# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


__lowercase											=      '''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''



def lowerCamelCase						(     SCREAMING_SNAKE_CASE=None			):
    '''simple docstring'''







    if subparsers is not None:
        __UpperCamelCase  :List[Any]    						=	subparsers.add_parser('''tpu-config'''						,    description=_description			)
    else:
        __UpperCamelCase  :Any    						=	argparse.ArgumentParser('''Accelerate tpu-config command'''						,    description=_description			)
    # Core arguments
    __UpperCamelCase  :List[str]    						=	parser.add_argument_group(
        '''Config Arguments'''						,    '''Arguments that can be configured through `accelerate config`.'''			)
    config_args.add_argument(
        '''--config_file'''						,    type=SCREAMING_SNAKE_CASE						,    default=SCREAMING_SNAKE_CASE						,    help='''Path to the config file to use for accelerate.'''						,    )
    config_args.add_argument(
        '''--tpu_name'''						,    default=SCREAMING_SNAKE_CASE						,    help='''The name of the TPU to use. If not specified, will use the TPU specified in the config file.'''						,    )
    config_args.add_argument(
        '''--tpu_zone'''						,    default=SCREAMING_SNAKE_CASE						,    help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.'''						,    )
    __UpperCamelCase  :Union[str, Any]    						=	parser.add_argument_group('''TPU Arguments'''						,    '''Arguments for options ran inside the TPU.'''			)
    pod_args.add_argument(
        '''--use_alpha'''						,    action='''store_true'''						,    help='''Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.'''						,    )
    pod_args.add_argument(
        '''--command_file'''						,    default=SCREAMING_SNAKE_CASE						,    help='''The path to the file containing the commands to run on the pod on startup.'''						,    )
    pod_args.add_argument(
        '''--command'''						,    action='''append'''						,    nargs='''+'''						,    help='''A command to run on the pod. Can be passed multiple times.'''						,    )
    pod_args.add_argument(
        '''--install_accelerate'''						,    action='''store_true'''						,    help='''Whether to install accelerate on the pod. Defaults to False.'''						,    )
    pod_args.add_argument(
        '''--accelerate_version'''						,    default='''latest'''						,    help='''The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.'''						,    )
    pod_args.add_argument(
        '''--debug'''						,    action='''store_true'''						,    help='''If set, will print the command that would be run instead of running it.'''			)

    if subparsers is not None:
        parser.set_defaults(func=SCREAMING_SNAKE_CASE			)
    return parser



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Optional[int]    						=	None

    # Get the default from the config file if it exists.
    if args.config_file is not None or os.path.isfile(SCREAMING_SNAKE_CASE			):
        __UpperCamelCase  :Optional[Any]    						=	load_config_from_file(args.config_file			)
        if not args.command_file and defaults.command_file is not None and not args.command:
            __UpperCamelCase  :str    						=	defaults.command_file
        if not args.command and defaults.commands is not None:
            __UpperCamelCase  :Optional[int]    						=	defaults.commands
        if not args.tpu_name:
            __UpperCamelCase  :Tuple    						=	defaults.tpu_name
        if not args.tpu_zone:
            __UpperCamelCase  :Optional[Any]    						=	defaults.tpu_zone
    if args.accelerate_version == "dev":
        __UpperCamelCase  :Any    						=	'''git+https://github.com/huggingface/accelerate.git'''
    elif args.accelerate_version == "latest":
        __UpperCamelCase  :List[Any]    						=	'''accelerate -U'''
    elif isinstance(parse(args.accelerate_version			)						,    SCREAMING_SNAKE_CASE			):
        __UpperCamelCase  :Union[str, Any]    						=	f"""accelerate=={args.accelerate_version}"""

    if not args.command_file and not args.command:
        raise ValueError('''You must specify either a command file or a command to run on the pod.'''			)

    if args.command_file:
        with open(args.command_file						,    '''r'''			) as f:
            __UpperCamelCase  :Tuple    						=	[f.read().splitlines()]

    # To turn list of lists into list of strings
    if isinstance(args.command[0]						,    SCREAMING_SNAKE_CASE			):
        __UpperCamelCase  :Tuple    						=	[line for cmd in args.command for line in cmd]
    # Default to the shared folder and install accelerate
    __UpperCamelCase  :Optional[int]    						=	['''cd /usr/share''']
    if args.install_accelerate:
        new_cmd += [f"""pip install {args.accelerate_version}"""]
    new_cmd += args.command
    __UpperCamelCase  :Optional[int]    						=	'''; '''.join(SCREAMING_SNAKE_CASE			)

