





'''simple docstring'''

import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402


__lowerCamelCase : List[Any]											=			{
    "return_dict": False,
    "output_hidden_states": True,
    "output_attentions": True,
    "torchscript": True,
    "torch_dtype": "float16",
    "use_bfloat16": True,
    "tf_legacy_loss": True,
    "pruned_heads": {"a": 1},
    "tie_word_embeddings": False,
    "is_decoder": True,
    "cross_attention_hidden_size": 128,
    "add_cross_attention": True,
    "tie_encoder_decoder": True,
    "max_length": 50,
    "min_length": 3,
    "do_sample": True,
    "early_stopping": True,
    "num_beams": 3,
    "num_beam_groups": 3,
    "diversity_penalty": 0.5,
    "temperature": 2.0,
    "top_k": 10,
    "top_p": 0.7,
    "typical_p": 0.2,
    "repetition_penalty": 0.8,
    "length_penalty": 0.8,
    "no_repeat_ngram_size": 5,
    "encoder_no_repeat_ngram_size": 5,
    "bad_words_ids": [1, 2, 3],
    "num_return_sequences": 3,
    "chunk_size_feed_forward": 5,
    "output_scores": True,
    "return_dict_in_generate": True,
    "forced_bos_token_id": 2,
    "forced_eos_token_id": 3,
    "remove_invalid_values": True,
    "architectures": ["BertModel"],
    "finetuning_task": "translation",
    "id2label": {0: "label"},
    "label2id": {"label": "0"},
    "tokenizer_class": "BertTokenizerFast",
    "prefix": "prefix",
    "bos_token_id": 6,
    "pad_token_id": 7,
    "eos_token_id": 8,
    "sep_token_id": 9,
    "decoder_start_token_id": 10,
    "exponential_decay_length_penalty": (5, 1.01),
    "suppress_tokens": [0, 1],
    "begin_suppress_tokens": 2,
    "task_specific_params": {"translation": "some_params"},
    "problem_type": "regression",
}

@is_staging_test
class       UpperCAmelCase (						unittest.TestCase  ):




		@classmethod
		def 				UpperCAmelCase__   (cls      :	Tuple				)					-> Dict:
						lowercase         = TOKEN
						HfFolder.save_token(A__				)




		@classmethod
		def 				UpperCAmelCase__   (cls      :	int				)					-> int:
						try:
										delete_repo(token=cls._token							,			repo_id="test-config"				)
						except HTTPError:
										pass

						try:
										delete_repo(token=cls._token							,			repo_id="valid_org/test-config-org"				)
						except HTTPError:
										pass

						try:
										delete_repo(token=cls._token							,			repo_id="test-dynamic-config"				)
						except HTTPError:
										pass




		def 				UpperCAmelCase__   (self      :	int				)					-> str:
						lowercase         = BertConfig(
						    vocab_size=9_9							,			hidden_size=3_2							,			num_hidden_layers=5							,			num_attention_heads=4							,			intermediate_size=3_7				)
						config.push_to_hub("test-config"							,			use_auth_token=self._token				)

						lowercase         = BertConfig.from_pretrained(f'{USER}/test-config'				)
						for k, v in config.to_dict().items():
										if k != "transformers_version":
														self.assertEqual(A__							,			getattr(A__							,			A__				)				)

        # Reset repo
						delete_repo(token=self._token							,			repo_id="test-config"				)

						# Push to hub via save_pretrained
						with tempfile.TemporaryDirectory() as tmp_dir:
										config.save_pretrained(A__							,			repo_id="test-config"							,			push_to_hub=A__							,			use_auth_token=self._token				)

						lowercase         = BertConfig.from_pretrained(f'{USER}/test-config'				)
						for k, v in config.to_dict().items():
										if k != "transformers_version":
														self.assertEqual(A__							,			getattr(A__							,			A__				)				)




		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> List[Any]:
						lowercase         = BertConfig(
						    vocab_size=9_9							,			hidden_size=3_2							,			num_hidden_layers=5							,			num_attention_heads=4							,			intermediate_size=3_7				)
						config.push_to_hub("valid_org/test-config-org"							,			use_auth_token=self._token				)

						lowercase         = BertConfig.from_pretrained("valid_org/test-config-org"				)
						for k, v in config.to_dict().items():
										if k != "transformers_version":
														self.assertEqual(A__							,			getattr(A__							,			A__				)				)

        # Reset repo
						delete_repo(token=self._token							,			repo_id="valid_org/test-config-org"				)

						# Push to hub via save_pretrained
						with tempfile.TemporaryDirectory() as tmp_dir:
										config.save_pretrained(
										    A__							,			repo_id="valid_org/test-config-org"							,			push_to_hub=A__							,			use_auth_token=self._token				)

						lowercase         = BertConfig.from_pretrained("valid_org/test-config-org"				)
						for k, v in config.to_dict().items():
										if k != "transformers_version":
														self.assertEqual(A__							,			getattr(A__							,			A__				)				)





