



'''simple docstring'''

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


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


UpperCAmelCase			=    'lfs-multipart-upload'

class __snake_case(      _lowerCAmelCase					):


						'''simple docstring'''



						@staticmethod
						def 				__snake_case    (					A_      )	->	Tuple:
							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=A_	,       help="""Local path to repository you want to configure."""      )
							enable_parser.set_defaults(func=lambda A_      : LfsEnableCommand(A_      )      )

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







class __snake_case:


						'''simple docstring'''



						def __init__(					self	,       A_      )	->	List[Any]:
							lowerCAmelCase    =	args




						def 				__snake_case    (					self      )	->	List[str]:
							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(A_      ):
								print("""This does not look like a valid git repo."""      )
								exit(1      )
							subprocess.run(
							    """git config lfs.customtransfer.multipart.path transformers-cli""".split()	,       check=A_	,       cwd=A_      )
							subprocess.run(
							    f'git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}'.split()	,       check=A_	,       cwd=A_	,       )
							print("""Local repo set up for largefiles"""      )







def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Dict							)			->						str:







	"""simple docstring"""
	lowerCAmelCase    =	json.dumps(_SCREAMING_SNAKE_CASE							) + """\n"""
	sys.stdout.write(_SCREAMING_SNAKE_CASE							)
	sys.stdout.flush()






def 		_snake_case		(					)			->						Optional[Dict]:







	"""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 __snake_case(      _lowerCAmelCase					):


						'''simple docstring'''



						def __init__(					self	,       A_	,       A_	,       A_      )	->	List[str]:
							lowerCAmelCase    =	filepath
							lowerCAmelCase    =	seek_from
							lowerCAmelCase    =	read_limit
							lowerCAmelCase    =	0


						def __enter__(					self      )	->	Optional[Any]:
							lowerCAmelCase    =	open(self.filepath	,       """rb"""      )
							self.f.seek(self.seek_from      )
							return self


						def __len__(					self      )	->	List[Any]:
							lowerCAmelCase    =	os.fstat(self.f.fileno()      ).st_size
							return min(self.read_limit	,       total_length - self.seek_from      )


						def 				__snake_case    (					self	,       A_=-1      )	->	Optional[int]:
							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(A_	,       A_      )      )
							self.n_seen += len(A_      )
							return data


						def __iter__(					self      )	->	Optional[int]:
							yield self.read(n=4 * 1024 * 1024      )




						def __exit__(					self	,       *A_      )	->	str:
							self.f.close()







class __snake_case:


						'''simple docstring'''



						def __init__(					self	,       A_      )	->	List[Any]:
							lowerCAmelCase    =	args




						def 				__snake_case    (					self      )	->	int:
							# 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    =	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(A_      ):
									with FileSlice(A_	,       seek_from=i * chunk_size	,       read_limit=A_      ) as data:
										lowerCAmelCase    =	requests.put(A_	,       data=A_      )
										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(
								    A_	,       json={
								        """oid""": oid,
								        """parts""": parts,
								    }	,       )
								r.raise_for_status()

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




'''simple docstring'''

import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

@skip_mps
class __snake_case(      _lowerCAmelCase		,							_lowerCAmelCase		,							_lowerCAmelCase		,							unittest.TestCase					):


						'''simple docstring'''



						UpperCAmelCase							:    Optional[Any]           =		StableDiffusionModelEditingPipeline
						UpperCAmelCase							:    List[str]           =		TEXT_TO_IMAGE_PARAMS
						UpperCAmelCase							:    Optional[Any]           =		TEXT_TO_IMAGE_BATCH_PARAMS
						UpperCAmelCase							:    Optional[int]           =		TEXT_TO_IMAGE_IMAGE_PARAMS
						UpperCAmelCase							:    Optional[int]           =		TEXT_TO_IMAGE_IMAGE_PARAMS


						def 				__snake_case    (					self      )	->	Optional[Any]:
							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	,       )
							lowerCAmelCase    =	DDIMScheduler()
							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	,       )
							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	,       )
							lowerCAmelCase    =	CLIPTextModel(A_      )
							lowerCAmelCase    =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )

							lowerCAmelCase    =	{
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """vae""": vae,
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """safety_checker""": None,
							    """feature_extractor""": None,
							}
							return components


						def 				__snake_case    (					self	,       A_	,       A_=0      )	->	Optional[int]:
							lowerCAmelCase    =	torch.manual_seed(A_      )
							lowerCAmelCase    =	{
							    """prompt""": """A field of roses""",
							    """generator""": generator,
							    # Setting height and width to None to prevent OOMs on CPU.
							    """height""": None,
							    """width""": None,
							    """num_inference_steps""": 2,
							    """guidance_scale""": 6.0,
							    """output_type""": """numpy""",
							}
							return inputs


						def 				__snake_case    (					self      )	->	List[str]:
							lowerCAmelCase    =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase    =	self.get_dummy_components()
							lowerCAmelCase    =	StableDiffusionModelEditingPipeline(**A_      )
							lowerCAmelCase    =	sd_pipe.to(A_      )
							sd_pipe.set_progress_bar_config(disable=A_      )

							lowerCAmelCase    =	self.get_dummy_inputs(A_      )
							lowerCAmelCase    =	sd_pipe(**A_      ).images
							lowerCAmelCase    =	image[0, -3:, -3:, -1]
							assert image.shape == (1, 64, 64, 3)

							lowerCAmelCase    =	np.array([0.4_7_5_5, 0.5_1_3_2, 0.4_9_7_6, 0.3_9_0_4, 0.3_5_5_4, 0.4_7_6_5, 0.5_1_3_9, 0.5_1_5_8, 0.4_8_8_9]      )

