



'''simple docstring'''



import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)
def 					UpperCAmelCase   (						A  :				Tuple					,		A  :				Any				):
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			XCLIPTextConfig()

							# derive patch size from model name
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			model_name.find('''patch'''				)
							SCREAMING_SNAKE_CASE					:      str     					=			int(model_name[start_idx + len('''patch'''				) : start_idx + len('''patch'''				) + 2]				)
							SCREAMING_SNAKE_CASE					:      int     					=			XCLIPVisionConfig(patch_size=A					,		num_frames=A				)

							if "large" in model_name:
														SCREAMING_SNAKE_CASE					:      Dict     					=			768
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			3072
														SCREAMING_SNAKE_CASE					:      Any     					=			12

														SCREAMING_SNAKE_CASE					:      List[Any]     					=			1024
														SCREAMING_SNAKE_CASE					:      Any     					=			4096
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			16
														SCREAMING_SNAKE_CASE					:      Tuple     					=			24
														SCREAMING_SNAKE_CASE					:      Tuple     					=			768
														SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			3072

							if model_name == "xclip-large-patch14-16-frames":
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			336

							SCREAMING_SNAKE_CASE					:      Tuple     					=			XCLIPConfig.from_text_vision_configs(A					,		A				)

							if "large" in model_name:
														SCREAMING_SNAKE_CASE					:      Dict     					=			768

							return config
def 					UpperCAmelCase   (						A  :				str				):
							# text encoder
							if name == "token_embedding.weight":
														SCREAMING_SNAKE_CASE					:      List[str]     					=			name.replace('''token_embedding.weight'''					,		'''text_model.embeddings.token_embedding.weight'''				)
							if name == "positional_embedding":
														SCREAMING_SNAKE_CASE					:      List[Any]     					=			name.replace('''positional_embedding'''					,		'''text_model.embeddings.position_embedding.weight'''				)
							if "ln_1" in name:
														SCREAMING_SNAKE_CASE					:      Dict     					=			name.replace('''ln_1'''					,		'''layer_norm1'''				)
							if "ln_2" in name:
														SCREAMING_SNAKE_CASE					:      List[Any]     					=			name.replace('''ln_2'''					,		'''layer_norm2'''				)
							if "c_fc" in name:
														SCREAMING_SNAKE_CASE					:      Any     					=			name.replace('''c_fc'''					,		'''fc1'''				)
							if "c_proj" in name:
														SCREAMING_SNAKE_CASE					:      Any     					=			name.replace('''c_proj'''					,		'''fc2'''				)
							if name.startswith('''transformer.resblocks'''				):
														SCREAMING_SNAKE_CASE					:      List[Any]     					=			name.replace('''transformer.resblocks'''					,		'''text_model.encoder.layers'''				)
							if "attn.out_proj" in name and "message" not in name:
														SCREAMING_SNAKE_CASE					:      int     					=			name.replace('''attn.out_proj'''					,		'''self_attn.out_proj'''				)
							if "ln_final" in name:
														SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			name.replace('''ln_final'''					,		'''text_model.final_layer_norm'''				)
							# visual encoder
							if name == "visual.class_embedding":
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			name.replace('''visual.class_embedding'''					,		'''vision_model.embeddings.class_embedding'''				)
							if name == "visual.positional_embedding":
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			name.replace('''visual.positional_embedding'''					,		'''vision_model.embeddings.position_embedding.weight'''				)
							if name.startswith('''visual.transformer.resblocks'''				):
														SCREAMING_SNAKE_CASE					:      List[Any]     					=			name.replace('''visual.transformer.resblocks'''					,		'''vision_model.encoder.layers'''				)
							if "visual.conv1" in name:
														SCREAMING_SNAKE_CASE					:      int     					=			name.replace('''visual.conv1'''					,		'''vision_model.embeddings.patch_embedding'''				)
							if "visual.ln_pre" in name:
														SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			name.replace('''visual.ln_pre'''					,		'''vision_model.pre_layernorm'''				)
							if "visual.ln_post" in name:
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			name.replace('''visual.ln_post'''					,		'''vision_model.post_layernorm'''				)
							if "visual.proj" in name:
														SCREAMING_SNAKE_CASE					:      str     					=			name.replace('''visual.proj'''					,		'''visual_projection.weight'''				)
							if "text_projection" in name:
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			name.replace('''text_projection'''					,		'''text_projection.weight'''				)
							# things on top
							if "prompts_visual_proj" in name:
														SCREAMING_SNAKE_CASE					:      List[str]     					=			name.replace('''prompts_visual_proj'''					,		'''prompts_visual_projection'''				)
							if "prompts_visual_ln" in name:
														SCREAMING_SNAKE_CASE					:      int     					=			name.replace('''prompts_visual_ln'''					,		'''prompts_visual_layernorm'''				)
							# mit
							if name == "mit.positional_embedding":
														SCREAMING_SNAKE_CASE					:      str     					=			name.replace('''positional'''					,		'''position'''				)
							if name.startswith('''mit.resblocks'''				):
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			name.replace('''mit.resblocks'''					,		'''mit.encoder.layers'''				)
							# prompts generator
							if name.startswith('''prompts_generator.norm'''				):
														SCREAMING_SNAKE_CASE					:      List[str]     					=			name.replace('''prompts_generator.norm'''					,		'''prompts_generator.layernorm'''				)