		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> List[str]:
						CustomConfig.register_for_auto_class()
						lowercase         = CustomConfig(attribute=4_2				)

						config.push_to_hub("test-dynamic-config"							,			use_auth_token=self._token				)

						# This has added the proper auto_map field to the config
						self.assertDictEqual(config.auto_map							,			{"AutoConfig": "custom_configuration.CustomConfig"}				)

						lowercase         = AutoConfig.from_pretrained(f'{USER}/test-dynamic-config'							,			trust_remote_code=A__				)
						# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
						self.assertEqual(new_config.__class__.__name__							,			"CustomConfig"				)
						self.assertEqual(new_config.attribute							,			4_2				)




class       UpperCAmelCase (						unittest.TestCase  ):




		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> Optional[int]:
						lowercase         = GPTaConfig()

						# attempt to modify each of int/float/bool/str config records and verify they were updated
						lowercase         = c.n_embd + 1  # int
						lowercase         = c.resid_pdrop + 1.0  # float
						lowercase         = not c.scale_attn_weights  # bool
						lowercase         = c.summary_type + "foo"  # str
						c.update_from_string(
						    f'n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}'				)
						self.assertEqual(A__							,			c.n_embd							,			"mismatch for key: n_embd"				)
						self.assertEqual(A__							,			c.resid_pdrop							,			"mismatch for key: resid_pdrop"				)
						self.assertEqual(A__							,			c.scale_attn_weights							,			"mismatch for key: scale_attn_weights"				)
						self.assertEqual(A__							,			c.summary_type							,			"mismatch for key: summary_type"				)




		def 				UpperCAmelCase__   (self      :	str				)					-> Union[str, Any]:
						lowercase         = PretrainedConfig()
						lowercase         = [key for key in base_config.__dict__ if key not in config_common_kwargs]
						# If this part of the test fails, you have arguments to addin config_common_kwargs above.
						self.assertListEqual(
						    A__							,			["is_encoder_decoder", "_name_or_path", "_commit_hash", "transformers_version"]				)
						lowercase         = [key for key, value in config_common_kwargs.items() if value == getattr(A__							,			A__				)]
						if len(A__				) > 0:
										raise ValueError(
										    "The following keys are set with the default values in"
										    " `test_configuration_common.config_common_kwargs` pick another value for them:"
										    f' {", ".join(A__				)}.'				)




		def 				UpperCAmelCase__   (self      :	Tuple				)					-> Optional[Any]:
						with self.assertRaises(A__				):
										# config is in subfolder, the following should not work without specifying the subfolder
										lowercase         = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder"				)

						lowercase         = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder"							,			subfolder="bert"				)

						self.assertIsNotNone(A__				)




		def 				UpperCAmelCase__   (self      :	int				)					-> List[Any]:
						# A mock response for an HTTP head request to emulate server down
						lowercase         = mock.Mock()
						lowercase         = 5_0_0
						lowercase         = {}
						lowercase         = HTTPError
						lowercase         = {}

						# Download this model to make sure it's in the cache.
						lowercase         = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert"				)

						# Under the mock environment we get a 500 error when trying to reach the model.
						with mock.patch("requests.Session.request"							,			return_value=A__				) as mock_head:
										lowercase         = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert"				)
										# This check we did call the fake head request
										mock_head.assert_called()




		def 				UpperCAmelCase__   (self      :	str				)					-> str:
						# This test is for deprecated behavior and can be removed in v5
						lowercase         = BertConfig.from_pretrained(
						    "https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json"				)




		def 				UpperCAmelCase__   (self      :	str				)					-> int:
						lowercase         = AutoConfig.from_pretrained("bert-base-cased"				)
						lowercase         = ["config.4.0.0.json"]

						with tempfile.TemporaryDirectory() as tmp_dir:
										configuration.save_pretrained(A__				)
										lowercase         = 2
										json.dump(configuration.to_dict()							,			open(os.path.join(A__							,			"config.4.0.0.json"				)							,			"w"				)				)

										# This should pick the new configuration file as the version of Transformers is > 4.0.0
										lowercase         = AutoConfig.from_pretrained(A__				)
										self.assertEqual(new_configuration.hidden_size							,			2				)

										# Will need to be adjusted if we reach v42 and this test is still here.
										# Should pick the old configuration file as the version of Transformers is < 4.42.0
										lowercase         = ["config.42.0.0.json"]
										lowercase         = 7_6_8
										configuration.save_pretrained(A__				)
										shutil.move(os.path.join(A__							,			"config.4.0.0.json"				)							,			os.path.join(A__							,			"config.42.0.0.json"				)				)
										lowercase         = AutoConfig.from_pretrained(A__				)
										self.assertEqual(new_configuration.hidden_size							,			7_6_8				)





