


'''simple docstring'''







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

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


try:
   import torch

   A   :  Dict       		=				True
except ImportError:
   A   :  List[Any]       		=				False


try:
   from torch.hub import _get_torch_home

   A   :  str       		=				_get_torch_home()
except ImportError:
   A   :  Tuple       		=				os.path.expanduser(
       os.getenv('''TORCH_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''torch'''))
   )

A   :  Dict       		=				os.path.join(torch_cache_home, '''transformers''')

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






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

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






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


class 				__lowerCamelCase				:
    """simple docstring"""
    a									=					{}


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

           _A  :			Optional[Any]											=      d


    def __repr__(						self    :							List[Any]):
           return str(list((self._pointer.keys())))


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


    def      A					(						self    :							Optional[int]):
           return self._pointer


    def      A					(						self    :							Dict   ,							SCREAMING_SNAKE_CASE    :							List[Any]   ,							SCREAMING_SNAKE_CASE    :							Any):
           with open(F'{file_name}'   ,							'w') as stream:
                  dump(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)


    def      A					(						self    :							Tuple   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]   ,							SCREAMING_SNAKE_CASE    :							Tuple):
           with open(F'{file_name}'   ,							'w') as stream:
                  json.dump(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)


    @staticmethod
    def      A					(						SCREAMING_SNAKE_CASE    :							Tuple):
           with open(SCREAMING_SNAKE_CASE) as stream:
                  _A  :			Tuple											=      load(SCREAMING_SNAKE_CASE   ,							Loader=SCREAMING_SNAKE_CASE)
           return data


    def __str__(						self    :							Optional[int]):
           _A  :			Optional[int]											=      '    '
           if self._name != "root":
                  _A  :			int											=      F'{t * (self._level-1)}{self._name}:\n'
           else:
                  _A  :			Dict											=      ''
           _A  :			Any											=      self._level
           for i, (k, v) in enumerate(self._pointer.items()):
                  if isinstance(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE):
                         r += F'{t * (self._level)}{v}\n'
                         self._level += 1
                  else:
                         r += F'{t * (self._level)}{k}: {v} ({type(SCREAMING_SNAKE_CASE).__name__})\n'
                  _A  :			List[Any]											=      level
           return r[:-1]


    @classmethod
    def      A					(						cls    :							int   ,							SCREAMING_SNAKE_CASE    :							str   ,							**SCREAMING_SNAKE_CASE    :							Optional[Any]):
           _A						,				_A  :			List[Any]											=      cls.get_config_dict(SCREAMING_SNAKE_CASE   ,							**SCREAMING_SNAKE_CASE)
           return cls(SCREAMING_SNAKE_CASE)


    @classmethod
    def      A					(						cls    :							Tuple   ,							SCREAMING_SNAKE_CASE    :							str   ,							**SCREAMING_SNAKE_CASE    :							Dict):
           _A  :			Tuple											=      kwargs.pop('cache_dir'   ,							SCREAMING_SNAKE_CASE)
           _A  :			Any											=      kwargs.pop('force_download'   ,							SCREAMING_SNAKE_CASE)
           _A  :			List[Any]											=      kwargs.pop('resume_download'   ,							SCREAMING_SNAKE_CASE)
           _A  :			Any											=      kwargs.pop('proxies'   ,							SCREAMING_SNAKE_CASE)
           _A  :			int											=      kwargs.pop('local_files_only'   ,							SCREAMING_SNAKE_CASE)

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

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

                  _A  :			Dict											=      Config.load_yaml(SCREAMING_SNAKE_CASE)

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

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

           return Config.load_yaml(SCREAMING_SNAKE_CASE), kwargs






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

       # Hugging face functions below






def    lowerCAmelCase__ (       lowerCamelCase  :    Optional[int]     ):
       _A  :			Tuple											=      urlparse(lowerCamelCase     )
       return parsed.scheme in ("http", "https")






def    lowerCAmelCase__ (       lowerCamelCase  :    str						,lowerCamelCase  :    str						,lowerCamelCase  :    Optional[int]=True     ):
       _A  :			Optional[Any]											=      CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
       _A  :			List[str]											=      '/' not in model_id
       if legacy_format:
              return F'{endpoint}/{model_id}-{filename}'
       else:
              return F'{endpoint}/{model_id}/{filename}'






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






def    lowerCAmelCase__ (       lowerCamelCase  :    Tuple						,lowerCamelCase  :    List[Any]=None						,lowerCamelCase  :    Union[str, Any]=False						,lowerCamelCase  :    List[str]=None						,lowerCamelCase  :    List[str]=10						,lowerCamelCase  :    Union[str, Any]=False						,lowerCamelCase  :    List[str]=None						,lowerCamelCase  :    Optional[Any]=False						,):
       if cache_dir is None:
              _A  :			int											=      TRANSFORMERS_CACHE
       if isinstance(lowerCamelCase						,lowerCamelCase     ):
              _A  :			int											=      str(lowerCamelCase     )

       os.makedirs(lowerCamelCase						,exist_ok=lowerCamelCase     )

