
"""simple docstring"""


import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


A_    							=					get_logger(__name__)


A_    							=					Path(__file__).parent / """model_card_template.md"""
A_    							=					uuida().hex
A_    							=					os.getenv("""HF_HUB_OFFLINE""", """""").upper() in ENV_VARS_TRUE_VALUES
A_    							=					os.getenv("""DISABLE_TELEMETRY""", """""").upper() in ENV_VARS_TRUE_VALUES
A_    							=					HUGGINGFACE_CO_RESOLVE_ENDPOINT + """/api/telemetry/"""






def  lowercase						(	lowerCAmelCase__ = None						):
			lowerCamelCase_					=  f"diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}"
			if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
						return ua + "; telemetry/off"
			if is_torch_available():
						ua += f"; torch/{_torch_version}"
			if is_flax_available():
						ua += f"; jax/{_jax_version}"
						ua += f"; flax/{_flax_version}"
			if is_onnx_available():
						ua += f"; onnxruntime/{_onnxruntime_version}"
			# CI will set this value to True
			if os.environ.get('''DIFFUSERS_IS_CI'''	,''''''						).upper() in ENV_VARS_TRUE_VALUES:
						ua += "; is_ci/true"
			if isinstance(lowerCAmelCase__	,lowerCAmelCase__						):
						ua += "; " + "; ".join(f"{k}/{v}" for k, v in user_agent.items()						)
			elif isinstance(lowerCAmelCase__	,lowerCAmelCase__						):
						ua += "; " + user_agent
			return ua






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__ = None	,lowerCAmelCase__ = None						):
			if token is None:
						lowerCamelCase_					=  HfFolder.get_token()
			if organization is None:
						lowerCamelCase_					=  whoami(lowerCAmelCase__						)['''name''']
						return f"{username}/{model_id}"
			else:
						return f"{organization}/{model_id}"






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__						):
			if not is_jinja_available():
						raise ValueError(
						    '''Modelcard rendering is based on Jinja templates.'''
						    ''' Please make sure to have `jinja` installed before using `create_model_card`.'''
						    ''' To install it, please run `pip install Jinja2`.'''						)

			if hasattr(lowerCAmelCase__	,'''local_rank'''						) and args.local_rank not in [-1, 0]:
						return

			lowerCamelCase_					=  args.hub_token if hasattr(lowerCAmelCase__	,'''hub_token'''						) else None
			lowerCamelCase_					=  get_full_repo_name(lowerCAmelCase__	,token=lowerCAmelCase__						)

			lowerCamelCase_					=  ModelCard.from_template(
			    card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
			        language='''en'''	,license='''apache-2.0'''	,library_name='''diffusers'''	,tags=[]	,datasets=args.dataset_name	,metrics=[]	,)	,template_path=lowerCAmelCase__	,model_name=lowerCAmelCase__	,repo_name=lowerCAmelCase__	,dataset_name=args.dataset_name if hasattr(lowerCAmelCase__	,'''dataset_name'''						) else None	,learning_rate=args.learning_rate	,train_batch_size=args.train_batch_size	,eval_batch_size=args.eval_batch_size	,gradient_accumulation_steps=(
			        args.gradient_accumulation_steps if hasattr(lowerCAmelCase__	,'''gradient_accumulation_steps'''						) else None
			    )	,adam_betaa=args.adam_betaa if hasattr(lowerCAmelCase__	,'''adam_beta1'''						) else None	,adam_betaa=args.adam_betaa if hasattr(lowerCAmelCase__	,'''adam_beta2'''						) else None	,adam_weight_decay=args.adam_weight_decay if hasattr(lowerCAmelCase__	,'''adam_weight_decay'''						) else None	,adam_epsilon=args.adam_epsilon if hasattr(lowerCAmelCase__	,'''adam_epsilon'''						) else None	,lr_scheduler=args.lr_scheduler if hasattr(lowerCAmelCase__	,'''lr_scheduler'''						) else None	,lr_warmup_steps=args.lr_warmup_steps if hasattr(lowerCAmelCase__	,'''lr_warmup_steps'''						) else None	,ema_inv_gamma=args.ema_inv_gamma if hasattr(lowerCAmelCase__	,'''ema_inv_gamma'''						) else None	,ema_power=args.ema_power if hasattr(lowerCAmelCase__	,'''ema_power'''						) else None	,ema_max_decay=args.ema_max_decay if hasattr(lowerCAmelCase__	,'''ema_max_decay'''						) else None	,mixed_precision=args.mixed_precision	,)

			lowerCamelCase_					=  os.path.join(args.output_dir	,'''README.md'''						)
			model_card.save(lowerCAmelCase__						)