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


						def 				__snake_case    (					self      )	->	str:
							lowerCAmelCase    =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase    =	self.get_dummy_components()
							lowerCAmelCase    =	StableDiffusionModelEditingPipeline(**A_      )
							lowerCAmelCase    =	sd_pipe.to(A_      )
							sd_pipe.set_progress_bar_config(disable=A_      )

							lowerCAmelCase    =	self.get_dummy_inputs(A_      )
							lowerCAmelCase    =	"""french fries"""
							lowerCAmelCase    =	sd_pipe(**A_	,       negative_prompt=A_      )
							lowerCAmelCase    =	output.images
							lowerCAmelCase    =	image[0, -3:, -3:, -1]

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

							lowerCAmelCase    =	np.array([0.4_9_9_2, 0.5_1_0_1, 0.5_0_0_4, 0.3_9_4_9, 0.3_6_0_4, 0.4_7_3_5, 0.5_2_1_6, 0.5_2_0_4, 0.4_9_1_3]      )

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


						def 				__snake_case    (					self      )	->	Optional[int]:
							lowerCAmelCase    =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase    =	self.get_dummy_components()
							lowerCAmelCase    =	EulerAncestralDiscreteScheduler(
							    beta_start=0.0_0_0_8_5	,       beta_end=0.0_1_2	,       beta_schedule="""scaled_linear"""      )
							lowerCAmelCase    =	StableDiffusionModelEditingPipeline(**A_      )
							lowerCAmelCase    =	sd_pipe.to(A_      )
							sd_pipe.set_progress_bar_config(disable=A_      )

							lowerCAmelCase    =	self.get_dummy_inputs(A_      )
							lowerCAmelCase    =	sd_pipe(**A_      ).images
							lowerCAmelCase    =	image[0, -3:, -3:, -1]

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

							lowerCAmelCase    =	np.array([0.4_7_4_7, 0.5_3_7_2, 0.4_7_7_9, 0.4_9_8_2, 0.5_5_4_3, 0.4_8_1_6, 0.5_2_3_8, 0.4_9_0_4, 0.5_0_2_7]      )

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


						def 				__snake_case    (					self      )	->	Optional[Any]:
							lowerCAmelCase    =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase    =	self.get_dummy_components()
							lowerCAmelCase    =	PNDMScheduler()
							lowerCAmelCase    =	StableDiffusionModelEditingPipeline(**A_      )
							lowerCAmelCase    =	sd_pipe.to(A_      )
							sd_pipe.set_progress_bar_config(disable=A_      )

							lowerCAmelCase    =	self.get_dummy_inputs(A_      )
							# the pipeline does not expect pndm so test if it raises error.
							with self.assertRaises(A_      ):
								lowerCAmelCase    =	sd_pipe(**A_      ).images


						def 				__snake_case    (					self      )	->	str:
							super().test_inference_batch_single_identical(expected_max_diff=5e-3      )




						def 				__snake_case    (					self      )	->	Any:
							super().test_attention_slicing_forward_pass(expected_max_diff=5e-3      )







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


						'''simple docstring'''



						def 				__snake_case    (					self      )	->	int:
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()


						def 				__snake_case    (					self	,       A_=0      )	->	Dict:
							lowerCAmelCase    =	torch.manual_seed(A_      )
							lowerCAmelCase    =	{
							    """prompt""": """A field of roses""",
							    """generator""": generator,
							    """num_inference_steps""": 3,
							    """guidance_scale""": 7.5,
							    """output_type""": """numpy""",
							}
							return inputs


						def 				__snake_case    (					self      )	->	int:
							lowerCAmelCase    =	"""CompVis/stable-diffusion-v1-4"""
							lowerCAmelCase    =	StableDiffusionModelEditingPipeline.from_pretrained(A_	,       safety_checker=A_      )
							pipe.to(A_      )
							pipe.set_progress_bar_config(disable=A_      )
							pipe.enable_attention_slicing()

							lowerCAmelCase    =	self.get_inputs()
							lowerCAmelCase    =	pipe(**A_      ).images
							lowerCAmelCase    =	image[0, -3:, -3:, -1].flatten()

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

							lowerCAmelCase    =	np.array(
							    [0.6_7_4_9_4_9_6, 0.6_3_8_6_4_5_3, 0.5_1_4_4_3_2_6_7, 0.6_6_0_9_4_9_0_5, 0.6_1_9_2_1_2_1_5, 0.5_4_9_1_3_3_2, 0.5_7_4_4_4_1_7, 0.5_8_0_7_5_1_0_6, 0.5_1_7_4_6_5_8]      )

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

							# make sure image changes after editing
							pipe.edit_model("""A pack of roses"""	,       """A pack of blue roses"""      )

							lowerCAmelCase    =	pipe(**A_      ).images
							lowerCAmelCase    =	image[0, -3:, -3:, -1].flatten()

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

							assert np.abs(expected_slice - image_slice      ).max() > 1e-1




						def 				__snake_case    (					self      )	->	str:
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							lowerCAmelCase    =	"""CompVis/stable-diffusion-v1-4"""
							lowerCAmelCase    =	DDIMScheduler.from_pretrained(A_	,       subfolder="""scheduler"""      )
							lowerCAmelCase    =	StableDiffusionModelEditingPipeline.from_pretrained(
							    A_	,       scheduler=A_	,       safety_checker=A_      )
							lowerCAmelCase    =	pipe.to(A_      )
							pipe.set_progress_bar_config(disable=A_      )
							pipe.enable_attention_slicing(1      )
							pipe.enable_sequential_cpu_offload()

							lowerCAmelCase    =	self.get_inputs()
							lowerCAmelCase    =	pipe(**A_      )

							lowerCAmelCase    =	torch.cuda.max_memory_allocated()
							# make sure that less than 4.4 GB is allocated
							assert mem_bytes < 4.4 * 10**9