
'''simple docstring'''





import gc
import os
import tempfile
import unittest

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNetaDConditionModel
from diffusers.loaders import AttnProcsLayers, LoraLoaderMixin
from diffusers.models.attention_processor import (
    Attention,
    AttnProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
    XFormersAttnProcessor,
)
from diffusers.utils import TEXT_ENCODER_ATTN_MODULE, floats_tensor, torch_device





def 					_a      (		lowerCamelCase							):
 lowerCamelCase	:			Tuple           =   {}
 for name in unet.attn_processors.keys():
  lowerCamelCase	:			List[str]           =   None if name.endswith("""attn1.processor"""							) else unet.config.cross_attention_dim
  if name.startswith("""mid_block"""							):
   lowerCamelCase	:			Union[str, Any]           =   unet.config.block_out_channels[-1]
  elif name.startswith("""up_blocks"""							):
   lowerCamelCase	:			str           =   int(name[len("""up_blocks."""							)]							)
   lowerCamelCase	:			Optional[int]           =   list(reversed(unet.config.block_out_channels							)							)[block_id]
  elif name.startswith("""down_blocks"""							):
   lowerCamelCase	:			Any           =   int(name[len("""down_blocks."""							)]							)
   lowerCamelCase	:			Optional[Any]           =   unet.config.block_out_channels[block_id]
  lowerCamelCase	:			str           =   (
      LoRAAttnProcessor2_0 if hasattr(lowerCamelCase,			"""scaled_dot_product_attention"""							) else LoRAAttnProcessor
  )
  lowerCamelCase	:			str           =   lora_attn_processor_class(
      hidden_size=lowerCamelCase,			cross_attention_dim=lowerCamelCase							)
 lowerCamelCase	:			Optional[Any]           =   AttnProcsLayers(lowerCamelCase							)
 return lora_attn_procs, unet_lora_layers





def 					_a      (		lowerCamelCase							):
 lowerCamelCase	:			List[str]           =   {}
 lowerCamelCase	:			Any           =   (
     LoRAAttnProcessor2_0 if hasattr(lowerCamelCase,			"""scaled_dot_product_attention"""							) else LoRAAttnProcessor
 )
 for name, module in text_encoder.named_modules():
  if name.endswith(lowerCamelCase							):
   lowerCamelCase	:			str           =   lora_attn_processor_class(
       hidden_size=module.out_proj.out_features,			cross_attention_dim=lowerCamelCase							)
 return text_lora_attn_procs





def 					_a      (		lowerCamelCase							):
 lowerCamelCase	:			Optional[int]           =   create_text_encoder_lora_attn_procs(lowerCamelCase							)
 lowerCamelCase	:			Tuple           =   AttnProcsLayers(lowerCamelCase							)
 return text_encoder_lora_layers





def 					_a      (		lowerCamelCase,			lowerCamelCase=False							):







 for _, attn_proc in text_lora_attn_procs.items():
  # set up.weights
  for layer_name, layer_module in attn_proc.named_modules():
   if layer_name.endswith("""_lora"""							):
    lowerCamelCase	:			Any           =   (
        torch.randn_like(layer_module.up.weight							)
        if randn_weight
        else torch.zeros_like(layer_module.up.weight							)
    )
    lowerCamelCase	:			Union[str, Any]           =   torch.nn.Parameter(lowerCamelCase							)





class 							A__	(			unittest.TestCase):
      def 		UpperCamelCase__							(   self   ):
       torch.manual_seed(0   )
       lowerCamelCase	:			Tuple           =   UNetaDConditionModel(
           block_out_channels=(3_2, 6_4)					,						layers_per_block=2					,						sample_size=3_2					,						in_channels=4					,						out_channels=4					,						down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")					,						up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")					,						cross_attention_dim=3_2					,						)
       lowerCamelCase	:			str           =   DDIMScheduler(
           beta_start=0.00_085					,						beta_end=0.012					,						beta_schedule="""scaled_linear"""					,						clip_sample=__magic_name__					,						set_alpha_to_one=__magic_name__					,						steps_offset=1					,						)
       torch.manual_seed(0   )
       lowerCamelCase	:			Any           =   AutoencoderKL(
           block_out_channels=[3_2, 6_4]					,						in_channels=3					,						out_channels=3					,						down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]					,						up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]					,						latent_channels=4					,						)
       lowerCamelCase	:			str           =   CLIPTextConfig(
           bos_token_id=0					,						eos_token_id=2					,						hidden_size=3_2					,						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					,						)
       lowerCamelCase	:			Optional[Any]           =   CLIPTextModel(__magic_name__   )
       lowerCamelCase	:			Union[str, Any]           =   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

