
"""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					:	Union[str, Any]							=		True
except ImportError:
 a					:	str							=		False


try:
 from torch.hub import _get_torch_home

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

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


def  _UpperCamelCase	(						_A=OBJECTS					,      _A=ATTRIBUTES      )    ->  List[str]:





    """simple docstring"""

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

    _UpperCAmelCase          =   []
    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      )    ->  int:





    """simple docstring"""

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




class 		a_						:
    a      :				Dict     =				{}







    def __init__(			self			:       Tuple     ,					__UpperCamelCase			:       dict     ,					__UpperCamelCase			:       str = "root"     ,					__UpperCamelCase			:       Tuple=0     )		->Tuple:






        '''simple docstring'''






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

        _UpperCAmelCase          =   d







    def __repr__(			self			:       Any     )		->int:






        '''simple docstring'''






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







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






        '''simple docstring'''






        _UpperCAmelCase          =   val
        _UpperCAmelCase          =   val
        _UpperCAmelCase          =   key.split("""."""     )
        _UpperCAmelCase          =   len(__UpperCamelCase     ) - 1
        _UpperCAmelCase          =   self._pointer
        if len(__UpperCamelCase     ) > 1:
            for i, l in enumerate(__UpperCamelCase     ):
                if hasattr(self     ,					__UpperCamelCase     ) and isinstance(getattr(self     ,					__UpperCamelCase     )     ,					__UpperCamelCase     ):
                    setattr(getattr(self     ,					__UpperCamelCase     )     ,					""".""".join(levels[i:]     )     ,					__UpperCamelCase     )
                if l == last_level:
                    _UpperCAmelCase          =   val
                else:
                    _UpperCAmelCase          =   pointer[l]







    def      _snake_case					(			self			:       Tuple     )		->Any:






        '''simple docstring'''






        return self._pointer







    def      _snake_case					(			self			:       Dict     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       List[Any]     )		->List[Any]:






        '''simple docstring'''






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







    def      _snake_case					(			self			:       List[str]     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       List[Any]     )		->Optional[Any]:






        '''simple docstring'''






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







    @staticmethod
    def      _snake_case					(			__UpperCamelCase			:       Dict     )		->Union[str, Any]:






        '''simple docstring'''






        with open(__UpperCamelCase     ) as stream:
            _UpperCAmelCase          =   load(__UpperCamelCase     ,					Loader=__UpperCamelCase     )
        return data







    def __str__(			self			:       Dict     )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   """    """
        if self._name != "root":
            _UpperCAmelCase          =   f"""{t * (self._level-1)}{self._name}:\n"""
        else:
            _UpperCAmelCase          =   """"""
        _UpperCAmelCase          =   self._level
        for i, (k, v) in enumerate(self._pointer.items()     ):
            if isinstance(__UpperCamelCase     ,					__UpperCamelCase     ):
                r += f"""{t * (self._level)}{v}\n"""
                self._level += 1
            else:
                r += f"""{t * (self._level)}{k}: {v} ({type(__UpperCamelCase     ).__name__})\n"""
            _UpperCAmelCase          =   level
        return r[:-1]







    @classmethod
    def      _snake_case					(			cls			:       List[Any]     ,					__UpperCamelCase			:       str     ,					**__UpperCamelCase			:       List[Any]     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   cls.get_config_dict(__UpperCamelCase     ,					**__UpperCamelCase     )
        return cls(__UpperCamelCase     )







    @classmethod
    def      _snake_case					(			cls			:       Tuple     ,					__UpperCamelCase			:       str     ,					**__UpperCamelCase			:       Optional[int]     )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   kwargs.pop("""cache_dir"""     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   kwargs.pop("""force_download"""     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   kwargs.pop("""resume_download"""     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   kwargs.pop("""proxies"""     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   kwargs.pop("""local_files_only"""     ,					__UpperCamelCase     )

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

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

            _UpperCAmelCase          =   Config.load_yaml(__UpperCamelCase     )

        except EnvironmentError:
            _UpperCAmelCase          =   """Can't load config for"""
            raise EnvironmentError(__UpperCamelCase     )

