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

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


try:
      import torch

      a__				:      str    		=     True
except ImportError:
      a__				:      Any    		=     False


try:
      from torch.hub import _get_torch_home

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

a__				:      Tuple    		=     os.path.join(torch_cache_home, '''transformers''')

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







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



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[str]				      = []
    with open(a__   ) as f:
        for object in f.readlines():
            vg_classes.append(object.split(''','''   )[0].lower().strip()   )

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







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



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






class     a_       :



       """simple docstring"""
       __SCREAMING_SNAKE_CASE       :					Union[str, Any]						=  {}






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

           SCREAMING_SNAKE_CASE				:				List[str]				      = d






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






       def __setattr__(		self				,       _lowerCamelCase				,       _lowerCamelCase   )					->List[str]:
           SCREAMING_SNAKE_CASE				:				List[Any]				      = val
           SCREAMING_SNAKE_CASE				:				int				      = val
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = key.split('''.'''   )
           SCREAMING_SNAKE_CASE				:				str				      = len(_lowerCamelCase   ) - 1
           SCREAMING_SNAKE_CASE				:				Dict				      = 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:
                       SCREAMING_SNAKE_CASE				:				List[str]				      = val
                   else:
                       SCREAMING_SNAKE_CASE				:				Tuple				      = pointer[l]






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






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase   )					->Optional[int]:
           with open(F"""{file_name}"""				,       '''w'''   ) as stream:
               dump(_lowerCamelCase				,       _lowerCamelCase   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase   )					->List[Any]:
           with open(F"""{file_name}"""				,       '''w'''   ) as stream:
               json.dump(_lowerCamelCase				,       _lowerCamelCase   )






       @staticmethod
       def 	__lowerCAmelCase     (		_lowerCamelCase   )					->Any:
           with open(_lowerCamelCase   ) as stream:
               SCREAMING_SNAKE_CASE				:				Tuple				      = load(_lowerCamelCase				,       Loader=_lowerCamelCase   )
           return data






       def __str__(		self   )					->str:
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = '''    '''
           if self._name != "root":
               SCREAMING_SNAKE_CASE				:				List[str]				      = F"""{t * (self._level-1)}{self._name}:\n"""
           else:
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = ''''''
           SCREAMING_SNAKE_CASE				:				Tuple				      = 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"""
               SCREAMING_SNAKE_CASE				:				int				      = level
           return r[:-1]






       @classmethod
       def 	__lowerCAmelCase     (		cls				,       _lowerCamelCase				,       **_lowerCamelCase   )					->List[str]:
           SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				Optional[int]				      = cls.get_config_dict(_lowerCamelCase				,       **_lowerCamelCase   )
           return cls(_lowerCamelCase   )






       @classmethod
       def 	__lowerCAmelCase     (		cls				,       _lowerCamelCase				,       **_lowerCamelCase   )					->int:
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = kwargs.pop('''cache_dir'''				,       _lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = kwargs.pop('''force_download'''				,       _lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = kwargs.pop('''resume_download'''				,       _lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				List[Any]				      = kwargs.pop('''proxies'''				,       _lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				str				      = kwargs.pop('''local_files_only'''				,       _lowerCamelCase   )

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

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

               SCREAMING_SNAKE_CASE				:				Tuple				      = Config.load_yaml(_lowerCamelCase   )

           except EnvironmentError:
               SCREAMING_SNAKE_CASE				:				Optional[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 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[str]				      = torch.load('''dump.pt''' ,						map_location=in_tensor.device   )
    SCREAMING_SNAKE_CASE				:				Tuple				      = in_tensor.numpy()
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = out_tensor.numpy()[0]
    print(na.shape ,						na[0, 0, :5]   )
    print(na.shape ,						na[0, 0, :5]   )
    assert np.allclose(a__ ,						a__ ,						rtol=0.01 ,						atol=0.1   ), (
        F"""{sum([1 for x in np.isclose(a__ ,						a__ ,						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_(    a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				int				      = urlparse(a__   )
    return parsed.scheme in ("http", "https")







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



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Tuple				      = CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = '''/''' not in model_id
    if legacy_format:
        return F"""{endpoint}/{model_id}-{filename}"""
    else:
        return F"""{endpoint}/{model_id}/{filename}"""







