





"""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				:






							def __init__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__=13      ,					lowerCAmelCase__=7      ,					lowerCAmelCase__=True      ,					lowerCAmelCase__=True      ,					lowerCAmelCase__=True      ,					lowerCAmelCase__=True      ,					lowerCAmelCase__=99      ,					lowerCAmelCase__=64      ,					lowerCAmelCase__=5      ,					lowerCAmelCase__=4      ,					lowerCAmelCase__=37      ,					lowerCAmelCase__="gelu"      ,					lowerCAmelCase__=0.1      ,					lowerCAmelCase__=0.1      ,					lowerCAmelCase__=512      ,					lowerCAmelCase__=16      ,					lowerCAmelCase__=2      ,					lowerCAmelCase__=0.02      ,					lowerCAmelCase__=3      ,					lowerCAmelCase__=4      ,					lowerCAmelCase__=None      ,					)	-> Optional[Any]:
								a      :		Dict	=				parent
								a      :		List[str]	=				batch_size
								a      :		Any	=				seq_length
								a      :		Dict	=				is_training
								a      :		List[str]	=				use_input_mask
								a      :		Any	=				use_token_type_ids
								a      :		List[Any]	=				use_labels
								a      :		Tuple	=				vocab_size
								a      :		List[Any]	=				hidden_size
								a      :		Optional[int]	=				num_hidden_layers
								a      :		Union[str, Any]	=				num_attention_heads
								a      :		str	=				intermediate_size
								a      :		Dict	=				hidden_act
								a      :		int	=				hidden_dropout_prob
								a      :		Dict	=				attention_probs_dropout_prob
								a      :		int	=				max_position_embeddings
								a      :		Optional[Any]	=				type_vocab_size
								a      :		Any	=				type_sequence_label_size
								a      :		List[str]	=				initializer_range
								a      :		List[Any]	=				num_labels
								a      :		Optional[int]	=				num_choices
								a      :		Optional[Any]	=				scope
								a      :		List[str]	=				vocab_size - 1






							def     __a   (   self      )	-> List[Any]:
								a      :		Optional[int]	=				ids_tensor([self.batch_size, self.seq_length]      ,					self.vocab_size      )

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

								a      :		List[str]	=				None
								if self.use_labels:
									a      :		Dict	=				ids_tensor([self.batch_size, self.seq_length]      ,					self.num_labels      )

								a      :		Any	=				self.get_config()

								return config, input_ids, input_mask, token_labels






							def     __a   (   self      )	-> Any:
								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]:
								a,       a,       a,       a      :		List[Any]	=				self.prepare_config_and_inputs()

								a      :		int	=				True

								return config, input_ids, input_mask, token_labels






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> int:
								a      :		Dict	=				GPTNeoXModel(config=lowerCAmelCase__      )
								model.to(lowerCAmelCase__      )
								model.eval()
								a      :		List[Any]	=				model(lowerCAmelCase__      ,					attention_mask=lowerCAmelCase__      )
								a      :		Dict	=				model(lowerCAmelCase__      )
								self.parent.assertEqual(result.last_hidden_state.shape      ,					(self.batch_size, self.seq_length, self.hidden_size)      )






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> Union[str, Any]:
								a      :		str	=				True
								a      :		Dict	=				GPTNeoXModel(lowerCAmelCase__      )
								model.to(lowerCAmelCase__      )
								model.eval()
								a      :		List[str]	=				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      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> Optional[int]:
								a      :		Optional[int]	=				GPTNeoXForCausalLM(config=lowerCAmelCase__      )
								model.to(lowerCAmelCase__      )
								model.eval()
								a      :		Tuple	=				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      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> int:
								a      :		Union[str, Any]	=				self.num_labels
								a      :		Optional[int]	=				GPTNeoXForQuestionAnswering(lowerCAmelCase__      )
								model.to(lowerCAmelCase__      )
								model.eval()
								a      :		str	=				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      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> Union[str, Any]:
								a      :		Union[str, Any]	=				self.num_labels
								a      :		Union[str, Any]	=				GPTNeoXForSequenceClassification(lowerCAmelCase__      )
								model.to(lowerCAmelCase__      )
								model.eval()
								a      :		Tuple	=				ids_tensor([self.batch_size]      ,					self.type_sequence_label_size      )
								a      :		Dict	=				model(lowerCAmelCase__      ,					attention_mask=lowerCAmelCase__      ,					labels=lowerCAmelCase__      )
								self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.num_labels)      )






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> Union[str, Any]:
								a      :		List[str]	=				self.num_labels
								a      :		Tuple	=				GPTNeoXForTokenClassification(lowerCAmelCase__      )
								model.to(lowerCAmelCase__      )
								model.eval()
								a      :		List[str]	=				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      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> Union[str, Any]:
								a      :		Any	=				True
								a      :		Union[str, Any]	=				GPTNeoXForCausalLM(config=lowerCAmelCase__      )
								model.to(lowerCAmelCase__      )
								model.eval()

