






import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
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 ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


def __A    (      __lowerCAmelCase	)->  Union[str, Any]:





						"""simple docstring"""



						_UpperCAmelCase									=			[tensor.shape for tensor in tensor_list]
						return all(shape == shapes[0] for shape in shapes[1:]	)





class __lowerCamelCase (      snake_case__     ,       snake_case__     ,       snake_case__     ,       unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=      StableDiffusionLatentUpscalePipeline
	UpperCamelCase__								=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
	    "height",
	    "width",
	    "cross_attention_kwargs",
	    "negative_prompt_embeds",
	    "prompt_embeds",
	}
	UpperCamelCase__								=      PipelineTesterMixin.required_optional_params - {"num_images_per_prompt"}
	UpperCamelCase__								=      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	UpperCamelCase__								=      frozenset(
	    [])  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	UpperCamelCase__								=      frozenset([])

	UpperCamelCase__								=      True

	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			1
							_UpperCAmelCase									=			4
							_UpperCAmelCase									=			(16, 16)

							_UpperCAmelCase									=			floats_tensor((batch_size, num_channels) + sizes       ,    rng=random.Random(0    )    ).to(UpperCAmelCase    )
							return image

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							torch.manual_seed(0    )
							_UpperCAmelCase									=			UNetaDConditionModel(
							    act_fn='gelu'       ,    attention_head_dim=8       ,    norm_num_groups=UpperCAmelCase       ,    block_out_channels=[32, 32, 64, 64]       ,    time_cond_proj_dim=160       ,    conv_in_kernel=1       ,    conv_out_kernel=1       ,    cross_attention_dim=32       ,    down_block_types=(
							        'KDownBlock2D',
							        'KCrossAttnDownBlock2D',
							        'KCrossAttnDownBlock2D',
							        'KCrossAttnDownBlock2D',
							    )       ,    in_channels=8       ,    mid_block_type=UpperCAmelCase       ,    only_cross_attention=UpperCAmelCase       ,    out_channels=5       ,    resnet_time_scale_shift='scale_shift'       ,    time_embedding_type='fourier'       ,    timestep_post_act='gelu'       ,    up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')       ,    )
							_UpperCAmelCase									=			AutoencoderKL(
							    block_out_channels=[32, 32, 64, 64]       ,    in_channels=3       ,    out_channels=3       ,    down_block_types=[
							        'DownEncoderBlock2D',
							        'DownEncoderBlock2D',
							        'DownEncoderBlock2D',
							        'DownEncoderBlock2D',
							    ]       ,    up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']       ,    latent_channels=4       ,    )
							_UpperCAmelCase									=			EulerDiscreteScheduler(prediction_type='sample'    )
							_UpperCAmelCase									=			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='quick_gelu'       ,    projection_dim=512       ,    )
							_UpperCAmelCase									=			CLIPTextModel(UpperCAmelCase    )
							_UpperCAmelCase									=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

							_UpperCAmelCase									=			{
							    'unet': model.eval(),
							    'vae': vae.eval(),
							    'scheduler': scheduler,
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							}

							return components

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase=0    ):
							"""simple docstring"""







							if str(UpperCAmelCase    ).startswith('mps'    ):
													_UpperCAmelCase									=			torch.manual_seed(UpperCAmelCase    )
							else:
													_UpperCAmelCase									=			torch.Generator(device=UpperCAmelCase    ).manual_seed(UpperCAmelCase    )
							_UpperCAmelCase									=			{
							    'prompt': 'A painting of a squirrel eating a burger',
							    'image': self.dummy_image.cpu(),
							    'generator': generator,
							    'num_inference_steps': 2,
							    'output_type': 'numpy',
							}
							return inputs

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			'cpu'

							_UpperCAmelCase									=			self.get_dummy_components()
							_UpperCAmelCase									=			self.pipeline_class(**UpperCAmelCase    )
							pipe.to(UpperCAmelCase    )
							pipe.set_progress_bar_config(disable=UpperCAmelCase    )

							_UpperCAmelCase									=			self.get_dummy_inputs(UpperCAmelCase    )
							_UpperCAmelCase									=			pipe(**UpperCAmelCase    ).images
							_UpperCAmelCase									=			image[0, -3:, -3:, -1]

							self.assertEqual(image.shape       ,    (1, 256, 256, 3)    )
							_UpperCAmelCase									=			np.array(
							    [0.47_22_24_12, 0.41_92_16_33, 0.44_71_74_34, 0.46_87_41_92, 0.42_58_82_58, 0.46_15_07_26, 0.4_67_75_34, 0.45_58_38_32, 0.48_57_90_55]    )
							_UpperCAmelCase									=			np.abs(image_slice.flatten() - expected_slice    ).max()
							self.assertLessEqual(UpperCAmelCase       ,    1e-3    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							super().test_attention_slicing_forward_pass(expected_max_diff=7e-3    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							super().test_inference_batch_single_identical(expected_max_diff=7e-3    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							super().test_save_load_local(expected_max_difference=3e-3    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							super().test_save_load_optional_components(expected_max_difference=3e-3    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			[
							    'DDIMScheduler',
							    'DDPMScheduler',
							    'PNDMScheduler',
							    'HeunDiscreteScheduler',
							    'EulerAncestralDiscreteScheduler',
							    'KDPM2DiscreteScheduler',
							    'KDPM2AncestralDiscreteScheduler',
							    'DPMSolverSDEScheduler',
							]
							_UpperCAmelCase									=			self.get_dummy_components()
							_UpperCAmelCase									=			self.pipeline_class(**UpperCAmelCase    )

