


import gc
import unittest

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

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class 	a__						(    lowerCAmelCase_    ,							unittest.TestCase     ):
       lowerCamelCase__:  Dict				     =  UnCLIPPipeline
       lowerCamelCase__:  str				     =  TEXT_TO_IMAGE_PARAMS - {
           """negative_prompt""",
           """height""",
           """width""",
           """negative_prompt_embeds""",
           """guidance_scale""",
           """prompt_embeds""",
           """cross_attention_kwargs""",
       }
       lowerCamelCase__:  int				     =  TEXT_TO_IMAGE_BATCH_PARAMS
       lowerCamelCase__:  Dict				     =  [
           """generator""",
           """return_dict""",
           """prior_num_inference_steps""",
           """decoder_num_inference_steps""",
           """super_res_num_inference_steps""",
       ]
       lowerCamelCase__:  Optional[int]				     =  False

       @property
       def     UpperCAmelCase( self   :  List[Any]     ):
            return 3_2

       @property
       def     UpperCAmelCase( self   :  List[str]     ):
            return 3_2

       @property
       def     UpperCAmelCase( self   :  Optional[int]     ):
            return self.time_input_dim

       @property
       def     UpperCAmelCase( self   :  List[Any]     ):
            return self.time_input_dim * 4

       @property
       def     UpperCAmelCase( self   :  Dict     ):
            return 1_0_0

       @property
       def     UpperCAmelCase( self   :  int     ):
            a_     :  Tuple				      =     CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )
            return tokenizer

       @property
       def     UpperCAmelCase( self   :  List[Any]     ):
            torch.manual_seed(0     )
            a_     :  int				      =     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=3_7   ,				layer_norm_eps=1E-05   ,				num_attention_heads=4   ,				num_hidden_layers=5   ,				pad_token_id=1   ,				vocab_size=1_0_0_0   ,				)
            return CLIPTextModelWithProjection(lowerCamelCase_     )

       @property
       def     UpperCAmelCase( self   :  List[Any]     ):
            torch.manual_seed(0     )

            a_     :  List[str]				      =     {
                """num_attention_heads""": 2,
                """attention_head_dim""": 1_2,
                """embedding_dim""": self.text_embedder_hidden_size,
                """num_layers""": 1,
            }

            a_     :  Union[str, Any]				      =     PriorTransformer(**lowerCamelCase_     )
            return model

       @property
       def     UpperCAmelCase( self   :  Dict     ):
            torch.manual_seed(0     )

            a_     :  Dict				      =     {
                """clip_embeddings_dim""": self.text_embedder_hidden_size,
                """time_embed_dim""": self.time_embed_dim,
                """cross_attention_dim""": self.cross_attention_dim,
            }

            a_     :  str				      =     UnCLIPTextProjModel(**lowerCamelCase_     )
            return model

       @property
       def     UpperCAmelCase( self   :  int     ):
            torch.manual_seed(0     )

            a_     :  str				      =     {
                """sample_size""": 3_2,
                # RGB in channels
                """in_channels""": 3,
                # Out channels is double in channels because predicts mean and variance
                """out_channels""": 6,
                """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
                """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
                """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
                """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
                """layers_per_block""": 1,
                """cross_attention_dim""": self.cross_attention_dim,
                """attention_head_dim""": 4,
                """resnet_time_scale_shift""": """scale_shift""",
                """class_embed_type""": """identity""",
            }

            a_     :  Tuple				      =     UNetaDConditionModel(**lowerCamelCase_     )
            return model

       @property
       def     UpperCAmelCase( self   :  List[Any]     ):
            return {
                "sample_size": 6_4,
                "layers_per_block": 1,
                "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
                "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
                "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
                "in_channels": 6,
                "out_channels": 3,
            }

       @property
       def     UpperCAmelCase( self   :  List[Any]     ):
            torch.manual_seed(0     )

            a_     :  int				      =     UNetaDModel(**self.dummy_super_res_kwargs     )
            return model