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

       _A  :			int											=      url_to_filename(lowerCamelCase						,lowerCamelCase     )

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

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

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

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

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

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

                     _A  :			Tuple											=      _resumable_file_manager
                     if os.path.exists(lowerCamelCase     ):
                            _A  :			List[str]											=      os.stat(lowerCamelCase     ).st_size
                     else:
                            _A  :			Tuple											=      0
              else:
                     _A  :			Any											=      partial(tempfile.NamedTemporaryFile						,dir=lowerCamelCase						,delete=lowerCamelCase     )
                     _A  :			Any											=      0

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

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

              os.replace(temp_file.name						,lowerCamelCase     )

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

       return cache_path






def    lowerCAmelCase__ (       lowerCamelCase  :    Tuple						,lowerCamelCase  :    Optional[Any]=None     ):
       _A  :			Tuple											=      url.encode('utf-8'     )
       _A  :			str											=      shaaaa(lowerCamelCase     )
       _A  :			List[Any]											=      url_hash.hexdigest()

       if etag:
              _A  :			Dict											=      etag.encode('utf-8'     )
              _A  :			Optional[int]											=      shaaaa(lowerCamelCase     )
              filename += "." + etag_hash.hexdigest()

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

       return filename






def    lowerCAmelCase__ (       lowerCamelCase  :    Any						,lowerCamelCase  :    Optional[Any]=None						,lowerCamelCase  :    Tuple=False						,lowerCamelCase  :    Dict=None						,lowerCamelCase  :    Tuple=False						,lowerCamelCase  :    Any=None						,lowerCamelCase  :    Dict=False						,lowerCamelCase  :    Any=False						,lowerCamelCase  :    Dict=False						,):
       if cache_dir is None:
              _A  :			Optional[Any]											=      TRANSFORMERS_CACHE
       if isinstance(lowerCamelCase						,lowerCamelCase     ):
              _A  :			Tuple											=      str(lowerCamelCase     )
       if isinstance(lowerCamelCase						,lowerCamelCase     ):
              _A  :			Optional[Any]											=      str(lowerCamelCase     )

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

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

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

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

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

              return output_path_extracted

       return output_path






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






def    lowerCAmelCase__ (       lowerCamelCase  :    Tuple     ):
       _A  :			str											=      requests.get(lowerCamelCase     )
       _A  :			Dict											=      np.array(Image.open(BytesIO(response.content     )     )     )
       return img






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






def    lowerCAmelCase__ (       ):
       print(F'{os.path.abspath(os.path.join(lowerCamelCase						,os.pardir     )     )}/demo.ipynb'     )






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






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










'''simple docstring'''







import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    SummarizationPipeline,
    TFPreTrainedModel,
    pipeline,
)
from transformers.testing_utils import get_gpu_count, is_pipeline_test, require_tf, require_torch, slow, torch_device
from transformers.tokenization_utils import TruncationStrategy

from .test_pipelines_common import ANY


A   :  Optional[Any]       		=				-1 if torch_device == '''cpu''' else 0


@is_pipeline_test
class 				__lowerCamelCase				( unittest.TestCase ):
    """simple docstring"""
    a									=					MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
    a									=					TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING


    def      A					(						self    :							Union[str, Any]   ,							SCREAMING_SNAKE_CASE    :							Tuple   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]   ,							SCREAMING_SNAKE_CASE    :							Dict):
           _A  :			str											=      SummarizationPipeline(model=SCREAMING_SNAKE_CASE   ,							tokenizer=SCREAMING_SNAKE_CASE)
           return summarizer, ["(CNN)The Palestinian Authority officially became", "Some other text"]


    def      A					(						self    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							Tuple   ,							SCREAMING_SNAKE_CASE    :							Optional[int]):
           _A  :			str											=      summarizer.model

           _A  :			Tuple											=      summarizer('(CNN)The Palestinian Authority officially became')
           self.assertEqual(SCREAMING_SNAKE_CASE   ,							[{'summary_text': ANY(SCREAMING_SNAKE_CASE)}])

           _A  :			Optional[Any]											=      summarizer(
               '(CNN)The Palestinian Authority officially became '   ,							num_beams=2   ,							min_length=2   ,							max_length=5   ,							)
           self.assertEqual(SCREAMING_SNAKE_CASE   ,							[{'summary_text': ANY(SCREAMING_SNAKE_CASE)}])

