

'''simple docstring'''
# This model implementation is heavily based on:

import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()
class  _snake_case						(__SCREAMING_SNAKE_CASE       ,       __SCREAMING_SNAKE_CASE       ,       __SCREAMING_SNAKE_CASE       ,       unittest.TestCase):
			__A   :     Optional[Any]          =StableDiffusionControlNetInpaintPipeline
			__A   :     Tuple          =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			__A   :     Optional[Any]          =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			__A   :     Optional[int]          =frozenset({"control_image"})  # skip `image` and `mask` for now, only test for control_image
			__A   :     int          =TEXT_TO_IMAGE_IMAGE_PARAMS






			def 						UpperCamelCase__				(   self			):
				torch.manual_seed(0			)
				UpperCAmelCase_			:	Optional[Any]      =   UNetaDConditionModel(
				    block_out_channels=(32, 64)       ,layers_per_block=2       ,sample_size=32       ,in_channels=9       ,out_channels=4       ,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")       ,up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")       ,cross_attention_dim=32       ,)
				torch.manual_seed(0			)
				UpperCAmelCase_			:	Tuple      =   ControlNetModel(
				    block_out_channels=(32, 64)       ,layers_per_block=2       ,in_channels=4       ,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")       ,cross_attention_dim=32       ,conditioning_embedding_out_channels=(16, 32)       ,)
				torch.manual_seed(0			)
				UpperCAmelCase_			:	str      =   DDIMScheduler(
				    beta_start=0.00085       ,beta_end=0.012       ,beta_schedule="scaled_linear"       ,clip_sample=_snake_case       ,set_alpha_to_one=_snake_case       ,)
				torch.manual_seed(0			)
				UpperCAmelCase_			:	Union[str, Any]      =   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			)
				UpperCAmelCase_			:	Optional[Any]      =   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=10_00       ,)
				UpperCAmelCase_			:	Optional[int]      =   CLIPTextModel(_snake_case			)
				UpperCAmelCase_			:	str      =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

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






			def 						UpperCamelCase__				(   self       ,_snake_case       ,_snake_case=0			):
				if str(_snake_case			).startswith("mps"			):
					UpperCAmelCase_			:	Any      =   torch.manual_seed(_snake_case			)
				else:
					UpperCAmelCase_			:	Dict      =   torch.Generator(device=_snake_case			).manual_seed(_snake_case			)

				UpperCAmelCase_			:	List[str]      =   2
				UpperCAmelCase_			:	List[str]      =   randn_tensor(
				    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)       ,generator=_snake_case       ,device=torch.device(_snake_case			)       ,)
				UpperCAmelCase_			:	List[str]      =   floats_tensor((1, 3, 32, 32)       ,rng=random.Random(_snake_case			)			).to(_snake_case			)
				UpperCAmelCase_			:	str      =   init_image.cpu().permute(0       ,2       ,3       ,1			)[0]

				UpperCAmelCase_			:	int      =   Image.fromarray(np.uinta(_snake_case			)			).convert("RGB"			).resize((64, 64)			)
				UpperCAmelCase_			:	int      =   Image.fromarray(np.uinta(init_image + 4			)			).convert("RGB"			).resize((64, 64)			)

				UpperCAmelCase_			:	Optional[Any]      =   {
				    "prompt": "A painting of a squirrel eating a burger",
				    "generator": generator,
				    "num_inference_steps": 2,
				    "guidance_scale": 6.0,
				    "output_type": "numpy",
				    "image": image,
				    "mask_image": mask_image,
				    "control_image": control_image,
				}

				return inputs






			def 						UpperCamelCase__				(   self			):
				return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3			)






			@unittest.skipIf(
			    torch_device != "cuda" or not is_xformers_available()       ,reason="XFormers attention is only available with CUDA and `xformers` installed"       ,)
			def 						UpperCamelCase__				(   self			):
				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3			)






			def 						UpperCamelCase__				(   self			):
				self._test_inference_batch_single_identical(expected_max_diff=2E-3			)




class  _snake_case						(__SCREAMING_SNAKE_CASE):
			__A   :     Tuple          =StableDiffusionControlNetInpaintPipeline
			__A   :     Union[str, Any]          =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			__A   :     Tuple          =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			__A   :     List[Any]          =frozenset([])