       @property
       def     UpperCAmelCase( self   :  Any     ):
            # seeded differently to get different unet than `self.dummy_super_res_first`
            torch.manual_seed(1     )

            a_     :  Dict				      =     UNetaDModel(**self.dummy_super_res_kwargs     )
            return model

       def     UpperCAmelCase( self   :  Union[str, Any]     ):
            a_     :  List[Any]				      =     self.dummy_prior
            a_     :  List[Any]				      =     self.dummy_decoder
            a_     :  Union[str, Any]				      =     self.dummy_text_proj
            a_     :  Tuple				      =     self.dummy_text_encoder
            a_     :  Dict				      =     self.dummy_tokenizer
            a_     :  List[Any]				      =     self.dummy_super_res_first
            a_     :  str				      =     self.dummy_super_res_last

            a_     :  List[Any]				      =     UnCLIPScheduler(
                variance_type="""fixed_small_log"""   ,				prediction_type="""sample"""   ,				num_train_timesteps=1_0_0_0   ,				clip_sample_range=5.0   ,				)

            a_     :  Any				      =     UnCLIPScheduler(
                variance_type="""learned_range"""   ,				prediction_type="""epsilon"""   ,				num_train_timesteps=1_0_0_0   ,				)

            a_     :  str				      =     UnCLIPScheduler(
                variance_type="""fixed_small_log"""   ,				prediction_type="""epsilon"""   ,				num_train_timesteps=1_0_0_0   ,				)

            a_     :  Union[str, Any]				      =     {
                """prior""": prior,
                """decoder""": decoder,
                """text_proj""": text_proj,
                """text_encoder""": text_encoder,
                """tokenizer""": tokenizer,
                """super_res_first""": super_res_first,
                """super_res_last""": super_res_last,
                """prior_scheduler""": prior_scheduler,
                """decoder_scheduler""": decoder_scheduler,
                """super_res_scheduler""": super_res_scheduler,
            }

            return components

       def     UpperCAmelCase( self   :  Tuple   ,				lowerCamelCase_   :  Union[str, Any]   ,				lowerCamelCase_   :  Union[str, Any]=0     ):
            if str(lowerCamelCase_     ).startswith("""mps"""     ):
                 a_     :  Tuple				      =     torch.manual_seed(lowerCamelCase_     )
            else:
                 a_     :  Any				      =     torch.Generator(device=lowerCamelCase_     ).manual_seed(lowerCamelCase_     )
            a_     :  str				      =     {
                """prompt""": """horse""",
                """generator""": generator,
                """prior_num_inference_steps""": 2,
                """decoder_num_inference_steps""": 2,
                """super_res_num_inference_steps""": 2,
                """output_type""": """numpy""",
            }
            return inputs

       def     UpperCAmelCase( self   :  Dict     ):
            a_     :  Dict				      =     """cpu"""

            a_     :  List[str]				      =     self.get_dummy_components()

            a_     :  Optional[Any]				      =     self.pipeline_class(**lowerCamelCase_     )
            a_     :  Optional[int]				      =     pipe.to(lowerCamelCase_     )

            pipe.set_progress_bar_config(disable=lowerCamelCase_     )

            a_     :  Optional[Any]				      =     pipe(**self.get_dummy_inputs(lowerCamelCase_     )     )
            a_     :  Optional[Any]				      =     output.images

            a_     :  Any				      =     pipe(
                **self.get_dummy_inputs(lowerCamelCase_     )   ,				return_dict=lowerCamelCase_   ,				)[0]

            a_     :  int				      =     image[0, -3:, -3:, -1]
            a_     :  List[Any]				      =     image_from_tuple[0, -3:, -3:, -1]

            assert image.shape == (1, 6_4, 6_4, 3)

            a_     :  Dict				      =     np.array(
                [
                    0.9_9_9_7,
                    0.9_9_8_8,
                    0.0_0_2_8,
                    0.9_9_9_7,
                    0.9_9_8_4,
                    0.9_9_6_5,
                    0.0_0_2_9,
                    0.9_9_8_6,
                    0.0_0_2_5,
                ]     )