							return name
def 					UpperCAmelCase   (						A  :				Dict					,		A  :				str				):
							for key in orig_state_dict.copy().keys():
														SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			orig_state_dict.pop(A				)

														if "attn.in_proj" in key:
																					SCREAMING_SNAKE_CASE					:      List[Any]     					=			key.split('''.'''				)
																					if key.startswith('''visual'''				):
																												SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			key_split[3]
																												SCREAMING_SNAKE_CASE					:      str     					=			config.vision_config.hidden_size
																												if "message_attn" in key:
																																			if "weight" in key:
																																										SCREAMING_SNAKE_CASE					:      List[str]     					=			val[
																																										    :dim, :
																																										]
																																										SCREAMING_SNAKE_CASE					:      Tuple     					=			val[
																																										    dim : dim * 2, :
																																										]
																																										SCREAMING_SNAKE_CASE					:      int     					=			val[
																																										    -dim:, :
																																										]
																																			else:
																																										SCREAMING_SNAKE_CASE					:      List[str]     					=			val[
																																										    :dim
																																										]
																																										SCREAMING_SNAKE_CASE					:      List[Any]     					=			val[
																																										    dim : dim * 2
																																										]
																																										SCREAMING_SNAKE_CASE					:      Optional[int]     					=			val[
																																										    -dim:
																																										]
																												else:
																																			if "weight" in key:
																																										SCREAMING_SNAKE_CASE					:      Any     					=			val[
																																										    :dim, :
																																										]
																																										SCREAMING_SNAKE_CASE					:      Any     					=			val[
																																										    dim : dim * 2, :
																																										]
																																										SCREAMING_SNAKE_CASE					:      Tuple     					=			val[
																																										    -dim:, :
																																										]
																																			else:
																																										SCREAMING_SNAKE_CASE					:      Tuple     					=			val[:dim]
																																										SCREAMING_SNAKE_CASE					:      Tuple     					=			val[
																																										    dim : dim * 2
																																										]
																																										SCREAMING_SNAKE_CASE					:      Dict     					=			val[-dim:]
																					elif key.startswith('''mit'''				):
																												SCREAMING_SNAKE_CASE					:      str     					=			key_split[2]
																												SCREAMING_SNAKE_CASE					:      List[Any]     					=			config.vision_config.mit_hidden_size
																												if "weight" in key:
																																			SCREAMING_SNAKE_CASE					:      int     					=			val[:dim, :]
																																			SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			val[dim : dim * 2, :]
																																			SCREAMING_SNAKE_CASE					:      List[str]     					=			val[-dim:, :]
																												else:
																																			SCREAMING_SNAKE_CASE					:      Tuple     					=			val[:dim]
																																			SCREAMING_SNAKE_CASE					:      List[Any]     					=			val[dim : dim * 2]
																																			SCREAMING_SNAKE_CASE					:      Optional[int]     					=			val[-dim:]
																					else:
																												SCREAMING_SNAKE_CASE					:      str     					=			key_split[2]
																												SCREAMING_SNAKE_CASE					:      Dict     					=			config.text_config.hidden_size
																												if "weight" in key:
																																			SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			val[:dim, :]
																																			SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			val[
																																			    dim : dim * 2, :
																																			]
																																			SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			val[-dim:, :]
																												else:
																																			SCREAMING_SNAKE_CASE					:      Tuple     					=			val[:dim]
																																			SCREAMING_SNAKE_CASE					:      List[str]     					=			val[
																																			    dim : dim * 2
																																			]
																																			SCREAMING_SNAKE_CASE					:      Optional[int]     					=			val[-dim:]
														else:
																					SCREAMING_SNAKE_CASE					:      Dict     					=			rename_key(A				)
																					if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
																												SCREAMING_SNAKE_CASE					:      Any     					=			val.T
																					SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			val

