



import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()





class  __lowerCAmelCase			(					unittest.TestCase	):




				def 							snake_case       (							self		):



									"""simple docstring"""





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




				@property
				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   1
									_lowerCAmelCase      =   3
									_lowerCAmelCase      =   (32, 32)

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




				@property
				def 							snake_case       (							self		):



									"""simple docstring"""





									torch.manual_seed(0		)
									_lowerCAmelCase      =   UNetaDConditionModel(
									    block_out_channels=(32, 64)						,    layers_per_block=2						,    sample_size=32						,    in_channels=4						,    out_channels=4						,    down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")						,    up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")						,    cross_attention_dim=32						,    )
									return model




				@property
				def 							snake_case       (							self		):



									"""simple docstring"""





									torch.manual_seed(0		)
									_lowerCAmelCase      =   AutoencoderKL(
									    block_out_channels=[32, 64]						,    in_channels=3						,    out_channels=3						,    down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]						,    up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]						,    latent_channels=4						,    )
									return model




				@property
				def 							snake_case       (							self		):



									"""simple docstring"""





									torch.manual_seed(0		)
									_lowerCAmelCase      =   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						,    )
									return CLIPTextModel(__UpperCamelCase		)




				@property
				def 							snake_case       (							self		):



									"""simple docstring"""





									def extract(*_snake_case						,    **_snake_case		):





														class  __lowerCAmelCase			:




																		def __init__(							self		):



																							"""simple docstring"""





																							_lowerCAmelCase      =   torch.ones([0]		)




																		def 							snake_case       (							self						,    _snake_case		):



																							"""simple docstring"""





																							self.pixel_values.to(__UpperCamelCase		)
																							return self

														return Out()

									return extract




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
									_lowerCAmelCase      =   self.dummy_cond_unet
									_lowerCAmelCase      =   DDIMScheduler(
									    beta_start=0.0_0085						,    beta_end=0.012						,    beta_schedule="""scaled_linear"""						,    clip_sample=__UpperCamelCase						,    set_alpha_to_one=__UpperCamelCase						,    )

									_lowerCAmelCase      =   self.dummy_vae
									_lowerCAmelCase      =   self.dummy_text_encoder
									_lowerCAmelCase      =   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)

									# make sure here that pndm scheduler skips prk
									_lowerCAmelCase      =   StableDiffusionPipeline(
									    unet=__UpperCamelCase						,    scheduler=__UpperCamelCase						,    vae=__UpperCamelCase						,    text_encoder=__UpperCamelCase						,    tokenizer=__UpperCamelCase						,    safety_checker=__UpperCamelCase						,    feature_extractor=self.dummy_extractor						,    )
									_lowerCAmelCase      =   sd_pipe.to(__UpperCamelCase		)
									sd_pipe.set_progress_bar_config(disable=__UpperCamelCase		)

									_lowerCAmelCase      =   """A painting of a squirrel eating a burger"""

									_lowerCAmelCase      =   torch.Generator(device=__UpperCamelCase		).manual_seed(0		)
									_lowerCAmelCase      =   sd_pipe([prompt]						,    generator=__UpperCamelCase						,    guidance_scale=6.0						,    num_inference_steps=2						,    output_type="""np"""		)
									_lowerCAmelCase      =   output.images

									_lowerCAmelCase      =   torch.Generator(device=__UpperCamelCase		).manual_seed(0		)
									_lowerCAmelCase      =   sd_pipe(
									    [prompt]						,    generator=__UpperCamelCase						,    guidance_scale=6.0						,    num_inference_steps=2						,    output_type="""np"""						,    return_dict=__UpperCamelCase						,    )[0]

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

									assert image.shape == (1, 64, 64, 3)
									_lowerCAmelCase      =   np.array([0.5753, 0.6114, 0.5001, 0.5034, 0.5470, 0.4729, 0.4971, 0.4867, 0.4867]		)

									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 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
									_lowerCAmelCase      =   self.dummy_cond_unet
									_lowerCAmelCase      =   PNDMScheduler(skip_prk_steps=__UpperCamelCase		)
									_lowerCAmelCase      =   self.dummy_vae
									_lowerCAmelCase      =   self.dummy_text_encoder
									_lowerCAmelCase      =   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)