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

        return Config.load_yaml(__UpperCamelCase     ), kwargs







def  _UpperCamelCase	(						_A      )    ->  Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   torch.load("""dump.pt"""					,      map_location=in_tensor.device      )
    _UpperCAmelCase          =   in_tensor.numpy()
    _UpperCAmelCase          =   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()      )*1_0_0:.4f} %"""
        " element-wise mismatch"
    )
    raise Exception("""tensors are all good"""      )

    # Hugging face functions below


def  _UpperCamelCase	(						_A      )    ->  Any:





    """simple docstring"""

    _UpperCAmelCase          =   urlparse(_A      )
    return parsed.scheme in ("http", "https")


def  _UpperCamelCase	(						_A					,      _A					,      _A=True      )    ->  str:





    """simple docstring"""

    _UpperCAmelCase          =   CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
    _UpperCAmelCase          =   """/""" 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					,      )    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   """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
    _UpperCAmelCase          =   {"""user-agent""": ua}
    if resume_size > 0:
        _UpperCAmelCase          =   """bytes=%d-""" % (resume_size,)
    _UpperCAmelCase          =   requests.get(_A					,      stream=_A					,      proxies=_A					,      headers=_A      )
    if response.status_code == 4_1_6:  # Range not satisfiable
        return
    _UpperCAmelCase          =   response.headers.get("""Content-Length"""      )
    _UpperCAmelCase          =   resume_size + int(_A      ) if content_length is not None else None
    _UpperCAmelCase          =   tqdm(
        unit="""B"""					,      unit_scale=_A					,      total=_A					,      initial=_A					,      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(_A      )      )
            temp_file.write(_A      )
    progress.close()


def  _UpperCamelCase	(						_A					,      _A=None					,      _A=False					,      _A=None					,      _A=1_0					,      _A=False					,      _A=None					,      _A=False					,      )    ->  str:





    """simple docstring"""

    if cache_dir is None:
        _UpperCAmelCase          =   TRANSFORMERS_CACHE
    if isinstance(_A					,      _A      ):
        _UpperCAmelCase          =   str(_A      )

    os.makedirs(_A					,      exist_ok=_A      )

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

    _UpperCAmelCase          =   url_to_filename(_A					,      _A      )

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

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

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

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

    return cache_path


def  _UpperCamelCase	(						_A					,      _A=None      )    ->  Optional[int]:





    """simple docstring"""

    _UpperCAmelCase          =   url.encode("""utf-8"""      )
    _UpperCAmelCase          =   shaaaa(_A      )
    _UpperCAmelCase          =   url_hash.hexdigest()

    if etag:
        _UpperCAmelCase          =   etag.encode("""utf-8"""      )
        _UpperCAmelCase          =   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					,      )    ->  Dict:





    """simple docstring"""

    if cache_dir is None:
        _UpperCAmelCase          =   TRANSFORMERS_CACHE
    if isinstance(_A					,      _A      ):
        _UpperCAmelCase          =   str(_A      )
    if isinstance(_A					,      _A      ):
        _UpperCAmelCase          =   str(_A      )

    if is_remote_url(_A      ):
        # URL, so get it from the cache (downloading if necessary)
        _UpperCAmelCase          =   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.
        _UpperCAmelCase          =   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/"
        _UpperCAmelCase          =   os.path.split(_A      )
        _UpperCAmelCase          =   output_file.replace("""."""					,      """-"""      ) + """-extracted"""
        _UpperCAmelCase          =   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
        _UpperCAmelCase          =   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      ):
                _UpperCAmelCase          =   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=","      )    ->  Optional[int]:





    """simple docstring"""

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


def  _UpperCamelCase	(						_A      )    ->  Dict:





    """simple docstring"""

    _UpperCAmelCase          =   requests.get(_A      )
    _UpperCAmelCase          =   np.array(Image.open(BytesIO(response.content      )      )      )
    return img


def  _UpperCamelCase	(						_A      )    ->  str:





    """simple docstring"""

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


def  _UpperCamelCase	(						)    ->  Optional[Any]:





    """simple docstring"""

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


def  _UpperCamelCase	(						_A					,      _A="RGB"      )    ->  Any:





    """simple docstring"""

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







def  _UpperCamelCase	(						_A					,      _A=1      )    ->  int:





    """simple docstring"""

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

"""simple docstring"""



import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel




class 		a_						:







    def __init__(			self			:       Tuple     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       List[Any]=99     ,					__UpperCamelCase			:       Optional[int]=13     ,					__UpperCamelCase			:       List[str]=7     ,					__UpperCamelCase			:       List[Any]=9     ,					__UpperCamelCase			:       List[Any]=True     ,					__UpperCamelCase			:       str=True     ,					__UpperCamelCase			:       int=False     ,					__UpperCamelCase			:       int=32     ,					__UpperCamelCase			:       Dict=5     ,					__UpperCamelCase			:       Optional[int]=4     ,					__UpperCamelCase			:       Any=37     ,					__UpperCamelCase			:       List[Any]=8     ,					__UpperCamelCase			:       Optional[int]=0.1     ,					__UpperCamelCase			:       str=0.0_0_2     ,					__UpperCamelCase			:       Union[str, Any]=1     ,					__UpperCamelCase			:       List[Any]=0     ,					__UpperCamelCase			:       Tuple=0     ,					__UpperCamelCase			:       Optional[int]=None     ,					__UpperCamelCase			:       Any=None     ,					)		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   parent
        _UpperCAmelCase          =   batch_size
        _UpperCAmelCase          =   encoder_seq_length
        _UpperCAmelCase          =   decoder_seq_length
        # For common tests
        _UpperCAmelCase          =   self.decoder_seq_length
        _UpperCAmelCase          =   is_training
        _UpperCAmelCase          =   use_attention_mask
        _UpperCAmelCase          =   use_labels
        _UpperCAmelCase          =   vocab_size
        _UpperCAmelCase          =   hidden_size
        _UpperCAmelCase          =   num_hidden_layers
        _UpperCAmelCase          =   num_attention_heads
        _UpperCAmelCase          =   d_ff
        _UpperCAmelCase          =   relative_attention_num_buckets
        _UpperCAmelCase          =   dropout_rate
        _UpperCAmelCase          =   initializer_factor
        _UpperCAmelCase          =   eos_token_id
        _UpperCAmelCase          =   pad_token_id
        _UpperCAmelCase          =   decoder_start_token_id
        _UpperCAmelCase          =   None
        _UpperCAmelCase          =   decoder_layers







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






        '''simple docstring'''






        return TaConfig.from_pretrained("""google/umt5-base"""     )







    def      _snake_case					(			self			:       List[Any]     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Tuple=None     ,					__UpperCamelCase			:       Optional[Any]=None     ,					__UpperCamelCase			:       Optional[Any]=None     ,					__UpperCamelCase			:       Any=None     ,					__UpperCamelCase			:       str=None     ,					)		->int:






        '''simple docstring'''






        if attention_mask is None:
            _UpperCAmelCase          =   input_ids.ne(config.pad_token_id     )
        if decoder_attention_mask is None:
            _UpperCAmelCase          =   decoder_input_ids.ne(config.pad_token_id     )
        if head_mask is None:
            _UpperCAmelCase          =   torch.ones(config.num_hidden_layers     ,					config.num_attention_heads     ,					device=__UpperCamelCase     )
        if decoder_head_mask is None:
            _UpperCAmelCase          =   torch.ones(config.num_decoder_layers     ,					config.num_attention_heads     ,					device=__UpperCamelCase     )
        if cross_attn_head_mask is None:
            _UpperCAmelCase          =   torch.ones(
                config.num_decoder_layers     ,					config.num_attention_heads     ,					device=__UpperCamelCase     )
        return {
            "input_ids": input_ids,
            "decoder_input_ids": decoder_input_ids,
            "attention_mask": attention_mask,
            "decoder_attention_mask": decoder_attention_mask,
            "head_mask": head_mask,
            "decoder_head_mask": decoder_head_mask,
            "cross_attn_head_mask": cross_attn_head_mask,
        }