							return orig_state_dict
def 					UpperCAmelCase   (						A  :				Optional[int]				):
							if num_frames == 8:
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			'''eating_spaghetti_8_frames.npy'''
							elif num_frames == 16:
														SCREAMING_SNAKE_CASE					:      List[Any]     					=			'''eating_spaghetti.npy'''
							elif num_frames == 32:
														SCREAMING_SNAKE_CASE					:      List[Any]     					=			'''eating_spaghetti_32_frames.npy'''
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			hf_hub_download(
							    repo_id='''hf-internal-testing/spaghetti-video'''					,		filename=A					,		repo_type='''dataset'''					,		)
							SCREAMING_SNAKE_CASE					:      str     					=			np.load(A				)
							return list(A				)
def 					UpperCAmelCase   (						A  :				Union[str, Any]					,		A  :				Union[str, Any]=None					,		A  :				Union[str, Any]=False				):
							SCREAMING_SNAKE_CASE					:      Any     					=			{
							    # fully supervised kinetics-400 checkpoints
							    '''xclip-base-patch32''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth''',
							    '''xclip-base-patch32-16-frames''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth'''
							    ),
							    '''xclip-base-patch16''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth''',
							    '''xclip-base-patch16-16-frames''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth'''
							    ),
							    '''xclip-large-patch14''': '''https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb''',
							    '''xclip-large-patch14-16-frames''': '''https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f''',
							    # fully supervised kinetics-600 checkpoints
							    '''xclip-base-patch16-kinetics-600''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth'''
							    ),
							    '''xclip-base-patch16-kinetics-600-16-frames''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth'''
							    ),
							    '''xclip-large-patch14-kinetics-600''': '''https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be''',
							    # few shot
							    '''xclip-base-patch16-hmdb-2-shot''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth'''
							    ),
							    '''xclip-base-patch16-hmdb-4-shot''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth'''
							    ),
							    '''xclip-base-patch16-hmdb-8-shot''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth'''
							    ),
							    '''xclip-base-patch16-hmdb-16-shot''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth'''
							    ),
							    '''xclip-base-patch16-ucf-2-shot''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth'''
							    ),
							    '''xclip-base-patch16-ucf-4-shot''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth'''
							    ),
							    '''xclip-base-patch16-ucf-8-shot''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth'''
							    ),
							    '''xclip-base-patch16-ucf-16-shot''': (
							        '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth'''
							    ),
							    # zero shot
							    '''xclip-base-patch16-zero-shot''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth''',
							}

							SCREAMING_SNAKE_CASE					:      int     					=			model_to_url[model_name]
							SCREAMING_SNAKE_CASE					:      int     					=			8
							if "16-frames" in model_name:
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			16
							elif "shot" in model_name:
														SCREAMING_SNAKE_CASE					:      Dict     					=			32

