






import copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


snake_case_   :	int							      =logging.getLogger(__name__)



def      UpperCAmelCase						( lowerCAmelCase__	):


		'''simple docstring'''

		random.seed(lowerCAmelCase__	)
		np.random.seed(lowerCAmelCase__	)
		torch.manual_seed(lowerCAmelCase__	)
		torch.cuda.manual_seed_all(lowerCAmelCase__	)



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		model.eval()
		__A    =							next(model.parameters()	).device
		__A    =							1
		__A    =							torch.zeros((eval_batch_size, context_len)					,     dtype=torch.long					,     device=lowerCAmelCase__	)
		__A    =							DataLoader(lowerCAmelCase__					,     shuffle=lowerCAmelCase__					,     batch_size=lowerCAmelCase__	)
		__A    =							torch.zeros(1					,     device=lowerCAmelCase__	)
		__A    =							0
		for batch in eval_dataloader:
				batch.to(lowerCAmelCase__	)
				# pad
				context.zero_()
				for i in range(lowerCAmelCase__	):
						__A    =							batch[i]
				__A    =							model(lowerCAmelCase__					,     labels=lowerCAmelCase__	)
				eval_loss += outputs[0].sum().item()
				nb_eval_examples += batch.size(0	)
		__A    =							eval_loss / nb_eval_examples
		__A    =							torch.exp(lowerCAmelCase__	)
		model.train()
		return perplexity



def      UpperCAmelCase						( lowerCAmelCase__="gpt2"	):


		'''simple docstring'''

		__A    =							GPTaLMHeadModel.from_pretrained(lowerCAmelCase__					,     output_hidden_states=lowerCAmelCase__	)
		torch.save(model.state_dict()					,     model_name + "local.pt"	)
		return model



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							copy.deepcopy(lowerCAmelCase__	)
		model.to(lowerCAmelCase__	)

		__A    =							["bias", "LayerNorm.weight"]
		__A    =							[
		    {
		        "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay	)],
		        "weight_decay": 0.0,
		    },
		    {"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay	)], "weight_decay": 0.0},
		]
		__A    =							AdamW(lowerCAmelCase__					,     lr=5E-5					,     eps=1E-8	)
		__A    =							get_linear_schedule_with_warmup(lowerCAmelCase__					,     0					,     lowerCAmelCase__	)
		torch.cuda.empty_cache()
		return model, lm_optimizer, lm_scheduler



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							np.array(lowerCAmelCase__	).mean()
		__A    =							np.array(lowerCAmelCase__	).std()
		__A    =							(real_perps - avg) / std
		__A    =							list(zip(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	)	)
		joblib.dump(lowerCAmelCase__					,     lowerCAmelCase__	)



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__="dev.jbl"					,     lowerCAmelCase__=recopy_gpta					,     ):


		'''simple docstring'''

		__A    =							[]
		__A    =							[]
		__A    =							[]

		# Initialize the transformer model
		__A    =							copy.deepcopy(lowerCAmelCase__	)
		orig_model.to(device="cpu"	)
		torch.cuda.empty_cache()

		# Compute perplexity of initial transformer model for comparison
		model.train()
		__A ,   __A ,   __A    =							recopy_model(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	)

		for step in tqdm(range(lowerCAmelCase__	)	):
				__A    =							torch.zeros((1, context_len)					,     dtype=torch.long					,     device=lowerCAmelCase__	)
				__A    =							random.choice(lowerCAmelCase__	)
				__A    =							random.randint(0					,     len(story[0]	) - context_len - 1	)
				__A    =							story[0][start : start + context_len]
				lm_optimizer.zero_grad()
				__A    =							model(lowerCAmelCase__					,     labels=lowerCAmelCase__	)
				__A    =							outputs[0]
				__A    =							compute_perplexity(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	)
				model.train()
				lm_loss.backward()
				# Do LM backprop
				torch.nn.utils.clip_grad_norm_(model.parameters()					,     3.0	)
				lm_optimizer.step()
				lm_scheduler.step()  # Update learning rate schedule

				# Compute perplexity after back-propagating on the selected context
				__A    =							compute_perplexity(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	)

				# Periodically save the stored (X, IG(X)) pairs
				if step % 1000 == 0 and step > 1:
						intermittent_save(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	)

				# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
				__A ,   __A ,   __A    =							recopy_model(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	)

				past_perps.append(past_perp.item()	)
				real_perps.append(orig_perp - real_perp.item()	)
				contexts.append(np.array(context.cpu()	)	)

		intermittent_save(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	)



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__="data/tokenized_stories_train_wikitext103.jbl"					,     lowerCAmelCase__=100					,     lowerCAmelCase__=1026					,     lowerCAmelCase__=True	):


		'''simple docstring'''