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

       def     UpperCAmelCase( self   :  Union[str, Any]     ):
            a_     :  Optional[Any]				      =     torch.device("""cpu"""     )


            class 	a__						:
                   lowerCamelCase__:  List[str]				     =  1

            a_     :  Optional[int]				      =     self.get_dummy_components()

            a_     :  Tuple				      =     self.pipeline_class(**lowerCamelCase_     )
            a_     :  int				      =     pipe.to(lowerCamelCase_     )

            a_     :  Dict				      =     components["""prior"""]
            a_     :  Dict				      =     components["""decoder"""]
            a_     :  Tuple				      =     components["""super_res_first"""]
            a_     :  List[Any]				      =     components["""tokenizer"""]
            a_     :  List[Any]				      =     components["""text_encoder"""]

            a_     :  List[str]				      =     torch.Generator(device=lowerCamelCase_     ).manual_seed(0     )
            a_     :  Optional[Any]				      =     prior.dtype
            a_     :  int				      =     1

            a_     :  List[Any]				      =     (batch_size, prior.config.embedding_dim)
            a_     :  Tuple				      =     pipe.prepare_latents(
                lowerCamelCase_   ,				dtype=lowerCamelCase_   ,				device=lowerCamelCase_   ,				generator=lowerCamelCase_   ,				latents=lowerCamelCase_   ,				scheduler=DummyScheduler()     )
            a_     :  List[str]				      =     (batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
            a_     :  Tuple				      =     pipe.prepare_latents(
                lowerCamelCase_   ,				dtype=lowerCamelCase_   ,				device=lowerCamelCase_   ,				generator=lowerCamelCase_   ,				latents=lowerCamelCase_   ,				scheduler=DummyScheduler()     )

            a_     :  Dict				      =     (
                batch_size,
                super_res_first.config.in_channels // 2,
                super_res_first.config.sample_size,
                super_res_first.config.sample_size,
            )
            a_     :  str				      =     pipe.prepare_latents(
                lowerCamelCase_   ,				dtype=lowerCamelCase_   ,				device=lowerCamelCase_   ,				generator=lowerCamelCase_   ,				latents=lowerCamelCase_   ,				scheduler=DummyScheduler()     )

            pipe.set_progress_bar_config(disable=lowerCamelCase_     )

            a_     :  str				      =     """this is a prompt example"""

            a_     :  Optional[int]				      =     torch.Generator(device=lowerCamelCase_     ).manual_seed(0     )
            a_     :  Tuple				      =     pipe(
                [prompt]   ,				generator=lowerCamelCase_   ,				prior_num_inference_steps=2   ,				decoder_num_inference_steps=2   ,				super_res_num_inference_steps=2   ,				prior_latents=lowerCamelCase_   ,				decoder_latents=lowerCamelCase_   ,				super_res_latents=lowerCamelCase_   ,				output_type="""np"""   ,				)
            a_     :  str				      =     output.images

            a_     :  str				      =     tokenizer(
                lowerCamelCase_   ,				padding="""max_length"""   ,				max_length=tokenizer.model_max_length   ,				return_tensors="""pt"""   ,				)
            a_     :  str				      =     text_encoder(text_inputs.input_ids     )
            a_     :  List[Any]				      =     text_inputs.attention_mask

            a_     :  Any				      =     torch.Generator(device=lowerCamelCase_     ).manual_seed(0     )
            a_     :  Tuple				      =     pipe(
                generator=lowerCamelCase_   ,				prior_num_inference_steps=2   ,				decoder_num_inference_steps=2   ,				super_res_num_inference_steps=2   ,				prior_latents=lowerCamelCase_   ,				decoder_latents=lowerCamelCase_   ,				super_res_latents=lowerCamelCase_   ,				text_model_output=lowerCamelCase_   ,				text_attention_mask=lowerCamelCase_   ,				output_type="""np"""   ,				)[0]

            # make sure passing text embeddings manually is identical
            assert np.abs(image - image_from_text     ).max() < 1E-4

       @skip_mps
       def     UpperCAmelCase( self   :  Union[str, Any]     ):
            a_     :  Union[str, Any]				      =     torch_device == """cpu"""

            self._test_attention_slicing_forward_pass(test_max_difference=lowerCamelCase_   ,				expected_max_diff=0.0_1     )

