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    :      Any	 =						True
except ImportError:
							_lowerCamelCase    :      Any	 =						False


try:
							from torch.hub import _get_torch_home

							_lowerCamelCase    :      Any	 =						_get_torch_home()
except ImportError:
							_lowerCamelCase    :      Optional[Any]	 =						os.path.expanduser(
							    os.getenv('''TORCH_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''torch'''))
							)

_lowerCamelCase    :      Union[str, Any]	 =						os.path.join(torch_cache_home, '''transformers''')

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





def 		__lowerCamelCase							(UpperCAmelCase__   :							Union[str, Any]=OBJECTS					,       UpperCAmelCase__   :							Optional[int]=ATTRIBUTES     ):
					SCREAMING_SNAKE_CASE				=				[]
					with open(snake_case__     ) as f:
										for object in f.readlines():
															vg_classes.append(object.split(","     )[0].lower().strip()     )

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





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


class   lowercase			:
							lowercase__  :				str									=							{}




							def __init__(  self      :    List[str]		,							_UpperCamelCase      :    Dict		,							_UpperCamelCase      :    Optional[Any] = "root"		,							_UpperCamelCase      :    Any=0					)    -> Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				name
												SCREAMING_SNAKE_CASE				=				level
												SCREAMING_SNAKE_CASE				=				{}
												for k, v in dictionary.items():
																	if v is None:
																						raise ValueError()
																	SCREAMING_SNAKE_CASE				=				copy.deepcopy(__a					)
																	SCREAMING_SNAKE_CASE				=				copy.deepcopy(__a					)
																	if isinstance(__a		,							__a					):
																						SCREAMING_SNAKE_CASE				=				Config(__a		,							name=__a		,							level=level + 1					)
																	SCREAMING_SNAKE_CASE				=				v
																	setattr(self		,							__a		,							__a					)

												SCREAMING_SNAKE_CASE				=				d




							def __repr__(  self      :    Optional[Any]					)    -> Union[str, Any]:



												'''simple docstring'''

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




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				val
												SCREAMING_SNAKE_CASE				=				val
												SCREAMING_SNAKE_CASE				=				key.split("."					)
												SCREAMING_SNAKE_CASE				=				len(__a					) - 1
												SCREAMING_SNAKE_CASE				=				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:
																											SCREAMING_SNAKE_CASE				=				val
																						else:
																											SCREAMING_SNAKE_CASE				=				pointer[l]




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



												'''simple docstring'''

												return self._pointer




							def 		__snake_case(  self      :    int		,							_UpperCamelCase      :    List[str]		,							_UpperCamelCase      :    Any					)    -> Optional[int]:



												'''simple docstring'''

												with open(F"{file_name}"		,							"w"					) as stream:
																	dump(__a		,							__a					)




							def 		__snake_case(  self      :    Optional[Any]		,							_UpperCamelCase      :    int		,							_UpperCamelCase      :    str					)    -> int:



												'''simple docstring'''

												with open(F"{file_name}"		,							"w"					) as stream:
																	json.dump(__a		,							__a					)




							@staticmethod
							def 		__snake_case(  _UpperCamelCase      :    Any					)    -> Dict:



												'''simple docstring'''

												with open(__a					) as stream:
																	SCREAMING_SNAKE_CASE				=				load(__a		,							Loader=__a					)
												return data




							def __str__(  self      :    str					)    -> str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				"    "
												if self._name != "root":
																	SCREAMING_SNAKE_CASE				=				F"{t * (self._level-1)}{self._name}:\n"
												else:
																	SCREAMING_SNAKE_CASE				=				""
												SCREAMING_SNAKE_CASE				=				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"
																	SCREAMING_SNAKE_CASE				=				level
												return r[:-1]




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				cls.get_config_dict(__a		,							**__a					)
												return cls(__a					)





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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				kwargs.pop("cache_dir"		,							__a					)
												SCREAMING_SNAKE_CASE				=				kwargs.pop("force_download"		,							__a					)
												SCREAMING_SNAKE_CASE				=				kwargs.pop("resume_download"		,							__a					)
												SCREAMING_SNAKE_CASE				=				kwargs.pop("proxies"		,							__a					)
												SCREAMING_SNAKE_CASE				=				kwargs.pop("local_files_only"		,							__a					)