       lowerCamelCase    ,					lowerCamelCase	:			Optional[int]           =   create_unet_lora_layers(__magic_name__   )
       lowerCamelCase	:			Any           =   create_text_encoder_lora_layers(__magic_name__   )

       lowerCamelCase	:			List[Any]           =   {
           """unet""": unet,
           """scheduler""": scheduler,
           """vae""": vae,
           """text_encoder""": text_encoder,
           """tokenizer""": tokenizer,
           """safety_checker""": None,
           """feature_extractor""": None,
       }
       lowerCamelCase	:			Any           =   {
           """unet_lora_layers""": unet_lora_layers,
           """text_encoder_lora_layers""": text_encoder_lora_layers,
           """unet_lora_attn_procs""": unet_lora_attn_procs,
       }
       return pipeline_components, lora_components
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			Dict           =   1
       lowerCamelCase	:			str           =   1_0
       lowerCamelCase	:			Any           =   4
       lowerCamelCase	:			List[str]           =   (3_2, 3_2)

       lowerCamelCase	:			int           =   torch.manual_seed(0   )
       lowerCamelCase	:			str           =   floats_tensor((batch_size, num_channels) + sizes   )
       lowerCamelCase	:			Optional[Any]           =   torch.randint(1					,						__magic_name__					,						size=(batch_size, sequence_length)					,						generator=__magic_name__   )

       lowerCamelCase	:			List[str]           =   {
           """prompt""": """A painting of a squirrel eating a burger""",
           """generator""": generator,
           """num_inference_steps""": 2,
           """guidance_scale""": 6.0,
           """output_type""": """numpy""",
       }

       return noise, input_ids, pipeline_inputs

       # copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			List[Any]           =   7_7

       lowerCamelCase	:			Tuple           =   torch.randint(2					,						5_6					,						size=(1, max_seq_length)					,						generator=torch.manual_seed(0   )   )

       lowerCamelCase	:			Optional[Any]           =   {}
       lowerCamelCase	:			Dict           =   inputs
       return prepared_inputs
      def 		UpperCamelCase__							(   self					,						__magic_name__   ):
       lowerCamelCase    ,					lowerCamelCase	:			Union[str, Any]           =   self.get_dummy_components()
       LoraLoaderMixin.save_lora_weights(
           save_directory=__magic_name__					,						unet_lora_layers=lora_components["""unet_lora_layers"""]					,						text_encoder_lora_layers=lora_components["""text_encoder_lora_layers"""]					,						)
       self.assertTrue(os.path.isfile(os.path.join(__magic_name__					,						"""pytorch_lora_weights.bin"""   )   )   )
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase    ,					lowerCamelCase	:			Optional[int]           =   self.get_dummy_components()
       lowerCamelCase	:			int           =   StableDiffusionPipeline(**__magic_name__   )
       lowerCamelCase	:			List[Any]           =   sd_pipe.to(__magic_name__   )
       sd_pipe.set_progress_bar_config(disable=__magic_name__   )

       lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase	:			str           =   self.get_dummy_inputs()

       lowerCamelCase	:			Union[str, Any]           =   sd_pipe(**__magic_name__   ).images
       lowerCamelCase	:			Any           =   original_images[0, -3:, -3:, -1]

       with tempfile.TemporaryDirectory() as tmpdirname:
        LoraLoaderMixin.save_lora_weights(
            save_directory=__magic_name__					,						unet_lora_layers=lora_components["""unet_lora_layers"""]					,						text_encoder_lora_layers=lora_components["""text_encoder_lora_layers"""]					,						)
        self.assertTrue(os.path.isfile(os.path.join(__magic_name__					,						"""pytorch_lora_weights.bin"""   )   )   )
        sd_pipe.load_lora_weights(__magic_name__   )

       lowerCamelCase	:			Any           =   sd_pipe(**__magic_name__   ).images
       lowerCamelCase	:			str           =   lora_images[0, -3:, -3:, -1]

