


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: Optional[int]					):






            '''simple docstring'''




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

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

            __UpperCamelCase =			model.to_bettertransformer()

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

            __UpperCamelCase =			model.generate(**A_					)

            __UpperCamelCase =			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(A_					)

                 __UpperCamelCase =			AutoModelForSeqaSeqLM.from_pretrained(A_					)

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

                 __UpperCamelCase =			model_reloaded.generate(**A_					)
                 self.assertTrue(torch.allclose(A_,A_					)					)







       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            __UpperCamelCase =			'hf-internal-testing/tiny-random-t5'
            __UpperCamelCase =			AutoModelForSeqaSeqLM.from_pretrained(A_					)

            __UpperCamelCase =			model.to_bettertransformer()

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

                 __UpperCamelCase =			model.reverse_bettertransformer()
                 model.save_pretrained(A_					)






from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

snake_case__				:	str					       =					{
    'facebook/dpr-ctx_encoder-single-nq-base': (
        'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-question_encoder-single-nq-base': (
        'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-reader-single-nq-base': (
        'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-ctx_encoder-multiset-base': (
        'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'
    ),
    'facebook/dpr-question_encoder-multiset-base': (
        'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'
    ),
    'facebook/dpr-reader-multiset-base': (
        'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'
    ),
}





class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							"dpr"



       def __init__(						self				:				Union[str, Any]		,				__a				:				Optional[Any]=30_522		,				__a				:				List[Any]=768		,				__a				:				List[Any]=12		,				__a				:				Dict=12		,				__a				:				Union[str, Any]=3_072		,				__a				:				Any="gelu"		,				__a				:				Any=0.1		,				__a				:				Any=0.1		,				__a				:				Tuple=512		,				__a				:				int=2		,				__a				:				Optional[Any]=0.02		,				__a				:				List[Any]=1e-12		,				__a				:				int=0		,				__a				:				int="absolute"		,				__a				:				int = 0		,				**__a				:				Optional[int]		,				)					->Tuple:
          super().__init__(pad_token_id=__a		,				**__a							)

          lowerCamelCase_						:   str		= vocab_size
          lowerCamelCase_						:   List[Any]		= hidden_size
          lowerCamelCase_						:   Optional[int]		= num_hidden_layers
          lowerCamelCase_						:   Optional[Any]		= num_attention_heads
          lowerCamelCase_						:   List[Any]		= hidden_act
          lowerCamelCase_						:   Optional[Any]		= intermediate_size
          lowerCamelCase_						:   List[Any]		= hidden_dropout_prob
          lowerCamelCase_						:   Any		= attention_probs_dropout_prob
          lowerCamelCase_						:   Any		= max_position_embeddings
          lowerCamelCase_						:   Tuple		= type_vocab_size
          lowerCamelCase_						:   Dict		= initializer_range
          lowerCamelCase_						:   List[Any]		= layer_norm_eps
          lowerCamelCase_						:   List[str]		= projection_dim
          lowerCamelCase_						:   Optional[int]		= position_embedding_type





