
'''simple docstring'''
import random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class     _lowerCAmelCase					(					__snake_case  ,							unittest.TestCase   ):







							'''simple docstring'''

							lowerCAmelCase_      		=		KandinskyVaaPriorEmbaEmbPipeline
							lowerCAmelCase_      		=		["prompt", "image"]
							lowerCAmelCase_      		=		["prompt", "image"]
							lowerCAmelCase_      		=		[
							    "num_images_per_prompt",
							    "strength",
							    "generator",
							    "num_inference_steps",
							    "latents",
							    "negative_prompt",
							    "guidance_scale",
							    "output_type",
							    "return_dict",
							]
							lowerCAmelCase_      		=		False

							@property
							def  lowercase							(self						)  ->     Tuple:
														return 32

							@property
							def  lowercase							(self						)  ->     str:
														return 32

							@property
							def  lowercase							(self						)  ->     Dict:
														return self.time_input_dim

							@property
							def  lowercase							(self						)  ->     Union[str, Any]:
														return self.time_input_dim * 4

							@property
							def  lowercase							(self						)  ->     List[str]:
														return 100

							@property
							def  lowercase							(self						)  ->     Optional[int]:
														_snake_case     =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""						)
														return tokenizer

							@property
							def  lowercase							(self						)  ->     Any:
														torch.manual_seed(0						)
														_snake_case     =						CLIPTextConfig(
														    bos_token_id=0						,						eos_token_id=2						,						hidden_size=self.text_embedder_hidden_size						,						projection_dim=self.text_embedder_hidden_size						,						intermediate_size=37						,						layer_norm_eps=1e-0_5						,						num_attention_heads=4						,						num_hidden_layers=5						,						pad_token_id=1						,						vocab_size=1000						,						)
														return CLIPTextModelWithProjection(UpperCAmelCase						)

							@property
							def  lowercase							(self						)  ->     List[Any]:
														torch.manual_seed(0						)

														_snake_case     =						{
														    """num_attention_heads""": 2,
														    """attention_head_dim""": 12,
														    """embedding_dim""": self.text_embedder_hidden_size,
														    """num_layers""": 1,
														}

														_snake_case     =						PriorTransformer(**UpperCAmelCase						)
														# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
														_snake_case     =						nn.Parameter(torch.ones(model.clip_std.shape						)						)
														return model

							@property
							def  lowercase							(self						)  ->     int:
														torch.manual_seed(0						)
														_snake_case     =						CLIPVisionConfig(
														    hidden_size=self.text_embedder_hidden_size						,						image_size=224						,						projection_dim=self.text_embedder_hidden_size						,						intermediate_size=37						,						num_attention_heads=4						,						num_channels=3						,						num_hidden_layers=5						,						patch_size=14						,						)

														_snake_case     =						CLIPVisionModelWithProjection(UpperCAmelCase						)
														return model

							@property
							def  lowercase							(self						)  ->     List[str]:
														_snake_case     =						CLIPImageProcessor(
														    crop_size=224						,						do_center_crop=UpperCAmelCase						,						do_normalize=UpperCAmelCase						,						do_resize=UpperCAmelCase						,						image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]						,						image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]						,						resample=3						,						size=224						,						)

														return image_processor

							def  lowercase							(self						)  ->     Dict:
														_snake_case     =						self.dummy_prior
														_snake_case     =						self.dummy_image_encoder
														_snake_case     =						self.dummy_text_encoder
														_snake_case     =						self.dummy_tokenizer
														_snake_case     =						self.dummy_image_processor

														_snake_case     =						UnCLIPScheduler(
														    variance_type="""fixed_small_log"""						,						prediction_type="""sample"""						,						num_train_timesteps=1000						,						clip_sample=UpperCAmelCase						,						clip_sample_range=10.0						,						)

														_snake_case     =						{
														    """prior""": prior,
														    """image_encoder""": image_encoder,
														    """text_encoder""": text_encoder,
														    """tokenizer""": tokenizer,
														    """scheduler""": scheduler,
														    """image_processor""": image_processor,
														}

														return components

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase=0						)  ->     List[str]:
														if str(UpperCAmelCase						).startswith("""mps"""						):
																					_snake_case     =						torch.manual_seed(UpperCAmelCase						)
														else:
																					_snake_case     =						torch.Generator(device=UpperCAmelCase						).manual_seed(UpperCAmelCase						)

														_snake_case     =						floats_tensor((1, 3, 64, 64)						,						rng=random.Random(UpperCAmelCase						)						).to(UpperCAmelCase						)
														_snake_case     =						image.cpu().permute(0						,						2						,						3						,						1						)[0]
														_snake_case     =						Image.fromarray(np.uinta(UpperCAmelCase						)						).convert("""RGB"""						).resize((256, 256)						)

														_snake_case     =						{
														    """prompt""": """horse""",
														    """image""": init_image,
														    """strength""": 0.5,
														    """generator""": generator,
														    """guidance_scale""": 4.0,
														    """num_inference_steps""": 2,
														    """output_type""": """np""",
														}
														return inputs

							def  lowercase							(self						)  ->     str:
														_snake_case     =						"""cpu"""

