self.get_config()

									return config, input_ids, input_mask, token_labels

						def        __a		(	self				:      Union[str, Any]   )							->		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=__lowerCamelCase							,							initializer_range=self.initializer_range							,							pad_token_id=self.pad_token_id							,							)

						def        __a		(	self				:      List[Any]   )							->		Tuple:


									'''simple docstring'''

									lowercase		,lowercase		,lowercase		,lowercase							=		self.prepare_config_and_inputs()

									lowercase							=		True

									return config, input_ids, input_mask, token_labels

						def        __a		(	self				:      Any							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      Optional[Any]   )							->		int:


									'''simple docstring'''

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

						def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      Dict   )							->		List[Any]:


									'''simple docstring'''

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

						def        __a		(	self				:      Tuple							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      Dict   )							->		int:


									'''simple docstring'''

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

						def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      List[str]   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		self.num_labels
									lowercase							=		GPTNeoXForQuestionAnswering(__lowerCamelCase   )
									model.to(__lowerCamelCase   )
									model.eval()
									lowercase							=		model(__lowerCamelCase							,							attention_mask=__lowerCamelCase   )
									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				:      str							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      int   )							->		List[str]:


									'''simple docstring'''

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

						def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      Dict   )							->		Optional[Any]:


									'''simple docstring'''

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

						def        __a		(	self				:      int							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      Optional[int]							,							__lowerCamelCase				:      int   )							->		Dict:


									'''simple docstring'''

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

									# first forward pass
									lowercase							=		model(__lowerCamelCase							,							attention_mask=__lowerCamelCase							,							use_cache=__lowerCamelCase   )
									lowercase							=		outputs.past_key_values

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

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

									lowercase							=		model(__lowerCamelCase							,							attention_mask=__lowerCamelCase							,							output_hidden_states=__lowerCamelCase   )
									lowercase							=		output_from_no_past['''hidden_states'''][0]
									lowercase							=		model(
									    __lowerCamelCase							,							attention_mask=__lowerCamelCase							,							past_key_values=__lowerCamelCase							,							output_hidden_states=__lowerCamelCase							,							)['''hidden_states'''][0]

									# select random slice
									lowercase							=		ids_tensor((1,)							,							output_from_past.shape[-1]   ).item()
									lowercase							=		output_from_no_past[:, -3:, random_slice_idx].detach()
									lowercase							=		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(__lowerCamelCase							,							__lowerCamelCase							,							atol=1E-3   )   )



						def        __a		(	self				:      str   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		self.prepare_config_and_inputs()
									lowercase		,lowercase		,lowercase		,lowercase							=		config_and_inputs
									lowercase							=		{'''input_ids''': input_ids, '''attention_mask''': input_mask}
									return config, inputs_dict






@require_torch
class      __lowercase					(    _A       ,     _A       ,     _A       ,     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        __a		(	self				:      Union[str, Any]   )							->		Tuple:


									'''simple docstring'''

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

						def        __a		(	self				:      List[str]   )							->		Tuple:


									'''simple docstring'''

									self.config_tester.run_common_tests()

						def        __a		(	self				:      int   )							->		List[str]:


									'''simple docstring'''

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

						def        __a		(	self				:      Any   )							->		Any:


									'''simple docstring'''

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

						def        __a		(	self				:      Dict   )							->		Tuple:


									'''simple docstring'''

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

									lowercase							=		None

									self.model_tester.create_and_check_model_as_decoder(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase   )

						def        __a		(	self				:      List[str]   )							->		List[str]:


									'''simple docstring'''

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

						def        __a		(	self				:      Tuple   )							->		List[Any]:


									'''simple docstring'''

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

						def        __a		(	self				:      str   )							->		Any:


									'''simple docstring'''

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

						def        __a		(	self				:      Dict   )							->		List[str]:


									'''simple docstring'''

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

						def        __a		(	self				:      int   )							->		List[str]:


									'''simple docstring'''

									lowercase							=		self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase   )

						@unittest.skip(reason='''Feed forward chunking is not implemented'''   )
						def        __a		(	self				:      Union[str, Any]   )							->		Optional[Any]:


									'''simple docstring'''

									pass



						@parameterized.expand([('''linear''',), ('''dynamic''',)]   )
						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      Optional[int]   )							->		Union[str, Any]:


									'''simple docstring'''

									lowercase		,lowercase							=		self.model_tester.prepare_config_and_inputs_for_common()
									lowercase							=		ids_tensor([1, 10]							,							config.vocab_size   )
									lowercase							=		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
									lowercase							=		GPTNeoXModel(__lowerCamelCase   )
									original_model.to(__lowerCamelCase   )
									original_model.eval()
									lowercase							=		original_model(__lowerCamelCase   ).last_hidden_state
									lowercase							=		original_model(__lowerCamelCase   ).last_hidden_state

									set_seed(42   )  # Fixed seed at init time so the two models get the same random weights
									lowercase							=		{'''type''': scaling_type, '''factor''': 10.0}
									lowercase							=		GPTNeoXModel(__lowerCamelCase   )
									scaled_model.to(__lowerCamelCase   )
									scaled_model.eval()
									lowercase							=		scaled_model(__lowerCamelCase   ).last_hidden_state
									lowercase							=		scaled_model(__lowerCamelCase   ).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(__lowerCamelCase							,							__lowerCamelCase							,							atol=1E-5   )   )
									else:
												self.assertFalse(torch.allclose(__lowerCamelCase							,							__lowerCamelCase							,							atol=1E-5   )   )

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






@require_torch
class      __lowercase					(    unittest.TestCase    ):

						@slow
						def        __a		(	self				:      Optional[int]   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		AutoTokenizer.from_pretrained('''EleutherAI/pythia-410m-deduped'''   )
									for checkpointing in [True, False]:
												lowercase							=		GPTNeoXForCausalLM.from_pretrained('''EleutherAI/pythia-410m-deduped'''   )

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

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

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

												self.assertEqual(__lowerCamelCase							,							__lowerCamelCase   )






from __future__ import annotations

from math import pi






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       dict[str, float]:



			"""simple docstring"""







			if (inductance, frequency, reactance).count(0       ) != 1:
						raise ValueError('''One and only one argument must be 0'''       )
			if inductance < 0:
						raise ValueError('''Inductance cannot be negative'''       )
			if frequency < 0:
						raise ValueError('''Frequency cannot be negative'''       )
			if reactance < 0:
						raise ValueError('''Inductive reactance cannot be negative'''       )
			if inductance == 0:
						return {"inductance": reactance / (2 * pi * frequency)}
			elif frequency == 0:
						return {"frequency": reactance / (2 * pi * inductance)}
			elif reactance == 0:
						return {"reactance": 2 * pi * frequency * inductance}
			else:
						raise ValueError('''Exactly one argument must be 0'''       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()