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



    """simple docstring"""



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







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



    """simple docstring"""



    if cache_dir is None:
        SCREAMING_SNAKE_CASE				:				int				      = TRANSFORMERS_CACHE
    if isinstance(a__ ,						a__   ):
        SCREAMING_SNAKE_CASE				:				List[str]				      = str(a__   )

    os.makedirs(a__ ,						exist_ok=a__   )

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

    SCREAMING_SNAKE_CASE				:				Any				      = url_to_filename(a__ ,						a__   )

    # get cache path to put the file
    SCREAMING_SNAKE_CASE				:				Optional[int]				      = os.path.join(a__ ,						a__   )

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

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

        if resume_download:
            SCREAMING_SNAKE_CASE				:				Dict				      = cache_path + '''.incomplete'''

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

            SCREAMING_SNAKE_CASE				:				str				      = _resumable_file_manager
            if os.path.exists(a__   ):
                SCREAMING_SNAKE_CASE				:				List[str]				      = os.stat(a__   ).st_size
            else:
                SCREAMING_SNAKE_CASE				:				List[str]				      = 0
        else:
            SCREAMING_SNAKE_CASE				:				str				      = partial(tempfile.NamedTemporaryFile ,						dir=a__ ,						delete=a__   )
            SCREAMING_SNAKE_CASE				:				List[str]				      = 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''' ,						a__ ,						temp_file.name ,						)

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

        os.replace(temp_file.name ,						a__   )

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

    return cache_path







def 				UpperCAmelCase_(    a__ ,						a__=None   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[Any]				      = url.encode('''utf-8'''   )
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = shaaaa(a__   )
    SCREAMING_SNAKE_CASE				:				int				      = url_hash.hexdigest()

    if etag:
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = etag.encode('''utf-8'''   )
        SCREAMING_SNAKE_CASE				:				Tuple				      = shaaaa(a__   )
        filename += "." + etag_hash.hexdigest()

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

    return filename







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



    """simple docstring"""



    if cache_dir is None:
        SCREAMING_SNAKE_CASE				:				str				      = TRANSFORMERS_CACHE
    if isinstance(a__ ,						a__   ):
        SCREAMING_SNAKE_CASE				:				Tuple				      = str(a__   )
    if isinstance(a__ ,						a__   ):
        SCREAMING_SNAKE_CASE				:				str				      = str(a__   )

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

    if extract_compressed_file:
        if not is_zipfile(a__   ) and not tarfile.is_tarfile(a__   ):
            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				:				Tuple				      = os.path.split(a__   )
        SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = output_file.replace('''.''' ,						'''-'''   ) + '''-extracted'''
        SCREAMING_SNAKE_CASE				:				Any				      = os.path.join(a__ ,						a__   )

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

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

        return output_path_extracted

    return output_path







def 				UpperCAmelCase_(    a__ ,						a__=","   ):



    """simple docstring"""



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







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				int				      = requests.get(a__   )
    SCREAMING_SNAKE_CASE				:				str				      = np.array(Image.open(BytesIO(response.content   )   )   )
    return img







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



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







def 				UpperCAmelCase_(    ):



    """simple docstring"""



    print(F"""{os.path.abspath(os.path.join(a__ ,						os.pardir   )   )}/demo.ipynb"""   )







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



    """simple docstring"""



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







def 				UpperCAmelCase_(    a__ ,						a__=1   ):



    """simple docstring"""



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


from __future__ import annotations

import unittest

from transformers import AlbertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import TF_MODEL_FOR_PRETRAINING_MAPPING
      from transformers.models.albert.modeling_tf_albert import (
          TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFAlbertForMaskedLM,
          TFAlbertForMultipleChoice,
          TFAlbertForPreTraining,
          TFAlbertForQuestionAnswering,
          TFAlbertForSequenceClassification,
          TFAlbertForTokenClassification,
          TFAlbertModel,
      )






class     a_       :