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






        '''simple docstring'''






        _UpperCAmelCase          =   ids_tensor([self.batch_size, self.encoder_seq_length]     ,					self.vocab_size     )
        _UpperCAmelCase          =   ids_tensor([self.batch_size, self.decoder_seq_length]     ,					self.vocab_size     )

        # we need to clamp the input ids here to avoid having pad token in between
        # this is because for NllbMoe the position_ids are prepared such that
        # all pad tokens have pos id = 2 and rest are between 2..seq_length
        # and the seq_length here is seq_length - num_pad_tokens
        # but when using past, there is no way of knowing if the past input ids had
        # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
        # position_ids being off by num_pad_tokens in past input
        _UpperCAmelCase          =   input_ids.clamp(self.pad_token_id + 1     )
        _UpperCAmelCase          =   decoder_input_ids.clamp(self.pad_token_id + 1     )

        _UpperCAmelCase          =   self.get_config()
        _UpperCAmelCase          =   config.num_attention_heads
        _UpperCAmelCase          =   self.prepare_inputs_dict(__UpperCamelCase     ,					__UpperCamelCase     ,					__UpperCamelCase     )
        return config, input_dict







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






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.prepare_config_and_inputs()
        return config, inputs_dict







    def      _snake_case					(			self			:       Dict     )		->List[str]:






        '''simple docstring'''






        return TaConfig(
            vocab_size=1_66     ,					d_model=self.hidden_size     ,					d_ff=self.d_ff     ,					d_kv=self.hidden_size // self.num_attention_heads     ,					num_layers=self.num_hidden_layers     ,					num_decoder_layers=self.decoder_layers     ,					num_heads=self.num_attention_heads     ,					relative_attention_num_buckets=self.relative_attention_num_buckets     ,					dropout_rate=self.dropout_rate     ,					initializer_factor=self.initializer_factor     ,					eos_token_id=self.eos_token_id     ,					bos_token_id=self.pad_token_id     ,					pad_token_id=self.pad_token_id     ,					decoder_start_token_id=self.decoder_start_token_id     ,					)







    def      _snake_case					(			self			:       Tuple     )		->Dict:






        '''simple docstring'''






        return TaConfig(
            vocab_size=self.vocab_size     ,					d_model=self.hidden_size     ,					d_ff=self.d_ff     ,					d_kv=self.hidden_size // self.num_attention_heads     ,					num_layers=self.num_hidden_layers     ,					num_decoder_layers=self.decoder_layers     ,					num_heads=self.num_attention_heads     ,					relative_attention_num_buckets=self.relative_attention_num_buckets     ,					dropout_rate=self.dropout_rate     ,					initializer_factor=self.initializer_factor     ,					eos_token_id=self.eos_token_id     ,					bos_token_id=self.pad_token_id     ,					pad_token_id=self.pad_token_id     ,					decoder_start_token_id=self.decoder_start_token_id     ,					)







    def      _snake_case					(			self			:       List[Any]     ,					__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       List[str]     ,					)		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase          =   UMTaModel(config=__UpperCamelCase     )
        model.to(__UpperCamelCase     )
        model.eval()
        _UpperCAmelCase          =   model(
            input_ids=__UpperCamelCase     ,					decoder_input_ids=__UpperCamelCase     ,					attention_mask=__UpperCamelCase     ,					decoder_attention_mask=__UpperCamelCase     ,					)
        _UpperCAmelCase          =   model(input_ids=__UpperCamelCase     ,					decoder_input_ids=__UpperCamelCase     )
        _UpperCAmelCase          =   result.last_hidden_state
        _UpperCAmelCase          =   result.past_key_values
        _UpperCAmelCase          =   result.encoder_last_hidden_state

        self.parent.assertEqual(encoder_output.size()     ,					(self.batch_size, self.encoder_seq_length, self.hidden_size)     )
        self.parent.assertEqual(decoder_output.size()     ,					(self.batch_size, self.decoder_seq_length, self.hidden_size)     )
        # There should be `num_layers` key value embeddings stored in decoder_past
        self.parent.assertEqual(len(__UpperCamelCase     )     ,					config.num_layers     )
        # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
        self.parent.assertEqual(len(decoder_past[0]     )     ,					4     )







    def      _snake_case					(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       List[str]     ,					)		->str:






        '''simple docstring'''






        _UpperCAmelCase          =   UMTaModel(config=__UpperCamelCase     ).get_decoder().to(__UpperCamelCase     ).eval()
        # first forward pass
        _UpperCAmelCase          =   model(__UpperCamelCase     ,					use_cache=__UpperCamelCase     )
        _UpperCAmelCase          =   model(__UpperCamelCase     )
        _UpperCAmelCase          =   model(__UpperCamelCase     ,					use_cache=__UpperCamelCase     )

        self.parent.assertTrue(len(__UpperCamelCase     ) == len(__UpperCamelCase     )     )
        self.parent.assertTrue(len(__UpperCamelCase     ) == len(__UpperCamelCase     ) + 1     )

        _UpperCAmelCase    ,_UpperCAmelCase          =   outputs.to_tuple()

        # create hypothetical next token and extent to next_input_ids
        _UpperCAmelCase          =   ids_tensor((self.batch_size, 1)     ,					config.vocab_size     )

        # append to next input_ids and
        _UpperCAmelCase          =   torch.cat([input_ids, next_tokens]     ,					dim=-1     )

        _UpperCAmelCase          =   model(__UpperCamelCase     )["""last_hidden_state"""]
        _UpperCAmelCase          =   model(__UpperCamelCase     ,					past_key_values=__UpperCamelCase     )["""last_hidden_state"""]

        # select random slice
        _UpperCAmelCase          =   ids_tensor((1,)     ,					output_from_past.shape[-1]     ).item()
        _UpperCAmelCase          =   output_from_no_past[:, -1, random_slice_idx].detach()
        _UpperCAmelCase          =   output_from_past[:, 0, random_slice_idx].detach()

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(__UpperCamelCase     ,					__UpperCamelCase     ,					atol=1e-3     )     )







    def      _snake_case					(			self			:       Optional[int]     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       Dict     ,					)		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase          =   UMTaModel(config=__UpperCamelCase     ).to(__UpperCamelCase     ).half().eval()
        _UpperCAmelCase          =   model(**__UpperCamelCase     )["""last_hidden_state"""]
        self.parent.assertFalse(torch.isnan(__UpperCamelCase     ).any().item()     )







@require_torch
class 		a_						( _UpperCAmelCase     ,						_UpperCAmelCase     ,						_UpperCAmelCase     ,						unittest.TestCase	):
    a      :				List[str]     =				(
        (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
    )
    a      :				Tuple     =				(UMTaForConditionalGeneration,) if is_torch_available() else ()
    a      :				Optional[Any]     =				(
        {
            'conversational': UMTaForConditionalGeneration,
            'feature-extraction': UMTaModel,
            'summarization': UMTaForConditionalGeneration,
            'text2text-generation': UMTaForConditionalGeneration,
            'translation': UMTaForConditionalGeneration,
            'question-answering': UMTaForQuestionAnswering,
        }
        if is_torch_available()
        else {}
    )
    a      :				Any     =				True
    a      :				Optional[int]     =				False
    a      :				Any     =				False
    a      :				Optional[int]     =				True
    a      :				Optional[Any]     =				True
    # The small UMT5 model needs higher percentages for CPU/MP tests
    a      :				int     =				[0.8, 0.9]







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






        '''simple docstring'''






        _UpperCAmelCase          =   UMTaModelTester(self     )