									# make sure here that pndm scheduler skips prk
									_lowerCAmelCase      =   StableDiffusionPipeline(
									    unet=__UpperCamelCase						,    scheduler=__UpperCamelCase						,    vae=__UpperCamelCase						,    text_encoder=__UpperCamelCase						,    tokenizer=__UpperCamelCase						,    safety_checker=__UpperCamelCase						,    feature_extractor=self.dummy_extractor						,    )
									_lowerCAmelCase      =   sd_pipe.to(__UpperCamelCase		)
									sd_pipe.set_progress_bar_config(disable=__UpperCamelCase		)

									_lowerCAmelCase      =   """A painting of a squirrel eating a burger"""
									_lowerCAmelCase      =   torch.Generator(device=__UpperCamelCase		).manual_seed(0		)
									_lowerCAmelCase      =   sd_pipe([prompt]						,    generator=__UpperCamelCase						,    guidance_scale=6.0						,    num_inference_steps=2						,    output_type="""np"""		)

									_lowerCAmelCase      =   output.images

									_lowerCAmelCase      =   torch.Generator(device=__UpperCamelCase		).manual_seed(0		)
									_lowerCAmelCase      =   sd_pipe(
									    [prompt]						,    generator=__UpperCamelCase						,    guidance_scale=6.0						,    num_inference_steps=2						,    output_type="""np"""						,    return_dict=__UpperCamelCase						,    )[0]

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

									assert image.shape == (1, 64, 64, 3)
									_lowerCAmelCase      =   np.array([0.5122, 0.5712, 0.4825, 0.5053, 0.5646, 0.4769, 0.5179, 0.4894, 0.4994]		)

									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 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   StableDiffusionPipeline.from_pretrained(
									    """hf-internal-testing/tiny-stable-diffusion-lms-pipe"""						,    safety_checker=__UpperCamelCase		)
									assert isinstance(__UpperCamelCase						,    __UpperCamelCase		)
									assert isinstance(pipe.scheduler						,    __UpperCamelCase		)
									assert pipe.safety_checker is None

									_lowerCAmelCase      =   pipe("""example prompt"""						,    num_inference_steps=2		).images[0]
									assert image is not None

									# check that there's no error when saving a pipeline with one of the models being None
									with tempfile.TemporaryDirectory() as tmpdirname:
														pipe.save_pretrained(__UpperCamelCase		)
														_lowerCAmelCase      =   StableDiffusionPipeline.from_pretrained(__UpperCamelCase		)

									# sanity check that the pipeline still works
									assert pipe.safety_checker is None
									_lowerCAmelCase      =   pipe("""example prompt"""						,    num_inference_steps=2		).images[0]
									assert image is not None




				@unittest.skipIf(torch_device != """cuda"""						,    """This test requires a GPU"""		)
				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   self.dummy_cond_unet
									_lowerCAmelCase      =   PNDMScheduler(skip_prk_steps=__UpperCamelCase		)
									_lowerCAmelCase      =   self.dummy_vae
									_lowerCAmelCase      =   self.dummy_text_encoder
									_lowerCAmelCase      =   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)

									# put models in fp16
									_lowerCAmelCase      =   unet.half()
									_lowerCAmelCase      =   vae.half()
									_lowerCAmelCase      =   bert.half()

									# make sure here that pndm scheduler skips prk
									_lowerCAmelCase      =   StableDiffusionPipeline(
									    unet=__UpperCamelCase						,    scheduler=__UpperCamelCase						,    vae=__UpperCamelCase						,    text_encoder=__UpperCamelCase						,    tokenizer=__UpperCamelCase						,    safety_checker=__UpperCamelCase						,    feature_extractor=self.dummy_extractor						,    )
									_lowerCAmelCase      =   sd_pipe.to(__UpperCamelCase		)
									sd_pipe.set_progress_bar_config(disable=__UpperCamelCase		)

									_lowerCAmelCase      =   """A painting of a squirrel eating a burger"""
									_lowerCAmelCase      =   sd_pipe([prompt]						,    num_inference_steps=2						,    output_type="""np"""		).images