		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> Dict:
						# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
						lowercase         = "hf-internal-testing/test-two-configs"

						import transformers as new_transformers

						lowercase         = "v4.0.0"
						lowercase   ,							lowercase         = new_transformers.models.auto.AutoConfig.from_pretrained(
						    A__							,			return_unused_kwargs=A__				)
						self.assertEqual(new_configuration.hidden_size							,			2				)
						# This checks `_configuration_file` ia not kept in the kwargs by mistake.
						self.assertDictEqual(A__							,			{}				)

						# Testing an older version by monkey-patching the version in the module it's used.
						import transformers as old_transformers

						lowercase         = "v3.0.0"
						lowercase         = old_transformers.models.auto.AutoConfig.from_pretrained(A__				)
						self.assertEqual(old_configuration.hidden_size							,			7_6_8				)











'''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

			__lowerCamelCase : Optional[Any]											=			True
except ImportError:
			__lowerCamelCase : Dict											=			False


try:
			from torch.hub import _get_torch_home

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

__lowerCamelCase : Tuple											=			os.path.join(torch_cache_home, "transformers")

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







def 							UpperCAmelCase_    (	lowerCAmelCase_=OBJECTS		,							lowerCAmelCase_=ATTRIBUTES							):

				"""simple docstring"""






				lowercase         = []
				with open(lowerCAmelCase_							) as f:
								for object in f.readlines():
												vg_classes.append(object.split(","							)[0].lower().strip()							)

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







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






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

class       UpperCAmelCase :
		UpperCAmelCase					:	int 			=		{}




		def __init__(self      :	List[str]							,			A__      :	dict							,			A__      :	str = "root"							,			A__      :	Optional[Any]=0				)					-> str:
						lowercase         = name
						lowercase         = level
						lowercase         = {}
						for k, v in dictionary.items():
										if v is None:
														raise ValueError()
										lowercase         = copy.deepcopy(A__				)
										lowercase         = copy.deepcopy(A__				)
										if isinstance(A__							,			A__				):
														lowercase         = Config(A__							,			name=A__							,			level=level + 1				)
										lowercase         = v
										setattr(self							,			A__							,			A__				)

						lowercase         = d




		def __repr__(self      :	Optional[Any]				)					-> Optional[int]:
						return str(list((self._pointer.keys())				)				)




		def __setattr__(self      :	str							,			A__      :	List[str]							,			A__      :	Dict				)					-> int:
						lowercase         = val
						lowercase         = val
						lowercase         = key.split("."				)
						lowercase         = len(A__				) - 1
						lowercase         = self._pointer
						if len(A__				) > 1:
										for i, l in enumerate(A__				):
														if hasattr(self							,			A__				) and isinstance(getattr(self							,			A__				)							,			A__				):
																		setattr(getattr(self							,			A__				)							,			".".join(levels[i:]				)							,			A__				)
														if l == last_level:
																		lowercase         = val
														else:
																		lowercase         = pointer[l]




		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> Any:
						return self._pointer




		def 				UpperCAmelCase__   (self      :	int							,			A__      :	Optional[int]							,			A__      :	int				)					-> int:
						with open(f'{file_name}'							,			"w"				) as stream:
										dump(A__							,			A__				)




		def 				UpperCAmelCase__   (self      :	int							,			A__      :	Optional[int]							,			A__      :	List[str]				)					-> List[str]:
						with open(f'{file_name}'							,			"w"				) as stream:
										json.dump(A__							,			A__				)




		@staticmethod
		def 				UpperCAmelCase__   (A__      :	Tuple				)					-> Optional[int]:
						with open(A__				) as stream:
										lowercase         = load(A__							,			Loader=A__				)
						return data




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




		@classmethod
		def 				UpperCAmelCase__   (cls      :	Any							,			A__      :	str							,			**A__      :	Optional[Any]				)					-> Optional[int]:
						lowercase   ,							lowercase         = cls.get_config_dict(A__							,			**A__				)
						return cls(A__				)





		@classmethod
		def 				UpperCAmelCase__   (cls      :	List[str]							,			A__      :	str							,			**A__      :	Optional[int]				)					-> int:
						lowercase         = kwargs.pop("cache_dir"							,			A__				)
						lowercase         = kwargs.pop("force_download"							,			A__				)
						lowercase         = kwargs.pop("resume_download"							,			A__				)
						lowercase         = kwargs.pop("proxies"							,			A__				)
						lowercase         = kwargs.pop("local_files_only"							,			A__				)

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

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

										lowercase         = Config.load_yaml(A__				)

						except EnvironmentError:
										lowercase         = "Can't load config for"
										raise EnvironmentError(A__				)

