

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     :






					'''simple docstring'''



					def __init__(				self				,      lowerCamelCase__				,      lowerCamelCase__=1_3				,      lowerCamelCase__=7				,      lowerCamelCase__=True				,      lowerCamelCase__=True				,      lowerCamelCase__=True				,      lowerCamelCase__=True				,      lowerCamelCase__=9_9				,      lowerCamelCase__=6_4				,      lowerCamelCase__=5				,      lowerCamelCase__=4				,      lowerCamelCase__=3_7				,      lowerCamelCase__="gelu"				,      lowerCamelCase__=0.1				,      lowerCamelCase__=0.1				,      lowerCamelCase__=5_1_2				,      lowerCamelCase__=1_6				,      lowerCamelCase__=2				,      lowerCamelCase__=0.02				,      lowerCamelCase__=3				,      lowerCamelCase__=4				,      lowerCamelCase__=None				,      ):
						UpperCAmelCase__:	Dict									= parent
						UpperCAmelCase__:	str									= batch_size
						UpperCAmelCase__:	List[str]									= seq_length
						UpperCAmelCase__:	Dict									= is_training
						UpperCAmelCase__:	int									= use_input_mask
						UpperCAmelCase__:	Optional[int]									= use_token_type_ids
						UpperCAmelCase__:	List[Any]									= use_labels
						UpperCAmelCase__:	int									= vocab_size
						UpperCAmelCase__:	List[Any]									= hidden_size
						UpperCAmelCase__:	int									= num_hidden_layers
						UpperCAmelCase__:	Dict									= num_attention_heads
						UpperCAmelCase__:	Union[str, Any]									= intermediate_size
						UpperCAmelCase__:	Optional[Any]									= hidden_act
						UpperCAmelCase__:	Dict									= hidden_dropout_prob
						UpperCAmelCase__:	Optional[int]									= attention_probs_dropout_prob
						UpperCAmelCase__:	List[Any]									= max_position_embeddings
						UpperCAmelCase__:	List[Any]									= type_vocab_size
						UpperCAmelCase__:	str									= type_sequence_label_size
						UpperCAmelCase__:	Tuple									= initializer_range
						UpperCAmelCase__:	int									= num_labels
						UpperCAmelCase__:	Union[str, Any]									= num_choices
						UpperCAmelCase__:	List[Any]									= scope
						UpperCAmelCase__:	Union[str, Any]									= vocab_size - 1





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Dict									= ids_tensor([self.batch_size, self.seq_length]				,      self.vocab_size						)

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

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

						UpperCAmelCase__:	Any									= self.get_config()

						return config, input_ids, input_mask, token_labels





					def    _UpperCAmelCase    (				self						):
						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    _UpperCAmelCase    (				self						):
						UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	str									= self.prepare_config_and_inputs()

						UpperCAmelCase__:	List[str]									= True

						return config, input_ids, input_mask, token_labels





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





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	Tuple									= True
						UpperCAmelCase__:	List[str]									= GPTNeoXModel(lowerCamelCase__						)
						model.to(lowerCamelCase__						)
						model.eval()
						UpperCAmelCase__:	Tuple									= model(lowerCamelCase__				,      attention_mask=lowerCamelCase__						)
						self.parent.assertEqual(result.last_hidden_state.shape				,      (self.batch_size, self.seq_length, self.hidden_size)						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	Optional[int]									= GPTNeoXForCausalLM(config=lowerCamelCase__						)
						model.to(lowerCamelCase__						)
						model.eval()
						UpperCAmelCase__:	Dict									= model(lowerCamelCase__				,      attention_mask=lowerCamelCase__				,      labels=lowerCamelCase__						)
						self.parent.assertEqual(result.logits.shape				,      (self.batch_size, self.seq_length, self.vocab_size)						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	int									= self.num_labels
						UpperCAmelCase__:	Tuple									= GPTNeoXForQuestionAnswering(lowerCamelCase__						)
						model.to(lowerCamelCase__						)
						model.eval()
						UpperCAmelCase__:	List[Any]									= 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    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	List[Any]									= self.num_labels
						UpperCAmelCase__:	List[str]									= GPTNeoXForSequenceClassification(lowerCamelCase__						)
						model.to(lowerCamelCase__						)
						model.eval()
						UpperCAmelCase__:	int									= ids_tensor([self.batch_size]				,      self.type_sequence_label_size						)
						UpperCAmelCase__:	Any									= model(lowerCamelCase__				,      attention_mask=lowerCamelCase__				,      labels=lowerCamelCase__						)
						self.parent.assertEqual(result.logits.shape				,      (self.batch_size, self.num_labels)						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	Dict									= self.num_labels
						UpperCAmelCase__:	int									= GPTNeoXForTokenClassification(lowerCamelCase__						)
						model.to(lowerCamelCase__						)
						model.eval()
						UpperCAmelCase__:	str									= model(lowerCamelCase__				,      attention_mask=lowerCamelCase__				,      labels=lowerCamelCase__						)
						self.parent.assertEqual(result.logits.shape				,      (self.batch_size, self.seq_length, self.num_labels)						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	Optional[int]									= True
						UpperCAmelCase__:	Any									= GPTNeoXForCausalLM(config=lowerCamelCase__						)
						model.to(lowerCamelCase__						)
						model.eval()