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__ = None						):
			if resolved_file is None or commit_hash is not None:
						return commit_hash
			lowerCamelCase_					=  str(Path(lowerCAmelCase__						).as_posix()						)
			lowerCamelCase_					=  re.search(R'''snapshots/([^/]+)/'''	,lowerCAmelCase__						)
			if search is None:
						return None
			lowerCamelCase_					=  search.groups()[0]
			return commit_hash if REGEX_COMMIT_HASH.match(lowerCAmelCase__						) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
A_    							=					os.path.expanduser(
    os.getenv("""HF_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """huggingface"""))
)
A_    							=					os.path.join(hf_cache_home, """diffusers""")






def  lowercase						(	lowerCAmelCase__ = None	,lowerCAmelCase__ = None						):
			if new_cache_dir is None:
						lowerCamelCase_					=  DIFFUSERS_CACHE
			if old_cache_dir is None:
						lowerCamelCase_					=  old_diffusers_cache

			lowerCamelCase_					=  Path(lowerCAmelCase__						).expanduser()
			lowerCamelCase_					=  Path(lowerCAmelCase__						).expanduser()
			for old_blob_path in old_cache_dir.glob('''**/blobs/*'''						):
						if old_blob_path.is_file() and not old_blob_path.is_symlink():
									lowerCamelCase_					=  new_cache_dir / old_blob_path.relative_to(lowerCAmelCase__						)
									new_blob_path.parent.mkdir(parents=lowerCAmelCase__	,exist_ok=lowerCAmelCase__						)
									os.replace(lowerCAmelCase__	,lowerCAmelCase__						)
									try:
												os.symlink(lowerCAmelCase__	,lowerCAmelCase__						)
									except OSError:
												logger.warning(
												    '''Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded.'''						)
			# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


A_    							=					os.path.join(DIFFUSERS_CACHE, """version_diffusers_cache.txt""")
if not os.path.isfile(cache_version_file):
						A_    							=					0
else:
						with open(cache_version_file) as f:
												try:
																		A_    							=					int(f.read())
												except ValueError:
																		A_    							=					0

if cache_version < 1:
						A_    							=					os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
						if old_cache_is_not_empty:
												logger.warning(
												    """The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your """
												    """existing cached models. This is a one-time operation, you can interrupt it or run it """
												    """later by calling `diffusers.utils.hub_utils.move_cache()`."""
												)
												try:
																		move_cache()
												except Exception as e:
																		A_    							=					"""\n""".join(traceback.format_tb(e.__traceback__))
																		logger.error(
																		    f"There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease "
																		    """file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole """
																		    """message and we will do our best to help."""
																		)

if cache_version < 1:
						try:
												os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
												with open(cache_version_file, """w""") as f:
																		f.write("""1""")
						except Exception:
												logger.warning(
												    f"There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure "
												    """the directory exists and can be written to."""
												)






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__ = None						):
			if variant is not None:
						lowerCamelCase_					=  weights_name.split('''.'''						)
						lowerCamelCase_					=  splits[:-1] + [variant] + splits[-1:]
						lowerCamelCase_					=  '''.'''.join(lowerCAmelCase__						)