			def 						UpperCamelCase__				(   self			):
				torch.manual_seed(0			)
				UpperCAmelCase_			:	List[str]      =   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       ,)
				torch.manual_seed(0			)
				UpperCAmelCase_			:	Optional[Any]      =   ControlNetModel(
				    block_out_channels=(32, 64)       ,layers_per_block=2       ,in_channels=4       ,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")       ,cross_attention_dim=32       ,conditioning_embedding_out_channels=(16, 32)       ,)
				torch.manual_seed(0			)
				UpperCAmelCase_			:	Any      =   DDIMScheduler(
				    beta_start=0.00085       ,beta_end=0.012       ,beta_schedule="scaled_linear"       ,clip_sample=_snake_case       ,set_alpha_to_one=_snake_case       ,)
				torch.manual_seed(0			)
				UpperCAmelCase_			:	Dict      =   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			)
				UpperCAmelCase_			:	Union[str, Any]      =   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=10_00       ,)
				UpperCAmelCase_			:	Any      =   CLIPTextModel(_snake_case			)
				UpperCAmelCase_			:	List[Any]      =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

				UpperCAmelCase_			:	Optional[Any]      =   {
				    "unet": unet,
				    "controlnet": controlnet,
				    "scheduler": scheduler,
				    "vae": vae,
				    "text_encoder": text_encoder,
				    "tokenizer": tokenizer,
				    "safety_checker": None,
				    "feature_extractor": None,
				}
				return components




class  _snake_case						(__SCREAMING_SNAKE_CASE       ,       __SCREAMING_SNAKE_CASE       ,       unittest.TestCase):
			__A   :     Optional[int]          =StableDiffusionControlNetInpaintPipeline
			__A   :     Tuple          =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			__A   :     Optional[Any]          =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS






			def 						UpperCamelCase__				(   self			):
				torch.manual_seed(0			)
				UpperCAmelCase_			:	str      =   UNetaDConditionModel(
				    block_out_channels=(32, 64)       ,layers_per_block=2       ,sample_size=32       ,in_channels=9       ,out_channels=4       ,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")       ,up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")       ,cross_attention_dim=32       ,)
				torch.manual_seed(0			)

				def init_weights(_snake_case			):
					if isinstance(_snake_case       ,torch.nn.Convad			):
						torch.nn.init.normal(m.weight			)
						m.bias.data.fill_(1.0			)

				UpperCAmelCase_			:	Optional[int]      =   ControlNetModel(
				    block_out_channels=(32, 64)       ,layers_per_block=2       ,in_channels=4       ,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")       ,cross_attention_dim=32       ,conditioning_embedding_out_channels=(16, 32)       ,)
				controlneta.controlnet_down_blocks.apply(_snake_case			)

				torch.manual_seed(0			)
				UpperCAmelCase_			:	List[str]      =   ControlNetModel(
				    block_out_channels=(32, 64)       ,layers_per_block=2       ,in_channels=4       ,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")       ,cross_attention_dim=32       ,conditioning_embedding_out_channels=(16, 32)       ,)
				controlneta.controlnet_down_blocks.apply(_snake_case			)

				torch.manual_seed(0			)
				UpperCAmelCase_			:	List[str]      =   DDIMScheduler(
				    beta_start=0.00085       ,beta_end=0.012       ,beta_schedule="scaled_linear"       ,clip_sample=_snake_case       ,set_alpha_to_one=_snake_case       ,)
				torch.manual_seed(0			)
				UpperCAmelCase_			:	int      =   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			)
				UpperCAmelCase_			:	Any      =   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=10_00       ,)
				UpperCAmelCase_			:	Dict      =   CLIPTextModel(_snake_case			)
				UpperCAmelCase_			:	str      =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

				UpperCAmelCase_			:	Tuple      =   MultiControlNetModel([controlneta, controlneta]			)

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






			def 						UpperCamelCase__				(   self       ,_snake_case       ,_snake_case=0			):
				if str(_snake_case			).startswith("mps"			):
					UpperCAmelCase_			:	Any      =   torch.manual_seed(_snake_case			)
				else:
					UpperCAmelCase_			:	List[Any]      =   torch.Generator(device=_snake_case			).manual_seed(_snake_case			)

				UpperCAmelCase_			:	Any      =   2

				UpperCAmelCase_			:	Optional[Any]      =   [
				    randn_tensor(
				        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)       ,generator=_snake_case       ,device=torch.device(_snake_case			)       ,),
				    randn_tensor(
				        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)       ,generator=_snake_case       ,device=torch.device(_snake_case			)       ,),
				]
				UpperCAmelCase_			:	int      =   floats_tensor((1, 3, 32, 32)       ,rng=random.Random(_snake_case			)			).to(_snake_case			)
				UpperCAmelCase_			:	Optional[Any]      =   init_image.cpu().permute(0       ,2       ,3       ,1			)[0]