						# first forward pass
						UpperCAmelCase__:	int									= model(lowerCamelCase__				,      attention_mask=lowerCamelCase__				,      use_cache=lowerCamelCase__						)
						UpperCAmelCase__:	Dict									= outputs.past_key_values

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

						UpperCAmelCase__:	Dict									= model(lowerCamelCase__				,      attention_mask=lowerCamelCase__				,      output_hidden_states=lowerCamelCase__						)
						UpperCAmelCase__:	List[str]									= output_from_no_past["hidden_states"][0]
						UpperCAmelCase__:	int									= model(
						    lowerCamelCase__				,      attention_mask=lowerCamelCase__				,      past_key_values=lowerCamelCase__				,      output_hidden_states=lowerCamelCase__				,      )["hidden_states"][0]

						# select random slice
						UpperCAmelCase__:	Optional[int]									= ids_tensor((1,)				,      output_from_past.shape[-1]						).item()
						UpperCAmelCase__:	Any									= output_from_no_past[:, -3:, random_slice_idx].detach()
						UpperCAmelCase__:	Optional[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(lowerCamelCase__				,      lowerCamelCase__				,      atol=1e-3						)						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Optional[int]									= self.prepare_config_and_inputs()
						UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	Dict									= config_and_inputs
						UpperCAmelCase__:	Dict									= {"input_ids": input_ids, "attention_mask": input_mask}
						return config, inputs_dict

@require_torch
class __UpperCamelCase     (				_a					,_a					,_a					,unittest.TestCase		):






					'''simple docstring'''



					__magic_name__        =   (
					    (
					        GPTNeoXModel,
					        GPTNeoXForCausalLM,
					        GPTNeoXForQuestionAnswering,
					        GPTNeoXForSequenceClassification,
					        GPTNeoXForTokenClassification,
					    )
					    if is_torch_available()
					    else ()
					)
					__magic_name__        =   (GPTNeoXForCausalLM,) if is_torch_available() else ()
					__magic_name__        =   (
					    {
					        "feature-extraction": GPTNeoXModel,
					        "question-answering": GPTNeoXForQuestionAnswering,
					        "text-classification": GPTNeoXForSequenceClassification,
					        "text-generation": GPTNeoXForCausalLM,
					        "token-classification": GPTNeoXForTokenClassification,
					        "zero-shot": GPTNeoXForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					__magic_name__        =   False
					__magic_name__        =   False
					__magic_name__        =   False
					__magic_name__        =   False





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	str									= GPTNeoXModelTester(self						)
						UpperCAmelCase__:	Any									= ConfigTester(self				,      config_class=lowerCamelCase__				,      hidden_size=6_4				,      num_attention_heads=8						)





					def    _UpperCAmelCase    (				self						):
						self.config_tester.run_common_tests()





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	Any									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	Any									= self.model_tester.prepare_config_and_inputs_for_decoder()
						self.model_tester.create_and_check_model_as_decoder(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						# 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(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	int									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_decoder_model_past_large_inputs(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Optional[int]									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_causal_lm(*lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Optional[int]									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	List[Any]									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Any									= 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    _UpperCAmelCase    (				self						):
						pass





