'''simple docstring'''





import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class        __A      ( UpperCamelCase__		,	unittest.TestCase	):
    a__       :						Dict          =  KandinskyVaaControlnetPipeline
    a__       :						Dict          =  ["""image_embeds""", """negative_image_embeds""", """hint"""]
    a__       :						List[Any]          =  ["""image_embeds""", """negative_image_embeds""", """hint"""]
    a__       :						Optional[int]          =  [
        """generator""",
        """height""",
        """width""",
        """latents""",
        """guidance_scale""",
        """num_inference_steps""",
        """return_dict""",
        """guidance_scale""",
        """num_images_per_prompt""",
        """output_type""",
        """return_dict""",
    ]
    a__       :						Union[str, Any]          =  False



    @property
    def 					_lowercase						(self    :    Union[str, Any] ):
         return 32



    @property
    def 					_lowercase						(self    :    str ):
         return 32



    @property
    def 					_lowercase						(self    :    int ):
         return self.time_input_dim



    @property
    def 					_lowercase						(self    :    Optional[Any] ):
         return self.time_input_dim * 4



    @property
    def 					_lowercase						(self    :    int ):
         return 100



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

         UpperCAmelCase_           =   {
             "in_channels": 8,
             # Out channels is double in channels because predicts mean and variance
             "out_channels": 8,
             "addition_embed_type": "image_hint",
             "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,
             "encoder_hid_dim": self.text_embedder_hidden_size,
             "encoder_hid_dim_type": "image_proj",
             "cross_attention_dim": self.cross_attention_dim,
             "attention_head_dim": 4,
             "resnet_time_scale_shift": "scale_shift",
             "class_embed_type": None,
         }

         UpperCAmelCase_           =   UNetaDConditionModel(**__a )
         return model



    @property
    def 					_lowercase						(self    :    Optional[int] ):
         return {
             "block_out_channels": [32, 32, 64, 64],
             "down_block_types": [
                 "DownEncoderBlock2D",
                 "DownEncoderBlock2D",
                 "DownEncoderBlock2D",
                 "AttnDownEncoderBlock2D",
             ],
             "in_channels": 3,
             "latent_channels": 4,
             "layers_per_block": 1,
             "norm_num_groups": 8,
             "norm_type": "spatial",
             "num_vq_embeddings": 12,
             "out_channels": 3,
             "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
             "vq_embed_dim": 4,
         }



    @property
    def 					_lowercase						(self    :    Tuple ):
         torch.manual_seed(0 )
         UpperCAmelCase_           =   VQModel(**self.dummy_movq_kwargs )
         return model



    def 					_lowercase						(self    :    str ):
         UpperCAmelCase_           =   self.dummy_unet
         UpperCAmelCase_           =   self.dummy_movq

         UpperCAmelCase_           =   DDIMScheduler(
             num_train_timesteps=1000   ,			beta_schedule="linear"   ,			beta_start=0.0_00_85   ,			beta_end=0.0_12   ,			clip_sample=__a   ,			set_alpha_to_one=__a   ,			steps_offset=1   ,			prediction_type="epsilon"   ,			thresholding=__a   ,			)

         UpperCAmelCase_           =   {
             "unet": unet,
             "scheduler": scheduler,
             "movq": movq,
         }
         return components



    def 					_lowercase						(self    :    str   ,			__a    :    str   ,			__a    :    List[str]=0 ):
         UpperCAmelCase_           =   floats_tensor((1, self.text_embedder_hidden_size)   ,			rng=random.Random(__a ) ).to(__a )
         UpperCAmelCase_           =   floats_tensor((1, self.text_embedder_hidden_size)   ,			rng=random.Random(seed + 1 ) ).to(
             __a )

         # create hint
         UpperCAmelCase_           =   floats_tensor((1, 3, 64, 64)   ,			rng=random.Random(__a ) ).to(__a )

         if str(__a ).startswith("mps" ):
              UpperCAmelCase_           =   torch.manual_seed(__a )
         else:
              UpperCAmelCase_           =   torch.Generator(device=__a ).manual_seed(__a )
         UpperCAmelCase_           =   {
             "image_embeds": image_embeds,
             "negative_image_embeds": negative_image_embeds,
             "hint": hint,
             "generator": generator,
             "height": 64,
             "width": 64,
             "guidance_scale": 4.0,
             "num_inference_steps": 2,
             "output_type": "np",
         }
         return inputs






    def 					_lowercase						(self    :    Union[str, Any] ):
         UpperCAmelCase_           =   "cpu"

         UpperCAmelCase_           =   self.get_dummy_components()

         UpperCAmelCase_           =   self.pipeline_class(**__a )
         UpperCAmelCase_           =   pipe.to(__a )

         pipe.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   pipe(**self.get_dummy_inputs(__a ) )
         UpperCAmelCase_           =   output.images

         UpperCAmelCase_           =   pipe(
             **self.get_dummy_inputs(__a )   ,			return_dict=__a   ,			)[0]

         UpperCAmelCase_           =   image[0, -3:, -3:, -1]
         UpperCAmelCase_           =   image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)