								# first forward pass
								a      :		List[str]	=				model(lowerCAmelCase__      ,					attention_mask=lowerCAmelCase__      ,					use_cache=lowerCAmelCase__      )
								a      :		Tuple	=				outputs.past_key_values

								# create hypothetical multiple next token and extent to next_input_ids
								a      :		Tuple	=				ids_tensor((self.batch_size, 3)      ,					config.vocab_size      )
								a      :		Union[str, Any]	=				ids_tensor((self.batch_size, 3)      ,					vocab_size=2      )

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

								a      :		List[str]	=				model(lowerCAmelCase__      ,					attention_mask=lowerCAmelCase__      ,					output_hidden_states=lowerCAmelCase__      )
								a      :		Union[str, Any]	=				output_from_no_past["hidden_states"][0]
								a      :		int	=				model(
								    lowerCAmelCase__      ,					attention_mask=lowerCAmelCase__      ,					past_key_values=lowerCAmelCase__      ,					output_hidden_states=lowerCAmelCase__      ,					)["hidden_states"][0]

								# select random slice
								a      :		int	=				ids_tensor((1,)      ,					output_from_past.shape[-1]      ).item()
								a      :		Union[str, Any]	=				output_from_no_past[:, -3:, random_slice_idx].detach()
								a      :		str	=				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      )	-> Any:
								a      :		Any	=				self.prepare_config_and_inputs()
								a,       a,       a,       a      :		int	=				config_and_inputs
								a      :		Dict	=				{"input_ids": input_ids, "attention_mask": input_mask}
								return config, inputs_dict


@require_torch
class        __UpperCamelCase				(						a__		,       a__		,       a__		,       unittest.TestCase						):
							lowerCamelCase   :					List[Any]      					=(
							    (
							        GPTNeoXModel,
							        GPTNeoXForCausalLM,
							        GPTNeoXForQuestionAnswering,
							        GPTNeoXForSequenceClassification,
							        GPTNeoXForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							lowerCamelCase   :					str      					=(GPTNeoXForCausalLM,) if is_torch_available() else ()
							lowerCamelCase   :					List[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   :					List[str]      					=False
							lowerCamelCase   :					Optional[Any]      					=False
							lowerCamelCase   :					int      					=False
							lowerCamelCase   :					List[Any]      					=False






							def     __a   (   self      )	-> Optional[int]:
								a      :		Optional[int]	=				GPTNeoXModelTester(self      )
								a      :		Tuple	=				ConfigTester(self      ,					config_class=lowerCAmelCase__      ,					hidden_size=64      ,					num_attention_heads=8      )






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






							def     __a   (   self      )	-> List[str]:
								a,       a,       a,       a      :		Optional[int]	=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )






							def     __a   (   self      )	-> Optional[Any]:
								a,       a,       a,       a      :		List[Any]	=				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:
								# This regression test was failing with PyTorch < 1.3
								a,       a,       a,       a      :		str	=				self.model_tester.prepare_config_and_inputs_for_decoder()

								a      :		Optional[int]	=				None

								self.model_tester.create_and_check_model_as_decoder(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )






							def     __a   (   self      )	-> List[Any]:
								a,       a,       a,       a      :		List[str]	=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_decoder_model_past_large_inputs(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )






							def     __a   (   self      )	-> Dict:
								a      :		Optional[Any]	=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_causal_lm(*lowerCAmelCase__      )






							def     __a   (   self      )	-> str:
								a      :		Optional[int]	=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__      )






							def     __a   (   self      )	-> List[str]:
								a      :		int	=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__      )






							def     __a   (   self      )	-> int:
								a      :		Tuple	=				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      )	-> int:
								pass






							@parameterized.expand([("linear",), ("dynamic",)]      )
							def     __a   (   self      ,					lowerCAmelCase__      )	-> List[Any]:
								a,       a      :		Optional[int]	=				self.model_tester.prepare_config_and_inputs_for_common()
								a      :		List[str]	=				ids_tensor([1, 10]      ,					config.vocab_size      )
								a      :		List[str]	=				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
								a      :		str	=				GPTNeoXModel(lowerCAmelCase__      )
								original_model.to(lowerCAmelCase__      )
								original_model.eval()
								a      :		str	=				original_model(lowerCAmelCase__      ).last_hidden_state
								a      :		Dict	=				original_model(lowerCAmelCase__      ).last_hidden_state

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






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

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

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

									a      :		Optional[Any]	=				model.generate(**lowerCAmelCase__      ,					do_sample=lowerCAmelCase__      ,					max_new_tokens=20      )
									a      :		str	=				tokenizer.batch_decode(lowerCAmelCase__      )[0]

									self.assertEqual(lowerCAmelCase__      ,					lowerCAmelCase__      )









"""simple docstring"""




from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class        __UpperCamelCase				(						a__						):






							def __init__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      ,					)	-> int:
								super().__init__()
								self.register_modules(transformer=lowerCAmelCase__      ,					vae=lowerCAmelCase__      ,					scheduler=lowerCAmelCase__      )