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

												try:
																	# Load from URL or cache if already cached
																	SCREAMING_SNAKE_CASE				=				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

																	SCREAMING_SNAKE_CASE				=				Config.load_yaml(__a					)

												except EnvironmentError:
																	SCREAMING_SNAKE_CASE				=				"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 		__lowerCamelCase							(UpperCAmelCase__   :							Optional[Any]     ):
					SCREAMING_SNAKE_CASE				=				torch.load("dump.pt"					,       map_location=in_tensor.device     )
					SCREAMING_SNAKE_CASE				=				in_tensor.numpy()
					SCREAMING_SNAKE_CASE				=				out_tensor.numpy()[0]
					print(na.shape					,       na[0, 0, :5]     )
					print(na.shape					,       na[0, 0, :5]     )
					assert np.allclose(snake_case__					,       snake_case__					,       rtol=0.01					,       atol=0.1     ), (
					    F"{sum([1 for x in np.isclose(snake_case__					,       snake_case__					,       rtol=0.01					,       atol=0.1     ).flatten() if x is False]     )/len(na.flatten()     )*1_0_0:.4f} %"
					    " element-wise mismatch"
					)
					raise Exception("tensors are all good"     )

					# Hugging face functions below





def 		__lowerCamelCase							(UpperCAmelCase__   :							Dict     ):
					SCREAMING_SNAKE_CASE				=				urlparse(snake_case__     )
					return parsed.scheme in ("http", "https")





def 		__lowerCamelCase							(UpperCAmelCase__   :							str					,       UpperCAmelCase__   :							str					,       UpperCAmelCase__   :							List[Any]=True     ):
					SCREAMING_SNAKE_CASE				=				CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
					SCREAMING_SNAKE_CASE				=				"/" not in model_id
					if legacy_format:
										return F"{endpoint}/{model_id}-{filename}"
					else:
										return F"{endpoint}/{model_id}/{filename}"





def 		__lowerCamelCase							(UpperCAmelCase__   :							List[Any]					,       UpperCAmelCase__   :							List[Any]					,       UpperCAmelCase__   :							Optional[Any]=None					,       UpperCAmelCase__   :							List[Any]=0					,       UpperCAmelCase__   :							str=None					,       ):
					SCREAMING_SNAKE_CASE				=				"python/{}".format(sys.version.split()[0]     )
					if _torch_available:
										ua += "; torch/{}".format(torch.__version__     )
					if isinstance(snake_case__					,       snake_case__     ):
										ua += "; " + "; ".join("{}/{}".format(snake_case__					,       snake_case__     ) for k, v in user_agent.items()     )
					elif isinstance(snake_case__					,       snake_case__     ):
										ua += "; " + user_agent
					SCREAMING_SNAKE_CASE				=				{"user-agent": ua}
					if resume_size > 0:
										SCREAMING_SNAKE_CASE				=				"bytes=%d-" % (resume_size,)
					SCREAMING_SNAKE_CASE				=				requests.get(snake_case__					,       stream=snake_case__					,       proxies=snake_case__					,       headers=snake_case__     )
					if response.status_code == 4_1_6:  # Range not satisfiable
										return
					SCREAMING_SNAKE_CASE				=				response.headers.get("Content-Length"     )
					SCREAMING_SNAKE_CASE				=				resume_size + int(snake_case__     ) if content_length is not None else None
					SCREAMING_SNAKE_CASE				=				tqdm(
					    unit="B"					,       unit_scale=snake_case__					,       total=snake_case__					,       initial=snake_case__					,       desc="Downloading"					,       )
					for chunk in response.iter_content(chunk_size=1_0_2_4     ):
										if chunk:  # filter out keep-alive new chunks
															progress.update(len(snake_case__     )     )
															temp_file.write(snake_case__     )
					progress.close()





def 		__lowerCamelCase							(UpperCAmelCase__   :							str					,       UpperCAmelCase__   :							Dict=None					,       UpperCAmelCase__   :							List[Any]=False					,       UpperCAmelCase__   :							str=None					,       UpperCAmelCase__   :							List[str]=1_0					,       UpperCAmelCase__   :							List[str]=False					,       UpperCAmelCase__   :							List[str]=None					,       UpperCAmelCase__   :							Dict=False					,       ):
					if cache_dir is None:
										SCREAMING_SNAKE_CASE				=				TRANSFORMERS_CACHE
					if isinstance(snake_case__					,       snake_case__     ):
										SCREAMING_SNAKE_CASE				=				str(snake_case__     )

					os.makedirs(snake_case__					,       exist_ok=snake_case__     )