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





@nightly
@require_torch_gpu
class  __lowerCAmelCase			(					unittest.TestCase	):




				def 							snake_case       (							self		):



									"""simple docstring"""





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




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   """cuda"""
									_lowerCAmelCase      =   StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""		)
									_lowerCAmelCase      =   pipe.to(__UpperCamelCase		)
									pipe.set_progress_bar_config(disable=__UpperCamelCase		)

									_lowerCAmelCase      =   """a photo of a cat"""
									_lowerCAmelCase      =   {
									    """editing_prompt""": ["""sunglasses"""],
									    """reverse_editing_direction""": [False],
									    """edit_warmup_steps""": 10,
									    """edit_guidance_scale""": 6,
									    """edit_threshold""": 0.95,
									    """edit_momentum_scale""": 0.5,
									    """edit_mom_beta""": 0.6,
									}

									_lowerCAmelCase      =   3
									_lowerCAmelCase      =   7

									# no sega enabled
									_lowerCAmelCase      =   torch.Generator(__UpperCamelCase		)
									generator.manual_seed(__UpperCamelCase		)
									_lowerCAmelCase      =   pipe(
									    [prompt]						,    generator=__UpperCamelCase						,    guidance_scale=__UpperCamelCase						,    num_inference_steps=50						,    output_type="""np"""						,    width=512						,    height=512						,    )

									_lowerCAmelCase      =   output.images
									_lowerCAmelCase      =   image[0, -3:, -3:, -1]
									_lowerCAmelCase      =   [
									    0.3467_3113,
									    0.3849_2733,
									    0.3759_7352,
									    0.3408_6335,
									    0.3565_0748,
									    0.3557_9205,
									    0.338_4763,
									    0.3434_0236,
									    0.357_3271,
									]

									assert image.shape == (1, 512, 512, 3)

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

									# with sega enabled
									# generator = torch.manual_seed(seed)
									generator.manual_seed(__UpperCamelCase		)
									_lowerCAmelCase      =   pipe(
									    [prompt]						,    generator=__UpperCamelCase						,    guidance_scale=__UpperCamelCase						,    num_inference_steps=50						,    output_type="""np"""						,    width=512						,    height=512						,    **__UpperCamelCase						,    )

									_lowerCAmelCase      =   output.images
									_lowerCAmelCase      =   image[0, -3:, -3:, -1]
									_lowerCAmelCase      =   [
									    0.4188_7826,
									    0.3772_8766,
									    0.3013_8272,
									    0.4141_6335,
									    0.4166_4985,
									    0.3628_3392,
									    0.3619_1246,
									    0.4336_4465,
									    0.4300_1732,
									]

									assert image.shape == (1, 512, 512, 3)

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




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   """cuda"""
									_lowerCAmelCase      =   StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""		)
									_lowerCAmelCase      =   pipe.to(__UpperCamelCase		)
									pipe.set_progress_bar_config(disable=__UpperCamelCase		)

									_lowerCAmelCase      =   """an image of a crowded boulevard, realistic, 4k"""
									_lowerCAmelCase      =   {
									    """editing_prompt""": """crowd, crowded, people""",
									    """reverse_editing_direction""": True,
									    """edit_warmup_steps""": 10,
									    """edit_guidance_scale""": 8.3,
									    """edit_threshold""": 0.9,
									    """edit_momentum_scale""": 0.5,
									    """edit_mom_beta""": 0.6,
									}

									_lowerCAmelCase      =   9
									_lowerCAmelCase      =   7

									# no sega enabled
									_lowerCAmelCase      =   torch.Generator(__UpperCamelCase		)
									generator.manual_seed(__UpperCamelCase		)
									_lowerCAmelCase      =   pipe(
									    [prompt]						,    generator=__UpperCamelCase						,    guidance_scale=__UpperCamelCase						,    num_inference_steps=50						,    output_type="""np"""						,    width=512						,    height=512						,    )

									_lowerCAmelCase      =   output.images
									_lowerCAmelCase      =   image[0, -3:, -3:, -1]
									_lowerCAmelCase      =   [
									    0.4349_7998,
									    0.9181_4065,
									    0.754_0739,
									    0.5558_0205,
									    0.846_7265,
									    0.538_9691,
									    0.6257_4506,
									    0.5889_7763,
									    0.5092_6757,
									]

									assert image.shape == (1, 512, 512, 3)