							SCREAMING_SNAKE_CASE					:      List[Any]     					=			get_xclip_config(A					,		A				)
							SCREAMING_SNAKE_CASE					:      Dict     					=			XCLIPModel(A				)
							model.eval()

							if "drive" in checkpoint_url:
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			'''pytorch_model.bin'''
														gdown.cached_download(A					,		A					,		quiet=A				)
														SCREAMING_SNAKE_CASE					:      List[Any]     					=			torch.load(A					,		map_location='''cpu'''				)['''model''']
							else:
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			torch.hub.load_state_dict_from_url(A				)['''model''']

							SCREAMING_SNAKE_CASE					:      Any     					=			convert_state_dict(A					,		A				)

							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			XCLIPModel(A				)
							SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Any     					=			model.load_state_dict(A					,		strict=A				)
							assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
							model.eval()

							SCREAMING_SNAKE_CASE					:      str     					=			336 if model_name == '''xclip-large-patch14-16-frames''' else 224
							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			VideoMAEImageProcessor(size=A				)
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''				)
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			CLIPTokenizerFast.from_pretrained('''openai/clip-vit-base-patch32'''				)
							SCREAMING_SNAKE_CASE					:      str     					=			XCLIPProcessor(image_processor=A					,		tokenizer=A				)

							SCREAMING_SNAKE_CASE					:      Any     					=			prepare_video(A				)
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			processor(
							    text=['''playing sports''', '''eating spaghetti''', '''go shopping''']					,		videos=A					,		return_tensors='''pt'''					,		padding=A				)

							print('''Shape of pixel values:'''					,		inputs.pixel_values.shape				)

							with torch.no_grad():
														SCREAMING_SNAKE_CASE					:      Any     					=			model(**A				)

							# Verify outputs
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			outputs.logits_per_video
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			logits_per_video.softmax(dim=1				)
							print('''Probs:'''					,		A				)
							# kinetics-400
							if model_name == "xclip-base-patch32":
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			torch.tensor([[0.0019, 0.9951, 0.0030]]				)
							elif model_name == "xclip-base-patch32-16-frames":
														SCREAMING_SNAKE_CASE					:      Dict     					=			torch.tensor([[7.0_9_9_9e-0_4, 9.9_8_8_3e-0_1, 4.5_5_8_0e-0_4]]				)
							elif model_name == "xclip-base-patch16":
														SCREAMING_SNAKE_CASE					:      Any     					=			torch.tensor([[0.0083, 0.9681, 0.0236]]				)
							elif model_name == "xclip-base-patch16-16-frames":
														SCREAMING_SNAKE_CASE					:      List[str]     					=			torch.tensor([[7.6_9_3_7e-0_4, 9.9_7_2_8e-0_1, 1.9_4_7_3e-0_3]]				)
							elif model_name == "xclip-large-patch14":
														SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			torch.tensor([[0.0062, 0.9864, 0.0075]]				)
							elif model_name == "xclip-large-patch14-16-frames":
														SCREAMING_SNAKE_CASE					:      Any     					=			torch.tensor([[3.3_8_7_7e-0_4, 9.9_9_3_7e-0_1, 2.8_8_8_8e-0_4]]				)
							# kinetics-600
							elif model_name == "xclip-base-patch16-kinetics-600":
														SCREAMING_SNAKE_CASE					:      str     					=			torch.tensor([[0.0555, 0.8914, 0.0531]]				)
							elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
														SCREAMING_SNAKE_CASE					:      int     					=			torch.tensor([[3.8_5_5_4e-0_4, 9.9_9_2_9e-0_1, 3.2_7_5_4e-0_4]]				)
							elif model_name == "xclip-large-patch14-kinetics-600":
														SCREAMING_SNAKE_CASE					:      Any     					=			torch.tensor([[0.0036, 0.9920, 0.0045]]				)
							# few shot
							elif model_name == "xclip-base-patch16-hmdb-2-shot":
														SCREAMING_SNAKE_CASE					:      Any     					=			torch.tensor([[7.1_8_9_0e-0_6, 9.9_9_9_4e-0_1, 5.6_5_5_9e-0_5]]				)
							elif model_name == "xclip-base-patch16-hmdb-4-shot":
														SCREAMING_SNAKE_CASE					:      int     					=			torch.tensor([[1.0_3_2_0e-0_5, 9.9_9_9_3e-0_1, 6.2_4_3_5e-0_5]]				)
							elif model_name == "xclip-base-patch16-hmdb-8-shot":
														SCREAMING_SNAKE_CASE					:      Any     					=			torch.tensor([[4.1_3_7_7e-0_6, 9.9_9_9_0e-0_1, 9.8_3_8_6e-0_5]]				)
							elif model_name == "xclip-base-patch16-hmdb-16-shot":
														SCREAMING_SNAKE_CASE					:      int     					=			torch.tensor([[4.1_3_4_7e-0_5, 9.9_9_6_2e-0_1, 3.3_4_1_1e-0_4]]				)
							elif model_name == "xclip-base-patch16-ucf-2-shot":
														SCREAMING_SNAKE_CASE					:      List[Any]     					=			torch.tensor([[8.5_8_5_7e-0_5, 9.9_9_2_8e-0_1, 6.3_2_9_1e-0_4]]				)
							elif model_name == "xclip-base-patch16-ucf-4-shot":
														SCREAMING_SNAKE_CASE					:      Dict     					=			torch.tensor([[8.5_8_5_7e-0_5, 9.9_9_2_8e-0_1, 6.3_2_9_1e-0_4]]				)
							elif model_name == "xclip-base-patch16-ucf-8-shot":
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			torch.tensor([[0.0027, 0.9904, 0.0070]]				)
							elif model_name == "xclip-base-patch16-ucf-16-shot":
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			torch.tensor([[9.8_2_1_9e-0_4, 9.9_5_9_3e-0_1, 3.0_8_6_3e-0_3]]				)
							# zero shot
							elif model_name == "xclip-base-patch16-zero-shot":
														SCREAMING_SNAKE_CASE					:      Dict     					=			torch.tensor([[3.5_0_8_2e-0_4, 9.9_7_8_5e-0_1, 1.7_9_6_6e-0_3]]				)
							else:
														raise ValueError(F"""Model name {model_name} not supported"""				)
							assert torch.allclose(A					,		A					,		atol=1e-3				)
							print('''Looks ok!'''				)