       # Outputs shouldn't match.
       self.assertFalse(torch.allclose(torch.from_numpy(__magic_name__   )					,						torch.from_numpy(__magic_name__   )   )   )
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase    ,					lowerCamelCase	:			Any           =   self.get_dummy_components()
       lowerCamelCase	:			int           =   StableDiffusionPipeline(**__magic_name__   )
       lowerCamelCase	:			Tuple           =   sd_pipe.to(__magic_name__   )
       sd_pipe.set_progress_bar_config(disable=__magic_name__   )

       lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase	:			Dict           =   self.get_dummy_inputs()

       lowerCamelCase	:			Tuple           =   sd_pipe(**__magic_name__   ).images
       lowerCamelCase	:			int           =   original_images[0, -3:, -3:, -1]

       with tempfile.TemporaryDirectory() as tmpdirname:
        LoraLoaderMixin.save_lora_weights(
            save_directory=__magic_name__					,						unet_lora_layers=lora_components["""unet_lora_layers"""]					,						text_encoder_lora_layers=lora_components["""text_encoder_lora_layers"""]					,						safe_serialization=__magic_name__					,						)
        self.assertTrue(os.path.isfile(os.path.join(__magic_name__					,						"""pytorch_lora_weights.safetensors"""   )   )   )
        sd_pipe.load_lora_weights(__magic_name__   )

       lowerCamelCase	:			str           =   sd_pipe(**__magic_name__   ).images
       lowerCamelCase	:			Optional[int]           =   lora_images[0, -3:, -3:, -1]

       # Outputs shouldn't match.
       self.assertFalse(torch.allclose(torch.from_numpy(__magic_name__   )					,						torch.from_numpy(__magic_name__   )   )   )
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase    ,					lowerCamelCase	:			str           =   self.get_dummy_components()
       lowerCamelCase	:			int           =   lora_components["""unet_lora_attn_procs"""]
       lowerCamelCase	:			Tuple           =   StableDiffusionPipeline(**__magic_name__   )
       lowerCamelCase	:			Union[str, Any]           =   sd_pipe.to(__magic_name__   )
       sd_pipe.set_progress_bar_config(disable=__magic_name__   )

       lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase	:			List[Any]           =   self.get_dummy_inputs()

       lowerCamelCase	:			str           =   sd_pipe(**__magic_name__   ).images
       lowerCamelCase	:			Any           =   original_images[0, -3:, -3:, -1]

       with tempfile.TemporaryDirectory() as tmpdirname:
        lowerCamelCase	:			Dict           =   sd_pipe.unet
        unet.set_attn_processor(__magic_name__   )
        unet.save_attn_procs(__magic_name__   )
        self.assertTrue(os.path.isfile(os.path.join(__magic_name__					,						"""pytorch_lora_weights.bin"""   )   )   )
        sd_pipe.load_lora_weights(__magic_name__   )

       lowerCamelCase	:			Any           =   sd_pipe(**__magic_name__   ).images
       lowerCamelCase	:			Optional[int]           =   lora_images[0, -3:, -3:, -1]

       # Outputs shouldn't match.
       self.assertFalse(torch.allclose(torch.from_numpy(__magic_name__   )					,						torch.from_numpy(__magic_name__   )   )   )
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase    ,					lowerCamelCase	:			Dict           =   self.get_dummy_components()
       lowerCamelCase	:			Optional[int]           =   StableDiffusionPipeline(**__magic_name__   )

       lowerCamelCase	:			int           =   self.get_dummy_tokens()

       # inference without lora
       lowerCamelCase	:			List[Any]           =   pipe.text_encoder(**__magic_name__   )[0]
       assert outputs_without_lora.shape == (1, 7_7, 3_2)

       # create lora_attn_procs with zeroed out up.weights
       lowerCamelCase	:			Optional[int]           =   create_text_encoder_lora_attn_procs(pipe.text_encoder   )
       set_lora_up_weights(__magic_name__					,						randn_weight=__magic_name__   )

       # monkey patch
       pipe._modify_text_encoder(__magic_name__   )

       # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
       del text_attn_procs
       gc.collect()

