






'''simple docstring'''





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

a__       =     logging.get_logger(__name__)


class    _lowerCAmelCase			(		lowercase__      ):



				"""simple docstring"""



				_lowercase				:   int      =  '''vision-encoder-decoder'''
				_lowercase				:   Dict      =  True



				def __init__(      self    :    int			,		**UpperCamelCase__    :    List[str]):






											'''simple docstring'''







											super().__init__(**__lowerCamelCase)
											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}''')

											snake_case__	  =       kwargs.pop("""encoder""")
											snake_case__	  =       encoder_config.pop("""model_type""")
											snake_case__	  =       kwargs.pop("""decoder""")
											snake_case__	  =       decoder_config.pop("""model_type""")

											snake_case__	  =       AutoConfig.for_model(__lowerCamelCase			,		**__lowerCamelCase)
											snake_case__	  =       AutoConfig.for_model(__lowerCamelCase			,		**__lowerCamelCase)
											snake_case__	  =       True



				@classmethod
				def 					__magic_name__							(      cls    :    Tuple			,		UpperCamelCase__    :    PretrainedConfig			,		UpperCamelCase__    :    PretrainedConfig			,		**UpperCamelCase__    :    int):






											'''simple docstring'''







											logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config""")
											snake_case__	  =       True
											snake_case__	  =       True

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



				def 					__magic_name__							(      self    :    Dict):






											'''simple docstring'''







											snake_case__	  =       copy.deepcopy(self.__dict__)
											snake_case__	  =       self.encoder.to_dict()
											snake_case__	  =       self.decoder.to_dict()
											snake_case__	  =       self.__class__.model_type
											return output


class    _lowerCAmelCase			(		lowercase__      ):



				"""simple docstring"""



				_lowercase				:   Dict      =  version.parse('''1.11'''      )



				@property
				def 					__magic_name__							(      self    :    Dict):






											'''simple docstring'''







											return OrderedDict(
											    [
											        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
											    ])



				@property
				def 					__magic_name__							(      self    :    Any):






											'''simple docstring'''







											return 1E-4



				@property
				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







											return OrderedDict({"""last_hidden_state""": {0: """batch""", 1: """encoder_sequence"""}})


class    _lowerCAmelCase			(		lowercase__      ):



				"""simple docstring"""



				@property
				def 					__magic_name__							(      self    :    Tuple):






											'''simple docstring'''







											snake_case__	  =       OrderedDict()
											snake_case__	  =       {0: "batch", 1: "past_decoder_sequence + sequence"}
											snake_case__	  =       {0: "batch", 1: "past_decoder_sequence + sequence"}
											snake_case__	  =       {0: "batch", 1: "encoder_sequence"}

											return common_inputs



				def 					__magic_name__							(      self    :    Optional[int]			,		UpperCamelCase__    :    "PreTrainedTokenizerBase"			,		UpperCamelCase__    :    int = -1			,		UpperCamelCase__    :    int = -1			,		UpperCamelCase__    :    bool = False			,		UpperCamelCase__    :    Optional["TensorType"] = None			,		):






											'''simple docstring'''







											import torch

											snake_case__	  =       OrderedDict()

											snake_case__	  =       super().generate_dummy_inputs(
											    __lowerCamelCase			,		batch_size=__lowerCamelCase			,		seq_length=__lowerCamelCase			,		is_pair=__lowerCamelCase			,		framework=__lowerCamelCase)

											snake_case__	  =       dummy_input["input_ids"].shape
											snake_case__	  =       (batch, encoder_sequence, self._config.encoder_hidden_size)
											snake_case__	  =       dummy_input.pop("""input_ids""")
											snake_case__	  =       dummy_input.pop("""attention_mask""")
											snake_case__	  =       torch.zeros(__lowerCamelCase)

											return common_inputs


class    _lowerCAmelCase			(		lowercase__      ):



				"""simple docstring"""



				@property
				def 					__magic_name__							(      self    :    Tuple):






											'''simple docstring'''







											pass



				def 					__magic_name__							(      self    :    List[Any]			,		UpperCamelCase__    :    PretrainedConfig):






											'''simple docstring'''







											return VisionEncoderDecoderEncoderOnnxConfig(__lowerCamelCase)



				def 					__magic_name__							(      self    :    str			,		UpperCamelCase__    :    PretrainedConfig			,		UpperCamelCase__    :    PretrainedConfig			,		UpperCamelCase__    :    str = "default"):






											'''simple docstring'''







											snake_case__	  =       encoder_config.hidden_size
											return VisionEncoderDecoderDecoderOnnxConfig(__lowerCamelCase			,		__lowerCamelCase)








import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout

def _UpperCAmelCase   (	a						:	List[Any] ):
							snake_case__	  =       FileLock(str(tmpdir / """foo.lock""" ) )
							snake_case__	  =       FileLock(str(tmpdir / """foo.lock""" ) )
							snake_case__	  =       0.01
							with locka.acquire():
														with pytest.raises(a ):
																					snake_case__	  =       time.time()
																					locka.acquire(a )
																					assert time.time() - _start > timeout



def _UpperCAmelCase   (	a						:	str ):
							snake_case__	  =       """a""" * 1000 + """.lock"""
							snake_case__	  =       FileLock(str(tmpdir / filename ) )
							assert locka._lock_file.endswith(""".lock""" )
							assert not locka._lock_file.endswith(a )
							assert len(os.path.basename(locka._lock_file ) ) <= 255
							snake_case__	  =       FileLock(tmpdir / filename )
							with locka.acquire():
														with pytest.raises(a ):
																					locka.acquire(0 )