			return weights_name






def  lowercase						(	lowerCAmelCase__	,*,
    lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__=None	,):
			lowerCamelCase_					=  str(lowerCAmelCase__						)
			if os.path.isfile(lowerCAmelCase__						):
						return pretrained_model_name_or_path
			elif os.path.isdir(lowerCAmelCase__						):
						if os.path.isfile(os.path.join(lowerCAmelCase__	,lowerCAmelCase__						)						):
									# Load from a PyTorch checkpoint
									lowerCamelCase_					=  os.path.join(lowerCAmelCase__	,lowerCAmelCase__						)
									return model_file
						elif subfolder is not None and os.path.isfile(
						    os.path.join(lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						)						):
									lowerCamelCase_					=  os.path.join(lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						)
									return model_file
						else:
									raise EnvironmentError(
									    f"Error no file named {weights_name} found in directory {pretrained_model_name_or_path}."						)
			else:
						# 1. First check if deprecated way of loading from branches is used
						if (
						    revision in DEPRECATED_REVISION_ARGS
						    and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
						    and version.parse(version.parse(lowerCAmelCase__						).base_version						) >= version.parse('''0.20.0'''						)
						):
									try:
												lowerCamelCase_					=  hf_hub_download(
												    lowerCAmelCase__	,filename=_add_variant(lowerCAmelCase__	,lowerCAmelCase__						)	,cache_dir=lowerCAmelCase__	,force_download=lowerCAmelCase__	,proxies=lowerCAmelCase__	,resume_download=lowerCAmelCase__	,local_files_only=lowerCAmelCase__	,use_auth_token=lowerCAmelCase__	,user_agent=lowerCAmelCase__	,subfolder=lowerCAmelCase__	,revision=revision or commit_hash	,)
												warnings.warn(
												    f"Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead."	,lowerCAmelCase__	,)
												return model_file
									except:  # noqa: E722
												warnings.warn(
												    f"You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(lowerCAmelCase__	,lowerCAmelCase__						)} file in the 'main' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {_add_variant(lowerCAmelCase__	,lowerCAmelCase__						)}' so that the correct variant file can be added."	,lowerCAmelCase__	,)
						try:
									# 2. Load model file as usual
									lowerCamelCase_					=  hf_hub_download(
									    lowerCAmelCase__	,filename=lowerCAmelCase__	,cache_dir=lowerCAmelCase__	,force_download=lowerCAmelCase__	,proxies=lowerCAmelCase__	,resume_download=lowerCAmelCase__	,local_files_only=lowerCAmelCase__	,use_auth_token=lowerCAmelCase__	,user_agent=lowerCAmelCase__	,subfolder=lowerCAmelCase__	,revision=revision or commit_hash	,)
									return model_file

						except RepositoryNotFoundError:
									raise EnvironmentError(
									    f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier "
									    '''listed on \'https://huggingface.co/models\'\nIf this is a private repository, make sure to pass a '''
									    '''token having permission to this repo with `use_auth_token` or log in with `huggingface-cli '''
									    '''login`.'''						)
						except RevisionNotFoundError:
									raise EnvironmentError(
									    f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for "
									    '''this model name. Check the model page at '''
									    f"'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."						)
						except EntryNotFoundError:
									raise EnvironmentError(
									    f"{pretrained_model_name_or_path} does not appear to have a file named {weights_name}."						)
						except HTTPError as err:
									raise EnvironmentError(
									    f"There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}"						)
						except ValueError:
									raise EnvironmentError(
									    f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"
									    f" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"
									    f" directory containing a file named {weights_name} or"
									    ''' \nCheckout your internet connection or see how to run the library in'''
									    ''' offline mode at \'https://huggingface.co/docs/diffusers/installation#offline-mode\'.'''						)
						except EnvironmentError:
									raise EnvironmentError(
									    f"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from "
									    '''\'https://huggingface.co/models\', make sure you don\'t have a local directory with the same name. '''
									    f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
									    f"containing a file named {weights_name}"						)





"""simple docstring"""


import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile

import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load


try:
						import torch

						A_    							=					True
except ImportError:
						A_    							=					False


try:
						from torch.hub import _get_torch_home

						A_    							=					_get_torch_home()
except ImportError:
						A_    							=					os.path.expanduser(
						    os.getenv("""TORCH_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """torch"""))
						)

A_    							=					os.path.join(torch_cache_home, """transformers""")