					@parameterized.expand([("linear",), ("dynamic",)]						)
					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__							,						UpperCAmelCase__:	Any									= self.model_tester.prepare_config_and_inputs_for_common()
						UpperCAmelCase__:	Optional[int]									= ids_tensor([1, 1_0]				,      config.vocab_size						)
						UpperCAmelCase__:	Optional[int]									= ids_tensor([1, int(config.max_position_embeddings * 1.5						)]				,      config.vocab_size						)

						set_seed(4_2						)  # Fixed seed at init time so the two models get the same random weights
						UpperCAmelCase__:	Any									= GPTNeoXModel(lowerCamelCase__						)
						original_model.to(lowerCamelCase__						)
						original_model.eval()
						UpperCAmelCase__:	Dict									= original_model(lowerCamelCase__						).last_hidden_state
						UpperCAmelCase__:	int									= original_model(lowerCamelCase__						).last_hidden_state

						set_seed(4_2						)  # Fixed seed at init time so the two models get the same random weights
						UpperCAmelCase__:	Union[str, Any]									= {"type": scaling_type, "factor": 10.0}
						UpperCAmelCase__:	List[str]									= GPTNeoXModel(lowerCamelCase__						)
						scaled_model.to(lowerCamelCase__						)
						scaled_model.eval()
						UpperCAmelCase__:	Optional[int]									= scaled_model(lowerCamelCase__						).last_hidden_state
						UpperCAmelCase__:	str									= 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 __UpperCamelCase     (				unittest.TestCase		):






					'''simple docstring'''



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

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

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

							UpperCAmelCase__:	Tuple									= model.generate(**lowerCamelCase__				,      do_sample=lowerCamelCase__				,      max_new_tokens=2_0						)
							UpperCAmelCase__:	Tuple									= tokenizer.batch_decode(lowerCamelCase__						)[0]

							self.assertEqual(lowerCamelCase__				,      lowerCamelCase__						)


from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean



def 			_A	(      SCREAMING_SNAKE_CASE      ):

	# A local function to see if a dot lands in the circle.
	def is_in_circle(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ) -> bool:
		UpperCAmelCase__:	Any									= sqrt((x**2) + (y**2)      )
		# Our circle has a radius of 1, so a distance
		# greater than 1 would land outside the circle.
		return distance_from_centre <= 1

	# The proportion of guesses that landed in the circle
	UpperCAmelCase__:	Optional[int]									= mean(
	    int(is_in_circle(uniform(-1.0	,1.0      )	,uniform(-1.0	,1.0      )      )      )
	    for _ in range(SCREAMING_SNAKE_CASE      )      )
	# The ratio of the area for circle to square is pi/4.
	UpperCAmelCase__:	Optional[int]									= proportion * 4
	print(f"The estimated value of pi is {pi_estimate}"      )
	print(f"The numpy value of pi is {pi}"      )
	print(f"The total error is {abs(pi - pi_estimate      )}"      )



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE = 0.0	,SCREAMING_SNAKE_CASE = 1.0	,):
	return mean(
	    function_to_integrate(uniform(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )      ) for _ in range(SCREAMING_SNAKE_CASE      )      ) * (max_value - min_value)



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE = 0.0	,SCREAMING_SNAKE_CASE = 1.0      ):

	def identity_function(SCREAMING_SNAKE_CASE      ) -> float:
		return x

	UpperCAmelCase__:	Tuple									= area_under_curve_estimator(
	    SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	Optional[Any]									= (max_value * max_value - min_value * min_value) / 2

	print("******************"      )
	print(f"Estimating area under y=x where x varies from {min_value} to {max_value}"      )
	print(f"Estimated value is {estimated_value}"      )
	print(f"Expected value is {expected_value}"      )
	print(f"Total error is {abs(estimated_value - expected_value      )}"      )
	print("******************"      )



def 			_A	(      SCREAMING_SNAKE_CASE      ):

	def function_to_integrate(SCREAMING_SNAKE_CASE      ) -> float:
		return sqrt(4.0 - x * x      )

	UpperCAmelCase__:	Union[str, Any]									= area_under_curve_estimator(
	    SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,0.0	,2.0      )

	print("******************"      )
	print("Estimating pi using area_under_curve_estimator"      )
	print(f"Estimated value is {estimated_value}"      )
	print(f"Expected value is {pi}"      )
	print(f"Total error is {abs(estimated_value - pi      )}"      )
	print("******************"      )


if __name__ == "__main__":
					import doctest

					doctest.testmod()