								# create a imagenet -> id dictionary for easier use
								a      :		str	=				{}
								if idalabel is not None:
									for key, value in idalabel.items():
										for label in value.split(","      ):
											a      :		int	=				int(lowerCAmelCase__      )
									a      :		Any	=				dict(sorted(self.labels.items()      )      )






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

								if not isinstance(lowerCAmelCase__      ,					lowerCAmelCase__      ):
									a      :		Optional[Any]	=				list(lowerCAmelCase__      )

								for l in label:
									if l not in self.labels:
										raise ValueError(
										    f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""      )

								return [self.labels[l] for l in label]






							@torch.no_grad()
							def __call__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = 4.0      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = 50      ,					lowerCAmelCase__ = "pil"      ,					lowerCAmelCase__ = True      ,					)	-> Union[ImagePipelineOutput, Tuple]:
								a      :		Dict	=				len(lowerCAmelCase__      )
								a      :		Tuple	=				self.transformer.config.sample_size
								a      :		Tuple	=				self.transformer.config.in_channels

								a      :		Optional[int]	=				randn_tensor(
								    shape=(batch_size, latent_channels, latent_size, latent_size)      ,					generator=lowerCAmelCase__      ,					device=self.device      ,					dtype=self.transformer.dtype      ,					)
								a      :		Tuple	=				torch.cat([latents] * 2      ) if guidance_scale > 1 else latents

								a      :		List[str]	=				torch.tensor(lowerCAmelCase__      ,					device=self.device      ).reshape(-1      )
								a      :		str	=				torch.tensor([1000] * batch_size      ,					device=self.device      )
								a      :		Dict	=				torch.cat([class_labels, class_null]      ,					0      ) if guidance_scale > 1 else class_labels

								# set step values
								self.scheduler.set_timesteps(lowerCAmelCase__      )

								for t in self.progress_bar(self.scheduler.timesteps      ):
									if guidance_scale > 1:
										a      :		Any	=				latent_model_input[: len(lowerCAmelCase__      ) // 2]
										a      :		Tuple	=				torch.cat([half, half]      ,					dim=0      )
									a      :		List[str]	=				self.scheduler.scale_model_input(lowerCAmelCase__      ,					lowerCAmelCase__      )

									a      :		Dict	=				t
									if not torch.is_tensor(lowerCAmelCase__      ):
										# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
										# This would be a good case for the `match` statement (Python 3.10+)
										a      :		Optional[int]	=				latent_model_input.device.type == "mps"
										if isinstance(lowerCAmelCase__      ,					lowerCAmelCase__      ):
											a      :		Union[str, Any]	=				torch.floataa if is_mps else torch.floataa
										else:
											a      :		str	=				torch.intaa if is_mps else torch.intaa
										a      :		List[str]	=				torch.tensor([timesteps]      ,					dtype=lowerCAmelCase__      ,					device=latent_model_input.device      )
									elif len(timesteps.shape      ) == 0:
										a      :		List[Any]	=				timesteps[None].to(latent_model_input.device      )
									# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
									a      :		Dict	=				timesteps.expand(latent_model_input.shape[0]      )
									# predict noise model_output
									a      :		Union[str, Any]	=				self.transformer(
									    lowerCAmelCase__      ,					timestep=lowerCAmelCase__      ,					class_labels=lowerCAmelCase__      ).sample

									# perform guidance
									if guidance_scale > 1:
										a,       a      :		Any	=				noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
										a,       a      :		Union[str, Any]	=				torch.split(lowerCAmelCase__      ,					len(lowerCAmelCase__      ) // 2      ,					dim=0      )

										a      :		Dict	=				uncond_eps + guidance_scale * (cond_eps - uncond_eps)
										a      :		Optional[int]	=				torch.cat([half_eps, half_eps]      ,					dim=0      )

										a      :		Optional[int]	=				torch.cat([eps, rest]      ,					dim=1      )

									# learned sigma
									if self.transformer.config.out_channels // 2 == latent_channels:
										a,       a      :		str	=				torch.split(lowerCAmelCase__      ,					lowerCAmelCase__      ,					dim=1      )
									else:
										a      :		Any	=				noise_pred

									# compute previous image: x_t -> x_t-1
									a      :		Optional[int]	=				self.scheduler.step(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ).prev_sample

								if guidance_scale > 1:
									a,       a      :		Tuple	=				latent_model_input.chunk(2      ,					dim=0      )
								else:
									a      :		Tuple	=				latent_model_input

								a      :		Optional[Any]	=				1 / self.vae.config.scaling_factor * latents
								a      :		Any	=				self.vae.decode(lowerCAmelCase__      ).sample

								a      :		Tuple	=				(samples / 2 + 0.5).clamp(0      ,					1      )

								# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
								a      :		Optional[Any]	=				samples.cpu().permute(0      ,					2      ,					3      ,					1      ).float().numpy()

								if output_type == "pil":
									a      :		int	=				self.numpy_to_pil(lowerCAmelCase__      )

								if not return_dict:
									return (samples,)

								return ImagePipelineOutput(images=lowerCAmelCase__      )


