'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


_lowercase		:				Tuple	       =		logging.get_logger(__name__)

_lowercase		:				Any	       =		{
    "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 					__magic_name__		(					_UpperCAmelCase):
			UpperCamelCase__       = '''distilbert'''
			UpperCamelCase__       = {
			    '''hidden_size''': '''dim''',
			    '''num_attention_heads''': '''n_heads''',
			    '''num_hidden_layers''': '''n_layers''',
			}







			def __init__(			self       :						List[Any]       ,    lowercase_       :						str=30522       ,    lowercase_       :						List[Any]=512       ,    lowercase_       :						Any=False       ,    lowercase_       :						List[str]=6       ,    lowercase_       :						str=12       ,    lowercase_       :						Optional[Any]=768       ,    lowercase_       :						Any=4 * 768       ,    lowercase_       :						Optional[int]=0.1       ,    lowercase_       :						int=0.1       ,    lowercase_       :						Any="gelu"       ,    lowercase_       :						Tuple=0.02       ,    lowercase_       :						List[str]=0.1       ,    lowercase_       :						Dict=0.2       ,    lowercase_       :						str=0       ,    **lowercase_       :						List[str]       ,    ):
										lowercase_      :    Dict       		= vocab_size
										lowercase_      :    str       		= max_position_embeddings
										lowercase_      :    Optional[Any]       		= sinusoidal_pos_embds
										lowercase_      :    Optional[Any]       		= n_layers
										lowercase_      :    List[Any]       		= n_heads
										lowercase_      :    int       		= dim
										lowercase_      :    str       		= hidden_dim
										lowercase_      :    Optional[Any]       		= dropout
										lowercase_      :    Any       		= attention_dropout
										lowercase_      :    Dict       		= activation
										lowercase_      :    Tuple       		= initializer_range
										lowercase_      :    str       		= qa_dropout
										lowercase_      :    List[str]       		= seq_classif_dropout
										super().__init__(**lowercase_       ,    pad_token_id=lowercase_					)





class 					__magic_name__		(					_UpperCAmelCase):







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
										if self.task == "multiple-choice":
																	lowercase_      :    Any       		= {0: """batch""", 1: """choice""", 2: """sequence"""}
										else:
																	lowercase_      :    Union[str, Any]       		= {0: """batch""", 1: """sequence"""}
										return OrderedDict(
										    [
										        ("""input_ids""", dynamic_axis),
										        ("""attention_mask""", dynamic_axis),
										    ]					)






'''simple docstring'''




from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
						import tensorflow as tf

						from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM





@require_tf
@require_sentencepiece
@require_tokenizers
class 					__magic_name__		(					unittest.TestCase):







			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										lowercase_      :    Any       		= TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""					)
										lowercase_      :    Dict       		= AutoTokenizer.from_pretrained("""google/mt5-small"""					)

										lowercase_      :    Union[str, Any]       		= tokenizer("""Hello there"""       ,    return_tensors="""tf"""					).input_ids
										lowercase_      :    List[str]       		= tokenizer("""Hi I am"""       ,    return_tensors="""tf"""					).input_ids

										lowercase_      :    Optional[Any]       		= model(lowercase_       ,    labels=lowercase_					).loss
										lowercase_      :    Optional[int]       		= -tf.math.reduce_mean(lowercase_					).numpy()

										lowercase_      :    Optional[int]       		= -21.22_81_68
										self.assertTrue(abs(mtf_score - EXPECTED_SCORE					) < 2E-4					)