					SCREAMING_SNAKE_CASE				=				None
					if not local_files_only:
										try:
															SCREAMING_SNAKE_CASE				=				requests.head(snake_case__					,       allow_redirects=snake_case__					,       proxies=snake_case__					,       timeout=snake_case__     )
															if response.status_code == 2_0_0:
																				SCREAMING_SNAKE_CASE				=				response.headers.get("ETag"     )
										except (EnvironmentError, requests.exceptions.Timeout):
															# etag is already None
															pass

					SCREAMING_SNAKE_CASE				=				url_to_filename(snake_case__					,       snake_case__     )

					# get cache path to put the file
					SCREAMING_SNAKE_CASE				=				os.path.join(snake_case__					,       snake_case__     )

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

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

										if resume_download:
															SCREAMING_SNAKE_CASE				=				cache_path + ".incomplete"

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

															SCREAMING_SNAKE_CASE				=				_resumable_file_manager
															if os.path.exists(snake_case__     ):
																				SCREAMING_SNAKE_CASE				=				os.stat(snake_case__     ).st_size
															else:
																				SCREAMING_SNAKE_CASE				=				0
										else:
															SCREAMING_SNAKE_CASE				=				partial(tempfile.NamedTemporaryFile					,       dir=snake_case__					,       delete=snake_case__     )
															SCREAMING_SNAKE_CASE				=				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"					,       snake_case__					,       temp_file.name					,       )

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

										os.replace(temp_file.name					,       snake_case__     )

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

					return cache_path





def 		__lowerCamelCase							(UpperCAmelCase__   :							int					,       UpperCAmelCase__   :							int=None     ):
					SCREAMING_SNAKE_CASE				=				url.encode("utf-8"     )
					SCREAMING_SNAKE_CASE				=				shaaaa(snake_case__     )
					SCREAMING_SNAKE_CASE				=				url_hash.hexdigest()

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

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

					return filename





def 		__lowerCamelCase							(UpperCAmelCase__   :							Optional[Any]					,       UpperCAmelCase__   :							Tuple=None					,       UpperCAmelCase__   :							Optional[int]=False					,       UpperCAmelCase__   :							int=None					,       UpperCAmelCase__   :							List[Any]=False					,       UpperCAmelCase__   :							int=None					,       UpperCAmelCase__   :							int=False					,       UpperCAmelCase__   :							List[Any]=False					,       UpperCAmelCase__   :							int=False					,       ):
					if cache_dir is None:
										SCREAMING_SNAKE_CASE				=				TRANSFORMERS_CACHE
					if isinstance(snake_case__					,       snake_case__     ):
										SCREAMING_SNAKE_CASE				=				str(snake_case__     )
					if isinstance(snake_case__					,       snake_case__     ):
										SCREAMING_SNAKE_CASE				=				str(snake_case__     )

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

					if extract_compressed_file:
										if not is_zipfile(snake_case__     ) and not tarfile.is_tarfile(snake_case__     ):
															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/"
										SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				os.path.split(snake_case__     )
										SCREAMING_SNAKE_CASE				=				output_file.replace("."					,       "-"     ) + "-extracted"
										SCREAMING_SNAKE_CASE				=				os.path.join(snake_case__					,       snake_case__     )

