
"""simple docstring"""

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import CLIPImageProcessor
class    _UpperCAmelCase							(						unittest.TestCase							):




      def __init__(							self      ,				lowercase_      ,				lowercase_=7      ,				lowercase_=3      ,				lowercase_=1_8      ,				lowercase_=3_0      ,				lowercase_=4_0_0      ,				lowercase_=True      ,				lowercase_=None      ,				lowercase_=True      ,				lowercase_=None      ,				lowercase_=True      ,				lowercase_=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]      ,				lowercase_=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]      ,				lowercase_=True      ,				)  ->    List[str]:
          UpperCAmelCase  	=    size if size is not None else {'''shortest_edge''': 2_0}
          UpperCAmelCase  	=    crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
          UpperCAmelCase  	=    parent
          UpperCAmelCase  	=    batch_size
          UpperCAmelCase  	=    num_channels
          UpperCAmelCase  	=    image_size
          UpperCAmelCase  	=    min_resolution
          UpperCAmelCase  	=    max_resolution
          UpperCAmelCase  	=    do_resize
          UpperCAmelCase  	=    size
          UpperCAmelCase  	=    do_center_crop
          UpperCAmelCase  	=    crop_size
          UpperCAmelCase  	=    do_normalize
          UpperCAmelCase  	=    image_mean
          UpperCAmelCase  	=    image_std
          UpperCAmelCase  	=    do_convert_rgb




      def 			a_					(							self							)  ->    Union[str, Any]:
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_convert_rgb": self.do_convert_rgb,
          }




      def 			a_					(							self      ,				lowercase_=False      ,				lowercase_=False      ,				lowercase_=False							)  ->    Tuple:
          assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

          if equal_resolution:
              UpperCAmelCase  	=    []
              for i in range(self.batch_size							):
                  image_inputs.append(
                      np.random.randint(
                          2_5_5      ,				size=(self.num_channels, self.max_resolution, self.max_resolution)      ,				dtype=np.uinta							)							)
          else:
              UpperCAmelCase  	=    []
              for i in range(self.batch_size							):
                  UpperCAmelCase  	=    np.random.choice(np.arange(self.min_resolution      ,				self.max_resolution							)      ,				2							)
                  image_inputs.append(np.random.randint(2_5_5      ,				size=(self.num_channels, width, height)      ,				dtype=np.uinta							)							)

          if not numpify and not torchify:
              # PIL expects the channel dimension as last dimension
              UpperCAmelCase  	=    [Image.fromarray(np.moveaxis(lowerCAmelCase__      ,				0      ,				-1							)							) for x in image_inputs]

          if torchify:
              UpperCAmelCase  	=    [torch.from_numpy(lowerCAmelCase__							) for x in image_inputs]

          return image_inputs

@require_torch
@require_vision
class    _UpperCAmelCase							(						_lowerCAmelCase   ,	unittest.TestCase							):
      __SCREAMING_SNAKE_CASE      :							str												=		CLIPImageProcessor if is_vision_available() else None




      def 			a_					(							self							)  ->    Optional[int]:
          UpperCAmelCase  	=    CLIPImageProcessingTester(self							)




      @property
      def 			a_					(							self							)  ->    List[Any]:
          return self.image_processor_tester.prepare_image_processor_dict()




      def 			a_					(							self							)  ->    Tuple:
          UpperCAmelCase  	=    self.image_processing_class(**self.image_processor_dict							)
          self.assertTrue(hasattr(lowerCAmelCase__      ,				'do_resize'							)							)
          self.assertTrue(hasattr(lowerCAmelCase__      ,				'size'							)							)
          self.assertTrue(hasattr(lowerCAmelCase__      ,				'do_center_crop'							)							)
          self.assertTrue(hasattr(lowerCAmelCase__      ,				'center_crop'							)							)
          self.assertTrue(hasattr(lowerCAmelCase__      ,				'do_normalize'							)							)
          self.assertTrue(hasattr(lowerCAmelCase__      ,				'image_mean'							)							)
          self.assertTrue(hasattr(lowerCAmelCase__      ,				'image_std'							)							)
          self.assertTrue(hasattr(lowerCAmelCase__      ,				'do_convert_rgb'							)							)




      def 			a_					(							self							)  ->    Optional[int]:
          UpperCAmelCase  	=    self.image_processing_class.from_dict(self.image_processor_dict							)
          self.assertEqual(image_processor.size      ,				{'shortest_edge': 2_0}							)
          self.assertEqual(image_processor.crop_size      ,				{'height': 1_8, 'width': 1_8}							)