       @skip_mps
       def     UpperCAmelCase( self   :  str     ):
            a_     :  str				      =     torch_device == """cpu"""
            a_     :  List[Any]				      =     True
            a_     :  str				      =     [
                """prior_num_inference_steps""",
                """decoder_num_inference_steps""",
                """super_res_num_inference_steps""",
            ]

            self._test_inference_batch_single_identical(
                test_max_difference=lowerCamelCase_   ,				relax_max_difference=lowerCamelCase_   ,				additional_params_copy_to_batched_inputs=lowerCamelCase_   ,				)

       def     UpperCAmelCase( self   :  Tuple     ):
            a_     :  Tuple				      =     [
                """prior_num_inference_steps""",
                """decoder_num_inference_steps""",
                """super_res_num_inference_steps""",
            ]

            if torch_device == "mps":
                 # TODO: MPS errors with larger batch sizes
                 a_     :  List[Any]				      =     [2, 3]
                 self._test_inference_batch_consistent(
                     batch_sizes=lowerCamelCase_   ,				additional_params_copy_to_batched_inputs=lowerCamelCase_   ,				)
            else:
                 self._test_inference_batch_consistent(
                     additional_params_copy_to_batched_inputs=lowerCamelCase_     )

       @skip_mps
       def     UpperCAmelCase( self   :  Union[str, Any]     ):
            return super().test_dict_tuple_outputs_equivalent()

       @skip_mps
       def     UpperCAmelCase( self   :  Any     ):
            return super().test_save_load_local()

       @skip_mps
       def     UpperCAmelCase( self   :  List[str]     ):
            return super().test_save_load_optional_components()


@nightly
class 	a__						(    unittest.TestCase     ):

       def     UpperCAmelCase( self   :  List[Any]     ):
            # clean up the VRAM after each test
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()

       def     UpperCAmelCase( self   :  List[Any]     ):
            a_     :  Optional[int]				      =     load_numpy(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                """/unclip/karlo_v1_alpha_horse_cpu.npy"""     )

            a_     :  Dict				      =     UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""     )
            pipeline.set_progress_bar_config(disable=lowerCamelCase_     )

            a_     :  List[Any]				      =     torch.manual_seed(0     )
            a_     :  Union[str, Any]				      =     pipeline(
                """horse"""   ,				num_images_per_prompt=1   ,				generator=lowerCamelCase_   ,				output_type="""np"""   ,				)

            a_     :  Optional[Any]				      =     output.images[0]

            assert image.shape == (2_5_6, 2_5_6, 3)
            assert np.abs(expected_image - image     ).max() < 1E-1


@slow
@require_torch_gpu
class 	a__						(    unittest.TestCase     ):

       def     UpperCAmelCase( self   :  List[Any]     ):
            # clean up the VRAM after each test
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()

       def     UpperCAmelCase( self   :  Optional[int]     ):
            a_     :  Optional[Any]				      =     load_numpy(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                """/unclip/karlo_v1_alpha_horse_fp16.npy"""     )

            a_     :  Optional[Any]				      =     UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""   ,				torch_dtype=torch.floataa     )
            a_     :  int				      =     pipeline.to(lowerCamelCase_     )
            pipeline.set_progress_bar_config(disable=lowerCamelCase_     )

            a_     :  Dict				      =     torch.Generator(device="""cpu"""     ).manual_seed(0     )
            a_     :  Tuple				      =     pipeline(
                """horse"""   ,				generator=lowerCamelCase_   ,				output_type="""np"""   ,				)

            a_     :  List[str]				      =     output.images[0]

            assert image.shape == (2_5_6, 2_5_6, 3)

            assert_mean_pixel_difference(lowerCamelCase_   ,				lowerCamelCase_     )

       def     UpperCAmelCase( self   :  Tuple     ):
            torch.cuda.empty_cache()
            torch.cuda.reset_max_memory_allocated()
            torch.cuda.reset_peak_memory_stats()

            a_     :  Tuple				      =     UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""   ,				torch_dtype=torch.floataa     )
            a_     :  List[str]				      =     pipe.to(lowerCamelCase_     )
            pipe.set_progress_bar_config(disable=lowerCamelCase_     )
            pipe.enable_attention_slicing()
            pipe.enable_sequential_cpu_offload()

            a_     :  List[Any]				      =     pipe(
                """horse"""   ,				num_images_per_prompt=1   ,				prior_num_inference_steps=2   ,				decoder_num_inference_steps=2   ,				super_res_num_inference_steps=2   ,				output_type="""np"""   ,				)

            a_     :  Optional[Any]				      =     torch.cuda.max_memory_allocated()
            # make sure that less than 7 GB is allocated
            assert mem_bytes < 7 * 1_0**9










"""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)