    # Then send it to gcloud
    # Eventually try to use google-api-core to do this instead of subprocess
    __UpperCamelCase  :Any    						=	['''gcloud''']
    if args.use_alpha:
        cmd += ["alpha"]
    cmd += [
        "compute",
        "tpus",
        "tpu-vm",
        "ssh",
        args.tpu_name,
        "--zone",
        args.tpu_zone,
        "--command",
        args.command,
        "--worker",
        "all",
    ]
    if args.debug:
        print(f"""Running {' '.join(SCREAMING_SNAKE_CASE			)}"""			)
        return
    subprocess.run(SCREAMING_SNAKE_CASE			)
    print('''Successfully setup pod.'''			)



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :Optional[int]    						=	tpu_command_parser()
    __UpperCamelCase  :Optional[int]    						=	parser.parse_args()

    tpu_command_launcher(SCREAMING_SNAKE_CASE			)






import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()


class 			lowerCamelCase_  (     UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			unittest.TestCase				):






    '''simple docstring'''






    a__			:		List[str]      			=   StableUnCLIPImgaImgPipeline
    a__			:		Optional[Any]      			=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS
    a__			:		Optional[int]      			=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    a__			:		str      			=   frozenset(
        []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    a__			:		List[Any]      			=   frozenset([]				)







    def     UpperCamelCase__     (			self) ->			List[str]:
        __UpperCamelCase  :Optional[Any]    						=	32
        __UpperCamelCase  :int    						=	embedder_hidden_size

        # image encoding components

        __UpperCamelCase  :Dict    						=	CLIPImageProcessor(crop_size=32				,				size=32)

        torch.manual_seed(0)
        __UpperCamelCase  :Tuple    						=	CLIPVisionModelWithProjection(
            CLIPVisionConfig(
                hidden_size=__lowercase				,				projection_dim=__lowercase				,				num_hidden_layers=5				,				num_attention_heads=4				,				image_size=32				,				intermediate_size=37				,				patch_size=1				,				))

        # regular denoising components

        torch.manual_seed(0)
        __UpperCamelCase  :Union[str, Any]    						=	StableUnCLIPImageNormalizer(embedding_dim=__lowercase)
        __UpperCamelCase  :Any    						=	DDPMScheduler(beta_schedule='''squaredcos_cap_v2''')

        torch.manual_seed(0)
        __UpperCamelCase  :List[Any]    						=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

        torch.manual_seed(0)
        __UpperCamelCase  :str    						=	CLIPTextModel(
            CLIPTextConfig(
                bos_token_id=0				,				eos_token_id=2				,				hidden_size=__lowercase				,				projection_dim=32				,				intermediate_size=37				,				layer_norm_eps=1E-0_5				,				num_attention_heads=4				,				num_hidden_layers=5				,				pad_token_id=1				,				vocab_size=1_000				,				))

        torch.manual_seed(0)
        __UpperCamelCase  :Any    						=	UNetaDConditionModel(
            sample_size=32				,				in_channels=4				,				out_channels=4				,				down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')				,				up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')				,				block_out_channels=(32, 64)				,				attention_head_dim=(2, 4)				,				class_embed_type='''projection'''				,				projection_class_embeddings_input_dim=embedder_projection_dim * 2				,				cross_attention_dim=__lowercase				,				layers_per_block=1				,				upcast_attention=__lowercase				,				use_linear_projection=__lowercase				,				)

        torch.manual_seed(0)
        __UpperCamelCase  :Dict    						=	DDIMScheduler(
            beta_schedule='''scaled_linear'''				,				beta_start=0.0_00_85				,				beta_end=0.0_12				,				prediction_type='''v_prediction'''				,				set_alpha_to_one=__lowercase				,				steps_offset=1				,				)

        torch.manual_seed(0)
        __UpperCamelCase  :Any    						=	AutoencoderKL()

        __UpperCamelCase  :Dict    						=	{
            # image encoding components
            '''feature_extractor''': feature_extractor,
            '''image_encoder''': image_encoder.eval(),
            # image noising components
            '''image_normalizer''': image_normalizer.eval(),
            '''image_noising_scheduler''': image_noising_scheduler,
            # regular denoising components
            '''tokenizer''': tokenizer,
            '''text_encoder''': text_encoder.eval(),
            '''unet''': unet.eval(),
            '''scheduler''': scheduler,
            '''vae''': vae.eval(),
        }

        return components







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase=0				,				__lowercase=True) ->			Tuple:
        if str(__lowercase).startswith('''mps'''):
            __UpperCamelCase  :int    						=	torch.manual_seed(__lowercase)
        else:
            __UpperCamelCase  :int    						=	torch.Generator(device=__lowercase).manual_seed(__lowercase)