          UpperCAmelCase  	=    self.image_processing_class.from_dict(self.image_processor_dict      ,				size=4_2      ,				crop_size=8_4							)
          self.assertEqual(image_processor.size      ,				{'shortest_edge': 4_2}							)
          self.assertEqual(image_processor.crop_size      ,				{'height': 8_4, 'width': 8_4}							)




      def 			a_					(							self							)  ->    Dict:
          pass




      def 			a_					(							self							)  ->    Optional[Any]:
          UpperCAmelCase  	=    self.image_processing_class(**self.image_processor_dict							)
          # create random PIL images
          UpperCAmelCase  	=    self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase__							)
          for image in image_inputs:
              self.assertIsInstance(lowerCAmelCase__      ,				Image.Image							)

          # Test not batched input
          UpperCAmelCase  	=    image_processing(image_inputs[0]      ,				return_tensors='pt'							).pixel_values
          self.assertEqual(
              encoded_images.shape      ,				(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )      ,				)

          # Test batched
          UpperCAmelCase  	=    image_processing(lowerCAmelCase__      ,				return_tensors='pt'							).pixel_values
          self.assertEqual(
              encoded_images.shape      ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )      ,				)




      def 			a_					(							self							)  ->    str:
          UpperCAmelCase  	=    self.image_processing_class(**self.image_processor_dict							)
          # create random numpy tensors
          UpperCAmelCase  	=    self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase__      ,				numpify=lowerCAmelCase__							)
          for image in image_inputs:
              self.assertIsInstance(lowerCAmelCase__      ,				np.ndarray							)

          # Test not batched input
          UpperCAmelCase  	=    image_processing(image_inputs[0]      ,				return_tensors='pt'							).pixel_values
          self.assertEqual(
              encoded_images.shape      ,				(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )      ,				)

          # Test batched
          UpperCAmelCase  	=    image_processing(lowerCAmelCase__      ,				return_tensors='pt'							).pixel_values
          self.assertEqual(
              encoded_images.shape      ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )      ,				)




      def 			a_					(							self							)  ->    Tuple:
          UpperCAmelCase  	=    self.image_processing_class(**self.image_processor_dict							)
          # create random PyTorch tensors
          UpperCAmelCase  	=    self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase__      ,				torchify=lowerCAmelCase__							)
          for image in image_inputs:
              self.assertIsInstance(lowerCAmelCase__      ,				torch.Tensor							)

          # Test not batched input
          UpperCAmelCase  	=    image_processing(image_inputs[0]      ,				return_tensors='pt'							).pixel_values
          self.assertEqual(
              encoded_images.shape      ,				(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )      ,				)

          # Test batched
          UpperCAmelCase  	=    image_processing(lowerCAmelCase__      ,				return_tensors='pt'							).pixel_values
          self.assertEqual(
              encoded_images.shape      ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )      ,				)

@require_torch
@require_vision
class    _UpperCAmelCase							(						_lowerCAmelCase   ,	unittest.TestCase							):
      __SCREAMING_SNAKE_CASE      :							Tuple												=		CLIPImageProcessor if is_vision_available() else None




      def 			a_					(							self							)  ->    List[str]:
          UpperCAmelCase  	=    CLIPImageProcessingTester(self      ,				num_channels=4							)
          UpperCAmelCase  	=    3




      @property
      def 			a_					(							self							)  ->    List[str]:
          return self.image_processor_tester.prepare_image_processor_dict()




      def 			a_					(							self							)  ->    List[str]:
          UpperCAmelCase  	=    self.image_processing_class(**self.image_processor_dict							)
          self.assertTrue(hasattr(lowerCAmelCase__      ,				'do_resize'							)							)
          self.assertTrue(hasattr(lowerCAmelCase__      ,				'size'							)							)
          self.assertTrue(hasattr(lowerCAmelCase__      ,				'do_center_crop'							)							)
          self.assertTrue(hasattr(lowerCAmelCase__      ,				'center_crop'							)							)
          self.assertTrue(hasattr(lowerCAmelCase__      ,				'do_normalize'							)							)
          self.assertTrue(hasattr(lowerCAmelCase__      ,				'image_mean'							)							)
          self.assertTrue(hasattr(lowerCAmelCase__      ,				'image_std'							)							)
          self.assertTrue(hasattr(lowerCAmelCase__      ,				'do_convert_rgb'							)							)