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

									# with sega enabled
									# generator = torch.manual_seed(seed)
									generator.manual_seed(__UpperCamelCase		)
									_lowerCAmelCase      =   pipe(
									    [prompt]						,    generator=__UpperCamelCase						,    guidance_scale=__UpperCamelCase						,    num_inference_steps=50						,    output_type="""np"""						,    width=512						,    height=512						,    **__UpperCamelCase						,    )

									_lowerCAmelCase      =   output.images
									_lowerCAmelCase      =   image[0, -3:, -3:, -1]
									_lowerCAmelCase      =   [
									    0.308_9719,
									    0.3050_0144,
									    0.2901_6042,
									    0.3063_0964,
									    0.32_5687,
									    0.2941_9225,
									    0.290_8091,
									    0.2872_3598,
									    0.2769_6294,
									]

									assert image.shape == (1, 512, 512, 3)

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




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   """cuda"""
									_lowerCAmelCase      =   StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""		)
									_lowerCAmelCase      =   pipe.to(__UpperCamelCase		)
									pipe.set_progress_bar_config(disable=__UpperCamelCase		)

									_lowerCAmelCase      =   """a castle next to a river"""
									_lowerCAmelCase      =   {
									    """editing_prompt""": ["""boat on a river, boat""", """monet, impression, sunrise"""],
									    """reverse_editing_direction""": False,
									    """edit_warmup_steps""": [15, 18],
									    """edit_guidance_scale""": 6,
									    """edit_threshold""": [0.9, 0.8],
									    """edit_momentum_scale""": 0.5,
									    """edit_mom_beta""": 0.6,
									}

									_lowerCAmelCase      =   48
									_lowerCAmelCase      =   7

									# no sega enabled
									_lowerCAmelCase      =   torch.Generator(__UpperCamelCase		)
									generator.manual_seed(__UpperCamelCase		)
									_lowerCAmelCase      =   pipe(
									    [prompt]						,    generator=__UpperCamelCase						,    guidance_scale=__UpperCamelCase						,    num_inference_steps=50						,    output_type="""np"""						,    width=512						,    height=512						,    )

									_lowerCAmelCase      =   output.images
									_lowerCAmelCase      =   image[0, -3:, -3:, -1]
									_lowerCAmelCase      =   [
									    0.7516_3555,
									    0.7603_7145,
									    0.6_1785,
									    0.918_9673,
									    0.862_7701,
									    0.8518_9694,
									    0.851_2813,
									    0.8701_2076,
									    0.831_2857,
									]

									assert image.shape == (1, 512, 512, 3)

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

									# with sega enabled
									# generator = torch.manual_seed(seed)
									generator.manual_seed(__UpperCamelCase		)
									_lowerCAmelCase      =   pipe(
									    [prompt]						,    generator=__UpperCamelCase						,    guidance_scale=__UpperCamelCase						,    num_inference_steps=50						,    output_type="""np"""						,    width=512						,    height=512						,    **__UpperCamelCase						,    )

									_lowerCAmelCase      =   output.images
									_lowerCAmelCase      =   image[0, -3:, -3:, -1]
									_lowerCAmelCase      =   [
									    0.7355_3365,
									    0.753_7271,
									    0.7434_1905,
									    0.6648_0356,
									    0.647_2925,
									    0.6303_9416,
									    0.6481_2905,
									    0.674_9717,
									    0.651_7102,
									]

									assert image.shape == (1, 512, 512, 3)

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




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   """cuda"""
									_lowerCAmelCase      =   StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""						,    torch_dtype=torch.floataa		)
									_lowerCAmelCase      =   pipe.to(__UpperCamelCase		)
									pipe.set_progress_bar_config(disable=__UpperCamelCase		)

									_lowerCAmelCase      =   """a photo of a cat"""
									_lowerCAmelCase      =   {
									    """editing_prompt""": ["""sunglasses"""],
									    """reverse_editing_direction""": [False],
									    """edit_warmup_steps""": 10,
									    """edit_guidance_scale""": 6,
									    """edit_threshold""": 0.95,
									    """edit_momentum_scale""": 0.5,
									    """edit_mom_beta""": 0.6,
									}

