





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        snake_case_		:







		def __init__(		self			,     __lowercase			,     __lowercase=1_3			,     __lowercase=7			,     __lowercase=True			,     __lowercase=True			,     __lowercase=True			,     __lowercase=True			,     __lowercase=9_9			,     __lowercase=6_4			,     __lowercase=5			,     __lowercase=4			,     __lowercase=3_7			,     __lowercase="gelu"			,     __lowercase=0.1			,     __lowercase=0.1			,     __lowercase=5_1_2			,     __lowercase=1_6			,     __lowercase=2			,     __lowercase=0.0_2			,     __lowercase=3			,     __lowercase=4			,     __lowercase=None			,     )				->   int:
				lowerCamelCase   : Optional[int]								=parent
				lowerCamelCase   : Any								=batch_size
				lowerCamelCase   : List[str]								=seq_length
				lowerCamelCase   : Union[str, Any]								=is_training
				lowerCamelCase   : int								=use_input_mask
				lowerCamelCase   : int								=use_token_type_ids
				lowerCamelCase   : Any								=use_labels
				lowerCamelCase   : Optional[Any]								=vocab_size
				lowerCamelCase   : List[Any]								=hidden_size
				lowerCamelCase   : Optional[int]								=num_hidden_layers
				lowerCamelCase   : Optional[int]								=num_attention_heads
				lowerCamelCase   : List[str]								=intermediate_size
				lowerCamelCase   : List[str]								=hidden_act
				lowerCamelCase   : str								=hidden_dropout_prob
				lowerCamelCase   : Optional[int]								=attention_probs_dropout_prob
				lowerCamelCase   : int								=max_position_embeddings
				lowerCamelCase   : Dict								=type_vocab_size
				lowerCamelCase   : Optional[Any]								=type_sequence_label_size
				lowerCamelCase   : List[str]								=initializer_range
				lowerCamelCase   : Optional[int]								=num_labels
				lowerCamelCase   : List[str]								=num_choices
				lowerCamelCase   : List[Any]								=scope
				lowerCamelCase   : Optional[Any]								=vocab_size - 1







		def 			__lowercase   (		self   )				->   Any:
				lowerCamelCase   : Optional[Any]								=ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size   )

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

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

				lowerCamelCase   : Tuple								=self.get_config()

				return config, input_ids, input_mask, token_labels







		def 			__lowercase   (		self   )				->   str:
				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 			__lowercase   (		self   )				->   Optional[Any]:
				lowerCamelCase   : List[str]								=self.prepare_config_and_inputs()

				lowerCamelCase   : Dict								=True

				return config, input_ids, input_mask, token_labels







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase			,     __lowercase   )				->   Optional[int]:
				lowerCamelCase   : Optional[int]								=GPTNeoXModel(config=A_   )
				model.to(A_   )
				model.eval()
				lowerCamelCase   : Any								=model(A_			,     attention_mask=A_   )
				lowerCamelCase   : Dict								=model(A_   )
				self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.seq_length, self.hidden_size)   )







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase			,     __lowercase   )				->   Optional[int]:
				lowerCamelCase   : Dict								=True
				lowerCamelCase   : Optional[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 			__lowercase   (		self			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase   )				->   Tuple:
				lowerCamelCase   : Optional[Any]								=GPTNeoXForCausalLM(config=A_   )
				model.to(A_   )
				model.eval()
				lowerCamelCase   : int								=model(A_			,     attention_mask=A_			,     labels=A_   )
				self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length, self.vocab_size)   )







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase   )				->   Any:
				lowerCamelCase   : Union[str, Any]								=self.num_labels
				lowerCamelCase   : Union[str, Any]								=GPTNeoXForQuestionAnswering(A_   )
				model.to(A_   )
				model.eval()
				lowerCamelCase   : int								=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 			__lowercase   (		self			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase   )				->   str:
				lowerCamelCase   : Dict								=self.num_labels
				lowerCamelCase   : List[str]								=GPTNeoXForSequenceClassification(A_   )
				model.to(A_   )
				model.eval()
				lowerCamelCase   : str								=ids_tensor([self.batch_size]			,     self.type_sequence_label_size   )
				lowerCamelCase   : List[str]								=model(A_			,     attention_mask=A_			,     labels=A_   )
				self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_labels)   )