		__A    =							joblib.load(lowerCAmelCase__	)
		print("data loaded"	)
		__A    =							[]
		if trim:
				for i, example in enumerate(lowerCAmelCase__	):
						if len(example[0]	) > min_len:
								__A    =							random.randint(0					,     len(example[0]	) - context_len - 1	)
								objective_set.append(example[0, start : start + context_len]	)
						if len(lowerCAmelCase__	) >= number:
								break
				__A    =							[]
				for j in range(i + 1					,     len(lowerCAmelCase__	)	):
						if len(data[j][0]	) > min_len:
								train_data.append(data[j]	)
		else:
				__A    =							data[0:number]
				__A    =							data[number:]

		joblib.dump(lowerCAmelCase__					,     "objective_set.jbl"	)
		print("objective set saved"	)
		return train_data, objective_set



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__=50					,     lowerCAmelCase__="secondary_learner.pt"	):


		'''simple docstring'''

		__A    =							torch.device("cuda:0" if torch.cuda.is_available() else "cpu"	)
		# We will use the first 512 pairs from our dataset as a test set for
		# our secondary learner and the rest to train
		__A    =							train_dataset[:512]
		__A    =							train_dataset[512:]
		__A    =							DataLoader(lowerCAmelCase__					,     shuffle=lowerCAmelCase__					,     batch_size=lowerCAmelCase__	)
		__A    =							DataLoader(lowerCAmelCase__					,     shuffle=lowerCAmelCase__					,     batch_size=lowerCAmelCase__	)

		# secondary learner model set up
		__A    =							nn.MSELoss()
		__A    =							nn.MSELoss(reduction="sum"	)
		secondary_learner.to(lowerCAmelCase__	)
		__A    =							torch.optim.Adam(secondary_learner.parameters()					,     lr=0.00_001	)
		secondary_learner.train()

		# TODO in original code this is written as number of actual batches seen
		# not number of items seen but other places it is number of items instead.
		# improve consistency! changed this to epochs for clarity
		__A    =							float("inf"	)
		# Iterate through batches until we've used max_steps batches
		for epoch in range(int(lowerCAmelCase__	)	):
				__A    =							0.0
				secondary_learner.train()
				for step, batch in enumerate(lowerCAmelCase__	):
						__A    =							batch[0].to(lowerCAmelCase__	)
						__A    =							batch[1].to(lowerCAmelCase__	)
						__A    =							secondary_learner(lowerCAmelCase__	)
						q_optimizer.zero_grad()
						__A    =							loss(lowerCAmelCase__					,     real_q.float()	)
						q_loss.backward()
						q_optimizer.step()
						tr_q_loss += q_loss.item()

						# model trains fairly quickly so we won't wait for a full epoch
						# eval is triggered at eval_freq and end of epochs
						if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(lowerCAmelCase__	)):
								__A    =							tr_q_loss / (step + 1)

								secondary_learner.eval()
								__A    =							0.0
								__A    =							0.0
								__A    =							[]
								__A    =							[]
								# Compute performance of the secondary learner after this batch
								for stepa, batcha in enumerate(lowerCAmelCase__	):
										__A    =							batcha[0].to(lowerCAmelCase__	)
										__A    =							batcha[1].to(lowerCAmelCase__	)
										__A    =							secondary_learner(lowerCAmelCase__	)
										q_lossa += test_loss(lowerCAmelCase__					,     lowerCAmelCase__	).item()
										sum_qa += torch.sum(lowerCAmelCase__	).item()
										for ei, i in enumerate(predicted_qa.cpu().detach().numpy()	):
												predicted.append(i.item()	)
										for ei, i in enumerate(real_qa.cpu().detach().numpy()	):
												actual.append(i.item()	)

								q_lossa /= len(lowerCAmelCase__	)
								print(
								    "Epoch: "					,     lowerCAmelCase__					,     "step: "					,     lowerCAmelCase__					,     "Avg. q:"					,     sum_qa / len(lowerCAmelCase__	)					,     "Train Loss: "					,     lowerCAmelCase__					,     "Test Loss: "					,     lowerCAmelCase__					,     )
								if q_lossa < best_test_loss:
										joblib.dump((predicted, actual)					,     "pred_vs_actual.jbl"	)
										torch.save(secondary_learner.state_dict()					,     lowerCAmelCase__	)
										__A    =							q_lossa

						secondary_learner.train()
		return secondary_learner





class        a__      (	nn.Module		):






		def __init__(      self							, lowercase__						)				->				Union[str, Any]:
				super(lowercase__							, self						).__init__()
				__A    =							model.transformer.wte
				__A    =							copy.deepcopy(model.transformer.wte.weight						)
				__A    =							nn.Convad(self.embeddings.weight.size(1						)							, 256							, 3							, padding=1						)
				__A    =							nn.Sequential(nn.Linear(256							, 32						)							, nn.Dropout(p=0.1						)							, nn.Linear(32							, 32						)							, nn.Linear(32							, 1						)						)






		def 					_lowerCamelCase					(      self							, lowercase__						)				->				str:
				__A    =							torch.max(self.conv(self.embeddings(lowercase__						).squeeze(1						).transpose(1							, 2						)						)							, 2						)[0]
				__A    =							self.fc(lowercase__						)
				return qs.squeeze(1						)