														_snake_case     =						self.get_dummy_components()

														_snake_case     =						self.pipeline_class(**UpperCAmelCase						)
														_snake_case     =						pipe.to(UpperCAmelCase						)

														pipe.set_progress_bar_config(disable=UpperCAmelCase						)

														_snake_case     =						pipe(**self.get_dummy_inputs(UpperCAmelCase						)						)
														_snake_case     =						output.image_embeds

														_snake_case     =						pipe(
														    **self.get_dummy_inputs(UpperCAmelCase						)						,						return_dict=UpperCAmelCase						,						)[0]

														_snake_case     =						image[0, -10:]
														_snake_case     =						image_from_tuple[0, -10:]

														assert image.shape == (1, 32)

														_snake_case     =						np.array(
														    [
														        0.107_1284,
														        1.333_0271,
														        0.6126_0223,
														        -0.669_1065,
														        -0.384_6852,
														        -1.030_3661,
														        0.2271_6111,
														        0.0334_8901,
														        0.3004_0675,
														        -0.2480_5029,
														    ]						)

														assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
														assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2

							@skip_mps
							def  lowercase							(self						)  ->     Optional[Any]:
														_snake_case     =						torch_device == """cpu"""
														_snake_case     =						True
														_snake_case     =						False

														self._test_inference_batch_single_identical(
														    test_max_difference=UpperCAmelCase						,						relax_max_difference=UpperCAmelCase						,						test_mean_pixel_difference=UpperCAmelCase						,						)





							@skip_mps
							def  lowercase							(self						)  ->     List[str]:
														_snake_case     =						torch_device == """cpu"""
														_snake_case     =						False

														self._test_attention_slicing_forward_pass(
														    test_max_difference=UpperCAmelCase						,						test_mean_pixel_difference=UpperCAmelCase						,						)







'''simple docstring'''
import os
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
				print('#################### AVAILABLE DEVICES ####################')
				print(jax.devices())
				print('###########################################################')

				# setup for wandb sweep
				__lowerCAmelCase  							= Args()
				__lowerCAmelCase  							= wandb.init(project='bigbird-natural-questions', config=args.__dict__)
				__lowerCAmelCase  							= dict(logger.config)
				del wandb_args["batch_size"]
				__lowerCAmelCase  							= replace(args, **wandb_args)
				__lowerCAmelCase  							= args.base_dir + '-' + wandb.run.id
				__lowerCAmelCase  							= replace(args, base_dir=base_dir)
				print(args)

				__lowerCAmelCase  							= load_dataset('json', data_files=args.tr_data_path)['train']
				__lowerCAmelCase  							= load_dataset('json', data_files=args.val_data_path)['train']

				# drop extra batch for now
				__lowerCAmelCase  							= range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
				__lowerCAmelCase  							= tr_dataset.shuffle().select(indices)
				__lowerCAmelCase  							= range(len(val_dataset) - len(val_dataset) % args.batch_size)
				__lowerCAmelCase  							= val_dataset.shuffle().select(indices)

				if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
								__lowerCAmelCase  							= tr_dataset.shuffle().select(range(80_000))
								__lowerCAmelCase  							= val_dataset.shuffle().select(range(8_000))

				print(tr_dataset)
				print(val_dataset)

				__lowerCAmelCase  							= FlaxBigBirdForNaturalQuestions.from_pretrained(
				    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
				)
				__lowerCAmelCase  							= BigBirdTokenizerFast.from_pretrained(args.model_id)
				__lowerCAmelCase  							= DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_096)

				__lowerCAmelCase  							= {
				    'lr': args.lr,
				    'init_lr': args.init_lr,
				    'warmup_steps': args.warmup_steps,
				    'num_train_steps': args.max_epochs * (len(tr_dataset) // args.batch_size),
				    'weight_decay': args.weight_decay,
				}
				__lowerCAmelCase							,       __lowerCAmelCase  							= build_tx(**tx_args)

				__lowerCAmelCase  							= Trainer(
				    args=args,
				    data_collator=data_collator,
				    model_save_fn=model.save_pretrained,
				    train_step_fn=train_step,
				    val_step_fn=val_step,
				    logger=logger,
				    scheduler_fn=lr,
				)

				__lowerCAmelCase  							= None
				__lowerCAmelCase  							= trainer.create_state(model, tx, num_train_steps=tx_args['num_train_steps'], ckpt_dir=ckpt_dir)
				try:
								trainer.train(state, tr_dataset, val_dataset)
				except KeyboardInterrupt:
								print('Oooops; TRAINING STOPPED UNFORTUNATELY')

				print('SAVING WEIGHTS IN `final-weights`')
				__lowerCAmelCase  							= jax_utils.unreplicate(state.params)
				model.save_pretrained(os.path.join(args.base_dir, 'final-weights'), params=params)