							if pytorch_dump_folder_path is not None:
														print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""				)
														model.save_pretrained(A				)

							if push_to_hub:
														print('''Pushing model, processor and slow tokenizer files to the hub...'''				)
														model.push_to_hub(A					,		organization='''nielsr'''				)
														processor.push_to_hub(A					,		organization='''nielsr'''				)
														slow_tokenizer.push_to_hub(A					,		organization='''nielsr'''				)


if __name__ == "__main__":
							lowerCAmelCase_     :       Optional[int]								=						argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--model_name',
							    default='xclip-base-patch32',
							    type=str,
							    help='Name of the model.',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
							)
							parser.add_argument(
							    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
							)

							lowerCAmelCase_     :       List[Any]								=						parser.parse_args()
							convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''



import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
lowerCAmelCase_     :       Optional[Any]								=						'.'


if __name__ == "__main__":
							lowerCAmelCase_     :       Optional[Any]								=						os.path.join(REPO_PATH, 'utils/documentation_tests.txt')
							lowerCAmelCase_     :       Dict								=						[]
							lowerCAmelCase_     :       Any								=						[]
							with open(doctest_file_path) as fp:
														for line in fp:
																					lowerCAmelCase_     :       List[str]								=						line.strip()
																					lowerCAmelCase_     :       Optional[Any]								=						os.path.join(REPO_PATH, line)
																					if not (os.path.isfile(path) or os.path.isdir(path)):
																												non_existent_paths.append(line)
																					all_paths.append(path)
							if len(non_existent_paths) > 0:
														lowerCAmelCase_     :       Dict								=						'\n'.join(non_existent_paths)
														raise ValueError(f'`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}')
							if all_paths != sorted(all_paths):
														raise ValueError('Files in `utils/documentation_tests.txt` are not in alphabetical order.')