									_lowerCAmelCase      =   3
									_lowerCAmelCase      =   7

									# no sega enabled
									_lowerCAmelCase      =   torch.Generator(__UpperCamelCase		)
									generator.manual_seed(__UpperCamelCase		)
									_lowerCAmelCase      =   pipe(
									    [prompt]						,    generator=__UpperCamelCase						,    guidance_scale=__UpperCamelCase						,    num_inference_steps=50						,    output_type="""np"""						,    width=512						,    height=512						,    )

									_lowerCAmelCase      =   output.images
									_lowerCAmelCase      =   image[0, -3:, -3:, -1]
									_lowerCAmelCase      =   [
									    0.3488_7695,
									    0.387_6953,
									    0.375,
									    0.3442_3828,
									    0.358_1543,
									    0.3571_7773,
									    0.338_3789,
									    0.3457_0312,
									    0.35_9375,
									]

									assert image.shape == (1, 512, 512, 3)

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

									# with sega enabled
									# generator = torch.manual_seed(seed)
									generator.manual_seed(__UpperCamelCase		)
									_lowerCAmelCase      =   pipe(
									    [prompt]						,    generator=__UpperCamelCase						,    guidance_scale=__UpperCamelCase						,    num_inference_steps=50						,    output_type="""np"""						,    width=512						,    height=512						,    **__UpperCamelCase						,    )

									_lowerCAmelCase      =   output.images
									_lowerCAmelCase      =   image[0, -3:, -3:, -1]
									_lowerCAmelCase      =   [
									    0.4228_5156,
									    0.3691_4062,
									    0.2907_7148,
									    0.4204_1016,
									    0.4191_8945,
									    0.3549_8047,
									    0.361_8164,
									    0.442_3828,
									    0.4311_5234,
									]

									assert image.shape == (1, 512, 512, 3)

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








import json
import os
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from contextlib import AbstractContextManager
from typing import Dict, List, Optional

import requests

from ..utils import logging
from . import BaseTransformersCLICommand


A__     				=	logging.get_logger(__name__)  # pylint: disable=invalid-name


A__     				=	"""lfs-multipart-upload"""





class  __lowerCAmelCase			(					lowerCamelCase__	):




				@staticmethod
				def 							snake_case       (							_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase      =   parser.add_parser(
									    """lfs-enable-largefiles"""						,    help=(
									        """Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB."""
									    )						,    )
									enable_parser.add_argument("""path"""						,    type=_snake_case						,    help="""Local path to repository you want to configure."""		)
									enable_parser.set_defaults(func=lambda _snake_case		: LfsEnableCommand(_snake_case		)		)

									_lowerCAmelCase      =   parser.add_parser(
									    _snake_case						,    help=(
									        """Deprecated: use `huggingface-cli` instead. """
									        """Command will get called by git-lfs, do not call it directly."""
									    )						,    )
									upload_parser.set_defaults(func=lambda _snake_case		: LfsUploadCommand(_snake_case		)		)





class  __lowerCAmelCase			:




				def __init__(							self						,    _snake_case		):



									"""simple docstring"""





									_lowerCAmelCase      =   args




				def 							snake_case       (							self		):



									"""simple docstring"""





									warnings.warn(
									    """Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead."""		)
									_lowerCAmelCase      =   os.path.abspath(self.args.path		)
									if not os.path.isdir(_snake_case		):
														print("""This does not look like a valid git repo."""		)
														exit(1		)
									subprocess.run(
									    """git config lfs.customtransfer.multipart.path transformers-cli""".split()						,    check=_snake_case						,    cwd=_snake_case		)
									subprocess.run(
									    F'git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}'.split()						,    check=_snake_case						,    cwd=_snake_case						,    )
									print("""Local repo set up for largefiles"""		)


def 		_UpperCAmelCase    (				snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase      =   json.dumps(snake_case      ) + """\n"""
					sys.stdout.write(snake_case      )
					sys.stdout.flush()


def 		_UpperCAmelCase    (				):





					"""simple docstring"""