      def 			a_					(							self							)  ->    Any:
          pass




      def 			a_					(							self							)  ->    Optional[int]:
          UpperCAmelCase  	=    self.image_processing_class(**self.image_processor_dict							)
          # create random PIL images
          UpperCAmelCase  	=    self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase__							)
          for image in image_inputs:
              self.assertIsInstance(lowerCAmelCase__      ,				Image.Image							)

          # Test not batched input
          UpperCAmelCase  	=    image_processing(image_inputs[0]      ,				return_tensors='pt'							).pixel_values
          self.assertEqual(
              encoded_images.shape      ,				(
                  1,
                  self.expected_encoded_image_num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )      ,				)

          # Test batched
          UpperCAmelCase  	=    image_processing(lowerCAmelCase__      ,				return_tensors='pt'							).pixel_values
          self.assertEqual(
              encoded_images.shape      ,				(
                  self.image_processor_tester.batch_size,
                  self.expected_encoded_image_num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )      ,				)










"""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

						UpperCamelCase_           =True
except ImportError:
						UpperCamelCase_           =False


try:
						from torch.hub import _get_torch_home

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

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

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





def        a_				(							_lowercase=OBJECTS       ,						_lowercase=ATTRIBUTES   ):
						_UpperCamelCase			:					Optional[int]     =    []
						with open(_lowercase   ) as f:
												for object in f.readlines():
																		vg_classes.append(object.split(''','''   )[0].lower().strip()   )

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





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




class  _a						:
				UpperCamelCase								=    {}






				def __init__( self		:    List[Any], lowerCAmelCase__		:    dict, lowerCAmelCase__		:    str = "root", lowerCAmelCase__		:    Optional[int]=0							)	->      Tuple:




										'''simple docstring'''



										_UpperCamelCase			:					Union[str, Any]     =    name
										_UpperCamelCase			:					Optional[int]     =    level
										_UpperCamelCase			:					List[str]     =    {}
										for k, v in dictionary.items():
																if v is None:
																						raise ValueError()
																_UpperCamelCase			:					str     =    copy.deepcopy(lowerCAmelCase__							)
																_UpperCamelCase			:					str     =    copy.deepcopy(lowerCAmelCase__							)
																if isinstance(lowerCAmelCase__, lowerCAmelCase__							):
																						_UpperCamelCase			:					List[str]     =    Config(lowerCAmelCase__, name=lowerCAmelCase__, level=level + 1							)
																_UpperCamelCase			:					Any     =    v
																setattr(self, lowerCAmelCase__, lowerCAmelCase__							)

										_UpperCamelCase			:					Optional[Any]     =    d






				def __repr__( self		:    List[str]							)	->      List[Any]:




										'''simple docstring'''



										return str(list((self._pointer.keys())							)							)






				def __setattr__( self		:    int, lowerCAmelCase__		:    List[str], lowerCAmelCase__		:    List[Any]							)	->      int:




										'''simple docstring'''



										_UpperCamelCase			:					int     =    val
										_UpperCamelCase			:					int     =    val
										_UpperCamelCase			:					List[str]     =    key.split('''.'''							)
										_UpperCamelCase			:					List[str]     =    len(lowerCAmelCase__							) - 1
										_UpperCamelCase			:					Optional[Any]     =    self._pointer
										if len(lowerCAmelCase__							) > 1:
																for i, l in enumerate(lowerCAmelCase__							):
																						if hasattr(self, lowerCAmelCase__							) and isinstance(getattr(self, lowerCAmelCase__							), lowerCAmelCase__							):
																												setattr(getattr(self, lowerCAmelCase__							), '''.'''.join(levels[i:]							), lowerCAmelCase__							)
																						if l == last_level:
																												_UpperCamelCase			:					Optional[Any]     =    val
																						else:
																												_UpperCamelCase			:					Dict     =    pointer[l]






				def 				snake_case	( self		:    int							)	->      Union[str, Any]:




										'''simple docstring'''



										return self._pointer






				def 				snake_case	( self		:    int, lowerCAmelCase__		:    List[str], lowerCAmelCase__		:    Union[str, Any]							)	->      Tuple:




										'''simple docstring'''



										with open(f"""{file_name}""", '''w'''							) as stream:
																dump(lowerCAmelCase__, lowerCAmelCase__							)






				def 				snake_case	( self		:    Tuple, lowerCAmelCase__		:    Optional[Any], lowerCAmelCase__		:    Tuple							)	->      Optional[Any]:




										'''simple docstring'''



										with open(f"""{file_name}""", '''w'''							) as stream:
																json.dump(lowerCAmelCase__, lowerCAmelCase__							)






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    List[Any]							)	->      List[str]:




										'''simple docstring'''



										with open(lowerCAmelCase__							) as stream:
																_UpperCamelCase			:					List[str]     =    load(lowerCAmelCase__, Loader=lowerCAmelCase__							)
										return data






				def __str__( self		:    Tuple							)	->      Union[str, Any]:




										'''simple docstring'''



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






				@classmethod
				def 				snake_case	( cls		:    Optional[int], lowerCAmelCase__		:    str, **lowerCAmelCase__		:    Tuple							)	->      List[str]:




										'''simple docstring'''



										_UpperCamelCase   ,							_UpperCamelCase			:					int     =    cls.get_config_dict(lowerCAmelCase__, **lowerCAmelCase__							)
										return cls(lowerCAmelCase__							)






				@classmethod
				def 				snake_case	( cls		:    str, lowerCAmelCase__		:    str, **lowerCAmelCase__		:    Tuple							)	->      Tuple:




										'''simple docstring'''



										_UpperCamelCase			:					List[Any]     =    kwargs.pop('''cache_dir''', lowerCAmelCase__							)
										_UpperCamelCase			:					Optional[int]     =    kwargs.pop('''force_download''', lowerCAmelCase__							)
										_UpperCamelCase			:					List[str]     =    kwargs.pop('''resume_download''', lowerCAmelCase__							)
										_UpperCamelCase			:					str     =    kwargs.pop('''proxies''', lowerCAmelCase__							)
										_UpperCamelCase			:					Any     =    kwargs.pop('''local_files_only''', lowerCAmelCase__							)

										if os.path.isdir(lowerCAmelCase__							):
																_UpperCamelCase			:					Any     =    os.path.join(lowerCAmelCase__, lowerCAmelCase__							)
										elif os.path.isfile(lowerCAmelCase__							) or is_remote_url(lowerCAmelCase__							):
																_UpperCamelCase			:					Union[str, Any]     =    pretrained_model_name_or_path
										else:
																_UpperCamelCase			:					str     =    hf_bucket_url(lowerCAmelCase__, filename=lowerCAmelCase__, use_cdn=lowerCAmelCase__							)

										try:
																# Load from URL or cache if already cached
																_UpperCamelCase			:					Union[str, Any]     =    cached_path(
																    lowerCAmelCase__, cache_dir=lowerCAmelCase__, force_download=lowerCAmelCase__, proxies=lowerCAmelCase__, resume_download=lowerCAmelCase__, local_files_only=lowerCAmelCase__, )
																# Load config dict
																if resolved_config_file is None:
																						raise EnvironmentError

																_UpperCamelCase			:					int     =    Config.load_yaml(lowerCAmelCase__							)

										except EnvironmentError:
																_UpperCamelCase			:					List[Any]     =    '''Can\'t load config for'''
																raise EnvironmentError(lowerCAmelCase__							)

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

										return Config.load_yaml(lowerCAmelCase__							), kwargs





def        a_				(							_lowercase   ):
						_UpperCamelCase			:					List[Any]     =    torch.load('''dump.pt'''       ,						map_location=in_tensor.device   )
						_UpperCamelCase			:					Tuple     =    in_tensor.numpy()
						_UpperCamelCase			:					str     =    out_tensor.numpy()[0]
						print(na.shape       ,						na[0, 0, :5]   )
						print(na.shape       ,						na[0, 0, :5]   )
						assert np.allclose(_lowercase       ,						_lowercase       ,						rtol=0.01       ,						atol=0.1   ), (
						    F"""{sum([1 for x in np.isclose(_lowercase       ,						_lowercase       ,						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        a_				(							_lowercase   ):
						_UpperCamelCase			:					Dict     =    urlparse(_lowercase   )
						return parsed.scheme in ("http", "https")





