


"""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 						_UpperCamelCase						:
							'''simple docstring'''







							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=0.0_2					,     __a=3					,     __a=4					,     __a=None					,     ):
										__lowerCAmelCase       					=    parent
										__lowerCAmelCase       					=    batch_size
										__lowerCAmelCase       					=    seq_length
										__lowerCAmelCase       					=    is_training
										__lowerCAmelCase       					=    use_input_mask
										__lowerCAmelCase       					=    use_token_type_ids
										__lowerCAmelCase       					=    use_labels
										__lowerCAmelCase       					=    vocab_size
										__lowerCAmelCase       					=    hidden_size
										__lowerCAmelCase       					=    num_hidden_layers
										__lowerCAmelCase       					=    num_attention_heads
										__lowerCAmelCase       					=    intermediate_size
										__lowerCAmelCase       					=    hidden_act
										__lowerCAmelCase       					=    hidden_dropout_prob
										__lowerCAmelCase       					=    attention_probs_dropout_prob
										__lowerCAmelCase       					=    max_position_embeddings
										__lowerCAmelCase       					=    type_vocab_size
										__lowerCAmelCase       					=    type_sequence_label_size
										__lowerCAmelCase       					=    initializer_range
										__lowerCAmelCase       					=    num_labels
										__lowerCAmelCase       					=    num_choices
										__lowerCAmelCase       					=    scope
										__lowerCAmelCase       					=    vocab_size - 1

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    ids_tensor([self.batch_size, self.seq_length]					,     self.vocab_size			)

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

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

										__lowerCAmelCase       					=    self.get_config()

										return config, input_ids, input_mask, token_labels

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

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.prepare_config_and_inputs()

										__lowerCAmelCase       					=    True

										return config, input_ids, input_mask, token_labels

							def     snake_case       (  self					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    GPTNeoXModel(config=A_			)
										model.to(A_			)
										model.eval()
										__lowerCAmelCase       					=    model(A_					,     attention_mask=A_			)
										__lowerCAmelCase       					=    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			):
										__lowerCAmelCase       					=    True
										__lowerCAmelCase       					=    GPTNeoXModel(A_			)
										model.to(A_			)
										model.eval()
										__lowerCAmelCase       					=    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			):
										__lowerCAmelCase       					=    GPTNeoXForCausalLM(config=A_			)
										model.to(A_			)
										model.eval()
										__lowerCAmelCase       					=    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			):
										__lowerCAmelCase       					=    self.num_labels
										__lowerCAmelCase       					=    GPTNeoXForQuestionAnswering(A_			)
										model.to(A_			)
										model.eval()
										__lowerCAmelCase       					=    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			):
										__lowerCAmelCase       					=    self.num_labels
										__lowerCAmelCase       					=    GPTNeoXForSequenceClassification(A_			)
										model.to(A_			)
										model.eval()
										__lowerCAmelCase       					=    ids_tensor([self.batch_size]					,     self.type_sequence_label_size			)
										__lowerCAmelCase       					=    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			):
										__lowerCAmelCase       					=    self.num_labels
										__lowerCAmelCase       					=    GPTNeoXForTokenClassification(A_			)
										model.to(A_			)
										model.eval()
										__lowerCAmelCase       					=    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			):
										__lowerCAmelCase       					=    True
										__lowerCAmelCase       					=    GPTNeoXForCausalLM(config=A_			)
										model.to(A_			)
										model.eval()

										# first forward pass
										__lowerCAmelCase       					=    model(A_					,     attention_mask=A_					,     use_cache=A_			)
										__lowerCAmelCase       					=    outputs.past_key_values

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

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

										__lowerCAmelCase       					=    model(A_					,     attention_mask=A_					,     output_hidden_states=A_			)
										__lowerCAmelCase       					=    output_from_no_past["hidden_states"][0]
										__lowerCAmelCase       					=    model(
										    A_					,     attention_mask=A_					,     past_key_values=A_					,     output_hidden_states=A_					,     )["hidden_states"][0]