       """simple docstring"""






       def __init__(		self				,       _lowerCamelCase				,       _lowerCamelCase=13				,       _lowerCamelCase=7				,       _lowerCamelCase=True				,       _lowerCamelCase=True				,       _lowerCamelCase=True				,       _lowerCamelCase=True				,       _lowerCamelCase=99				,       _lowerCamelCase=16				,       _lowerCamelCase=32				,       _lowerCamelCase=2				,       _lowerCamelCase=4				,       _lowerCamelCase=37				,       _lowerCamelCase="gelu"				,       _lowerCamelCase=0.1				,       _lowerCamelCase=0.1				,       _lowerCamelCase=512				,       _lowerCamelCase=16				,       _lowerCamelCase=2				,       _lowerCamelCase=0.0_2				,       _lowerCamelCase=3				,       _lowerCamelCase=4				,       _lowerCamelCase=None				,       )					->List[Any]:
           SCREAMING_SNAKE_CASE				:				int				      = parent
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = 13
           SCREAMING_SNAKE_CASE				:				int				      = 7
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = True
           SCREAMING_SNAKE_CASE				:				List[str]				      = True
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = True
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = True
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = 99
           SCREAMING_SNAKE_CASE				:				str				      = 16
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = 32
           SCREAMING_SNAKE_CASE				:				str				      = 2
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = 4
           SCREAMING_SNAKE_CASE				:				int				      = 37
           SCREAMING_SNAKE_CASE				:				Any				      = '''gelu'''
           SCREAMING_SNAKE_CASE				:				str				      = 0.1
           SCREAMING_SNAKE_CASE				:				Dict				      = 0.1
           SCREAMING_SNAKE_CASE				:				Tuple				      = 512
           SCREAMING_SNAKE_CASE				:				Any				      = 16
           SCREAMING_SNAKE_CASE				:				str				      = 2
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = 0.0_2
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = 3
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = 4
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = None






       def 	__lowerCAmelCase     (		self   )					->List[str]:
           SCREAMING_SNAKE_CASE				:				Tuple				      = ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size   )

           SCREAMING_SNAKE_CASE				:				Optional[int]				      = None
           if self.use_input_mask:
               SCREAMING_SNAKE_CASE				:				Optional[int]				      = random_attention_mask([self.batch_size, self.seq_length]   )

           SCREAMING_SNAKE_CASE				:				Dict				      = None
           if self.use_token_type_ids:
               SCREAMING_SNAKE_CASE				:				Dict				      = ids_tensor([self.batch_size, self.seq_length]				,       self.type_vocab_size   )

           SCREAMING_SNAKE_CASE				:				str				      = None
           SCREAMING_SNAKE_CASE				:				Any				      = None
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = None
           if self.use_labels:
               SCREAMING_SNAKE_CASE				:				str				      = ids_tensor([self.batch_size]				,       self.type_sequence_label_size   )
               SCREAMING_SNAKE_CASE				:				List[str]				      = ids_tensor([self.batch_size, self.seq_length]				,       self.num_labels   )
               SCREAMING_SNAKE_CASE				:				Tuple				      = ids_tensor([self.batch_size]				,       self.num_choices   )

           SCREAMING_SNAKE_CASE				:				List[Any]				      = AlbertConfig(
               vocab_size=self.vocab_size				,       hidden_size=self.hidden_size				,       embedding_size=self.embedding_size				,       num_hidden_layers=self.num_hidden_layers				,       num_attention_heads=self.num_attention_heads				,       intermediate_size=self.intermediate_size				,       hidden_act=self.hidden_act				,       hidden_dropout_prob=self.hidden_dropout_prob				,       attention_probs_dropout_prob=self.attention_probs_dropout_prob				,       max_position_embeddings=self.max_position_embeddings				,       type_vocab_size=self.type_vocab_size				,       initializer_range=self.initializer_range				,       )

           return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase   )					->List[str]:
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = TFAlbertModel(config=_lowerCamelCase   )
           # inputs = {'input_ids': input_ids,
           #           'attention_mask': input_mask,
           #           'token_type_ids': token_type_ids}
           # sequence_output, pooled_output = model(**inputs)
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = model(_lowerCamelCase   )

           SCREAMING_SNAKE_CASE				:				List[str]				      = [input_ids, input_mask]
           SCREAMING_SNAKE_CASE				:				List[str]				      = model(_lowerCamelCase   )