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

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

										return output_path_extracted

					return output_path





def 		__lowerCamelCase							(UpperCAmelCase__   :							Dict					,       UpperCAmelCase__   :							Optional[int]=","     ):
					assert isinstance(snake_case__					,       snake_case__     )
					if os.path.isfile(snake_case__     ):
										with open(snake_case__     ) as f:
															SCREAMING_SNAKE_CASE				=				eval(f.read()     )
					else:
										SCREAMING_SNAKE_CASE				=				requests.get(snake_case__     )
										try:
															SCREAMING_SNAKE_CASE				=				requests.json()
										except Exception:
															SCREAMING_SNAKE_CASE				=				req.content.decode()
															assert data is not None, "could not connect"
															try:
																				SCREAMING_SNAKE_CASE				=				eval(snake_case__     )
															except Exception:
																				SCREAMING_SNAKE_CASE				=				data.split("\n"     )
										req.close()
					return data





def 		__lowerCamelCase							(UpperCAmelCase__   :							List[Any]     ):
					SCREAMING_SNAKE_CASE				=				requests.get(snake_case__     )
					SCREAMING_SNAKE_CASE				=				np.array(Image.open(BytesIO(response.content     )     )     )
					return img





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





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





def 		__lowerCamelCase							(UpperCAmelCase__   :							Dict					,       UpperCAmelCase__   :							Dict="RGB"     ):
					assert isinstance(snake_case__					,       snake_case__     )
					if os.path.isfile(snake_case__     ):
										SCREAMING_SNAKE_CASE				=				cva.imread(snake_case__     )
					else:
										SCREAMING_SNAKE_CASE				=				get_image_from_url(snake_case__     )
										assert img is not None, F"could not connect to: {im}"
					SCREAMING_SNAKE_CASE				=				cva.cvtColor(snake_case__					,       cva.COLOR_BGR2RGB     )
					if input_format == "RGB":
										SCREAMING_SNAKE_CASE				=				img[:, :, ::-1]
					return img





def 		__lowerCamelCase							(UpperCAmelCase__   :							Tuple					,       UpperCAmelCase__   :							List[str]=1     ):
					return (images[i : i + batch] for i in range(0					,       len(snake_case__     )					,       snake_case__     ))







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     lowercase						(       unittest.TestCase					):



 '''simple docstring'''


 def __init__(self       ,       __a       ,       __a=7       ,       __a=3       ,       __a=18       ,       __a=30       ,       __a=400       ,       __a=True       ,       __a=None       ,       __a=True       ,       __a=None       ,       __a=True       ,       __a=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]       ,       __a=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]       ,       __a=True       ,       ) ->					Optional[int]:






  """simple docstring"""





  UpperCAmelCase__							=				size if size is not None else {'shortest_edge': 20}
  UpperCAmelCase__							=				crop_size if crop_size is not None else {'height': 18, 'width': 18}
  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   UpperCamelCase__							(self ) ->					List[str]:






  """simple docstring"""





  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   UpperCamelCase__							(self       ,       __a=False       ,       __a=False       ,       __a=False ) ->					Union[str, Any]:






  """simple docstring"""





  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(
            255       ,       size=(self.num_channels, self.max_resolution, self.max_resolution)       ,       dtype=np.uinta ) )
  else:
   UpperCAmelCase__							=				[]
   for i in range(self.batch_size ):
    UpperCAmelCase__				,    UpperCAmelCase__							=				np.random.choice(np.arange(self.min_resolution       ,       self.max_resolution )       ,       2 )
    image_inputs.append(np.random.randint(255       ,       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(__a       ,       0       ,       -1 ) ) for x in image_inputs]

  if torchify:
   UpperCAmelCase__							=				[torch.from_numpy(__a ) for x in image_inputs]

  return image_inputs







@require_torch
@require_vision
class     lowercase						(       _UpperCamelCase ,				unittest.TestCase					):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE												=				CLIPImageProcessor if is_vision_available() else None




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






  """simple docstring"""





  UpperCAmelCase__							=				CLIPImageProcessingTester(self )




 @property
 def   UpperCamelCase__							(self ) ->					List[str]:






  """simple docstring"""





  return self.image_processor_tester.prepare_image_processor_dict()




 def   UpperCamelCase__							(self ) ->					Any:






  """simple docstring"""