					_lowerCAmelCase      =   json.loads(sys.stdin.readline().strip()      )

					if "terminate" in (msg.get("""type"""      ), msg.get("""event"""      )):
										# terminate message received
										return None

					if msg.get("""event"""      ) not in ("download", "upload"):
										logger.critical("""Received unexpected message"""      )
										sys.exit(1      )

					return msg





class  __lowerCAmelCase			(					lowerCamelCase__	):




				def __init__(							self						,    _snake_case						,    _snake_case						,    _snake_case		):



									"""simple docstring"""





									_lowerCAmelCase      =   filepath
									_lowerCAmelCase      =   seek_from
									_lowerCAmelCase      =   read_limit
									_lowerCAmelCase      =   0




				def __enter__(							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   open(self.filepath						,    """rb"""		)
									self.f.seek(self.seek_from		)
									return self




				def __len__(							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   os.fstat(self.f.fileno()		).st_size
									return min(self.read_limit						,    total_length - self.seek_from		)




				def 							snake_case       (							self						,    _snake_case=-1		):



									"""simple docstring"""





									if self.n_seen >= self.read_limit:
														return b""
									_lowerCAmelCase      =   self.read_limit - self.n_seen
									_lowerCAmelCase      =   self.f.read(remaining_amount if n < 0 else min(_snake_case						,    _snake_case		)		)
									self.n_seen += len(_snake_case		)
									return data




				def __iter__(							self		):



									"""simple docstring"""





									yield self.read(n=4 * 1024 * 1024		)




				def __exit__(							self						,    *_snake_case		):



									"""simple docstring"""





									self.f.close()





class  __lowerCAmelCase			:




				def __init__(							self						,    _snake_case		):



									"""simple docstring"""





									_lowerCAmelCase      =   args




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   json.loads(sys.stdin.readline().strip()		)
									if not (init_msg.get("""event"""		) == "init" and init_msg.get("""operation"""		) == "upload"):
														write_msg({"""error""": {"""code""": 32, """message""": """Wrong lfs init operation"""}}		)
														sys.exit(1		)

									# The transfer process should use the information it needs from the
									# initiation structure, and also perform any one-off setup tasks it
									# needs to do. It should then respond on stdout with a simple empty
									# confirmation structure, as follows:
									write_msg({}		)

									# After the initiation exchange, git-lfs will send any number of
									# transfer requests to the stdin of the transfer process, in a serial sequence.
									while True:
														_lowerCAmelCase      =   read_msg()
														if msg is None:
																			# When all transfers have been processed, git-lfs will send
																			# a terminate event to the stdin of the transfer process.
																			# On receiving this message the transfer process should
																			# clean up and terminate. No response is expected.
																			sys.exit(0		)

														_lowerCAmelCase      =   msg["""oid"""]
														_lowerCAmelCase      =   msg["""path"""]
														_lowerCAmelCase      =   msg["""action"""]["""href"""]
														_lowerCAmelCase      =   msg["""action"""]["""header"""]
														_lowerCAmelCase      =   int(header.pop("""chunk_size"""		)		)
														_lowerCAmelCase      =   list(header.values()		)

														_lowerCAmelCase      =   []
														for i, presigned_url in enumerate(_snake_case		):
																			with FileSlice(_snake_case						,    seek_from=i * chunk_size						,    read_limit=_snake_case		) as data:
																								_lowerCAmelCase      =   requests.put(_snake_case						,    data=_snake_case		)
																								r.raise_for_status()
																								parts.append(
																								    {
																								        """etag""": r.headers.get("""etag"""		),
																								        """partNumber""": i + 1,
																								    }		)
																								# In order to support progress reporting while data is uploading / downloading,
																								# the transfer process should post messages to stdout
																								write_msg(
																								    {
																								        """event""": """progress""",
																								        """oid""": oid,
																								        """bytesSoFar""": (i + 1) * chunk_size,
																								        """bytesSinceLast""": chunk_size,
																								    }		)
																								# Not precise but that's ok.

														_lowerCAmelCase      =   requests.post(
														    _snake_case						,    json={
														        """oid""": oid,
														        """parts""": parts,
														    }						,    )
														r.raise_for_status()

														write_msg({"""event""": """complete""", """oid""": oid}		)