    @unittest.skip("""Test has a segmentation fault on torch 1.8.0"""     )
    def      _snake_case					(			self			:       Union[str, Any]     )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        _UpperCAmelCase          =   UMTaModel(config_and_inputs[0]     ).to(__UpperCamelCase     )
        with tempfile.TemporaryDirectory() as tmpdirname:
            torch.onnx.export(
                __UpperCamelCase     ,					(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])     ,					f"""{tmpdirname}/t5_test.onnx"""     ,					export_params=__UpperCamelCase     ,					opset_version=9     ,					input_names=["""input_ids""", """decoder_input_ids"""]     ,					)







    @unittest.skipIf(torch_device == """cpu"""     ,					"""Cant do half precision"""     )
    def      _snake_case					(			self			:       Tuple     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model_fpaa_forward(*__UpperCamelCase     )







    def      _snake_case					(			self			:       Any     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        _UpperCAmelCase          =   config_and_inputs[0]
        _UpperCAmelCase          =   UMTaForConditionalGeneration(__UpperCamelCase     ).eval()
        model.to(__UpperCamelCase     )

        _UpperCAmelCase          =   {
            """head_mask""": torch.zeros(config.num_layers     ,					config.num_heads     ,					device=__UpperCamelCase     ),
            """decoder_head_mask""": torch.zeros(config.num_decoder_layers     ,					config.num_heads     ,					device=__UpperCamelCase     ),
            """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers     ,					config.num_heads     ,					device=__UpperCamelCase     ),
        }

        for attn_name, (name, mask) in zip(__UpperCamelCase     ,					head_masking.items()     ):
            _UpperCAmelCase          =   {name: mask}
            # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
            if name == "head_mask":
                _UpperCAmelCase          =   torch.ones(
                    config.num_decoder_layers     ,					config.num_heads     ,					device=__UpperCamelCase     )

            _UpperCAmelCase          =   model.generate(
                config_and_inputs[1]["""input_ids"""]     ,					num_beams=1     ,					max_length=3     ,					output_attentions=__UpperCamelCase     ,					return_dict_in_generate=__UpperCamelCase     ,					**__UpperCamelCase     ,					)
            # We check the state of decoder_attentions and cross_attentions just from the last step
            _UpperCAmelCase          =   out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
            self.assertEqual(sum([w.sum().item() for w in attn_weights]     )     ,					0.0     )







    @unittest.skip("""Does not work on the tiny model as we keep hitting edge cases."""     )
    def      _snake_case					(			self			:       Tuple     )		->List[Any]:






        '''simple docstring'''






        pass







@require_torch
@require_sentencepiece
@require_tokenizers
class 		a_						( unittest.TestCase	):







    @slow
    @unittest.skip(
        """Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged"""     )
    def      _snake_case					(			self			:       Optional[Any]     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""     ,					return_dict=__UpperCamelCase     ).to(__UpperCamelCase     )
        _UpperCAmelCase          =   AutoTokenizer.from_pretrained("""google/umt5-small"""     ,					use_fast=__UpperCamelCase     ,					legacy=__UpperCamelCase     )
        _UpperCAmelCase          =   [
            """Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
            """No se como puedo <extra_id_0>.""",
            """This is the reason why we <extra_id_0> them.""",
            """The <extra_id_0> walks in <extra_id_1>, seats""",
            """A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
        ]
        _UpperCAmelCase          =   tokenizer(__UpperCamelCase     ,					return_tensors="""pt"""     ,					padding=__UpperCamelCase     ).input_ids
        # fmt: off
        _UpperCAmelCase          =   torch.tensor(
            [
                [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
                [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
            ]     )
        # fmt: on
        torch.testing.assert_allclose(__UpperCamelCase     ,					__UpperCamelCase     )

        _UpperCAmelCase          =   model.generate(input_ids.to(__UpperCamelCase     )     )
        _UpperCAmelCase          =   [
            """<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
            """<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
            """<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
            """<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
            """<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
        ]
        _UpperCAmelCase          =   tokenizer.batch_decode(__UpperCamelCase     )
        self.assertEqual(__UpperCamelCase     ,					__UpperCamelCase     )