       # inference with lora
       lowerCamelCase	:			int           =   pipe.text_encoder(**__magic_name__   )[0]
       assert outputs_with_lora.shape == (1, 7_7, 3_2)

       assert torch.allclose(
           __magic_name__					,						__magic_name__   ), "lora_up_weight are all zero, so the lora outputs should be the same to without lora outputs"

       # create lora_attn_procs with randn up.weights
       lowerCamelCase	:			List[str]           =   create_text_encoder_lora_attn_procs(pipe.text_encoder   )
       set_lora_up_weights(__magic_name__					,						randn_weight=__magic_name__   )

       # monkey patch
       pipe._modify_text_encoder(__magic_name__   )

       # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
       del text_attn_procs
       gc.collect()

       # inference with lora
       lowerCamelCase	:			List[str]           =   pipe.text_encoder(**__magic_name__   )[0]
       assert outputs_with_lora.shape == (1, 7_7, 3_2)

       assert not torch.allclose(
           __magic_name__					,						__magic_name__   ), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase    ,					lowerCamelCase	:			List[Any]           =   self.get_dummy_components()
       lowerCamelCase	:			str           =   StableDiffusionPipeline(**__magic_name__   )

       lowerCamelCase	:			Optional[int]           =   self.get_dummy_tokens()

       # inference without lora
       lowerCamelCase	:			Dict           =   pipe.text_encoder(**__magic_name__   )[0]
       assert outputs_without_lora.shape == (1, 7_7, 3_2)

       # create lora_attn_procs with randn up.weights
       lowerCamelCase	:			Dict           =   create_text_encoder_lora_attn_procs(pipe.text_encoder   )
       set_lora_up_weights(__magic_name__					,						randn_weight=__magic_name__   )

       # monkey patch
       pipe._modify_text_encoder(__magic_name__   )

       # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
       del text_attn_procs
       gc.collect()

       # inference with lora
       lowerCamelCase	:			Any           =   pipe.text_encoder(**__magic_name__   )[0]
       assert outputs_with_lora.shape == (1, 7_7, 3_2)

       assert not torch.allclose(
           __magic_name__					,						__magic_name__   ), "lora outputs should be different to without lora outputs"

       # remove monkey patch
       pipe._remove_text_encoder_monkey_patch()

       # inference with removed lora
       lowerCamelCase	:			Any           =   pipe.text_encoder(**__magic_name__   )[0]
       assert outputs_without_lora_removed.shape == (1, 7_7, 3_2)

       assert torch.allclose(
           __magic_name__					,						__magic_name__   ), "remove lora monkey patch should restore the original outputs"
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase    ,					lowerCamelCase	:			Tuple           =   self.get_dummy_components()
       lowerCamelCase	:			List[Any]           =   StableDiffusionPipeline(**__magic_name__   )
       lowerCamelCase	:			Any           =   sd_pipe.to(__magic_name__   )
       sd_pipe.set_progress_bar_config(disable=__magic_name__   )

       lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase	:			Dict           =   self.get_dummy_inputs()

       with tempfile.TemporaryDirectory() as tmpdirname:
        LoraLoaderMixin.save_lora_weights(
            save_directory=__magic_name__					,						unet_lora_layers=lora_components["""unet_lora_layers"""]					,						text_encoder_lora_layers=lora_components["""text_encoder_lora_layers"""]					,						)
        self.assertTrue(os.path.isfile(os.path.join(__magic_name__					,						"""pytorch_lora_weights.bin"""   )   )   )
        sd_pipe.load_lora_weights(__magic_name__   )

       lowerCamelCase	:			Optional[Any]           =   sd_pipe(**__magic_name__   ).images
       lowerCamelCase	:			Dict           =   lora_images[0, -3:, -3:, -1]

       lowerCamelCase	:			List[str]           =   sd_pipe(**__magic_name__					,						cross_attention_kwargs={"""scale""": 0.5}   ).images
       lowerCamelCase	:			List[str]           =   lora_images_with_scale[0, -3:, -3:, -1]

