import subprocess
from argparse import ArgumentParser
from typing import List, Union

from huggingface_hub.hf_api import HfFolder, create_repo, whoami
from requests.exceptions import HTTPError

from . import BaseTransformersCLICommand


class   lowercase			(   a   ):




							@staticmethod
							def 		__snake_case(  _UpperCamelCase      :    ArgumentParser					)    -> str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				parser.add_parser("login"		,							help="Log in using the same credentials as on huggingface.co"					)
												login_parser.set_defaults(func=lambda _UpperCamelCase					: LoginCommand(_UpperCamelCase					)					)
												SCREAMING_SNAKE_CASE				=				parser.add_parser("whoami"		,							help="Find out which huggingface.co account you are logged in as."					)
												whoami_parser.set_defaults(func=lambda _UpperCamelCase					: WhoamiCommand(_UpperCamelCase					)					)
												SCREAMING_SNAKE_CASE				=				parser.add_parser("logout"		,							help="Log out"					)
												logout_parser.set_defaults(func=lambda _UpperCamelCase					: LogoutCommand(_UpperCamelCase					)					)

												# new system: git-based repo system
												SCREAMING_SNAKE_CASE				=				parser.add_parser(
												    "repo"		,							help="Deprecated: use `huggingface-cli` instead. Commands to interact with your huggingface.co repos."		,							)
												SCREAMING_SNAKE_CASE				=				repo_parser.add_subparsers(
												    help="Deprecated: use `huggingface-cli` instead. huggingface.co repos related commands"					)
												SCREAMING_SNAKE_CASE				=				repo_subparsers.add_parser(
												    "create"		,							help="Deprecated: use `huggingface-cli` instead. Create a new repo on huggingface.co"					)
												repo_create_parser.add_argument(
												    "name"		,							type=_UpperCamelCase		,							help="Name for your model's repo. Will be namespaced under your username to build the model id."		,							)
												repo_create_parser.add_argument("--organization"		,							type=_UpperCamelCase		,							help="Optional: organization namespace."					)
												repo_create_parser.add_argument("-y"		,							"--yes"		,							action="store_true"		,							help="Optional: answer Yes to the prompt"					)
												repo_create_parser.set_defaults(func=lambda _UpperCamelCase					: RepoCreateCommand(_UpperCamelCase					)					)



class   lowercase			:
							lowercase__  :				str									=							"""\u001b[1m"""
							lowercase__  :				Dict									=							"""\u001b[31m"""
							lowercase__  :				str									=							"""\u001b[90m"""
							lowercase__  :				List[str]									=							"""\u001b[0m"""




							@classmethod
							def 		__snake_case(  cls      :    Union[str, Any]		,							_UpperCamelCase      :    Optional[int]					)    -> str:



												'''simple docstring'''

												return F"{cls._bold}{s}{cls._reset}"




							@classmethod
							def 		__snake_case(  cls      :    str		,							_UpperCamelCase      :    Tuple					)    -> List[Any]:



												'''simple docstring'''

												return F"{cls._bold}{cls._red}{s}{cls._reset}"





							@classmethod
							def 		__snake_case(  cls      :    Tuple		,							_UpperCamelCase      :    Any					)    -> Optional[int]:



												'''simple docstring'''

												return F"{cls._gray}{s}{cls._reset}"





def 		__lowerCamelCase							(UpperCAmelCase__   :							List[List[Union[str, int]]]					,       UpperCAmelCase__   :							List[str]     ):
					SCREAMING_SNAKE_CASE				=				[max(len(str(UpperCAmelCase__     )     ) for x in col     ) for col in zip(*UpperCAmelCase__					,       UpperCAmelCase__     )]
					SCREAMING_SNAKE_CASE				=				("{{:{}}} " * len(UpperCAmelCase__     )).format(*UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				[]
					lines.append(row_format.format(*UpperCAmelCase__     )     )
					lines.append(row_format.format(*["-" * w for w in col_widths]     )     )
					for row in rows:
										lines.append(row_format.format(*UpperCAmelCase__     )     )
					return "\n".join(UpperCAmelCase__     )


class   lowercase			:




							def __init__(  self      :    List[Any]		,							_UpperCamelCase      :    Any					)    -> str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				args



class   lowercase			(   a   ):




							def 		__snake_case(  self      :    Tuple					)    -> Tuple:



												'''simple docstring'''

												print(
												    ANSI.red(
												        "ERROR! `huggingface-cli login` uses an outdated login mechanism "
												        "that is not compatible with the Hugging Face Hub backend anymore. "
												        "Please use `huggingface-cli login instead."					)					)



class   lowercase			(   a   ):




							def 		__snake_case(  self      :    Optional[Any]					)    -> Any:



												'''simple docstring'''

												print(
												    ANSI.red(
												        "WARNING! `transformers-cli whoami` is deprecated and will be removed in v5. Please use "
												        "`huggingface-cli whoami` instead."					)					)
												SCREAMING_SNAKE_CASE				=				HfFolder.get_token()
												if token is None:
																	print("Not logged in"					)
																	exit()
												try:
																	SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				whoami(_UpperCamelCase					)
																	print(_UpperCamelCase					)
																	if orgs:
																						print(ANSI.bold("orgs: "					)		,							",".join(_UpperCamelCase					)					)
												except HTTPError as e:
																	print(_UpperCamelCase					)
																	print(ANSI.red(e.response.text					)					)
																	exit(1					)



class   lowercase			(   a   ):




							def 		__snake_case(  self      :    int					)    -> Optional[int]:



												'''simple docstring'''

												print(
												    ANSI.red(
												        "ERROR! `transformers-cli logout` uses an outdated logout mechanism "
												        "that is not compatible with the Hugging Face Hub backend anymore. "
												        "Please use `huggingface-cli logout instead."					)					)



class   lowercase			(   a   ):




							def 		__snake_case(  self      :    Tuple					)    -> List[str]:



												'''simple docstring'''

												print(
												    ANSI.red(
												        "WARNING! Managing repositories through transformers-cli is deprecated. "
												        "Please use `huggingface-cli` instead."					)					)
												SCREAMING_SNAKE_CASE				=				HfFolder.get_token()
												if token is None:
																	print("Not logged in"					)
																	exit(1					)
												try:
																	SCREAMING_SNAKE_CASE				=				subprocess.check_output(["git", "--version"]					).decode("utf-8"					)
																	print(ANSI.gray(stdout.strip()					)					)
												except FileNotFoundError:
																	print("Looks like you do not have git installed, please install."					)

												try:
																	SCREAMING_SNAKE_CASE				=				subprocess.check_output(["git-lfs", "--version"]					).decode("utf-8"					)
																	print(ANSI.gray(stdout.strip()					)					)
												except FileNotFoundError:
																	print(
																	    ANSI.red(
																	        "Looks like you do not have git-lfs installed, please install."
																	        " You can install from https://git-lfs.github.com/."
																	        " Then run `git lfs install` (you only have to do this once)."					)					)
												print(""					)

												SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				whoami(_UpperCamelCase					)
												SCREAMING_SNAKE_CASE				=				self.args.organization if self.args.organization is not None else user
												SCREAMING_SNAKE_CASE				=				F"{namespace}/{self.args.name}"
												print(F"You are about to create {ANSI.bold(_UpperCamelCase					)}"					)

												if not self.args.yes:
																	SCREAMING_SNAKE_CASE				=				input("Proceed? [Y/n] "					).lower()
																	if not (choice == "" or choice == "y" or choice == "yes"):
																						print("Abort"					)
																						exit()
												try:
																	SCREAMING_SNAKE_CASE				=				create_repo(_UpperCamelCase		,							name=self.args.name		,							organization=self.args.organization					)
												except HTTPError as e:
																	print(_UpperCamelCase					)
																	print(ANSI.red(e.response.text					)					)
																	exit(1					)
												print("\nYour repo now lives at:"					)
												print(F"  {ANSI.bold(_UpperCamelCase					)}"					)
												print("\nYou can clone it locally with the command below, and commit/push as usual."					)
												print(F"\n  git clone {url}"					)
												print(""					)





from typing import Any, Dict, List, Mapping

import numpy as np
import torch

from ...utils import is_cython_available, requires_backends


if is_cython_available():
							import pyximport

							pyximport.install(setup_args={'''include_dirs''': np.get_include()})
							from . import algos_graphormer  # noqa E402





def 		__lowerCamelCase							(UpperCAmelCase__   :							List[Any]					,       UpperCAmelCase__   :							int = 5_1_2     ):
					SCREAMING_SNAKE_CASE				=				x.shape[1] if len(x.shape     ) > 1 else 1
					SCREAMING_SNAKE_CASE				=				1 + np.arange(0					,       feature_num * offset					,       UpperCAmelCase__					,       dtype=np.intaa     )
					SCREAMING_SNAKE_CASE				=				x + feature_offset
					return x





def 		__lowerCamelCase							(UpperCAmelCase__   :							int					,       UpperCAmelCase__   :							Optional[Any]=True     ):
					requires_backends(UpperCAmelCase__					,       ["cython"]     )

					if keep_features and "edge_attr" in item.keys():  # edge_attr
										SCREAMING_SNAKE_CASE				=				np.asarray(item["edge_attr"]					,       dtype=np.intaa     )
					else:
										SCREAMING_SNAKE_CASE				=				np.ones((len(item["edge_index"][0]     ), 1)					,       dtype=np.intaa     )  # same embedding for all

					if keep_features and "node_feat" in item.keys():  # input_nodes
										SCREAMING_SNAKE_CASE				=				np.asarray(item["node_feat"]					,       dtype=np.intaa     )
					else:
										SCREAMING_SNAKE_CASE				=				np.ones((item["num_nodes"], 1)					,       dtype=np.intaa     )  # same embedding for all

					SCREAMING_SNAKE_CASE				=				np.asarray(item["edge_index"]					,       dtype=np.intaa     )