           # Some models (Switch Transformers, LED, T5, LongT5, etc) can handle long sequences.
           _A  :			Union[str, Any]											=      [
               'SwitchTransformersConfig',
               'T5Config',
               'LongT5Config',
               'LEDConfig',
               'PegasusXConfig',
               'FSMTConfig',
               'M2M100Config',
               'ProphetNetConfig',  # positional embeddings up to a fixed maximum size (otherwise clamping the values)
           ]
           if model.config.__class__.__name__ not in model_can_handle_longer_seq:
                  # Too long and exception is expected.
                  # For TF models, if the weights are initialized in GPU context, we won't get expected index error from
                  # the embedding layer.
                  if not (
                      isinstance(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)
                      and get_gpu_count() > 0
                      and len(summarizer.model.trainable_weights) > 0
                  ):
                         with self.assertRaises(SCREAMING_SNAKE_CASE):
                                _A  :			Any											=      summarizer('This ' * 1000)
           _A  :			int											=      summarizer('This ' * 1000   ,							truncation=TruncationStrategy.ONLY_FIRST)


    @require_torch
    def      A					(						self    :							int):
           _A  :			Tuple											=      pipeline(task='summarization'   ,							model='sshleifer/tiny-mbart'   ,							framework='pt')
           _A  :			List[str]											=      summarizer('This is a small test')
           self.assertEqual(
               SCREAMING_SNAKE_CASE   ,							[
                   {
                       'summary_text': 'เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป'
                   }
               ]   ,							)


    @require_tf
    def      A					(						self    :							int):
           _A  :			Optional[Any]											=      pipeline(task='summarization'   ,							model='sshleifer/tiny-mbart'   ,							framework='tf')
           _A  :			Any											=      summarizer('This is a small test')
           self.assertEqual(
               SCREAMING_SNAKE_CASE   ,							[
                   {
                       'summary_text': 'เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป'
                   }
               ]   ,							)


    @require_torch
    @slow
    def      A					(						self    :							List[Any]):
           _A  :			int											=      pipeline(task='summarization'   ,							device=SCREAMING_SNAKE_CASE)
           _A  :			int											=      (
               ' (CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on'
               ' Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The'
               ' formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based.'
               ' The Palestinians signed the ICC\'s founding Rome Statute in January, when they also accepted its'
               ' jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including East'
               ' Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination into the'
               ' situation in Palestinian territories, paving the way for possible war crimes investigations against'
               ' Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and'
               ' the United States, neither of which is an ICC member, opposed the Palestinians\' efforts to join the'
               ' body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday\'s ceremony, said it was a'
               ' move toward greater justice. "As Palestine formally becomes a State Party to the Rome Statute today, the'
               ' world is also a step closer to ending a long era of impunity and injustice," he said, according to an'
               ' ICC news release. "Indeed, today brings us closer to our shared goals of justice and peace." Judge'
               ' Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the'
               ' Palestinians. "As the Rome Statute today enters into force for the State of Palestine, Palestine'
               ' acquires all the rights as well as responsibilities that come with being a State Party to the Statute.'
               ' These are substantive commitments, which cannot be taken lightly," she said. Rights group Human Rights'
               ' Watch welcomed the development. "Governments seeking to penalize Palestine for joining the ICC should'
               ' immediately end their pressure, and countries that support universal acceptance of the court\'s treaty'
               ' should speak out to welcome its membership," said Balkees Jarrah, international justice counsel for the'
               ' group. \"What\'s objectionable is the attempts to undermine international justice, not Palestine\'s'
               ' decision to join a treaty to which over 100 countries around the world are members." In January, when'
               ' the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an'
               ' outrage, saying the court was overstepping its boundaries. The United States also said it "strongly"'
               ' disagreed with the court\'s decision. \"As we have said repeatedly, we do not believe that Palestine is a'
               ' state and therefore we do not believe that it is eligible to join the ICC," the State Department said in'
               ' a statement. It urged the warring sides to resolve their differences through direct negotiations. "We'
               ' will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,"'
               ' it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the'
               ' territories as "Palestine." While a preliminary examination is not a formal investigation, it allows the'
               ' court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou'
               ' Bensouda said her office would "conduct its analysis in full independence and impartiality." The war'
               ' between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry'
               ' will include alleged war crimes committed since June. The International Criminal Court was set up in'
               ' 2002 to prosecute genocide, crimes against humanity and war crimes. CNN\'s Vasco Cotovio, Kareem Khadder'
               ' and Faith Karimi contributed to this report.'
           )
           _A  :			Optional[Any]											=      (
               ' The Palestinian Authority becomes the 123rd member of the International Criminal Court . The move gives'
               ' the court jurisdiction over alleged crimes in Palestinian territories . Israel and the United States'
               ' opposed the Palestinians\' efforts to join the court . Rights group Human Rights Watch welcomes the move,'
               ' says governments seeking to penalize Palestine should end pressure .'
           )
           _A  :			Union[str, Any]											=      summarizer(SCREAMING_SNAKE_CASE)
           self.assertEqual(result[0]['summary_text']   ,							SCREAMING_SNAKE_CASE)






