

from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
   from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__UpperCAmelCase      = logging.get_logger(__name__)

__UpperCAmelCase      = {
    """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/config.json""",
    """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/config.json""",
    """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json""",
    """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json""",
    """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json""",
    """microsoft/deberta-xlarge-mnli""": """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json""",
}



class SCREAMING_SNAKE_CASE	(  a_ ):
  """simple docstring"""






  lowerCamelCase    :			Optional[int]						      ="deberta"







  def __init__(			self :    int       ,							lowerCAmelCase :    List[str]=5_02_65       ,							lowerCAmelCase :    Tuple=7_68       ,							lowerCAmelCase :    List[Any]=12       ,							lowerCAmelCase :    Any=12       ,							lowerCAmelCase :    Optional[int]=30_72       ,							lowerCAmelCase :    Optional[Any]="gelu"       ,							lowerCAmelCase :    Optional[int]=0.1       ,							lowerCAmelCase :    Optional[Any]=0.1       ,							lowerCAmelCase :    List[str]=5_12       ,							lowerCAmelCase :    Dict=0       ,							lowerCAmelCase :    Optional[Any]=0.02       ,							lowerCAmelCase :    int=1e-7       ,							lowerCAmelCase :    List[Any]=False       ,							lowerCAmelCase :    List[Any]=-1       ,							lowerCAmelCase :    Dict=0       ,							lowerCAmelCase :    List[Any]=True       ,							lowerCAmelCase :    Tuple=None       ,							lowerCAmelCase :    Union[str, Any]=0       ,							lowerCAmelCase :    Optional[Any]="gelu"       ,							**lowerCAmelCase :    str       ,							)							->	int:






     """simple docstring"""







     super().__init__(**lowerCAmelCase    )

     __lowerCAmelCase  :			Dict					    =						hidden_size
     __lowerCAmelCase  :			List[str]					    =						num_hidden_layers
     __lowerCAmelCase  :			Union[str, Any]					    =						num_attention_heads
     __lowerCAmelCase  :			Any					    =						intermediate_size
     __lowerCAmelCase  :			Union[str, Any]					    =						hidden_act
     __lowerCAmelCase  :			Union[str, Any]					    =						hidden_dropout_prob
     __lowerCAmelCase  :			List[str]					    =						attention_probs_dropout_prob
     __lowerCAmelCase  :			Optional[int]					    =						max_position_embeddings
     __lowerCAmelCase  :			int					    =						type_vocab_size
     __lowerCAmelCase  :			Optional[int]					    =						initializer_range
     __lowerCAmelCase  :			List[Any]					    =						relative_attention
     __lowerCAmelCase  :			List[str]					    =						max_relative_positions
     __lowerCAmelCase  :			List[Any]					    =						pad_token_id
     __lowerCAmelCase  :			Optional[int]					    =						position_biased_input

     # Backwards compatibility
     if type(lowerCAmelCase    ) == str:
        __lowerCAmelCase  :			List[Any]					    =						[x.strip() for x in pos_att_type.lower().split("""|"""    )]

     __lowerCAmelCase  :			int					    =						pos_att_type
     __lowerCAmelCase  :			int					    =						vocab_size
     __lowerCAmelCase  :			List[str]					    =						layer_norm_eps

     __lowerCAmelCase  :			Any					    =						kwargs.get("""pooler_hidden_size"""       ,							lowerCAmelCase    )
     __lowerCAmelCase  :			Dict					    =						pooler_dropout
     __lowerCAmelCase  :			Optional[int]					    =						pooler_hidden_act






class SCREAMING_SNAKE_CASE	(  a_ ):
  """simple docstring"""






  @property
  def        SCREAMING_SNAKE_CASE    (			self :    Union[str, Any]    )							->	Mapping[str, Mapping[int, str]]:






     """simple docstring"""







     if self.task == "multiple-choice":
        __lowerCAmelCase  :			Tuple					    =						{0: """batch""", 1: """choice""", 2: """sequence"""}
     else:
        __lowerCAmelCase  :			Tuple					    =						{0: """batch""", 1: """sequence"""}
     if self._config.type_vocab_size > 0:
        return OrderedDict(
            [("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)]    )
     else:
        return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)]    )







  @property
  def        SCREAMING_SNAKE_CASE    (			self :    Any    )							->	int:






     """simple docstring"""







     return 12







  def        SCREAMING_SNAKE_CASE    (			self :    Any       ,							lowerCAmelCase :    Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]       ,							lowerCAmelCase :    int = -1       ,							lowerCAmelCase :    int = -1       ,							lowerCAmelCase :    int = -1       ,							lowerCAmelCase :    bool = False       ,							lowerCAmelCase :    Optional["TensorType"] = None       ,							lowerCAmelCase :    int = 3       ,							lowerCAmelCase :    int = 40       ,							lowerCAmelCase :    int = 40       ,							lowerCAmelCase :    "PreTrainedTokenizerBase" = None       ,							)							->	Mapping[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :			Tuple					    =						super().generate_dummy_inputs(preprocessor=lowerCAmelCase       ,							framework=lowerCAmelCase    )
     if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
        del dummy_inputs["token_type_ids"]
     return dummy_inputs









# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json





class 	lowerCAmelCase__(       __lowercase			):






       '''simple docstring'''


       @classmethod
       def 			UpperCamelCase_	(					cls							)	->   Tuple:
            return cls







       @slow
       @require_torch_gpu
       def 			UpperCamelCase_	(					self							)	->   Any:
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							self.get_auto_remove_tmp_dir()
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							F"""
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        """.split()

            # we want this test to run even if there is only one GPU, but if there are more we use them all
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							get_gpu_count()
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							F"""
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        """.split()
            _SCREAMING_SNAKE_CASE				:		int  			=							[sys.executable] + distributed_args + args
            execute_subprocess_async(__lowerCamelCase			,  env=self.get_env()							)

            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							os.path.join(__lowerCamelCase			,  "test_bleu.json"							)
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							load_json(__lowerCamelCase							)
            # print(metrics)
            self.assertGreaterEqual(metrics["bleu"]			,  2_5							)