										# select random slice
										__lowerCAmelCase       					=    ids_tensor((1,)					,     output_from_past.shape[-1]			).item()
										__lowerCAmelCase       					=    output_from_no_past[:, -3:, random_slice_idx].detach()
										__lowerCAmelCase       					=    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			):
										__lowerCAmelCase       					=    self.prepare_config_and_inputs()
										__lowerCAmelCase       					=    config_and_inputs
										__lowerCAmelCase       					=    {"input_ids": input_ids, "attention_mask": input_mask}
										return config, inputs_dict
@require_torch
class 						_UpperCamelCase						(     __snake_case ,__snake_case ,__snake_case ,unittest.TestCase	):
							'''simple docstring'''







							__UpperCAmelCase		:					str          =(
							    (
							        GPTNeoXModel,
							        GPTNeoXForCausalLM,
							        GPTNeoXForQuestionAnswering,
							        GPTNeoXForSequenceClassification,
							        GPTNeoXForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							__UpperCAmelCase		:					str          =(GPTNeoXForCausalLM,) if is_torch_available() else ()
							__UpperCAmelCase		:					Optional[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		:					Any          =False
							__UpperCAmelCase		:					List[str]          =False
							__UpperCAmelCase		:					Union[str, Any]          =False
							__UpperCAmelCase		:					Dict          =False

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    GPTNeoXModelTester(self			)
										__lowerCAmelCase       					=    ConfigTester(self					,     config_class=A_					,     hidden_size=64					,     num_attention_heads=8			)

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

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(A_					,     A_					,     A_			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    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			):
										__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs_for_decoder()

										__lowerCAmelCase       					=    None

										self.model_tester.create_and_check_model_as_decoder(A_					,     A_					,     A_			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    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			):
										__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_causal_lm(*A_			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*A_			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*A_			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    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			):
										pass



							@parameterized.expand([("linear",), ("dynamic",)]			)
							def     snake_case       (  self					,     __a			):
										__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs_for_common()
										__lowerCAmelCase       					=    ids_tensor([1, 10]					,     config.vocab_size			)
										__lowerCAmelCase       					=    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       					=    GPTNeoXModel(A_			)
										original_model.to(A_			)
										original_model.eval()
										__lowerCAmelCase       					=    original_model(A_			).last_hidden_state
										__lowerCAmelCase       					=    original_model(A_			).last_hidden_state

										set_seed(42			)  # Fixed seed at init time so the two models get the same random weights
										__lowerCAmelCase       					=    {"type": scaling_type, "factor": 1_0.0}
										__lowerCAmelCase       					=    GPTNeoXModel(A_			)
										scaled_model.to(A_			)
										scaled_model.eval()
										__lowerCAmelCase       					=    scaled_model(A_			).last_hidden_state
										__lowerCAmelCase       					=    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	):
							'''simple docstring'''







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

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

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

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

													self.assertEqual(A_					,     A_			)








"""simple docstring"""


import string


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    ""
			for i in sequence:
						__lowerCAmelCase       					=    ord(_UpperCamelCase   )
						if 65 <= extract <= 90:
									output += chr(155 - extract   )
						elif 97 <= extract <= 122:
									output += chr(219 - extract   )
						else:
									output += i
			return output


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    string.ascii_letters
			__lowerCAmelCase       					=    string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
			return "".join(
			    letters_reversed[letters.index(_UpperCamelCase   )] if c in letters else c for c in sequence   )


def _lowerCamelCase			(	):

			'''simple docstring'''




			from timeit import timeit

			print("Running performance benchmarks..."   )
			__lowerCAmelCase       					=    "from string import printable ; from __main__ import atbash, atbash_slow"
			print(f"> atbash_slow(): {timeit('atbash_slow(printable)'					,				setup=_UpperCamelCase   )} seconds"   )
			print(f">      atbash(): {timeit('atbash(printable)'					,				setup=_UpperCamelCase   )} seconds"   )


if __name__ == "__main__":
	for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
		print(f'''{example} encrypted in atbash: {atbash(example)}''')
	benchmark()