					SCREAMING_SNAKE_CASE				=				convert_to_single_emb(UpperCAmelCase__     ) + 1
					SCREAMING_SNAKE_CASE				=				item["num_nodes"]

					if len(edge_attr.shape     ) == 1:
										SCREAMING_SNAKE_CASE				=				edge_attr[:, None]
					SCREAMING_SNAKE_CASE				=				np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]					,       dtype=np.intaa     )
					SCREAMING_SNAKE_CASE				=				convert_to_single_emb(UpperCAmelCase__     ) + 1

					# node adj matrix [num_nodes, num_nodes] bool
					SCREAMING_SNAKE_CASE				=				np.zeros([num_nodes, num_nodes]					,       dtype=UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				True

					SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				algos_graphormer.floyd_warshall(UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				np.amax(UpperCAmelCase__     )

					SCREAMING_SNAKE_CASE				=				algos_graphormer.gen_edge_input(UpperCAmelCase__					,       UpperCAmelCase__					,       UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				np.zeros([num_nodes + 1, num_nodes + 1]					,       dtype=np.single     )  # with graph token

					# combine
					SCREAMING_SNAKE_CASE				=				input_nodes + 1  # we shift all indices by one for padding
					SCREAMING_SNAKE_CASE				=				attn_bias
					SCREAMING_SNAKE_CASE				=				attn_edge_type
					SCREAMING_SNAKE_CASE				=				shortest_path_result.astype(np.intaa     ) + 1  # we shift all indices by one for padding
					SCREAMING_SNAKE_CASE				=				np.sum(UpperCAmelCase__					,       axis=1     ).reshape(-1     ) + 1  # we shift all indices by one for padding
					SCREAMING_SNAKE_CASE				=				item["in_degree"]  # for undirected graph
					SCREAMING_SNAKE_CASE				=				input_edges + 1  # we shift all indices by one for padding
					if "labels" not in item:
										SCREAMING_SNAKE_CASE				=				item["y"]

					return item


class   lowercase			:




							def __init__(  self      :    int		,							_UpperCamelCase      :    Any=20		,							_UpperCamelCase      :    List[str]=False					)    -> Union[str, Any]:



												'''simple docstring'''

												if not is_cython_available():
																	raise ImportError("Graphormer preprocessing needs Cython (pyximport)"					)

												SCREAMING_SNAKE_CASE				=				spatial_pos_max
												SCREAMING_SNAKE_CASE				=				on_the_fly_processing





							def __call__(  self      :    Union[str, Any]		,							_UpperCamelCase      :    List[dict]					)    -> Dict[str, Any]:



												'''simple docstring'''

												if self.on_the_fly_processing:
																	SCREAMING_SNAKE_CASE				=				[preprocess_item(_UpperCamelCase					) for i in features]

												if not isinstance(features[0]		,							_UpperCamelCase					):
																	SCREAMING_SNAKE_CASE				=				[vars(_UpperCamelCase					) for f in features]
												SCREAMING_SNAKE_CASE				=				{}

												SCREAMING_SNAKE_CASE				=				max(len(i["input_nodes"]					) for i in features					)
												SCREAMING_SNAKE_CASE				=				len(features[0]["input_nodes"][0]					)
												SCREAMING_SNAKE_CASE				=				len(features[0]["attn_edge_type"][0][0]					)
												SCREAMING_SNAKE_CASE				=				max(len(i["input_edges"][0][0]					) for i in features					)
												SCREAMING_SNAKE_CASE				=				len(features[0]["input_edges"][0][0][0]					)
												SCREAMING_SNAKE_CASE				=				len(_UpperCamelCase					)

												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							max_node_num + 1		,							max_node_num + 1		,							dtype=torch.float					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(
												    _UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)

												for ix, f in enumerate(_UpperCamelCase					):
																	for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
																						SCREAMING_SNAKE_CASE				=				torch.tensor(f[k]					)

																	if len(f["attn_bias"][1:, 1:][f["spatial_pos"] >= self.spatial_pos_max]					) > 0:
																						SCREAMING_SNAKE_CASE				=				float("-inf"					)

																	SCREAMING_SNAKE_CASE				=				f["attn_bias"]
																	SCREAMING_SNAKE_CASE				=				f[
																	    "attn_edge_type"
																	]
																	SCREAMING_SNAKE_CASE				=				f["spatial_pos"]
																	SCREAMING_SNAKE_CASE				=				f["in_degree"]
																	SCREAMING_SNAKE_CASE				=				f["input_nodes"]
																	SCREAMING_SNAKE_CASE				=				f["input_edges"]

												SCREAMING_SNAKE_CASE				=				batch["in_degree"]

												SCREAMING_SNAKE_CASE				=				features[0]["labels"]
												if len(_UpperCamelCase					) == 1:  # one task
																	if isinstance(sample[0]		,							_UpperCamelCase					):  # regression
																						SCREAMING_SNAKE_CASE				=				torch.from_numpy(np.concatenate([i["labels"] for i in features]					)					)
																	else:  # binary classification
																						SCREAMING_SNAKE_CASE				=				torch.from_numpy(np.concatenate([i["labels"] for i in features]					)					)
												else:  # multi task classification, left to float to keep the NaNs
																	SCREAMING_SNAKE_CASE				=				torch.from_numpy(np.stack([i["labels"] for i in features]		,							axis=0					)					)

												return batch




