






import time
from contextlib import contextmanager
from pathlib import Path

import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder


snake_case__   :						Union[str, Any]													=   """__DUMMY_TRANSFORMERS_USER__"""
snake_case__   :						Optional[int]													=   """Dummy User"""
snake_case__   :						Any													=   """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"""

snake_case__   :						List[Any]													=   """https://hub-ci.huggingface.co"""
snake_case__   :						Dict													=   CI_HUB_ENDPOINT + """/datasets/{repo_id}/resolve/{revision}/{path}"""
snake_case__   :						Dict													=   CI_HUB_ENDPOINT + """/{repo_id}/resolve/{revision}/{filename}"""
snake_case__   :						Optional[int]													=   Path("""~/.huggingface/hub_ci_token""").expanduser()






@pytest.fixture
def      _snake_case     (__lowercase):
   monkeypatch.setattr(
       'huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE'     ,   __lowercase)






@pytest.fixture
def      _snake_case     (__lowercase):
   monkeypatch.setattr('datasets.config.HF_ENDPOINT'     ,   __lowercase)
   monkeypatch.setattr('datasets.config.HUB_DATASETS_URL'     ,   __lowercase)






@pytest.fixture
def      _snake_case     (__lowercase):
   monkeypatch.setattr('huggingface_hub.hf_api.HfFolder.path_token'     ,   __lowercase)






@pytest.fixture
def      _snake_case     (__lowercase     ,   __lowercase):
   HfFolder.save_token(__lowercase)
   yield
   HfFolder.delete_token()






@pytest.fixture(scope='session')
def      _snake_case     ():
   return HfApi(endpoint=__lowercase)






@pytest.fixture(scope='session')
def      _snake_case     (__lowercase):
   UpperCamelCase_						=  HfFolder.get_token()
   HfFolder.save_token(__lowercase)
   yield CI_HUB_USER_TOKEN
   if previous_token is not None:
      HfFolder.save_token(__lowercase)






@pytest.fixture
def      _snake_case     (__lowercase):
   def _cleanup_repo(__lowercase):
      hf_api.delete_repo(__lowercase     ,   token=__lowercase     ,   repo_type='dataset')

   return _cleanup_repo






@pytest.fixture
def      _snake_case     (__lowercase):
   @contextmanager
   def _temporary_repo(__lowercase):
      try:
         yield repo_id
      finally:
         cleanup_repo(__lowercase)

   return _temporary_repo






@pytest.fixture(scope='session')
def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   UpperCamelCase_						=  f"""repo_txt_data-{int(time.time() * 10e3)}"""
   UpperCamelCase_						=  f"""{CI_HUB_USER}/{repo_name}"""
   hf_api.create_repo(__lowercase     ,   token=__lowercase     ,   repo_type='dataset'     ,   private=__lowercase)
   hf_api.upload_file(
       token=__lowercase     ,   path_or_fileobj=str(__lowercase)     ,   path_in_repo='data/text_data.txt'     ,   repo_id=__lowercase     ,   repo_type='dataset'     ,   )
   yield repo_id
   try:
      hf_api.delete_repo(__lowercase     ,   token=__lowercase     ,   repo_type='dataset')
   except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
      pass






@pytest.fixture()
def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   return hf_private_dataset_repo_txt_data_






@pytest.fixture(scope='session')
def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   UpperCamelCase_						=  f"""repo_zipped_txt_data-{int(time.time() * 10e3)}"""
   UpperCamelCase_						=  f"""{CI_HUB_USER}/{repo_name}"""
   hf_api.create_repo(__lowercase     ,   token=__lowercase     ,   repo_type='dataset'     ,   private=__lowercase)
   hf_api.upload_file(
       token=__lowercase     ,   path_or_fileobj=str(__lowercase)     ,   path_in_repo='data.zip'     ,   repo_id=__lowercase     ,   repo_type='dataset'     ,   )
   yield repo_id
   try:
      hf_api.delete_repo(__lowercase     ,   token=__lowercase     ,   repo_type='dataset')
   except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
      pass






@pytest.fixture()
def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   return hf_private_dataset_repo_zipped_txt_data_






@pytest.fixture(scope='session')
def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   UpperCamelCase_						=  f"""repo_zipped_img_data-{int(time.time() * 10e3)}"""
   UpperCamelCase_						=  f"""{CI_HUB_USER}/{repo_name}"""
   hf_api.create_repo(__lowercase     ,   token=__lowercase     ,   repo_type='dataset'     ,   private=__lowercase)
   hf_api.upload_file(
       token=__lowercase     ,   path_or_fileobj=str(__lowercase)     ,   path_in_repo='data.zip'     ,   repo_id=__lowercase     ,   repo_type='dataset'     ,   )
   yield repo_id
   try:
      hf_api.delete_repo(__lowercase     ,   token=__lowercase     ,   repo_type='dataset')
   except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
      pass