		@classmethod
		def 					_lowerCamelCase					(      cls							, lowercase__							, lowercase__						)				->				List[str]:
				__A    =							cls(lowercase__						)  # this calls __init__
				__A    =							torch.load(lowercase__						)
				secondary_learner.load_state_dict(lowercase__						)
				__A    =							model.transformer.wte
				__A    =							copy.deepcopy(model.transformer.wte.weight						)
				return secondary_learner


import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class       __snake_case (  lowerCamelCase__       , lowerCamelCase__       , unittest.TestCase ):
 __lowerCamelCase     : int 	=   StableDiffusionXLPipeline
 __lowerCamelCase     : Any 	=   TEXT_TO_IMAGE_PARAMS
 __lowerCamelCase     : List[str] 	=   TEXT_TO_IMAGE_BATCH_PARAMS
 __lowerCamelCase     : Optional[int] 	=   TEXT_TO_IMAGE_IMAGE_PARAMS
 __lowerCamelCase     : Union[str, Any] 	=   TEXT_TO_IMAGE_IMAGE_PARAMS
 def 						UpperCAmelCase__							(	self    )						->				Optional[Any]:
       '''simple docstring'''





       torch.manual_seed(0    )
       UpperCAmelCase     :			Union[str, Any]     	=UNetaDConditionModel(
           block_out_channels=(32, 64)		, layers_per_block=2		, sample_size=32		, in_channels=4		, out_channels=4		, down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		, up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		, attention_head_dim=(2, 4)		, use_linear_projection=snake_case__		, addition_embed_type='''text_time'''		, addition_time_embed_dim=8		, transformer_layers_per_block=(1, 2)		, projection_class_embeddings_input_dim=80		, cross_attention_dim=64		, )
       UpperCAmelCase     :			Optional[int]     	=EulerDiscreteScheduler(
           beta_start=0.0_0085		, beta_end=0.012		, steps_offset=1		, beta_schedule='''scaled_linear'''		, timestep_spacing='''leading'''		, )
       torch.manual_seed(0    )
       UpperCAmelCase     :			int     	=AutoencoderKL(
           block_out_channels=[32, 64]		, in_channels=3		, out_channels=3		, down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		, up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		, latent_channels=4		, sample_size=128		, )
       torch.manual_seed(0    )
       UpperCAmelCase     :			Optional[Any]     	=CLIPTextConfig(
           bos_token_id=0		, eos_token_id=2		, hidden_size=32		, intermediate_size=37		, layer_norm_eps=1e-05		, num_attention_heads=4		, num_hidden_layers=5		, pad_token_id=1		, vocab_size=1000		, hidden_act='''gelu'''		, projection_dim=32		, )
       UpperCAmelCase     :			List[str]     	=CLIPTextModel(snake_case__    )
       UpperCAmelCase     :			int     	=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		, local_files_only=snake_case__    )

       UpperCAmelCase     :			Tuple     	=CLIPTextModelWithProjection(snake_case__    )
       UpperCAmelCase     :			int     	=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		, local_files_only=snake_case__    )

       UpperCAmelCase     :			Optional[int]     	={
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''text_encoder_2''': text_encoder_a,
           '''tokenizer_2''': tokenizer_a,
           # "safety_checker": None,
           # "feature_extractor": None,
       }
       return components
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__=0    )						->				Optional[Any]:
       '''simple docstring'''





       if str(snake_case__    ).startswith('''mps'''    ):
             UpperCAmelCase     :			Union[str, Any]     	=torch.manual_seed(snake_case__    )
       else:
             UpperCAmelCase     :			Optional[int]     	=torch.Generator(device=snake_case__    ).manual_seed(snake_case__    )
       UpperCAmelCase     :			Any     	={
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 5.0,
           '''output_type''': '''numpy''',
       }
       return inputs
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[Any]     	='''cpu'''  # ensure determinism for the device-dependent torch.Generator
       UpperCAmelCase     :			List[Any]     	=self.get_dummy_components()
       UpperCAmelCase     :			Tuple     	=StableDiffusionXLPipeline(**snake_case__    )
       UpperCAmelCase     :			Optional[Any]     	=sd_pipe.to(snake_case__    )
       sd_pipe.set_progress_bar_config(disable=snake_case__    )