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase			,     __lowercase			,     __lowercase   )				->   Dict:
				lowerCamelCase   : Optional[int]								=self.num_labels
				lowerCamelCase   : Optional[int]								=GPTNeoXForTokenClassification(A_   )
				model.to(A_   )
				model.eval()
				lowerCamelCase   : Union[str, Any]								=model(A_			,     attention_mask=A_			,     labels=A_   )
				self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length, self.num_labels)   )







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase			,     __lowercase   )				->   str:
				lowerCamelCase   : int								=True
				lowerCamelCase   : Tuple								=GPTNeoXForCausalLM(config=A_   )
				model.to(A_   )
				model.eval()

				# first forward pass
				lowerCamelCase   : str								=model(A_			,     attention_mask=A_			,     use_cache=A_   )
				lowerCamelCase   : List[Any]								=outputs.past_key_values

				# create hypothetical multiple next token and extent to next_input_ids
				lowerCamelCase   : Dict								=ids_tensor((self.batch_size, 3)			,     config.vocab_size   )
				lowerCamelCase   : List[str]								=ids_tensor((self.batch_size, 3)			,     vocab_size=2   )

				# append to next input_ids and
				lowerCamelCase   : Union[str, Any]								=torch.cat([input_ids, next_tokens]			,     dim=-1   )
				lowerCamelCase   : Dict								=torch.cat([input_mask, next_mask]			,     dim=-1   )

				lowerCamelCase   : Optional[Any]								=model(A_			,     attention_mask=A_			,     output_hidden_states=A_   )
				lowerCamelCase   : Any								=output_from_no_past["hidden_states"][0]
				lowerCamelCase   : List[str]								=model(
				    A_			,     attention_mask=A_			,     past_key_values=A_			,     output_hidden_states=A_			,     )["hidden_states"][0]

				# select random slice
				lowerCamelCase   : Union[str, Any]								=ids_tensor((1,)			,     output_from_past.shape[-1]   ).item()
				lowerCamelCase   : Tuple								=output_from_no_past[:, -3:, random_slice_idx].detach()
				lowerCamelCase   : 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(A_			,     A_			,     atol=1e-3   )   )







		def 			__lowercase   (		self   )				->   Optional[Any]:
				lowerCamelCase   : Optional[int]								=self.prepare_config_and_inputs()
				lowerCamelCase   : Optional[Any]								=config_and_inputs
				lowerCamelCase   : List[str]								={"input_ids": input_ids, "attention_mask": input_mask}
				return config, inputs_dict

@require_torch
class        snake_case_		(			__snake_case						,					__snake_case						,					__snake_case						,					unittest.TestCase):
		lowerCamelCase    :str						=     (
		    (
		        GPTNeoXModel,
		        GPTNeoXForCausalLM,
		        GPTNeoXForQuestionAnswering,
		        GPTNeoXForSequenceClassification,
		        GPTNeoXForTokenClassification,
		    )
		    if is_torch_available()
		    else ()
		)
		lowerCamelCase    :str						=     (GPTNeoXForCausalLM,) if is_torch_available() else ()
		lowerCamelCase    :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 {}
		)
		lowerCamelCase    :Any						=     False
		lowerCamelCase    :List[str]						=     False
		lowerCamelCase    :Union[str, Any]						=     False
		lowerCamelCase    :Dict						=     False







		def 			__lowercase   (		self   )				->   int:
				lowerCamelCase   : Union[str, Any]								=GPTNeoXModelTester(self   )
				lowerCamelCase   : Any								=ConfigTester(self			,     config_class=A_			,     hidden_size=6_4			,     num_attention_heads=8   )







		def 			__lowercase   (		self   )				->   Union[str, Any]:
				self.config_tester.run_common_tests()







		def 			__lowercase   (		self   )				->   Optional[Any]:
				lowerCamelCase   : str								=self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(A_			,     A_			,     A_   )







		def 			__lowercase   (		self   )				->   Any:
				lowerCamelCase   : List[Any]								=self.model_tester.prepare_config_and_inputs_for_decoder()
				self.model_tester.create_and_check_model_as_decoder(A_			,     A_			,     A_   )