							# make sure that PNDM does not need warm-up
							pipe.scheduler.register_to_config(skip_prk_steps=UpperCAmelCase    )

							pipe.to(UpperCAmelCase    )
							pipe.set_progress_bar_config(disable=UpperCAmelCase    )
							_UpperCAmelCase									=			self.get_dummy_inputs(UpperCAmelCase    )
							_UpperCAmelCase									=			2

							_UpperCAmelCase									=			[]
							for scheduler_enum in KarrasDiffusionSchedulers:
													if scheduler_enum.name in skip_schedulers:
																			# no sigma schedulers are not supported
																			# no schedulers
																			continue

													_UpperCAmelCase									=			getattr(UpperCAmelCase       ,    scheduler_enum.name    )
													_UpperCAmelCase									=			scheduler_cls.from_config(pipe.scheduler.config    )
													_UpperCAmelCase									=			pipe(**UpperCAmelCase    )[0]
													outputs.append(UpperCAmelCase    )

							assert check_same_shape(UpperCAmelCase    )





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





	"""simple docstring"""






	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			torch.manual_seed(33    )

							_UpperCAmelCase									=			StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'       ,    torch_dtype=torch.floataa    )
							pipe.to('cuda'    )

							_UpperCAmelCase									=			StableDiffusionLatentUpscalePipeline.from_pretrained(
							    'stabilityai/sd-x2-latent-upscaler'       ,    torch_dtype=torch.floataa    )
							upscaler.to('cuda'    )

							_UpperCAmelCase									=			'a photo of an astronaut high resolution, unreal engine, ultra realistic'

							_UpperCAmelCase									=			pipe(UpperCAmelCase       ,    generator=UpperCAmelCase       ,    output_type='latent'    ).images

							_UpperCAmelCase									=			upscaler(
							    prompt=UpperCAmelCase       ,    image=UpperCAmelCase       ,    num_inference_steps=20       ,    guidance_scale=0       ,    generator=UpperCAmelCase       ,    output_type='np'       ,    ).images[0]

							_UpperCAmelCase									=			load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'    )
							assert np.abs((expected_image - image).mean()    ) < 5e-2

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			torch.manual_seed(33    )

							_UpperCAmelCase									=			StableDiffusionLatentUpscalePipeline.from_pretrained(
							    'stabilityai/sd-x2-latent-upscaler'       ,    torch_dtype=torch.floataa    )
							upscaler.to('cuda'    )

							_UpperCAmelCase									=			'the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'

							_UpperCAmelCase									=			load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'    )

							_UpperCAmelCase									=			upscaler(
							    prompt=UpperCAmelCase       ,    image=UpperCAmelCase       ,    num_inference_steps=20       ,    guidance_scale=0       ,    generator=UpperCAmelCase       ,    output_type='np'       ,    ).images[0]

							_UpperCAmelCase									=			load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'    )
							assert np.abs((expected_image - image).max()    ) < 5e-2








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)
_a      	=  logging.getLogger()

_a      	=  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							os.makedirs(UpperCAmelCase       ,    exist_ok=UpperCAmelCase    )
							_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(UpperCAmelCase       ,    F"""{split}.{field}"""    )       ,    'w'    ) as f:
																									f.write(UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase = "pytorch"    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.get_auto_remove_tmp_dir()
							_UpperCAmelCase									=			os.path.join(UpperCAmelCase       ,    'output'    )
							_UpperCAmelCase									=			os.path.join(UpperCAmelCase       ,    'data'    )
							self._create_dummy_data(data_dir=UpperCAmelCase    )

							_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(UpperCAmelCase       ,    env=self.get_env()    )

							_UpperCAmelCase									=			os.path.join(UpperCAmelCase       ,    'metrics.json'    )
							with open(UpperCAmelCase    ) as f:
													_UpperCAmelCase									=			json.load(UpperCAmelCase    )
							return result

	@require_torch_gpu
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self._run_finetune(gpus=1    )
							self.assertGreaterEqual(result['test'][0]['test_avg_em']       ,    0.2    )

	@require_torch_multi_gpu
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self._run_finetune(gpus=2    )
							self.assertGreaterEqual(result['test'][0]['test_avg_em']       ,    0.2    )

	@require_torch_gpu
	@require_ray
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_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        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self._run_finetune(gpus=1       ,    distributed_retriever='ray'    )
							self.assertGreaterEqual(result['test'][0]['test_avg_em']       ,    0.2    )