       # Outputs shouldn't match.
       self.assertFalse(
           torch.allclose(torch.from_numpy(__magic_name__   )					,						torch.from_numpy(__magic_name__   )   )   )
      def 		UpperCamelCase__							(   self   ):



       with tempfile.TemporaryDirectory() as tmpdirname:
        self.create_lora_weight_file(__magic_name__   )

        lowerCamelCase    ,					lowerCamelCase	:			List[str]           =   self.get_dummy_components()
        lowerCamelCase	:			Dict           =   StableDiffusionPipeline(**__magic_name__   )
        lowerCamelCase	:			Union[str, Any]           =   sd_pipe.to(__magic_name__   )
        sd_pipe.set_progress_bar_config(disable=__magic_name__   )

        # check if vanilla attention processors are used
        for _, module in sd_pipe.unet.named_modules():
         if isinstance(__magic_name__					,						__magic_name__   ):
          self.assertIsInstance(module.processor					,						(AttnProcessor, AttnProcessor2_0)   )

            # load LoRA weight file
        sd_pipe.load_lora_weights(__magic_name__   )

        # check if lora attention processors are used
        for _, module in sd_pipe.unet.named_modules():
         if isinstance(__magic_name__					,						__magic_name__   ):
          lowerCamelCase	:			Optional[int]           =   (
              LoRAAttnProcessor2_0 if hasattr(__magic_name__					,						"""scaled_dot_product_attention"""   ) else LoRAAttnProcessor
          )
          self.assertIsInstance(module.processor					,						__magic_name__   )
      @unittest.skipIf(torch_device != """cuda"""					,						"""This test is supposed to run on GPU"""   )
      def 		UpperCamelCase__							(   self   ):



       with tempfile.TemporaryDirectory() as tmpdirname:
        self.create_lora_weight_file(__magic_name__   )

        lowerCamelCase    ,					lowerCamelCase	:			Dict           =   self.get_dummy_components()
        lowerCamelCase	:			Optional[int]           =   StableDiffusionPipeline(**__magic_name__   )
        lowerCamelCase	:			Tuple           =   sd_pipe.to(__magic_name__   )
        sd_pipe.set_progress_bar_config(disable=__magic_name__   )

        # enable XFormers
        sd_pipe.enable_xformers_memory_efficient_attention()

        # check if xFormers attention processors are used
        for _, module in sd_pipe.unet.named_modules():
         if isinstance(__magic_name__					,						__magic_name__   ):
          self.assertIsInstance(module.processor					,						__magic_name__   )

            # load LoRA weight file
        sd_pipe.load_lora_weights(__magic_name__   )

        # check if lora attention processors are used
        for _, module in sd_pipe.unet.named_modules():
         if isinstance(__magic_name__					,						__magic_name__   ):
          self.assertIsInstance(module.processor					,						__magic_name__   )






      @unittest.skipIf(torch_device != """cuda"""					,						"""This test is supposed to run on GPU"""   )
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase    ,					lowerCamelCase	:			Any           =   self.get_dummy_components()
       lowerCamelCase	:			List[str]           =   StableDiffusionPipeline(**__magic_name__   )
       lowerCamelCase	:			int           =   sd_pipe.to(__magic_name__   )
       sd_pipe.set_progress_bar_config(disable=__magic_name__   )

       lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase	:			Union[str, Any]           =   self.get_dummy_inputs()

       # enable XFormers
       sd_pipe.enable_xformers_memory_efficient_attention()

       lowerCamelCase	:			Union[str, Any]           =   sd_pipe(**__magic_name__   ).images
       lowerCamelCase	:			str           =   original_images[0, -3:, -3:, -1]

       with tempfile.TemporaryDirectory() as tmpdirname:
        LoraLoaderMixin.save_lora_weights(
            save_directory=__magic_name__					,						unet_lora_layers=lora_components["""unet_lora_layers"""]					,						text_encoder_lora_layers=lora_components["""text_encoder_lora_layers"""]					,						)
        self.assertTrue(os.path.isfile(os.path.join(__magic_name__					,						"""pytorch_lora_weights.bin"""   )   )   )
        sd_pipe.load_lora_weights(__magic_name__   )

       lowerCamelCase	:			List[Any]           =   sd_pipe(**__magic_name__   ).images
       lowerCamelCase	:			Any           =   lora_images[0, -3:, -3:, -1]

       # Outputs shouldn't match.
       self.assertFalse(torch.allclose(torch.from_numpy(__magic_name__   )					,						torch.from_numpy(__magic_name__   )   )   )