  UpperCAmelCase__							=				self.image_processing_class(**self.image_processor_dict )
  self.assertTrue(hasattr(__a       ,       'do_resize' ) )
  self.assertTrue(hasattr(__a       ,       'size' ) )
  self.assertTrue(hasattr(__a       ,       'do_center_crop' ) )
  self.assertTrue(hasattr(__a       ,       'center_crop' ) )
  self.assertTrue(hasattr(__a       ,       'do_normalize' ) )
  self.assertTrue(hasattr(__a       ,       'image_mean' ) )
  self.assertTrue(hasattr(__a       ,       'image_std' ) )
  self.assertTrue(hasattr(__a       ,       'do_convert_rgb' ) )




 def   UpperCamelCase__							(self ) ->					str:






  """simple docstring"""





  UpperCAmelCase__							=				self.image_processing_class.from_dict(self.image_processor_dict )
  self.assertEqual(image_processor.size       ,       {'shortest_edge': 20} )
  self.assertEqual(image_processor.crop_size       ,       {'height': 18, 'width': 18} )

  UpperCAmelCase__							=				self.image_processing_class.from_dict(self.image_processor_dict       ,       size=42       ,       crop_size=84 )
  self.assertEqual(image_processor.size       ,       {'shortest_edge': 42} )
  self.assertEqual(image_processor.crop_size       ,       {'height': 84, 'width': 84} )




 def   UpperCamelCase__							(self ) ->					List[Any]:






  """simple docstring"""





  pass




 def   UpperCamelCase__							(self ) ->					Dict:






  """simple docstring"""





  UpperCAmelCase__							=				self.image_processing_class(**self.image_processor_dict )
  # create random PIL images
  UpperCAmelCase__							=				self.image_processor_tester.prepare_inputs(equal_resolution=__a )
  for image in image_inputs:
   self.assertIsInstance(__a       ,       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(__a       ,       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   UpperCamelCase__							(self ) ->					Optional[Any]:






  """simple docstring"""





  UpperCAmelCase__							=				self.image_processing_class(**self.image_processor_dict )
  # create random numpy tensors
  UpperCAmelCase__							=				self.image_processor_tester.prepare_inputs(equal_resolution=__a       ,       numpify=__a )
  for image in image_inputs:
   self.assertIsInstance(__a       ,       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(__a       ,       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   UpperCamelCase__							(self ) ->					Tuple:






  """simple docstring"""





  UpperCAmelCase__							=				self.image_processing_class(**self.image_processor_dict )
  # create random PyTorch tensors
  UpperCAmelCase__							=				self.image_processor_tester.prepare_inputs(equal_resolution=__a       ,       torchify=__a )
  for image in image_inputs:
   self.assertIsInstance(__a       ,       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(__a       ,       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     lowercase						(       _UpperCamelCase ,				unittest.TestCase					):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE												=				CLIPImageProcessor if is_vision_available() else None




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






  """simple docstring"""





  UpperCAmelCase__							=				CLIPImageProcessingTester(self       ,       num_channels=4 )
  UpperCAmelCase__							=				3




 @property
 def   UpperCamelCase__							(self ) ->					Optional[int]:






  """simple docstring"""





  return self.image_processor_tester.prepare_image_processor_dict()




 def   UpperCamelCase__							(self ) ->					List[str]:






  """simple docstring"""





  UpperCAmelCase__							=				self.image_processing_class(**self.image_processor_dict )
  self.assertTrue(hasattr(__a       ,       'do_resize' ) )
  self.assertTrue(hasattr(__a       ,       'size' ) )
  self.assertTrue(hasattr(__a       ,       'do_center_crop' ) )
  self.assertTrue(hasattr(__a       ,       'center_crop' ) )
  self.assertTrue(hasattr(__a       ,       'do_normalize' ) )
  self.assertTrue(hasattr(__a       ,       'image_mean' ) )
  self.assertTrue(hasattr(__a       ,       'image_std' ) )
  self.assertTrue(hasattr(__a       ,       'do_convert_rgb' ) )




 def   UpperCamelCase__							(self ) ->					Tuple:






  """simple docstring"""





  pass




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






  """simple docstring"""





  UpperCAmelCase__							=				self.image_processing_class(**self.image_processor_dict )
  # create random PIL images
  UpperCAmelCase__							=				self.image_processor_tester.prepare_inputs(equal_resolution=__a )
  for image in image_inputs:
   self.assertIsInstance(__a       ,       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(__a       ,       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'],
      )       ,       )



