





'''simple docstring'''

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   UpperCAmelCase      (			unittest.TestCase							):
 def 	UpperCAmelCase_				(							self :List[str]				)->				Optional[Any]:
     A__							=     "hf-internal-testing/tiny-random-t5"
     A__							=     AutoTokenizer.from_pretrained(lowercase_				)
     A__							=     AutoModelForSeqaSeqLM.from_pretrained(lowercase_				)

     A__							=     tokenizer("This is me"		,			return_tensors="pt"				)

     A__							=     model.to_bettertransformer()

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

     A__							=     model.generate(**lowercase_				)

     A__							=     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_				)

         A__							=     AutoModelForSeqaSeqLM.from_pretrained(lowercase_				)

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

         A__							=     model_reloaded.generate(**lowercase_				)
         self.assertTrue(torch.allclose(lowercase_		,			lowercase_				)				)



 def 	UpperCAmelCase_				(							self :str				)->				Any:
     A__							=     "hf-internal-testing/tiny-random-t5"
     A__							=     AutoModelForSeqaSeqLM.from_pretrained(lowercase_				)

     A__							=     model.to_bettertransformer()

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

         A__							=     model.reverse_bettertransformer()
         model.save_pretrained(lowercase_				)










'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


__lowerCAmelCase				:      Optional[Any]						      =logging.get_logger(__name__)

__lowerCAmelCase				:      str						      ={
    "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
    "distilbert-base-uncased-distilled-squad": (
        "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
    "distilbert-base-cased-distilled-squad": (
        "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
    "distilbert-base-multilingual-cased": (
        "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
    ),
    "distilbert-base-uncased-finetuned-sst-2-english": (
        "https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
    ),
}






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							"""distilbert"""
 __lowercase  						=							{
     """hidden_size""": """dim""",
     """num_attention_heads""": """n_heads""",
     """num_hidden_layers""": """n_layers""",
 }
 def __init__(							self :Tuple		,			lowercase_ :List[str]=3_05_22		,			lowercase_ :Tuple=5_12		,			lowercase_ :List[str]=False		,			lowercase_ :int=6		,			lowercase_ :Optional[int]=12		,			lowercase_ :Optional[int]=7_68		,			lowercase_ :str=4 * 7_68		,			lowercase_ :List[Any]=0.1		,			lowercase_ :str=0.1		,			lowercase_ :Optional[int]="gelu"		,			lowercase_ :Optional[Any]=0.0_2		,			lowercase_ :Optional[Any]=0.1		,			lowercase_ :List[Any]=0.2		,			lowercase_ :str=0		,			**lowercase_ :Union[str, Any]		,			)->				str:
     A__							=     vocab_size
     A__							=     max_position_embeddings
     A__							=     sinusoidal_pos_embds
     A__							=     n_layers
     A__							=     n_heads
     A__							=     dim
     A__							=     hidden_dim
     A__							=     dropout
     A__							=     attention_dropout
     A__							=     activation
     A__							=     initializer_range
     A__							=     qa_dropout
     A__							=     seq_classif_dropout
     super().__init__(**lowercase_		,			pad_token_id=lowercase_				)






class   UpperCAmelCase      (			UpperCamelCase__							):
 @property
 def 	UpperCAmelCase_				(							self :List[str]				)->				Mapping[str, Mapping[int, str]]:
     if self.task == "multiple-choice":
         A__							=     {0: "batch", 1: "choice", 2: "sequence"}
     else:
         A__							=     {0: "batch", 1: "sequence"}
     return OrderedDict(
         [
             ("input_ids", dynamic_axis),
             ("attention_mask", dynamic_axis),
         ]				)