A_    							=					"""https://cdn.huggingface.co"""
A_    							=					"""https://s3.amazonaws.com/models.huggingface.co/bert"""
A_    							=					"""/""".join(str(Path(__file__).resolve()).split("""/""")[:-1])
A_    							=					os.path.join(PATH, """config.yaml""")
A_    							=					os.path.join(PATH, """attributes.txt""")
A_    							=					os.path.join(PATH, """objects.txt""")
A_    							=					os.getenv("""PYTORCH_PRETRAINED_BERT_CACHE""", default_cache_path)
A_    							=					os.getenv("""PYTORCH_TRANSFORMERS_CACHE""", PYTORCH_PRETRAINED_BERT_CACHE)
A_    							=					os.getenv("""TRANSFORMERS_CACHE""", PYTORCH_TRANSFORMERS_CACHE)
A_    							=					"""pytorch_model.bin"""
A_    							=					"""config.yaml"""






def  lowercase						(	lowerCAmelCase__=OBJECTS	,lowerCAmelCase__=ATTRIBUTES						):
			lowerCamelCase_					=  []
			with open(lowerCAmelCase__						) as f:
						for object in f.readlines():
									vg_classes.append(object.split(''','''						)[0].lower().strip()						)

			lowerCamelCase_					=  []
			with open(lowerCAmelCase__						) as f:
						for object in f.readlines():
									vg_attrs.append(object.split(''','''						)[0].lower().strip()						)
			return vg_classes, vg_attrs






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  OrderedDict()
			with open(lowerCAmelCase__	,'''rb'''						) as f:
						lowerCamelCase_					=  pkl.load(lowerCAmelCase__						)['''model''']
			for k in copy.deepcopy(list(ckp.keys()						)						):
						lowerCamelCase_					=  ckp.pop(lowerCAmelCase__						)
						if isinstance(lowerCAmelCase__	,np.ndarray						):
									lowerCamelCase_					=  torch.tensor(lowerCAmelCase__						)
						else:
									assert isinstance(lowerCAmelCase__	,torch.tensor						), type(lowerCAmelCase__						)
						lowerCamelCase_					=  v
			return r




class  __lowerCamelCase  :
				a__:      Union[str, Any]          =    {}




				def __init__(				self							,						UpperCAmelCase							,						UpperCAmelCase = "root"							,						UpperCAmelCase=0   ):
							lowerCamelCase_					=  name
							lowerCamelCase_					=  level
							lowerCamelCase_					=  {}
							for k, v in dictionary.items():
										if v is None:
													raise ValueError()
										lowerCamelCase_					=  copy.deepcopy(UpperCAmelCase   )
										lowerCamelCase_					=  copy.deepcopy(UpperCAmelCase   )
										if isinstance(UpperCAmelCase							,						UpperCAmelCase   ):
													lowerCamelCase_					=  Config(UpperCAmelCase							,						name=UpperCAmelCase							,						level=level + 1   )
										lowerCamelCase_					=  v
										setattr(self							,						UpperCAmelCase							,						UpperCAmelCase   )

							lowerCamelCase_					=  d




				def __repr__(				self   ):
							return str(list((self._pointer.keys())   )   )




				def __setattr__(				self							,						UpperCAmelCase							,						UpperCAmelCase   ):
							lowerCamelCase_					=  val
							lowerCamelCase_					=  val
							lowerCamelCase_					=  key.split('''.'''   )
							lowerCamelCase_					=  len(UpperCAmelCase   ) - 1
							lowerCamelCase_					=  self._pointer
							if len(UpperCAmelCase   ) > 1:
										for i, l in enumerate(UpperCAmelCase   ):
													if hasattr(self							,						UpperCAmelCase   ) and isinstance(getattr(self							,						UpperCAmelCase   )							,						UpperCAmelCase   ):
																setattr(getattr(self							,						UpperCAmelCase   )							,						'''.'''.join(levels[i:]   )							,						UpperCAmelCase   )
													if l == last_level:
																lowerCamelCase_					=  val
													else:
																lowerCamelCase_					=  pointer[l]




				def        UpperCAmelCase__						(				self   ):
							return self._pointer




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase   ):
							with open(f"{file_name}"							,						'''w'''   ) as stream:
										dump(UpperCAmelCase							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase   ):
							with open(f"{file_name}"							,						'''w'''   ) as stream:
										json.dump(UpperCAmelCase							,						UpperCAmelCase   )