       UpperCAmelCase     :			Optional[Any]     	=self.get_dummy_inputs(snake_case__    )
       UpperCAmelCase     :			Optional[int]     	=sd_pipe(**snake_case__    ).images
       UpperCAmelCase     :			str     	=image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       UpperCAmelCase     :			Dict     	=np.array([0.5873, 0.6128, 0.4797, 0.5122, 0.5674, 0.4639, 0.5227, 0.5149, 0.4747]    )

       assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
 def 						UpperCAmelCase__							(	self    )						->				List[str]:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[Any]     	=self.get_dummy_components()
       UpperCAmelCase     :			List[Any]     	=StableDiffusionXLPipeline(**snake_case__    )
       UpperCAmelCase     :			List[str]     	=sd_pipe.to(snake_case__    )
       UpperCAmelCase     :			Optional[int]     	=sd_pipe.to(snake_case__    )
       sd_pipe.set_progress_bar_config(disable=snake_case__    )

       # forward without prompt embeds
       UpperCAmelCase     :			int     	=self.get_dummy_inputs(snake_case__    )
       UpperCAmelCase     :			int     	=3 * ['''this is a negative prompt''']
       UpperCAmelCase     :			Union[str, Any]     	=negative_prompt
       UpperCAmelCase     :			Union[str, Any]     	=3 * [inputs['''prompt''']]

       UpperCAmelCase     :			int     	=sd_pipe(**snake_case__    )
       UpperCAmelCase     :			str     	=output.images[0, -3:, -3:, -1]

       # forward with prompt embeds
       UpperCAmelCase     :			Any     	=self.get_dummy_inputs(snake_case__    )
       UpperCAmelCase     :			List[str]     	=3 * ['''this is a negative prompt''']
       UpperCAmelCase     :			int     	=3 * [inputs.pop('''prompt'''    )]

       (
           (
           UpperCAmelCase
       )	,    (
           UpperCAmelCase
       )	,    (
           UpperCAmelCase
       )	,    (
           UpperCAmelCase
       )	,    
       )     :			Union[str, Any]     	=sd_pipe.encode_prompt(snake_case__		, negative_prompt=snake_case__    )

       UpperCAmelCase     :			Union[str, Any]     	=sd_pipe(
           **snake_case__		, prompt_embeds=snake_case__		, negative_prompt_embeds=snake_case__		, pooled_prompt_embeds=snake_case__		, negative_pooled_prompt_embeds=snake_case__		, )
       UpperCAmelCase     :			Union[str, Any]     	=output.images[0, -3:, -3:, -1]

       # make sure that it's equal
       assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()    ).max() < 1e-4
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       super().test_attention_slicing_forward_pass(expected_max_diff=3e-3    )






 def 						UpperCAmelCase__							(	self    )						->				Any:
       '''simple docstring'''





       super().test_inference_batch_single_identical(expected_max_diff=3e-3    )







@slow
@require_torch_gpu
class       __snake_case (  unittest.TestCase ):
 def 						UpperCAmelCase__							(	self    )						->				List[str]:
       '''simple docstring'''





       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__="cpu"		, snake_case__=torch.floataa		, snake_case__=0    )						->				Any:
       '''simple docstring'''





       UpperCAmelCase     :			str     	=torch.Generator(device=snake_case__    ).manual_seed(snake_case__    )
       UpperCAmelCase     :			Optional[Any]     	=np.random.RandomState(snake_case__    ).standard_normal((1, 4, 64, 64)    )
       UpperCAmelCase     :			List[Any]     	=torch.from_numpy(snake_case__    ).to(device=snake_case__		, dtype=snake_case__    )
       UpperCAmelCase     :			Optional[Any]     	={
           '''prompt''': '''a photograph of an astronaut riding a horse''',
           '''latents''': latents,
           '''generator''': generator,
           '''num_inference_steps''': 3,
           '''guidance_scale''': 7.5,
           '''output_type''': '''numpy''',
       }
       return inputs






 def 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





       UpperCAmelCase     :			List[Any]     	=DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''    )
       pipe.to(snake_case__    )
       pipe.set_progress_bar_config(disable=snake_case__    )

       UpperCAmelCase     :			List[Any]     	=self.get_inputs(snake_case__    )
       UpperCAmelCase     :			str     	=pipe(**snake_case__    ).images
       UpperCAmelCase     :			int     	=image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       UpperCAmelCase     :			Any     	=np.array([0.4_9493, 0.4_7896, 0.4_0798, 0.5_4214, 0.5_3212, 0.4_8202, 0.4_7656, 0.4_6329, 0.4_8506]    )
       assert np.abs(image_slice - expected_slice    ).max() < 7e-3