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

						return Config.load_yaml(A__				), kwargs







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = torch.load("dump.pt"		,							map_location=in_tensor.device							)
				lowercase         = in_tensor.numpy()
				lowercase         = 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 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = urlparse(lowerCAmelCase_							)
				return parsed.scheme in ("http", "https")







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_=True							):

				"""simple docstring"""






				lowercase         = CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
				lowercase         = "/" not in model_id
				if legacy_format:
								return f'{endpoint}/{model_id}-{filename}'
				else:
								return f'{endpoint}/{model_id}/{filename}'







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_=None		,							lowerCAmelCase_=0		,							lowerCAmelCase_=None		,							):

				"""simple docstring"""






				lowercase         = "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
				lowercase         = {"user-agent": ua}
				if resume_size > 0:
								lowercase         = "bytes=%d-" % (resume_size,)
				lowercase         = requests.get(lowerCAmelCase_		,							stream=lowerCAmelCase_		,							proxies=lowerCAmelCase_		,							headers=lowerCAmelCase_							)
				if response.status_code == 416:  # Range not satisfiable
								return
				lowercase         = response.headers.get("Content-Length"							)
				lowercase         = resume_size + int(lowerCAmelCase_							) if content_length is not None else None
				lowercase         = tqdm(
				    unit="B"		,							unit_scale=lowerCAmelCase_		,							total=lowerCAmelCase_		,							initial=lowerCAmelCase_		,							desc="Downloading"		,							)
				for chunk in response.iter_content(chunk_size=1024							):
								if chunk:  # filter out keep-alive new chunks
												progress.update(len(lowerCAmelCase_							)							)
												temp_file.write(lowerCAmelCase_							)
				progress.close()







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_=None		,							lowerCAmelCase_=False		,							lowerCAmelCase_=None		,							lowerCAmelCase_=10		,							lowerCAmelCase_=False		,							lowerCAmelCase_=None		,							lowerCAmelCase_=False		,							):

				"""simple docstring"""






				if cache_dir is None:
								lowercase         = TRANSFORMERS_CACHE
				if isinstance(lowerCAmelCase_		,							lowerCAmelCase_							):
								lowercase         = str(lowerCAmelCase_							)

				os.makedirs(lowerCAmelCase_		,							exist_ok=lowerCAmelCase_							)

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

				lowercase         = url_to_filename(lowerCAmelCase_		,							lowerCAmelCase_							)

				# get cache path to put the file
				lowercase         = 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:
												lowercase         = [
												    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.
				lowercase         = 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:
												lowercase         = cache_path + ".incomplete"

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

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

								lowercase         = {"url": url, "etag": etag}
								lowercase         = cache_path + ".json"
								with open(lowerCAmelCase_		,							"w"							) as meta_file:
												json.dump(lowerCAmelCase_		,							lowerCAmelCase_							)

				return cache_path







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_=None							):

				"""simple docstring"""






				lowercase         = url.encode("utf-8"							)
				lowercase         = shaaaa(lowerCAmelCase_							)
				lowercase         = url_hash.hexdigest()

				if etag:
								lowercase         = etag.encode("utf-8"							)
								lowercase         = shaaaa(lowerCAmelCase_							)
								filename += "." + etag_hash.hexdigest()

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

				return filename







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_=None		,							lowerCAmelCase_=False		,							lowerCAmelCase_=None		,							lowerCAmelCase_=False		,							lowerCAmelCase_=None		,							lowerCAmelCase_=False		,							lowerCAmelCase_=False		,							lowerCAmelCase_=False		,							):

				"""simple docstring"""






				if cache_dir is None:
								lowercase         = TRANSFORMERS_CACHE
				if isinstance(lowerCAmelCase_		,							lowerCAmelCase_							):
								lowercase         = str(lowerCAmelCase_							)
				if isinstance(lowerCAmelCase_		,							lowerCAmelCase_							):
								lowercase         = str(lowerCAmelCase_							)

				if is_remote_url(lowerCAmelCase_							):
								# URL, so get it from the cache (downloading if necessary)
								lowercase         = 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.
								lowercase         = 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/"
								lowercase   ,							lowercase         = os.path.split(lowerCAmelCase_							)
								lowercase         = output_file.replace("."		,							"-"							) + "-extracted"
								lowercase         = 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
								lowercase         = 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_							):
																lowercase         = 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 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_=","							):

				"""simple docstring"""






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







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = requests.get(lowerCAmelCase_							)
				lowercase         = np.array(Image.open(BytesIO(response.content							)							)							)
				return img







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






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







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				print(f'{os.path.abspath(os.path.join(lowerCAmelCase_		,							os.pardir							)							)}/demo.ipynb'							)







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_="RGB"							):

				"""simple docstring"""






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







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_=1							):

				"""simple docstring"""






				return (images[i : i + batch] for i in range(0		,							len(lowerCAmelCase_							)		,							lowerCAmelCase_							))