@pytest.fixture()
def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   return hf_private_dataset_repo_zipped_img_data_













import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
 from ... import PreTrainedTokenizerBase, TensorType

snake_case__   :						List[str]													=   logging.get_logger(__name__)


class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = """vision-encoder-decoder"""
  A_						       = True




  def __init__(  self		,		**_UpperCAmelCase			)      ->			Dict:
     super().__init__(**_UpperCAmelCase			)
     if "encoder" not in kwargs or "decoder" not in kwargs:
        raise ValueError(
            f"""A configuraton of type {self.model_type} cannot be instantiated because """
            f"""not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}"""			)

     UpperCamelCase_						=  kwargs.pop('encoder'			)
     UpperCamelCase_						=  encoder_config.pop('model_type'			)
     UpperCamelCase_						=  kwargs.pop('decoder'			)
     UpperCamelCase_						=  decoder_config.pop('model_type'			)

     UpperCamelCase_						=  AutoConfig.for_model(_UpperCAmelCase		,		**_UpperCAmelCase			)
     UpperCamelCase_						=  AutoConfig.for_model(_UpperCAmelCase		,		**_UpperCAmelCase			)
     UpperCamelCase_						=  True




  @classmethod
  def  _UpperCAmelCase  (  cls		,		_UpperCAmelCase		,		_UpperCAmelCase		,		**_UpperCAmelCase			)      ->			PretrainedConfig:
     logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'			)
     UpperCamelCase_						=  True
     UpperCamelCase_						=  True

     return cls(encoder=encoder_config.to_dict()		,		decoder=decoder_config.to_dict()		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			int:
     UpperCamelCase_						=  copy.deepcopy(self.__dict__			)
     UpperCamelCase_						=  self.encoder.to_dict()
     UpperCamelCase_						=  self.decoder.to_dict()
     UpperCamelCase_						=  self.__class__.model_type
     return output




class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = version.parse("""1.11"""     )




  @property
  def  _UpperCAmelCase  (  self			)      ->			Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
         ]			)




  @property
  def  _UpperCAmelCase  (  self			)      ->			float:
     return 1e-4




  @property
  def  _UpperCAmelCase  (  self			)      ->			Mapping[str, Mapping[int, str]]:
     return OrderedDict({'last_hidden_state': {0: 'batch', 1: 'encoder_sequence'}}			)




class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  @property
  def  _UpperCAmelCase  (  self			)      ->			Mapping[str, Mapping[int, str]]:
     UpperCamelCase_						=  OrderedDict()
     UpperCamelCase_						=  {0: 'batch', 1: 'past_decoder_sequence + sequence'}
     UpperCamelCase_						=  {0: 'batch', 1: 'past_decoder_sequence + sequence'}
     UpperCamelCase_						=  {0: 'batch', 1: 'encoder_sequence'}

     return common_inputs




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase = -1		,		_UpperCAmelCase = -1		,		_UpperCAmelCase = False		,		_UpperCAmelCase = None		,		)      ->			Mapping[str, Any]:
     import torch

     UpperCamelCase_						=  OrderedDict()

     UpperCamelCase_						=  super().generate_dummy_inputs(
         _UpperCAmelCase		,		batch_size=_UpperCAmelCase		,		seq_length=_UpperCAmelCase		,		is_pair=_UpperCAmelCase		,		framework=_UpperCAmelCase			)

     UpperCamelCase_		,   UpperCamelCase_						=  dummy_input['input_ids'].shape
     UpperCamelCase_						=  (batch, encoder_sequence, self._config.encoder_hidden_size)
     UpperCamelCase_						=  dummy_input.pop('input_ids'			)
     UpperCamelCase_						=  dummy_input.pop('attention_mask'			)
     UpperCamelCase_						=  torch.zeros(_UpperCAmelCase			)

     return common_inputs




class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  @property
  def  _UpperCAmelCase  (  self			)      ->			None:
     pass




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			OnnxConfig:
     return VisionEncoderDecoderEncoderOnnxConfig(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = "default"			)      ->			OnnxConfig:
     UpperCamelCase_						=  encoder_config.hidden_size
     return VisionEncoderDecoderDecoderOnnxConfig(_UpperCAmelCase		,		_UpperCAmelCase			)