def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase=True   ):
						_UpperCamelCase			:					List[str]     =    CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
						_UpperCamelCase			:					Optional[int]     =    '''/''' not in model_id
						if legacy_format:
												return F"""{endpoint}/{model_id}-{filename}"""
						else:
												return F"""{endpoint}/{model_id}/{filename}"""





def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase=None       ,						_lowercase=0       ,						_lowercase=None       ,						):
						_UpperCamelCase			:					str     =    '''python/{}'''.format(sys.version.split()[0]   )
						if _torch_available:
												ua += "; torch/{}".format(torch.__version__   )
						if isinstance(_lowercase       ,						_lowercase   ):
												ua += "; " + "; ".join('''{}/{}'''.format(_lowercase       ,						_lowercase   ) for k, v in user_agent.items()   )
						elif isinstance(_lowercase       ,						_lowercase   ):
												ua += "; " + user_agent
						_UpperCamelCase			:					Dict     =    {'''user-agent''': ua}
						if resume_size > 0:
												_UpperCamelCase			:					List[str]     =    '''bytes=%d-''' % (resume_size,)
						_UpperCamelCase			:					Optional[int]     =    requests.get(_lowercase       ,						stream=_lowercase       ,						proxies=_lowercase       ,						headers=_lowercase   )
						if response.status_code == 416:  # Range not satisfiable
												return
						_UpperCamelCase			:					Optional[Any]     =    response.headers.get('''Content-Length'''   )
						_UpperCamelCase			:					Any     =    resume_size + int(_lowercase   ) if content_length is not None else None
						_UpperCamelCase			:					int     =    tqdm(
						    unit='''B'''       ,						unit_scale=_lowercase       ,						total=_lowercase       ,						initial=_lowercase       ,						desc='''Downloading'''       ,						)
						for chunk in response.iter_content(chunk_size=1024   ):
												if chunk:  # filter out keep-alive new chunks
																		progress.update(len(_lowercase   )   )
																		temp_file.write(_lowercase   )
						progress.close()





def        a_				(							_lowercase       ,						_lowercase=None       ,						_lowercase=False       ,						_lowercase=None       ,						_lowercase=10       ,						_lowercase=False       ,						_lowercase=None       ,						_lowercase=False       ,						):
						if cache_dir is None:
												_UpperCamelCase			:					Any     =    TRANSFORMERS_CACHE
						if isinstance(_lowercase       ,						_lowercase   ):
												_UpperCamelCase			:					int     =    str(_lowercase   )

						os.makedirs(_lowercase       ,						exist_ok=_lowercase   )

						_UpperCamelCase			:					Optional[Any]     =    None
						if not local_files_only:
												try:
																		_UpperCamelCase			:					Optional[Any]     =    requests.head(_lowercase       ,						allow_redirects=_lowercase       ,						proxies=_lowercase       ,						timeout=_lowercase   )
																		if response.status_code == 200:
																								_UpperCamelCase			:					str     =    response.headers.get('''ETag'''   )
												except (EnvironmentError, requests.exceptions.Timeout):
																		# etag is already None
																		pass

						_UpperCamelCase			:					Optional[Any]     =    url_to_filename(_lowercase       ,						_lowercase   )

						# get cache path to put the file
						_UpperCamelCase			:					Any     =    os.path.join(_lowercase       ,						_lowercase   )

						# 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(_lowercase   ):
																		return cache_path
												else:
																		_UpperCamelCase			:					Dict     =    [
																		    file
																		    for file in fnmatch.filter(os.listdir(_lowercase   )       ,						filename + '''.*'''   )
																		    if not file.endswith('''.json'''   ) and not file.endswith('''.lock'''   )
																		]
																		if len(_lowercase   ) > 0:
																								return os.path.join(_lowercase       ,						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(_lowercase   ) and not force_download:
												return cache_path

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

												if resume_download:
																		_UpperCamelCase			:					Any     =    cache_path + '''.incomplete'''

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

																		_UpperCamelCase			:					List[str]     =    _resumable_file_manager
																		if os.path.exists(_lowercase   ):
																								_UpperCamelCase			:					str     =    os.stat(_lowercase   ).st_size
																		else:
																								_UpperCamelCase			:					Tuple     =    0
												else:
																		_UpperCamelCase			:					Tuple     =    partial(tempfile.NamedTemporaryFile       ,						dir=_lowercase       ,						delete=_lowercase   )
																		_UpperCamelCase			:					Optional[int]     =    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'''       ,						_lowercase       ,						temp_file.name       ,						)