				@staticmethod
				def        UpperCAmelCase__						(				UpperCAmelCase   ):
							with open(UpperCAmelCase   ) as stream:
										lowerCamelCase_					=  load(UpperCAmelCase							,						Loader=UpperCAmelCase   )
							return data




				def __str__(				self   ):
							lowerCamelCase_					=  '''    '''
							if self._name != "root":
										lowerCamelCase_					=  f"{t * (self._level-1)}{self._name}:\n"
							else:
										lowerCamelCase_					=  ''''''
							lowerCamelCase_					=  self._level
							for i, (k, v) in enumerate(self._pointer.items()   ):
										if isinstance(UpperCAmelCase							,						UpperCAmelCase   ):
													r += f"{t * (self._level)}{v}\n"
													self._level += 1
										else:
													r += f"{t * (self._level)}{k}: {v} ({type(UpperCAmelCase   ).__name__})\n"
										lowerCamelCase_					=  level
							return r[:-1]




				@classmethod
				def        UpperCAmelCase__						(				cls							,						UpperCAmelCase							,						**UpperCAmelCase   ):
							lowerCamelCase_		, lowerCamelCase_					=  cls.get_config_dict(UpperCAmelCase							,						**UpperCAmelCase   )
							return cls(UpperCAmelCase   )





				@classmethod
				def        UpperCAmelCase__						(				cls							,						UpperCAmelCase							,						**UpperCAmelCase   ):
							lowerCamelCase_					=  kwargs.pop('''cache_dir'''							,						UpperCAmelCase   )
							lowerCamelCase_					=  kwargs.pop('''force_download'''							,						UpperCAmelCase   )
							lowerCamelCase_					=  kwargs.pop('''resume_download'''							,						UpperCAmelCase   )
							lowerCamelCase_					=  kwargs.pop('''proxies'''							,						UpperCAmelCase   )
							lowerCamelCase_					=  kwargs.pop('''local_files_only'''							,						UpperCAmelCase   )

							if os.path.isdir(UpperCAmelCase   ):
										lowerCamelCase_					=  os.path.join(UpperCAmelCase							,						UpperCAmelCase   )
							elif os.path.isfile(UpperCAmelCase   ) or is_remote_url(UpperCAmelCase   ):
										lowerCamelCase_					=  pretrained_model_name_or_path
							else:
										lowerCamelCase_					=  hf_bucket_url(UpperCAmelCase							,						filename=UpperCAmelCase							,						use_cdn=UpperCAmelCase   )

							try:
										# Load from URL or cache if already cached
										lowerCamelCase_					=  cached_path(
										    UpperCAmelCase							,						cache_dir=UpperCAmelCase							,						force_download=UpperCAmelCase							,						proxies=UpperCAmelCase							,						resume_download=UpperCAmelCase							,						local_files_only=UpperCAmelCase							,						)
										# Load config dict
										if resolved_config_file is None:
													raise EnvironmentError

										lowerCamelCase_					=  Config.load_yaml(UpperCAmelCase   )

							except EnvironmentError:
										lowerCamelCase_					=  '''Can\'t load config for'''
										raise EnvironmentError(UpperCAmelCase   )

							if resolved_config_file == config_file:
										print('''loading configuration file from path'''   )
							else:
										print('''loading configuration file cache'''   )

							return Config.load_yaml(UpperCAmelCase   ), kwargs






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  torch.load('''dump.pt'''	,map_location=in_tensor.device						)
			lowerCamelCase_					=  in_tensor.numpy()
			lowerCamelCase_					=  out_tensor.numpy()[0]
			print(na.shape	,na[0, 0, :5]						)
			print(na.shape	,na[0, 0, :5]						)
			assert np.allclose(lowerCAmelCase__	,lowerCAmelCase__	,rtol=0.01	,atol=0.1						), (
			    f"{sum([1 for x in np.isclose(lowerCAmelCase__	,lowerCAmelCase__	,rtol=0.01	,atol=0.1						).flatten() if x is False]						)/len(na.flatten()						)*100:.4f} %"
			    " element-wise mismatch"
			)
			raise Exception('''tensors are all good'''						)

			# Hugging face functions below






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  urlparse(lowerCAmelCase__						)
			return parsed.scheme in ("http", "https")