           SCREAMING_SNAKE_CASE				:				Any				      = model(_lowerCamelCase   )

           self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.hidden_size)   )
           self.parent.assertEqual(result.pooler_output.shape				,       (self.batch_size, self.hidden_size)   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase   )					->List[Any]:
           SCREAMING_SNAKE_CASE				:				str				      = self.num_labels
           SCREAMING_SNAKE_CASE				:				List[str]				      = TFAlbertForPreTraining(config=_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Any				      = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
           SCREAMING_SNAKE_CASE				:				int				      = model(_lowerCamelCase   )
           self.parent.assertEqual(result.prediction_logits.shape				,       (self.batch_size, self.seq_length, self.vocab_size)   )
           self.parent.assertEqual(result.sop_logits.shape				,       (self.batch_size, self.num_labels)   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase   )					->str:
           SCREAMING_SNAKE_CASE				:				str				      = TFAlbertForMaskedLM(config=_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				int				      = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = model(_lowerCamelCase   )
           self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.vocab_size)   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase   )					->int:
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = self.num_labels
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = TFAlbertForSequenceClassification(config=_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
           SCREAMING_SNAKE_CASE				:				Tuple				      = model(_lowerCamelCase   )
           self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_labels)   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase   )					->Dict:
           SCREAMING_SNAKE_CASE				:				List[str]				      = TFAlbertForQuestionAnswering(config=_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
           SCREAMING_SNAKE_CASE				:				int				      = model(_lowerCamelCase   )
           self.parent.assertEqual(result.start_logits.shape				,       (self.batch_size, self.seq_length)   )
           self.parent.assertEqual(result.end_logits.shape				,       (self.batch_size, self.seq_length)   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase   )					->Any:
           SCREAMING_SNAKE_CASE				:				List[Any]				      = self.num_choices
           SCREAMING_SNAKE_CASE				:				Any				      = TFAlbertForMultipleChoice(config=_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Tuple				      = tf.tile(tf.expand_dims(_lowerCamelCase				,       1   )				,       (1, self.num_choices, 1)   )
           SCREAMING_SNAKE_CASE				:				Tuple				      = tf.tile(tf.expand_dims(_lowerCamelCase				,       1   )				,       (1, self.num_choices, 1)   )
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = tf.tile(tf.expand_dims(_lowerCamelCase				,       1   )				,       (1, self.num_choices, 1)   )
           SCREAMING_SNAKE_CASE				:				Tuple				      = {
               '''input_ids''': multiple_choice_inputs_ids,
               '''attention_mask''': multiple_choice_input_mask,
               '''token_type_ids''': multiple_choice_token_type_ids,
           }
           SCREAMING_SNAKE_CASE				:				str				      = model(_lowerCamelCase   )
           self.parent.assertListEqual(list(result['''logits'''].shape   )				,       [self.batch_size, self.num_choices]   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase   )					->Optional[Any]:
           SCREAMING_SNAKE_CASE				:				str				      = self.num_labels
           SCREAMING_SNAKE_CASE				:				List[Any]				      = TFAlbertForTokenClassification(config=_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = {
               '''input_ids''': input_ids,
               '''attention_mask''': input_mask,
               '''token_type_ids''': token_type_ids,
           }
           SCREAMING_SNAKE_CASE				:				str				      = model(_lowerCamelCase   )
           self.parent.assertListEqual(list(result['''logits'''].shape   )				,       [self.batch_size, self.seq_length, self.num_labels]   )






       def 	__lowerCAmelCase     (		self   )					->List[Any]:
           SCREAMING_SNAKE_CASE				:				List[Any]				      = self.prepare_config_and_inputs()
           (
               (
               SCREAMING_SNAKE_CASE
           )      ,							(
               SCREAMING_SNAKE_CASE
           )      ,							(
               SCREAMING_SNAKE_CASE
           )      ,							(
               SCREAMING_SNAKE_CASE
           )      ,							(
               SCREAMING_SNAKE_CASE
           )      ,							(
               SCREAMING_SNAKE_CASE
           )      ,							(
               SCREAMING_SNAKE_CASE
           )      ,							
           )				:				Any				      = config_and_inputs
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
           return config, inputs_dict






