
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        lowerCAmelCase__      :

						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=5_12       ,							a=16       ,							a=2       ,							a=0.02       ,							a=3       ,							a=4       ,							a=None       ,							)					->      Dict:


								'''simple docstring'''

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

						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

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

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

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

								_UpperCamelCase						    =	self.get_config()

								return config, input_ids, input_mask, token_labels

						def  A_				(			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=a       ,							initializer_range=self.initializer_range       ,							pad_token_id=self.pad_token_id       ,							)

						def  A_				(			self				)					->      str:


								'''simple docstring'''

								_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	self.prepare_config_and_inputs()

								_UpperCamelCase						    =	True

								return config, input_ids, input_mask, token_labels

						def  A_				(			self       ,							a       ,							a       ,							a				)					->      Dict:


								'''simple docstring'''

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

						def  A_				(			self       ,							a       ,							a       ,							a				)					->      Dict:


								'''simple docstring'''

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

						def  A_				(			self       ,							a       ,							a       ,							a       ,							a				)					->      Any:


								'''simple docstring'''

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

						def  A_				(			self       ,							a       ,							a       ,							a       ,							a				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	self.num_labels
								_UpperCamelCase						    =	GPTNeoXForQuestionAnswering(a				)
								model.to(a				)
								model.eval()
								_UpperCamelCase						    =	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  A_				(			self       ,							a       ,							a       ,							a       ,							a				)					->      Any:


								'''simple docstring'''

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

						def  A_				(			self       ,							a       ,							a       ,							a       ,							a				)					->      List[Any]:


								'''simple docstring'''

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

						def  A_				(			self       ,							a       ,							a       ,							a				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	True
								_UpperCamelCase						    =	GPTNeoXForCausalLM(config=a				)
								model.to(a				)
								model.eval()

								# first forward pass
								_UpperCamelCase						    =	model(a       ,							attention_mask=a       ,							use_cache=a				)
								_UpperCamelCase						    =	outputs.past_key_values

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

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

								_UpperCamelCase						    =	model(a       ,							attention_mask=a       ,							output_hidden_states=a				)
								_UpperCamelCase						    =	output_from_no_past["""hidden_states"""][0]
								_UpperCamelCase						    =	model(
								    a       ,							attention_mask=a       ,							past_key_values=a       ,							output_hidden_states=a       ,							)["""hidden_states"""][0]

								# select random slice
								_UpperCamelCase						    =	ids_tensor((1,)       ,							output_from_past.shape[-1]				).item()
								_UpperCamelCase						    =	output_from_no_past[:, -3:, random_slice_idx].detach()
								_UpperCamelCase						    =	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  A_				(			self				)					->      List[Any]:


								'''simple docstring'''

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







@require_torch
class        lowerCAmelCase__      (   __lowercase		,	__lowercase		,	__lowercase		,	unittest.TestCase	):
						UpperCamelCase_							: Any	=		(
						    (
						        GPTNeoXModel,
						        GPTNeoXForCausalLM,
						        GPTNeoXForQuestionAnswering,
						        GPTNeoXForSequenceClassification,
						        GPTNeoXForTokenClassification,
						    )
						    if is_torch_available()
						    else ()
						)
						UpperCamelCase_							: Any	=		(GPTNeoXForCausalLM,) if is_torch_available() else ()
						UpperCamelCase_							: Any	=		(
						    {
						        "feature-extraction": GPTNeoXModel,
						        "question-answering": GPTNeoXForQuestionAnswering,
						        "text-classification": GPTNeoXForSequenceClassification,
						        "text-generation": GPTNeoXForCausalLM,
						        "token-classification": GPTNeoXForTokenClassification,
						        "zero-shot": GPTNeoXForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						UpperCamelCase_							: List[Any]	=		False
						UpperCamelCase_							: Dict	=		False
						UpperCamelCase_							: Dict	=		False
						UpperCamelCase_							: Dict	=		False

						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	GPTNeoXModelTester(self				)
								_UpperCamelCase						    =	ConfigTester(self       ,							config_class=a       ,							hidden_size=64       ,							num_attention_heads=8				)

						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								self.config_tester.run_common_tests()

						def  A_				(			self				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(a       ,							a       ,							a				)

						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_model_as_decoder(a       ,							a       ,							a				)

						def  A_				(			self				)					->      Any:


								'''simple docstring'''

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

								_UpperCamelCase						    =	None

								self.model_tester.create_and_check_model_as_decoder(a       ,							a       ,							a				)

						def  A_				(			self				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_decoder_model_past_large_inputs(a       ,							a       ,							a				)

						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_causal_lm(*a				)

						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*a				)

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


								'''simple docstring'''

								_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*a				)

						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								_UpperCamelCase						    =	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  A_				(			self				)					->      Optional[int]:


								'''simple docstring'''

								pass







						@parameterized.expand([("""linear""",), ("""dynamic""",)]				)
						def  A_				(			self       ,							a				)					->      Union[str, Any]:


								'''simple docstring'''

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

								set_seed(42				)  # Fixed seed at init time so the two models get the same random weights
								_UpperCamelCase						    =	{"""type""": scaling_type, """factor""": 10.0}
								_UpperCamelCase						    =	GPTNeoXModel(a				)
								scaled_model.to(a				)
								scaled_model.eval()
								_UpperCamelCase						    =	scaled_model(a				).last_hidden_state
								_UpperCamelCase						    =	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        lowerCAmelCase__      (   unittest.TestCase	):

						@slow
						def  A_				(			self				)					->      List[str]:


								'''simple docstring'''

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

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

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

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

										self.assertEqual(a       ,							a				)





from math import isclose, sqrt


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				tuple[float, float, float]:

		"""simple docstring"""






		_UpperCamelCase						    =	point_y / 4 / point_x
		_UpperCamelCase						    =	2 * normal_gradient / (1 + normal_gradient * normal_gradient)
		_UpperCamelCase						    =	(1 - normal_gradient * normal_gradient) / (
		    1 + normal_gradient * normal_gradient
		)
		_UpperCamelCase						    =	(sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

		# to find the next point, solve the simultaeneous equations:
		# y^2 + 4x^2 = 100
		# y - b = m * (x - a)
		# ==> A x^2 + B x + C = 0
		_UpperCamelCase						    =	outgoing_gradient**2 + 4
		_UpperCamelCase						    =	2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
		_UpperCamelCase						    =	(point_y - outgoing_gradient * point_x) ** 2 - 1_0_0

		_UpperCamelCase						    =	(
		    -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
		) / (2 * quadratic_term)
		_UpperCamelCase						    =	(
		    -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
		) / (2 * quadratic_term)

		# two solutions, one of which is our input point
		_UpperCamelCase						    =	x_minus if isclose(lowerCAmelCase						,  lowerCAmelCase ) else x_plus
		_UpperCamelCase						    =	point_y + outgoing_gradient * (next_x - point_x)

		return next_x, next_y, outgoing_gradient


def 							__A(lowerCAmelCase = 1.4						,  lowerCAmelCase = -9.6 )		->				int:

		"""simple docstring"""






		_UpperCamelCase						    =	0
		_UpperCamelCase						    =	first_x_coord
		_UpperCamelCase						    =	first_y_coord
		_UpperCamelCase						    =	(10.1 - point_y) / (0.0 - point_x)

		while not (-0.01 <= point_x <= 0.01 and point_y > 0):
				_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	next_point(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )
				num_reflections += 1

		return num_reflections


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