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__=True						):
			lowerCamelCase_					=  CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
			lowerCamelCase_					=  '''/''' not in model_id
			if legacy_format:
						return f"{endpoint}/{model_id}-{filename}"
			else:
						return f"{endpoint}/{model_id}/{filename}"






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__=None	,lowerCAmelCase__=0	,lowerCAmelCase__=None	,):
			lowerCamelCase_					=  '''python/{}'''.format(sys.version.split()[0]						)
			if _torch_available:
						ua += "; torch/{}".format(torch.__version__						)
			if isinstance(lowerCAmelCase__	,lowerCAmelCase__						):
						ua += "; " + "; ".join('''{}/{}'''.format(lowerCAmelCase__	,lowerCAmelCase__						) for k, v in user_agent.items()						)
			elif isinstance(lowerCAmelCase__	,lowerCAmelCase__						):
						ua += "; " + user_agent
			lowerCamelCase_					=  {'''user-agent''': ua}
			if resume_size > 0:
						lowerCamelCase_					=  '''bytes=%d-''' % (resume_size,)
			lowerCamelCase_					=  requests.get(lowerCAmelCase__	,stream=lowerCAmelCase__	,proxies=lowerCAmelCase__	,headers=lowerCAmelCase__						)
			if response.status_code == 416:  # Range not satisfiable
						return
			lowerCamelCase_					=  response.headers.get('''Content-Length'''						)
			lowerCamelCase_					=  resume_size + int(lowerCAmelCase__						) if content_length is not None else None
			lowerCamelCase_					=  tqdm(
			    unit='''B'''	,unit_scale=lowerCAmelCase__	,total=lowerCAmelCase__	,initial=lowerCAmelCase__	,desc='''Downloading'''	,)
			for chunk in response.iter_content(chunk_size=1_024						):
						if chunk:  # filter out keep-alive new chunks
									progress.update(len(lowerCAmelCase__						)						)
									temp_file.write(lowerCAmelCase__						)
			progress.close()






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__=None	,lowerCAmelCase__=False	,lowerCAmelCase__=None	,lowerCAmelCase__=10	,lowerCAmelCase__=False	,lowerCAmelCase__=None	,lowerCAmelCase__=False	,):
			if cache_dir is None:
						lowerCamelCase_					=  TRANSFORMERS_CACHE
			if isinstance(lowerCAmelCase__	,lowerCAmelCase__						):
						lowerCamelCase_					=  str(lowerCAmelCase__						)

			os.makedirs(lowerCAmelCase__	,exist_ok=lowerCAmelCase__						)

			lowerCamelCase_					=  None
			if not local_files_only:
						try:
									lowerCamelCase_					=  requests.head(lowerCAmelCase__	,allow_redirects=lowerCAmelCase__	,proxies=lowerCAmelCase__	,timeout=lowerCAmelCase__						)
									if response.status_code == 200:
												lowerCamelCase_					=  response.headers.get('''ETag'''						)
						except (EnvironmentError, requests.exceptions.Timeout):
									# etag is already None
									pass

			lowerCamelCase_					=  url_to_filename(lowerCAmelCase__	,lowerCAmelCase__						)

			# get cache path to put the file
			lowerCamelCase_					=  os.path.join(lowerCAmelCase__	,lowerCAmelCase__						)

			# etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
			# try to get the last downloaded one
			if etag is None:
						if os.path.exists(lowerCAmelCase__						):
									return cache_path
						else:
									lowerCamelCase_					=  [
									    file
									    for file in fnmatch.filter(os.listdir(lowerCAmelCase__						)	,filename + '''.*'''						)
									    if not file.endswith('''.json'''						) and not file.endswith('''.lock'''						)
									]
									if len(lowerCAmelCase__						) > 0:
												return os.path.join(lowerCAmelCase__	,matching_files[-1]						)
									else:
												# If files cannot be found and local_files_only=True,
												# the models might've been found if local_files_only=False
												# Notify the user about that
												if local_files_only:
															raise ValueError(
															    '''Cannot find the requested files in the cached path and outgoing traffic has been'''
															    ''' disabled. To enable model look-ups and downloads online, set \'local_files_only\''''
															    ''' to False.'''						)
												return None