        __UpperCamelCase  :Tuple    						=	floats_tensor((1, 3, 32, 32)				,				rng=random.Random(__lowercase)).to(__lowercase)

        if pil_image:
            __UpperCamelCase  :Tuple    						=	input_image * 0.5 + 0.5
            __UpperCamelCase  :Any    						=	input_image.clamp(0				,				1)
            __UpperCamelCase  :Any    						=	input_image.cpu().permute(0				,				2				,				3				,				1).float().numpy()
            __UpperCamelCase  :int    						=	DiffusionPipeline.numpy_to_pil(__lowercase)[0]

        return {
            "prompt": "An anime racoon running a marathon",
            "image": input_image,
            "generator": generator,
            "num_inference_steps": 2,
            "output_type": "np",
        }







    @skip_mps
    def     UpperCamelCase__     (			self) ->			Tuple:
        __UpperCamelCase  :int    						=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
        __UpperCamelCase  :Dict    						=	self.get_dummy_components()
        __UpperCamelCase  :Dict    						=	StableUnCLIPImgaImgPipeline(**__lowercase)
        __UpperCamelCase  :int    						=	sd_pipe.to(__lowercase)
        sd_pipe.set_progress_bar_config(disable=__lowercase)

        __UpperCamelCase  :Optional[int]    						=	self.get_dummy_inputs(__lowercase)
        inputs.update({'''image_embeds''': None})
        __UpperCamelCase  :Any    						=	sd_pipe(**__lowercase).images
        __UpperCamelCase  :int    						=	image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        __UpperCamelCase  :Union[str, Any]    						=	np.array([0.38_72, 0.72_24, 0.56_01, 0.47_41, 0.68_72, 0.58_14, 0.46_36, 0.38_67, 0.50_78])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3







    def     UpperCamelCase__     (			self) ->			Dict:
        __UpperCamelCase  :Union[str, Any]    						=	torch_device in ['''cpu''', '''mps''']

        self._test_attention_slicing_forward_pass(test_max_difference=__lowercase)







    def     UpperCamelCase__     (			self) ->			Optional[int]:
        __UpperCamelCase  :List[Any]    						=	torch_device in ['''cpu''', '''mps''']

        self._test_inference_batch_single_identical(test_max_difference=__lowercase)







    @unittest.skipIf(
        torch_device != '''cuda''' or not is_xformers_available()				,				reason='''XFormers attention is only available with CUDA and `xformers` installed'''				,				)
    def     UpperCamelCase__     (			self) ->			Optional[Any]:
        self._test_xformers_attention_forwardGenerator_pass(test_max_difference=__lowercase)


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






    '''simple docstring'''






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







    def     UpperCamelCase__     (			self) ->			str:
        __UpperCamelCase  :Union[str, Any]    						=	load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')

        __UpperCamelCase  :str    						=	load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy''')

        __UpperCamelCase  :Optional[int]    						=	StableUnCLIPImgaImgPipeline.from_pretrained(
            '''fusing/stable-unclip-2-1-l-img2img'''				,				torch_dtype=torch.floataa)
        pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)
        # stable unclip will oom when integration tests are run on a V100,
        # so turn on memory savings
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        __UpperCamelCase  :Union[str, Any]    						=	torch.Generator(device='''cpu''').manual_seed(0)
        __UpperCamelCase  :str    						=	pipe(__lowercase				,				'''anime turle'''				,				generator=__lowercase				,				output_type='''np''')

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

        assert image.shape == (768, 768, 3)

        assert_mean_pixel_difference(__lowercase				,				__lowercase)







    def     UpperCamelCase__     (			self) ->			List[Any]:
        __UpperCamelCase  :Dict    						=	load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')

        __UpperCamelCase  :str    						=	load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy''')

        __UpperCamelCase  :Tuple    						=	StableUnCLIPImgaImgPipeline.from_pretrained(
            '''fusing/stable-unclip-2-1-h-img2img'''				,				torch_dtype=torch.floataa)
        pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)
        # stable unclip will oom when integration tests are run on a V100,
        # so turn on memory savings
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        __UpperCamelCase  :int    						=	torch.Generator(device='''cpu''').manual_seed(0)
        __UpperCamelCase  :int    						=	pipe(__lowercase				,				'''anime turle'''				,				generator=__lowercase				,				output_type='''np''')

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

        assert image.shape == (768, 768, 3)

        assert_mean_pixel_difference(__lowercase				,				__lowercase)







    def     UpperCamelCase__     (			self) ->			Union[str, Any]:
        __UpperCamelCase  :List[Any]    						=	load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')

        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        __UpperCamelCase  :Any    						=	StableUnCLIPImgaImgPipeline.from_pretrained(
            '''fusing/stable-unclip-2-1-h-img2img'''				,				torch_dtype=torch.floataa)
        __UpperCamelCase  :Any    						=	pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        __UpperCamelCase  :Tuple    						=	pipe(
            __lowercase				,				'''anime turtle'''				,				num_inference_steps=2				,				output_type='''np'''				,				)

        __UpperCamelCase  :str    						=	torch.cuda.max_memory_allocated()
        # make sure that less than 7 GB is allocated
        assert mem_bytes < 7 * 10**9