				UpperCAmelCase_			:	List[str]      =   Image.fromarray(np.uinta(_snake_case			)			).convert("RGB"			).resize((64, 64)			)
				UpperCAmelCase_			:	Optional[Any]      =   Image.fromarray(np.uinta(init_image + 4			)			).convert("RGB"			).resize((64, 64)			)

				UpperCAmelCase_			:	Union[str, Any]      =   {
				    "prompt": "A painting of a squirrel eating a burger",
				    "generator": generator,
				    "num_inference_steps": 2,
				    "guidance_scale": 6.0,
				    "output_type": "numpy",
				    "image": image,
				    "mask_image": mask_image,
				    "control_image": control_image,
				}

				return inputs






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	Dict      =   self.get_dummy_components()
				UpperCAmelCase_			:	Union[str, Any]      =   self.pipeline_class(**_snake_case			)
				pipe.to(_snake_case			)

				UpperCAmelCase_			:	Optional[Any]      =   10.0
				UpperCAmelCase_			:	str      =   4

				UpperCAmelCase_			:	Any      =   self.get_dummy_inputs(_snake_case			)
				UpperCAmelCase_			:	List[str]      =   steps
				UpperCAmelCase_			:	List[str]      =   scale
				UpperCAmelCase_			:	List[str]      =   pipe(**_snake_case			)[0]

				UpperCAmelCase_			:	List[str]      =   self.get_dummy_inputs(_snake_case			)
				UpperCAmelCase_			:	Optional[int]      =   steps
				UpperCAmelCase_			:	Optional[Any]      =   scale
				UpperCAmelCase_			:	List[str]      =   pipe(**_snake_case       ,control_guidance_start=0.1       ,control_guidance_end=0.2			)[0]

				UpperCAmelCase_			:	str      =   self.get_dummy_inputs(_snake_case			)
				UpperCAmelCase_			:	Optional[int]      =   steps
				UpperCAmelCase_			:	Any      =   scale
				UpperCAmelCase_			:	Optional[int]      =   pipe(**_snake_case       ,control_guidance_start=[0.1, 0.3]       ,control_guidance_end=[0.2, 0.7]			)[0]

				UpperCAmelCase_			:	Tuple      =   self.get_dummy_inputs(_snake_case			)
				UpperCAmelCase_			:	Tuple      =   steps
				UpperCAmelCase_			:	Optional[int]      =   scale
				UpperCAmelCase_			:	Tuple      =   pipe(**_snake_case       ,control_guidance_start=0.4       ,control_guidance_end=[0.5, 0.8]			)[0]

				# make sure that all outputs are different
				assert np.sum(np.abs(output_a - output_a			)			) > 1E-3
				assert np.sum(np.abs(output_a - output_a			)			) > 1E-3
				assert np.sum(np.abs(output_a - output_a			)			) > 1E-3






			def 						UpperCamelCase__				(   self			):
				return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3			)






			@unittest.skipIf(
			    torch_device != "cuda" or not is_xformers_available()       ,reason="XFormers attention is only available with CUDA and `xformers` installed"       ,)
			def 						UpperCamelCase__				(   self			):
				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3			)






			def 						UpperCamelCase__				(   self			):
				self._test_inference_batch_single_identical(expected_max_diff=2E-3			)






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	Optional[Any]      =   self.get_dummy_components()
				UpperCAmelCase_			:	Tuple      =   self.pipeline_class(**_snake_case			)
				pipe.to(_snake_case			)
				pipe.set_progress_bar_config(disable=_snake_case			)
				with tempfile.TemporaryDirectory() as tmpdir:
					try:
						# save_pretrained is not implemented for Multi-ControlNet
						pipe.save_pretrained(_snake_case			)
					except NotImplementedError:
						pass




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






			def 						UpperCamelCase__				(   self			):
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	str      =   ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"			)

				UpperCAmelCase_			:	Dict      =   StableDiffusionControlNetInpaintPipeline.from_pretrained(
				    "runwayml/stable-diffusion-inpainting"       ,safety_checker=_snake_case       ,controlnet=_snake_case			)
				pipe.enable_model_cpu_offload()
				pipe.set_progress_bar_config(disable=_snake_case			)