         UpperCAmelCase_           =   np.array(
             [0.6_95_98_26, 0.86_82_79, 0.7_55_80_92, 0.68_76_94_67, 0.85_80_58_04, 0.65_97_74_96, 0.44_88_53_02, 0.5_95_91_11, 0.4_25_15_95] )

         assert (
             np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
         ), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""

         assert (
             np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
         ), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""




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



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






    def 					_lowercase						(self    :    List[str] ):
         UpperCAmelCase_           =   load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy" )

         UpperCAmelCase_           =   load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/kandinskyv22/hint_image_cat.png" )
         UpperCAmelCase_           =   torch.from_numpy(np.array(__a ) ).float() / 2_55.0
         UpperCAmelCase_           =   hint.permute(2   ,			0   ,			1 ).unsqueeze(0 )

         UpperCAmelCase_           =   KandinskyVaaPriorPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-2-prior"   ,			torch_dtype=torch.floataa )
         pipe_prior.to(__a )

         UpperCAmelCase_           =   KandinskyVaaControlnetPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-2-controlnet-depth"   ,			torch_dtype=torch.floataa )
         UpperCAmelCase_           =   pipeline.to(__a )
         pipeline.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   "A robot, 4k photo"

         UpperCAmelCase_           =   torch.Generator(device="cuda" ).manual_seed(0 )
         UpperCAmelCase_    , UpperCAmelCase_           =   pipe_prior(
             __a   ,			generator=__a   ,			num_inference_steps=5   ,			negative_prompt=""   ,			).to_tuple()

         UpperCAmelCase_           =   torch.Generator(device="cuda" ).manual_seed(0 )
         UpperCAmelCase_           =   pipeline(
             image_embeds=__a   ,			negative_image_embeds=__a   ,			hint=__a   ,			generator=__a   ,			num_inference_steps=100   ,			output_type="np"   ,			)

         UpperCAmelCase_           =   output.images[0]

         assert image.shape == (512, 512, 3)

         assert_mean_pixel_difference(__a   ,			__a )





'''simple docstring'''





import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
SCREAMING_SNAKE_CASE_:     Optional[int]  =logging.getLogger()

SCREAMING_SNAKE_CASE_:     Union[str, Any]  =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class        __A      ( UpperCamelCase__	):



    def 					_lowercase						(self    :    Optional[Any]   ,			__a    :    str ):
         os.makedirs(__a   ,			exist_ok=__a )
         UpperCAmelCase_           =   {"source": "What is love ?", "target": "life"}
         UpperCAmelCase_           =   {"train": 12, "val": 2, "test": 2}
         for split in ["train", "test", "val"]:
              for field in ["source", "target"]:
                   UpperCAmelCase_           =   "\n".join([contents[field]] * n_lines[split] )
                   with open(os.path.join(__a   ,			f"""{split}.{field}""" )   ,			"w" ) as f:
                        f.write(__a )



    def 					_lowercase						(self    :    Optional[int]   ,			__a    :    int   ,			__a    :    str = "pytorch" ):
         UpperCAmelCase_           =   self.get_auto_remove_tmp_dir()
         UpperCAmelCase_           =   os.path.join(__a   ,			"output" )
         UpperCAmelCase_           =   os.path.join(__a   ,			"data" )
         self._create_dummy_data(data_dir=__a )

         UpperCAmelCase_           =   f"""
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            """.split()

         if gpus > 0:
              testargs.append(f"""--gpus={gpus}""" )
              if is_apex_available():
                   testargs.append("--fp16" )
         else:
              testargs.append("--gpus=0" )
              testargs.append("--distributed_backend=ddp_cpu" )
              testargs.append("--num_processes=2" )

         UpperCAmelCase_           =   [sys.executable, str(Path(finetune_rag.__file__ ).resolve() )] + testargs
         execute_subprocess_async(__a   ,			env=self.get_env() )

         UpperCAmelCase_           =   os.path.join(__a   ,			"metrics.json" )
         with open(__a ) as f:
              UpperCAmelCase_           =   json.load(__a )
         return result



    @require_torch_gpu
    def 					_lowercase						(self    :    Optional[int] ):
         UpperCAmelCase_           =   self._run_finetune(gpus=1 )
         self.assertGreaterEqual(result["test"][0]["test_avg_em"]   ,			0.2 )



    @require_torch_multi_gpu
    def 					_lowercase						(self    :    Dict ):
         UpperCAmelCase_           =   self._run_finetune(gpus=2 )
         self.assertGreaterEqual(result["test"][0]["test_avg_em"]   ,			0.2 )



    @require_torch_gpu
    @require_ray
    def 					_lowercase						(self    :    Optional[int] ):
         UpperCAmelCase_           =   self._run_finetune(gpus=1   ,			distributed_retriever="ray" )
         self.assertGreaterEqual(result["test"][0]["test_avg_em"]   ,			0.2 )






    @require_torch_multi_gpu
    @require_ray
    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   self._run_finetune(gpus=1   ,			distributed_retriever="ray" )
         self.assertGreaterEqual(result["test"][0]["test_avg_em"]   ,			0.2 )