    # From now on, etag is not None.
			if os.path.exists(lowerCAmelCase__						) and not force_download:
						return cache_path

			# Prevent parallel downloads of the same file with a lock.
			lowerCamelCase_					=  cache_path + '''.lock'''
			with FileLock(lowerCAmelCase__						):
						# If the download just completed while the lock was activated.
						if os.path.exists(lowerCAmelCase__						) and not force_download:
									# Even if returning early like here, the lock will be released.
									return cache_path

						if resume_download:
									lowerCamelCase_					=  cache_path + '''.incomplete'''

									@contextmanager
									def _resumable_file_manager():
												with open(lowerCAmelCase__	,'''a+b'''						) as f:
															yield f

									lowerCamelCase_					=  _resumable_file_manager
									if os.path.exists(lowerCAmelCase__						):
												lowerCamelCase_					=  os.stat(lowerCAmelCase__						).st_size
									else:
												lowerCamelCase_					=  0
						else:
									lowerCamelCase_					=  partial(tempfile.NamedTemporaryFile	,dir=lowerCAmelCase__	,delete=lowerCAmelCase__						)
									lowerCamelCase_					=  0

						# Download to temporary file, then copy to cache dir once finished.
						# Otherwise you get corrupt cache entries if the download gets interrupted.
						with temp_file_manager() as temp_file:
									print(
									    '''%s not found in cache or force_download set to True, downloading to %s'''	,lowerCAmelCase__	,temp_file.name	,)

									http_get(
									    lowerCAmelCase__	,lowerCAmelCase__	,proxies=lowerCAmelCase__	,resume_size=lowerCAmelCase__	,user_agent=lowerCAmelCase__	,)

						os.replace(temp_file.name	,lowerCAmelCase__						)

						lowerCamelCase_					=  {'''url''': url, '''etag''': etag}
						lowerCamelCase_					=  cache_path + '''.json'''
						with open(lowerCAmelCase__	,'''w'''						) as meta_file:
									json.dump(lowerCAmelCase__	,lowerCAmelCase__						)

			return cache_path






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__=None						):
			lowerCamelCase_					=  url.encode('''utf-8'''						)
			lowerCamelCase_					=  shaaaa(lowerCAmelCase__						)
			lowerCamelCase_					=  url_hash.hexdigest()

			if etag:
						lowerCamelCase_					=  etag.encode('''utf-8'''						)
						lowerCamelCase_					=  shaaaa(lowerCAmelCase__						)
						filename += "." + etag_hash.hexdigest()

			if url.endswith('''.h5'''						):
						filename += ".h5"

			return filename






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__=None	,lowerCAmelCase__=False	,lowerCAmelCase__=None	,lowerCAmelCase__=False	,lowerCAmelCase__=None	,lowerCAmelCase__=False	,lowerCAmelCase__=False	,lowerCAmelCase__=False	,):
			if cache_dir is None:
						lowerCamelCase_					=  TRANSFORMERS_CACHE
			if isinstance(lowerCAmelCase__	,lowerCAmelCase__						):
						lowerCamelCase_					=  str(lowerCAmelCase__						)
			if isinstance(lowerCAmelCase__	,lowerCAmelCase__						):
						lowerCamelCase_					=  str(lowerCAmelCase__						)

			if is_remote_url(lowerCAmelCase__						):
						# URL, so get it from the cache (downloading if necessary)
						lowerCamelCase_					=  get_from_cache(
						    lowerCAmelCase__	,cache_dir=lowerCAmelCase__	,force_download=lowerCAmelCase__	,proxies=lowerCAmelCase__	,resume_download=lowerCAmelCase__	,user_agent=lowerCAmelCase__	,local_files_only=lowerCAmelCase__	,)
			elif os.path.exists(lowerCAmelCase__						):
						# File, and it exists.
						lowerCamelCase_					=  url_or_filename
			elif urlparse(lowerCAmelCase__						).scheme == "":
						# File, but it doesn't exist.
						raise EnvironmentError('''file {} not found'''.format(lowerCAmelCase__						)						)
			else:
						# Something unknown
						raise ValueError('''unable to parse {} as a URL or as a local path'''.format(lowerCAmelCase__						)						)

			if extract_compressed_file:
						if not is_zipfile(lowerCAmelCase__						) and not tarfile.is_tarfile(lowerCAmelCase__						):
									return output_path