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


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


_lowerCamelCase           ="""lfs-multipart-upload"""





class 							A__	(			__SCREAMING_SNAKE_CASE):
      @staticmethod
      def 		UpperCamelCase__							(   __magic_name__   ):
       lowerCamelCase	:			List[Any]           =   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=__magic_name__					,						help="""Local path to repository you want to configure."""   )
       enable_parser.set_defaults(func=lambda __magic_name__   : LfsEnableCommand(__magic_name__   )   )

       lowerCamelCase	:			Union[str, Any]           =   parser.add_parser(
           __magic_name__					,						help=(
               """Deprecated: use `huggingface-cli` instead. """
               """Command will get called by git-lfs, do not call it directly."""
           )					,						)
       upload_parser.set_defaults(func=lambda __magic_name__   : LfsUploadCommand(__magic_name__   )   )





class 							A__	:
      def __init__(   self					,						__magic_name__   ):
       lowerCamelCase	:			int           =   args






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





def 					_a      (		lowerCamelCase							):
 lowerCamelCase	:			Dict           =   json.dumps(lowerCamelCase							) + """\n"""
 sys.stdout.write(lowerCamelCase							)
 sys.stdout.flush()





def 					_a      (		):
 lowerCamelCase	:			Tuple           =   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 							A__	(			__SCREAMING_SNAKE_CASE):
      def __init__(   self					,						__magic_name__					,						__magic_name__					,						__magic_name__   ):
       lowerCamelCase	:			List[str]           =   filepath
       lowerCamelCase	:			Dict           =   seek_from
       lowerCamelCase	:			str           =   read_limit
       lowerCamelCase	:			Tuple           =   0
      def __enter__(   self   ):
       lowerCamelCase	:			str           =   open(self.filepath					,						"""rb"""   )
       self.f.seek(self.seek_from   )
       return self
      def __len__(   self   ):
       lowerCamelCase	:			Any           =   os.fstat(self.f.fileno()   ).st_size
       return min(self.read_limit					,						total_length - self.seek_from   )
      def 		UpperCamelCase__							(   self					,						__magic_name__=-1   ):
       if self.n_seen >= self.read_limit:
        return b""
       lowerCamelCase	:			Tuple           =   self.read_limit - self.n_seen
       lowerCamelCase	:			Dict           =   self.f.read(remaining_amount if n < 0 else min(__magic_name__					,						__magic_name__   )   )
       self.n_seen += len(__magic_name__   )
       return data
      def __iter__(   self   ):
       yield self.read(n=4 * 1_0_2_4 * 1_0_2_4   )






      def __exit__(   self					,						*__magic_name__   ):
       self.f.close()





class 							A__	:
      def __init__(   self					,						__magic_name__   ):
       lowerCamelCase	:			Dict           =   args






      def 		UpperCamelCase__							(   self   ):
       # Immediately after invoking a custom transfer process, git-lfs
       # sends initiation data to the process over stdin.
       # This tells the process useful information about the configuration.
       lowerCamelCase	:			Dict           =   json.loads(sys.stdin.readline().strip()   )
       if not (init_msg.get("""event"""   ) == "init" and init_msg.get("""operation"""   ) == "upload"):
        write_msg({"""error""": {"""code""": 3_2, """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	:			Dict           =   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	:			Optional[int]           =   msg["""oid"""]
        lowerCamelCase	:			Union[str, Any]           =   msg["""path"""]
        lowerCamelCase	:			Any           =   msg["""action"""]["""href"""]
        lowerCamelCase	:			Optional[int]           =   msg["""action"""]["""header"""]
        lowerCamelCase	:			Dict           =   int(header.pop("""chunk_size"""   )   )
        lowerCamelCase	:			List[str]           =   list(header.values()   )

        lowerCamelCase	:			str           =   []
        for i, presigned_url in enumerate(__magic_name__   ):
         with FileSlice(__magic_name__					,						seek_from=i * chunk_size					,						read_limit=__magic_name__   ) as data:
          lowerCamelCase	:			Dict           =   requests.put(__magic_name__					,						data=__magic_name__   )
          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	:			Optional[Any]           =   requests.post(
            __magic_name__					,						json={
                """oid""": oid,
                """parts""": parts,
            }					,						)
        r.raise_for_status()

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