		def 			__lowercase   (		self   )				->   Any:
				lowerCamelCase   : Dict								=self.model_tester.prepare_config_and_inputs_for_decoder()

				lowerCamelCase   : Optional[int]								=None

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







		def 			__lowercase   (		self   )				->   str:
				lowerCamelCase   : int								=self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_decoder_model_past_large_inputs(A_			,     A_			,     A_   )







		def 			__lowercase   (		self   )				->   Optional[int]:
				lowerCamelCase   : Dict								=self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_causal_lm(*A_   )







		def 			__lowercase   (		self   )				->   Dict:
				lowerCamelCase   : List[str]								=self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_question_answering(*A_   )







		def 			__lowercase   (		self   )				->   Dict:
				lowerCamelCase   : Optional[int]								=self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_sequence_classification(*A_   )







		def 			__lowercase   (		self   )				->   Union[str, Any]:
				lowerCamelCase   : int								=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 			__lowercase   (		self   )				->   Any:
				pass







		@parameterized.expand([('''linear''',), ('''dynamic''',)]   )
		def 			__lowercase   (		self			,     __lowercase   )				->   List[str]:
				lowerCamelCase   : List[Any]								=self.model_tester.prepare_config_and_inputs_for_common()
				lowerCamelCase   : Tuple								=ids_tensor([1, 1_0]			,     config.vocab_size   )
				lowerCamelCase   : List[str]								=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
				lowerCamelCase   : Dict								=GPTNeoXModel(A_   )
				original_model.to(A_   )
				original_model.eval()
				lowerCamelCase   : List[Any]								=original_model(A_   ).last_hidden_state
				lowerCamelCase   : Optional[Any]								=original_model(A_   ).last_hidden_state

				set_seed(4_2   )  # Fixed seed at init time so the two models get the same random weights
				lowerCamelCase   : Union[str, Any]								={"type": scaling_type, "factor": 1_0.0}
				lowerCamelCase   : List[Any]								=GPTNeoXModel(A_   )
				scaled_model.to(A_   )
				scaled_model.eval()
				lowerCamelCase   : Dict								=scaled_model(A_   ).last_hidden_state
				lowerCamelCase   : 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        snake_case_		(			unittest.TestCase):







		@slow
		def 			__lowercase   (		self   )				->   List[Any]:
				lowerCamelCase   : Optional[Any]								=AutoTokenizer.from_pretrained('''EleutherAI/pythia-410m-deduped'''   )
				for checkpointing in [True, False]:
						lowerCamelCase   : str								=GPTNeoXForCausalLM.from_pretrained('''EleutherAI/pythia-410m-deduped'''   )

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

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

						lowerCamelCase   : List[Any]								=model.generate(**A_			,     do_sample=A_			,     max_new_tokens=2_0   )
						lowerCamelCase   : int								=tokenizer.batch_decode(A_   )[0]

						self.assertEqual(A_			,     A_   )








import string


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     str:
		lowerCamelCase   : Optional[Any]								=''''''
		for i in sequence:
				lowerCamelCase   : int								=ord(SCREAMING_SNAKE_CASE_		)
				if 6_5 <= extract <= 9_0:
						output += chr(1_5_5 - extract		)
				elif 9_7 <= extract <= 1_2_2:
						output += chr(2_1_9 - extract		)
				else:
						output += i
		return output


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     str:
		lowerCamelCase   : Tuple								=string.ascii_letters
		lowerCamelCase   : int								=string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
		return "".join(
		    letters_reversed[letters.index(SCREAMING_SNAKE_CASE_		)] if c in letters else c for c in sequence		)


def 					A__	(       )   ->     None:
		from timeit import timeit

		print('''Running performance benchmarks...'''		)
		lowerCamelCase   : Tuple								='''from string import printable ; from __main__ import atbash, atbash_slow'''
		print(F"> atbash_slow(): {timeit('atbash_slow(printable)'							,       setup=SCREAMING_SNAKE_CASE_		)} seconds"		)
		print(F">      atbash(): {timeit('atbash(printable)'							,       setup=SCREAMING_SNAKE_CASE_		)} seconds"		)


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