@require_tf
class     a_       (			a__				,    a__				,    unittest.TestCase							):



       """simple docstring"""
       __SCREAMING_SNAKE_CASE       :					int						=  (
           (
               TFAlbertModel,
               TFAlbertForPreTraining,
               TFAlbertForMaskedLM,
               TFAlbertForSequenceClassification,
               TFAlbertForQuestionAnswering,
               TFAlbertForTokenClassification,
               TFAlbertForMultipleChoice,
           )
           if is_tf_available()
           else ()
       )
       __SCREAMING_SNAKE_CASE       :					Optional[int]						=  (
           {
               'feature-extraction': TFAlbertModel,
               'fill-mask': TFAlbertForMaskedLM,
               'question-answering': TFAlbertForQuestionAnswering,
               'text-classification': TFAlbertForSequenceClassification,
               'token-classification': TFAlbertForTokenClassification,
               'zero-shot': TFAlbertForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       __SCREAMING_SNAKE_CASE       :					Tuple						=  False
       __SCREAMING_SNAKE_CASE       :					Any						=  False






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase=False   )					->int:
           SCREAMING_SNAKE_CASE				:				Tuple				      = super()._prepare_for_class(_lowerCamelCase				,       _lowerCamelCase				,       return_labels=_lowerCamelCase   )

           if return_labels:
               if model_class in get_values(_lowerCamelCase   ):
                   SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = tf.zeros(self.model_tester.batch_size				,       dtype=tf.intaa   )

           return inputs_dict






       def 	__lowerCAmelCase     (		self   )					->Tuple:
           SCREAMING_SNAKE_CASE				:				Dict				      = TFAlbertModelTester(self   )
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = ConfigTester(self				,       config_class=_lowerCamelCase				,       hidden_size=37   )






       def 	__lowerCAmelCase     (		self   )					->Union[str, Any]:
           self.config_tester.run_common_tests()






       def 	__lowerCAmelCase     (		self   )					->str:
           SCREAMING_SNAKE_CASE				:				Tuple				      = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_albert_model(*_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self   )					->str:
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_albert_for_pretraining(*_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self   )					->Any:
           SCREAMING_SNAKE_CASE				:				Dict				      = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_albert_for_masked_lm(*_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self   )					->Optional[Any]:
           SCREAMING_SNAKE_CASE				:				List[str]				      = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_albert_for_multiple_choice(*_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self   )					->Dict:
           SCREAMING_SNAKE_CASE				:				List[Any]				      = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_albert_for_sequence_classification(*_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self   )					->str:
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_albert_for_question_answering(*_lowerCamelCase   )






       @slow
       def 	__lowerCAmelCase     (		self   )					->int:
           for model_name in TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               SCREAMING_SNAKE_CASE				:				Tuple				      = TFAlbertModel.from_pretrained(_lowerCamelCase   )
               self.assertIsNotNone(_lowerCamelCase   )






@require_tf
class     a_       (			unittest.TestCase							):



       """simple docstring"""






       @slow
       def 	__lowerCAmelCase     (		self   )					->List[Any]:
           SCREAMING_SNAKE_CASE				:				Dict				      = TFAlbertForPreTraining.from_pretrained('''albert-base-v2'''   )
           SCREAMING_SNAKE_CASE				:				str				      = tf.constant([[0, 1, 2, 3, 4, 5]]   )
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = model(_lowerCamelCase   )[0]

           SCREAMING_SNAKE_CASE				:				List[str]				      = [1, 6, 3_0000]
           self.assertEqual(output.shape				,       _lowerCamelCase   )

           SCREAMING_SNAKE_CASE				:				Dict				      = tf.constant(
               [
                   [
                       [4.5_9_5_6_6_8, 0.7_4_4_6_2_7_5_4, -1.8_1_8_1_4_7],
                       [4.5_9_5_4_3_4_7, 0.7_4_5_4_1_8_4, -1.8_1_8_8_2_5_8],
                       [4.5_9_5_4_9_0_5, 0.7_4_4_8_2_3_5, -1.8_1_8_2_3_1_6],
                   ]
               ]   )
           tf.debugging.assert_near(output[:, :3, :3]				,       _lowerCamelCase				,       atol=1e-4   )
