
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


__lowerCAmelCase					   =       logging.get_logger(__name__)

__lowerCAmelCase					   =       {
    'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
    ),
    'microsoft/deberta-v2-xxlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
    ),
}





class     lowerCamelCase      (       __lowerCamelCase					):
	UpperCamelCase_  :					Tuple    		=	'deberta-v2'







	def __init__(     self      :str   ,     lowercase      :Tuple=1_2_8_1_0_0   ,     lowercase      :Any=1_5_3_6   ,     lowercase      :Union[str, Any]=2_4   ,     lowercase      :Any=2_4   ,     lowercase      :Union[str, Any]=6_1_4_4   ,     lowercase      :Union[str, Any]="gelu"   ,     lowercase      :List[Any]=0.1   ,     lowercase      :Any=0.1   ,     lowercase      :str=5_1_2   ,     lowercase      :List[Any]=0   ,     lowercase      :int=0.02   ,     lowercase      :str=1e-7   ,     lowercase      :List[Any]=False   ,     lowercase      :Optional[Any]=-1   ,     lowercase      :Any=0   ,     lowercase      :Any=True   ,     lowercase      :int=None   ,     lowercase      :Optional[Any]=0   ,     lowercase      :int="gelu"   ,     **lowercase      :str   ,     )		->      Any:







				"""simple docstring"""






				super().__init__(**lowercase			)

				SCREAMING_SNAKE_CASE     						= hidden_size
				SCREAMING_SNAKE_CASE     						= num_hidden_layers
				SCREAMING_SNAKE_CASE     						= num_attention_heads
				SCREAMING_SNAKE_CASE     						= intermediate_size
				SCREAMING_SNAKE_CASE     						= hidden_act
				SCREAMING_SNAKE_CASE     						= hidden_dropout_prob
				SCREAMING_SNAKE_CASE     						= attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE     						= max_position_embeddings
				SCREAMING_SNAKE_CASE     						= type_vocab_size
				SCREAMING_SNAKE_CASE     						= initializer_range
				SCREAMING_SNAKE_CASE     						= relative_attention
				SCREAMING_SNAKE_CASE     						= max_relative_positions
				SCREAMING_SNAKE_CASE     						= pad_token_id
				SCREAMING_SNAKE_CASE     						= position_biased_input

				# Backwards compatibility
				if type(lowercase			) == str:
							SCREAMING_SNAKE_CASE     						= [x.strip() for x in pos_att_type.lower().split('''|'''			)]

				SCREAMING_SNAKE_CASE     						= pos_att_type
				SCREAMING_SNAKE_CASE     						= vocab_size
				SCREAMING_SNAKE_CASE     						= layer_norm_eps

				SCREAMING_SNAKE_CASE     						= kwargs.get('''pooler_hidden_size'''   ,     lowercase			)
				SCREAMING_SNAKE_CASE     						= pooler_dropout
				SCREAMING_SNAKE_CASE     						= pooler_hidden_act





class     lowerCamelCase      (       __lowerCamelCase					):







	@property
	def  snake_case__     (     self      :Tuple			)		->      Mapping[str, Mapping[int, str]]:







				"""simple docstring"""






				if self.task == "multiple-choice":
							SCREAMING_SNAKE_CASE     						= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
				else:
							SCREAMING_SNAKE_CASE     						= {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  snake_case__     (     self      :Union[str, Any]			)		->      int:







				"""simple docstring"""






				return 1_2







	def  snake_case__     (     self      :Tuple   ,     lowercase      :Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]   ,     lowercase      :int = -1   ,     lowercase      :int = -1   ,     lowercase      :int = -1   ,     lowercase      :bool = False   ,     lowercase      :Optional["TensorType"] = None   ,     lowercase      :int = 3   ,     lowercase      :int = 4_0   ,     lowercase      :int = 4_0   ,     lowercase      :"PreTrainedTokenizerBase" = None   ,     )		->      Mapping[str, Any]:







				"""simple docstring"""






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

import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
							import torch





@require_torch
@require_optimum
@slow
class     lowerCamelCase      (       unittest.TestCase					):







	def  snake_case__     (     self      :List[Any]			)		->      Optional[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= '''hf-internal-testing/tiny-random-t5'''
				SCREAMING_SNAKE_CASE     						= AutoTokenizer.from_pretrained(lowercase			)
				SCREAMING_SNAKE_CASE     						= AutoModelForSeqaSeqLM.from_pretrained(lowercase			)

				SCREAMING_SNAKE_CASE     						= tokenizer('''This is me'''   ,     return_tensors='''pt'''			)

				SCREAMING_SNAKE_CASE     						= model.to_bettertransformer()

				self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

				SCREAMING_SNAKE_CASE     						= model.generate(**lowercase			)

				SCREAMING_SNAKE_CASE     						= model.reverse_bettertransformer()

				self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

				with tempfile.TemporaryDirectory() as tmpdirname:
							model.save_pretrained(lowercase			)

							SCREAMING_SNAKE_CASE     						= AutoModelForSeqaSeqLM.from_pretrained(lowercase			)

							self.assertFalse(
							    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

							SCREAMING_SNAKE_CASE     						= model_reloaded.generate(**lowercase			)
							self.assertTrue(torch.allclose(lowercase   ,     lowercase			)			)







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







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= '''hf-internal-testing/tiny-random-t5'''
				SCREAMING_SNAKE_CASE     						= AutoModelForSeqaSeqLM.from_pretrained(lowercase			)

				SCREAMING_SNAKE_CASE     						= model.to_bettertransformer()

				with tempfile.TemporaryDirectory() as tmpdirname:
							with self.assertRaises(lowercase			):
										model.save_pretrained(lowercase			)

							SCREAMING_SNAKE_CASE     						= model.reverse_bettertransformer()
							model.save_pretrained(lowercase			)