				UpperCAmelCase_			:	Optional[Any]      =   torch.Generator(device="cpu"			).manual_seed(0			)
				UpperCAmelCase_			:	Optional[int]      =   load_image(
				    "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"			).resize((5_12, 5_12)			)

				UpperCAmelCase_			:	str      =   load_image(
				    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
				    "/stable_diffusion_inpaint/input_bench_mask.png"			).resize((5_12, 5_12)			)

				UpperCAmelCase_			:	Dict      =   "pitch black hole"

				UpperCAmelCase_			:	Tuple      =   load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"			).resize((5_12, 5_12)			)

				UpperCAmelCase_			:	Tuple      =   pipe(
				    _snake_case       ,image=_snake_case       ,mask_image=_snake_case       ,control_image=_snake_case       ,generator=_snake_case       ,output_type="np"       ,num_inference_steps=3       ,)

				UpperCAmelCase_			:	str      =   output.images[0]

				assert image.shape == (5_12, 5_12, 3)

				UpperCAmelCase_			:	Dict      =   load_numpy(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy"			)

				assert np.abs(expected_image - image			).max() < 9E-2






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	List[str]      =   ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint"			)

				UpperCAmelCase_			:	List[Any]      =   StableDiffusionControlNetInpaintPipeline.from_pretrained(
				    "runwayml/stable-diffusion-v1-5"       ,safety_checker=_snake_case       ,controlnet=_snake_case			)
				UpperCAmelCase_			:	Any      =   DDIMScheduler.from_config(pipe.scheduler.config			)
				pipe.enable_model_cpu_offload()
				pipe.set_progress_bar_config(disable=_snake_case			)

				UpperCAmelCase_			:	Optional[Any]      =   torch.Generator(device="cpu"			).manual_seed(33			)

				UpperCAmelCase_			:	Optional[int]      =   load_image(
				    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"			)
				UpperCAmelCase_			:	Dict      =   init_image.resize((5_12, 5_12)			)

				UpperCAmelCase_			:	List[Any]      =   load_image(
				    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"			)
				UpperCAmelCase_			:	str      =   mask_image.resize((5_12, 5_12)			)

				UpperCAmelCase_			:	int      =   "a handsome man with ray-ban sunglasses"

				def make_inpaint_condition(_snake_case       ,_snake_case			):
					UpperCAmelCase_			:	Optional[int]      =   np.array(image.convert("RGB"			)			).astype(np.floataa			) / 255.0
					UpperCAmelCase_			:	Any      =   np.array(image_mask.convert("L"			)			).astype(np.floataa			) / 255.0

					assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
					UpperCAmelCase_			:	str      =   -1.0  # set as masked pixel
					UpperCAmelCase_			:	List[Any]      =   np.expand_dims(_snake_case       ,0			).transpose(0       ,3       ,1       ,2			)
					UpperCAmelCase_			:	List[str]      =   torch.from_numpy(_snake_case			)
					return image

				UpperCAmelCase_			:	List[Any]      =   make_inpaint_condition(_snake_case       ,_snake_case			)

				UpperCAmelCase_			:	str      =   pipe(
				    _snake_case       ,image=_snake_case       ,mask_image=_snake_case       ,control_image=_snake_case       ,guidance_scale=9.0       ,eta=1.0       ,generator=_snake_case       ,num_inference_steps=20       ,output_type="np"       ,)
				UpperCAmelCase_			:	Tuple      =   output.images[0]

				assert image.shape == (5_12, 5_12, 3)

				UpperCAmelCase_			:	Dict      =   load_numpy(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy"			)

				assert np.abs(expected_image - image			).max() < 9E-2







'''simple docstring'''
# 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 os
from typing import List


def       a__    (  _SCREAMING_SNAKE_CASE					:    List[str]     ,							_SCREAMING_SNAKE_CASE					:    str							)			-> Optional[Any]:


	"""simple docstring"""





	if name != "training_function" and name != "main":
		raise ValueError(F'''Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''''							)
	UpperCAmelCase_ , UpperCAmelCase_			:	int      =   [], False
	for line in lines:
		if not found_start and F'''def {name}''' in line:
			UpperCAmelCase_			:	List[str]      =   True
			good_lines.append(_SCREAMING_SNAKE_CASE							)
			continue
		if found_start:
			if name == "training_function" and "def main" in line:
				return good_lines
			if name == "main" and "if __name__" in line:
				return good_lines
			good_lines.append(_SCREAMING_SNAKE_CASE							)


def       a__    (  _SCREAMING_SNAKE_CASE					:    List[str]							)			-> Optional[Any]:


	"""simple docstring"""





	return [line for line in lines if not line.lstrip().startswith("#"							) and line != "\n"]





def       a__    (  _SCREAMING_SNAKE_CASE					:    str     ,							_SCREAMING_SNAKE_CASE					:    str     ,							_SCREAMING_SNAKE_CASE					:    bool     ,							_SCREAMING_SNAKE_CASE					:    str = None							)			-> int:


	"""simple docstring"""





	with open(_SCREAMING_SNAKE_CASE     ,							"r"							) as f:
		UpperCAmelCase_			:	Union[str, Any]      =   f.readlines()
	with open(os.path.abspath(os.path.join("examples"     ,							"nlp_example.py"							)							)     ,							"r"							) as f:
		UpperCAmelCase_			:	Dict      =   f.readlines()
	with open(_SCREAMING_SNAKE_CASE     ,							"r"							) as f:
		UpperCAmelCase_			:	Any      =   f.readlines()
	if secondary_filename is not None:
		with open(_SCREAMING_SNAKE_CASE     ,							"r"							) as f:
			UpperCAmelCase_			:	Tuple      =   f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
	if parser_only:
		UpperCAmelCase_			:	List[str]      =   clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE     ,							"main"							)							)
		UpperCAmelCase_			:	Tuple      =   clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE     ,							"main"							)							)
		UpperCAmelCase_			:	Tuple      =   clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE     ,							"main"							)							)
		if secondary_filename is not None:
			UpperCAmelCase_			:	List[Any]      =   clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE     ,							"main"							)							)
	else:
		UpperCAmelCase_			:	Union[str, Any]      =   clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE     ,							"training_function"							)							)
		UpperCAmelCase_			:	str      =   clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE     ,							"training_function"							)							)
		UpperCAmelCase_			:	List[Any]      =   clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE     ,							"training_function"							)							)
		if secondary_filename is not None:
			UpperCAmelCase_			:	Tuple      =   clean_lines(
			    get_function_contents_by_name(_SCREAMING_SNAKE_CASE     ,							"training_function"							)							)

	UpperCAmelCase_			:	List[Any]      =   "train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"

	# Specific code in our script that differs from the full version, aka what is new
	UpperCAmelCase_			:	Dict      =   []
	UpperCAmelCase_			:	Any      =   []  # We keep track of the idxs just in case it's a repeated statement
	UpperCAmelCase_			:	Optional[int]      =   iter(_SCREAMING_SNAKE_CASE							)
	for i in range(len(_SCREAMING_SNAKE_CASE							) - 1							):
		if i not in passed_idxs:
			UpperCAmelCase_			:	str      =   next(_SCREAMING_SNAKE_CASE							)
			if (line not in full_file_func) and (line.lstrip() != _dl_line):
				if "TESTING_MOCKED_DATALOADERS" not in line:
					new_feature_code.append(_SCREAMING_SNAKE_CASE							)
					passed_idxs.append(_SCREAMING_SNAKE_CASE							)
				else:
					# Skip over the `config['num_epochs'] = 2` statement
					UpperCAmelCase_			:	List[Any]      =   next(_SCREAMING_SNAKE_CASE							)

    # Extract out just the new parts from the full_file_training_func
	UpperCAmelCase_			:	Tuple      =   []
	UpperCAmelCase_			:	Optional[Any]      =   []  # We keep track of the idxs just in case it's a repeated statement
	for i, line in enumerate(_SCREAMING_SNAKE_CASE							):
		if i not in passed_idxs:
			if (line not in full_file_func) and (line.lstrip() != _dl_line):
				if "TESTING_MOCKED_DATALOADERS" not in line:
					new_full_example_parts.append(_SCREAMING_SNAKE_CASE							)
					passed_idxs.append(_SCREAMING_SNAKE_CASE							)

    # Finally, get the overall diff
	UpperCAmelCase_			:	Union[str, Any]      =   [line for line in new_feature_code if line not in new_full_example_parts]
	if secondary_filename is not None:
		UpperCAmelCase_			:	int      =   [line for line in full_file_contents if line not in secondary_file_func]
		UpperCAmelCase_			:	str      =   [line for line in diff_from_example if line not in diff_from_two]

	return diff_from_example