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

												os.replace(temp_file.name       ,						_lowercase   )

												_UpperCamelCase			:					List[str]     =    {'''url''': url, '''etag''': etag}
												_UpperCamelCase			:					List[Any]     =    cache_path + '''.json'''
												with open(_lowercase       ,						'''w'''   ) as meta_file:
																		json.dump(_lowercase       ,						_lowercase   )

						return cache_path





def        a_				(							_lowercase       ,						_lowercase=None   ):
						_UpperCamelCase			:					str     =    url.encode('''utf-8'''   )
						_UpperCamelCase			:					Any     =    shaaaa(_lowercase   )
						_UpperCamelCase			:					Union[str, Any]     =    url_hash.hexdigest()

						if etag:
												_UpperCamelCase			:					List[Any]     =    etag.encode('''utf-8'''   )
												_UpperCamelCase			:					Union[str, Any]     =    shaaaa(_lowercase   )
												filename += "." + etag_hash.hexdigest()

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

						return filename





def        a_				(							_lowercase       ,						_lowercase=None       ,						_lowercase=False       ,						_lowercase=None       ,						_lowercase=False       ,						_lowercase=None       ,						_lowercase=False       ,						_lowercase=False       ,						_lowercase=False       ,						):
						if cache_dir is None:
												_UpperCamelCase			:					Union[str, Any]     =    TRANSFORMERS_CACHE
						if isinstance(_lowercase       ,						_lowercase   ):
												_UpperCamelCase			:					Tuple     =    str(_lowercase   )
						if isinstance(_lowercase       ,						_lowercase   ):
												_UpperCamelCase			:					str     =    str(_lowercase   )

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

						if extract_compressed_file:
												if not is_zipfile(_lowercase   ) and not tarfile.is_tarfile(_lowercase   ):
																		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/"
												_UpperCamelCase   ,							_UpperCamelCase			:					str     =    os.path.split(_lowercase   )
												_UpperCamelCase			:					str     =    output_file.replace('''.'''       ,						'''-'''   ) + '''-extracted'''
												_UpperCamelCase			:					Tuple     =    os.path.join(_lowercase       ,						_lowercase   )

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

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

												return output_path_extracted

						return output_path





def        a_				(							_lowercase       ,						_lowercase=","   ):
						assert isinstance(_lowercase       ,						_lowercase   )
						if os.path.isfile(_lowercase   ):
												with open(_lowercase   ) as f:
																		_UpperCamelCase			:					int     =    eval(f.read()   )
						else:
												_UpperCamelCase			:					List[str]     =    requests.get(_lowercase   )
												try:
																		_UpperCamelCase			:					List[str]     =    requests.json()
												except Exception:
																		_UpperCamelCase			:					Tuple     =    req.content.decode()
																		assert data is not None, "could not connect"
																		try:
																								_UpperCamelCase			:					int     =    eval(_lowercase   )
																		except Exception:
																								_UpperCamelCase			:					Union[str, Any]     =    data.split('''\n'''   )
												req.close()
						return data





def        a_				(							_lowercase   ):
						_UpperCamelCase			:					Union[str, Any]     =    requests.get(_lowercase   )
						_UpperCamelCase			:					List[Any]     =    np.array(Image.open(BytesIO(response.content   )   )   )
						return img





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





def        a_				(							):
						print(F"""{os.path.abspath(os.path.join(_lowercase       ,						os.pardir   )   )}/demo.ipynb"""   )





def        a_				(							_lowercase       ,						_lowercase="RGB"   ):
						assert isinstance(_lowercase       ,						_lowercase   )
						if os.path.isfile(_lowercase   ):
												_UpperCamelCase			:					Tuple     =    cva.imread(_lowercase   )
						else:
												_UpperCamelCase			:					List[str]     =    get_image_from_url(_lowercase   )
												assert img is not None, F"""could not connect to: {im}"""
						_UpperCamelCase			:					Any     =    cva.cvtColor(_lowercase       ,						cva.COLOR_BGR2RGB   )
						if input_format == "RGB":
												_UpperCamelCase			:					List[Any]     =    img[:, :, ::-1]
						return img






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