						# Path where we extract compressed archives
						# We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
						lowerCamelCase_		, lowerCamelCase_					=  os.path.split(lowerCAmelCase__						)
						lowerCamelCase_					=  output_file.replace('''.'''	,'''-'''						) + '''-extracted'''
						lowerCamelCase_					=  os.path.join(lowerCAmelCase__	,lowerCAmelCase__						)

						if os.path.isdir(lowerCAmelCase__						) and os.listdir(lowerCAmelCase__						) and not force_extract:
									return output_path_extracted

						# Prevent parallel extractions
						lowerCamelCase_					=  output_path + '''.lock'''
						with FileLock(lowerCAmelCase__						):
									shutil.rmtree(lowerCAmelCase__	,ignore_errors=lowerCAmelCase__						)
									os.makedirs(lowerCAmelCase__						)
									if is_zipfile(lowerCAmelCase__						):
												with ZipFile(lowerCAmelCase__	,'''r'''						) as zip_file:
															zip_file.extractall(lowerCAmelCase__						)
															zip_file.close()
									elif tarfile.is_tarfile(lowerCAmelCase__						):
												lowerCamelCase_					=  tarfile.open(lowerCAmelCase__						)
												tar_file.extractall(lowerCAmelCase__						)
												tar_file.close()
									else:
												raise EnvironmentError('''Archive format of {} could not be identified'''.format(lowerCAmelCase__						)						)

						return output_path_extracted

			return output_path






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__=","						):
			assert isinstance(lowerCAmelCase__	,lowerCAmelCase__						)
			if os.path.isfile(lowerCAmelCase__						):
						with open(lowerCAmelCase__						) as f:
									lowerCamelCase_					=  eval(f.read()						)
			else:
						lowerCamelCase_					=  requests.get(lowerCAmelCase__						)
						try:
									lowerCamelCase_					=  requests.json()
						except Exception:
									lowerCamelCase_					=  req.content.decode()
									assert data is not None, "could not connect"
									try:
												lowerCamelCase_					=  eval(lowerCAmelCase__						)
									except Exception:
												lowerCamelCase_					=  data.split('''\n'''						)
						req.close()
			return data






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  requests.get(lowerCAmelCase__						)
			lowerCamelCase_					=  np.array(Image.open(BytesIO(response.content						)						)						)
			return img






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  url.split('''/'''						)[-1]
			if fn not in os.listdir(os.getcwd()						):
						wget.download(lowerCAmelCase__						)
			with open(lowerCAmelCase__	,'''rb'''						) as stream:
						lowerCamelCase_					=  pkl.load(lowerCAmelCase__						)
			lowerCamelCase_					=  weights.pop('''model'''						)
			lowerCamelCase_					=  {}
			for k, v in model.items():
						lowerCamelCase_					=  torch.from_numpy(lowerCAmelCase__						)
						if "running_var" in k:
									lowerCamelCase_					=  torch.tensor([0]						)
									lowerCamelCase_					=  k.replace('''running_var'''	,'''num_batches_tracked'''						)
									lowerCamelCase_					=  zero
			return new






def  lowercase						(	):
			print(f"{os.path.abspath(os.path.join(lowerCAmelCase__	,os.pardir						)						)}/demo.ipynb"						)






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__="RGB"						):
			assert isinstance(lowerCAmelCase__	,lowerCAmelCase__						)
			if os.path.isfile(lowerCAmelCase__						):
						lowerCamelCase_					=  cva.imread(lowerCAmelCase__						)
			else:
						lowerCamelCase_					=  get_image_from_url(lowerCAmelCase__						)
						assert img is not None, f"could not connect to: {im}"
			lowerCamelCase_					=  cva.cvtColor(lowerCAmelCase__	,cva.COLOR_BGR2RGB						)
			if input_format == "RGB":
						lowerCamelCase_					=  img[:, :, ::-1]
			return img






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__=1						):
			return (images[i : i + batch] for i in range(0	,len(lowerCAmelCase__						)	,lowerCAmelCase__						))



